International Journal of Human Capital in Urban Management
Quarterly Publication

Estimation of exposure to fine particulate air pollution using GIS-based modeling approach in an urban area in Tehran

https://doi.org/10.22034/ijhcum.2016.04.007
Volume 1, Issue 4
Autumn 2016
Pages 295-300
International Journal of Human Capital in Urban Management

Document Type : CASE STUDY

Authors

M. Memarianfard
A. Mostafa Hatami
M. Memarianfard
M. Ajam

Khaje Nasir Toosi University of Technology, Department of Civil Engineering, Tehran, Iran

Abstract
In many industrialized areas, the highest concentration of particulate matter, as a major concern on public health, is being felt worldwide problem. Since the air pollution assessment and its evaluation with considering spatial dispersion analysis because of various factors are complex, in this paper, GIS-based modeling approach was utilized to zoning PM2.5 dispersion over Tehran, during one year, from 21 March 2014 to 20 March 2015. The RBF method was applied to obtain the zoning maps and determining the highest concentration of PM2.5 in the 22 Tehran’s regions for each season. The RMSEmin values according to the number of neighbors and types of functions in the radial basis function method, including completely regularized spline, Spline with tension, Multiquadric function, Inverse multiquadric function, and Thin-plate spline  for each month have been assessed. By performing analysis on the errors, the numbers of neighbors were estimated. The numbers of neighbors in the model for each function were varied from 2 to 30. The results indicate that the models with 3 and 4 neighbors have the best performance with the lowest RMSE values with using RBF method. The highest PM2.5 concentrations have been occurred in the summer and winter especially at the center, south, and in some cases at northeast of the city.

Keywords

Air pollution
Completely regularized spline (CRS)
Inverse multiquadric function (IMF)
Multiquadric function (MF)
PM2.5 concentrations
Radial basis function (RBF) method
RMSEmin
Spline with tension (SWT)
Tehran's regions
Thin-plate spline (TPS)
Subjects
Alizadeh-Choobari, O.; Bidokhti, A. A.; Ghafarian, P.; Najafi, M. S.; (2016a). Temporal and spatial variations of particulate matter and gaseous pollutants in the urban area of Tehran. Atmos. Environ., 141: 443–453 (11 pages).
Alizadeh-Choobari, O.; Ghafarian, P.; Adibi, P.; (2016b). Inter-annual variations and trends of the urban warming in Tehran. Atmos. Res., 170: 176–185 (10 pages).
 Cyrys, J., Hochadel, M., Gehring, U., Hoek, G., Diegmann, V., Brunekreef, B., and Heinrich, J., (2005). GIS-Based estimation of exposure to particulate matter and NO2 in an urban area: Stochastic versus dispersion modeling. Environ. Health Persp., 3(8): 987-992 (6 pages).
 Han, L.J.; Zhou, W.Q.; Li, W.F.; Li, L., (2014). Impact of urbanization level on urban air quality: a case of fine particles (PM2.5) in Chinese cities. Environ. Pollut., 194: 163–170 (8 pages).
Hu, X., Waller, L.A., Lyapustin, A., Wang, Y., Al-Hamdan, M.Z., and Crosson, W.L., (2014). Estimating ground-level PM2.5 concentrations in the southeastern United States using MAIACAOD retrievals and a two-stage model. Remote Sens Environ, 140: 220–32 (13 pages).
 Huang, R.J.; Zhang, Y.; Bozzetti, C.; Ho, K.F.; Cao, J.J.; Han, Y.; Daellenbach, K.R.; Slowik, J.G.; Platt, S.M.; Canonaco, F.; Zotter, P., (2014). High secondary aerosol contribution to particulate pollution during haze events in China. Nature, 514(7521): 218-222 (5 pages).
 Lalehpour, M., (2016). Recognition of management structure and spatial planning in Tehran metropolitan area. J. Urban Manage., 5(1): 3-15 (13 pages).
Lary, D. J.; Faruque, F. S.; Malakar, N.; Moore, A.; Roscoe, B.; Adams, Z. L.; Eggelston, Y., (2014). Estimating the global abundance of ground level presence of particulate matter (PM2.5). Geospatial Health, 8(3): 611–630 (20 pages).
Lin, J.T.; Pan, D.; Davis, S.J.; Zhang, Q.; He, K.B.; Wang, C.; Streets, D.G.;Wuebbles, D.J.; Guan, D.B., (2014). China's international trade and air pollution in the United States. Proc. Natl. Acad. Sci. U. S. A. 111(5): 1736–1741 (6 pages).
Liu, Z.; Guan, D.B.; Crawford-Brown, D.; Zhang, Q.; He, K.B.; Liu, J.G., (2013). A low-carbon road map for China. Nat., 500(7461): 143–145 (3 pages).
Lu, W.; Wang, W.; Wang, X.; Yan, S.; Lam, J.; (2004). Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong. Environ. Res., 96(1): 79–87 (9 pages).
Memarianfard, M.; Hatami, A.M.; Memarianfard, M., (2017). Artificial neural network forecast application for fine particulate matter concentration using meteorological data. Global J. Environ. Sci. Manage., 3(2). (In Press)
Nayeb Yazdi, M.; Delavarrafiee, M.; Arhami, M., (2015). Evaluating near highway air pollutant levels and estimating emission factors: Case study of Tehran, Iran. Sci. Total Environ., 538: 375–384 (10 pages).
Redzuan, M.; Kiani, A.; Salari sardri, F.;Edalati, A.; Abbasi, Z.; (2013). Comparative analysis of traditional and new approach to urban management in Iran. Prog. Manage. Sci., 1(1): 24-35 (12 pages).
Saleh, A. H.; Bahaa, Z.; Hasan, G., (2014). Mapping Dispersion of Urban Area Particulate Matter Over Kirkuk City Using Geographic Information System. Environ. Earth Sci., 4(8): 80-87 (8 pages).
Song, C.; Pei, T.; Yao, L., (2015). Analysis of the characteristics and evolution modes of PM2.5 pollution episodes in Beijing, China during 2013. Int. J. Environ. Res. Public Health, 12(3): 1099-1111 (13 pages).
Statistical Centre of Iran, (2012). Iran Statistical Year Book, Available at: www.amar.org
 Trizio, L.; Angiuli, L.; Menegotto, M.; Fedele, F.; Giua, R.; Mazzone, F.; Carducci, A.G.C.; Bellotti, R.; Assennato, G., (2016). Effect of the Apulia air quality plan on PM10 and benzo (a) pyrene exceedances. Global J. Environ. Sci. Manage., 2(2): 95-104 (10 pages).
Vakili, M.; Sabbagh-Yazdi, S.; Kalhor, K.; Khosrojerdi, S., (2015). Using Artificial Neural Networks for Prediction of Global Solar Radiation in Tehran Considering Particulate Matter Air Pollution. Energy Procedia, 74: 1205-1212 (8 pages).
 Wang, J.; Ogawa, S., (2015). Effects of Meteorological Conditions on PM2.5 Concentrations in Nagasaki, Japan, Int. J. Environ. Res. Public Health, 12: 9089-9101 (13 pages).
WHC, (2016). World Heritage List, Available at: http://whc.unesco.org/en/list/
Zhang, Q.; He, K.B.; Huo, H., (2012). Policy: Cleaning China's air. Nat., 484(7393): 161-162 (2 pages).
 
 
 

  • PM2.5 spatial prediction has been estimated across 22 Tehran’s regions.
  • The GIS approach was utilized for distribution modeling.
  • The RMSEmin values according to the number of neighbors and types of functions including CRS, SWT, MF, IMF, and TPS in RBF method have been assessed.
  • The highest PM2.5 concentrations have been carried out during a certain period of times.

 

 

  • Receive Date 08 June 2016
  • Revise Date 14 August 2016
  • Accept Date 26 September 2016

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