Spatio – Temporal Estimation of Carbon Monoxide and Nitrogen Dioxide based on Remote Sensing Data and Ancillary Data in Tehran

Document Type : Research Paper

Authors

1 Department of Remote Sensing and Geographic Information System, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran

2 Department of Remote Sensing and Geographic Information System, Faculty of Humanities, Tarbiat Modares University, Tehran, IranIran

Abstract

Air pollution is one of the most important consequences of human activities, which not only threatens human health but also negatively affects all elements of the environment, including plants and animals. Tehran, the capital of Iran, and the administrative, political and economic center of the country, is no exception which is constantly struggling with these hazard. So far, many linear and nonlinear models have been applied to model air pollution. In this research, 8 pollutant measurement stations distributed over Tehran were selected according to the availability of their recorded data. In order to provide a model predicting pollutants, spatially and temporally, the combination of spatial and temporal features extracted of remote sensing data and environmental data was modeled using multilayer perceptron artificial neural network. The input data include meteorological data, topography, traffic index, population data, air pollutant concentrations for the last days, and land use map. In addition, vegetation cover, distance from heat islands, and the land surface temperature derived from remotely sensed data were used as remotely sensed attributes. In order to increase the accuracy of modeling, wavelet transform and feature selection methods were used on input attributes of the model. Random forest feature selection method was applied on the input data in order to reduce the number of input attributes,. The results of the model evaluation indicated that the model was efficient in estimating the concentrations of pollutants. Temporally, carbon monoxide and nitrogen dioxide were predicted with error estimation of 13% and 11.5%, respectively. Besides, these pollutants were spatially predicted with the estimation error less than 17%.

Keywords

References

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Geography and Environmental Sustainability
Volume 10, Issue 3 - Serial Number 36
December 2020
Pages 107-124

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