滦河秋季大型底栖动物群落结构空间格局与关键环境影响因子识别

掌握水生生物的空间分布特征及其关键影响因子是科学制定生物多样性保护策略的基础.于2016年秋季对滦河53个样点开展了大型底栖动物和环境因子的现场调查,分析了大型底栖动物群落结构与空间分布特征,并识别出影响大型底栖动物物种空间分布的关键环境因子.结果表明:①滦河（河北省）共采集并鉴定出大型底栖动物206种,隶属于8纲22目70科144属,以纹石蚕（Hydropsyche sp.）和东方蜉蝣（Ephemera orientalis）为优势种.②根据滦河大型底栖动物群落结构相似性,53个样点在空间上被分为3组.第1组分布在滦河干流的中下游及支流下游,无指示物种;第2组分布在支流上游,指示种为纹石蚕、朝大蚊（Antocha sp.）和中华小长臂虾（Palaemonetes sinensis）;第3组分布在滦河干流中上游,指示种为东方蜉蝣和中华齿米虾（Neocaridina denticulate sinensis）.③第3组的分类单元数显著高于第1组和第2组;第1组的多样性指数显著高于第2组;第1组的均匀度指数显著高于其他2组.单因素方差分析（one-way ANOVA）发现,pH、TN、底质、河道变化、水质状况和河岸土地利用类型等环境因子在第2组与第3组之间差异显著,第2组明显高于第3组.典范对应分析（CCA）表明,栖境复杂性、河水水量状况和河岸土地利用类型是影响滦河大型底栖动物空间分布的关键环境因子.研究显示,降低河岸带干扰强度、恢复河道生境多样性是未来滦河大型底栖动物多样性保护恢复的重要管理措施.      

可靠性强化试验技术在某型伺服作动器研制中的适用性探索

目的探索可靠性强化试验技术在典型机电液一体化产品伺服作动器研制过程中的适用性。方法以某型伺服作动器为研究对象,从故障激发的角度对可靠性强化试验技术的应用进行可行性分析,在响应调查和应力分析的基础上,结合产品的工作特点设计适用于该类伺服作动器的可靠性强化试验方案,包含低温步进应力试验、高温步进应力试验、快速温度循环试验10个循环、振动步进应力试验(包含气锤式三轴向六自由度超高斯随机振动方式及电磁振动台随机振动方式)及综合环境应力试验5个循环,并依此进行试验。结果在快速温度循环试验及综合环境应力试验过程中,均有效地激发出了产品的漏油故障,与相似产品外场暴露的漏油故障模式相吻合。结论可靠性强化试验技术可有效地应用于典型机电液一体化产品伺服作动器的研制过程,设计的可靠性强化试验方案可有效地激发外场出现频率较高的故障,可作为该类产品研制的试验手段之一。     

海洋环境低水位加速腐蚀研究进展

以海洋环境中一个非常重要的局部腐蚀形式——低水位加速腐蚀(ALWC)为对象,自其检测、发生原因、防护三个方面就国内外的文献报告进行综述分析。在检测方面,首先介绍了常用的宏观观察这一被动形式,并突出其特征,然后介绍了利用海水中可溶性无机氮含量作为ALWC发生概率预测这一主动形式,分析其优势与不足。在发生原因方面,在对将ALWC认定为一种典型的微生物腐蚀(MIC)形式的认知过程进行介绍之后,重点分析了微生物对ALWC作用机制不清晰的原因,并建议在后续研究中突出动态演变过程,结合高通量测序等分子生物学技术,确定在不同的阶段影响ALWC的关键微生物,且进一步在大气-海水体系下研究典型菌株及其协同作用的影响,提出微生物对ALWC的作用机制。在防护方面,根据新建和已建钢结构设施分别对传统和针对ALWC所具有的MIC与局部腐蚀特性的新型高效防护方法进行了介绍,并分析了防护方法的优缺点。     

空间站控制力矩陀螺微振动测试技术

目的分析控制力矩陀螺的微振动扰动特性。方法通过获取陀螺连接点对结构的扰振力输出、关键部位的微振动响应来获取产品的微振动扰动特性,通过隔振的方式对外部环境的干扰进行隔离,进而降低测试的背景噪声,通过运用高精度测试传感器来实现微小扰动力信号、加速度信号的测量,利用结构有限元法及单点激励多点响应的模态分析方法校核系统刚度,进而确定测试频率范围是否满足需求。结果可同时获得空间站控制力矩陀螺连接点处的输出扰振力、质心处的输出扰振力合力、合力矩以及关键部位的微振动响应,可有效隔离外部干扰,扰振力背景噪声的时域信号最大值可控制在0.07 N以内,RMS值在0.02 N以内,加速度信号的时域背景噪声最大值可控制在1.5 mg以内,RMS值在0.1 mg以内。可识别微小扰振信号,加速度传感器分辨率为5×10-5 grms,力传感器的测量分辨率为1×10-2 N,力矩测量分辨率小于1×10-2 N·m。测试频带在320 Hz以内。结论满足型号需求,并投入型号使用。     

经济手段防治白色污染的博弈分析

经济手段在环境管理中的作用及涉及到政府与各利益关系人之间的博弈过程。本文就政府与一次性餐具生产者，销售者之间的博弈关系，分析如何应用经济手段使其在防治“白色污染”中发挥更好的作用。     

A pilot project to detect and forecast harmful algal blooms in the northern Gulf of Mexico

More timely access to data and information on the initiation, evolution and effects of harmful algal blooms can reduce adverse impacts on valued natural resources and human health. To achieve this in the northern Gulf of Mexico, a pilot project was initiated to develop a user-driven, end-to-end (measurements to applications) observing system. A key strategy of the project is to coordinate existing state, federal and academic programs at an unprecedented level of collaboration and partnership. Resource managers charged with protection of public health and aquatic resources require immediate notice of algal events and a forecast of when, where and what adverse effects will likely occur. Further, managers require integrated analyses and interpretations, rather than raw data, to make effective decisions. Consequently, a functional observing system must collect and transform diverse measurements into usable forecasts. Data needed to support development of forecasts will include such properties as sea surface temperature, winds, currents and waves; precipitation and freshwater flows with related discharges of sediment and nutrients; salinity, dissolved oxygen, and chlorophyll concentrations (in vivo fluorescence); and remotely-sensed spatial images of sea surface chlorophyll concentrations. These data will be provided via a mixture of discrete and autonomous in situ sensing with near real-time data telemetry, and remote sensing from space (SeaWiFS), aircraft (hyperspectral imagery) or land (high-frequency radar). With calibration across these platforms, the project will ultimately provide a 4-dimensional visualization of harmful algae events in a time frame suitable to resource managers.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

Benchmarking for best practice environmental management

Benchmarking of environmental performance to demonstrate theachievement of best practice environmental management is acomponent of a new form of licensing of industrialdischarges in Western Australia. The paper describes theapproaches to benchmarking for the critical environmentalissues for an alumina refinery and wastewater treatmentplant. It also describes the lessons learnt from thebenchmarking process on appropriate methods, the benefitsand difficulties in the benchmarking process, and changesthat would assist benchmarking for best practiceenvironmental management.     

Optimal Timing to Adopt an Environmental Policy in a Strategic Framework

In this paper, the problem of optimal timing, when to adopt an environmental policy in a strategic framework is considered. Using real options theory and some basic tools of game theory, we show that, under certain assumptions, a country behaving strategically should wait longer before adopting such a policy than if it behaves unstrategically or within a larger entity. Such a postponed decision is sub-optimal as regards to the environment protection.     

Determination of Heavy Metals in Honey in Kahramanmaraş City, Turkey

Heavy metals in honey are of interest not only for quality control, but also for determination environmental contamination. The objective of this work was to determine distribution of the levels of selected heavy metals. Levels of copper (Cu), cadmium (Cd), manganese (Mn), Iron (Fe), magnesium (Mg) and nickel (Ni) in honey samples (21) in Kahramanmaraş region were determined by atomic absorption spectrometer (AAS). The mean values for Cu, Cd, Mn, Fe, and Mg were 0.01, 0.32, 0.03, 0.36 and 10.45 ppm, respectively. Nickel was not detected in honey samples. Kahramanmaraş honey samples were not free of heavy metals but the levels of them were found well below the permitted levels. The results suggested that honey may be useful for assessing the presence of environmental contaminants.