Spatial Distribution of The Carbon Monoxid Using Common and Modern Interpolation Methods (Case study of Tehran)

Air pollution is a major threat to public health, especially in the metropolises (Kalo et al., 2020). Due to the disadvantages of air pollution, understanding the various aspects of this issue is of great importance. Producing accurate air pollution maps plays an important role in managing and quantifying existing and future health risks (Alimissis et al., 2018). Estimating the spatial distribution of air pollution continuously over a wide geographical area, especially in areas that have not been measured is a major concern in health studies (Masroor et al., 2020). Although spatial interpolation methods have been widely used in various applications to estimate unknown values in unsampled locations, many fundamental problems remain unresolved (Kalo et al., 2020). The superior methods extracted in previous research show that the results obtained in one phenomenon or one area are not extendable to all phenomena and places. Therefore, the evaluation and selection of interpolation techniques play an important role in the spatial zoning of CO pollution. Based on the results presented by García-Santos et al. (2020) and given reviewing the methods used in previous research, Inverse Distance Weight (IDW), Kriging (simple, ordinary, and universal), and Radial Base Function (RBF) methods were selected as common and classical methods of evaluation. New interpolation methods including artificial neural networks (ANN) and fuzzy-based methods have been developed in various fields. Alimissis et al. (2018) expressed the ability of ANN in predicting the pollutants of nitrogen dioxide, nitrogen monoxide, carbon monoxide, sulfur, and ozone. ANN and linear interpolations have also been used for daily nitrous oxide measurements (Bigaignon et al., 2020). In performed research, only the temporal forecast of air pollution in each station is considered and no spatial zoning is done. Tutmez and Hatipoglu (2010) compared the Takagi-Sugeno fuzzy method with fuzzy clustering and Universal Kriging in nitrate modeling so that their study demonstrated the superiority of fuzzy methods. Since the spatial distribution of air pollutants is one of the major concerns of Tehran and authorities, the main objective of this research is to evaluate the capability of some proposed methods’ functionality (e.g., ANN and Fuzzy Sugeno by Fuzzy C-means Clustering) along with the common interpolation methods (e.g., IDW, RBF and Simple Kriging (SK), Ordinary Kriging (OK) and Universal Kriging (OK)) in estimating carbon monoxide gas pollution.