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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Detalles Bibliográficos
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
Descripción
Sumario: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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