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A simplified material flow analysis employing local expert judgment and its impact on uncertainty

Material flow analysis (MFA) is an effective tool for waste management, but low- and middle-income countries lack essential data for MFA. This study proposed a simplified MFA (sMFA) utilizing local expert judgment (LEJ) and examining the impact of simplification on its uncertainty. A stochastic sMFA...

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Autores principales: Naing, Wutyi, Harada, Hidenori, Fujii, Shigeo, Hmwe, Chaw Su Su
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Japan 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10307724/
https://www.ncbi.nlm.nih.gov/pubmed/37397279
http://dx.doi.org/10.1007/s10163-023-01660-5
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author Naing, Wutyi
Harada, Hidenori
Fujii, Shigeo
Hmwe, Chaw Su Su
author_facet Naing, Wutyi
Harada, Hidenori
Fujii, Shigeo
Hmwe, Chaw Su Su
author_sort Naing, Wutyi
collection PubMed
description Material flow analysis (MFA) is an effective tool for waste management, but low- and middle-income countries lack essential data for MFA. This study proposed a simplified MFA (sMFA) utilizing local expert judgment (LEJ) and examining the impact of simplification on its uncertainty. A stochastic sMFA model was developed for nitrogen and phosphorus in urban Mandalay, Myanmar. This model was compared with the intensive MFA (iMFA) model employing intensive surveys for primary data collection. For the total loadings to the environment, the medians of the sMFA were higher by 3% and 11%, respectively, for nitrogen and phosphorus than those of the iMFA. The widths of the 80% confidence intervals of these loadings in the sMFA, normalized by those in the iMFA, were − 0.05 and − 0.11, respectively. The three largest flows to the environment were the same for the two models: on-site sanitation effluent/leakage, greywater, and industrial wastewater. Large median gaps between the models were observed for industrial wastewater, fecal sludge, and human excreta, associated with informal waste management, whereby LEJ did not work well. Overall, the sMFA demonstrated a good estimation of nitrogen and phosphorus flows with limited increase of uncertainty, still requiring focused attention on informal waste streams. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10163-023-01660-5.
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spelling pubmed-103077242023-06-30 A simplified material flow analysis employing local expert judgment and its impact on uncertainty Naing, Wutyi Harada, Hidenori Fujii, Shigeo Hmwe, Chaw Su Su J Mater Cycles Waste Manag Original Article Material flow analysis (MFA) is an effective tool for waste management, but low- and middle-income countries lack essential data for MFA. This study proposed a simplified MFA (sMFA) utilizing local expert judgment (LEJ) and examining the impact of simplification on its uncertainty. A stochastic sMFA model was developed for nitrogen and phosphorus in urban Mandalay, Myanmar. This model was compared with the intensive MFA (iMFA) model employing intensive surveys for primary data collection. For the total loadings to the environment, the medians of the sMFA were higher by 3% and 11%, respectively, for nitrogen and phosphorus than those of the iMFA. The widths of the 80% confidence intervals of these loadings in the sMFA, normalized by those in the iMFA, were − 0.05 and − 0.11, respectively. The three largest flows to the environment were the same for the two models: on-site sanitation effluent/leakage, greywater, and industrial wastewater. Large median gaps between the models were observed for industrial wastewater, fecal sludge, and human excreta, associated with informal waste management, whereby LEJ did not work well. Overall, the sMFA demonstrated a good estimation of nitrogen and phosphorus flows with limited increase of uncertainty, still requiring focused attention on informal waste streams. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10163-023-01660-5. Springer Japan 2023-05-02 2023 /pmc/articles/PMC10307724/ /pubmed/37397279 http://dx.doi.org/10.1007/s10163-023-01660-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Naing, Wutyi
Harada, Hidenori
Fujii, Shigeo
Hmwe, Chaw Su Su
A simplified material flow analysis employing local expert judgment and its impact on uncertainty
title A simplified material flow analysis employing local expert judgment and its impact on uncertainty
title_full A simplified material flow analysis employing local expert judgment and its impact on uncertainty
title_fullStr A simplified material flow analysis employing local expert judgment and its impact on uncertainty
title_full_unstemmed A simplified material flow analysis employing local expert judgment and its impact on uncertainty
title_short A simplified material flow analysis employing local expert judgment and its impact on uncertainty
title_sort simplified material flow analysis employing local expert judgment and its impact on uncertainty
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10307724/
https://www.ncbi.nlm.nih.gov/pubmed/37397279
http://dx.doi.org/10.1007/s10163-023-01660-5
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