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.     

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.     

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.     

Development and case study of a science-based software platform to support policy making on air quality

This article describes the development and implementations of a novel software platform that supports real-time, science-based policy making on air quality through a user-friendly interface. The software, RSM-VAT, uses a response surface modeling (RSM) methodology and serves as a visualization and analysis tool (VAT) for three-dimensional air quality data obtained by atmospheric models. The software features a number of powerful and intuitive data visualization functions for illustrating the complex nonlinear relationship between emission reductions and air quality benefits. The case study of contiguous U.S. demonstrates that the enhanced RSM-VAT is capable of reproducing the air quality model results with Normalized Mean Bias < 2% and assisting in air quality policy making in near real time.     

Atmospheric sulfur hexafluoride in-situ measurements at the Shangdianzi regional background station in China

We present in-situ measurements of atmospheric sulfur hexafluoride（SF6） conducted by an automated gas chromatograph–electron capture detector system and a gas chromatography/mass spectrometry system at a regional background site, Shangdianzi,in China, from June 2009 to May 2011, using the System for Observation of Greenhouse gases in Europe and Asia and Advanced Global Atmospheric Gases Experiment（AGAGE）techniques. The mean background and polluted mixing ratios for SF6 during the study period were 7.22 × 10-12（mol/mol, hereinafter） and 8.66 × 10-12, respectively. The averaged SF6 background mixing ratios at Shangdianzi were consistent with those obtained at other AGAGE stations located at similar latitudes（Trinidad Head and Mace Head）, but larger than AGAGE stations in the Southern Hemisphere（Cape Grim and Cape Matatula）. SF6 background mixing ratios increased rapidly during our study period, with a positive growth rate at 0.30 × 10-12year-1. The peak to peak amplitude of the seasonal cycle for SF6 background conditions was 0.07 × 10-12, while the seasonal fluctuation of polluted conditions was 2.16 × 10-12. During the study period, peak values of SF6 mixing ratios occurred in autumn when local surface horizontal winds originated from W/WSW/SW/SWS/S sectors, while lower levels of SF6 mixing ratios appeared as winds originated from N/NNE/NE/ENE/E sectors.     

The impact of privatization approaches on the productivity growth of the water industry: A case study of Chile

The water industry faces the challenge of implementing privatization reforms. This process mainly adopts the following two approaches: the privatization of public water and sewerage services (WSS) and the privatization of water companies’ ownership. This paper investigates the impact of both privatization approaches on changes in productivity in the water industry. In doing so, the Luenberger productivity indicator (LPI) was computed for a sample of Chilean water and sewerage companies (WaSCs) for the period 1997–2013. Unlike the most commonly applied index (Malmquist productivity index), the LPI simultaneously takes into account output expansion and input contraction. The results evidenced that productivity in the Chilean water industry decreased after its privatization. However, in the sub-periods in which the ownership of WaSCs was privatized, the productivity of the water industry increased due to technical improvements. On the other hand, the concession of WWS to private WaSCs involved a regression in productivity. From a policy perspective, regulators and managers of WaSCs at the international level can learn important lessons from the Chilean case to improve the productivity of the water industries in their countries.     

Assessing the efficiency of Chilean water and sewerage companies accounting for uncertainty

Efficiency assessment of water and sewerage companies (WaSCs) has attracted considerable attention both for water company managers and water regulators. Within the methodological approaches that can be applied to estimate efficiency scores, data envelopment analysis (DEA) is the most widely applied technique. In spite of the positive features of DEA, it presents a major drawback which is its deterministic nature. In other words, conventional DEA models do not account for uncertainty in the data. To overcome this limitation, we assess, for the first time, the efficiency of a sample of Chilean WaSCs by using a DEA model with statistical tolerance in the data. Hence, 81 efficiency scores are estimated for each WaSC rather than a single score as with conventional DEA models. The results illustrate that outputs exhibit larger uncertainty than inputs. Moreover, WaSCs efficiency scores change significantly under the best-case and worst-case scenarios. The ranking of the WaSCs allows for the identification of which of them has the highest performance based on their efficiency scores. This information is essential to enhance efficiency and innovation in the water industry. Moreover, the introduction of uncertainty in the efficiency assessment allows for the prediction and ranking of future performance of WaSCs.     

Referee analyses: A better approach than data validation

Data validation, as prescribed in guidance documents provided by the United States Environmental Protection Agency (EPA), yields legal evidence that an analytical laboratory has performed analyses according to predetermined specifications. This validation process, because it involves only the checking of procedural documentation, provides minimal information concerning the technical validity of the data. Unfortunately, in the minds of many, the performance of such a data validation has become synonymous with technical validity. A better approach is to implement a routine program of referee analyses. That is, submit a predetermined number of samples from the project to a second laboratory. A routine program of this type provides a self-checking and self-correcting mechanism for determining whether data are technically valid. Publication No. 3874, Environmental Sciences Division, ORNL. Managed by Martin-Marietta Energy Systems, Inc. for the US Department of Energy under Contract No. DE-AC05-840R21400.