Sulfur dioxide AQI modeling by artificial neural network in Tehran between 2007 and 2013

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 SO[2] 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 SO[2]except 2008 [0.037 ppm] was less than the EPA standard [0.14 and 0.03 ppm, respectively]. Trend of SO[2] AQI showed the variation of SO[2]during different days, but the study declined overtime and the predicted trend is higher than the actual trend

Conclusion: The trend of SO[2] 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 SO[2] emissions strategy and lack of effective parameters in SO[2] emissions in predicting model

[Investigation of Sick Building Syndrome symptoms in inhabitants of Ekbatan town]

Sick Building Syndrome has mostly been examined in office environments than in residential spaces. However, in this research, this problem is surveyed in residential buildings of Ekbatan town. Three hundred and thirty cases were, randomly, chosen from among the inhabitants of Ekbatan town. The questionnaires involved questions about the irritative and mental symptoms. A relation was established between the intensification of these symptoms and the physical conditions of buildings e.g. light, ventilation and other factors such as gender and age. Symptoms of Sick Building Syndrome were positive in 56.4 percent of cases. The strongest symptoms observed among the residents include eye irritation during using the computer [8.8%], sore throat [8.5%] and nose irritation [6.4%] near the garbage shooting. The results showed that the residents of buildings without natural sunlight and appropriate central air-conditioning system with [P= 0.04, OR= 1.60] have higher chances of sick building syndrome than residents of buildings exposed to sunlight and good air-conditioning system [with P=0.001, OR=2.41]. The factors influencing the Sick Building Syndrome in this town include inefficient central air-conditioning system, double windows, improper operation and maintenance of shooting system, improper cleaning, and lack of compressor. The neighboring location of Ekbatan town with the polluted areas such as Azadi's West Terminal and Mehr Abad International Airport is among the factors which contribute to the prevalence of the syndrome, as well