Life Cycle Impact Assessment of Greywater Recycling Technologies for New Developments
Life Cycle Impact Assessment of Greywater Recycling Technologies for New Developments
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DOI：
10.1007/s10661-006-9422-3
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发表时间：
2007-01
影响因子：
3
通讯作者：
F. Memon;Z. Zheng;David Butler;C. Shirley-Smith;S. Lui;Christos Makropoulos;Lisa M. Avery
中科院分区：
文献类型：
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作者：
F. Memon;Z. Zheng;David Butler;C. Shirley-Smith;S. Lui;Christos Makropoulos;Lisa M. Avery
本文介绍了生命周期影响评估（LCIA）的四个处理技术，目前正在调查回收从家庭产生的灰水，并介绍了一个新的LCIA工具集的发展，在这项研究中产生的。研究的技术包括芦苇床，膜生物反应器（MBR），膜化学反应器（MCR）和创新的绿色屋顶水循环系统（GROW）。这些技术的建设和运营阶段所需的材料和能源已经量化为20个开发规模。收集的所有信息都用于为每种技术准备生命周期清单。清单已被用作Simapro软件执行LCIA的输入。采用了两种评价方法（CML-2和Eco-indc-99）。对于CML方法，对结果进行处理以表达十个影响类别的环境性能，包括气候变化/全球变暖、非生物资源消耗、酸化、富营养化和人类毒性。对于Eco-indc-99方法，结果显示在三个通用环境指标中：人类健康、生态系统和自然资源。LCIA结果获得的20个发展规模已被用来开发一个工具集，使用自适应神经模糊推理系统技术。研究结果表明，基于自然处理过程（GROW和芦苇床）的技术对环境的影响很小。
This paper presents the life cycle impact assessment (LCIA) of four treatment technologies currently under investigation for recycling the greywater generated from households and describes the development of a new LCIA tool set produced in this study. The technologies investigated include reed beds, membrane bioreactors (MBR); membrane chemical reactors (MCR) and an innovative green roof water recycling system (GROW). The materials and energy required for the construction and operation phases of these technologies have been quantified for 20 development scales. All of the information gathered is used to prepare life cycle inventories for each technology. The inventories have been used as an input to Simapro Software for performing LCIA. Two assessment methods (CML-2 and Eco-indc-99) have been employed. For the CML method, the results were processed to express the environmental performance in ten impact categories including climate change/global warming, depletion of abiotic resources, acidification, eutrophication, and human toxicity. For the Eco-indc-99 method, results are shown in three generic environmental indicators: human health, eco system and natural resources. LCIA results obtained for 20 development scales have been used to develop a tool set using adaptive neuro-fuzzy inference system technique. The study results indicate that the technologies based on natural treatment processes (GROW and reed beds) have low environmental impact.