Application of geostatistical methods for estimation of Tehran Air pollutants spatial distribution

Subject Areas : environmental management

Mansour Halimi ¹ ( PhD of Climatology, Tarbiat Modares University, Tehran, Iran.* (Corresponding Author) )
Zahra Zarei Chaghabalaki ² ( - PhD of Climatology, Lorestan University, Lorestan, Iran. )
Vahide Sayad ³ ( MSc of climatology, Tarbiat Modares University, Tehran, Iran )
Hasan Jems ⁴ ( - MSc of Geography, Sistan va Balouchestan University, Iran. )

Received: 2014-03-02 Accepted : 2014-10-19 Published : 2017-08-23

Keywords: Air pollution, Geostatistical schema, Kriging, Tehran,

Abstract :

Background and Objective: Ambient air quality is a major concern in highly urbanized and industrialized regions such as Tehran. Method: In this paper, the spatial distribution of 4 air pollutants in Tehran atmosphere was analyzed. The analyzed air pullutants were Carbon monoxide (CO), Nitrogen Dioxide (NO2), Ozone (O3) and atmospheric particulate matters less than 10 micrometers in diameter (PM10). For this purpose, 4 common geostatistical interpolation methods namely: Ordinary Kriging (OK), Universal Kriging (UK), Sample Kriging (SK), and Ordinary Cokriging (COK) with Gaussian modeled semivariogram, were used to estimate the continuous surface for the 4 mentioned air pollutants. The data were collected from 21 air quality monitoring stations located in different districts of Tehran during a 2-year period from 2012 to 2013. The Kriging interpolation schemes are stochastic, local, gradual and exact interpolators. After preprocessing the collected data, they were imported to GIS by using metric coordinate system (UTM Zone 39). Finally, the Kriging predicted map was evaluate using 3 statistical indices of validation namely: Mean Absolute Error (MBE), Mean Bias Error (MAE), and Root Mean Square Error (RMSE) that can be divide into systematic and unsystematic errors (RMSEs, RMSEu). Findings: The results indicated that, using 2 auxiliary variables, the OCK is the optimum schema for spatial estimation of CO and NO2 pollutants in Tehran. Moreover, SK is found out as the best model for spatial estimation of NO2 and CO. According to optimal model, the highest concentrations of ozone (O3) and particulate matters greater than 10 microns (PM10) are observed in the marginal areas of Tehran, while the highest concentrations of CO, NO2 are observed in the central and northern districts of Tehran such as districts 1 to 4. Conclusion: The developed optimized model can be used for real time estimation of any pollutants in Tehran atmosphere by updating the observed data

References:

 

1-      Stijn Janssen, Gerwin Dumon, Frans Fierens, Clemens Mensink, (2008),  Spatial interpolation of air pollution measurements using CORINE land cover data, Atmospheric Environment 42 4884–4903

2-      Yanosky JD, Paciorek CJ, Schwartz J, Laden F, Puett R, ShuHH.( 2008), Spatio-temporal modeling of chronic PM10exposure for the Nurses' Health Study. Atmos Environ;42 (18):4047–62

3-      Liang, W., Wei, H. and Kuo, H., (2009). Association between daily mortality from respiratory and cardiovascular diseases and air pollution in Taiwan.  Environmental Research, 109, pp. 51-58.

4-      Hosseinpour, A.R., Forouzanfar, M.H., Yunesian, M., Asghari, F., Holakouie Naieni, K. and Farhood, D., (2005). Air pollution and hospitalization due to angina pectoris in Tehran, Iran: A time-series study. Environmental Research,99,pp. 126-131 [In Persian]

5-      Ghorbani, M. and Younesian, M., (1389). Research Projects in Air pollution Epidemiology.  Iranian Epidemiology Journal. 5, pp. 44-52 [In Persian]

6-      Rajarathnam, U., Sehgal M., Nairy S., Patnayak R.C., Chhabra S.K., Kilnani, K.V., R and Committee., HHR, (2011). Time Series study on air pollution and mortality in Dehli. Res Rep Health Eff Inst,Mar,pp. 47-74

7-      D. Vienneau, K. de Hoogh, D. Briggs, (2009), A GIS-based method for modelling air pollution exposures across Europe, Science of the Total Environment 408 , 255–266

8-      Wong D.W., Lester Yuan And Susan A. Perlin, (2004) , Comparison of spatial interpolation methods for the estimation of air quality data, Journal of Exposure Analysis and Environmental Epidemiology,14, 404–415

9-      Gerboles Michel and Reuter Hannes I,( 2010), Estimation of the measurement uncertainty of ambient air pollution datasets using geostatistical analysis European Commission, Joint Research Centre Institute for Environment and Sustainability, , Pages 1-37

10-  Moscato Umberto, Esposito Teresa, Vanini Giancarlo., (2011), Nitrous oxide pollution: a geostatistical method toassess spatial distribution of anaesthetic gases andhospital staff exposure exposure, human responses and building investigations ,Pages  487-492

11-  Gretchen T. Goldman ,James A. Mulholland, Armistead G. Russell, Katherine Gass , Matthew J. Strickland, Paige E. (2012),Tolbert. Characterization of ambient air pollution measurement error in a time-series health study using a geostatistical simulation approach Atmospheric Environment Volume 57, September, Pages 101–108

12-  Willmott, C. J., (1984). On the evaluation of model performance in physical geography. In Spatial Statistics and Models, ed. G. L. Gaile, and C. J. Willmott, pp. 443-460.

13-  Sharolyn Anderson , (2004), An Evaluation of Spatial Interpolation Methods on Air Temperature in Phoenix, AZ. G. Lorentz, and e. al, pp. 203-67. New York: Academic Press