A preliminary investigation of the environmental impact of a thermal power plant in relation to PCB contamination

Purpose

The most significant application of polychlorinated biphenyls (PCBs) is in transformers and capacitors. Therefore, power plants are important suspected sources for entry of PCBs into the environment. In this context, the levels and distribution of PCBs in sediment, soil, ash, and sludge samples were investigated around Seyitömer thermal power plant, Kütahya, Turkey. Moreover, identity and contribution of PCB mixtures were predicted using the chemical mass balance (CMB) receptor model.

Methods

United States Environmental Protection Agency methods were applied during sample preparation, extraction (3540C), cleanup (3660B, 3665A, 3630C), and analysis (8082A).

Results

ΣPCB concentrations in the region ranged from not detected to 385 ng/g dry weight, with relatively higher contamination in sediments in comparison to soil, sludge, and ash samples collected from around the power plant. Congener profiles of the sediment and soil samples show penta-, hexa-, and hepta-chlorobiphenyls as the major homolog groups. The results from the CMB model indicate that PCB contamination is largely due to Clophen A60/A40 and Aroclor 1254/1254(late)/1260 release into the sediment and sludge samples around the thermal power plant.

Conclusions

Since there are no other sources of PCBs in the region and the identity of PCB sources estimated by the CMB model mirrors PCB mixtures contained in transformers formerly used in the plant, the environmental contamination observed especially in sediments is attributed to the power plant. Release of PCBs over time, as indicated by the significant concentrations observed even in surface samples, emphasizes the importance of the need for better environmental management.

     

Proposing of new building scheme and composite towards global warming mitigation for Malaysia

The building sector has been regarded as a potential sector where there is large capacity to reduce the climate change effect. This study has proposed solutions to mitigate environmental impacts and achieve low CO₂ emission from residential sector. Therefore, full life cycle assessment (LCA) has been run to assess the CO₂ emission and its effect on the atmosphere and climate change. Based on the result, timber scheme is the best choice due to releasing less CO₂ emissions to the atmosphere. However, house builders in Malaysia have almost completely neglected timber as a building material, with timber use as building components reduced to 5%. In this study, LCA Software was used to assess CO₂ emissions from different wall construction. The alternative building scheme has been made by reinforce steel stud, wooden beam and timber wall (S8) to improve the scheme deficiency while releasing less CO₂ emissions compared to other schemes. Therefore, S8 has a decreased CO₂ effect by 85% less than precast concrete frame and 90% less than brick over their lifetime. (S8) increased the load bearing compared to conventional timber beam. Thus, new scheme S8 could be replaced by current scheme and promote more adjustable scheme for Malaysian housing.     

Investigation of Nanoparticle Additives to Biodiesel for Improvement of the Performance and Exhaust Emissions in a Compression Ignition Engine

Worldwide energy demand has been growing steadily during the past five decades and most experts believe that this trend will continue to rise. The amount of emitted harmful emission gases increases in parallel with increasing energy consumption. This increase has forced many countries to take various precautions, and various restrictions on emitted emissions have been carried. In this study, effects of addition of oxygen containing nanoparticle additives to biodiesel on fuel properties and effects on diesel engine performance and exhaust emissions were investigated. Two different nanoparticle additives, namely MgO and SiO₂, were added to biodiesel at the addition dosage of 25 and 50 ppm. Fuel properties, engine performance, and exhaust emission characteristics of obtained modified fuels were examined. As a result of this study, engine emission values NO_x and CO were decreased and engine performance values slightly increased with the addition of nanoparticle additives.     

The modeling and analysis of transesterification reaction conditions in the selection of optimal biodiesel yield and viscosity

Among alternative fuels, biodiesel has been emphasized as a substantial candidate for diesel engines because of many advantages. However, the main shortcomings preventing more widespread use of biodiesel are high production cost and viscosity. In order to simultaneously overcome both of these shortcomings, the reaction conditions for the transesterification of waste cooking oil (WCO) were optimized using Taguchi and the full factorial design approaches. The analyses of signal to noise ratio and variance were also performed to identify the dominance of reaction conditions on viscosity and biodiesel yield. As a result, the optimal reaction conditions giving the lowest kinematic viscosity (3.991 cSt) and the highest biodiesel yield (98.19%) were determined to be as follows: sodium methoxide amount of 1.00 wt%, reaction time of 60 min, reaction temperature of 55 °C, and methanol to oil molar ratio of 6:1. The catalyst amount and methanol to oil molar ratio were found to be the most significant conditions influencing on the viscosity (10.36% and 78.87% contributions) and the yield (58.48% and 20.17% contributions), respectively. Finally, all physicochemical properties of final waste cooking oil biodiesel (WCOB) produced under optimal reaction conditions were found to meet the EN 14214.     

Application of response surface methodology for the optimization of biodiesel production from yellow mustard (Sinapis alba L.) seed oil

In the present study, response surface methodology (RSM) involving central composite design (CCD) was applied to optimize the reaction parameters of biodiesel production from yellow mustard (Sinapis alba L.) seed oil during the single-step transesterification process. A total of 30 experiments were designed and performed to determine under the effects of variables on the biodiesel yield such as methanol to oil molar ratio (2:1–10:1), catalyst concentration (0.2–1.0 wt.% NaOH), reaction temperature (50–70°C), and reaction time (30–90 min). The second order polynomial model was used to predict the biodiesel yield and coefficient of determination (R²) was found to be at 0.9818. The optimum biodiesel yield was calculated as 96.695% from the model with the following reaction conditions: 7.41:1 of methanol to oil molar ratio, 0.63 wt. % NaOH of catalyst concentration, 61.84°C of reaction temperature, and 62.12 min of reaction time. It is seen that the regression model results were in agreement with the experimental data. The results showed that RSM is a suitable statistical technique for optimizing the reaction parameters in the transesterification process in order to maximize the biodiesel yield.     

Traffic accident reconstruction and an approach for prediction of fault rates using artificial neural networks: A case study in Turkey

Objective: Currently, in Turkey, fault rates in traffic accidents are determined according to the initiative of accident experts (no speed analyses of vehicles just considering accident type) and there are no specific quantitative instructions on fault rates related to procession of accidents which just represents the type of collision (side impact, head to head, rear end, etc.) in No. 2918 Turkish Highway Traffic Act (THTA 1983). The aim of this study is to introduce a scientific and systematic approach for determination of fault rates in most frequent property damage–only (PDO) traffic accidents in Turkey.

Methods: In this study, data (police reports, skid marks, deformation, crush depth, etc.) collected from the most frequent and controversial accident types (4 sample vehicle–vehicle scenarios) that consist of PDO were inserted into a reconstruction software called vCrash. Sample real-world scenarios were simulated on the software to generate different vehicle deformations that also correspond to energy-equivalent speed data just before the crash. These values were used to train a multilayer feedforward artificial neural network (MFANN), function fitting neural network (FITNET, a specialized version of MFANN), and generalized regression neural network (GRNN) models within 10-fold cross-validation to predict fault rates without using software. The performance of the artificial neural network (ANN) prediction models was evaluated using mean square error (MSE) and multiple correlation coefficient (R).

Results: It was shown that the MFANN model performed better for predicting fault rates (i.e., lower MSE and higher R) than FITNET and GRNN models for accident scenarios 1, 2, and 3, whereas FITNET performed the best for scenario 4. The FITNET model showed the second best results for prediction for the first 3 scenarios. Because there is no training phase in GRNN, the GRNN model produced results much faster than MFANN and FITNET models. However, the GRNN model had the worst prediction results. The R values for prediction of fault rates were close to 1 for all folds and scenarios.

Conclusions: This study focuses on exhibiting new aspects and scientific approaches for determining fault rates of involvement in most frequent PDO accidents occurring in Turkey by discussing some deficiencies in THTA and without regard to initiative and/or experience of experts. This study yields judicious decisions to be made especially on forensic investigations and events involving insurance companies. Referring to this approach, injury/fatal and/or pedestrian-related accidents may be analyzed as future work by developing new scientific models.     

渭干—库车河三角洲绿洲土壤盐渍化空间分布特征遥感监测研究

渭干-库车河三角洲绿洲是位于塔里木盆地北缘的具有典型干旱特征的绿洲。干旱地区生态环境脆弱,土壤盐渍化严重破坏了国民经济的生产和生态环境的平衡,所以对干旱区盐渍化问题进行系统的研究是非常必要的。本文采用研究区的2001年8月6日遥感图像进行K-L和K-T变换,经过一系列波段结合．我们得到TM3、KL3、KT2波段结合是盐碱地信息提取的最佳波段结合;然后把研究区的等高线矢量图转换成DEM立体图,以及分类结果和DEM之间进行对比,结果表明,立体图的凹区和分类图的盐碱地基本重合在一起,从而我们得到导致该地区的盐渍化问题的自然和人文因素中,地形因素是占在主导地位的。     

SnO2薄膜光波导传感元件检测二甲苯蒸气

利用SnO2薄膜光波导传感元件,研究了检测二甲苯蒸气的新方法.采用溶胶-凝胶法制备了SnO2薄膜/锡掺杂玻璃光波导元件,将该元件固定在光波导气体检测系统,对挥发性有机物蒸气进行检测.实验结果表明,室温下该敏感元件对二甲苯蒸气有较高的选择性响应.对于体积分数为2.5×10-7的二甲苯蒸气,响应和恢复时间分别为5s和36s.该元件具有响应快、可逆性好、灵敏度高等特点.     

Engine Performance and Emission Characteristics of Plastic Oil Produced from Waste Polyethylene and Its Blends with Diesel Fuel

The overall objective of this study was to explore the utility of waste plastics as a potential source of diesel fuel. An experimental study was conducted to evaluate the use of various blends of plastic oil produced from waste polyethylene (WPE) with diesel fuel (D). WPE was degraded thermally and catalytically using sodium aluminum silicate as a catalyst. The oil collected at optimum conditions (414°C–480°C range and 1 h reaction time) was fractionated at different temperatures and fuel properties of the fractions were measured. Plastic oil was blended with diesel fuel at the volumetric ratios of 5%, 10%, 15%, 20%, and 100%. Fuel properties of blends are found comparable with those of diesel fuel within the EN 590 Diesel Fuel standard and they can also be used as fuel in compression ignition engines without any modification. Engine performance and exhaust emission studies of 5% WPE-D (WPE5) blend were performed. Experimental results showed that carbon monoxide (CO) emission is decreased by 20.63%, carbon dioxide (CO₂) emission is increased by 3.34%, and oxides of nitrogen (NO_x) emission is increased by 9.17% with WPE-D (WPE5) blend compared to diesel fuel.