Particle concentrations in data centers

Cooling buildings with large airflow rates of outside air when temperatures are favorable is an established energy-saving measure. In data centers, this strategy is not widely used, owing to concerns that it would cause increased indoor levels of particles of outdoor origin, which could damage electronic equipment. However, environmental conditions typical of data centers and the associated potential for equipment failure are not well characterized. This study presents the first published measurements of particle concentrations in operating data centers. Indoor and outdoor particle measurements were taken at eight different sites in northern California for particulate matter 0.3–5.0 μm in diameter. One of the data centers has an energy-efficient design that employs outside air for cooling, while the rest use conventional cooling methods. Ratios of measured particle concentrations in the conventional data centers to the corresponding outside concentrations were significantly lower than those typically found in office or residential buildings. Estimates using a material-balance model match well with empirical results, indicating that the dominant particle sources and losses have been identified. Measurements taken at the more energy-efficient site show nearly an order of magnitude increase in particle concentration when ventilation rates were high. The model indicates that this increase may be even higher when including particles smaller than the monitoring-equipment size limitation. Even with the increases, the measured particle concentrations are still below concentration limits recommended in industry standards.     

Determination of the optimum MM5 configuration for long term CMAQ simulations of aerosol bound pollutants in Europe

Realistic meteorological fields are a prerequisite for the determination of pollutant concentrations and depositions by means of a chemistry transport model. Different configurations of the 5th generation NCAR/Penn State University mesoscale meteorological model MM5 were tested to determine the optimum set up for long term hindcasts that cover several months up to years. Four dimensional data assimilation (FDDA) significantly enhances the spatio temporal representation of temperature, humidity and wind. Best agreement with radiosonde observations could be achieved when temperature, humidity and wind were grid nudged every 6 h. The quality of the resulting meteorological fields showed no significant systematic temporal or spatial variation over Europe in a model run of the year 2000. It was found that the hydrological cycle was not correctly reproduced by the model when no nudging was applied. The relevant model run showed too high relative humidity and too high rainfall when compared to observations. This led to considerably lower aerosol concentrations close to ground and a shift in the deposition patterns of particle bound pollutants like the carcinogenic benzo(a)pyrene (B(a)P). Guest Editor: Dr. S. T. Rao.     

Network-based exploration and visualisation of ecological data

Networks – structured graphs consisting of sets of nodes connected by edges – provide a rich framework for data visualisation and exploratory analyses. Although rarely used for the visualisation of ecological data, networks are well suited to this purpose, including data that one might not normally think of as a network. We present a simple method for transforming a data matrix into network format, and show how this can be used as the basis for interactive exploratory analyses of ecological data.The method is demonstrated using a database of marine zooplankton samples acquired in the Southern Ocean. The network analyses revealed zooplankton community structures that are in good agreement with previously published results. Variations in community structure were observed to be related to the temporal and spatial pattern of sampling, as well as to physical environmental factors such as sea ice cover. The analyses also revealed a number of errors in the data, including taxon identification errors and instrument failures.The method allows the analyst to generate networks from different combinations of variables in the data set, and to examine the effects of varying parameters such as the scales of spatial, temporal, and taxonomic aggregation. This flexibility allows the analyst to rapidly gain a number of perspectives on the data and provides a powerful mechanism for exploration.     

城市级环境信息系统总体方案的研究与开发

在分析我国城市级环境信息现状和需求的基础上,提出了我国城市级环境信息系统的总体方案，系统依靠现有的环境数据和管理软件建立城市环境信息基础数据库，依照数据仓库的组织方式对环境数据重新进行整合，并在此基础上开发出数据查询分析应用和地理信息系统，同时结合城市环保局的办公自动化系统，建立了一个统一的环境信息发布平台，提高了环境信息的共享水平。     

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.     

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.