Waste Segregation Research Articles

Comprehensive Assessment of Pharmaceutical Waste Disposal Practices in Ishaka-Bushenyi Municipality: Implications for Public Health and Environmental Sustainability

Proper management of pharmaceutical waste is crucial for safeguarding public health and the environment. This study aimed to comprehensively assess pharmaceutical waste management practices in hospitals, pharmacies, and households within Bushenyi-Ishaka Municipality, Uganda. The study employed a qualitative case study design, gathering data through observations, interviews, and questionnaires administered to five pharmacies, two hospitals, and 100 households. The results revealed that the mean amount of pharmaceutical waste generated by pharmacies and hospitals in Ishaka was 40.5 kg ± 32.39 kg per month, of which 26.83 kg ± 29.10 kg were disposed of safely. However, 60% of the waste was categorized as hazardous. The study found that while most facilities used labeled or color-coded containers for waste segregation, the practice of segregation was inconsistent, with 71.43% of the facilities not segregating their waste. Incineration (42.86%) and dilution followed by flushing in protected soak pits (42.86%) were the primary disposal methods employed by the facilities. Concerningly, only 4% of households returned unused or expired medications to pharmacies, with the majority disposing of them in dustbins (46%) or flushing them down toilets (41%). The findings also highlighted gaps in awareness and training, with 50% of pharmacy staff not receiving any training on proper pharmaceutical waste disposal, and only 14.29% of the facilities having established waste management guidelines. These deficiencies in knowledge and practices contribute to the suboptimal management of pharmaceutical waste, posing significant risks to public health and the environment. The study underscores the urgent need for comprehensive policies, targeted training programs, and community-based initiatives to enhance pharmaceutical waste management practices in Bushenyi-Ishaka Municipality and similar settings. Addressing these challenges is crucial for mitigating the adverse impacts of improper pharmaceutical waste disposal and promoting sustainable healthcare waste management. Keywords: Pharmaceutical waste, Waste management, Disposal practices, Awareness, Hazardous waste.

IOT based Waste Segregation Using Deep Learning

In the rapidly urbanizing world, effective waste management is crucial to maintain environmental sustainability. This project presents an innovative solution utilizing Internet of Things (IoT) technology and deep learning Convolutional Neural Networks (CNN) algorithm for automated waste segregation. We propose a smart waste segregation system equipped with sensors to detect the level of waste in bins. The system utilizes a CNN model trained using Deep Learning to classify waste items into different categories, ensuring proper segregation. When a bin reaches its full capacity, the IoT sensors trigger an alert mechanism, sending real-time notifications to the concerned authorities or waste management personnel. This proactive approach not only optimizes waste collection but also promotes recycling by ensuring the segregated waste reaches the appropriate recycling facilities. The integration of IoT devices and deep learning algorithms enhances the efficiency of waste management processes, reducing environmental pollution and promoting a sustainable future. Key Words: IoT, waste management, deep learning, CNN algorithm, waste segregation, environmental sustainability, real-time monitoring, sensor technology, recycling, smart cities, automation.

Awareness About Solid Waste Management – A Study with Reference to Home Makers in Chennai

This study's objective is to evaluate the levels of knowledge and practices that are linked with the management of solid waste among homemakers in Chennai. Specifically, the research will focus on the city of Chennai. Managing garbage in an effective manner is becoming an urgent need for those who want to live a sustainable lifestyle. This is due to the fact that the population is expanding and the urbanisation rate is rising. This research evaluates the knowledge, of homemakers in regard to the disposal of garbage.
 The ubiquitous waste management strategies that are used in households are investigated in this study, with a special emphasis placed on the role that homemakers play as key factors in shaping these practices. In this study, the grasp of concepts such as recycling, segregation of the solid waste, and the impact that improper waste disposal has on the environment is investigated. The purpose of the insights that were gathered from the study is to bring to light potential areas that may be improved via intervention and development.
 The purpose of this awareness is to provide homemakers with the information they need to adopt waste management practices that are socially and ecologically responsible. This will ultimately result in a cleaner and greener Chennai. The findings of this study have the potential to serve as a valuable resource for residents, government, community, and environmental activists, therefore fostering a collective commitment to the implementation of responsible waste management practices.

Garbage Classification Using Deep Learning

Garbage disposal is a critical responsibility in maintaining a healthy and green environment. The recycling business is thriving as inhabitants of India become more aware of the necessity of a clean environment in terms of reducing natural resource use and rubbish disposal. The amount of trash generated in daily life has an impact on land, water, and air, posing a major threat to aquatic animals and their habitats, as well as humans, if not properly managed. Conventional garbage disposal systems are on the rise, requiring accurate and efficient segmentation and recognition processes. This demand corresponds to an increase in the computing capacity of modern computer systems as well as more efficient picture recognition methods. To address this issue, the garbage categorization process is automated by using an image classifier with a convolutional neural network (CNN), which reduces waste segregation time and costs. The goal of automating the process is to reduce human intervention and increase productivity in the waste segregation process. In this paper, three distinct models are tested for greater accuracy: Simple CNN, ResNet50, and VGG16 are trained on various image datasets and used to extract features from images, which are then fed into a classifier for dump/trash categorization. The learned models are then loaded into the mobile application.

Multilevel-thermoplastic waste segregation using Archimedes Henry gas solubility trained deep quantum network

The management of plastic waste is complex process worldwide. The manual process to sort garbage is termed to be complex and expensive task and hence the scientists studied automatic sorting technique which can elevate the efficacy of recycling task. Moreover, the waste segregation models are adapted to several groups of materials to sort the waste. The usage of deep models is considered to be effective in classifying the thermoplastic waste segregation. The images are attained and delivered to pre-processing with median filter followed by segmentation. PSI-Net considering Archimedes Henry Gas Solubility Optimization (AHGSO) is adapted to segment pre-processed images. Thereafter, texture features along with statistical features are obtained for undergoing first level classification. The first level categorization is executed using Deep Quantum Neural Network (DQNN) wherein the model is trained using AHGSO and it classifies into three classes, namely “no plastic,”“Resin,” and “plastic.” In addition, the second level classification is done in case of plastic or resin using Deep Maxout Network (DMN)-AHGSO wherein DMN undergoes training by means of AHGSO. The DMN-AHGSO gained premium accuracy of 92.9%, F-measure of 92.6%, and precision of 92.9%.

Bridging the Gap: Determinants of Consumers’ Willingness to Pay for Environmentally Friendly Packages of Leafy Greens

Government and corporate policies have mandated a reduction in plastic packaging to combat issues of waste and climate change. This move towards sustainable packaging alternatives in the fresh food sector will increase costs for consumers. Much of the present research finds consumers are willing to pay more for sustainability, but their willingness to pay (WTP) does not align with real-world purchases, representing an attitude–behaviour gap. To combat this gap, it is posited that consumers’ current purchasing- and sustainability-related behaviours will meaningfully correlate with their WTP and bridge the attitude–behaviour gap. This research used an online survey (n = 476) to gauge consumers’ attitudes and behaviours regarding sustainability as it relates to packaging, biofortification, and WTP in the fresh leafy greens sector. Using binary logistic regression, this research finds that price- and sustainability-related purchasing habits and attitudes towards sustainable packaging meaningfully narrow the attitude–behaviour gap, but organic purchasing habits, waste segregation habits, and sustainability literacy do not. This research contributes the knowledge that, for environmentally friendly leafy greens, past price- and sustainability-related purchasing behaviour should be used instead of merely attitudes as an indication of WTP.

Advanced Waste Seclusion and Chucking System Using Deep Learning Techniques

Any material that is undesirable or unusable is considered waste. Waste can be in any form (Liquid, solid or gas) but generally, waste is a solid form. There are various types of waste like paper, displeasing food, torn clothes, dried plants, kitchen waste, etc., Skin disease, diarrhea, tuberculosis, whooping cough, pneumonia etc.., are some other common diseases spread due to immoral waste management. Separating waste allows us to salvage more items, preventing their scraping in landfills. By reducing landfills disposal Segregating waste is important not only to reduce its impact on the environment but also to prevent health issues that can arise from the disposal of waste and toxins. Although the problem of waste segregation is a big challenge, there are several methods for automatic segregation, which eliminates the need for human hands. The proposed system utilizes Faster R-CNN algorithms to segregate waste into bio-degradable and non-biodegradable categories which, effectively eliminating the need for human involvement in the segregation process. This method employs artificial intelligence techniques to achieve waste segregation.

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%.

A Pilot Study into the Use of Qualitative Methods to Improve the Awareness of Barriers to Sustainable Medical Waste Segregation within the United Kingdom’s National Health Service

Within the United Kingdom, most medical waste is incorrectly classified as hazardous and disposed of via incineration or alternative treatment. Currently, no research has been conducted on why such a large quantity of medical waste is erroneously segregated. This pilot study explores the barriers to correct segregation with the aim to decrease the volume of incinerated waste by investigating why medical waste is wrongly identified as hazardous. No previous data are available to compare results, and so this study demonstrates the significance of using qualitative methods (questionnaires and focus groups) to bring awareness to issues faced within medical facilities when segregating waste. The low availability of different bins as well as lack of space and the healthcare workers’ busy schedules were identified as main reasons for poor segregation. Bins were sparsely placed, and staff lacked time to find the appropriate one leading to incorrect segregation of non-hazardous waste. Lack of information around whether a material was recyclable or not led to less recycled waste. When ways to engage with this issue were discussed, most medical staff favoured quick forms of information provision, such as posters, whereas a participant proclaimed longer hands-on style sessions as more effective. The findings of this study provide evidence that governmental strategies focused on sustainable medical waste management should direct their attention to the placement and availability of bins, whilst including ‘on-the-ground’ personnel in their decision making. This pilot study showed the value in using qualitative methods when current data are lacking and can be repeated by other healthcare facilities to collectively grow a greater awareness of the sustainability issues faced by the UK healthcare waste management system.

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.

Recyclable plastic waste segregation with deep learning based hand-eye coordination

Plastic waste management is a critical concern in municipal solid waste management systems worldwide. Despite the efforts of waste management personnel to segregate waste manually, the existing challenges persist. In municipal waste facilities, individuals responsible for waste segregation face numerous obstacles. Consequently, a significant amount of plastic waste ends up in landfills, exacerbating the plastic waste problem. To overcome these challenges, this research focuses on developing an automated system capable of categorizing plastic waste based on its visual characteristics. The trained model exhibits high precision in identifying various types of plastic waste, including PET, HDPE, PVC, LDPE, PP, and PS. Specifically, the model achieves an Average Precision of 0.917 and an Average Recall of 0.801. Moreover, the model maintains a good balance between precision and recall. In real-time operation, an overhead camera locates the positions of both the waste items and the gripper. By calculating the positional difference between the waste and the gripper, the system achieves a higher level of segregation accuracy, resembling human-like hand-eye coordination. The proposed system offers a solution to the challenges faced in MSW facilities, where the timely segregation of waste is crucial. By automating the plastic waste categorization process, the system can significantly improve waste management practices, leading to a more sustainable approach to plastic waste disposal and recycling.

Analysis of Waste Separation Drivers in Urban Centers Using the Theory of Planned Behavior and the Norm Activation Model

Urban centers worldwide are grappling with complex waste management challenges, including efficient collection, transportation, processing, and an over-reliance on landfills. A promising approach to mitigate these issues lies in bolstering public participation in waste separation, which could significantly improve recycling efforts. To effectively encourage this practice, it is crucial to understand the underlying factors that motivate community engagement in waste segregation activities. This study utilizes the Theory of Planned Behavior and the Norm Activation Model to identify and analyze determinants influencing individuals' propensity to separate waste in the sampling area of Balikpapan City, Indonesia. Balikpapan, one of the cities in Indonesia, is currently facing several distinct challenges related to waste management. Through the empirical validation of eight hypotheses, it becomes apparent that the presence of market facilitators (H3) and the influence of past behavior (H5) play pivotal roles in shaping the intention to engage in waste separation. The findings suggest that providing accessible, well-maintained market facilities and initiatives designed to enrich the public's waste separation experience are essential strategies. Implementing these strategies could significantly improve waste separation practices within specific urban contexts such as Balikpapan, Indonesia, and other cities facing similar environmental management challenges.

Needle Stick Injury Among Healthcare Workers in a Tertiary Care Setting in Dehradun, Sub-Himalayan Region: A Four-Year Record-Based Study.

Needlestick injuries (NSIs) represent a significant occupational health risk in healthcare settings. These injuries, caused by contaminated sharps such as needles, vials, and scalpel blades, can lead to percutaneous exposure to infectious materials. Despite the severity of NSIs, they often go unreported, highlighting a critical gap in occupational safety protocols. This study aimed to investigate the occurrence of NSIs among healthcare workers (HCWs) by sex, profession, and working areas. It also sought to explore the underlying reasons for these injuries and the factors contributing to their underreporting. Adhering to the RECORD guidelines (Reporting of studies Conducted using Observational Routinely Collected Data), this record-based study involved a retrospective analysis of reported NSIs. Data were collected from voluntary reports by HCWs who experienced NSIs or exposure to potentially infectious materials such as blood and body fluids. Statistical analysis was conducted using IBM SPSS Statistics for Windows, Version 16 (Released 2007; IBM Corp., Armonk, New York) and Microsoft Excel 2010 (Microsoft Corporation, Redmond, Washington). Data from 142 participants indicated a higher proportion of females experiencing NSIs compared to males, with rates of 57.7% pre-COVID and 60.6% during COVID. There were notable shifts in NSI rates across professions, with increases observed among staff nurses and ward attendants/helpers. Analysis of injury circumstances revealed a decrease in sampling procedure-related injuries but an increase during intravenous procedures and biomedical waste segregation. Injuries occurring on the right-hand index finger decreased from 52.1% pre-COVID to 31% during COVID, while those on the left-hand index finger increased from 19.7% pre-COVID to 39.4% during COVID. Statistically significant associations were found between the injury site and the place of occurrence (p=0.021). Healthcare professionals commonly cleansed the site with disinfectants and used personal protective equipment (PPE) kits, with increased PPE usage noted during the COVID-19 pandemic. These findings emphasize the evolving dynamics of NSIs among HCWs and underscore the importance of tailored preventive measures during pandemics.

Heavy metal accumulation on microplastics in compost - The role of biofilm

Microplastics, small plastic pieces ( microplastics washed with distilled water > compost free from microplastics > chemically separated microplastic > new macro-plastic, with some exceptions. Heavy metal concentrations were significantly higher in samples from Kochi, due to a larger bio-film formation on the plastics as determined by TOC and SEM analyses. The findings of the study highlight the importance of source segregation and prevention of mixing organic waste with plastic waste in MSW, to avoid heavy metal transport in terrestrial environment through microplastics.

Plastic Solid Waste Management Assessment Among Selected Schools in Gwadabawa Local Government, Sokoto, Nigeria

The objective of this work was to assess the plastic solid waste management among selected schools in Gwadabawa Local Government, Sokoto, Nigeria. A descriptive survey (cross-sectional) study was employed and collected data from portions of schools and respondents. The findings indicate that, demographic characteristics of teacher respondents involved in this study are: Males are 66.7%, females are 33.3%; and the levels of education include, NCE/diploma (58.0%), BSc (26.7%), MSc (13.3%), and PhD (2.0%). Regards to students involved, 48.0% are males, 60.0% are females; 36.0% are 18-20 years old, and 64.0% are above 20 years old. The level of education of students include, 60.0% diploma students, and 40.0% certificates students. The effects are listed in the order of frequency as submitted by participants: Release of gases (20.5%), land pollution (17.0%), water pollution (15.0%), attraction of vermin (11.0%), breeding of diseases (10.5%), loss of biodiversity (7.5%), reduction of beauty of environment (7.5%), offensive odour (5.0%), flooding (5.0%), and particulates release (1.0%). Methods of waste elimination are: open disposal occurs among 37.5% schools; likewise, open disposal and burning, occur in 37.5%; while, use as fertilizer was recorded in 12.5% schools. Waste collection methods are either open dumping (80.0%), or use of containers (20.0%). The use of segregation of waste; therewith, only 20.0% practice segregation, the remaining 80.0% do not practice segregation, rather openly display mixtures of waste materials. The possible remedies to mitigate plastic solid waste mismanagement and effects include, education (awareness) and behavior change (53.3%) was mostly stated, funding (26.7%), improvisation (15.3%), scavenging (3.3%), and innovations (1.3%). Policy makers need to act urgently in diverse ways, and the people are supposed to be educated on ways of remediating plastic waste pollution.

Implementing Biodigester Technology for Sustainable Waste Management in Mojokerto City, Indonesia

Mojokerto City, located in East Java, grapples with a steadily rising waste generation trend accentuated by the COVID-19 pandemic. Drawing data from the National Waste Management Information System (SIPSN) and supplemented with an exhaustive literature review, this study scrutinizes the potential of biodigester technology as a solution to this mounting challenge. Mojokerto's waste, consistent in its composition from 2019 to 2022, is dominated by food waste, making it an ideal candidate for biodigester intervention. The study revealed that existing biodigester facilities in Mojokerto vary in their waste reduction efficiencies yet collectively demonstrate the viability of converting food waste to energy. For instance, the TPS 3R Magersari Berseri facility showcased an impressive waste reduction rate of 85.0%, processing 62.05 tons out of 73 tons of waste received annually. Comparative analyses of other facilities like TPS 3R Pulorejo "Cahaya Berseri" and TPS 3R Muria Berseri were also conducted, revealing varying degrees of efficiency. These facilities underscore the potential of biodigester technology in managing the rising tide of waste and contributing to sustainable energy generation. The findings of this study also call for increased awareness about food conservation, improved waste segregation at the source, and the promotion of composting and other organic waste processing methods, thereby optimizing the overall efficiency of waste management in the city.

Plastic Solid Waste Management Assessment Among Selected Schools in Gwadabawa Local Government, Sokoto, Nigeria

The objective of this work was to assess the plastic solid waste management among selected schools in Gwadabawa Local Government, Sokoto, Nigeria. A descriptive survey (cross-sectional) study was employed and collected data from portions of schools and respondents. The findings indicate that, demographic characteristics of teacher respondents involved in this study are: Males are 66.7%, females are 33.3%; and the levels of education include, NCE/diploma (58.0%), BSc (26.7%), MSc (13.3%), and PhD (2.0%). Regards to students involved, 48.0% are males, 60.0% are females; 36.0% are 18-20 years old, and 64.0% are above 20 years old. The level of education of students include, 60.0% diploma students, and 40.0% certificates students. The effects are listed in the order of frequency as submitted by participants: Release of gases (20.5%), land pollution (17.0%), water pollution (15.0%), attraction of vermin (11.0%), breeding of diseases (10.5%), loss of biodiversity (7.5%), reduction of beauty of environment (7.5%), offensive odour (5.0%), flooding (5.0%), and particulates release (1.0%). Methods of waste elimination are: open disposal occurs among 37.5% schools; likewise, open disposal and burning, occur in 37.5%; while, use as fertilizer was recorded in 12.5% schools. Waste collection methods are either open dumping (80.0%), or use of containers (20.0%). The use of segregation of waste; therewith, only 20.0% practice segregation, the remaining 80.0% do not practice segregation, rather openly display mixtures of waste materials. The possible remedies to mitigate plastic solid waste mismanagement and effects include, education (awareness) and behavior change (53.3%) was mostly stated, funding (26.7%), improvisation (15.3%), scavenging (3.3%), and innovations (1.3%). Policy makers need to act urgently in diverse ways, and the people are supposed to be educated on ways of remediating plastic waste pollution.

Quantitative and Qualitative Assessment of Hospital Waste Management at Capital’s Health Care Facility, Pakistan

The improper disposal of solid waste, particularly in developing countries, poses significant threats to the natural environment and public health, leading to increased pollution and disease prevalence. Hospital-generated solid waste, characterized by its heightened toxicity and contagion potential, further exacerbates these risks when mismanaged. This study undertakes an evaluation of the solid waste management (SWM) practices implemented at Pakistan Institute of Medical Sciences (PIMS) Hospital in Islamabad, with a specific focus on assessing their impact on the staff responsible for SWM. The data collection process encompasses on-site visits to check and compare the hospital wastes data available athospital servers as well as by the structured questionnaires. According to the gathered data, PIMS Hospital generates an annual total of 61,030 kilograms of hazardous waste and 254,866.9 kilograms of non-hazardous waste. Given its status as the capital's healthcare facility, PIMS Hospital maintains a well-established and satisfactorily functioning standard of SWM system. This system includes the segregation of hazardous and non-hazardous waste at the point of generation, facilitated by a color-coded scheme. Non-hazardous waste is then transported to a secure covered storage site using specialized trolleys, while hazardous waste is safely disposed of through the utilization of on-site, double-chambered incinerators. Data analysis underscores the heightened occupational risks faced by SWM personnel, including an increased likelihood of sharp injuries, and disease exposure, as well as the prevalent experience of stress and anxiety. The study's findings necessitate the imperative adoption of standardized practices for the proper handling, treatment, and disposal of solid waste by the involved staff. Furthermore, a critical recommendation arises from the need for routine health monitoring of SWM workers by qualified medical professionals through established mechanisms to mitigate the potential transmission of diseases to their family members. Such measures are paramount in safeguarding the health and well-being of both the SWM staff and the broader community.

The Degree of Implementation of Waste Management on Ship

Ships generating waste pose a significant environmental challenge in the maritime sector. This research aims to assess how effectively waste management practices are implemented by maritime personnel to mitigate the environmental impacts of onboard waste. Data gathered from survey questionnaires were analyzed using a quantitative research design, focusing on responses from chief officers and second engineers of ships. A four-point Likert scale was employed, and results were evaluated using frequency percentages, weighted means, and the Chi-square statistical analysis. The study aimed to profile the ship's crew and evaluate the implementation of waste management strategies, particularly regarding waste management plans and waste disposal onshore and ship-to-shore. The findings indicate that waste management implementation at the Port of Surigao's ships was fully achieved. These results underscore the importance of comprehensive waste management systems and training to ensure consistency in perceptions and practices among maritime officers. Therefore, the researchers suggested that ship operators can reduce environmental impact and promote sustainability in the maritime industry by developing a comprehensive waste management plan, providing crew training, installing effective waste segregation facilities, implementing recycling initiatives, and conducting regular audits.

In-depth analysis to develop a social marketing model to promote women's participation in waste segregation behaviour: A qualitative study

Waste separation is one of the key factors in managing solid waste and creating a healthy environment. Waste separation at source has always been associated with challenges. Therefore, this study was conducted to determine the perceptions of housewives and related parties regarding the factors that influence waste separation behaviour and to identify approaches to improve behaviour based on the social marketing framework. This study was conducted as a qualitative content analysis in Amol City in 2022. The data was collected through semi-structured individual interviews. A total of 25 housewives were selected as main participants and 5 stakeholders through purposive selection. The results of the study included lack of awareness of recyclable materials (product), personal, family and environmental barriers (price), lack of doorstep collection of dry waste (place), and lack of use of appropriate technology (promotion). The lack of financial resources, inappropriate political measures and the coronavirus pandemic were also the causes of this challenge. Most participants cited environmental and educational deficits as the main reason for not separating waste. It is possible to improve waste sorting behaviour at source through appropriate behavioural interventions at the individual, social and environmental levels. Researchers can use the results of this study to design, implement and evaluate waste segregation intervention programmes for housewives.