碳量子点掺杂氮化碳提高的光电化学阴极保护性能研究

目的研究g-C_3N_4/C-Dots-M复合材料的光电化学阴极保护性能。方法采用X射线衍射(XRD)、扫描电子显微镜(SEM)和紫外可见漫反射光谱技术对样品的晶体结构、微观形貌和光吸收性能进行表征。通过电化学测试可见光照射下g-C_3N_4/C-Dots-M复合材料光电极偶联316L不锈钢后的光电化学阴极保护电流密度和电位变化曲线,研究材料的光电化学阴极保护性能。结果可见光照射下,该偶联体系的光致混合电位下降至-0.43 V(vs. Ag/AgCl),光电化学阴极保护电流密度达到4.3μA/cm。结论得益于碳量子点优异的电子传导特性,g-C_3N_4/C-Dots-550复合材料的光电化学阴极保护性能比纯g-C_3N_4的明显增强。该复合材料在光电化学阴极保护方面展现了较大的应用潜力。     

包装箱用碳纤维增强复合材料贮存寿命研究

目的研究碳纤维增强复合材料贮存条件下的性能变化趋势和寿命评估。方法对碳纤维增强复合材料开展四个不同温度条件下的热氧老化试验,按试验周期定期取样开展冲击性能测试,对试验数据采用寿命预估方法进行处理,对材料性能进行预估。结果通过数据计算分别得到我国热带海洋、干热沙漠等典型气候条件下的碳纤维增强复合材料贮存寿命分别为17.21~35.89年。结论碳纤维增强复合材料具有较好的贮存性能,在较为严酷的热带海洋气候和给定的失效判据条件下,寿命预计为17.21年。试验和数据处理方法可以较好地预计材料的性能变化趋势和开展寿命评估。     

气体中毒窒息事故的环境应急监测

以株洲市攸县某矿业公司"5·7"重大中毒窒息事故的环境应急监测为例，详细阐述了该事故开展应急监测的全过程。针对锁定污染因子、查找污染源、判断污染团的扩散趋势以及监控敏感点的安全等监测目的设计监测方案。通过监测数据分析确定事故特征因子为CO，其周边环境空气中浓度随着与抽风口距离的增加而快速下降，监测到附近居民区环境空气质量未受事故影响。     

超临界二氧化碳萃取－GC／MS测定土壤中的多环芳烃

本文开发了一种采用超临界二氧化碳萃取土壤中多环芳烃、不须经过纯化步骤，直接可用于ＧＣ／ＭＳ分析的简便、高效的方法。本实验中超临界革取的流体是二氧化碳，改善剂是５％的二氯甲烷／甲醇，萃取温度为１２０℃、压力为３４ＭＰａ。ＧＣ／ＭＳ分析时除了采用外标外，还加入了６种同位素ＰＡＨｓ内标以校正各段ＰＡＨｓ的响应因子。采用本方法成功地测定了我国未开垦森林土壤中的ＰＡＨｓ。     

WK－1A型二氧化硫自动测定仪测定烟气中SO_2的方法研究

本文研究了仪器电导法测定烟气中ＳＯ２的监测方法，并与碘量法进行了比较，提出了吸收液测定的影响及吸收液的配制方法。本方法简单快速，能够满足烟气中ＳＯ２监测工作的要求。     

Photocatalytic degradation of phenol

In this study photocatalytic degradation of phenol in thepresence of UV irradiated TiO₂ catalyst andH₂O₂was investigated. Effects of TiO₂ andH₂O₂concentrations and pH on photocatalytic degradation were examined. The rate constants for photocatalytic degradation wereevaluated as a function of TiO₂ and H₂O₂ concentrations and pH of the solution. It was found thatphotodegradation is an effective method for the removal of phenoland disappearance of phenol obeyed first order kinetics. The amount of CO₂h produced during photocatalytic degradation wascorresponding to the complete mineralization. Photodegradationcan be an alternative method for the treatment of phenol containing wastewaters.     

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.     

Factors influencing Aerosol Characteristics over Urban Environment

Atmospheric aerosols are an important contributing factor to turbidity in urban areas besides having impact on health. Aerosol characteristics show a high degree of variability in space and time as anthropogenic share of total aerosol loading is quite substantial and is essential to monitor the aerosol features over long time scales. In the present study extensive observations of columnar aerosol optical depth (AOD), total columnar ozone (TCO) and precipitable water content (PWC) have been carried over a tropical urban city of Hyderabad, India. Significant variations of AOD have been observed during course of the day with low values of AOD during morning and evening hours and high values during afternoon hours. Spectral variation of AOD exhibits high AOD at smaller wavelengths and vice versa except a slight enhancement in AOD at 500 nm. Anomalies in AOD, particulate matter and black carbon concentrations have been observed during May, 2003. Back trajectory analysis of air mass during these episodes suggested variation in air mass trajectories. Analysis of the results suggests that air trajectories from land region north of study area cause high loading of atmospheric aerosols. The results are discussed in the paper.     

Higher Plants As Bioindicators Of Sulphur Dioxide Emissions In Urban Environments

The evaluation of certain vascular plants that grow in the city of Madrid as biomonitors of SO₂ air pollution in urban environments has been carried out. Total concentration of sulphur in leaves of the chosen higher plants as well as other parameters in close relation to this contaminant (visible injury symptoms, chlorophyll a- and b-content and peroxidase activity) have been determined in order to study the spatial distribution and temporal changes in SO₂ deposition. Results obtained show that coniferous species such as Pinus pinea, were more sensitive to SO₂ atmospheric concentration than leafy species as Quercux ilex subspecies ballota and, in the same way, bush species, such asPyracantha coccinea and Nerium oleander, were more sensitive than wooded species, such as Cedrus deodaraandPinus pinea, respectively. There is a higher accumulation of sulphur in vegetable species located near highways and dense traffic incidence roads and near areas with high density of population. The minimum values for accumulation of SO₂ were registered in winter and spring seasons (from January to April) due to the vegetative stop; while maximum values are obtained during the summer season (from June to September), due to the stoma opening. The highest increments in sulphur concentration, calculated as the difference between two consecutive months, are obtained in May and June for all considered species except forCedrus deodara and Pyracantha coccinea, both species have few seasonal changes during the whole year. Some species are more sensitive to natural washing than others, showing a decrease in sulphur concentration after rainfall periods.     

大气颗粒物中元素碳的直接测定

对原来用元素分析仪测定大气颗粒物样品中有机碳、元素碳的方法[1 ] 进行改进 ,将差减法间接测定元素碳改为一步直接测定元素碳。有机碳、元素碳的测量标准偏差的平均值分别为 0 35 %、0 34% ,提高了元素碳的测量精度 ,同时避免了误差传递 ,解决了差减法测定元素碳时出现负值的情况。