Mohammadali Ghorbani, Leila Naghipour, Vahid Karimi, Reza Farhoudi,
Volume 6, Issue 1 (5-2013)
Abstract
Background and Objectives: Weather pollution, caused by Ozone (O3) in metropolitans, is one of the major components of pollutants, which damage the environment and hurt all living organisms. Therefore, this study attempts to provide a model for the estimation of O3 concentration in Tabriz at two pollution monitoring stations: Abresan and Rastekuche.
Materials and Methods: In this research, Artificial neural networks (ANNs) were used to consider the impact of the meteorological and weather pollution parameters upon O3 concentration, and weight matrix of ANNs with Garson equation were used for sensitivity analysis of the input parameters to ANNs.
Results: The results indicate that the O3 concentration is simultaneously affected by the meteorological and the weather pollution parameters. Among the meteorological parameters used by ANNs, maximum temperature and among the air pollution parameters, carbon monoxide had the maximum effect.
Conclusion: The results are representative of the acceptable performance of ANNs to predict O3 concentration. In addition, the parameters used in the modeling process could assess variations of the ozone concentration at the investigated stations.
K Ezimand, Aa Kakroodi,
Volume 10, Issue 4 (3-2018)
Abstract
Background and Objective: Ground level ozone (O3) is one of most dangerous pollutants for human health in urban areas. The aim of this study was to identify the factors affecting the formation of ozone and modeling the spatial and temporal variations of ozone concentration in Tehran metropolitan area.
Materials and Methods: The data used in this research included meteorological data and pollution concentration data for 2014. First, we studied the impact and correlation of parameters to ozone concentration using the coefficient of Pearson, and then we did modeling of ozone concentration using a multivariate linear regression method.
Results: The developed model had the ability to describe 79% of the data changes for 2014. The temporal analysis of the ozone concentration showed that the best coefficient of determination of the model was R2 = 0.771 in the summer and R2 = 0.778 in July. These results also showed that among the air quality monitoring station of Tehran, station 4 had the lowest coefficient of determination (R2 = 0.6) and Aqdasieh station had the highest coefficient of determination (R2 = 0.79). Finally, the spatial distribution of the estimated ozone concentration was consistent with the measured ozone concentration at the station level.
Conclusion: According to the results, all the parameters related to air pollution concentration and meteorological parameters were effective parameters on modeling of ozone concentration on the ground level. The spatial distribution of ozone concentration in Tehran showed a greater concentration of ozone in the South and East than the North and West of the city.
