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Environmental Management

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28-07-2020

Optimal Selection of Sewage Treatment Technologies in Town Areas: A Coupled Multi-Criteria Decision-Making Model

Authors: Ben Liu, Jie Tang, Zhaoyang Li, Yu Yan, Jingshu Chen

Published in: Environmental Management | Issue 4/2020

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Abstract

In recent years, the development of sewage treatment technologies has made many treatment options available in towns. Selecting the most appropriate alternative (MAA) can make the best use of existing resources to achieve the optimal effect, which has become a topical issue in academic circles. The Liao River basin in China is an important area for agricultural cultivation and animal husbandry, but it also suffers from water shortages and pollution. In this study, the fuzzy set theory (FST), the Analytic Hierarchy Process (AHP), and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) were combined as a scientific and effective multi-criteria decision-making (MCDM) model to optimize the sewage treatment technologies in town areas of the Liao River basin. It was found that compared with natural treatment technologies (such as constructed wetlands, stabilization ponds, etc.) and combination technologies (i.e., a combination of various technologies), single small-scale sewage treatment plant technologies (such as activated sludge process or sequencing batch reactor with small daily capacity) were more suitable for those areas. The indicator of construction costs was critical in this model, and the fluctuation of its weight might change the MAA. This study aims to provide a decision support framework for the future optimization of sewage treatment technologies in towns by combining economic, environmental, and social issues, rather than just focusing on the technical aspects.

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Title: Optimal Selection of Sewage Treatment Technologies in Town Areas: A Coupled Multi-Criteria Decision-Making Model
Authors: Ben Liu
Jie Tang
Zhaoyang Li
Yu Yan
Jingshu Chen
Publication date: 28-07-2020
Publisher: Springer US
Published in: Environmental Management / Issue 4/2020
Print ISSN: 0364-152X
Electronic ISSN: 1432-1009
DOI: https://doi.org/10.1007/s00267-020-01338-w

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