Waste Classification Research Articles

An Assessment of Waste Management in Slum Communities in Freetown. It’s Implications on the Health and Safety of Inhabitants

Purpose: The study objective is to assess the state of Solid Waste Management (SWM) in slum communities in Freetown, its factors and effects on the safety and wellbeing of slum dwellers together with the challenges faced by authorities in combatting inadequate SWM system in slum and to provide recommendations with regards to interventions to promote adequate waste management in slums.
 Materials and Methods: The study took place in three slum areas around Freetown in Sierra Leone. A total sample size of 400 respondents comprising of household heads were selected. A self-administered structured questionnaire was given to a total of 400 respondents of household heads above eighteen years old. Only 392 of the 400 respondents that were initially targeted for the study completed their questionnaires at the end of the survey. The data collected was presented in tables and statistical analytical techniques or methods such as percentages and graphs were used to analyse the data and to find out the factors, effects and challenges associated with inappropriate solid waste management services in slum areas in Freetown.
 Findings: According to the study, it was found that the major categories of waste generated in households were biodegradable and food (39%) while plastic remains (31%). Most households stored their wastes in polythene bags (59.1%) and sacks (20.2%) before disposal and 10.3% of the households did not have waste storage containers and kept their wastes outside the house in the open. Poor knowledge in waste segregation practices with very minimal recycling, reuse of wastes and a resultant poor waste management system were observed in slum communities. The study helps provide a much-needed data on solid waste management (SWM) in Freetown for authorities and other stakeholders as there are limited sources of data on waste management in Sierra Leone. It also provides further literature on waste management issues for further research, policy development by the authorities and recommendations for best practice by communities and stakeholders.
 Implications to Practice and Policy: It was recommended that waste management should be prioritized as a social service, with adequate budget lines. Engage several stakeholders in the management of waste to generate a sense of responsibility and interest from all stakeholders.

Modeling and evaluating costs and greenhouse gas emissions in solid waste management based on system dynamics in a mega-city: The case of Xi'an

With the escalating generation of municipal solid waste (MSW) and the increasing utilization of collection and transportation (C&T) vehicles within the MSW classification system, the associated costs and greenhouse gas (GHG) emissions from waste drop-off, as well as the C&T processes, have risen sharply. However, the mesoscopic view of the waste drop-off and C&T system is limited. Therefore, we divided the waste classification management (WCM) system into three subsystems: generation and drop-off, C&T, and disposal. Based on system dynamics theory, the logical relationships between relevant parameters in each subsystem were established. A WCM system dynamics model, which focused mainly on the drop-off and C&T processes, was constructed using Anylogic software. Using sensitivity, scenario, and uncertainty analyses, the economic impact and GHG emissions of the WCM system were analyzed in detail. The results showed that the waste drop-off and C&T processes accounted for 54.29–57.87% ($47.31 per ton) and 24.54–26.83% ($21.39–21.93 per ton) of the total cost in the whole waste management chain, respectively. GHG emissions were mainly generated in disposal facilities (116.84–257.86 kg CO2-eq/t), followed by C&T (19.02–20.55 kg CO2-eq/t) and drop-off processes (12.68–18.78 kg CO2-eq/t). The system cost and GHG emissions were significantly influenced by the components of the source-separated waste and operational parameters of the applied C&T vehicles. This study not only provides a basis for WCM policy formulation but also a tool for the design and optimization of waste C&T systems.

Intelligent and sustainable waste classification model based on multi-objective beluga whale optimization and deep learning.

Resource recycling is considered necessary for sustainable development, especially in smart cities where increased urbanization and the variety of waste generated require the development of automated waste management models. The development of smart technology offers a possible alternative to traditional waste management techniques that are proving insufficient to reduce the harmful effects of trash on the environment. This paper proposes an intelligent waste classification model to enhance the classification of waste materials, focusing on the critical aspect of waste classification. The proposed model leverages the InceptionV3 deep learning architecture, augmented by multi-objective beluga whale optimization (MBWO) for hyperparameter optimization. In MBWO, sensitivity and specificity evaluation criteria are integrated linearly as the objective function to find the optimal values of the dropout period, learning rate, and batch size. A benchmark dataset, namely TrashNet is adopted to verify the proposed model's performance. By strategically integrating MBWO, the model achieves a considerable increase in accuracy and efficiency in identifying waste materials, contributing to more effective waste management strategies while encouraging sustainable waste management practices. The proposed intelligent waste classification model outperformed the state-of-the-art models with an accuracy of 97.75%, specificity of 99.55%, F1-score of 97.58%, and sensitivity of 98.88%.

Source analysis of chlorine in municipal solid waste under waste classification: a case study of Hangzhou, China.

Inorganic chlorine is susceptible to water and soil salinization due to its non-degradability and high mobility. To clarify the environmental risks associated with the active inorganic chlorine in municipal solid waste (MSW), the specific characteristics and contributions of inorganic chlorine in different MSW categories were investigated in this study. MSW samples were collected from eight representative waste classification residential areas in Hangzhou, China. It was found that the inorganic chlorine content in different MSW categories varied significantly (0-113 mg/g). Perishable waste, paper, and plastic were found to be the main sources of inorganic chlorine in MSW. A four-category classification system was used to quantify the contribution of inorganic chlorine from each waste category. It was found that the misclassification of inorganic chlorine contributions from perishable waste and other waste accounted for 51.96% and 48.04%, respectively. However, when correctly classified into the four-category system, their contributions were reduced to 67.14% and 30.65%, respectively. Therefore, MSW classification showed a significant reduction in the overall contribution of inorganic chlorine. The misclassification reduces the contribution of inorganic chlorine to 48.04%, while correct classification increases the reduction to 69.35%.

Characterization and distribution of macroplastics along the coastal municipalities of Partido district, Camarines Sur, Philippines

This study was conducted to characterize and investigate the distribution of macroplastics in the nine coastal municipalities of Partido district, Camarines Sur, Philippines. The study aimed to quantify and categorize the macroplastics along the coastal areas of the study site. The results reveal variations in the type, color, size, category, and amount of plastic waste among different municipalities. Notably, plastic fragments, cigarette butt, food packaging, and disposable utensils emerged as the predominant forms of macroplastics within the study area. The majority of the collected plastic particles fell within the size range of 0.5–1.00 cm, while transparent, white, and green were identified as the prevailing colors in the samples of plastic waste. Furthermore, both hard plastic and soft plastic fragments were frequently encountered, accounting for 35% and 31% of the total plastic, respectively. Sagňay, Tigaon, and Caramoan exhibited the highest densities of macroplastics, with values of 0.62±0.11, 0.60±0.05, and 0.59±0.06 pcs/m2, respectively. In contrast, Garchitorena displayed the lowest density at 0.26±0.04. The remaining municipalities exhibited densities ranging from 0.4 to 0.5 pcs/m2. The elevated macroplastics density observed can be attributed to factors such as their degree of urbanization, tourist activity, and population density. The findings of this study provide valuable insights into the composition and characteristics of macroplastics found along the coastal municipalities of Partido district, emphasizing the urgent need for effective strategies and policies to mitigate the possible impact to the environment.

The Role of Internet and Social Interactions in Advancing Waste Sorting Behaviors in Rural Communities

Addressing the global challenge of sustainable waste management, this research investigates the influence of social dynamics and digital connectivity on rural residents’ willingness to adopt waste classification practices, essential for sustainable environmental management. Through a comprehensive analysis of 5413 rural participants surveyed in the China Labor-force Dynamic Survey (CLDS), this study employs a novel mixed-methods approach. It integrates quantitative analysis with the Manski social interaction framework and a Recursive Bivariate Probit model to explore the intricate interplay between community interactions, internet access, and environmental behaviors. Our methodology stands out for its unique combination of social theory and econometric modeling to address a pressing environmental issue. Results highlight a significant effect of mobile internet use and social interactions within communities on enhancing willingness towards waste classification. Notably, digital connectivity emerges as a key facilitator of environmental engagement, mediating social influences, and fostering a collective approach to waste management. Considering these insights, we propose targeted policy interventions that blend digital strategies with traditional community engagement efforts. Recommendations include crafting digital literacy programs and leveraging social media to bolster community-centric environmental governance. By harnessing the synergistic potential of digital tools and social dynamics, these strategies aim to elevate the effectiveness of waste classification initiatives in rural China, offering a scalable model for environmental sustainability.

Intelligent waste classification approach based on improved multi-layered convolutional neural network

AbstractThis study aims to improve the performance of organic to recyclable waste through deep learning techniques. Negative impacts on environmental and Social development have been observed relating to the poor waste segregation schemes. Separating organic waste from recyclable waste can lead to a faster and more effective recycling process. Manual waste classification is a time-consuming, costly, and less accurate recycling process. Automated segregation in the proposed work uses Improved Deep Convolutional Neural Network (DCNN). The dataset of 2 class category with 25077 images is divided into 70% training and 30% testing images. The performance metrics used are classification Accuracy, Missed Detection Rate (MDR), and False Detection Rate (FDR). The results of Improved DCNN are compared with VGG16, VGG19, MobileNetV2, DenseNet121, and EfficientNetB0 after transfer learning. Experimental results show that the image classification accuracy of the proposed model reaches 93.28%.

Determination of volume of radioactive waste in activated VVER-1200 reactor structures

The results of calculation studies of the amount of solid high-level, intermediate-level, low-level, very lowlevel radioactive waste (HLW, ILW, LLW, VLLW, respectively) generated during neutron activation of structural materials adjacent to the core of the VVER-1200 reactor plant, depending on the time after the final stopping it. An assessment of the volumes of radioactive waste (RAW) of activation origin generated over 60 years of operation of a VVER-1200 type reactor was carried out on the basis of computational studies of the induced activity of structural and protective materials using reactor and Monte Carlo program codes (SERPENT 2, TVS-M, DYN3D , MCU-PD). Specific activities for each isotope, total specific activities and activities, masses and volumes of activated structural and protective reactor materials for various categories of radioactive waste were calculated after the specified period of operation of VVER-1200 and over time after the final shutdown of the reactor. The obtained dependences of changes in the masses and volumes of activated materials over time after the final shutdown of the reactor make it possible to determine these parameters of materials separately for each category of radioactive waste and in total at any point in time. Thus, the masses of activated materials related to radioactive waste will decrease by 264 tons (from 713 to 449 tons) over 200 years of aging, and the volumes will decrease by 74.3 m3 (from 169 to 94.7 m3). Forecast quantitative data on the volumes and mass of activated reactor structures, depending on the duration of their exposure after the final shutdown of the power unit, will make it possible to develop technical proposals, forecast plans and schedules for organizing a radioactive waste management system during the decommissioning of the VVER-1200 reactor plant, as well as possible technological and design solutions for the disposal system for these radioactive waste.

Multilevel thermoplastic waste segregation and classification with AHGSO using federated learning framework

PurposeAn abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.Design/methodology/approachIn this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.FindingsBy using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.Originality/valueThe multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

Analyzing recycled waste-infused mortars: Preparation and Examination of thermal, mechanical, and chemical characteristics

In response to the urgent need for innovative and sustainable solutions in the construction industry, this study provides an in-depth investigation and experimental evaluation of the thermal, mechanical, and chemical properties associated with the incorporation of three different categories of waste into mortar. These categories include expanded polystyrene waste (EPSW), expanded perlite waste (EPW), human hair waste (HHW), textile fiber (TFW), and paraffin waste (PW). Various samples were prepared by adding different proportions of these waste materials as sand substitutes in cement mortar. To enable a thorough assessment of the material properties, a comprehensive series of tests were carried out, including measurements of thermal conductivity, flexural strength, compressive strength, chemical composition and crystalline structures. The results revealed that, for a replacement level of 50%, there was a decrease in thermal conductivity by 28%, 55%, 44% and 67% for EPSW, EPW, TFW, HHW and PW mortars, respectively, which resulted in a decrease in compressive strength by 75%, 71%, 50%, 79% and 81%, respectively. Furthermore, the chemical characterization showed that the chemical composition of the mortar remained consistent, even when a substantial proportion of waste materials was introduced. The study emphases the sustainable construction key role in reducing building life cycle emissions.

Sustainable considerations in additive manufacturing processes: A review

Efficient waste management practices are becoming increasingly necessary due to the negative environmental and health impacts of waste generation and disposal. One type of waste that has received particular attention is electronic waste (e-waste). This category of waste has the potential to cause significant environmental harm if not disposed of properly. The management of e-waste is crucial in the electrical/electronic industry which has led to the creation of models and institutional legislature to promote sustainable production processes. Among these processes, Additive manufacturing otherwise referred to as 3D printing is particularly effective in reducing waste generation and energy requirements by reusing spent parts and products as feedstock. Sustainability in the manufacturing and production sectors can be promoted through the inculcation of certain practices. Of these practices, reusing e-waste that would otherwise be disposed of in landfills has the potential to promote environmental cost savings. This article introduces the potential of e-waste being integrated into the manufacturing sector to promote sustainable production. The article also addresses the problem of geometric e-waste generation and suggests an efficient way of reusing waste from the electrical and electronic industries.

STUDIES TO ESTABLISH THE SCALING FACTOR METHODOLOGY FOR LIQUID RADIOACTIVE WASTE GENERATED BY TRIGA REACTOR

During TRIGA reactor operation significant amounts of liquid radioactive waste are generated requiring appropriate management to ensure their safe disposal. Radiological characterization is an important activity needed to accomplish the waste acceptance criteria for different management steps including disposal. The purpose of this paper is to assess the activity concentrations of the (_1^3)H , (_38^90)Sr, (_28^63)Ni (difficult-to-measure), and gamma emitters (easy-to-measure) radionuclides in liquid radioactive waste generated by the TRIGA reactor operation and to establish a correlation among them to determine the scaling factors for this waste category. The radiological protocols applied for (_28^63)Ni and (_38^90)Sr, separation and purification involve extraction chromatography using selective resins, while for (_1^3)H separation the distillation technique was applied. The assessment of gamma emitters was carried out using gamma-ray spectrometry and the detection of (_1^3)H , (_38^90)Sr, and (_28^63)Ni was performed by liquid scintillation counting. The chemical yield of each experiment was determined by measuring the carrier added in each test by inductively coupled plasma optical emission spectrometry. The activity concentrations of (_38^90)Sr, and (_28^63)Ni obtained were correlated with the activity concentration of (_27^60)Co for establishing the scaling factor for the liquid radioactive waste stream. There were obtained good correlations but more experimental data are needed to determine the relevant scaling factors for these difficult-to-measure radionuclides. The scaling factors, once established, will improve and optimize the radiological characterization methodology applied for liquid radioactive waste as an easy and rapid method for assessment of concentration activities of difficult-to-measure radionuclides. Until now, no scaling factors data were reported for this liquid radioactive waste generated by TRIGA reactor operated by Institute for Nuclear Research Pitesti. The results obtained in this work represent preliminary data for establishing correlations between difficult-to-measure and easy-to-measure radionuclides and will be used for developing a scaling factor methodology. This will further be used to determine the inventory of difficult-to-measure radionuclides in different waste streams generated by TRIGA operation and decommissioning (foreseen after 2035).

SELECTING OF MATRIX COMPOSITION FOR IMMOBILIZATION OF IRRADIATED FUEL OF THE IGR REACTOR

Impulse Graphite Reactor (IGR) is a unique nuclear installation namely research reactor with the core consisting of uranium-graphite fuel elements with 90% enrichment by U235. Possible immobilization of the first core taken from the reactor in 1967 is studied here as part of the IGR reactor conversion to low-enriched uranium fuel. Irradiated fuel is related to the category of mediumactive waste according to the content of beta-emitters. Expected value of highly-enriched uranium fuel (HEU) burnup rate was less than 1 % mass. The experts from the Institute of Atomic Energy (IAE) proposed the way how to immobilize irradiated IGR reactor fuel into the cement matrix. First, the fuel is suggested to dilute with depleted uranium in order to lower the enrichment up to the <20 mass. % by U235. The properties of the matrix should meet the engineering criteria ruled by international and national standards. So, according to aforesaid, The IAE team is facing an actual problem of selection the matrix and understanding its characteristics.The work was aimed at selection of composition of cement mortar for making robust matrix to immobilize irradiated IGR reactor fuel.The paper describes techniques and results of the time identification (start) of solidification, gradual volume change, water separation, velocity and uniform cement mortar.

Garbage Object Detection Based on Improved YOLOv5

Traditional waste classification methods often rely on manual feature extraction and manual rule design, which cannot meet the needs of practical application scenarios. This thesis proposes a YOLOv5[1] target detection method for garbage classification, aiming to solve the problem of automation and efficiency in garbage classification. First, we introduce semi-supervision to enhance the model's detection robustness as well as its extensiveness. Second, we incorporate Mixup, which fuses multiple different datasets to obtain new images with occlusions, which are trained in order to improve the robustness of the model for targets with complex backgrounds. In addition, we introduce a mosaic data enhancement algorithm to defocus the target and improve the accuracy of target detection. The proposed garbage target detection method based on the improved YOLOv5 has made significant progress in improving the detection performance, providing more effective tools and technical support for environmental protection and urban management. This method is expected to be widely popularized in practical applications.

The interactive force between fat, oil, and grease (FOG) deposits and the effect of food waste disposers on kitchen drainage systems

Food waste disposers (FWDs) streamline kitchen waste management and facilitate waste classification, whether they would increase the potential of blockage in kitchen drainage system is still unknown. This study conducted a theoretical analysis of the interactive forces between fat, oil, and grease (FOG) deposits and their aggregation on pipe walls. The study involved grading food waste particles processed by FWDs using sieving and weighing techniques to determine the mean weight diameter (MWD) of various aggregations. A full-scale experimental system, implemented in a 60-m high test tower, simulated blockages in horizontal pipes of high-rise buildings. The effect of pipeline materials and particle sizes on blockage were examined by measuring the adhesion of deposits on horizontal pipes. Energy dispersive spectrometer (EDS) analysis suggested that liquid bridge force is a primary factor in aggregate formation. Hand-cut particles formed aggregates with the highest MWD value. Particle size analysis revealed that sizes ranging from 2.36 to 4.75 mm, 1.18–2.36 mm, and 0.60–1.18 mm constituted over 80 % of particles ground by FWDs, with an average size of 2.16 mm. Results of full-scale experiment indicate particle diameters, friction coefficients and lipophilic coefficient significantly affected the propensity of these aggregates to adhere to pipes. Notably, particles processed by FWDs tended to cause blockages more frequently than hand-cut particles. These findings elucidate the deposition mechanism of FOG deposits and offer strategies to reduce blockages in kitchen drainage systems, such as reducing current grinding particle size by 18 % to 1.77 mm or selecting pipes like cast iron and high-density polyethylene.

Can Internet Use Narrow the Gap between Farmers’ Willingness and Behavior in Waste Classification? Empirical Evidence from Rural Areas in Jiangsu Province, China

Household waste classification and treatment are important for environmental protection and sustainable development. The Logit model is used to analyze differences in farmers’ willingness and behavior regarding waste classification based on data from the China Land Economic Survey. Key findings include the following: (1) There is an evident discrepancy between waste classification willingness and action among rural residents. Despite nearly 90% of the sampled farmers expressing a willingness, nearly 40% do not practice waste classification. (2) Internet usage significantly reduces the discrepancy between farmers’ willingness and actual waste classification practices. This observation is valid even after robustness checks and endogeneity discussions. (3) There is a partial mediating effect between ecological awareness and knowledge perception on farmers’ willingness and behavior differences, which accounts for 12.9% and 52.6%, respectively, of the total impact. Notably, institutional limitations amplify the negative influence of Internet use on this discrepancy. (4) According to heterogeneity analysis, Internet use has a greater negative impact on farmers’ willingness and behavior in waste classification in suburban villages and villages with environmental governance projects. This study proposes policy suggestions such as strengthening the construction of digital infrastructure in rural areas, enhancing the promotion and training of waste classification among farmers, and improving incentives and restraint mechanisms for rural household waste classification.

Determine greenhouse gas emissions from landfills and suggest a household solid waste classification system in Dong Hoi, Quang Binh province

Determine greenhouse gas emissions from landfills and suggest a household solid waste classification system in Dong Hoi, Quang Binh province

An Improved Strategy to Effectively Manage Healthcare Waste after COVID-19 in Republic of Korea

During the coronavirus disease 2019 (COVID-19) pandemic, 24,289 tons of infectious waste was generated in 2021 in Korea, a 320% increase compared to that generated in 2020 (5788 tons). The disposal of other healthcare waste has been delayed because COVID-19 infectious waste must be disposed of first, leading to long-term concerns related to the lack of healthcare waste disposal capacity. To solve this problem, this study investigated healthcare waste classification systems in Korea and overseas. We analyzed the current state of healthcare waste in Korea and compared the treatment methods and healthcare waste treatment systems by country. The results showed that Korea has a strict healthcare waste management system compared to other countries, which relies on dedicated incinerators and transport. It is difficult to expand incinerators exclusively for healthcare waste due to site selection issues because they incinerate infectious waste. Therefore, to solve the healthcare waste disposal problem, Korea should improve its management system to reduce treatment amounts. This study suggests that general healthcare waste that is considered less infectious should be excluded from healthcare waste-exclusive treatment and that sterilization/grinding as an intermediate treatment method should be allowed.

Unveiling Waste Sorting Intentions: The Influence of Green Brand Opportunity Perception Among Cafes

Cafe managers play an important role in realizing waste sorting behavior in cafes. To encourage waste sorting behavior among cafe managers, it is necessary to understand intentions as the main predictor of behavior. This research aims to determine the influence of green brand opportunity perception on waste sorting intention. The method used in this research was quantitative correlational with a total of 103 cafe managers in the Greater Malang area (Malang City, Malang Regency, Batu City) subjects who were collected using purposive sampling techniques. The scales used in this research are the opportunity perception scale (???? = 0.93) adapted after Neill et al. (2017), and the waste classification intention scale (???? = 0.80) adapted after Tian et al. (2019). Data analysis used simple linear regression with results showing that there was a significant positive effect (B = 0.308; p < 0.001). The findings of this research reveal green brand opportunity perception as a predictor of waste sorting intention among cafe managers. The results of this research provide an understanding of the cognitive dynamics that occur in business actors to support environmentally friendly practices.

ASSESSING KNOWLEDGE, ATTITUDE, AND PRACTICE OF HEALTHCARE WORKERS REGARDING MEDICAL WASTE MANAGEMENT AT DISTRICT JAMSHORO, SINDH

This study aimed to evaluate the waste management knowledge, attitudes, and practices of three groups of healthcare workers: doctors, nurses, and paramedical staff. A cross-sectional study involved doctors, nurses, and technicians at the National Institute of Cardiovascular Disease (NICVD) and the Syed Abdullah Shah Institute of Medical and Health Science (SASIMS) in Shahwan Sharif, District Jamshoro, Sindh. A convenient non-probability sampling technique was used to select the study participants. A total of 370 healthcare workers consented to be part of the study. The data was obtained through the administration of questionnaires. Data were collected and compiled using Microsoft Excel and analyzed using SPSS version 22.0. This study had 39 (63.2%) males and 34 (36.8 %%) females. The median age of participants was 30.5 years, with a mean of 33.12 ± 8.2 years. The majority of the participants were paramedics (42.7%). They were followed by doctors (34.3%) and nurses (23%). The knowledge of healthcare workers indicated that there was a significant association between the waste categories (p = 0.02), type of waste (p = 0.00), color coding differentiation (p = 0.01), hospital policies (p = 0.00), infection control department (p = 0.03), risk toward healthcare workers (p = 0.00), human anatomical waste (p = 0.01), color-coding container used for wasted sharps and infectious solid waste (p = 0.00), and type of color-coding container (p = 0.00) This study highlights the discrepancies and inadequacies of knowledge and practice of proper healthcare waste management among healthcare workers. Healthcare workers with adequate knowledge were paramedics, doctors, and nurses. All healthcare personnel should have medical waste management guidelines ready and easily accessible.