乡镇垃圾分类循环利用现状——以闽侯县上街镇为例

由于我国乡镇经济的迅速发展以及乡镇村民生活水平加速提高,乡镇的垃圾种类及数量趋向于城市.乡镇村民居住较为分散,垃圾分类、收集、循环利用相较于城市困难,因此推进乡镇垃圾分类循环利用迫在眉睫.本文从闽侯县上街镇展开调查,以乡镇垃圾分类、回收循环利用为关键词展开分析,并从上街镇垃圾分类循环利用模式存在的问题进行分析、总结和建...     

基于LCA的废旧资源循环利用节能减排效果评估模式与方法研究——以吉林省某钢铁企业为例

促进废旧资源循环利用是加快推进我国生态文明建设,完成节能减排目标的必然选择。本文基于生命周期评价模式,从微观企业层面入手,构建产品全生命周期基准流程,引入能量输入与环境输出参数,建立废旧资源循环利用节能减排效果量化核算模型,评估再生产品的节能减排经济成效,并以吉林省某钢铁企业为例,评估"废钢-电炉"短流程和"铁矿石-高炉-转炉"长流程的能源、环境、成本差异,辨识影响废钢再循环节能减排效果的主要因素和重要环节。结果显示,再生钢铁全生命周期与原生钢铁全生命周期相比,节能588.48kgce/t,节能率为84%;主要污染物中SO2减排率最高,达92%;CO2总减排1 180.92 kg/t,减排率为67%;总成本却高出198元/t。其中,炼铁工序的节能量和减碳量最大,烧结工序SO2、NOx和烟(粉)尘减排量最大,焦化工序COD和氨氮减排量最大,回收、加工处理、炼钢环节节能量和减碳量以及SO2、NOx和烟(粉)尘减排量均为负。成本方面,再生钢铁生产成本高于原生钢铁308元/t,虽然再生钢铁由于污染减排可节省56元/t的排污费并获取54元/t的碳交易收益,但都不足以扭转电炉炼钢费用较高的现状。因此,国家应在电炉炼钢方面给予钢企及相关企业适当的财税扶持政策,在电价方面给予钢企一定的优惠或补贴,并完善废钢回收加工体系等,以促进废钢循环利用。基于LCA的废旧资源循环利用节能减排效果评估可以实现对产品生命周期全过程的资源、环境、成本的优化管理。     

An analysis of end-of-life terminology in the carbon fiber reinforced plastic industry

While many studies and reviews into the practices conducted by industry and academia to recycle and remanufacture carbon fibre-reinforced plastic (CFRP) exist, to date no investigation exists which regards the correctness of the use of the terms recycling and remanufacturing. As such, this paper seeks to analyse the CFRP reuse industry’s attempt to recycle and remanufacture manufacturing waste CFRP and end-of-life (EOL) CFRP with an emphasis on the terminology used to describe these practices. Firstly, this paper presents a justification of the importance of using EOL terminology correctly; outlining the benefits and problems associated with using the correct and incorrect terminology. This paper finds that in the case of CFRP remanufacturing, terminology is being applied incorrectly and in the case of CFRP recycling, particular care should be taken when applying the term recycled to CFRP or stating that CFRP has been recycled. Further, this paper proposes new terminology (in keeping with EU directives) which could be adopted by industry and academia working in this area. This paper also finds that in the case of remanufacture, CFRP is incapable of being remanufactured.     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.     

Predicting land cover using GIS, Bayesian and evolutionary algorithm methods

Modelling land cover change from existing land cover maps is a vital requirement for anyone wishing to understand how the landscape may change in the future. In order to test any land cover change model, existing data must be used. However, often it is not known which data should be applied to the problem, or whether relationships exist within and between complex datasets. Here we have developed and tested a model that applied evolutionary processes to Bayesian networks. The model was developed and tested on a dataset containing land cover information and environmental data, in order to show that decisions about which datasets should be used could be made automatically. Bayesian networks are amenable to evolutionary methods as they can be easily described using a binary string to which crossover and mutation operations can be applied. The method, developed to allow comparison with standard Bayesian network development software, was proved capable of carrying out a rapid and effective search of the space of possible networks in order to find an optimal or near-optimal solution for the selection of datasets that have causal links with one another. Comparison of land cover mapping in the North-East of Scotland was made with a commercial Bayesian software package, with the evolutionary method being shown to provide greater flexibility in its ability to adapt to incorporate/utilise available evidence/knowledge and develop effective and accurate network structures, at the cost of requiring additional computer programming skills. The dataset used to develop the models included GIS-based data taken from the Land Cover for Scotland 1988 (LCS88), Land Capability for Forestry (LCF), Land Capability for Agriculture (LCA), the soil map of Scotland and additional climatic variables.     

Choosing a Food Future: Differentiating Among Alternative Food Options

This article examines the diversity of food networks that fit within the alternative food system of the United States. While farmers’ markets, community supported agriculture schemes, and corporate organic food markets all fit within the alternative food system, they differ greatly in the conventions and beliefs that they represent. The alternative food system has divided into two movements: corporate, weak alternative food networks; and local, strong alternative food networks. The weak corporate version focuses on protecting the environment; however, it neglects issues concerning labor standards, animal welfare, rural communities, small-scale farmers, and human health. Local, strong alternative food networks not only assure environmental protection, but they also address the issues that weak alternatives neglect. Using three case studies from the Washington, D.C. metro area, the author explains that strong alternative food networks are better suited to create social and political change because they challenge the foundations of the conventional food system: standardized and generic products, price-based competition, consolidated power, and global scale. To affect true social and political change in the United States, the author recommends supporting strong alternative food networks by creating the requisite cultural and political space for them to succeed.     

Reflections on the use of Bayesian belief networks for adaptive management

A broad range of tools are available for integrated water resource management (IWRM). In the EU research project NeWater, a hypothesis exists that IWRM cannot be realised unless current management regimes undergo a transition toward adaptive management (AM). This includes a structured process of learning, dealing with complexity, uncertainty etc. We assume that it is no longer enough for managers and tool researchers to understand the complexity and uncertainty of the outer natural system-the environment. It is just as important, to understand what goes on in the complex and uncertain participatory processes between the water managers, different stakeholders, authorities and researchers when a specific tool and process is used for environmental management. The paper revisits a case study carried out 2001-2004 where the tool Bayesian networks (BNs) was tested for groundwater management with full stakeholder involvement. With the participation of two researchers (the authors) and two water managers previously involved in the case study, a qualitative interview was prepared and carried out in June 2006. The aim of this ex-post evaluation was to capture and explore the water managers' experience with Bayesian belief networks when used for integrated and adaptive water management and provide a narrative approach for tool enhancement.     

A preliminary process design and economic assessment of a catalyst rejuvenation process for waste disposal of refinery spent catalysts

Spent hydroprocessing catalysts from refineries have been classified as hazardous solid waste by the United States Environmental Protection Agency (USEPA), refiners must find a viable but economical solution to solve this serious environmental issue. Catalyst rejuvenation is an attractive option for minimizing the environmental problems associated with spent catalysts. In this study, a preliminary design for such a process and the corresponding economic analysis are performed to assess the proposed catalyst rejuvenation process for metal-fouled spent catalysts generated in residue hydroprocessing units. The scenarios used in the economic assessment are based on three options of process synthesis and two operator modes. It is found that the option of rejuvenating medium and lightly fouled spent catalyst produced by the refinery will be the best solution for refiners, both environmentally and economically.     

基于BP模型对城市交通噪声的数据处理和预测

城市交通噪声的预测和评价技术是城市交通可持续发展的重要研究内容,直接为城市交通规划中环境容量分析和环境影响评价服务。本文通过实测的大量数据,运用神经网络中的BP模型及其算法建立车辆数、道路宽度和交通噪声之间的关系,对城市道路交通噪声数据进行处理和预测。     

Formalising informal solid waste recycling at the Pomona dumpsite in Harare,Zimbabwe

The role of informal recycling in poverty alleviation and solid waste management in cities in developing countries has been receiving increased attention. This study explores the integration of the informal recycling sector with the Harare City Council's solid waste management system, focusing on the Pomona dumpsite. The extent of this integration was compared with interventions proposed in InteRa, a new way of evaluating the integration of informal recyclers with the waste management systems of cities in developing countries. Our results suggest that the Harare City Council, which had the vision of transforming itself into a world‐class city, failed to fully integrate the informal recycling sector. We suggest to policymakers that complete integration of the informal sector will not necessarily prevent cities from achieving such visions. Rather, addressing the neglected interventions may help in achieving their visions.