Environmental impacts of the Swiss collection and recovery systems for Waste Electrical and Electronic Equipment (WEEE): a follow-up

While Waste Electrical and Electronic Equipment (WEEE) collection and recovery have significantly gained in importance all over Europe in the last 15 years, comprehensive studies assessing the environmental loads and benefits of these systems still are not common. In this paper we present the results of a combined material flow analysis and life cycle assessment study, which aimed to calculate the overall environmental impacts of collection, pre-processing and end-processing for the existing Swiss WEEE collection and recovery systems, as well as of incineration and landfilling scenarios, in which the same amount of WEEE is either incinerated in a an MSWI plant or landfilled. According to the calculations based on the material flow data for the year 2009 and a new version of the ecoinvent life cycle inventory database (ecoinvent v2.01), collection, recovery and disposal result in significantly lower environmental impacts per t of WEEE for midpoint indicators such as global warming or ozone depletion and the endpoint indicator Eco-Indicator '99 points. A comparison between the environmental impacts of the WEEE recovery scenarios 2009 and 2004, both calculated with ecoinvent v2.01 data, shows that the impacts per t of WEEE in 2009 were slightly lower. This appears to be mainly due to the changes in the treatment of plastics (more recycling, less incineration). Compared to the overall environmental impacts of the recovery scenario 2004 obtained with an old version of ecoinvent (ecoinvent v1.1), the calculation with ecoinvent v2.01 results in an increase of the impacts by about 20%, which is primarily the consequence of a more adequate modeling of several WEEE fractions (e.g. metals, cables or CRT devices). In view of a further increase of the environmental benefits associated with the Swiss WEEE collection and recovery systems, the recovery of geochemically scarce metals should be further investigated, in particular.     

Soft computing for solar radiation potential assessment in Algeria

ABSTRACT

Precise estimation of solar radiation is a highly required parameter for the design and assessment of solar energy applications. Over the past years, many machine learning techniques have been proposed in order to improve the forecasting performance using different input attributes. The aim of this study is the forecasting of one day ahead of horizontal global solar radiation using a set of meteorological and geographical inputs. In this respect, the Gaussian process regression methodology (GPR) and least-square support vector machine (LS-SVM) with different kernels are evaluated in order to select the most appropriate forecasting model. In order to assess the proposed models, the southern Algerian city, Ghardaia regions, was selected for this study. A historical data of five years (2013–2017) of meteorological data collected at Renewable Energies (URAER) in Ghardaia city are used. The achieved results demonstrate that all the proposed models give approximately similar results in terms of statistical indicators. In term of processing time, all the models showed acceptable computational efficiency with less computational costs of the GPR model among all machine learning models.     

A fuzzy approach to construction project risk assessment

The increasing complexity and dynamism of construction projects have imposed substantial uncertainties and subjectivities in the risk analysis process. Most of the real-world risk analysis problems contain a mixture of quantitative and qualitative data; therefore quantitative risk assessment techniques are inadequate for prioritizing risks. This article presents a risk assessment methodology based on the Fuzzy Sets Theory, which is an effective tool to deal with subjective judgement, and on the Analytic Hierarchy Process (AHP), which is used to structure a large number of risks. The proposed methodology incorporates knowledge and experience acquired from many experts, since they carry out the risks identification and their structuring, and also the subjective judgements of the parameters which are considered to assess the overall risk factor: risk impact, risk probability and risk discrimination. All of these factors are expressed by qualitative scales which are defined by trapezoidal fuzzy numbers to capture the vagueness in the linguistic variables. The most notable differences with other fuzzy risk assessment methods are the use of an algorithm to handle the inconsistencies in the fuzzy preference relation when pair-wise comparison judgements are necessary, and the use of trapezoidal fuzzy numbers until the defuzzification step. An illustrative example on risk assessment of a rehabilitation project of a building is used to demonstrate the proposed methodology.     

软土路基沉降的优性组合预测

通过分析实测数据来预测沉降是实际工程中较多采用的方法。单一的预测方法各有其适用性,同时也存在着不足,据此提出既能综合利用多种预测方法提供的信息又能提高预测精度的组合预测。不同机理的沉降预测方法组合时,以误差的平方和或离差绝对值之和最小为准则的组合预测不一定能如实地反映其有效性。因此,采用以有效度最大为准则的优性组合预测,并通过具体工程实例分析,说明优性组合预测的有效性及加权系数的时变特性。     

A GIS-based human health risk assessment for urban green space planning—An example from Grugliasco (Italy)

The need to develop approaches for risk-based management of soil contamination, as well as the integration of the assessment of the human health risk (HHR) due to the soil contamination in the urban planning procedures has been the subject of recent attention of scientific literature and policy makers. The spatial analysis of environmental data offers multiple advantages for studying soil contamination and HHR assessment, facilitating the decision making process. The aim of this study was to explore the possibilities and benefits of spatial implementation of a quantitative HHR assessment methodology for a planning case in a typical urban environment where the soil is contaminated. The study area is located in the city of Grugliasco a part of the Turin (Italy) metropolitan area.The soils data were derived from a site specific soil survey and the land-use data from secondary sources. In the first step the soil contamination data were geo-statistically analysed and a spatial soil contamination data risk modelling procedure designed. In order to spatially assess the HHR computer routines were developed using GIS raster tools. The risk was evaluated for several different land uses for the planned naturalistic park area.The HHR assessment indicated that the contamination of soils with heavy metals in the area is not sufficient to induce considerable health problems due to typical human behaviour within the variety of urban land uses. An exception is the possibility of direct ingestion of contaminated soil which commonly occurs in playgrounds.The HHR evaluation in a planning case in the Grugliasco Municipality confirms the suitability of the selected planning option. The construction of the naturalistic park presents one solution for reducing the impacts of soil contamination on the health of citizens. The spatial HHR evaluation using GIS techniques is a diagnostic procedure for assessing the impacts of urban soil contamination, with which one can verify planning options, and provides an important step in the integration of human health protection within urban planning procedures.     

城镇燃气各类用户用气量预测方法研究

阐述了城镇燃气各类用户用气量的预测方法,介绍了因果分析法在城镇燃气用气量预测中的应用,结合某地区燃气负荷数据进行了模拟预测,预测结果达到要求。     

Ecological risk assessment of urban and industrial systems: a review

Numerous ecological risk assessment methodologies have been developed over the last twenty years around the world for evaluating urban and industrial systems and installations, by both the organisations responsible for implementing regulations and the scientific community. Although these methodologies share the general principle underlying their use, they differ widely with respect to the approaches chosen and the resources employed to apply them. Also, they may even have different objectives: prior assessment as part of an impact study before building a new installation, or retrospective assessment, for example, in view to explaining the reasons for an impact recorded or for forecasting additional expected impacts.This article provides a synthesis of the different approaches used around the world for carrying out each of the major steps common to all ecological risk assessment methodologies. The advantages and limitations of these different options are discussed in order to provide elements for formulating any new methodology adapted to a given scenario. To conclude, perspectives for improving the tools required for these methodologies are proposed, and the research works to which priority should be given are identified.     

Intercomparison of air quality data using principal component analysis, and forecasting of PM₁₀ and PM₂.₅ concentrations using artificial neural networks, in Thessaloniki and Helsinki

In this paper we propose a methodology consisting of specific computational intelligence methods, i.e. principal component analysis and artificial neural networks, in order to inter-compare air quality and meteorological data, and to forecast the concentration levels for environmental parameters of interest (air pollutants). We demonstrate these methods to data monitored in the urban areas of Thessaloniki and Helsinki in Greece and Finland, respectively. For this purpose, we applied the principal component analysis method in order to inter-compare the patterns of air pollution in the two selected cities. Then, we proceeded with the development of air quality forecasting models for both studied areas. On this basis, we formulated and employed a novel hybrid scheme in the selection process of input variables for the forecasting models, involving a combination of linear regression and artificial neural networks (multi-layer perceptron) models. The latter ones were used for the forecasting of the daily mean concentrations of PM₁₀ and PM_2.5 for the next day. Results demonstrated an index of agreement between measured and modelled daily averaged PM₁₀ concentrations, between 0.80 and 0.85, while the kappa index for the forecasting of the daily averaged PM₁₀ concentrations reached 60% for both cities. Compared with previous corresponding studies, these statistical parameters indicate an improved performance of air quality parameters forecasting. It was also found that the performance of the models for the forecasting of the daily mean concentrations of PM₁₀ was not substantially different for both cities, despite the major differences of the two urban environments under consideration.     

Reliability-based assessment of masonry arch bridges

The paper presents a methodology for the probabilistic assessment of masonry arches at the serviceability and Ultimate Limit States. First, it explains the definition of the different failure modes and corresponding limit state functions that may occur depending on the type of masonry construction (single-ring and multi-ring). The most reported modes of failure are the four-hinge mechanism, the ring separation in multi-ring arches and the slippage at the foundations. Because of the lack of reliable material data (in the statistic sense) or available response models, only those more prone to be analyzed using reliability-based methods are shown in this paper: four-hinge mechanism and ring separation.The possibility of fatigue failure of masonry arch bridges under service loads and the proposal of reliability-based assessment methods at the ultimate level of the four-hinge mechanism are also analyzed. Finally, the proposed methodology is applied to an existing bridge.     

Integrated approach for older adult friendly home staircase architectural design

The older adult population requires special consideration in terms of interior architectural design. This paper presents a methodology which aims to investigate the risk of falling associated with the architectural design of staircase elements, in order to suggest best practices to create elderly-friendly design that enhances safety for older adults. The proposed methodology uses the concept of evidence-based assessment to evaluate staircase elements such as handrail and step design. This paper also presents a scenario-based rating system that assesses the degree of the risk of falling for different types of architectural staircase design. The proposed rating system was incorporated into a mathematical model and a Design Assessment Tree (DAT) that calculate the degree of risk associated with architectural staircase design to facilitate a building information modeling (BIM) approach. A hypothetical case study is presented to illustrate the effectiveness of the proposed methodology and highlights the essential features of the proposed model.     

Matching housing supply and demand: an empirical study of Hong Kong's market

This paper has the objective of improving on the issue of forecasting new housing construction, and highlights differences between space demand and investment demand in housing markets. Further, it indicates how these differences will affect construction decisions. The first step is to identify the factors associated with estimating residential property prices in Hong Kong, based on a demand-supply adjustment process. Specifically, this study examines the role of population growth, transaction volume, inflation and interest rate in determining house prices. Second, based on these estimations, a methodology is developed to estimate the investment demand schedule and new construction of residential property.     

Application and Limitations of a Method Based on Pyrolysis Models to Simulate Railway Rolling Stock Fire Scenarios

This article relates the application of pyrolysis models to products used onboard trains, as part of a fire safety engineering design or assessment. It is based on work performed during the European Research program, TRANSFEU. As a first step in the methodology, risk analysis permits identification of the most critical scenarios to be studied, considering actual operational conditions and rules in the European railway network. The study of one such scenario has been performed using advanced numerical tools and a multi-scale approach from raw material to real scale, in order to validate the application of such techniques in fire risk assessment. Simulations have been compared to experimental data at each scale. This predictive method shows a good capability to properly reproduce fire growth, heat release rate, temperatures and carbon dioxide concentrations in a real-scale scenario. On the other hand, this study highlights poor prediction of concentrations of carbon monoxide and other toxic species.     

Using river distances in the space/time estimation of dissolved oxygen along two impaired river networks in New Jersey

Understanding surface water quality is a critical step towards protecting human health and ecological stability. Because of resource deficiencies and the large number of river miles needing assessment, there is a need for a methodology that can accurately depict river water quality where data do not exist. The objective of this research is to implement a methodology that incorporates a river metric into the space/time analysis of dissolved oxygen data for two impaired river basins. An efficient algorithm is developed to calculate river distances within the BMElib statistical package for space/time geostatistics. We find that using a river distance in a space/time context leads to an appreciable 10% reduction in the overall estimation error, and results in maps of DO that are more realistic than those obtained using a Euclidean distance. As a result river distance is used in the subsequent non-attainment assessment of DO for two impaired river basins in New Jersey.     

Toolbox for development and validation of grey-box building models for forecasting and control

As automatic sensing and information and communication technology get cheaper, building monitoring data becomes easier to obtain. The availability of data leads to new opportunities in the context of energy efficiency in buildings. This paper describes the development and validation of a data-driven grey-box modelling toolbox for buildings. The Python toolbox is based on a Modelica library with thermal building and Heating, Ventilation and Air-Conditioning models and the optimization framework in JModelica.org. The toolchain facilitates and automates the different steps in the system identification procedure, like data handling, model selection, parameter estimation and validation. To validate the methodology, different grey-box models are identified for a single-family dwelling with detailed monitoring data from two experiments. Validated models for forecasting and control can be identified. However, in one experiment the model performance is reduced, likely due to a poor information content in the identification data set.     

Improving project forecast accuracy by integrating earned value management with exponential smoothing and reference class forecasting

In this paper, the earned value management (EVM) project control methodology is integrated with the exponential smoothing forecasting approach. This results in an extension of the known EVM and earned schedule (ES) cost and time forecasting formulas. A clear correspondence between the established approaches and the newly introduced method – called the XSM – is identified, which could facilitate future implementation. More specifically, only one smoothing parameter is needed to calculate the enhanced EVM performance factor. Moreover, this parameter can be dynamically adjusted during project progress based on information of past performance and/or anticipated management actions. Additionally, the reference class forecasting (RCF) technique can be incorporated into the XSM. Results from 23 real-life projects show that, for both time and cost forecasting, the XSM exhibits a considerable overall performance improvement with respect to the most accurate project forecasting methods identified by previous research, especially when incorporating the RCF concept.     

Terrestrial laser scanning for the comprehensive structural health assessment of the Baptistery di San Giovanni in Florence,Italy: an integrative methodology for repeatable data acquisition,visualization and analysis

The ravages of time, natural and man-made disasters, pollution, fatigue, overexposure, mismanagement, and the unintended consequences of efforts to preserve our cultural patrimony, have all taken a major toll on historical structures. Structural health assessment is the first pivotal step towards creating a strategy for long-term life-cycle management. Historical structures provide an abundance of unique challenges that when combined serve as a great qualifying test for the study of as-built structures. This paper explores the diagnostic value of terrestrial laser scanning for the structural health assessment of the Baptistery di San Giovanni in Florence, Italy and proposes an integrative methodology for repeatable data acquisition, processing, visualization and analysis. The presented study proves that even under challenging circumstances, efficient documentation of entire structures is possible. The case study at the Baptistery demonstrates that even when objectives are not formed prior to the survey, comprehensive data sets of high quality and reliability will enable meaningful structural health assessments. With a reliable comprehensive baseline model in place, it can be annotated, qualitatively analyzed and recurring surveys can be conducted to track changes and damages throughout time.     

Innovation in artificial neural network learning: Learn-On-Demand methodology

The artificial neural networks represent the state of the art tool for forecasting and prediction. However, the technique relies heavily on the availability of adequate data for its training. There have been many attempts to overcome the problems associated with the acquisition of learning data. These include the use of simulation techniques, which prepare the data for pre-processing prior to learning. Nevertheless, these methods tend to undermine the specific nature of the application that is reflected in its data. Furthermore, it is evident that, in certain circumstances, the current learning methods, grouped under on-line and off-line, do not provide an effective learning solution and their advantages are mutually exclusive. With these problems in mind, this research proposes a method for rectifying these shortcomings. The solution focuses on the learning processes rather than data. The work offers a new learning mechanism, namely the “Learn-On-Demand” (LOD) methodology, which enables the ANN to learn where the lack of knowledge is evident. The proposed LOD methodology integrates into ANN's learning process. Having produced the algorithm for its implementation, the paper then produces the mathematical representation of the Learn-On-Demand methodology by integrating the new algorithm into existing methodologies. The need for this solution emerged out of a research in the field of construction, where Structured Systems Analysis and Design was sued as a platform for integrating a hybrid of AI techniques in order to develop an enhanced method of client briefing.     

Forecasting the sales of precast concrete building products

Sales of precast concrete building products are influenced by the general demand for construction. This demand is subject to substantial fluctuations, caused by such diverse factors as capital spending by Government, the general strength of the economy, the demand for housing — which in turn reflects mortgage interest rates -and also by seasonal factors and weather. These are some of the difficulties associated with sales forecasting in the precast concrete industry. Sales forecasting is crucial managerial practice and its accuracy is vital for any company's business survival. A survey of the current forecasting and planning practices in the industry concluded that forecasting, especially for major product groups, is fairly basic and not reliable. Against this background, a forecasting model has been developed to analyse historical data and forecast demand for 12 months ahead. Two forecasting methods were applied to historical data of 12 groups of products of a major manufacturer. The results of the forecasting model were encouraging and more accurate than the manufacturer's existing forecasting system. The authors interviewed the firm's marketing and sales staff to identify the advantages and disadvantages of the forecasting system and identify the factors which affect sales and forecasting in general. Some tangible indication of the practical use of this work is the support given to this research project by staff of this company, at all levels. The work described in this paper is part of a more general computerized capacity planning system for the precast industry. This would be suitable for major companies, most of whom produce a large number of different products in a number of different manufacturing plants dispersed throughout the UK.     

Methodological problems in investigating disease clusters.

Increased reporting of disease clusters is stretching health department resources while failing to uncover many significant environmental hazards. By reviewing the methodology by which health departments respond to such reports, we argue that there is a strong bias against investigating the most hazardous situations. Failure to find serious hazards is more a reflection of the methodology used than an assessment of the existence of environmental problems. The biases arise from the selection of situations that dictate full in-house consideration, the selection of the methodology used to evaluate reported or registry-based data, and the interpretation given to anomalous observations. We argue for more rigorous evaluation of statistical cluster detection methods and for use of more objective criteria in cluster investigation protocols.     

Spatio-Temporal Short-Term Urban Traffic Volume Forecasting Using Genetically Optimized Modular Networks

Abstract:   Current interest in short-term traffic volume forecasting focuses on incorporating temporal and spatial volume characteristics in the forecasting process. This article addresses the problem of integrating and optimizing predictive information from multiple locations of an urban signalized arterial and proposes a modular neural predictor consisting of temporal genetically optimized structures of multilayer perceptrons (MLP) that are fed with volume data from sequential locations to improve the accuracy of short-term forecasts. The results show that the proposed methodology provides more accurate forecasts compared to the conventional statistical methodologies applied, as well as to the static forms of neural networks.