PMID- 37036648
OWN - NLM
STAT- MEDLINE
DCOM- 20230510
LR  - 20230516
IS  - 1614-7499 (Electronic)
IS  - 0944-1344 (Print)
IS  - 0944-1344 (Linking)
VI  - 30
IP  - 21
DP  - 2023 May
TI  - Circular economy of medical waste: novel intelligent medical waste management 
      framework based on extension linear Diophantine fuzzy FDOSM and neural network 
      approach.
PG  - 60473-60499
LID - 10.1007/s11356-023-26677-z [doi]
AB  - Environmental 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.
CI  - (c) 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Chew, XinYing
AU  - Chew X
AD  - School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, 
      Malaysia.
FAU - Khaw, Khai Wah
AU  - Khaw KW
AD  - School of Management, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia.
FAU - Alnoor, Alhamzah
AU  - Alnoor A
AD  - Management Technical College, Southern Technical University, Basrah, Iraq. 
      alhamzah.malik@stu.edu.iq.
FAU - Ferasso, Marcos
AU  - Ferasso M
AD  - Economics and Business Sciences Department, Universidade Autonoma de Lisboa, 
      1169-023, Lisbon, Portugal.
AD  - Grupo de Investigacion de Estudios Organizacionales Sostenibles, Universidad 
      Autonoma de Chile, Santiago, Chile.
FAU - Al Halbusi, Hussam
AU  - Al Halbusi H
AD  - Department of Management, Ahmed Bin Mohammad Military College, Doha, Qatar.
FAU - Muhsen, Yousif Raad
AU  - Muhsen YR
AD  - Faculty of Engineering, Universiti Putra Malaysia, Seri Kembangan, Selangor, 
      Malaysia.
LA  - eng
PT  - Journal Article
DEP - 20230410
PL  - Germany
TA  - Environ Sci Pollut Res Int
JT  - Environmental science and pollution research international
JID - 9441769
RN  - 0 (Medical Waste)
SB  - IM
EIN - Environ Sci Pollut Res Int. 2023 Apr 25;:. PMID: 37097584
MH  - Humans
MH  - *Medical Waste
MH  - Pandemics
MH  - *COVID-19
MH  - *Waste Management
MH  - Incineration
PMC - PMC10088637
OTO - NOTNLM
OT  - COVID-19
OT  - Circular economy
OT  - Environmental pollution
OT  - Medical/healthcare waste
OT  - Multi-Criteria Decision Making
COIS- The authors declare no competing interests.
EDAT- 2023/04/11 06:00
MHDA- 2023/05/10 06:42
PMCR- 2023/04/11
CRDT- 2023/04/10 11:23
PHST- 2022/10/22 00:00 [received]
PHST- 2023/03/23 00:00 [accepted]
PHST- 2023/05/10 06:42 [medline]
PHST- 2023/04/11 06:00 [pubmed]
PHST- 2023/04/10 11:23 [entrez]
PHST- 2023/04/11 00:00 [pmc-release]
AID - 10.1007/s11356-023-26677-z [pii]
AID - 26677 [pii]
AID - 10.1007/s11356-023-26677-z [doi]
PST - ppublish
SO  - Environ Sci Pollut Res Int. 2023 May;30(21):60473-60499. doi: 
      10.1007/s11356-023-26677-z. Epub 2023 Apr 10.