石化特种设备管理现状及风险控制对策

为加强特种设备管理,提高风险控制水平,对中国特种设备的管理法规标准体系、中国石化特种设备管理现状及存在的问题,进行了讨论和思考,并提出了石化特种设备风险控制对策。     

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。     

发光细菌生物毒性抑制率优化研究

发光细菌生物毒对水生生态环境有极大的破坏作用,为保护水体环境,提出发光细菌生物毒性抑制率优化研究。完成测试样品调制;设置菌悬液特征参数,利用pH、RLU及D600进行同步检测,完成发光细菌生物毒性重现;分析磺胺类药品于根际土壤的形态分布和生态毒性,完成发光细菌生物毒性抑制率优化研究。实验证明,三种磺胺类药品对发光细菌生物毒性的相对抑制率都小于30%,为低毒性;其中有机溶剂对发光细菌生物毒性的抑制率最大,水溶态较大,结合态最小。     

高密度电法在原位修复土壤过程中的监控研究

采用高密度电阻率成像法（ERT）对上海市某有机污染场地土壤污染原地修复进行研究，通过持续监测药剂注入前后电阻率的改变来推断药剂和污染物的扩散路径。结果表明，药剂扩散的影响半径介于2 m～4 m之间；其影响深度根据注药点和土壤渗透性差异而有所不同，在16 m处注药的影响深度大于监测深度20 m，ERT技术在监控原位注入修复过程中具有较好的可行性和适用性。     

红壤丘陵区典型流域地表水水环境特征研究——以浏阳河流域为例

选取浏阳河流域为例,根据近十年河流断面监测数据,采用改进的综合污染指数法来评价水质污染程度,研究了该红壤丘陵区典型河流水质的时空变化特征,并结合土地利用和土壤特征等分析地表水质变化原因。结果表明,改进的综合污染指数法有较好的适用性;从时间特征上看,由于面源污染加剧,使得浏阳河近十年的水质污染呈增长趋势;从空间特征上看,浏阳河从上游到下游,河流污染呈增长趋势,上游水质较好,中游表现为重金属铅和汞的污染较大,而下游则是氨氮污染加剧。     

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.     

浅谈烟道气现场监测的工作管理

烟道气现场监测，易出差错。监测人员必须有良好的专业理论水平、丰富的工作经验、高度的敬业精神。本文就现场监测工作易出现问题进行了分析，列举了解决问题的方法     

Determination of PAHs by On-Line Polarization Synchronous Fluorescence with HPLC

A novel analytical method has been established for on-line simultaneous determination of fluorene and acenaphthene,chrysene and benzo[a]anthracene (B[a]A) by polarization synchronous fluoromet.y (PSF) coupled to high performance liquid chromatography (HPLC). The detection limits were: 0.039, 0.046, 0.016 and 0.042 mgL-1 for fluorene, acenaphthene, chrysene and B[a]A, respectively. The proposed method has been successfisily applied to simultaneous determination of these PAHs in environmental air and marine sediment samples.     

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.