Potential of secondary aerosol formation from Chinese gasoline engine exhaust

Light-duty gasoline vehicles have drawn public attention in China due to their significant primary emissions of particulate matter and volatile organic compounds(VOCs). However,little information on secondary aerosol formation from exhaust for Chinese vehicles and fuel conditions is available. In this study, chamber experiments were conducted to quantify the potential of secondary aerosol formation from the exhaust of a port fuel injection gasoline engine. The engine and fuel used are common in the Chinese market, and the fuel satisfies the China V gasoline fuel standard. Substantial secondary aerosol formation was observed during a 4–5 hr simulation, which was estimated to represent more than 10 days of equivalent atmospheric photo-oxidation in Beijing. As a consequence, the extreme case secondary organic aerosol(SOA) production was 426 ± 85 mg/kg-fuel, with high levels of precursors and OH exposure. The low hygroscopicity of the aerosols formed inside the chamber suggests that SOA was the dominant chemical composition. Fourteen percent of SOA measured in the chamber experiments could be explained through the oxidation of speciated single-ring aromatics. Unspeciated precursors, such as intermediate-volatility organic compounds and semi-volatile organic compounds, might be significant for SOA formation from gasoline VOCs. We concluded that reductions of emissions of aerosol precursor gases from vehicles are essential to mediate pollution in China.     

基于SOM-SVM组合分类器的涂层防护性能研究

目的为避免EIS,EN技术可能出现的问题,建立一个准确、高效的评价模型,以探究现役军用有机涂层防护性能。方法利用电化学阻抗谱(EIS)、电化学噪声(EN)技术分析了两种军车有机涂层在循环暴露试验中的腐蚀行为,提取低频阻抗模值|Z|_(0.1 Hz)与涂层噪声电阻R_n两种电化学评价参数作为自组织神经网络(SOM)的输入训练样本,同时结合支持向量机(SVM)方法建立涂层防护性能组合分类器。结果将涂层失效过程自适应地分为涂层防护性能良好、防护性能下降、基本失效三个阶段。结论所建立的SOM-SVM组合分类器对于辅助分析涂层防护性能具有可行性。     

大型空间环境模拟器真空系统配置技术研究

分别对国内外大型空间环境模拟器的真空系统配置进行了介绍,并对真空系统各个子系统的配置与技术特点进行了分析,重点针对大型空间环境模拟器真空系统中所需的真空抽气策略进行了研究,为后续超大型环境试验设备的研制和试验测试提供了参考。     

含弹内埋弹舱缩尺模型气动噪声特性研究

目的对含弹的内埋弹舱模型进行噪声仿真和试验研究。方法计算采用"CFD+CAA"的混合方法,采用DDES计算流场,基于M?hring声学类比方法得到测量点的噪声信息。采用该方法与空腔M219标准试验结果进行对比。内埋弹舱吹风试验在全消声室中进行,并采用传声器阵列识别主要噪声源。结果计算与试验得到的不同来流条件下,不同测量点的噪声频谱曲线基本一致。弹舱内最大声压级出现在中间弹翼附近的侧壁上,与声源识别的主要噪声源的位置一致。结论该研究可为内埋弹舱的设计提供参考和技术支持。     

Monitoring Air Quality in Mountains: Designing an Effective Network

A quantitatively robust yet parsimonious air-quality monitoring network in mountainous regions requires special attention to relevant spatial and temporal scales of measurement and inference. The design of monitoring networks should focus on the objectives required by public agencies, namely: 1) determine if some threshold has been exceeded (e.g., for regulatory purposes), and 2) identify spatial patterns and temporal trends (e.g., to protect natural resources). A short-term, multi-scale assessment to quantify spatial variability in air quality is a valuable asset in designing a network, in conjunction with an evaluation of existing data and simulation-model output. A recent assessment in Washington state (USA) quantified spatial variability in tropospheric ozone distribution ranging from a single watershed to the western third of the state. Spatial and temporal coherence in ozone exposure modified by predictable elevational relationships ( 1.3 ppbv ozone per 100 m elevation gain) extends from urban areas to the crest of the Cascade Range. This suggests that a sparse network of permanent analyzers is sufficient at all spatial scales, with the option of periodic intensive measurements to validate network design. It is imperative that agencies cooperate in the design of monitoring networks in mountainous regions to optimize data collection and financial efficiencies.     

Study on the effect of porous fence on air quality and traffic noise level around a double-decked road structure

Fence for traffic noise control sometimes causes adverse effect on air pollution. Thus in this study, performance of porous fence as a tool for control of both air pollution and noise pollution was evaluated. A two-dimensional numerical model for flow and pollutant concentration and an analytical model for traffic noise were utilized in the analysis of a double-decked road structure with fences only at ground (Case 1) and at both ground and upper deck (Case 2). Porous fences were assumed only at the ground level since the solid fences at the upper deck usually leads to desirable result on air pollution. Effects of the variable porosity on air quality and noise level near road were evaluated. Obtained results showed: (1) flow pattern in leeward of fence was drastically changed at 40–50% porosity in Case 1 and 50% in Case 2. The porosity larger than 40% excluded presence of a circulation behind the fence. (2) Effect of porous fence on air pollution was different in Cases 1 and 2. In Case 1, the porous fence generally resulted in the reduction of air pollution at the ground level; on the other hand, in Case 2, it rather led to increase of the concentration. (3) Traffic noise level was also largely changed by the porosity of the fence; an example of simultaneous evaluation of the effects of porous fence on both air and noise pollution in Case 1 showed that the fence of 60% porosity leads to reduction of air pollution by 20% compared with solid fence case, and reduction of noise pollution by 4–6% in dB compared with no fence case, at l m high and 10 m from the road.     

B-P网络应用于地下水水质监测点优选

以地下水水质评价分级标准作为训练样本,构造B-P网络模型对其进行训练,用训练好的B-P网络对某地的地下水水质监测点进行评判、优选。并与其它方法的结果进行比较,结果表明,B-P网络用于环境测点优选不仅原理直观,而且具有较好的客观性和实用性。     

基于B-P神经网络的环境质量评价方法

提出可将环境质量评价的无论是定量指标还是定性参数转化成"二进制"的"1"或"0",进而将这种二进制数引入B-P网络.通过实例探讨,这种新的B-P网络既适用于定量指标的水质参数又适用于定性指标的水质参数.     

ADMS模型解析城区总悬浮颗粒物来源

以污染源排放数据为基础,应用ADMS-城市扩散模型模拟分析了鞍山市尘各污染源对空气环境质量的贡献。结果表明,来自污染源的浓度贡献值占总量的52%;二次尘及外来尘浓度贡献占48%;矿山开采二次尘的浓度贡献占16%;鞍钢炼铁厂的浓度贡献占19.6%;鞍钢化工总厂浓度贡献占5.6%;供暖锅炉浓度贡献占9.3%。低、中、高架源浓度贡献百分比分别为68.1%、29.0%、2.9%。低架源吨排放量浓度贡献为高架源的2.4倍。