Life cycle assessment of Tehran Municipal solid waste during the COVID-19 pandemic and environmental impacts prediction using machine learning

Life cycle assessment and machine learning were combined to find the best option for Tehran's waste management for future pandemics. The ReCipe results showed the waste's destructive effects after COVID-19 were greater than before due to waste composition changes. Plastic waste has changed...

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Autores principales: Shekoohiyan, Sakine, Hadadian, Mobina, Heidari, Mohsen, Hosseinzadeh-Bandbafha, Homa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Authors. Published by Elsevier Ltd. 2023
Materias:
Case Report
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9998284/
https://www.ncbi.nlm.nih.gov/pubmed/37521456
http://dx.doi.org/10.1016/j.cscee.2023.100331
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author Shekoohiyan, Sakine
Hadadian, Mobina
Heidari, Mohsen
Hosseinzadeh-Bandbafha, Homa
author_facet Shekoohiyan, Sakine
Hadadian, Mobina
Heidari, Mohsen
Hosseinzadeh-Bandbafha, Homa
author_sort Shekoohiyan, Sakine
collection PubMed
description Life cycle assessment and machine learning were combined to find the best option for Tehran's waste management for future pandemics. The ReCipe results showed the waste's destructive effects after COVID-19 were greater than before due to waste composition changes. Plastic waste has changed from 7.5 to 11%. Environmental burdens of scenarios were Sc-1 (increase composting to 50%) > Sc-3 > Sc-4 > Sc-b2 > Sc-5 > Sc-2 (increase recycling from 9 to 20%). The artificial neural network and gradient-boosted regression tree could predict environmental impacts with high R(2). Based on the results, the environmental burdens of solid waste after COVID-19 should be investigated.
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spelling pubmed-99982842023-03-10 Life cycle assessment of Tehran Municipal solid waste during the COVID-19 pandemic and environmental impacts prediction using machine learning Shekoohiyan, Sakine Hadadian, Mobina Heidari, Mohsen Hosseinzadeh-Bandbafha, Homa Case Studies in Chemical and Environmental Engineering Case Report Life cycle assessment and machine learning were combined to find the best option for Tehran's waste management for future pandemics. The ReCipe results showed the waste's destructive effects after COVID-19 were greater than before due to waste composition changes. Plastic waste has changed from 7.5 to 11%. Environmental burdens of scenarios were Sc-1 (increase composting to 50%) > Sc-3 > Sc-4 > Sc-b2 > Sc-5 > Sc-2 (increase recycling from 9 to 20%). The artificial neural network and gradient-boosted regression tree could predict environmental impacts with high R(2). Based on the results, the environmental burdens of solid waste after COVID-19 should be investigated. The Authors. Published by Elsevier Ltd. 2023-06 2023-03-10 /pmc/articles/PMC9998284/ /pubmed/37521456 http://dx.doi.org/10.1016/j.cscee.2023.100331 Text en © 2023 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Case Report
Shekoohiyan, Sakine
Hadadian, Mobina
Heidari, Mohsen
Hosseinzadeh-Bandbafha, Homa
Life cycle assessment of Tehran Municipal solid waste during the COVID-19 pandemic and environmental impacts prediction using machine learning
title Life cycle assessment of Tehran Municipal solid waste during the COVID-19 pandemic and environmental impacts prediction using machine learning
title_full Life cycle assessment of Tehran Municipal solid waste during the COVID-19 pandemic and environmental impacts prediction using machine learning
title_fullStr Life cycle assessment of Tehran Municipal solid waste during the COVID-19 pandemic and environmental impacts prediction using machine learning
title_full_unstemmed Life cycle assessment of Tehran Municipal solid waste during the COVID-19 pandemic and environmental impacts prediction using machine learning
title_short Life cycle assessment of Tehran Municipal solid waste during the COVID-19 pandemic and environmental impacts prediction using machine learning
title_sort life cycle assessment of tehran municipal solid waste during the covid-19 pandemic and environmental impacts prediction using machine learning
topic Case Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9998284/
https://www.ncbi.nlm.nih.gov/pubmed/37521456
http://dx.doi.org/10.1016/j.cscee.2023.100331
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