Prediction of CO pollutant in Mashhad metropolis, Iran: Using multiple linear regression

Abstract

AbstractGiven the importance of carbon monoxide (CO) in acute health threats, studies have been conducted in different countries and cities (272 cities in China and 337 cities on a global scale) on the relationship between daily mortality and CO while the spatial analysis of factors affecting CO pollutant in cities, especially in developing countries, has rarely been done. Accordingly, this research has measured the effect of five environmental‐social variables (ESVs) on the spatial distribution of CO pollutants in the metropolis of Mashhad, Iran. CO concentration data were collected in 23 air pollutant monitoring stations in an area of 356 km2 in 2019. Then, the relationship between five variables and CO pollutant were measured using linear and multiple regression by Sentinel 2A and 3 satellite images in ArcGIS and TerrSet software. The results show that the mean CO concentration averages at 1.56 ppm in the whole city. But its range varies between 0.171 and 2.907 ppm, which is a low figure compared with presented standards and does not indicate a critical situation. The results of multiple regression indicate that 42% of the variance in CO concentration is explained by independent variables. Among five independent variables, the beta value of the land surface temperature (LST) and digital elevation model (DEM) variables is negative and positive for the other three variables, including population density, normalised difference vegetation index (NDVI) and normalised difference built‐up index (NDBI). It should be noted that the strongest correlating variable is population density. Prediction of the spatial distribution of CO pollutants shows the division of the city into three areas: (1) the south and southwest slopes of the city with a low concentration; (2) central areas with a medium concentration; and (3) northern and northeastern areas of the city with a high concentration where low‐income groups reside and there are more worn‐out vehicles, motorcycles and industrial workshops. Areas with high CO concentration need more attention from urban managers.