海水中阴极极化对X80钢应力腐蚀及氢脆敏感性的影响

目的研究不同p H值海水中阴极极化对X80管线钢应力腐蚀及氢脆的抑制作用。方法采用慢应变速率拉伸试验、电化学测试、微观组织观察等分析方法。结果 X80钢在天然海水中的析氢电位约为-940m V(vs.SCE,下同),海水p H为3.5时析氢电位发生正移。其应力腐蚀敏感性与极化电位有很大关系,随着极化电位负移,X80钢的氢脆敏感性增加。天然海水中当极化电位负于-950 m V时,断口出现准解理断裂特征形貌。在-1050 m V极化电位下,钢材进入氢脆断裂区发生脆性断裂。海水p H为3.5时,-900 m V钢材有发生氢脆的危险。结论与天然海水相比,X80钢在p H为3.5的酸性海水中具有较高的应力腐蚀敏感性,两种海水介质中X80钢的应力腐蚀敏感性均随极化电位负移而增加。     

玻璃纤维-铝合金层合板氙灯老化性能研究

目的研究氙灯环境对玻璃纤维-铝合金层合板性能的影响。方法通过研究玻璃纤维-铝合金层合板在氙灯老化后的力学性能、基体红外光谱、铝合金表面形貌及元素变化,分析层合板的氙灯老化机理。结果随氙灯老化时间的延长,树脂基体老化降解程度越高,树脂与纤维、铝合金的界面强度下降,使材料拉伸及弯曲性能降低,拉伸强度受老化影响最为显著的是正交结构层合板,老化84 d后强度降低13.7%,正交结构0°方向层合板的弯曲强度受氙灯老化影响降低4.6%,其他试样的弯曲强度降幅均低于5%,氙灯老化后的铝合金层表面的微凹坑数量及尺寸无明显变化,但凹坑氧元素随老化周期延长略微增加。结论氙灯环境主要影响玻璃纤维-铝合金层合板中的复合材料层及各界面,树脂基体的降解导致力学性能的衰退,而对于铝合金的影响并不明显。     

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。     

铍青铜弹簧片应力腐蚀断裂研究

目的研究传感器微动开关弹簧片应力腐蚀的机理,找到断裂原因。方法对弹簧片进行断口形貌观察、能谱成分分析,并进行产品模拟腐蚀实验。结果传感器内部意外进入雨水,在工作温度下形成湿热环境,开关盒中的游离氨在此环境下释放出来,在开关盒内部形成潮湿氨气氛,铍青铜弹簧片工作时凹面一侧承受静拉应力作用,从而在潮湿氨和拉应力共同作用下发生应力腐蚀开裂。结论通过采用无氨酚醛塑料制备开关盒,弹簧片的应力腐蚀得到了有效预防。     

发展我国环境监测技术产业的探讨

本文从发展我国环境监测技术产业的角度论述了环境监测技术产业的主要内容，分析了我国环境监测技术产业的发展和存在的问题，对发展我国环境监测技术产业提出了建议。     

济南市创建国家环保模范城市的调查与思考

从济南市环境保护现状调查入手,对照国家环境保护模范城市考核指标,分析济南市创建国家环境保护模范城市存在的问题,借鉴其他城市创建国家环境保护模范城市的成功经验,结合济南市实际,提出了有针对性的对策与建议。     

锅炉环境影响评价系统软件研究

用Ｃ语言作为开发语言，研制开发了锅炉环境影响评价系统软件，软件充分利用Ｃ语言模块化，过程化的优点，源程序由１２个模块组成。经编译连接生成一个可执行程序，用户只需输入１５个必需的原始参数，软件即可自动查询，计算大量的中间参数，并立即给出污染源强及污染物在周围环境中分布的预测和评价结果。     

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.