A new leak location method based on leakage acoustic waves for oil and gas pipelines

In order to study a new leak detection and location method for oil and natural gas pipelines based on acoustic waves, the propagation model is established and modified. Firstly, the propagation law in theory is obtained by analyzing the damping impact factors which cause the attenuation. Then, the dominant-energy frequency bands of leakage acoustic waves are obtained through experiments by wavelet transform analysis. Thirdly, the actual propagation model is modified by the correction factor based on the dominant-energy frequency bands. Then a new leak detection and location method is proposed based on the propagation law which is validated by the experiments for oil pipelines. Finally, the conclusions and the method are applied to the gas pipelines in experiments. The results indicate: the modified propagation model can be established by the experimental method; the new leak location method is effective and can be applied to both oil and gas pipelines and it has advantages over the traditional location method based on the velocity and the time difference. Conclusions can be drawn that the new leak detection and location method can effectively and accurately detect and locate the leakages in oil and natural gas pipelines.     

Analysis of the Trend and Seasonal Cycle of Carbon Monoxide Concentrations in an Urban Area (4 pp)

Background, Aim and Scope Air quality is an field of major concern in large cities. This problem has led administrations to introduce plans and regulations to reduce pollutant emissions. The analysis of variations in the concentration of pollutants is useful when evaluating the effectiveness of these plans. However, such an analysis cannot be undertaken using standard statistical techniques, due to the fact that concentrations of atmospheric pollutants often exhibit a lack of normality and are autocorrelated. On the other hand, if long-term trends of any pollutant’s emissions are to be detected, meteorological effects must be removed from the time series analysed, due to their strong masking effects. Materials and Methods The application of statistical methods to analyse temporal variations is illustrated using monthly carbon monoxide (CO) concentrations observed at an urban site. The sampling site is located at a street intersection in central Valencia (Spain) with a high traffic density. Valencia is the third largest city in Spain. It is a typical Mediterranean city in terms of its urban structure and climatology. The sampling site started operation in January 1994 and monitored CO ground level concentrations until February 2002. Its geographic coordinates are W0°22′52″ N39°28′05″ and its altitude is 11 m. Two nonparametric trend tests are applied. One of these is robust against serial correlation with regards to the false rejection rate, when observations have a strong persistence or when the sample size per month is small. A nonparametric analysis of the homogeneity of trends between seasons is also discussed. A multiple linear regression model is used with the transformed data, including the effect of meteorological variables. The method of generalized least squares is applied to estimate the model parameters to take into account the serial dependence of the residuals of this model. This study also assesses temporal changes using the Kolmogorov-Zurbenko (KZ) filter. The KZ filter has been shown to be an effective way to remove the influence of meteorological conditions on O₃ and PM to examine underlying trends. Results The nonparametric tests indicate a decreasing, significant trend in the sampled site. The application of the linear model yields a significant decrease every twelve months of 15.8% for the average monthly CO concentration. The 95% confidence interval for the trend ranges from 13.9% to 17.7%. The seasonal cycle also provides significant results. There are no differences in trends throughout the months. The percentage of CO variance explained by the linear model is 90.3%. The KZ filter separates out long, short-term and seasonal variations in the CO series. The estimated, significant, long-term trend every year results in 10.3% with this method. The 95% confidence interval ranges from 8.8% to 11.9%. This approach explains 89.9% of the CO temporal variations. Discussion The differences between the linear model and KZ filter trend estimations are due to the fact that the KZ filter performs the analysis on the smoothed data rather than the original data. In the KZ filter trend estimation, the effect of meteorological conditions has been removed. The CO short-term componentis attributable to weather and short-term fluctuations in emissions. There is a significant seasonal cycle. This component is a result of changes in the traffic, the yearly meteorological cycle and the interactions between these two factors. There are peaks during the autumn and winter months, which have more traffic density in the sampled site. There is a minimum during the month of August, reflecting the very low level of vehicle emissions which is a direct consequence of the holiday period. Conclusions The significant, decreasing trend implies to a certain extent that the urban environment in the area is improving. This trend results from changes in overall emissions, pollutant transport, climate, policy and economics. It is also due to the effect of introducing reformulated gasoline. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of CO. The KZ filter has been the most effective method to separate the CO series components and to obtain an estimate of the long-term trend due to changes in emissions, removing the effect of meteorological conditions. Recommendations and Perspectives Air quality managers and policy-makers must understand the link between climate and pollutants to select optimal pollutant reduction strategies and avoid exceeding emission directives. This paper analyses eight years of ambient CO data at a site with a high traffic density, and provides results that are useful for decision-making. The assessment of long-term changes in air pollutants to evaluate reduction strategies has to be done while taking into account meteorological variability     

A modular approach to Integrated Assessment modeling

In this paper, we present a new approach to model coupling that probably forms the methodological basis of a new generation of Integrated Assessment models. This approach respects the knowledge and expertise that is embodied in existing models and encourages their gradual evolution. Modularity is the guiding principle. Our approach is distinguished by the way modules are coupled which is based on an interplay of a job control module, a numerical coupling module, and a couple of stand-alone functional modules. The numerical coupling module - the core component - serves to treat the feedbacks between the functional modules. A first implemented example that couples an economic and a climate module by means of a two-phase meta-optimization is presented here. The algorithm and mathematical structure behind are discussed as well as important features such as convergence behavior and reliability.     

我国热带地区胶—茶—鸡农林复合系统物质循环研究

以海南省文昌市典型的胶—茶—鸡农林复合模式为研究对象 ,以单作胶园、胶茶间作园为对照 ,采用定量研究与定性分析相结合的方法 ,研究其物质循环规律。结果表明 ,胶—茶—鸡农林复合系统的氮、磷输入、输出量最大 ,系统外物质投入率比另两系统明显减少 ,系统内养分循环量增加 ,因此 ,该系统具有较合理的物质循环结构。     

高浓度废水预处理

从分析冲击炼油污水处理的原因出发，阐述了高浓度废水预处理的重要性；重点介绍了三种好氧生物法处理技术；其中ＳＢＲ法由于工程技术日趋成熟，可优先推广应用     

石油亚砜萃取巯基醋酸的研究

对石油亚砜(PSO)萃取巯基醋酸的条件和影响进行了实验研究,确定了石油亚砜通过氢键和巯基醋酸形成的萃合物的组成.为从废水中回收巯基醋酸以及石油硫醚的综合利用提供了依据.     

高级氧化技术机理及在水处理中的应用进展

综述了近年来发展迅速的高级氧化技术，主要包括Fenton法、臭氧氧化法、湿式氧化技术、超临界水氧化法、纳米光催化氧化法电化学催化降解法及超声降解法等。介绍了各种高级氧化技术的基本原理及在废水处理中的应用进展，并对其特点进行了评述。     

有效微生物技术在渗滤液处理中的应用与发展

介绍了有效微生物(EM)的组成、特点及其处理废水的机理;通过与传统的生化法处理垃圾渗滤液的比较,指出了利用EM技术处理垃圾渗滤液的优势,认为该技术不仅能够提高污染物的去除效率,降低污泥产量,而且能够缩短反应器的响应时间;并介绍了EM制剂在国内外的生产情况,对今后EM技术的发展前景提出了展望.     

SO2和氟化物对农作物的危害的鉴别与防治

SO2 和氟化物对农作物的危害可通过症状、镜检和受害叶片的化学分析来鉴别 ,并从风向、与污染源距离、障碍物和叶片成熟程度几方面与作物病虫等影响因素相区别。排放浓度、接触时和作物本身抗性是影响危害程度的主要因素。应从治理污染、选种抗性强树木、避开作物敏感生育期、合理种植、加强施肥等方面采取措施加以防治