Abstract
Facemasks have been extensively used during the COVID-19 pandemic, and this study aims to find out the environmental impacts of three types of prevalent facemasks in Tehran to select a suitable option for future epidemics. The environmental impacts of facemasks were investigated from cradle to grave in complete incineration and landfill scenarios using the ReCiPE (H) 2016 method. The environmental burden results at endpoint levels were predicted using machine learning algorithms. The results showed that 3-layer surgical masks, 3-dimensional (3D) masks, and washable masks were predominant in Tehran, accounting for 80.1% coverage. The life cycle assessment results showed that the stage of use and raw material consumption had the most destructive environmental impacts in two scenarios for total used facemasks. The most detrimental stages for 3-layer surgical, 3D, and washable facemasks were raw material consumption, packaging, and use, respectively. The contribution of use, raw material, packaging, end-of-life, production, and transportation stages in the incineration scenario was 41.54%, 31.69%, 12.45%, 7.45%, 6.33%, and 0.50%, respectively, while in the landfill scenario, it was 45.40%, 34.64%, 13.61%, -1.17%, 6.91%, and 0.60%, respectively. The comparison of the weighted values of the damage categories in the two scenarios was as follows: 3D masks > washable masks > 3-layer surgical masks. More than 85% of the damage was caused to human health. The ML results showed that artificial neural networks, gradient-boosting regression trees, and AdaBoost algorithms were able to predict the environmental impacts of used facemasks with high R2 and low error values. Based on the present findings, it is recommended to use the 3-layer surgical mask for future epidemics due to its lower environmental impacts. Therefore, if a different type of facemask is used in the future, it is necessary to reassess its environmental impacts in comparison to the recommended 3-layer surgical mask.
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The authors would like to acknowledge Tarbiat Modares University for providing technical and financial support.
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Fatemeh Parsaee took part in investigation, formal analysis, writing—original draft and review. Sakine Shekoohiyan was responsible for resources, conceptualization, methodology, validation, writing—original draft, writing—review & editing, formal analysis, project administration, and data collection. Gholamreza Moussavi contributed to conceptualization, methodology, writing—original draft, writing—review & editing, project administration, and advising.
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Parsaee, F., Shekoohiyan, S. & Moussavi, G. Life cycle assessment of Tehran’s COVID-19 facemasks and prediction of environmental impacts using machine learning. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-04787-z
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DOI: https://doi.org/10.1007/s10668-024-04787-z