en عمومى General مقاله اصیل Original Article <p style="text-align: left">Background: Air pollution and concerns about health impacts have been raised in metropolitan cities like Tehran. Trend and prediction of air pollutants can show the effectiveness of strategies for the management and control of air pollution. Artificial neural network (ANN) technique is widely used as a reliable method for modeling of air pollutants in urban areas. Therefore, the aim of current study was to evaluate the trend of sulfur dioxide (SO2) air quality index (AQI) in Tehran using ANN.<br> Methods: The dataset of SO2 concentration and AQI in Tehran between 2007 and 2013 for 2550 days were obtained from air quality monitoring fix stations belonging to the Department of Environment (DOE). These data were used as input for the ANN and nonlinear autoregressive (NAR) model using Matlab (R2014a) software.<br> Results: Daily and annual mean concentration of SO2 except 2008 (0.037 ppm) was less than the EPA standard (0.14 and 0.03 ppm, respectively). Trend of SO2 AQI showed the variation of SO2 during different days, but the study declined overtime and the predicted trend is higher than the actual trend.<br> Conclusion: The trend of SO2 AQI in this study, despite daily fluctuations in ambient air of Tehran over the period of the study have decreased and the difference between the predicted and actual trends can be related to various factors, such as change in management and control of SO2 emissions strategy and lack of effective parameters in SO2 emissions in predicting model.</p> Sulfur dioxide, Neural networks, Air quality index, Tehran 173 178 http://ehemj.com/browse.php?a_code=A-10-1-34&slc_lang=en&sid=1 Saeed Motesaddi 10031947532846002030 10031947532846002030 No Department of Environmental Health Engineering, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran Parviz Nowrouz Nowrouzp@gmail.com 10031947532846002031 10031947532846002031 Yes Environmental Health Engineering, Department of Environmental Health Engineering, School of Public Health, Sulfur dioxide AQI modeling by artificial neural network in Tehran between 2007 and 2013 Saeed Motesaddi 1 , Parviz Nowrouz 2* , Behrouz Alizadeh 3 , Fariba Khalili 4 , Reza Nemati 2 1 Associate Professor, Department of Environmental Health Engineering, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran 2 Shahid Beheshti University of Medical Sciences, Tehran, Iran Behrouz Alizadeh 10031947532846002032 10031947532846002032 No Department of Medical Informatics, School of Allied Medical Sciences, Shahid Beheshti University Ph.D Student of Environmental Health Engineering, Department of Environmental Health Engineering, School of Public Health, Sulfur dioxide AQI modeling by artificial neural network in Tehran between 2007 and 2013 Saeed Motesaddi 1 , Parviz Nowrouz 2* , Behrouz Alizadeh 3 , Fariba Khalili 4 , Reza Nemati 2 1 Associate Professor, Department of Environmental Health Engineering, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran 2 Shahid Beheshti University of Medical Sciences, Tehran, Iran 3 of Medical Sciences, Tehran, Iran Fariba Khalili 10031947532846002033 10031947532846002033 No Department of Environmental Health Engineering, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran Reza Nemati 10031947532846002034 10031947532846002034 No Department of Environmental Health Engineering, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran