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dc.contributor.authorChew, XinYing
dc.contributor.authorKhaw, Khai Wah
dc.contributor.authorAlnoor, Alhamzah
dc.contributor.authorFerasso, Marcos
dc.contributor.authorAl Halbusi, Hussam
dc.contributor.authorMuhsen, Yousif Raad
dc.date.accessioned2024-04-11T05:56:26Z
dc.date.available2024-04-11T05:56:26Z
dc.date.issued2023
dc.identifier10.1007/s11356-023-26677-z
dc.identifier.issn9441344
dc.identifier.urihttps://hdl.handle.net/20.500.12728/11110
dc.description.abstractEnvironmental pollution has been a major concern for researchers and policymakers. A number of studies have been conducted to enquire the causes of environmental pollution which suggested numerous policies and techniques as remedial measures. One such major source of environmental pollution, as reported by previous studies, has been the garbage resulting from disposed hospital wastes. The recent outbreak of the COVID-19 pandemic has resulted into mass generation of medical waste which seems to have further deteriorated the issue of environmental pollution. This necessitates active attention from both the researchers and policymakers for effective management of medical waste to prevent the harm to environment and human health. The issue of medical waste management is more important for countries lacking sophisticated medical infrastructure. Accordingly, the purpose of this study is to propose a novel application for identification and classification of 10 hospitals in Iraq which generated more medical waste during the COVID-19 pandemic than others in order to address the issue more effectively. We used the Multi-Criteria Decision Making (MCDM) method to this end. We integrated MCDM with other techniques including the Analytic Hierarchy Process (AHP), linear Diophantine fuzzy set decision by opinion score method (LDFN-FDOSM), and Artificial Neural Network (ANN) analysis to generate more robust results. We classified medical waste into five categories, i.e., general waste, sharp waste, pharmaceutical waste, infectious waste, and pathological waste. We consulted 313 experts to help in identifying the best and the worst medical waste management technique within the perspectives of circular economy using the neural network approach. The findings revealed that incineration technique, microwave technique, pyrolysis technique, autoclave chemical technique, vaporized hydrogen peroxide, dry heat, ozone, and ultraviolet light were the most effective methods to dispose of medical waste during the pandemic. Additionally, ozone was identified as the most suitable technique among all to serve the purpose of circular economy of medical waste. We conclude by discussing the practical implications to guide governments and policy makers to benefit from the circular economy of medical waste to turn pollutant hospitals into sustainable ones.es_ES
dc.description.sponsorshipMinistry of Higher Education Malaysia, Fundamental Research Grant Scheme [FRGS/1/2022/STG06/USM/02/4]es_ES
dc.language.isoenes_ES
dc.publisherSPRINGER HEIDELBERGes_ES
dc.subjectMedical/healthcare wastees_ES
dc.subjectCircular economyes_ES
dc.subjectCOVID-19es_ES
dc.subjectEnvironmental pollutiones_ES
dc.subjectMulti-Criteria Decision Makinges_ES
dc.titleCircular economy of medical waste: novel intelligent medical waste management framework based on extension linear Diophantine fuzzy FDOSM and neural network approaches_ES
dc.typeArticlees_ES


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