Assessment of Air Pollution Modelling using Spatial Interpolation Techniques
Author : M. Goutham Priya and S. JayalakshmiVolume 7 No.2 July-December 2018 pp 1-6
Abstract
Air pollution has become the growing worldwide threat. Effective assessment of air pollution modelling depends on significant, wide-spread and distinguished instrumental data which are not possible for a developing country like India to have established an extensive network. This shortcoming on the capability to precisely envisage the pollutant concentration at unmonitored stations can be accomplished by spatial interpolation. Spatial Interpolation is a process, in which a parameter can be predicted and enumerated from a limited number of sample data points which is used to model the data for air pollutants like NO2, SO2 and RSPM. This study establishes an assessment of interpolation techniques to produce fine-scale air quality data for Tamil Nadu, India. The interpolation techniques which are used to evaluate the air quality data are Kriging, Inverse Distance Weighting, Local Polynomial Interpolation, Global Polynomial Interpolation, Radial Basis Function, Kernel Interpolation, Diffusion Interpolation, Natural Neighbour and Spline. The accuracy assessment was done with the help of statistical error metrics such as Root Mean Square Error, Mean Average Error, Mean Average Percentage Error, Mean Average Relative Error and Index of Agreement. Cross-Validation is executed by considering 20% of the data points as test data and remaining 80% as training data respectively. Spline, Local Polynomial Interpolation and Global Polynomial Interpolation technique showed enhanced resemblance between the observed and estimated values for the pollutants NO2, SO2 and RSPM respectively. This study embarks the association between the air pollutant and their distribution through spatial interpolation using Geographical Information System.
Keywords
Air Pollution, GIS, Interpolation, Statistical Error Metrics
Full Text:
References
[1] M. N. Rao and H. V. N. Rao, “Air Pollution”, McGraw Hill Education Private Limited, New Delhi, India, pp. 1-2, 2016.
[2] Bhawna Dubey, “Application of air pollution models and remote sensing in Air Quality Management”, Indian Journal of Applied Research, Vol. 4, No. 5, pp. 266-268, 2014.
[3] Kanakiya r s, Singh S K, and Shah U, “GIS application for spatial and temporal analysis of the Air pollutants in Urban area”, International Journal of Advanced Remote Sensing and GIS, Vol.4, No. 1, ISSN 2320 – 0243, pp 1120-1129, 2015.
[4] Willmott.C.J and Matsuura K, “Smart Interpolation of annually averaged air temperature in the United States” Journal of Applied Meteorology, Vol. 34, pp. 2577 – 2586, 1995.
[5] Daniel Kurtzman and Ronen Kadmon, “Mapping of temperature variables in Israel: a comparison of different interpolation methods”, Climate Research, Vol. 13, pp. 33–43, 1999.
[6] Mustafa GÜLER and Tekin KARA, “Comparison of Different Interpolation Techniques for Modelling Temperatures in Middle Black Sea Region”, Journal of Agricultural Faculty of Gaziosmanpasa University, Vol.31, Issue.2, ISSN:1300-2910, pp 61 – 71, 2014.
[7] Xian Luo and Youpeng Xu, Yi Shi, “Comparison of Interpolation Methods for Spatial Precipitation under Diverse Orographic Effects”, IEEE, 2011.
[8] Alan Mair and Ali Fares, “Comparison of Rainfall Interpolation Methods in a Mountainous Region of a Tropical Island”, Journal of Hydrologic Engineering, Vol. 16, pp 371 – 383, 2011.
[9] Phatarapon Vorapracha, Pongtep Phonprasert, Suparada Khanaruksombat and nuchanaporn Pijarn, “A Comparison of Spatial Interpolation Methods for predicting concentrations of Particle Pollution (PM10)”, International Journal of Chemical, Environmental & Biological Sciences, Vol. 3, No. 4, pp. 302 – 306, ISSN: 2320 – 4087, 2015.
[10] D Rojas Avallaneda, “Spatial interpolation Techniques for estimating the levels of pollutant concentrations in the atmosphere”, Revista Mexicana De Fisica, Vol. 53, No. 6, pp. 447 – 454, 2007.
[11] Shareef M, Husain T and Alharbi B, “Optimization of Air Quality Monitoring Network Using GIS Based Interpolation Techniques”, Journal of Environmental Protection, Vol.7, pp. 895 – 911, 2016.
[12] Yue Sun, Shaozhong Kang, Fusheng Li and Lu Zhang, “Comparison of interpolation methods for depth to ground water and its temporal and spatial variations in the Minqin oasis of northwest china”, Environment Modelling and Software, Vol. 24, No. 10, pp. 1163 – 1170, 2009.
[13] Lamiaa Khazaz, Hassane Jarar Oulidi, Saida El Moutaki and Abdessamad Ghafiri, “Comparing and Evaluating Probabilistic and deterministic Spatial Interpolation Methods for Ground Water Level of Haouz in Morocco”, Journal of Geographic Information System, Vol. 7, pp. 631-642, 2015.