Vehicle exhaust is a major source of anthropogenic carbon dioxide (CO2) in metropolitan cities. Popular community mode (buses and taxies) and about 2.4 million private cars are the main emission sources of air pollution in Tehran. A case survey has conducted to measure CO2 in four popular vehicles, bus, taxi, private car and motorcycle, which moved in the city with respectively 7800, 82358, 560000 and 2.4 million per day in 2012. Results indicated that the contribution of CO2 emissions increased in the following order: private car, motorcycle, bus and taxi. The overall average for the contribution of CO2 emissions in the private car, motorcycle, bus, and taxi were 26372, 1648, 1433 and 374 tons per day, respectively. Our results also showed that the urban transport operation consume an estimated 178 and 4224 million liter diesel and petrol per year, respectively, that have released about 10 million tons of CO2. The average contribution of CO2 emissions of private cars in Tehran was higher (88%) than other vehicles. It was concluded that high volume of traffic, transport consumption of fossil fuels and shortage of adequate public transport system are responsible for the high CO2 level in environment in Tehran. Thus, it is to be expected that CO2 as a greenhouse gas has risen in Tehran more than ever in the following years and this would be a matter of concern for the authorities to have a comprehensive plan to mitigate this phenomena.

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, respectively. The random effects model data demonstrated that the F-value calculated was greater than the critical F-value approximately 59 % of the variability in the exposure was due to differences between groups. Based on these finding, the order of probability of the long-term mean exposure exceeding (Z) to the OEL of 10 mg/m 3 for total dust which were in kiln (100%), packing (100%), cement mill (90%), crusher (73%), raw mill (60%) administration (2.3%) and the maintenance parts (0%).

Determination of different facies in an underground reservoir with the aid of various applicable neural network methods can improve the reservoir modeling. Accordingly facies identification from well logs and cores data information is considered as the most prominent recent tasks of geological engineering. The aim of this study is to analyze and compare the five artificial neural networks (ANN) approaches with identification of various structures in a rock facies and evaluate their capability in contrast to the labor intensive conventional method. The selected networks considered are Backpropagation Neural Networks (BPNN), Radial Basis Function (RBF), Probabilistic Neural Networks (PNN), Competitive Learning (CL) and Learning Vector Quantizer (LVQ). All these methods have been applied in four wells of South Pars field, Iran. Data of three wells were employed for the networks training purpose and the fourth one was used to test and verify the trained network predictions. The results have demonstrated that all approaches have the ability of facies modeling with more than 65% of precision. According to the performed analysis, RBF, CL and LVQ methods could model the facies with the accuracy between 66 and 68 percent while PNN and BPNN techniques are capable of making predictions with more than 72% and 88.5% of precision, respectively. It can be concluded that the BPNN can generate most accurate results in comparison to the other type of networks but it is important to note that the other factors such as consuming the amount of time taken, simplicity and the less adjusted parameters as well as the acquired precisions should be considered. As a result, the model evaluation analysis used in this study can be useful for prospective surveys and cost benefit facies identification.