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Metabolism of urban wastewater: Ecological network analysis for Guangdong Province, China.

Authors :
Zheng, Boyue
Huang, Guohe
Liu, Lirong
Zhai, Mengyu
Guan, Yuru
Source :
Journal of Cleaner Production. Apr2019, Vol. 217, p510-519. 10p.
Publication Year :
2019

Abstract

Abstract Wastewater discharge is a burden on environmentally sustainable development, especially in the water-deficient area. Existing Studies on wastewater discharge is not comprehensive for lacking analysis of mutual flow and necessary components. In this study, a wastewater metabolism input-output model is developed to achieve sustainable development through a novel perspective to depict the industrial wastewater flow among sectors. Since chemical oxygen demand and ammonia nitrogen are indicators for studying the degree of wastewater pollution, this paper also considers their wastewater to make the research synthetic and systematic. A case study of Guangdong Province, China, is conducted to further illustrate the potential benefits of the model in investigation of the sectors interactions. The results show that the wastewater discharge of Guangdong Province is considerable, with industrial wastewater, chemical oxygen demand wastewater and ammonia-nitrogen wastewater being 7.53 billion tons, 852 thousand tons and 69 thousand tons respectively. Some typical sectors have been distinguished based on ecological network analysis and input-output analysis for mitigating wastewater discharge, such as electronic equipment manufacture, chemical materials and paper manufacture, and tertiary industry. The implementation of the "Replace Subsidies with Rewards" policy is conducive to the discharge reduction of the system. Graphical abstract Image 1 Highlights • Establishing a wastewater metabolism input-output model for Guangdong, China. • Industrial wastewater discharge is large and exploitation relationship is dominant. • Comparing the discharge status of cod wastewater and ammonia nitrogen wastewater. • Proposing some policies and regulations for main sectors to optimize the model. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09596526
Volume :
217
Database :
Academic Search Index
Journal :
Journal of Cleaner Production
Publication Type :
Academic Journal
Accession number :
134822057
Full Text :
https://doi.org/10.1016/j.jclepro.2019.01.222