专家笔谈：资源环境热点问题

重大的资源开发利用工程建设设想或实施往往容易引起社会各界的广泛关注。如何正确认识和科学评价各种资源环境热点问题是大家讨论的重点。文章认为任何重大资源开发工程必须遵循自然规律、生态规律、资源规律和经济规律,必须结合国家战略需求和地缘政治关系,开展前期深入的系统性综合调查,进行生态安全与资源工程安全评价和交叉学科论证,并对其科学价值、社会价值和经济价值等做出正确的判断。建议：未来的资源环境工程建设要谨防“大跃进”,要科学认识自然资源系统规律,守护好美丽的地球家园;要切实履行资源科学家的社会责任,提倡基于科学研究和科学态度的学术争鸣,科学问题最好还是由科学家去讨论;一些争议大的科研课题慎用公益性研发经费来支持。     

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。     

提升汇流环湿热环境适应性方法研究

目的提高汇流环在湿热环境下的适应能力。方法实物解剖,观察汇流环绝缘材料损坏情况、碳粉堆积情况,在不同温度、湿度下测试绝缘材料绝缘度、抗电强度,碳粉堆积和导电情况,比对不同绝缘材料性能,并找出最适合的绝缘材料,比对滚动汇流环、碳刷组件、刷丝组件,找到解决碳粉堆积的方法。结果汇流环受湿热影响会从接缝处和电缆连接处进入湿气,温度、湿度的上升会使得原绝缘材料性能下降,长时间使用汇流环,碳粉会在汇流环内堆积并受到湿热的影响而导电,局部密封汇流环可以减少湿气进入。PPO材料性能优异,可以替换原绝缘材料从而减少湿热的影响,使用刷丝组件替代碳刷可以避免碳粉的产生。结论综合运用局部密封、绝缘材料改进、碳刷组件改进三种措施,可以有效提高汇流环在湿热环境下的适应能力。     

一种模拟动力学边界条件的环境振动试验方法研究

目的提高环境振动试验的可靠性。方法以某典型钛合金蜂窝夹芯壁板试验件为例,提出一种模拟试验件动力学边界条件的试验方法,在试验件周围引入弹性连接件,再通过刚性转接工装与振动台台面连接,以试验件在试验安装状态下的共振频率及振型节线位置为优化目标,以弹性连接件的外形尺寸及厚度为优化变量,对弹性连接件进行优化设计,从而模拟试验件真实的动力学边界条件。结果弹性连接件的优化设计使得试验件在试验安装状态下前两阶共振频率与试验要求相差小于8%,而且振型节线位置与试验要求基本重合,达到了模拟试验件真实动力学边界条件的设计目标。结论对于壁板类试验件,在传统刚性夹具的基础上引入弹性连接件的试验方法,可以较好地模拟试验件真实动力学边界条件,提高环境振动试验的可靠性,并且这种方法对试验费用及试验周期影响较小,具有良好的工程应用前景。     

环境升温过程对常温固化环氧树脂热力学性能的影响

目的提高常温固化环氧树脂体系的高温使用性能。方法采用常温固化剂T31、中温固化剂IPDA以及高温固化剂DDM作为混合固化剂,对E-44型和AG-80型混合环氧树脂体系进行常温固化反应,并分析环境升温过程对固化物热力学性能的影响。通过DMA分析、热变形测量、固化度测试,分别评价室温固化环氧树脂在环境升温过程前后的玻璃化转变温度、热变形量及体系内部的固化反应程度变化,并通过吸水率测试和弯曲强度测试对玻璃纤维布增强常温固化环氧树脂基复合材料的耐湿热性能以及高温条件下的力学性能进行分析。结果环氧树脂常温固化物的tg为85.21℃,经1.5℃/min的平均升温速率加热至90℃之后,该环境升温过程使固化物的固化度增大至92%以上,tg增长为132.06℃的同时热变形温度增大。其复合材料耐湿热性能提高,且100℃时弯曲强度的保持率为65%,对于加热至120℃的环境升温过程,固化物的固化度接近96%,tg增长为144.45℃的同时热变形温度进一步提高,其复合材料耐湿热性能改善程度更加明显,且130℃时弯曲强度保持率仍接近60%。结论常温、中温、高温混合固化剂的合理复配有助于环氧树脂体系在环境升温变化的诱导条件下发生梯度式固化反应,使体系内部的交联固化程度迅速升至较高水平,可以有效提高其玻璃化转变温度,显著改善常温固化环氧树脂体系在高温条件下的热力学性能。     

A study on the interactions among several plant secondary compounds and aphids

The research, focused on the specific interactions among seven plant secondary compounds and aphids, was carried out and the preliminary results showed: (1) "T-typed tube method" was regarded as the best method compared with others designed to observe the interactions. (2) Cabbage aphid was able to be attracted by laurolene while it was not susceptible to α-pinene, β-pinene and diamylene. (3) Gossypol, a major secondary substance in cotton, was able to be implicated as feeding attractant to cotton aphid. (4) Rutin might be implicated repellent to cotton aphid.     

8-氨基喹啉-5-偶氮-对苯酚荧光光度法测定微量铬(Ⅵ)

在盐酸介质中,铬(Ⅵ)与AQAPOH形成可被氯仿萃取的荧光化合物,其λex/λem=295nm/400nm.线性范围为0-1.0μg Cr(Ⅵ)/25ml(水相),检测限为0.7μg/L.除Mn(Ⅶ)外,常见金属离子无干扰,可在数千倍Cr(Ⅲ)存在下测定废水中微量铬(Ⅵ).初步探讨了荧光反应的机理.     

Cumulative impact assessment in environmental planning: A coastal wetland watershed example

Several theoretical, analytical, and institutional difficulties have impeded the development and application of the assessment of cumulative environmental impacts. Watershed development on coastal wetlands offers an ideal context for evaluating the land disturbance target approach to cumulative impact assessment. A model land use planning system involving a time series approach was developed for Elkhorn Slough in California. The approach included four major components: evaluation of erosion susceptibility, measurement of land disturbance, establishment of a land disturbance target, and a comparison of existing and target land disturbance values. Further research is needed to test the transferability of the approach in a wide range of coastal watersheds and to verify the applicability of the methods to other cumulative impact problems.     

大气环境质量综合评价加权灰色关联模型的建立与应用

利用灰色理论建立大气环境质量综合评价加权灰色关联模型 ,运用污染贡献率确定权重系数。与其它评价模型相比 ,加权灰色关联模型还具有排序功能 ,实例表明该模型是简便有效的     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.