Garbage Generation Research Articles

E-Waste in construction: a comprehensive bibliometric analysis and review of the literature

Purpose The global construction industry faces both challenges and opportunities from electronic waste (e-waste). This study aims to present a bibliometric analysis and comprehensive literature assessment on e-waste in concrete construction materials. Design/methodology/approach This study studies 4,122 Scopus documents to examine garbage generation in different countries and inventive ways to integrate e-waste into construction as a sustainable strategy. This study lists famous researchers and their cooperation networks, demonstrating a robust and dynamic area with a surge in research output, notably from 2018 to 2022. Data is visually represented using VOS Viewer to show trends, patterns and study interests throughout time. Findings The findings imply that e-waste can improve construction materials’ mechanical characteristics and sustainability. The results are inconsistent and suggest further optimization. e-Waste into construction has garnered scientific interest for its environmental, life cycle, and economic impacts. This field has great potential for improving e-waste material use, developing sophisticated prediction models, studying environmental implications, economic analysis, policy formulation, novel construction methods, global cooperation and public awareness. This study shows that e-waste can be used in sustainable building. It stresses this area’s need for research and innovation. This lays the groundwork for using electronic trash in buildings, which promotes a circular economy and environmental sustainability. Research limitations/implications The findings underscore the critical role of ongoing research and innovation in leveraging e-waste for sustainable building practices. This study lays the groundwork for integrating e-waste into construction, contributing to the advancement of a circular economy and environmental sustainability. Social implications The social implications of integrating e-waste into construction are significant. Using e-waste not only addresses environmental concerns but also promotes social sustainability by creating new job opportunities in the recycling and construction sectors. It fosters community awareness and responsibility towards sustainable practices and waste management. Additionally, this approach can reduce construction costs, making building projects more accessible and potentially lowering housing prices. Originality/value This research contributes to the field by offering a bibliometric analysis and comprehensive assessment of e-waste in concrete construction materials, highlighting its global significance.

Smart-Waste Management System Based On Arduino and Motion Sensors

Abstract: Smart-Waste Management System involves the collection of the waste generated in separate dustbins that are equipped with Arduino based automated system. The segregation of the waste will be done by the IoT based system; the sensor will detect and segregate the waste into recyclable and non-recyclable waste. As with the increment in population the amount of garbage generation per year is increasing as well. Most of the garbage generated is left untreated and the garbage dumping places and garbage dumpsters are becoming more unhygienic and polluted. After the COVID-19 pandemic many people prefer contact less interaction. Automated-dustbins are the best ways to deal with this situation. The dustbins will be controlled by Arduino components allowing it to open when waste is detected; it will also notify the garbage collector when the dustbin capacity is full and the waste needs to be collected. So thus, the garbage collector need not to go collect the garbage unnecessarily. Secondly, after the waste is collected the wet garbage is being dumped and buried underground whereas the dry garbage needs to be segregated on the basis of recyclable and non-recyclable waste. The waste segregation is based on the sensor and system based on IoT that allows to segregate the waste into categories like wet, metallic, paper-plastics, etc. These will lead to save of fuel and time, at the same time automated system will lead to contactless collection of waste and segregation of waste will help to treat waste properly, classify them into categories and proceed them for further processes. This way the amount of unprocessed and untreated waste will get reduced to some extent; resulting in less pollution

Smart-Waste Management System Based On Arduino and Motion Sensors

Abstract: Smart-Waste Management System involves the collection of the waste generated in separate dustbins that are equipped with Arduino based automated system. The segregation of the waste will be done by the IoT based system; the sensor will detect and segregate the waste into recyclable and non-recyclable waste. As with the increment in population the amount of garbage generation per year is increasing as well. Most of the garbage generated is left untreated and the garbage dumping places and garbage dumpsters are becoming more unhygienic and polluted. After the COVID-19 pandemic many people prefer contact less interaction. Automated-dustbins are the best ways to deal with this situation. The dustbins will be controlled by Arduino components allowing it to open when waste is detected; it will also notify the garbage collector when the dustbin capacity is full and the waste needs to be collected. So thus, the garbage collector need not to go collect the garbage unnecessarily. Secondly, after the waste is collected the wet garbage is being dumped and buried underground whereas the dry garbage needs to be segregated on the basis of recyclable and non-recyclable waste. The waste segregation is based on the sensor and system based on IoT that allows to segregate the waste into categories like wet, metallic, paper-plastics, etc. These will lead to save of fuel and time, at the same time automated system will lead to contactless collection of waste and segregation of waste will help to treat waste properly, classify them into categories and proceed them for further processes. This way the amount of unprocessed and untreated waste will get reduced to some extent; resulting in less pollution

Environmental Impact, reduction of garbage generation from Pet products in Universities of the Country

Environmental Impact, reduction of garbage generation from Pet products in Universities of the Country

Review on the Impact of Waste in Nigerian Manufacturing Firms and Waste Management Practices Using Machine Learning

Abstract: The study examined the impact of waste in Nigeria manufacturing firm and waste management practices using machine learning. This paper presents ways of interaction of man with his environment in terms of waste management in Nigeria with the aim of promoting sustainable development. It shows the policies, the agents making these policies less likely to happen and the damage it can cause for the next generation.This article presents how data has been collected and analyzed through survey and series of progressive steps and present some guidelines when practicing waste management. It presents how a region can discover and implement some efficient technique for efficient waste management and comes up with enormous constructive results.In order to automate and optimize garbage generation, collection, transportation, treatment, and disposal, this study examines a variety of machine learning techniques. In order to provide accurate and efficient forecasts for trash creation, segregation, and collection, the system combines several machine learning techniques, such as decision trees (DT), random forests (RF), k-nearest neighbors (KNN), support vector machines (SVM), and clustering algorithms

SOCIOLOGICAL PERSPECTIVES ON SOLID WASTE MANAGEMENT

The purpose of this study was to outline sociological theories that are relevant to solid waste management. The study was motivated by the observation that the explosion of post-industrial, toxic, and municipal waste is one aspect of the wider discussion of environmental risk and global climate change. In order to go beyond the idea that garbage poses a harmful threat to people, the study employed a sociological perspective to view waste as a social problem. This allowed for a more critical analysis of waste as a reflection of historically and socially established power relations. An analysis of the sociological theories revealed that no other discipline is better suited to combine the terminology and methodologies of studies on social inequality, urban planning, and environmental risks than sociology. The research findings indicate that gender and socio-cultural factors have a significant impact on the formation of solid waste, with socialization processes being crucial in determining attitudes and actions related to waste management. Gender dynamics and garbage generation are closely linked. Women in Africa play a major role in waste management techniques. The study concludes that knowledge from society and social education is profound in comprehending solid waste processes outside the scope of scientific models. Consequently, the assessment of ordinary people’s perceptions and localized knowledge is just as important as theoretical frameworks and scientific teaching.

FConvNet: Leveraging Fused Convolution for Household Garbage Classification

Confronted with the fast social development, the speed of garbage generation has increased. Therefore, from every aspect, especially staying green and healthy, it is urgent and vital to ensure accurate and stable garbage classification. In the field of garbage classification, household garbage has a large volume and the classification process still requires manual intervention. Besides, most of the current studies focus on industrial waste and medical waste, while the existing studies on household garbage are not effective enough. Furthermore, these studies are mostly trained and validated in self-built datasets, making the experimental results insufficiently representative and severely limiting the development of research. Aiming to facilitate the study of household garbage classification, this paper proposes a simple yet effective network model (FConvNet) to improve classification performance. In this paper, an effective convolution block with fused convolution is designed to enhance the potential correlation of the features in the spatial dimension, and then a lightweight symmetric structure is constructed to infer more information about the feature representation. The experiments on public datasets have justified the effectiveness of FConvNet with accuracy rates of 95%, 95% and 97%, achieving better performance on public datasets compared to baselines. Additionally, an online system based on the WeChat mini program framework is designed to provide convenient household garbage classification services and enable the collection of uploaded images to perform model optimizations.

Development of modified compost unit for domestic biodegradable waste

As the human population grows, so does the amount of organic solid waste generated in cities. In India, waste generation has surged dramatically during the last decade, reaching 62 million tonnes per year. By 2030, garbage generation is expected to reach 165 million tonnes, necessitating the construction of a landfill the size of Bengaluru. Organic waste, on the other hand, can be diverted from landfills and composted, which is a more sustainable technique. The study was conducted to evaluate the performance of the rotary drum. The objective is to decrease informal refill, separate waste and increment the nature of fertilizer or excrement. This effort aims to turn solid waste to compost in as little as 4–8 weeks. The goal of this study was to see how well a composting barrel with a covering mechanism could reduce the release of foul odours during the composting of common household bio-waste. It is a promising idea to turn the substrates inside the barrel, however, the impact of turning frequency needs to be discussed more. For 30 days, physio-chemical parameters were assessed. Composts made from various organic wastes vary in quality and stability, which is also dependent on the composition of the raw material utilised in the composting process. The stability and maturity of compost are directly proportional to its quality. Finally, pH, temperature, and moisture content were analysed in the compost made with cow dung as a beginning pack. Physio-chemical changes were tracked for 4 to 8 weeks. Moisture content, temperature, odour and pH were all taken into consideration. It was found that temperature remained above 55 °C for a period of two days in the middle of composting duration. The pH drops to 7 in first 5 days, but eventually managed to stay neutral in the end. The moisture content reduced from 60 % to 54 % during the total composting period of 30 days.

Research on the Development of Green Hotels in the Context of Low Carbon Tourism

With the continuous implementation of national measures related to carbon neutrality strategy, the concept of a low-carbon economy has been deeply rooted in people’s hearts. Low-carbon tourism has gradually become an inevitable trend, and the tourism industry is also undergoing orderly transformation and upgrading. To promote the sustainable development of the tourism industry, the tourism and hotel industry should also shift towards a low-carbon and green model. Hotel activities can easily bring related environmental issues, such as energy consumption, garbage generation, ecological pollution, etc. Therefore, building a green hotel will bring huge social and economic benefits to the low-carbon tourism industry. This article will address the above issues and take a low-carbon economy as the starting point, proposing sustainable development strategies and suggestions for green hotels from both the internal and external environment of the hotel, creating a low-carbon management atmosphere, and promoting the low-carbon operation of the green hotel industry.

Composition of marine litter from fishing activities: a preliminary study at the fish landing base of Pantai Labu North Sumatera

Pantai Labu Fish Landing Base (PPI) is a type D fishing port that’s located in Paluh Sibaji Village, Pantai Labu District, Deli Serdang Regency. The existence of garbage in the sea is important to know because the generation of garbage in the sea can have a negative effect in several sectors, one of which is in carrying out fishing activities. The purpose of this study was to determine the composition of marine litter from fishing activities by fishing units at Pantai Labu Fish Landing Base. This research was conducted in January 2022 in Paluh Sibaji Village, Pantai Labu District, North Sumatra. Data collection method used purposive sampling. The results showed that the composition of inorganic waste generated from fishing activities consisted of plastic, metal, paper and cardboard. Types of waste made from plastic materials consist of plastic bags, food wrappers, plastic bottles, bottle caps, plastic cups, cigarette packs, foamed plastic, and monofilament fishing line.

State-Of-The-Art On Reversible Multiplier Architectures and Its Comparison for Future Quantum Computing

Multiplication is the key crucial operation in realizing digital signal processing (DSP) functions. It is accomplished by using diverse multiplier architectures. Multiplication operation is furthermost basic and normally used action in the central processing unit (CPU). The multiplication operation is the most fundamental and commonly performed activity in the central processing unit (CPU). An efficient multiplier design should have a high speed, small area, and a low power consumption. Compact, efficient multipliers with minimal power dissipation are needed. The proposed paper provides a thorough inspection of multipliers such as the Array multiplier, Booth multiplier, column bypass multiplier, Baugh-Wooley multiplier, and Vedic multiplier based on their operational activities and working, as well as their benefits and limits. A comparison of these multipliers' performance parameters such as speed, area, power consumption, quantum cost, garbage generation and circuit complexity.

Municipal Solid Waste Management: A Review of Machine Learning Applications

This study comprises of an analysis of various Machine Learning (ML) algorithms for municipal solid waste management to enhance waste management procedures and reduce the adverse environmental effects. The increasing population has resulted in substantial environmental hazards due to increased waste generation. Therefore, an effective waste management system with much more efficient and innovative waste management techniques is required to reduce the adverse effects that would occur due to the generation of massive waste. This study reviews various ML algorithms to automate and optimize garbage generation, collection, transportation, treatment, and disposal. To deliver and predict effective and precise waste generation, segregation, and collection forecasts, the system integrates multiple ML methods including decision trees (DT), k-nearest neighbours (KNN), support vector machines (SVM), random forests (RF), and clustering algorithms.

Does living alone increase the consumption of social resources?

China's living arrangement has changed as the economy grows and society makes progress. More and more people prefer to live alone. In 2018, a total of 240 million people chose to live alone in China, with an annual growth of 0.53% per year. How will the growing number of people living alone affect the resources consumption and the ecological environment? Based on the data from 1998 to 2017 at provincial level, this paper selects domestic water and electricity consumption to represent resources consumption, and household garbage generation to represent ecological environment, taking the proportion of single-person households in the total households as the explanatory variable and age, education, and household appliances as the control variables. This paper aims to apply dynamic panel models to analyze the impact of solitary population on resources consumption (water and electricity resources as representatives) and on waste generation. The results show that (1) people living alone consume more resources and generate more garbage, while household waste is influenced most, followed by household electricity consumption and household water consumption, (2) positive relations between age and resources consumption and waste generation have been identified, and (3) the energy-saving technology of home appliances is conducive to resources conservation and emission reduction.

A Deep Learning-Based Intelligent Garbage Detection System Using an Unmanned Aerial Vehicle

A population explosion has resulted in garbage generation on a large scale. The process of proper and automatic garbage collection is a challenging and tedious task for developing countries. This paper proposes a deep learning-based intelligent garbage detection system using an Unmanned Aerial Vehicle (UAV). The main aim of this paper is to provide a low-cost, accurate and easy-to-use solution for handling the garbage effectively. It also helps municipal corporations to detect the garbage areas in remote locations automatically. This automation was derived using two Convolutional Neural Network (CNN) models and images of solid waste were captured by the drone. Both models were trained on the collected image dataset at different learning rates, optimizers and epochs. This research uses symmetry during the sampling of garbage images. Homogeneity regarding resizing of images is generated due to the application of symmetry to extract their characteristics. The performance of two CNN models was evaluated with the state-of-the-art models using different performance evaluation metrics such as precision, recall, F1-score, and accuracy. The CNN1 model achieved better performance for automatic solid waste detection with 94% accuracy.

Preparation and Characterization of Irradiated Bioplastic from Cassava Peel – A Review

The conventional plastic bears unavoidable responsibility for the massive scale generation of garbage. The bulk of the market is mostly made up of fossil-based plastics and the worldwide environmental pollution generated by them is growing increasingly problematic. Agro-waste product is probably a good way to make bioplastic. In fact, biodegradable plastic is indeed a good alternative to replace the petroleum-based plastics. Besides, making good use of agriculture waste can assist in diminishing waste accumulation. Cassava peel is a type of waste from cultivating activities that has the potential in making bio-based plastics. This research will focus on the preparation and characterization of bioplastic from cassava peel. Several related characterization methods such as XRD, SEM, FTIR, TGA will be discussed. The main purpose of this review is to fabricate and produce bioplastic samples from cassava peel with the addition of different ratios of sorbitol and chitosan.

COMMUNITY READINESS ON NON-ORGANIC WASTE MANAGEMENT OF HAMLET 3, KASOMALANG KULON VILLAGE, SUBANG DISTRICT, INDONESIA

Waste management is a systematic, comprehensive and sustainable activity that includes the reduction and handling of waste. In Kasomalang Kulon Village precisely in Hamlet 3, waste management has not yet been carried out properly. Community education is conducted to gain more awareness of people in handling the waste properly. The purpose of this study is to identify non-organic waste that still has an economic value so that it can be recycled and reduce the amount of waste that enters the waste disposal site. The research also aims to know the readiness of the community in managing waste, and determine the processing needed. Prior to designing the management system, a sampling was conducted using sampling method based on SNI 19-3964-1994 to identify quantity and type of wastes. The community readiness was investigated by distributing questionnaires following the community education. The survey shows that the solid waste generation is 0.193 kg/person/day for permanent houses and 0.182 kg/person/day for semi-permanent houses and the average weight of garbage generation per day is 68.80 kg. Non-organic waste data from Kasomalang Kulon Village which has an economic value of 32% of the total waste generation which is 3,761 L/d with potency of approximately IDR 425,000 per week. In general, the KAP survey shows that community has a high level of knowledge, positive attitudes and good behavior, which indicates the readiness of the community in managing non-organic waste.

A deep learning approach based hardware solution to categorise garbage in environment

Garbage detection and disposal have become one of the major hassles in urban planning. Due to population influx in urban areas, the rate of garbage generation has increased exponentially along with garbage diversity. In this paper, we propose a hardware solution for garbage segregation at the base level based on deep learning architecture. The proposed deep-learning-based hardware solution SmartBin can segregate the garbage into biodegradable and non-biodegradable using Image classification through a Convolutional Neural Network System Architecture using a Real-time embedded system. Garbage detection via image classification aims for quick and efficient categorization of garbage present in the bin. However, this is an arduous task as garbage can be of any dimension, object, color, texture, unlike object detection of a particular entity where images of objects of that entity do share some similar characteristics and traits. The objective of this work is to compare the performance of various pre-trained Convolution Neural Network namely AlexNet, ResNet, VGG-16, and InceptionNet for garbage classification and test their working along with hardware components (PiCam, raspberry pi, infrared sensors, etc.) used for garbage detection in the bin. The InceptionNet Neural Network showed the best performance measure for the proposed model with an accuracy of 98.15% and a loss of 0.10 for the training set while it was 96.23% and 0.13 for the validation set.

Operational Technical Study of Waste Management in The Region of Ponorogo Disctrict

Waste is still a problem in various countries, especially developing countries. The problem of waste in big cities is different from small and medium cities. The amount of waste tends to increase along with population growth and an increase in living standards is not accompanied by the availability of adequate infrastructure, especially in waste management operations. Ponorogo Regency is one of the small cities in the province of East Java that has failed for 4 consecutive years to win Adipura because of poor evaluation on aspects of waste management. This study aims to examine the availability of infrastructure, especially technical waste management operations in small cities with a case study in Ponorogo Regency. The results show that in 2026 the projected population of Ponorogo Regency is 147,519 people with a volume of garbage generation of 442,556 m3/day. The amount requires availability of 246 units of carts, 16 units of dump truck, 71 units of containers at the transfer station, as well as the expansion of Mrican landfills to 5.4 hectares.

Influencing Factors of Urban Residents’ Garbage Classification and Recycling Behavior Driving Mechanism in Artificial Intelligence Environment

With the rapid development of urbanization, urban domestic waste is also increasing, if the garbage cannot be effectively classified, there will be a dilemma of garbage siege. In recent years, artificial intelligence technology has been applied in the field of garbage classification, which provides a convenient means for garbage classification. However, technological innovation can only alleviate the accumulated total amount of garbage, and finding the source of garbage generation is the key to solve the problem. Therefore, based on the theory of planned behavior, A-B-C theory and interpersonal behavior theory, from the policy level, the social level and the individual level, this paper puts forward some suggestions, it is composed of government, society, individual and other organizations, and puts forward the corresponding research hypotheses. Through interviewing and questionnaire survey in Guangzhou, the sample data are collected, and then regression analysis and structural equation model analysis are used to obtain the influence degree value of residents’ attitude towards garbage classification, subjective norms, perceived behavior control, situational factors, and government legal regulation on residents’ living garbage classification willingness. The results show that residents’ attitude towards domestic waste classification is significant and the government’s legal control factors are the key factors affecting the residents’ willingness of garbage classification.

PLA Composites Reinforced with Flax and Jute Fibers-A Review of Recent Trends, Processing Parameters and Mechanical Properties.

Multiple environmental concerns such as garbage generation, accumulation in disposal systems and recyclability are powerful drivers for the use of many biodegradable materials. Due to the new uses and requests of plastic users, the consumption of biopolymers is increasing day by day. Polylactic Acid (PLA) being one of the most promising biopolymers and researched extensively, it is emerging as a substitute for petroleum-based polymers. Similarly, owing to both environmental and economic benefits, as well as to their technical features, natural fibers are arising as likely replacements to synthetic fibers to reinforce composites for numerous products. This work reviews the current state of the art of PLA compounds reinforced with two of the high strength natural fibers for this application: flax and jute. Flax fibers are the most valuable bast-type fibers and jute is a widely available plant at an economic price across the entire Asian continent. The physical and chemical treatments of the fibers and the production processing of the green composites are exposed before reporting the main achievements of these materials for structural applications. Detailed information is summarized to understand the advances throughout the last decade and to settle the basis of the next generation of flax/jute reinforced PLA composites (200 Maximum).