Medical Waste Disposal Research Articles

Environmental Health-Based Medical Waste Management: Implementation of Local Government Responsibilities in Central Java

Purpose: Medical waste, generated by hospitals, clinics, and other healthcare facilities, has the potential for harm if not managed properly. The responsibility of the Central Java local government in managing medical waste is the main focus in this study. This study aims to determine the extent of the implementation of local government obligations in Central Java in carrying out their obligations to manage medical waste in maintaining environmental health. Methods/design/approach: This research uses descriptive analysis methods and case studies, by collecting data from primary and secondary sources, such as government policies, regulations, and medical waste management practices. Results: The results showed that the Central Java regional government has taken significant steps in an effort to manage medical waste by taking into account environmental health principles. They have issued regulations governing the collection, treatment, and disposal of medical waste, as well as providing guidelines to health facilities in the implementation of safe medical waste management practices. However, the study also revealed a number of challenges in implementing local government responsibilities. Some hospitals and clinics may still not fully comply with existing regulations, and medical waste management infrastructure may need to be improved in some areas. Conclusion: Environmental health-based medical waste management is a crucial aspect in maintaining public health and environmental sustainability in Central Java. Local governments have taken significant steps, but there are still challenges that need to be overcome. With further efforts in improving infrastructure, awareness, and supervision, it is expected that medical waste management in Central Java will become more effective, safe, and sustainable. It will support public health and contribute to the sustainable development goals in Central Java.

Research on transportation management model of COVID-19 medical waste: a case study in Beijing, China.

During the COVID-19 pandemic, disposable masks, protective clothing, gloves, and nasopharyngeal swabs collected by nucleic acid testing formed a large amount of medical waste. Medical waste has strict temporary storage time requirements in hospitals, which need to be transported to medical waste disposal plants within the specified time. However, as most of disposal plants are far away from downtown, they also need to be responsible for the transportation and disposal of medical waste in many hospitals, and put forward higher requirement for transportation routes. Rapid and safe disposal of all types of medical waste generated by COVID-19 is crucial to the prevention and control of the epidemic. This paper designs the transportation route optimization model using Anylogic simulation software based on the regional distribution of 118 tertiary hospitals and 2 large medical waste disposal plants in Beijing, China. At the same time, transportation routes of 118 tertiary hospitals in the morning peak, evening peak, all-day, and ordinary periods were simulated based on the Beijing traffic index in 2017. On this basis, through the analysis of the simulation data, the selection of medical waste transport routes for 118 tertiary hospitals in the morning peak, evening peak, all day, and ordinary periods is further clarified, so as to ensure that medical waste can be transported to the medical waste disposal plant in the shortest time. The shortest path and fastest speed transport mode, medical waste transport data set, and the selection of transport mode of 118 tertiary hospitals formed by this research provide certain reference experience for the rapid and safe transport of medical waste during the epidemic period, and also provides corresponding data support for medical waste transportation management in the post-epidemic era and medical waste transportation decision-making when facing major public health problems.

Construction and effect evaluation of a hierarchical training system for nosocomial infection based on hospitals at all levels

INTRODUCTION: Nosocomial infection is a critical global public health issue. The education of medical personnel can effectively enhance compliance with nosocomial infection protocols and reduce the incidence of such infections. However, the current training provided to third-party staff is inadequate, necessitating an urgent enhancement of their knowledge on nosocomial infection through effective and tailored training programs. OBJECTIVES: The objective is to establish a hierarchical training system for nosocomial infection, customized to meet the specific requirements of hospitals at all levels, and evaluate its efficacy. METHODS: A questionnaire survey was conducted among third-party staff members at hospitals of different levels to assess their understanding of nosocomial infection prevention measures. Based on the survey results, a hierarchical training system was developed for nosocomial infection among the participants. After the training, a post-training assessment was carried out to evaluate the participants' comprehension of nosocomial infections. RESULTS: A total of 561 third-party employees participated in the baseline hospital infection knowledge questionnaire. The baseline findings unveiled disparities in the extent to which third-party staff members across various tiers of medical institutions have mastered their knowledge on nosocomial infections. After undergoing hierarchical training, the deficiencies of hospitals at all levels have been rectified, thereby effectively enhancing the level of knowledge regarding nosocomial infections among third-party personnel. The results of multivariate analysis indicate that individuals with limited work experience should enhance their training in medical waste disposal and acquire a deeper understanding of personal protection measures related to nosocomial infections. Moreover, infrequent annual training sessions may impede the comprehension of nosocomial infection among third-party staff. CONCLUSION: The knowledge of hospital infection among third-party staff at all levels of medical institutions exhibits varying deficiencies. Implementing a hierarchical training approach is a meaningful strategy that effectively enhances the level of hospital infection knowledge among these staff members.

Optimal Route Determination Automation System for Covid-19 Medical Waste Disposal Based on 3D Building Modeling

Optimal Route Determination Automation System for Covid-19 Medical Waste Disposal Based on 3D Building Modeling

Comparative Study of Legal Responsibility of B3 Waste Producers in Medical B3 Waste Management Crowded in Hospitals Under the Law Indonesia and South Korean Law

This study uses a normative legal research type, while the nature of the research used in this writing is descriptive, analytical to analyze and describe the comparative legal responsibility of B3 waste producers in solid medical B3 waste management in hospitals in Indonesia and South Korea. The regulation of the legal responsibility of B3 waste producers in the management of solid medical B3 waste in hospitals in Indonesia is regulated in Law No. 32 of 2009 which states that everyone who produces B3 waste is obliged to manage the waste they produce, including in this case health service facilities that produce medical B3 waste. Medical B3 waste management activities include Reduction and sorting, Storage, Transportation, Processing, Burial and/or Landfill activities of B3 Waste. The management of solid medical B3 waste in hospitals in South Korea was regulated by the Medical Law under the Ministry of Health and Welfare until 1999. The Ministry of Environment announced several regulations for the definition, segregation, packaging, tracking, and final disposal of medical waste. Medical B3 waste management in Korea and Indonesia has similarities in legal, technical and operational aspects. This is one of them because Korea and Indonesia adhere to the same legal system, namely the continental European legal system (civil law).

Experimental and numerical investigation of medical waste disposal via plasma gasification

The COVID-19 pandemic has increased global awareness of proper medical waste disposal. Conventional methods like incineration and gasification are insufficient due to low energy efficiency and harmful emissions. Plasma gasification, however, offers significant advantages, including economical benefits, higher efficiency, lower emissions, and greater hydrogen production potential. Hence, plasma gasification can be an influential alternative to conventional methods with further developments. In this study, gasification characteristics of medical waste are investigated in a novel laboratory-scale updraft plasma gasifier. The gasifier utilizes a DC arc plasma torch with a maximum capacity of 5 kWh. Medical waste samples are prepared by mixing various consumables like face masks and cotton bandages, whereas waste capacity of the gasifier is estimated to be 7.0 kg/h for an equivalence ratio (ER) of 0.2. Simulations are conducted for ER values of 0.1, 0.2, 0.3, and 0.4 using ANSYS Fluent software, and the plasma jet is modeled as hot gas. Results demonstrate that integrating the throat configuration to the updraft plasma gasifier and increasing the ER value enhance H2 production and reduce CO content in the syngas. The minimum H2 content is found as 48.5% for ER = 0.1 with 24.1% CO, while the maximum H2 content is 54.7% for ER = 0.4 with 17.1% CO. The gasifier configuration achieves a carbon conversion rate (CCR) of 29.1% and a cold gas efficiency (CGE) of 28.5%.

Optimal Route Determination Automation System for Covid-19 Medical Waste Disposal Based on 3D Building Modeling

Urbanization is a contributing factor to global warming, as asphalt, concrete, and other light-absorbing materials replace vegetated areas, causing an increase in Land Surface Temperature (LST) and creating Surface Urban Heat Islands (SUHI). Although thermal satellite imagery has been a powerful tool in mapping LST and SUHI spatio-temporal changes, the number of studies in Africa, including Egypt, remains limited. Thus, in this research, an automated model was developed in ArcGIS and used to map LST and SUHI and detect Urban Hot Spots (UHS) in Alexandria city, Egypt, using Landsat 8 time series (2013 to 2021). The results revealed an increase of 41.31% in urban areas and a decrease of 49.51% in agricultural areas, a change that was demonstrated by a decline in the Normalized Difference Vegetation Index (NDVI) from 0.84 in 2013 to 0.53 in 2021. Correspondingly, LST and SUHI displayed an increasing pattern, with the highest recorded values observed in 2021. Thus, this study showed the negative impact of urbanization on Alexandria city’s temperature – a city that is already facing a climate catastrophe because of the sea level rise resulting from climate change. Furthermore, the developed estimation model can be similarly useful for climate change researchers and decision makers.

Research on Optimization of Medical Waste Emergency Disposal Transportation Network for Public Health Emergencies in the Context of Intelligent Transportation

In order to build a more comprehensive emergency disposal and transportation network system for medical waste, it is necessary to consider various uncertain factors and data characteristics. Therefore, in the context of intelligent transportation, this article considers the uncertainty of the quantity and regional population density of infectious medical waste generation as well as the emergency disposal of infectious medical waste under multi-cycle and multi-objective conditions, and it constructs a multi-cycle emergency disposal logistics network optimization model for infectious medical waste under uncertain conditions. Through deterministic transformation of the model and data mining of the medical waste disposal logistics network in Wuhan, China, the multi-objective model under uncertain conditions was also solved and sensitivity analyzed using the MOPSO-NSGA2 intelligent algorithm, verifying the effectiveness and superiority of the algorithm.

Knowledge About Medical Waste Management: How do Healthcare Professionals Learn and Practice?

The management and disposal of medical waste follow scientific protocols to ensure safety. However, the effective implementation of the medical waste management system depended on correct handling and segregation by the healthcare workers, during their daily activities. These professionals need to know about health waste management, since incorrect handling and disposal pose a potential risk to the environment and human health. This study aimed to analyze the knowledge regarding the management of medical waste of the healthcare teams working in the Family Health Strategy (FHS). Designed to provide preventive and basic healthcare using multidisciplinary professional teams, the FHS reflects many best practices. The research was carried out in a city that is the 3rd economy in southern Brazil. This was a cross-sectional descriptive study with a quantitative approach, and data were assessed by using self-administered and pretested questionnaire. The study sample consisted of 74 participants, including doctors, nurses, and nursing technicians. Descriptive statistics were carried out to express participants’ demographic information, mean knowledge score, and the teaching strategies used with the team. The main research results show that the healthcare teams working in the Family Health Strategy have, in part, knowledge about the correct management of waste. Besides, they are sensitive, in general, to its impact on the environment. Nevertheless, the results highlighted that more practical training would be needed, to help healthcare workers with everyday doubts. Furthermore, the results show that it requires attention to rethinking the strategies used to train and develop knowledge and skills in health services.

Designing a Tri-Objective, Sustainable, Closed-Loop, and Multi-Echelon Supply Chain During the COVID-19 and Lockdowns

Abstract This paper proposes a mathematical model of Sustainable Closed-Loop Supply Chain Networks (SCLSCNs). When an outbreak occurs, environmental, economic, and social aspects can be traded off. A novelty aspect of this paper is its emphasis on hygiene costs. As well as healthcare education, prevention, and control of COVID-19, this model offers job opportunities related to COVID-19 pandemic. COVID-19 damages lead to lost days each year, which is one of the negative social aspects of this model. COVID-19 was associated with two environmental novelties in this study. positive and negative effects of COVID-19 can be observed in the environmental context. As a result, there has been an increase in medical waste disposal and plastic waste disposal. Multi-objective mathematical modeling whit Weighted Tchebycheff method scalarization. In this process, the software Lingo is used. The COVID-19 pandemic still has a lot of research gaps because it’s a new disease. An SC model that is sustainable and hygienic will be developed to fill this gap in the COVID-19 condition disaster. Our new indicator of sustainability is demonstrated using a mixed-integer programming model with COVID-19-related issues in a Closed-Loop Supply Chain (CLSC) overview.

Determinants of Proper Disposal of Plastic Waste among Health Workers at Chukwuemeka Odumegwu Ojukwu University, Amaka, Awka, Anambra State

Improper disposal of plastic waste has being a huge global environmental issue over time. With the emergence of COVID-19 and high prevalence of various infectious diseases, production of single use personal protective equipment escalated. And they are mostly made of plastic, making plastic waste from healthcare services increase tremendously. These plastic wastes from the hospital may contain infectious or toxic substances that pose a significant threat to health workers, waste handlers and to the public at large for those that will come in contact with the waste if improperly disposed. The drastic increase in medical plastic waste is overwhelming the environment and so, has been tagged a major environmental burden. As a result, medical facilities are called upon to look into their waste management protocol to ensure they are safely disposing their waste to avoid infection of the public and waste handlers and also to ensure environmental safety. This study is to determine the proper disposal of healthcare waste among healthcare workers in Chukwuemeka Odumegwu Ojukwu University Teaching Hospital, Amaku, Awka, Anambra State. This study was carried out using a sample size of 712 staff. Simple random sampling without replacement was used in other to avoid obtaining a bias response. The association and predictability of the dependent variables by independent factors were determined statistically using Chi-square and regression methods, respectively. From the findings of the study, it was observed that 302(42.4%) of the total respondents used and disposed of their plastic wastes properly, whereas 410 (57.6%) disposed of their plastic waste improperly. Improper disposal of medical waste could be as a result of unavailability of proper means of waste disposal, bad or no healthcare management policy on waste management and other factors. Healthcare workers are advised to make use of proper means of plastic waste disposal so as to avoid the negative effect.

An advanced approach to multiple criteria optimization and compromise solutions under circular intuitionistic fuzzy uncertainty

The intention of this work is to create an advanced VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approach that deals with multiple criteria optimization and compromise solutions in the face of circular intuitionistic fuzzy (C-IF) uncertainty. There has been limited research on enhancing VIKOR techniques to manipulate multiple criteria decision analysis (MCDA) with sophisticated C-IF information. Specifically, the relative score function in current C-IF VIKOR methods loses its meaning when the radius of the C-IF number is zero, restricting its applicability to regular intuitionistic fuzzy contexts. Furthermore, converting C-IF information into intuitionistic fuzzy contents prematurely using existing C-IF VIKOR algorithms results in inadequate preservation of the C-IF connotation, hindering the primary objective of using C-IF theory to manage MCDA issues. This study addresses the limitations of the current relative score system in C-IF contexts and recommends a more legitimate and fair scoring function. The upgraded C-IF scoring function includes scaling and exponential parameters, making it more pragmatic and adaptable than the present relative scoring mechanism. By connecting the suggested C-IF scoring function with the recently published C-IF Minkowski distance measurements, the study establishes a parameterized C-IF VIKOR methodology to analyze MCDA issues arising from C-IF uncertainty. The suggested parameterized C-IF VIKOR model provides accessible assessments of individual regret, collective utility, and trade-off indices by utilizing three- and four-term C-IF metric ratios. The model constructs a synthesized mechanism to investigate the requirements for acceptable advantage and decision-making stability for locating agreeable compromise solutions. The viability and performance of the suggested technique are manifested through applications in waste disposal location selection, multi-expert supplier evaluation, and medical waste disposal techniques. Comparative analysis is executed to highlight implications for theory, practice, and future research. This study provides significant contributions, including (i) an innovative approach to the C-IF VIKOR methodology for broadening the coverage of higher-order fuzzy research models, (ii) a new C-IF scoring function for decision aiding to overcome the limitation of the current relative score function, and (iii) a novel conceptual framework of a parameterized VIKOR for empowering decision-makers with more leeway in adapting the VIKOR solution to intuitively judged outcomes.

Exploring Influential Factors with Structural Equation Modeling–Artificial Neural Network to Involve Medicine Users in Home Medicine Waste Management and Preventing Pharmacopollution

The appropriate management of home medical waste is of paramount importance due to the adverse consequences that arise from improper handling. Incorrect disposal practices can lead to pharmacopollution, which poses significant risks to environmental integrity and human well-being. Involving medicine users in waste management empowers them to take responsibility for their waste and make informed decisions to safeguard the environment and public health. The objective of this research was to contribute to the prevention of pharmacopollution by identifying influential factors that promote responsible disposal practices among medicine users. Factors such as attitude, marketing campaigns, collection points, safe handling, medical prescription, package contents, and public policies and laws were examined. To analyze the complex relationships and interactions among these factors, a dual-staged approach was employed, utilizing advanced statistical modeling techniques and deep learning artificial neural network algorithms. Data were collected from 952 respondents in Pernambuco, a state in northeastern Brazil known for high rates of pharmacopollution resulting from improper disposal of household medical waste. The results of the study indicated that the propositions related to safety in handling and medical prescription were statistically rejected in the structural equation modeling (SEM) model. However, in the artificial neural network (ANN) model, these two propositions were found to be important predictors of cooperative behavior, highlighting the ANN’s ability to capture complex, non-linear relationships between variables. The findings emphasize the significance of user cooperation and provide insights for the development of effective strategies and policies to address pharmacopollution.

Prediction of pyrolysis product yield of medical waste based on BP neural network

Predicting pyrolysis product yields becomes more and more important for the realization of harmless, reduction, and resource disposal of medical waste. Based on the gray correlation analysis, this study predicted the three-state yield of medical waste pyrolysis products by building a BP neural network model. A database of pyrolysis product yields distribution of multivariate organic solid waste was established by selecting 225 sets of literature data in the existing research on rapid pyrolysis of organic solid waste and 18 sets of experimental data obtained from independent experiments. By the combination of the S-type tansig function of the hidden layer with the linear transfer purelin function of the output layer, the transfer function was created to construct the mapping between input and output variables. The results showed that when the number of hidden layer neurons was 14 and the learning rate was 0.04, the predicted yields and experimental yields were in good agreement with the R2 of neural network training and testing samples were 0.943 and 0.889, respectively. Increasing temperature and prolonging solid residence time were beneficial to the conversion of solid phase products to gas and liquid phase products. The effect of gas residence time on the three-phase yield distribution was less than that of temperature and solid residence time.

Feasibility of thermal disposal masks in domestic waste incinerators: a study on the combustion characteristics, gaseous pollutant emissions characteristics and control of masks

ABSTRACT Discarded disposable medical masks generated due to the COVID−19 epidemic are overwhelming the current medical waste disposal facilities. Thermal disposal masks in domestic waste incinerator can both utilize the heat energy released by their combustion and dispose of the headache-inducing masks in a harmless manner. The masks are divided into 6 parts: white inside (WI), filter (FI), blue outside (BO), bonding of 3 parts (BP), ear strap (ES) and nose wire (NW). The highest burnout temperature (Tb) of each part of the mask was only 481.2°C and had a high HHV of 40.47 MJ/kg, which has a high energy potential as an energy source. The NOx, SO2, and HCl emission characteristics of each part of the masks were evaluated in a fixed-bed reactor. BP, BO, and ES contradict SO2 and HCl emission limits in China. The SO2 and HCl emission peaks of the masks with the addition of 10 wt% Ca(OH)2 did not exceed 60 mg/m3, which made the combustion emissions of the masks comply with GB 18,485–2014. Considering subsidies for disposal of masks and power generation, blending masks with a 1%−5% ratio enhances the subsidy revenue of waste incineration power plants by 12.63%−63.13%.

Medical waste disposal planning for healthcare units using spherical fuzzy CRITIC-WASPAS

Healthcare services create medical waste that may be dangerous to healthcare personnel, patients, the general public, and the environment. Medical waste disposal method selection is among the most important decisions that must be made by healthcare organizations, and such a problem has a number of contradictory criteria and alternatives. On the other hand, decision experts may have considerable uncertainty while evaluating these alternatives. In this paper, new fuzzy multi-criteria decision-making (MCDM) methodologies are provided for assessing the medical waste disposal alternatives. The CRiteria Importance Through Intercriteria Correlation (CRITIC) is used for obtaining criterion weights in an objective manner, and the Weighted Aggregated Sum Product ASsessment (WASPAS) approach is utilized to rank the alternatives. For modeling the uncertainty in the nature of the problem, the proposed methodology is developed in single and interval-valued spherical fuzzy environments. Single-valued spherical fuzzy sets enable users to model the membership, non-membership, and hesitancy parameters independently. On the other hand, interval-valued spherical fuzzy sets provide increased fuzziness modeling capacity. The step-by-step solution of the proposed methodologies are followed by sensitivity and comparative analyses, and a discussion. This study contributes to the work of both academics and practitioners in the healthcare industry, as well as other sectors facing similar types of decision-making problems.

An Investigation into the Behavior of Disposable Face Masks in Modified Bitumen for Sustainable Transportation Pathways

Since December 2019, COVID-19 infection rates have risen considerably, and the virus is currently widespread around the world. Following the COVID-19 outbreak, the production of medical waste has skyrocketed. Disposable face masks are considered medical waste. Alternative measures must be implemented to assist in reducing medical waste disposal, which can result in serious public health problems and have a negative influence on the environment. In this regard, this research was conducted to investigate the effect of disposable face mask (DFM) ash with varied rates ranging from 5% to 20% by weight on bitumen with a 5% increment to be utilised as an alternative material in asphalt pavement. A series of physical and rheological tests were conducted on the bitumen samples to study the behavior of the DFM ash in bituminous material. Overall, the physical and rheological test results revealed that introducing 20% DFM ash to the modified bitumen was unable to achieve the same properties as with the conventional SMA14 asphalt binder. However, the modified bitumen penetration grade PEN 60/70 has improved the properties and quality of the asphalt in the flexible pavement. In addition, utilising the DFM waste in road construction would be a sustainable technique for protecting the environment by minimising face mask waste caused by the COVID-19 epidemic while lowering the pavement’s construction cost. These research findings may be commercialised to generate revenue in the construction industry for sustainable transportation pathways.

Assessment of Biomedical Waste in West, Central, North Zones of Hyderabad

Waste disposal is especially important when it comes to medical supplies, as some waste can be contaminated with diseases and dangerous pathogens. Not all biomedical waste is treated the same way and different disposal companies use different methods such as autoclaving, incineration, chemicals and microwaves. Improper disposal of medical waste causes environmental pollution, unpleasant odours, the growth and reproduction of insects, rodents and nematodes, and injuries from blood-contaminated sharp objects, typhoid fever, cholera, hepatitis, etc. can lead to disease transmission. Thisstudy will reveal the current processes and environmental impacts of biomedical waste management by examining the quantity and quality of biomedical waste (BMW) generated by pharmacies, laboratories and multiple speciality hospitals.

Evaluating and selecting sustainable logistics service providers for medical waste disposal treatment in the healthcare industry

Health institutes are structurally growing depending on the population increase and escalating demands for health services. As a negative result, the health industry produces more healthcare waste daily. Its efforts and resources cannot be sufficient to dispose of such medical wastes as the quantity and variety continue to increase. Therefore, developing solution partnership relations with logistics service providers specialized in collecting, storing, and disposing healthcare waste may be a beneficial and fruitful step for solving this problem. However, the evaluation and selection of service suppliers for healthcare waste disposal may not always result in success. Hence, the present work develops a robust integrated methodology on the basis of Step-wise Weight Assessment Ratio Analysis (SWARA) and COmplex PRoportional ASsessment (COPRAS) techniques based on Interval-Valued Fermatean Fuzzy Sets (IVFFSs) for healthcare decision-makers to make more rational and optimal decisions concerning service supplier selection. The proposed model based on the IVFFSs can also overcome incredibly complicated uncertainties in the assessment processes. A large-scale international hospital chain in Turkey is investigated using the developed methodology to find reasonable and logical solutions to assess and select the best Medical Waste Disposal and Logistics (MWDL) firm. It is revealed that the most important criterion is “Storage Conditions” where “Republic Services, Inc” is the most successful supplier of MWDL. Finally, theoretical and practical implications are discussed in detail in order to provide useful managerial insights and decision aids to assist in improving the sustainability of healthcare industry.

Improved assessment model for health-care waste management based on dual 2-tuple linguistic rough number clouds

The management of medical waste disposal is a major challenge faced by different cities in developing countries. The choice of optimal medical waste treatment technology is a complex multi-criteria group decision-making problem that requires consideration of multiple alternatives against conflicting criteria. Decision makers evaluating disposal municipalities may conduct random evaluations from different linguistic term sets according to different preferences and contexts. Various fuzzy numbers based approaches have been studied in HCWM (health-care waste management) process, however they have certain limitations such as lacking manipulation tools of diverse information, additional adjustments and suppositions, ignoring randomness and multi-granularity in experts’ judgements. In response, this research article proposes a novel mathematical models by integrating 2-tuple linguistic setting into rough approximations and cloud theory. First, three novel linguistic manipulation models, namely, 2-tuple linguistic clouds, 2-tuple linguistic rough numbers and dual 2-tuple linguistic rough number (D2tLRN) clouds are developed to handle uncertainty with randomness and multi-granularity simultaneously. Secondly, a hybrid weighting scheme is utilized to evaluate the relative importance of waste factors using both subjective and objective aspects of uncertainty. Thirdly, the proposed D2tLRN cloud model is integrated with the technique for order of preference by similarity to ideal solution (TOPSIS) method to design an evaluation approach for the selection of HCWM technologies. Finally, the application of the proposed method is studied with an empirical case study of health care waste management in the most crowded municipality. The sensitivity analysis of the proposed approach is also discussed in detail to study the effect of various parameters on the results. The out-performance and significance of the proposed D2tLRN cloud TOPSIS method is illustrated by comparing it with existing approaches.