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

Sakine Shekoohiyan; Mobina Hadadian; Mohsen Heidari; Homa Hosseinzadeh-Bandbafha

doi:10.1016/j.cscee.2023.100331

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

Case Stud Chem Environ Eng. 2023 Jun:7:100331. doi: 10.1016/j.cscee.2023.100331. Epub 2023 Mar 10.

Authors

Sakine Shekoohiyan¹, Mobina Hadadian¹, Mohsen Heidari¹, Homa Hosseinzadeh-Bandbafha²

Affiliations

¹ Department of Environmental Health Engineering, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
² Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.

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.

Keywords: COVID-19; Life cycle assessment; Machine learning; Municipal solid waste.

Publication types

Case Reports