危化品道路运输事故应急救援中心选址模型研究

为快速提高危化品道路运输事故应急救援能力,降低事故后果,文章基于目前应急救援中心选址模型的不足,提出了一种新的双目标决策救援中心选址模型。通过综合考虑应急响应时间及应急处理能力对救援能力的影响,建立了救援中心点的最大网络覆盖模型;并采用较优的邻域搜索算法求解救援中心点的合理位置。最后以某地危化品运输路网为例,利用所建模型对事故应急救援中心的合理布局计算求解。结果表明:考虑应急响应时间及应急处理能力限制的应急救援中心选址模型,能够有效提高救援中心的综合应急能力,为有关部门合理规划危化品道路运输事故应急救援中心布局提供理论参考和决策依据。     

Modelling cheetah relocation success in southern Africa using an Iterative Bayesian Network Development Cycle

Relocation is one of the strategies used by conservationists to deal with problem cheetahs in southern Africa. The success of a relocation event and the factors that influence it within the broader context of long-term viability of wild cheetah metapopulations was the focus of a Bayesian Network (BN) modelling workshop in South Africa. Using a new heuristics, Iterative Bayesian Network Development Cycle (IBNDC), described in this paper, several networks were formulated to distinguish between the unique relocation experiences and conditions in Botswana and South Africa. There were many common underlying factors, despite the disparate relocation strategies and sites in the two countries. The benefit of relocation BNs goes beyond the identification and quantification of the factors influencing the success of relocations and population viability. They equip conservationists with a powerful communication tool in their negotiations with land and livestock owners, which is key to the long-term survival of cheetahs in southern Africa. Importantly, the IBNDC provides the ecological modeller with a methodological process that combines several BN design frameworks to facilitate the development of a BN in a multi-expert and multi-field domain.     

东部发达城市智慧水务建设探析

在互联网技术飞速发展,于各传统行业运用的建设大背景下,"智慧水务"的构想应运而生。结合当前水务发展遇到的实际问题和国家战略部署阐述了智慧水务的建设。最后提出应落实发展信息技术,加快技术融合,为智慧城市的建设添砖加瓦。     

全国辐射环境监测网络射频综合场强测量比对

在2011年全国辐射环境监测网络射频综合场强项目测量比对中,采用稳健统计方法对36家参比单位的78个比对测量结果进行统计分析。结果表明,全国各监测机构满意结果占87.3%,有问题结果占7.7%,离群结果占5%。对可能影响监测结果的若干因素进行了研究,如仪器型号等,反映了目前全国辐射环境监测网络射频电磁场测量项目的监测能力水平,为进一步开展质保工作提供了科学依据。     

国内外空气质量监测网络设计方法研究进展

如何设计效率高、代表性好、目的性强的空气质量监测网络,是长期以来空气质量监测工作的难点与关键。随着环境管理水平的提高,环境空气质量监测网络的设计成为当前研究的热点之一,国内外很多学者对其进行了广泛的研究。文章对美国、欧盟和我国关于空气质量监测网络设计的规范进行了简要评述和比较分析,并参考国内外一些关于空气质量监测网络设计的研究和应用实例,对当前主要的设计方法及应用进展进行了综述。     

Dynamic failure probability analysis of urban gas pipeline network

The gas pipeline network is an essential infrastructure for a smart city. It provides a much-needed energy source; however, it poses a significant risk to the community. Effective risk management assists in maintaining the operational safety of the network. The risk management of the network requires reliable dynamic failure probability analysis. This paper proposes a methodology of condition monitoring and dynamic failure probability analysis of urban gas pipeline network. The methodology begins with identifying key design and operational factors responsible for pipeline failure. Subsequently, a causation-based failure model is developed as the Bowtie model. The Bowtie model is transformed into a Bayesian network, which is analyzed using operational data. The key contributory factors of accident causation are monitored. The monitored data is used to analyze the updated failure probability of the network. The gas pipeline network's dynamic failure probability is combined with the potential consequences to assess the risk. The application of the approach is demonstrated in a section of the urban gas pipeline.     

Bayesian Decision Analysis for Evaluating Management Options to Promote Recovery of a Depleted Salmon Population

Abstract:  The endangered population of sockeye salmon (Oncorhynchus nerka) in Cultus Lake, British Columbia, Canada, migrates through commercial fishing areas along with other, much more abundant sockeye salmon populations, but it is not feasible to selectively harvest only the latter, abundant populations. This situation creates controversial trade-offs between recovery actions and economic revenue. We conducted a Bayesian decision analysis to evaluate options for recovery of Cultus Lake sockeye salmon. We used a stochastic population model that included 2 sources of uncertainty that are often omitted from such analyses: structural uncertainty in the magnitude of a potential Allee effect and implementation uncertainty (the deviation between targets and actual outcomes of management actions). Numerous state-dependent, time-independent management actions meet recovery objectives. These actions prescribe limitations on commercial harvest rates as a function of abundance of Cultus Lake sockeye salmon. We also quantified how much reduction in economic value of commercial harvests of the more abundant sockeye salmon populations would be expected for a given increase in the probability of recovery of the Cultus population. Such results illustrate how Bayesian decision analysis can rank options for dealing with conservation risks and can help inform trade-off discussions among decision makers and among groups that have competing objectives.     

Design of an Air-Quality Surveillance System for Buenos Aires City Integrated by a NO x Monitoring Network and Atmospheric Dispersion Models

This paper presents an air-quality surveillance system designed to detect the occurrence of air pollutant concentrations greater than a reference level in an urban area. The system is integrated by an air-quality monitoring network and atmospheric dispersion models simulations. An objective methodology to design an urban air-quality monitoring network is proposed. This methodology is based on the analysis of air-quality modelling results. The procedure is applied to design an air-quality monitoring network to control NO _x concentration levels in Buenos Aires City. Results indicate that six monitors will detect the occurrence of concentration greater than the air-quality guidelines with an efficiency of about 67%. Once a violation is detected, results of atmospheric dispersion models will help in the determination of affected areas. Four possible examples are included to illustrate the assistance that the results of atmospheric dispersion models can bring to a better estimation of possible affected areas in the city. Combining these results with the last census data, an estimation of the inhabitants possibly exposed is obtained.     

Ecosystem classification for EU habitat distribution assessment in sandy coastal environments: An application in central Italy

Many recent developments in coastal science have gone against the demands of European Union legislation. Coastal dune systems which cover small areas of the earth can host a high level of biodiversity. However, human pressure on coastal zones around the world has increased dramatically in the last 50 years. In addition to direct habitat loss, the rapid extinction of many species that are unique to these systems can be attributed to landscape deterioration through the lack of appropriate management. In this paper, we propose to use of an ecosystem classification technique that integrates potential natural vegetation distribution as a reference framework for coastal dune EU Habitats (92/43) distribution analysis and assessment. As an example, the present study analyses the EU Habitats distribution within a hierarchical ecosystem classification of the coastal dune systems of central Italy. In total, 24 land elements belonging to 8 land units, 5 land facets, 2 land systems and 2 land regions were identified for the coastal dunes of central Italy, based on diagnostic land attributes. In central Italy, coastal dune environments including all the beach area, mobile dunes and all the fixed-dune land elements contain or could potentially hold at least one EU habitat of interest. Almost all dune slack transitions present the potentiality for the spontaneous development of EU woodlands of interest. The precise information concerning these ecosystems distribution and ecological relationships that this method produces, makes it very effective in Natura 2000 European network assessment. This hierarchical ecosystem classification method facilitates the identification of areas to be surveyed and eventually bound, under the implementation of EU Habitat directive (92/43) including areas with highly disturbed coastal dune ecosystems.     

Probabilistic Modelling of Cattle Farm Distribution in Australia

A probabilistic Bayesian method called weights of evidence (WofE) was used to develop a synthetic dataset of cattle farm locations at a national scale across Australia. The synthetic dataset was required for the modelling of livestock movements with a view to assessing biosecurity implications. The WofE method is based on the analysis of spatial relationships between evidential patterns with respect to an event, such as the actual location of a farm. The evidential patterns of cattle farms were derived from maps of land use, land tenure, drainage systems, roads, settlements and long-term averaged rainfall. These evidential patterns were used for delineating and ranking land areas suitable for cattle farming. For each evidential pattern statistics such as a positive weight, a negative weight and a contrast were calculated for estimating the degree of correlation between the evidential patterns and known farm locations. The integrated evidential patterns of known farms were then used for estimating posterior probabilities and splitting land into five different classes according to its suitability for farming.