Waste Items Research Articles

A Multidisciplinary One Health Approach to Addressing Unsanitary Conditions: The Case of Cascais

Abstract Unsanitary conditions in domiciles often stem from complex social and health-related issues, involving the elderly and/or individuals with mental health problems. These situations typically lead to a decreased quality of life for those involved, directly and indirectly, and may pose a risk to public health depending on their magnitude. In Cascais municipality, identified cases of unsanitary conditions undergo a multidisciplinary analysis. It has been established a Local Technical Group for Domiciliary Unsanitary Situations, comprising various institutions such as local authorities, law enforcement agencies, civil protection, health and social security ministries, and the animal rescue center. This group operates under a One Health approach, encompassing human health, environmental health, and animal health. Interventions are determined by the working group, with the aim of promoting health and enhancing citizens’ quality of life. Cases of unsanitary conditions reported are subsequently discussed within the technical group, which has regular meetings, and may organize joint assessment visits and appropriate actions. This social and environmental initiative responds to the growing need to address unsanitary and cluttered living conditions, which significantly impact individuals’ physical, emotional, and social well-being. The objective is to restore housing conditions in homes by removing waste and unnecessary items, contributing to general individual well-being. While international studies suggest a correlation between unsanitary conditions and hoarding disorder, it’s crucial to differentiate between distinct and independent situations caused by various physical, psychological, and cognitive processes. The establishment of a Local Technical Group for Domiciliary Unsanitary Situations exemplifies a proactive approach to addressing complex health and social challenges, emphasizing collaboration, and holistic intervention strategies, always with a One Health approach. Key messages • Addressing unsanitary conditions is crucial for safeguarding public health, impacting physical, emotional, and social well-being. • The establishment of a Local Technical Group for Domiciliary Unsanitary Situations, under a One Health framework, reflects a collaborative approach to addressing complex health and social challenges.

Risk Analysis Of Musculoskeletal Disorders (MSDs) Of The Material Handling Process In Waste Processing Operators Using The Rapid Whole Body Assessment Method

: Businesses should never stop trying to include every employee in their operations and never skimp on safety precautions. One of the safety methods in the worker-centered approach implementation is the significance of ergonomics. Typically, material handling phases in production processes include physical labor that demands a lot of operator effort. Handling objects by hand raises the possibility of workplace mishaps, which can lead to musculoskeletal injuries. This study focuses on the different labor postures used for gathering, transferring, and positioning waste as well as the manual handling of waste items up to the shredding area. This study evaluates work postures that may result in symptoms of musculoskeletal diseases using the Rapid Entire Body Assessment (REBA) technique. There are three steps in this process. Prior to lowering the waste bucket to the designated place, gather the trash first, then hoist and carry it till it reaches the mechanical area. Three grades 9, 2, and 6 were obtained from the analysis utilizing the REBA (Rapid Entire Body Assessment) approach. Based on these findings, the first stage has to be improved right away. Thus, in order to meet the improvement objective, we created a telescopic reach that enables employees to change the height a device that aids in bettering workers' posture while picking up trash.

Nitrosamine and nicotine exposure after switching from filtered to unfiltered cigarette smoking: a cross-over clinical trial

BackgroundThe cellulose acetate filter is a plastic attachment on nearly all commercial cigarettes sold worldwide. It is the main component of discarded cigarette butts, and the most prevalent waste item...

Assessing plastic pollution in farmland: Impact of agricultural practices on bird nesting materials

Farmland, a vital terrestrial environment, has seen significant changes due to intensified agricultural practices and plastic pollution. Assessing plastic pollution in farmed areas remains challenging, and understanding its impacts on agricultural ecosystems is limited. The aim of the was to determine whether plastic pollution in agricultural landscapes depends on the type of crops. We used the Red-backed Shrike (Lanius collurio), an indicator bird species and investigated plastic contamination of 122 of its nests. We found plastic waste in 82.1 % of nests, totalling 6234 items weighing 197.2 g. On average, each nest contained 55.7 waste items, constituting approximately 4.3 % of the nest's mass. Plastic string fibres were predominant, making up 95.6 % of the items and 90.7 % of the mass. This variation may be due to different straw and hay management practices and varying levels of vigilance in removing plastic remnants. Contamination levels varied significantly among farmland types, with cultivated fields showing the highest contamination and meadows the lowest.

Regional Variations in Urban Trash: Connections between Litter Communities and Place

Litter is a pervasive social and environmental issue that continues to evade effective and sustainable mitigation strategies. As the nature of waste items can influence methods and rates of littering, an understanding of litter typologies associated with specific sites has the potential to inform targeted anti-littering efforts. In this study, data analysis methods from ecology were applied to litter surveys to evaluate patterns among urban litter items found in two types of streets in England (High Streets and Central Business Districts). The results indicate that sites characterised as a High Street (predominantly leisure activities such as shopping and dining) contained lower densities and less variety yet featured litter items with a higher potential for environmental contamination than sites categorised as Central Business Districts (identified by high numbers of professional workers and transport links). Although litter was significantly different between sites, the litter community structure was not. Our results suggest that litter typologies and associated activities can lead to specific knowledge of key influential items in a site and inform future evidence-based and sustainable mitigation systems.

Neutron-based analytical techniques for the elemental composition analysis of electronic waste samples: advantages and challenges

Recycling electronic waste (WEEE) is a fundamental aspect of the circular economy. To develop technology and assess its economic aspects, the composition characterization of the waste items is crucial. Neutron-based analytical techniques, such as the INAA, in-beam NAA, and PGAA, are applicable to measure the elemental composition of WEEE. These techniques are bulk representative and offer simultaneous determination of the relevant elements without sample dissolution. This study explores the suitability of neutron-based elemental analysis methods for measuring common WEEE items, e.g., printed circuit boards and integrated circuits. Matrix effects mostly related to the thermal neutron self-shielding, were identified and successfully corrected, ensuring accurate mass fraction measurements.

Home Waste Audit: A Community Science Activity to Increase Waste Literacy and Reduce Household Waste

AbstractThe amount of household waste produced worldwide is increasing every year. In combination with other actions to reduce waste production and improve waste management, community engagement and community‐focused programs are needed to motivate the public to change their behavior in such a way that reduces their waste generation and increases the accuracy of waste sorting. It is also helpful for people to become more waste literate to empower them to be part of the solution. The Home Waste Audit (HWA) is a community science activity designed to increase waste literacy and reduce household waste. In the HWA, participants record their waste for a set period of time, research their local waste streams, and complete surveys to share perceptions of household waste habits. Here, we present data from a HWA conducted in 2021 as a case study. Before the audit, 66% of participants underestimated their weekly waste generation. Throughout the HWA, weekly waste count among households decreased by 31%. Participants found purchasing items with less/no packaging and avoiding single‐use plastics challenging. Easier changes included learning which items can/cannot be recycled and repurposing waste items. Several changes to waste habits were maintained 1 year after participation. These results demonstrate that the HWA is an effective tool for individuals to be a part of the solution by learning about local waste streams, reducing waste production, and accurately managing their household waste.

Waste management systems in the context of sustainability

The present paper deals with waste management systems in the context of sustainability in selected municipalities. The author's team aimed to find the key waste groups according to the classification groups that have a major impact on the volume of waste and the cost of the system, thus facilitating management decision-making. In order to optimise the management of the entire waste management system from the perspective of the statutory municipality, it was first necessary to reduce the original list of 53 waste items to a significantly smaller number. With the help of waste management experts, criteria for reducing the number of waste input items were defined, resulting in a reduced statistical sample of 27 items. Each item is quantified by the amount of waste per year and the cost of the system per year. The mathematical apparatus consisted primarily of calculating average values as well as marginal slopes of waste quantity and cost. The interrelationships between waste groups were graphically recorded in perceptual maps. The individual analyses were carried out primarily for a statutory city, with secondary comparisons of the results with smaller towns and small villages. The analytical procedures verified that the catalogue of basic components of mixed municipal waste can be significantly reduced for decision-making in secondary resource management. The amount of waste selected in each group significantly impacts the cost of the whole waste management system. Of the original 15 waste groups, only three groups appear to be the main ones. The municipal waste group even has a decisive influence on both the quantity of waste and the cost of the whole system. Thanks to these findings, from the point of view of both the manager and the management of the statutory city, it is possible to focus on this core group, which contains the 13 original catalogue items.

STUDY ON ENGINEERING PROPERTIES OF SOIL USING STONE DUST AND BAGASSE ASH

In the discipline of civil engineering, soil stabilization is the process of enhancing and improving the engineering qualities of soil so that it can support large loads without failing. The safe and efficient disposal of waste material without causing harm to society is a significant concern in a well-managed ecosystem. Utilizing solid waste products to stabilize soil has become more popular recently as a successful way to manage trash from many sectors. This paper reviews the usage of various solid waste items that have been applied to stabilize soft soils. However, there are numerous approaches and strategies for stabilizing this soil. This research offers a method for stabilizing soft soil using waste products. An investigation into the stabilizing potential of bagasse ash and sisal fiber for highly compressible clayey soil has been prompted by the rising expense of building traditional stabilizers and the necessity of inexpensively consuming industrial and agricultural wastes for value trade. There is a growing demand for sustainable construction, which is why recent research has concentrated on sustainable resource consumption trends. We can employ natural and biodegradable resources, as well as waste products, to obtain. This study was a large-scale laboratory experiment to explore the use of natural fibers, industrial waste, and agricultural waste to improve the engineering qualities of moderately compressible clay. The purpose of this study was to determine whether Stone Dust and Bagasse Ash were suitable for stabilizing clayey soil. The results of testing for consistency limits, maximum dry density, optimal moisture content, UCS, and (soaked) CBR were obtained using percentages of 10%, 15%, and 20% by weight of dry soil for Bagasse Ash and 6%, 8%, and 10% for Stone Dust. In this work, expansive soil was stabilized using Stone Dust and Bagasse Ash. Bagasse Ash was fixed at 15%, after that Stone Dust was changed to 3%, 6%, and 9%. Key Words: Compaction test, CBR, UCS, Stone Dust, Bagasse Ash

Comparison of MCNP and Microshield Dose Savings Determinations for Remote Methods of Transuranic Contamination Characterization.

The maturation of robotic and remote systems presents opportunities to expand the use of technologies that have typically been restricted to high-dose/high-risk nuclear work for moderate- or low-risk work to further reduce radiation exposure to workers. This study quantifies the potential dose savings achieved through the use of robotic techniques for characterizing transuranic-contaminated waste items and compares dose estimates from a simplistic, user-friendly deterministic radiation transport code and a more robust, complex Monte Carlo code. Three scenarios of transuranic-contaminated waste items described in published reports are modeled using representative source geometries in MicroShield and MCNP radiation transport codes. Estimated dose rates are determined at points ranging from 30 cm to 300 cm from the face of the waste item to represent the increase in distance allowed by robotic or remote system implementation for characterization activities. The dose rate savings are then converted to detriment cost savings using a dollar-per-person-dose conversion factor to provide a financial context. The radiation transport simulations show no consistent bias in estimated dose rate by varying simulation methodology or using geometrical simplifications-in some cases, MicroShield produces higher dose rate estimates while MCNP estimates are higher in other cases. In the MCNP simulations, the volume source geometry consistently produces a higher dose rate than the slab source geometry, but the MicroShield dose rate estimates do not display the same trend. Dose savings range from 1.60 × 10-5 μSv h-1 to 1.75 × 101 μSv h-1 with associated detriment cost savings from < 0.010 USD/person-h to 14 USD/person-h.

Smart Handling of Biomedical Waste using Intelligent Deep Learning and IOT

Hospitals create an enormous volume of potentially hazardous trash. Currently, bandages, plastic papers, wasted pus, syringes, glucose drip bottles, other dangerous medical wastes, and containers are separated by hand, resulting in long-term health consequences such as tuberculosis, cancer, and infectious diseases. Rag pickers are currently responsible for the majority of waste separation. Efficient management of medical waste demands not only precise segregation but also real-time monitoring and notification systems to ensure timely intervention and optimized waste handling. This project proposes an integrated solution combining deep learning, image processing, Embedded Systems hardware and Internet of Things (IoT) technologies. Medical waste will be detected using the Deep Learning technique; A deep learning model is trained on a diverse dataset to classify medical waste items, and image processing techniques are utilized to enhance the accuracy of the classification process. This holistic approach offers an efficient and automated system for medical waste management integrating waste segregation, waste level monitoring, and timely notifications, thereby ensuring a sustainable and responsible waste management process in healthcare facilities.

Inter-observer reliability in transect-based observations of environmental waste in greater accra and kisumu: implications for waste management

Transect surveys are widely used to quantify mismanaged plastic waste discarded to the environment. However, very few studies have quantified inter-observer reliability in environmental waste surveys. The aim of this study was to assess inter-observer reliability for a set of environmental waste indicators derived from transect surveys in slum areas of two sub-Saharan African cities. Pairs of observers independently recorded counts of scattered waste items, large waste piles, and burnt waste along 64 environmental transects in Kisumu, Kenya, and Greater Accra, Ghana. Concordance correlation coefficients were used to measure inter-observer reliability for derived indicators measuring mismanaged waste density, waste composition, waste origins of policy concern, and waste disposal practices. Evidence across all observers consistently showed that single-use disposable diapers, discarded Personal Protective Equipment, and bagged or bottled water are locally important constituents of mismanaged waste in both cities that should be addressed through urban waste management strategies. In both cities, there was generally excellent inter-observer reliability for density of burnt waste and density of large waste piles (concordance correlation coefficient > 0.9 for all but one observer pair), but weak to moderate inter-observer reliability for scattered waste density and densities of specific waste items such as disposable nappies and discarded Personal Protective Equipment. Our study shows that beach litter survey protocols can be adapted for use in slums. However, to generate robust estimates of scattered waste, its composition, and waste origins of policy concern in slums, assessment of inter-observer reliability should be incorporated into field team training.

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.

CHARACTERISATION OF MACRO-PLASTIC WASTE ALONG THE PHILIPPINES' LONGEST COASTAL BOULEVARD: BASIS FOR SOLID WASTE MANAGEMENT AND POLICY FORMULATION

This study focused on the characterisation of macro-plastic waste in terms of types and quantities along the Philippines' longest coastal boulevard, considering density and cleanliness as a basis for solid waste management and policy formulation. Macro-plastic identification and counting were conducted in four (4) municipalities and eight (8) barangays using the standing stock survey method. There were six (6) types of macro-plastic waste, with the highest quantity consisting of beverage (37.2%) and food packaging (30.5%), followed by fishing gear (17.0%), plastic utensils (9.2%), toiletries (4.8%) and household wastes (1.4%). The computed overall density from a total of 3,978 macro-plastic waste items over 12,000 m2 of beach area sampled was 0.3 (CM), meaning there were 0.3 litter items of plastic per m2 throughout the whole boulevard. The computed overall beach cleanliness was 6.6, which means that the cleanliness status is moderate. Overall, most macro-plastic waste is generated by locals or visitors. In conclusion, a policy regulating the use, littering, and carrying of plastic along boulevards is recommended to prevent plastic pollution considering the province's growing ecotourism and the future expansion of the coastal boulevard.

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.

Corrosion Resistance of Scraped Copper in Velocity Varied Inland Water of Bangladesh

Copper (Cu) is widely used for electrical wires, equipment parts, and components in commercial, industrial, and marine fields due to its excellent electromechanical properties. With rapid scientific and technological advancement and economic development, the demand for copper is increasing continuously. As such, scraped items have become important to meet the rising demand considering the depletion level of copper ores in nature. But sometimes old copper contains tin (Sn), lead (Pb), etc., which may alter its properties. Thus, it is necessary to characterize different properties, including corrosion resistance, to explore the appropriate engineering applications of scraped copper waste items. In this context, the present research reports the study of the corrosion behavior of pure Cu and scraped Cu in the river water environment of Bangladesh for the submersed period of 36 days. The investigations have been carried out for three relative velocity conditions of fluid, i.e., 0, 2 and 4 m/s. A gravimetric analysis was done to find out the corrosion rate. The results of this study indicate that the presence of only 1%Sn and 1%Pb in copper has significantly increased the corrosion rate in all three velocity conditions. At the same time, scraped copper's average corrosion rate has been increased by about 10% while the relative velocity is changed from 0 to 4 m/s. Micrographs have confirmed the extent of corrosion severity resulting from chemical composition and fluid velocity alterations.

3R-Focused Web Application

3R-focused web application is an effective waste reduction and waste management which provides solution for reusing and reducing waste items in 3 effective ways: Reduce, Reuse, Recycle. With the base support of 3Rs (Reduce, Reuse and Recycle) strategy which acts as a sustainable and socioeconomic option for environment as well as people. The main objective of the system will remain to reduce, reuse, and recycle the items which are easily thrown away by people without any thought. Followed by the main objectives next comes various facilities like donating the reusable waste items, availability of various recycling ideas, giving the items which are not in reusable condition to the recycling centers, lending (renting) out the items to people. This web application will target all the three categories of waste reductions; reduce, reuse, and recycle by suggesting alternatives to reusing items and recycling them as well as the option of lending them out or simply donating them. Implementing these 3 approaches in one application will target all the netizens by providing a platform for easy access and real time communication between users for instructiveness and bigger support. It will solve the problem of having to make more than one connection and more than one action to reach the desired result by giving access to all the solutions needed in just one platform.

Intelligent system for solid waste classification using combination of image processing and machine learning models

ABSTRACT Solid waste is a major issue in all cities around the world. Classification and segregation of solid waste prior to reuse, recycle or recover is an important step towards sustainable waste management. Traditional manual sorting of solid waste is a labour-intensive process that may pose health risks to the workers. Currently, automated classification of solid waste using machine learning techniques is widely applied. This study aims to develop an automated waste classification model by testing traditional and deep machine learning models. To achieve that, both open and generated datasets were used in the model training and testing. The study results showed relatively low prediction capability of the traditional machine learning models like Random Forest (RF) and Support Vector Machine (SVM) as compared to the deep machine learning Convolutional Neural Network (CNN). The testing of the three models on a combined data set of Trashnet with local garbage data set resulted in accuracy of 62.5% for SVM, 72.0% for RF and 92.7% for CNN. JONET deep learning model has been developed using a combination of pre-trained base model (DenseNet 201) with a new architicture that contains a fully connected layer in the classification stage with 1024 neurons. The model is capable to identify six classes of solid waste items with various accuracies. When tested on the Trashnet dataset, the accuracy was 96.06%, while testing on the local garbage dataset gave an accuracy of 94.40%. JONET has been tested also on multi object images which gave an acceptable prediction accuracy.

An integrated deep-learning model for smart waste classification.

Efficient waste management is essential for human well-being and environmental health, as neglecting proper disposal practices can lead to financial losses and the depletion of natural resources. Given the rapid urbanization and population growth, developing an automated, innovative waste classification model becomes imperative. To address this need, our paper introduces a novel and robust solution - a smart waste classification model that leverages a hybrid deep learning model (Optimized DenseNet-121 + SVM) to categorize waste items using the TrashNet datasets. Our proposed approach uses the advanced deep learning model DenseNet-121, optimized for superior performance, to extract meaningful features from an expanded TrashNet dataset. These features are subsequently fed into a support vector machine (SVM) for precise classification. Employing data augmentation techniques further enhances classification accuracy while mitigating the risk of overfitting, especially when working with limited TrashNet data. The results of our experimental evaluation of this hybrid deep learning model are highly promising, with an impressive accuracy rate of 99.84%. This accuracy surpasses similar existing models, affirming the efficacy and potential of our approach to revolutionizing waste classification for a sustainable and cleaner future.

Environmental Perception of Solid Waste Management in the Hurtado Balneary, Colombia

Environmental perception serves as a powerful tool for identifying community needs and potential within their surroundings. This study assessed tourists’ environmental perception regarding solid waste and its impact on the Hurtado Balneary, located in Valledupar, Colombia. The investigation delved into the environmental attitudes and behaviors of 769 balneary users by employing a combination of observation, photographic documentation, and a comprehensive 21-question online survey. The findings are 98.8% of participants acknowledged the adverse effects of solid waste on the environment, with 98% demonstrating a willingness to participate in waste management efforts. Furthermore, 42.13% exhibited limited awareness regarding waste management practices, reflecting a concerning lack of public interest and commitment (41.61%). Statistically significant disparities (p > 0.05) emerged 59% of waste items were not appropriately disposed of in designated receptacles. Reasons for this included a lack of a sense of belonging (50.2%) and inadequate environmental education (45.6%). Ninety-six percent expressed the need for river conservation signage, while 59.8% did not undertake any form of environmental training. The study’s implications extend beyond the Hurtado Balneary, potentially informing solid waste management strategies in other aquatic environments. This investigation aligns with three Sustainable Development Goals: SDG 12, emphasizing responsible consumption and production; SDG 13, addressing climate action; and SDG 15, advocating for life on land and below water.