基于GPRS网络的环境自动监控数据传输系统的设计

分析了江苏省环境自动监控系统数据传输的现状，提出了一种基于GPKS网络的环境自动监控数据传输系统，并进行了详细的设计。     

水环境DSS系统中数据集市的开发应用

水环境综合整治是一种投资巨大、影响深远的系统工程,需要进行科学决策.近来年水环境决策支持技术发展很快,而作为其中两大支柱之一,水环境数据库的开发日益受到重视.数据仓库、数据集市等技术是对数据库技术的重要发展.为此在水环境数据库之上开发水环境数据集市进行了探讨.将已有的水环境数据库,按照水环境综合整治的主题,采取星型模型进行重新组织,即生成水环境数据集市.介绍了ECTL工具的开发,即将数据库转换为数据集市的抽取(Extract)、清洗(Cleanse)、转换(Transform)、装载(Load)工具;然后介绍了OLAP(OnLine Analytical Processing)工具的开发,即为实现对数据集市的应用而开发相应的联机分析处理工具,相应的联机分析功能包括查询、分类、可视化等.通过应用实例展示了水环境数据集市提供决策支持信息的能力.     

Performance of data acceptance criteria over 50 months from an automatic real-time environmental radiation surveillance network

The automatic real-time environmental radiation surveillance network of Catalonia (Spain) comprises two subnetworks; one with 9 aerosol monitors and the other with 8 Geiger monitors together with 2 water monitors located in the Ebre river. Since September 2006, several improvements were implemented in order to get better quality and quantity of data, allowing a more accurate data analysis. However, several causes (natural causes, equipment failure, artificial external causes and incidents in nuclear power plants) may produce radiological measured values mismatched with the own station background, whether spurious without significance or true radiological values. Thus, data analysis for a 50-month period was made and allowed to establish an easily implementable statistical criterion to find those values that require special attention. This criterion proved a very useful tool for creating a properly debugged database and to give a quick response to equipment failures or possible radiological incidents. This paper presents the results obtained from the criterion application, including the figures for the expected, raw and debugged data, percentages of missing data grouped by causes and radiological measurements from the networks. Finally, based on the discussed information, recommendations for the improvement of the network are identified to obtain better radiological information and analysis capabilities.     

Measuring and analyzing adaptive capacity at management levels of resilient systems

Resilience engineering (RE) is capable of handling disruptive events and controlling their consequences in process industries such as petrochemical plants. This study aims at analyzing the level of adaptive capacity and identifying effective factors on developing adaptive capacity in the organizational structure of process industries. The data of this study were obtained through direct observation and a structured questionnaire in a petrochemical plant. Managers at all levels participated in the survey. Data envelopment analysis (DEA), which is a mathematical approach, was used to compute and analyze the role of the factors contributing to adaptive capacity. The results indicated that reporting safety issues played a central role in enhancing adaptive capacity at all management levels. Both middle management and low-level management emphasized the importance of management commitment, whereas top-level management considered flexibility as a vital factor in managing disruptions and reducing accidents. The findings of this study could be useful for managers and other decision-makers to improve safety in process industries.     

Data fusion modeling for groundwater systems

Engineering projects involving hydrogeology are faced with uncertainties because the earth is heterogeneous, and typical data sets are fragmented and disparate. In theory, predictions provided by computer simulations using calibrated models constrained by geological boundaries provide answers to support management decisions, and geostatistical methods quantify safety margins. In practice, current methods are limited by the data types and models that can be included, computational demands, or simplifying assumptions. Data Fusion Modeling (DFM) removes many of the limitations and is capable of providing data integration and model calibration with quantified uncertainty for a variety of hydrological, geological, and geophysical data types and models. The benefits of DFM for waste management, water supply, and geotechnical applications are savings in time and cost through the ability to produce visual models that fill in missing data and predictive numerical models to aid management optimization. DFM has the ability to update field-scale models in real time using PC or workstation systems and is ideally suited for parallel processing implementation. DFM is a spatial state estimation and system identification methodology that uses three sources of information: measured data, physical laws, and statistical models for uncertainty in spatial heterogeneities. What is new in DFM is the solution of the causality problem in the data assimilation Kalman filter methods to achieve computational practicality. The Kalman filter is generalized by introducing information filter methods due to Bierman coupled with a Markov random field representation for spatial variation. A Bayesian penalty function is implemented with Gauss–Newton methods. This leads to a computational problem similar to numerical simulation of the partial differential equations (PDEs) of groundwater. In fact, extensions of PDE solver ideas to break down computations over space form the computational heart of DFM. State estimates and uncertainties can be computed for heterogeneous hydraulic conductivity fields in multiple geological layers from the usually sparse hydraulic conductivity data and the often more plentiful head data. Further, a system identification theory has been derived based on statistical likelihood principles. A maximum likelihood theory is provided to estimate statistical parameters such as Markov model parameters that determine the geostatistical variogram. Field-scale application of DFM at the DOE Savannah River Site is presented and compared with manual calibration. DFM calibration runs converge in less than 1 h on a Pentium Pro PC for a 3D model with more than 15,000 nodes. Run time is approximately linear with the number of nodes. Furthermore, conditional simulation is used to quantify the statistical variability in model predictions such as contaminant breakthrough curves.     

欠发达地区农户生产效率研究——以西藏自治区为例

以西藏自治区为例,在2009年农户抽样调查数据基础上,利用数据包络法(DEA)探讨西藏不同区域农户生产效率,认为西藏农户生产效率差异与农业生产中自用价值的计算有关,若考虑自用价值,在固定生产规模报酬(CRS)假设下,平均生产效率为0.653,其中拉萨市最高(0.784),日喀则市最低(0.277);若不计入自用价值,山南与那曲地区呈现最高生产效率。表明西藏农户收入中自用价值占了较大比例,扣除自用价值,收入大幅减少,影响生产效率。因此,提高农牧户生产商品率是增加农牧民收入的有效途径。     

Quality Science in the Courtroom: U.S. EPA Data Quality and Peer Review Policies and Procedures Compared to the Daubert Factors

Protection of the environment is critically dependent on the quality of data used in decision making. Whether the decisions are part of the scientific process or relate to application of the laws governing people and their living conditions, good quality data are required/needed by two disciplines with distinct differences. This paper examines some differences between science and the law, provides a brief history of science in law, discusses the effects of law on science, compares United States Environmental Protection Agency (U.S. EPA) guidance and U.S. Supreme Court credible science criteria. This paper further speculates on the future use of science data by the courts.     

Environmental information system for visualizing environmental impact assessment information

The Institute of Environmental Engineering at the University of Pannonia has undertaken the challenge to develop an online environmental information system. This system is able to receive and process the collected environmental data via Internet. The authors have attached importance to the presentation of the data and have included other comprehensible information for laymen as well in order to work out visualisation techniques that are expressive and attract attention for environmental questions through the developed information system. The ways of visualizing physical and chemical parameters of surface water and the effects of motorway construction were examined.     

Identification of managerial shaping factors in a petrochemical plant by resilience engineering and data envelopment analysis

Resilience engineering (RE) is a novel approach that is capable of controlling and limiting incidents and accidents. This study identifies managerial shaping factors in a petrochemical plant by RE and data envelopment analysis (DEA). To do this, a standard questionnaire containing resilient factors is completed by managers of a petrochemical plant. Then, the best DEA model is selected based on average efficiency and statistical test. In addition, sensitivity analysis are performed to identify the most important shaping factors. Reporting culture, management commitment, and preparedness are identified as the most important factors in this paper, respectively. Finally, the proposed model is validated and verified through statistical experiment. The proposed approach would help managers to have a comprehensive understanding of the plant with respect to the RE. To the best of our knowledge this is the first paper that examines resilient shaping factors in a petrochemical plant with respect to management and organization by DEA.     

A new methodology for the synthesis of an optimum flexible water networks

This paper presents a new optimization model for the synthesis of direct reuse/recycle water networks with data uncertainties. In this case, data uncertainties were originated from the varying parameters of the water sources and sinks, which fluctuate within a given range of values. The aim of the method is to design an optimum flexible water network (FWN) that ensures minimum fresh water consumption and yet fulfilling all process constraints and uncertain parameters. The resulting FWN also features the minimum number of pipelines, which will ease process operation and control. Due to the complexity of the problem, multistep solving method based on theorem of corner points has been developed. The FWN obtained using the proposed method also includes information on the maximum flow rate in the pipelines, which is essential for detailed network design stage. The resulting model is a mixed integer linear problem (MILP), which may be solved to obtain global optimum solution. Two case studies were used to elucidate the newly proposed method.