Needs-based sewerage prioritization: alternative to conventional cost-benefit analysis

This paper presents an empirical approach to select and prioritize sewerage projects within set budgetary limitations. The methodology includes a model which quantifies benefits of a sewerage project as an index or dimensionless number. The index considers need and urgency of sewerage and other project goals. Benefit is defined as the difference in anticipated impact between the current condition (without the project) and the expected condition with the project. Anticipated benefits primarily include reduction in environmental pollution, reduction of human diseases and morbidity, and other tangible and intangible improvement. This approach is a powerful decision tool for sewerage prioritization and an effective alternative to conventional cost-benefit analysis. Unlike conventional analysis, this approach makes no attempt to convert project benefits and other impacts into a monetary measure. This work recognizes that the decision to provide sewerage based solely on net benefits is not practical. Instead, benefit-cost ratios (B/C) are calculated utilizing cost-effectiveness approach. Using these ratios, 16 unserviced areas of Ensenada, Mexico are ranked. The prioritization rankings produced by this method must be further scrutinized and carefully reviewed for logic, accuracy of input data, and practicality of implementation. A similar framework may also be useful for prioritizing other public works projects.     

Prospective changes in irrigation water requirements caused by agricultural expansion and climate changes in the eastern arc mountains of Kenya

Water resources and land use are closely linked with each other and with regional climate, assembling a very complex system. The understanding of the interconnecting relations involved in this system is an essential step for elaborating public policies that can effectively lead to the sustainable use of water resources. In this study, an integrated modelling framework was assembled in order to investigate potential impacts of agricultural expansion and climate changes on Irrigation Water Requirements (IWR) in the Taita Hills, Kenya. The framework comprised a land use change simulation model, a reference evapotranspiration model and synthetic precipitation datasets generated through a Monte Carlo simulation. In order to generate plausible climate change scenarios, outputs from General Climate Models were used as reference to perturbing the Monte Carlo simulations. The results indicate that throughout the next 20 years the low availability of arable lands in the hills will drive agricultural expansion to areas with higher IWR in the foothills. If current trends persist, agricultural areas will occupy roughly 60% of the study area by 2030. This expansion will increase by approximately 40% the annual water volume necessary for irrigation. Climate change may slightly decrease crops' IWR in April and November by 2030, while in May a small increase will likely be observed. The integrated assessment of these environmental changes allowed a clear identification of priority regions for land use allocation policies and water resources management.     

An evaluation of environmental factors affecting species distributions

Many different models can be built to explain the distributions of species. Often there is no single model that is clearly better than the alternatives, and this leads to uncertainty over which environmental factors are limiting species’ distributions. We investigated the support for different environmental factors by determining the drop in model performance when selected predictors were excluded from the model building process. We used a paired t-test over 37 plant species so that an environmental factor was only deemed significant if it consistently improved the results for multiple species. Geology and winter minimum temperatures were found to be the environmental factors with the most support, with a significant drop in model performance when either of these factors was excluded. However, there was less support for summer maximum temperature, as other environmental factors could combine to produce similar model performance. Our method of evaluating environmental factors using multiple species will not be capable of detecting predictors that are only important for one or two species, but it is difficult to distinguish these from spurious correlations. The strength of the method is that it increases inference for factors that consistently affect the distributions of many species. We discourage the assessment of models against predefined benchmarks, such as an area under the curve (AUC) of more than 0.7, as many alternative models for the same species produce similar results. Therefore, the benchmarks do not provide any indication of how the performance of the selected model compares to alternative models, and they provide weak inference to accept any selected model.     

Model-based analysis of the influence of ecological processes on forest point pattern formation—A case study

Many different spatio-temporal individual-based models (IBM) for forests have been developed for studying the development of trees in space and time. Such models typically depend on various numerical parameters that represent the ecological processes of growth (G), inter-plant competition (C) and birth-and-death (B&D; also called regeneration and mortality). Until now little work has been done to systematically trace the influence of these processes and their model parameters on the spatial structure of forest ecosystems.This paper attempts to fill this gap by addressing an important aspect of forest structure, spatial variability, characterised by the mark variogram as a summary characteristic. The model used was inspired by components of various well-established IBMs including a shot-noise competition field. Time series data from monospecies forests in three different countries of the northern hemisphere provided ecological reference scenarios. Though a case study, the paper's methodology is rather general and can be applied to any model and forest ecosystem.Methods of sensitivity analysis revealed that only a small number of model parameters is crucial for forming spatial variability. Particularly important is the range of competition between trees; with increasing range the variability increases. Growth processes have considerable importance particularly with short observation periods and in young forests, whereas mortality processes become more influential in the long-term. Naturally, these statements depend upon the initial structure and on the length of the observation period.     

Integrating spatial support tools into strategic planning—SEA of the GMS North–South Economic Corridor Strategy and Action Plan

The GMS countries, supported by the Asian Development Bank, have adopted a holistic, multidimensional approach to strengthen infrastructural linkages and facilitate cross border trade through (i) the establishment of a trans-boundary road connecting two economic nodes across marginalised areas, followed by 2) facilitation of environmentally and socially sound investments in these newly connected areas as a means to develop livelihoods. The North–South Economic Corridor is currently in its second phase of development, with investment opportunities to be laid out in the NSEC Strategy and Action Plan (SAP). It targets the ecologically and culturally sensitive border area between PR China's Yunnan Province, Northern Lao PDR, and Thailand. A trans-boundary, cross-sectoral Strategic Environmental Assessment was conducted to support the respective governments in assessing potential environmental and social impacts, developing alternatives and mitigation options, and feeding the findings back into the SAP writing process. Given the spatial dimension of corridor development—both with regard to opportunities and risks—particular emphasis was put in the application of spatial modelling tools to help geographically locate and quantify impacts as a means to guide interventions and set priorities.     

What does it mean to transform knowledge into action in falls prevention research? Perspectives from the Canadian Institutes of Health Research

Introduction

The concept of knowledge translation as defined by the Canadian Institutes for Health Research and the Knowledge to Action Cycle, described by Graham et al (Graham et al., 2006), are used to make a case for the importance of using a conceptual model to describe moving knowledge into action in the area of falls prevention.

Method

There is a large body of research in the area of falls prevention. It would seem that in many areas it is clear what is needed to prevent falls and further syntheses can determine where the evidence is sufficiently robust to warrant its implementation as well as where the gaps are that require further basic research.

Conclusion

The phases of the action cycle highlight seven areas that should be paid attention to in order to maximize chances of successful implementation.     

基于大气退化物理模型恢复算法的露天矿车辆助驾技术

露天矿汽车运输是大型露天矿主要运输方式之一,是影响露天矿生产能力和经济效益的关键因素。本文分析了露天矿车辆运输事故原因,针对露天矿卡车运输道路环境特点与天气因素,通过安装于车体外的高清CCD摄像设备,来消除卡车盲区的安全隐患,并基于大气退化物理模型恢复算法,实现了在雨、雪、雾、灰尘等恶劣天气条件下的露天矿车辆运输道路环境探测影像增强技术,提高露天矿卡车司机的可视距离,降低事故发生率。本文的研究对于提高露天矿车辆运输安全,降低露天矿事故发生率具有重要意义,并以某大型露天煤矿为应用实例验证了其可行性。     

内嵌式蒙皮散热器气动阻力影响研究

目的 开展内嵌式蒙皮散热器对小型飞行器气动阻力影响研究,探明气动阻力产生的原因及影响因素。方法 利用数值仿真技术,对气动阻力增大的诱因进行理论分析,分别研究蒙皮散热器引流口半径、导流口半径和翅片厚度等结构参数对飞行器气动阻力及散热性能的影响,进而平衡蒙皮散热器散热能力和飞行器气动阻力等设计指标。结果 配置蒙皮散热器为电子设备提供热沉会导致小型飞行器气动阻力增大,原因是配置散热器诱导产生了额外的压差阻力和摩擦阻力。结论 增大引流口、导流口半径可减小压差阻力,增加翅片厚度,则可减小摩擦阻力,进而减小飞行器气动阻力。增加翅片厚度,可使气动阻力减少20%以上,同时也会导致传热性能的显著降低,增大引流口、导流口半径则可在一定程度促进传热。     

黄河三角洲石油开采区盐渍化农田土壤多环芳烃的分布特征与源解析

采集黄河三角洲石油开采区滨州市89个土壤样品(其中:83个农田土壤样品,6个石油开采区土壤样品),采用气相色谱-质谱仪(GC-MS)分析土壤中的16种EPA优控的多环芳烃(PAHs)组成与含量,运用主成分分析(PCA)和正定矩阵因子法(PMF)模型判断农田土壤中多环芳烃的来源.结果表明,农田土壤中16种PAHs总含量(以干重计)范围31. 5～1 399. 4μg·kg~(-1),其平均值为149. 8μg·kg~(-1). PAHs组成特征表明,该地区农田土壤中主要以4～6环多环芳烃为主,单体PAH间大多呈显著相关(P 0. 01),表明采样区某些单体PAH的污染来源具有一定的相似性.两种源解析方法对多环芳烃的来源判定有相似的结果,农田土壤中PAHs主要来源于燃煤、生物质燃烧、化石燃料燃烧以及柴油燃烧; 4类源的贡献率依次是汽油燃烧(24. 05%)、柴油燃烧(6. 17%)、低温热解过程(60. 67%)与煤燃烧(9. 11%).     

A novel approach combining bootstrapped non-intrusive reduced order models and unscented transform for the robust and efficient CFD analysis of accidental gas releases in congested plants

The risk assessment for safety-critical, complex systems is a very challenging computational problem when it is performed with high-fidelity models, e.g. CFD, like in the case of accidental gas releases in congested systems. Within this framework, a novel CFD approach, named Source Box Accident Model, has been recently proposed to efficiently model such phenomena by splitting the simulation of the gas release and its subsequent dispersion in the system in two steps. In this view, the present paper proposes a non-intrusive, Proper Orthogonal Decomposition-Radial Basis Functions reduced order model that exploits the two-step nature of the SBAM approach, to mimic the behaviour of the original, long-running CFD model code at a significantly lower computational cost. Moreover, the paper presents a methodology combining the bootstrap and unscented transform approaches to efficiently assess the ROM uncertainty in the safety-critical simulation output quantities of interest, e.g. the flammable volume. The results obtained in a test case involving a high pressure, accidental gas release in an off-shore Oil & Gas plant are in very satisfactory agreement with those produced by CFD, with a relative error smaller than 10% and a reduction in the computational time of about three orders of magnitude.