Stochastic grey water footprint model based on uncertainty analysis theory

The conventional grey water footprint (GWF) cannot deal with the uncertainties induced by the background information. To solve this problem, this study develops a stochastic GWF model based on probability theory and the maximum entropy principle. The stochastic GWF model further introduces the expec...

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Autores principales:	Xinkui Wang, Zengchuan Dong, Wenzhuo Wang, Yun Luo, Yaogeng Tan
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Grey water footprint Background value COD Uncertainty analysis Ningxia province Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/140518dd0c88446482653f6d1662a127
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spelling	oai:doaj.org-article:140518dd0c88446482653f6d1662a1272021-12-01T04:46:21ZStochastic grey water footprint model based on uncertainty analysis theory1470-160X10.1016/j.ecolind.2021.107444https://doaj.org/article/140518dd0c88446482653f6d1662a1272021-05-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21001096https://doaj.org/toc/1470-160XThe conventional grey water footprint (GWF) cannot deal with the uncertainties induced by the background information. To solve this problem, this study develops a stochastic GWF model based on probability theory and the maximum entropy principle. The stochastic GWF model further introduces the expectation calculation and water pollution risk (WPR) identification into assessment, which are used to comprehensively evaluate the GWF and quantify the potential water shortage risk induced by pollution, respectively. To verify its effectiveness, the stochastic GWF is applied to the evaluation of chemical oxygen demand (COD) in Ningxia province, China. Results show the following: (i) compared with the conventional GWF, the stochastic GWF significant is in advantage in terms of grade identification, pollution ranking, and risk recognizing. (ii) From 2011 to 2017, the GWF expectations of COD in Ningxia provinces are 7.52, 7.32, 7.15, 6.92, 3.95, and 3.36 billion m3, and the WPRs are 0.51, 0.10, 0.13, 0.06, 0.00, and 0.00, respectively. (iii) The WPRs are determined not only by the pollution load but also by climate change and the hydrological rhythm. (iv) Only using the mathematical expectation of the background parameter for evaluation may ignore the environmental risk in the water area with high background values, making the evaluation over-optimistic.Xinkui WangZengchuan DongWenzhuo WangYun LuoYaogeng TanElsevierarticleGrey water footprintBackground valueCODUncertainty analysisNingxia provinceEcologyQH540-549.5ENEcological Indicators, Vol 124, Iss , Pp 107444- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Grey water footprint Background value COD Uncertainty analysis Ningxia province Ecology QH540-549.5
spellingShingle	Grey water footprint Background value COD Uncertainty analysis Ningxia province Ecology QH540-549.5 Xinkui Wang Zengchuan Dong Wenzhuo Wang Yun Luo Yaogeng Tan Stochastic grey water footprint model based on uncertainty analysis theory
description	The conventional grey water footprint (GWF) cannot deal with the uncertainties induced by the background information. To solve this problem, this study develops a stochastic GWF model based on probability theory and the maximum entropy principle. The stochastic GWF model further introduces the expectation calculation and water pollution risk (WPR) identification into assessment, which are used to comprehensively evaluate the GWF and quantify the potential water shortage risk induced by pollution, respectively. To verify its effectiveness, the stochastic GWF is applied to the evaluation of chemical oxygen demand (COD) in Ningxia province, China. Results show the following: (i) compared with the conventional GWF, the stochastic GWF significant is in advantage in terms of grade identification, pollution ranking, and risk recognizing. (ii) From 2011 to 2017, the GWF expectations of COD in Ningxia provinces are 7.52, 7.32, 7.15, 6.92, 3.95, and 3.36 billion m3, and the WPRs are 0.51, 0.10, 0.13, 0.06, 0.00, and 0.00, respectively. (iii) The WPRs are determined not only by the pollution load but also by climate change and the hydrological rhythm. (iv) Only using the mathematical expectation of the background parameter for evaluation may ignore the environmental risk in the water area with high background values, making the evaluation over-optimistic.
format	article
author	Xinkui Wang Zengchuan Dong Wenzhuo Wang Yun Luo Yaogeng Tan
author_facet	Xinkui Wang Zengchuan Dong Wenzhuo Wang Yun Luo Yaogeng Tan
author_sort	Xinkui Wang
title	Stochastic grey water footprint model based on uncertainty analysis theory
title_short	Stochastic grey water footprint model based on uncertainty analysis theory
title_full	Stochastic grey water footprint model based on uncertainty analysis theory
title_fullStr	Stochastic grey water footprint model based on uncertainty analysis theory
title_full_unstemmed	Stochastic grey water footprint model based on uncertainty analysis theory
title_sort	stochastic grey water footprint model based on uncertainty analysis theory
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/140518dd0c88446482653f6d1662a127
work_keys_str_mv	AT xinkuiwang stochasticgreywaterfootprintmodelbasedonuncertaintyanalysistheory AT zengchuandong stochasticgreywaterfootprintmodelbasedonuncertaintyanalysistheory AT wenzhuowang stochasticgreywaterfootprintmodelbasedonuncertaintyanalysistheory AT yunluo stochasticgreywaterfootprintmodelbasedonuncertaintyanalysistheory AT yaogengtan stochasticgreywaterfootprintmodelbasedonuncertaintyanalysistheory
_version_	1718405765528551424

Stochastic grey water footprint model based on uncertainty analysis theory

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