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。     

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).     

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.     

The organic composition of diesel particulate matter, diesel fuel and engine oil of a non-road diesel generator

Diesel-powered equipment is known to emit significant quantities of fine particulate matter to the atmosphere. Numerous organic compounds can be adsorbed onto the surfaces of these inhalable particles, among which polycyclic aromatic hydrocarbons (PAHs) are considered potential occupational carcinogens. Guidelines have been established by various agencies regarding diesel emissions and various control technologies are under development. The purpose of this study is to identify, quantify and compare the organic compounds in diesel particulate matter (DPM) with the diesel fuel and engine oil used in a non-road diesel generator. Approximately 90 organic compounds were quantified (with molecular weight ranging from 120 to 350), which include alkanes, PAHs, alkylated PAHs, alkylbenzenes and alkanoic acids. The low sulfur diesel fuel contains 61% alkanes and 7.1% of PAHs. The identifiable portion of the engine oil contains mainly the alkanoic and benzoic acids. The composition of DPM suggests that they may be originated from unburned diesel fuel, engine oil evaporation and combustion generated products. Compared with diesel fuel, DPM contains fewer fractions of alkanes and more PAH compounds, with the shift toward higher molecular weight ones. The enrichment of compounds with higher molecular weight in DPM may be combustion related (pyrogenic).     

垃圾填埋场渗出液中有害成分的研究

研究垃圾填埋场渗出液中的有害成分。渗出液样品用适当的方法处理后 ,用气相色谱 -质谱联用方法和原子发射光谱法进行定性分析。然后用原子吸收分光光度法和反相高效液相色谱法定量测定样品中微量的 Cu、Zn、Pb、Cr、Cd、Hg、苯酚、对甲苯酚、萘、蒽     

内蒙古某灌域表层土壤六氯环己烷空间分布特征及影响因素

为修复灌区内土壤有机氯农药污染，降低六氯环己烷对土壤环境的危害，对内蒙古某灌域表层土壤进行采样，利用气相色谱测定50个土壤样品中六氯环己烷及其异构体的含量，并利用ArcGIS空间分析技术确定表层土壤中六氯环己烷的分布状况。样本中六氯环己烷的检出率为96%，各样本中六氯环己烷的浓度范围为未检出~23.0 ng/g，平均浓度为4.4 ng/g。4种异构体含量平均值顺序为γ-六氯环己烷>δ-六氯环己烷>β-六氯环己烷>α-六氯环己烷。灌域东北部和西南部残留浓度较高，并由这2个方向向中部地区逐渐递减，表层土壤中的六氯环己烷分布受土壤黏粒量、不同种植种类和灌溉水源的影响较大。虽受人类耕作的影响，但表层土壤中的六氯环己烷污染水平相对较低。     

上海汽车制造业涂装废气VOCs排放特征

于2015年10月对上海市嘉定区4家(A、B、C、D)汽车制造企业涂装废气ρ(VOCs)和组成特征进行了调查分析。结果表明,A、B、C、D厂涂装废气排放口ρ(VOCs)总为0. 743～6. 11 mg/m~3,主要检出物和最高检出值分别为:A厂二甲苯2. 06 mg/m~3、B厂二甲苯0. 578 mg/m~3、C厂甲苯2. 59 mg/m~3、D厂庚烷0. 274 mg/m~3;芳香烃类是A、B、C厂排放比例最高的VOCs组分,烷烃类是D厂排放比例最高的VOCs组分。指出,原、辅料种类影响排放物的主要成分,废气处理工艺类型影响排放物主要成分和浓度。     

环境样品中痕量锰的催化动力学测定法

对环境样品中痕量锰的催化动力学测定方法进行简要评述，引述文献１６篇。