Abstract
One of the most toxic pollutant gases produced by fossil fuels is carbon monoxide. Hence, the accurate and regular estimation and control of CO in the cities such as Tehran is inevitable. In this research, for the first time, CO concentration in ambient air was predicted based on 12 important urban and meteorological parameters by neural network. Also, the sensitivity analysis of the factors that effect on the concentration of carbon monoxide in Tehran was investigated based on the pollutant concentration predictive model. In this research, the daily statistical data of Tehran metropolis over the course of five consecutive years from 12 factors affecting the amount of carbon monoxide in Tehran, such as population, density, precipitation, temperature, urban traffic, wind speed, gasoil consumption, moisture, air flow, effective vision and air pressure was used. Based on this database, the artificial neural network with the best possible algorithm had been trained to predict this contaminant and root mean square error of model was equal to 2.54. Then, sensitivity analysis was done to find the most effective factor on the concentration of carbon monoxide, urban density and air pressure. In order to control this hazardous contaminant in urban management, these parameters should be taken into account. Based on the result, by preventing the construction of high towers in Tehran, wind speed average will increase and increasing in wind speed (25%) caused to reducing in carbon monoxide concentration (about 12%). Also, prevention of urban density (25%) will cause to prevention of increasing CO concentration (about 10%).
Highlights
Experiments and prediction in environmental issues are important
Diagram A represents the wind speed, which indicates that the maximum wind speed is between 0.8 and 1.3 meters per second, Diagram B represents the air pressure in Tehran, with the highest statistical pressure of 6.76 and Diagram C represents the moisture of the city of Tehran, monitored daily by observation stations over the course of five consecutive days
If any of these parameters are properly managed and controlled, the concentration of toxic and hazardous carbon monoxide contaminants in the city will be reduced. Each of these six parameters, respectively, urban density, gasoline consumption, population, gasoil consumption and traffic volume and air traffic have greater sensitivity and effect on increasing the CO concentration. This table increased each of these parameters using the model that was trained in the artificial neural network by 10%, 25% and 50%, and presented the rate of change in carbon monoxide concentration for each parameters, so that if the gasoline consumption in this city increases by 25%, the CO concentration will increase by 7.25% or if the population of Tehran increases by 10% in the coming years, the CO concentration will increase by 0.8%
Summary
Experiments and prediction in environmental issues are important. Wastewater and air pollutions are the main subjects of environmental engineering. Many researchers study this subject in recent years. Air pollution has become an undeniable problem in many countries. The world's policy makers, especially in the United States and Europe, have drafted international laws and treaties to reduce air pollution [1,2,3]. The rapid growth of countries' economies through industry and non-compliance with the international laws have exposed these countries into air pollution as a global environmental challenge [4]. One of the toxic and dangerous air pollutants is carbon monoxide, produced by incomplete combustion of hydrocarbons [5]. The sources of entry of this greenhouse gas can be different sources like as the car exhausts, steam boilers, smoke and tobacco and incorrect use of generators and charcoal furnaces [6, 7]
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