Tehran Air Pollution Estimation Improvement

Document Type : Persian Original Article

Authors

1 School of Surveying and Geo-spatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.

2 Dept. of Transportation Eng., Faculty of Civil & Transportation Engineering, University of Isfahan, Isfahan, Iran.

3 Environmental Scienecs Research Institute, Shahid Beheshti University, Tehran, Iran.

4 Karlsruhe Institue of Technology, Institute of Economics Econometrics and Statistics, Karlsruhe, Germany.

5 6 Environmental Software & Services GmbH, Veinna, Austria.

6 Dept. of GIS, School of Surveying and Geospatial Engineering, College of Engineering. Tehran, iran.

Abstract

air pollution modeling is essential for environmental management. we attempted to select the optimum interpolation method for modeling air pollution in Tehran using some selected methods and proposed method. For this purpose, different interpolation methods such as inverse distances interpolation, polynomials, K nearest neighbors and various existing kriging methods were employed. Finally, by evaluating all the methods, the optimum interpolation method for air pollution modeling in Tehran was proposed and implemented. Among the above-mentioned methods, Kriging has had the best results. Then, using genetic optimization and particle swarm optimization methods, the clustering intervals of the Kriging method were optimized in both regular and irregular intervals to determine the optimal number of clusters and the distance between the clusters at regular intervals in the conventional methods. A mathematical variogram was used, in the irregular interval method. the same experimental variogram employed and the aim was to minimize the error of each of these methods. Finally, the error of the regular intervals with the optimal cluster numbers and spacing was less than those of all the mentioned methods. To test the proposed model, this model has been implemented for all months of the year (March 2016 – March 2017), with an average %64 accuracy improvement compared to that of the regular kriging method. The least error in the methods for interpolation of air pollution has been at 4 (μg / m3), while an error of 1.8 (μg / m3) in this study was achieved which resulted to the %55 accuracy improvement of the interpolation of air pollution, approximately. As a result, in this research, the best interpolation method has been presented in relation to previous studies for air pollution modeling. the proposed method can be applied as a complementary method for air pollution modeling where information scarcity exists in air pollution contamination.

Keywords

References

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Journal of Soft Computing and Information Technology
Volume 9, Issue 2 - Serial Number 28
Summer
July 2020
Pages 87-99

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