Morphology and composition of particles emitted from conventional and alternative fuel vehicles

Size, morphology, and composition of airborne particles strongly affect human health and visibility, precipitation, and the kinetic characteristics of particles. In this study, the morphology and chemical composition of particles emitted from conventional (diesel and gasoline) and alternative (CNG and methanol) fuel vehicles were characterized through scanning electron microscopy (SEM) and energy-dispersive X-ray (EDX). The SEM images revealed that the size of primary particles (without agglomeration) was approximately 10 nm in the exhaust from all the tested vehicles. The particles emitted from gasoline vehicle (GV), CNG vehicle (CNGV), and methanol vehicle (MV) had the same median diameter, 62 nm, which was smaller than those from heavy diesel vehicle (HDV) and light diesel vehicle (LDV). Soot was observed in the HDV, LDV, and GV samples but not in the CNGV and MV. The fractal dimension, which was used to quantify the degree of irregularity of soot, was 1.752 ± 0.014, 1.789 ± 0.076, and 1.769 ± 0.006 in the exhaust from HDV, LDV, and GV samples, respectively. The particles discharged by all tested vehicles contained the elements C, O, Fe, and Na. The main element in the samples of HDV, LDV, and GV was C, while O was the main element in the samples of alternative fuel vehicles. The profiles of minor elements were more complex in the emissions of alternative fuel vehicles than those in the emissions of conventional fuel vehicles. The results improved our understanding of the morphology and elemental composition of particles emitted from vehicles powered by diesel, gasoline, CNG, and methanol.

     

基于EMD-HHT可定位长输天然气管道第三方破坏事件监测技术*

为适应目前管道安全监测需要,满足对扰动信号分类监测的实际需求,提出1种基于希尔伯变换和经验模态分解（EMD-HHT）的信号特征提取技术,利用基于φ-OTDR分布式光纤传感系统采集振动信号,通过EMD+HHT区分算法对管道沿线振动事件进行分解并提取6个典型特征向量,各特征事件数据经过EMD后选取IMF3为最终提取特征向量的原始数据,BP神经元网络可有效识别机械破坏、敲击破坏、车辆经过、人工挖掘、动力干扰5种事件。研究结果表明:在长输管道信号识别中,BP神经网络对5类事件平均识别率高达98.6%,该技术分类识别5类事件扰动信号,能够达到较高准确性,并且误报率平均在1.3%,能较好满足现场安全实时监测需求。研究结果对长输油气管道附近第三方破坏扰动信号分类监测具有一定参考意义。