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

ABSTRACT: 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 R². Based on the results, the environmental burdens of solid waste after COVID-19 should be investigated.

SUBMITTER: Shekoohiyan S 

PROVIDER: S-EPMC9998284 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature