鄂东鸭儿湖地区大气中有机氯农药特征及土-气交换

采用聚氨酯泡沫-被动采样法（PUF-PAS）采集鸭儿湖地区大气样,研究典型污染源地区大气中有机氯农药（OCPs）组成、来源及土-气交换现状。鸭儿湖地区大气中OCPs主要组成为艾氏剂（Aldrin）、滴滴涕（DDTs）、六六六（HCHs）、六氯苯（HCB）、甲氧滴滴涕（Methoxychlor）和a-硫丹（α-Endosulfan）,约占OCPs总量的84%。较高浓度Aldrin（平均浓度为161.25 pg/m³）广泛存在应引起高度重视。HCHs（平均浓度为89.64 pg/m³）和DDTs（平均浓度为92.29 pg/m³）普遍存在且含量高,污染程度比较明显。HCHs异构体中β-HCH高于α-HCH,远远高于γ-HCH和δ-HCH,说明HCHs经过长期降解已逐渐稳定下来;来源分析推测存在工业HCHs的使用或者受大气长距离传输影响。DDTs在各点位分布明显不同于HCHs,且各点位DDTs的六种组成均存在很大差别,可能由于点位地理位置以及农药使用情况导致;来源分析推测大气中DDTs主要来自历史残留。鸭儿湖地区OCPs土气交换研究表明,HCHs主要表现为从大气向土壤中沉降;DDTs在大多数点位源于土壤中历史残留的挥发,部分点位受到大气长距离传输影响。     

高速铁路选线对生态环境的影响分析研究

高速铁路选线要充分考虑对生态环境的影响,在路基、桥涵、隧道等形式通过环境敏感点时,要从工程技术和生态环境保护的角度出发,避免高速铁路建设和运营对当地的水资源环境、自然保护区造成破坏。根据高速铁路周边的环境信息,在路基填土高度上结合地形、地质和水文自然特征确定合理的填土高度。在高速铁路跨越生态保护区和湖泊等线路中,贯彻生态选线的原则,准确确定桥涵净空,在工程设计、施工上采用系列的生态保护措施。     

铝合金的实验室盐雾试验腐蚀行为图像特征提取

目的提取铝合金材料在盐雾箱试验中可以表征腐蚀程度的表面形貌图像特征量。方法首先,对采集于表面化学清洗过的试样原始图像进行图像增强等预处理,突出腐蚀部位;其次,基于数字图像处理的分形和小波分解方法提取出分形维数和小波能量特征值。结果与以质量损失量为基础的腐蚀深度特征值进行相关性对比,基于图像分析的特征提取法的准确度和精度比较高。结论该方法可以应用于对试样的腐蚀程度进行定性和定量分析,判断并预测试样的腐蚀速度。     

印制电路板海洋环境试验与实验室环境试验相关性研究

目的研究机载电子设备印制电路板西沙海洋大气环境与实验室环境试验的相关性。方法在西沙海域环境下开展两种印制电路板的棚下暴露试验,暴晒试验时间为3年,同时在实验室开展盐雾试验,分别通过测试绝缘电阻、品质因数研究两种试验环境的相关性。结果将以绝缘电阻、品质因数为基准相关性评价结果进行平均,得出印制电路板实验室加速试验和自然暴露试验存在着强相关。两种样品的加速系数分别是3.5,5.15。结论建立了印制电路板在西沙海洋环境试验和实验室盐雾环境试验的相关性,可为后续西沙环境试验的加速处理提供依据。     

绿色建筑的节能环保设计思路分析研究

绿色建筑具有透光好、保温性与通风性好等优点,为了实现节能减排,提高宜居性,进行绿色建筑的节能环保设计,考虑低碳节能效果,在建筑外墙使用抹整体式保温系统,楼顶使用现浇整体式保温系统进行建筑的保温墙设计,考虑建筑的通风性,建筑使用通透性的设计方案,设计空中楼顶花园,提高隔热防晒效果的同时,提高绿化覆盖面积。合理布局楼高和楼间距,提高建筑的采光性,设计景观生物廊道,采用底层架空式结构实现自然通风和采光,实现绿色建筑的节能优化设计。     

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.     

Analysis of BTEX and other substituted benzenes in water using headspace SPME-GC-FID: method validation

The analysis of BTEX and other substituted benzenes in water samples using solid phase microextraction (SPME) and quantification by gas chromatography with flame ionization detection (GC-FID) was validated. The best analytical conditions were obtained using PDMS/DVB/CAR fibre using headspace extraction (HS-SPME) at 50 [degree]C for 20 min without stirring. The linear range for each compound by HS-SPME with GC/FID was defined. The detection limits for these compounds obtained with PDMS/DVB/CAR fibre and GC/FID were: benzene (15 ng L(-1)), toluene (160 ng L(-1)), monochlorobenzene (54 ng L(-1)), ethylbenzene (32 ng L(-1)), m-xylene (56 ng L(-1)), p-xylene (69 ng L(-1)), styrene (35 ng L(-1)), o-xylene (42 ng L(-1)), m-dichlorobenzene (180 ng L(-1)), p-dichlorobenzene (230 ng L(-1)), o-dichlorobenzene (250 ng L(-1)) and trichlorobenzene (260 ng L(-1)). This headspace SPME-GC-FID method was compared with a previously validated method of analysis using closed-loop-stripping analysis (CLSA). The headspace SPME-GC-FID method is suitable for monitoring the production and distribution of potable water and was used, in field trials, for the analysis of samples from main intakes of water (surface or underground) and from the water supply system of a large area (Lisbon and neighbouring municipalities).     

略谈淮北市城市景观生态建设

为了保护淮北市城市生态环境，拟利用城市景观生态学原理对该市城市景观进行生态设计，包括城市自然组分、非自然组分设计。自然组分包括种群源、廊道、节点等内容，力争使自然组分成为环境质量的控制性组分，使城市生态良性循环、物种多样并持续存在和流动可达，物质循环、能量流动、信息传递畅通。     

火焰原子吸收分光光度法测定鱼内脏及河流底泥中的铜

采用火焰原子吸收分光光度法测定鱼内脏及河流底泥中的铜。鱼内脏经组织捣碎机捣成匀浆后，加硝酸消解；底泥则经氢氟酸和高氯酸消化。该方法具有灵敏度高，精密度和回收率较好等特点。     

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.