Background and Objective: Spatial distribution of contaminants is essential for soil contamination monitoring and maintaining environmental quality. Therefore, this study was conducted to assessment of Pb, Cd and Cu concentrations of soil samples in the vicinity of Shaz More
Background and Objective: Spatial distribution of contaminants is essential for soil contamination monitoring and maintaining environmental quality. Therefore, this study was conducted to assessment of Pb, Cd and Cu concentrations of soil samples in the vicinity of Shazand thermal power plant in 2013 and preparing the spatial distribution map of elements.
Material and Methodology: A total of 54 topsoil and subsoil samples were collected from nine sampling stations. In the laboratory, after preparation of soil samples, heavy metal concentrations were determined using ICP-OES. All statistical analyses were performed using the SPSS 18.0 statistical package. Also, spatial distribution maps of elements were prepared using Kriging interpolation method.
Findings: Based on the results obtained, the maximum mean concentration of Pb (µg/kg) in topsoil and subsoil samples were 10255 ± 577 and 8416 ± 415, respectively, while, the maximum mean concentration of Cd in topsoil and subsoil samples were 304 ± 29.0 and 303 ± 34.0 µg/kg, respectively, whereas, the maximum mean concentration of Cu in topsoils and subsoils samples were found to be 11839 ± 431 and 10473 ± 501 µg/kg, respectively. The results of statistical analyses showed that, the mean concentrations of Cu in both topsoil and subsoil specimens were significantly higher than permissible maximum permissible concentration established by WHO.
Discussion and Conclusion: Although the mean concentrations of Pb and Cd in soil samples were lower than WHO permissible limits, the establishment of environmental management system in industries of the study area and implementation of programs for the monitoring of heavy metals in soil samples is recommended.
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Several studies yet have been conducted in the field of climate change in different parts of the worldin order to investigate the impact of meteorological parameters changes on the performance of energysector. In this study, the effect of climate change on the performan More
Several studies yet have been conducted in the field of climate change in different parts of the worldin order to investigate the impact of meteorological parameters changes on the performance of energysector. In this study, the effect of climate change on the performance of energy generation sector atIran power plants in next decade has been investigated using the results of climate change calculationsin the country’s provinces obtained by downscaling through neural network. Calculations show thatthe efficiency of gas power plants averagely decreases by 0.6% per 1 oC temperature increase.Similarly, the efficiency of steam and combined cycle power plants averagely decreases by 0.5% and0.4% respectively. Considering the climate change consequences in Iran, the overall temperature willaveragely increase about 1.36 oC by the year 2025. Conduction a close investigation, the averagetemperature rise affecting the performance of power plants in the country - which would cause a dropin energy generation sector efficiency - was evaluated to be 1.13 oC. After making calculations andutilizing energy and environment software, it was found that the thermal power plants’ fuelconsumption will increase about 2.49%. The results revealed that the amount of carbon dioxideemission and social costs caused by emissions will increase by about 1.3% and 2%, respectively.
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Visible steam emitting from the cooling tower and H 2 S smell are obvious environmental manifestations of a geothermal facilit More
Visible steam emitting from the cooling tower and H 2 S smell are obvious environmental manifestations of a geothermal facility. They are usually coupled with concerns about exposure to the high toxicity of mercury and arsenic along with radon radiation. They have been, to a large extent, responsible for the perception that air quality is significantly affected by geothermal activities. In reality, air quality is affected by geothermal facilities a little, especially as compared to many other industries or fossil-fuel power plants. If the quality of air is considered in the construction and utilization of geothermal installations, worries about air quality will be removed. The key to success in air quality programs is the early collection of data, plant operational scenarios, educational programs, etc.The present study aims at predicting dispersion pattern of one of the main gases of Sabalan geothermal power plants to find a solution for its probable negative effects.
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Restructuring of power systems and integration of different renewable energy sources with complex dynamic behaviors and high structural uncertainties has made the issue of load frequency control more important. For a hybrid power system that includes a thermal power pla More
Restructuring of power systems and integration of different renewable energy sources with complex dynamic behaviors and high structural uncertainties has made the issue of load frequency control more important. For a hybrid power system that includes a thermal power plant taking into account nonlinear limitations such as the governor dead band and generator rate constraints and renewable energy sources including a wind turbine, solar-thermal power plant, electrolyzer, fuel cell, and plug-in electric vehicle, this paper proposes an adaptive wavelet neural network fractional order PID controller (AWNNFOPID) based on self-recursive wavelet neural networks and fractional order PID controller. To compare the performance of the proposed AWNNFOPID controller, four different scenarios are considered and the simulation results are compared with traditional I, PI, and PID controllers as well as with the optimized FOPID controller. The simulation results show that the proposed AWNNFOPID controller has better performances than the other control strategies used for the studied hybrid power system based on performance indicators such as settling time, rise time, maximum overshoot, maximum undershoot, integral time absolute error (ITAE), and integral absolute error (IAE).
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