Prediction of climate change induced temperature rise in regional scale using neural network

The objective of this paper is to develop an artificial neural network [ANN] model which can be used to predict temperature rise due to climate change in regional scale. In the present work data recorded over years 1985-2008 have been used at training and testing steps for ANN model. The multilayer perceptron [MLP] network architecture is used for this purpose. Three applied optimization methods are backpropagation [BP] [in both input selection and weight optimization], genetic algorithm [GA] [in both input selection and weight optimization] and combined GA-particle swarm optimization [PSO] [input selection by GA and weight optimization by PSO]. In this framework, natural and anthropogenic parameters which affect the incoming solar radiation are considered in order to predict the climate change induced temperature rise in regional scale. Inputs of ANN model are mean temperature, dew point temperature, relative humidity, wind speed, solar radiation, cloudiness, rainfall, station-level pressure [QFE] and greenhouse gases. For predicting monthly mean temperature, input data include one month, six months, 12 months and 24 months before recorded data. In this work, nine stations namely Tehran, Mashhad, Ramsar, Orumiyeh, Sanandaj, Yazd, Ahwaz, Bandar Abbas and Chabahar in nine different climatic region of Iran are chosen to determine the temperature rise over Iran. Results show that the averaged minimum square errors [MSE] are 0.0196, 0.0224 and 0.0228 for ANN-BP, ANN-GA and ANN-GA-PSO methods, respectively. The ANN model associated with BP optimization method predict annual mean temperature rise as 0.44, 0.49, 0.20, 0.12, 0.17, 0.46, 0.41, 0.06 and 0.01°C after 10 years for mentioned stations, respectively. These values show the average temperature rise of 0.26°C after 10 years [the base year is 2008] for Iran

[Comparison of numerical simulation of NOx with modeling of IAQX in indoor environments]

This study presents an evaluation between IAQX 1.0f and Fluent 6.3.26 in modeling of NOx dispersion in an indoor residential environment. Modeling predictions are compared with sampling results. Aresidential building with about 84 m2 area is modeled. In IAQX 1.0f the building is divided into five zones. Emission factors and absorption rate of sinks is estimated with US.EPA suggested factors. On the other hand, In the Fluent 6.3.26 model, the building was divided into 1777 cells, and the openings are defined by the boundary conditions of the inflow. In this model, pollution sources were simulated by boundary conditions of the mass inflow. Compared to IAQX 1.0f, Fluent 6.3.26 showed higher estimation of the concentrations in the zones of 1, 2 and 3. In comparison with the measurements, both models had underestimated results. The results of Fluent 6.3.26 were closer to the sampling results in the zones

Air quality deterioration in Tehran due to motorcycles

Since there is a rise of motorcycles population as well as other motor vehicles, it seems that air Pollution deterioration should be studied as one of its environmental impacts. The main objective of this study was to develop a number of scenarios in order to determine the amount of Tehran's air pollution attributable to motorcycles and select the best and the most probable case to be recommended for implementation. The first step was to collect data such as the number of active motorcycles, daily traffic volume, average traveling speed and actual emission factors. For this purpose, a detailed questionnaire was designed to be completed by field surveys and measurements. The collected data were compared with traffic volume data, manufacturing statistics and the latest production capacity forecast in this field. Finally, with this data and emission factors for each type of motorcycle, an emissions inventory model was chosen to provide annual emissions from motorcycles in Tehran in different scenarios. The results showed that in 2002, there has been about 450'000 active motorcycles [4-stroke 58%, 2-stroke 28%, and moped 14%] with average speed of 40 km/h and average mileage of 110 km/d. Five scenarios were developed. The best scenario was "Changing all motorcycles to 4-strokes under EU-97 standard" which would result in reduction of NMVOC by 75%, CO by 35% and PM10 by 88%