:: Volume 2, Issue 4 (Autumn 2015) ::
Environ. health eng. manag. 2015, 2(4): 173-178 Back to browse issues page
Sulfur dioxide AQI modeling by artificial neural network in Tehran between 2007 and 2013
Saeed Motesaddi, Parviz Nowrouz , Behrouz Alizadeh, Fariba Khalili, Reza Nemati
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 , Nowrouzp@gmail.com
Abstract:   (7537 Views)

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.
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.
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.
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.

Keywords: Sulfur dioxide, Neural networks, Air quality index, Tehran
eprint link: http://eprints.kmu.ac.ir/id/eprint/24698
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Type of Study: Original Article | Subject: General
Received: 2015/12/21 | Accepted: 2015/12/21 | Published: 2015/12/21


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Volume 2, Issue 4 (Autumn 2015) Back to browse issues page