SWM: Smart waste management for green environment

Smart waste management and air pollution forecasting: Harnessing Internet of things and fully Elman neural network.

The effective management of municipal solid waste is a critical global issue, affecting both urban and rural areas. To address the growing volume of solid waste, proactive planning is essential. Traditionally, solid waste is often disposed of without segregation, preventing recycling and the recovery of raw materials. Proper waste segregation is a fundamental requirement for effective solid waste management, allowing materials to be recycled efficiently. Emerging technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) offer powerful tools for identifying recyclable materials like glass, plastic, and metal within solid waste. The primary goal of this research is to contribute to a cleaner environment, reduce infant mortality, improve maternal health, and support efforts to combat HIV/AIDS, malaria, and other diseases. This study introduces an intelligent and smart solid waste management system (iSSWMs) designed to smartly collect and segregate solid waste. The proposed system focuses on three types of materials: plastic, glass, and metal. The first phase involves waste collection using smart bins connected to a mobile application, which sends notifications when the bins are full. In the second phase, we develop a deep learning-based mechanical model to segregate the waste, using the VGG-19 model, which achieved a performance accuracy of 99.7% during training. To the best of our knowledge, iSSWMs is a promising framework that integrates both waste collection and segregation through the use of cutting-edge technologies, delivering high accuracy and efficiency.

The increasing global urbanization has necessitated innovative solutions for waste management, particularly in the context of sustainable urban environments. IoT-based smart waste management and automated recycling systems have emerged as transformative technologies, offering highly efficient and eco-friendly methods to address urban waste challenges. This chapter provides an in-depth exploration of the role of advanced technologies in waste sorting, recycling, and composting, emphasizing automation, IoT integration, and artificial intelligence (AI). The application of robotic sorting systems, machine learning, sensor technologies, and automated composting processes are highlighted, with a focus on their impact on operational efficiency, cost reduction, and environmental sustainability. Additionally, the chapter examines key challenges and opportunities associated with deploying these technologies in large-scale urban waste management systems, including regulatory considerations, data privacy issues, and the need for seamless system integration. The integration of IoT-enabled devices for real-time monitoring and automated control is presented as a cornerstone for enhancing system performance and reducing human intervention. This chapter also discusses the potential for integrating circular economy principles through the development of intelligent waste management frameworks that foster sustainable urban living. The combination of these technologies not only supports efficient waste disposal but also contributes to the advancement of urban sustainability goals.

Continuous urbanization and industrialization has to led to increase in volume and type of waste generated. It is estimated that in 2006 the total amount of municipal solid waste generated globally reached 2.02 billion tones, representing a 7% annual increase since 2003 (Global Waste Management Market Report 2007). This poses a problem for local and national governments to ensure sustainable and effective waste management. Technology always helps mankind in making life easier. In public places, proper disposal of waste is not being followed which causes overflow of waste in dustbins that has become a threat to the environment. Segregation, management, transport and disposal of waste plays an important role to minimize the risk to the public and environment. The economic value of waste is best realized when it is segregated. Currently there is no such system of segregation of dry, wet and metallic wastes at a household level. This paper is an innovative way to revolutionize the waste management system using sensors. Segregation is achieved using respective automated sensors and the level of dustbins are monitored simultaneously. Presently there's no such system for segregation of dry and wet wastes at domestic level or social unit level. This paper proposes an automated sensor based waste management and segregation system. It is designed to sort the refuse into 2 categories: wet and dry (metal).

No city can be called as smart without smart waste management system and in the very paper we have developed an integrated platform for waste management where dustbins are installed with a network of sensors and they transmit real-time data indicating the fill percentage of the bin. The purpose of this study is to develop an efficient waste management system based on Internet of things (IoT) for mobile application. We have observed traditional waste management system of India and have identified a number of issues with current systems. They are unhygienic landfills, overflowing waste bins, limited collection, lack of storage in collection vehicles, and less awareness of separated waste collection, recycling, and reuse. Therefore, this study has been proposed as a solution to cope up with the above issues. This study provides a platform for domestic users, drivers of collection vehicle, and garbage collection authority to interact with solid waste management of the country. The idea of our project was to basically alert the concerned authorities whenever the dustbin of a particular region is full and needs to be taken care of. Our objective is to cover all the dustbins of VIT University and install our smart system into those dustbins so that they could be emptied as soon as they get filled to avoid overflow of garbage and increasing the quality of the environment around the campus. We are making a prototype of the above-mentioned system.KeywordsInternet of things (IoT)Real timeUltrasonic sensorWaste management

Background: This requires substantial support and resources in chemical waste management and recycling, such as automated technology, chemical data management systems, advanced materials, etc. The growing need for efficiency and sustainability in waste management systems is leading industries to adopt IoT-based applications in monitoring and optimizing chemical waste disposal and recycling systems. For instance, IoT-based techniques are adding real-time tracking, automated segregation, and predictive analytics, which can help make waste management much more efficient. Despite their benefits, myriad challenges remain to the adoption of IoT-enabled systems, including high implementation costs, regulatory inconsistencies, technical expertise gaps, and data security concerns. Tackling these issues is essential to improving waste management practices, ensuring compliance with regulations, and promoting environmental sustainability. Aim: The current paper investigates the trends, problems, and future directions in chemical waste management and recycling optimization with IoT-based approaches. It specifically explores the knowledge, level of use, and perceived effectiveness of IoT-based waste management systems, where it also aims to determine whether there are barriers and factors that influence the widespread implementation of IoT-based waste management systems. The findings of the study will inform the industries, policymakers, and the environmental agencies to encourage or accelerate the adoption of the IoT in waste management and propel the sustainable waste disposed of solutions. Methodology: Based on structured survey interviews with 110 respondents, including representatives from chemical industries, waste management companies, environmental non-government organizations, government agencies, and academic researchers. The study employed a cross-sectional mixed-methods design, integrating both quantitative and qualitative data for a comprehensive evaluation of IoT implementation in chemical waste management. Descriptive and inferential statistical analyses were used to assess awareness levels, adoption rates, challenges, and perceived benefits of IoT-assisted waste management. In order to position the findings in the context of existing literature, a systematic literature review was also conducted focusing on smart waste tracking, hazardous waste management, and IoT use cases for sustainability. Findings: The paper provides important insights to characterize the status of IoT-enabled waste management by finding out the awareness level about the IoT-based waste management along with its adoption barriers. While 58% of the respondents were aware of IoT-enabled waste tracking, only 30% had implemented IoT within their organizations. According to the report, regulatory compliance requirements (50%), cost efficiency (45%), and sustainability goals (40%) were the primary factors driving IoT adoption in waste management. The most important barriers identified were high implementation costs (50%), lack of technical expertise (40%), and concerns around data privacy and security (35%). Respondents believed that 55% of respondents said government incentives like grants and standard regulatory frameworks (48%) would greatly increase adoption rates. Conclusion: But to facilitate broader adoption, we need to solve for the cost, technical and regulatory challenges. The results underscore the role of tailored financial motivations, supportive government laws, and sectoraliszied training initiatives in adopting the use of IoT in waste management. Further work can address cost-benefit analysis, case studies in paradigm shift in implementing IoT-based systems and frameworks on secure, scalable and smart waste management. Transforming chemical waste management into a smart and eco-friendly system will require collaboration across sectors, between industries, policymakers, and technology providers

Purpose This study aims to explore how digital platforms (DPs) contribute to value co-creation in municipal solid waste (MSW) management systems. Design/methodology/approach The present paper conducts an explorative analysis using single case study methodology. The case in question involves a DPs operating in Italy. Findings Empirical analysis shows that DPs help engage citizens in MSW and reduce the fragmentation in waste management systems by fulfilling a brokerage role that connects citizens, municipalities and waste management companies. The development of bidirectional knowledge and resource flow among actors contributes to better waste recycling processes, as well as fosters economic, environmental and social value co-creation in a complex public service. Research limitations/implications This research is limited to a single case study within the Italian context, which may influence the generalizability of the findings. Future research could expand the scope to include multiple case studies across different geographical regions. Practical implications For practitioners and policymakers, this paper underscores the strategic benefits of adopting DPs in MSW management systems and thereby improving public service delivery. Social implications The case analysis highlights that DPs can assist public actors in achieving numerous sustainable development goals by enhancing recycling rates and activating learning mechanisms among citizens. Originality/value This study contributes to literature by connecting different fields of research (i.e. waste management and public management) and using network theory to show how DPs can contribute to the economic, environmental and social sustainability of MSW while generating relevant benefits for the actors involved.

Rapid urbanization and population growth have intensified the challenges of waste management across the globe. Traditional waste management systems often lack the intelligence and efficiency to handle modern waste generation patterns. In response, the integration of big data analytics and machine learning (ML) technologies into waste management has emerged as a transformative approach. This paper explores how smart waste management systems utilize real-time data collection, predictive analytics, and intelligent decision-making to enhance waste collection, reduce operational costs, and support environmental sustainability. The study highlights various applications of ML, including waste sorting, route optimization, and predictive maintenance, supported by big data platforms. It further discusses the challenges, such as data privacy, interoperability, and infrastructure limitations, and offers future directions for research and implementation. By leveraging digital intelligence, smart waste management represents a vital step toward achieving cleaner, smarter, and more sustainable cities.

Developments in Municipal Solid Waste Management (MSWM) systems depend on socio-cultural, political, economic, and environmental issues. The ability to assess and evaluate the level and nature of these relationships plays a critical role in measuring the performance level vis-à-vis sustainability of waste management systems. The current criteria adopted for assessing waste management performance do not capture an accurate and comprehensive representation of MSWM scenarios from collection to disposal. Hence the methodologies do not accurately measure the level of impacts resulting from waste management systems or, indirectly, the environmental, economic, social, and cultural costs of MSWM. This research develops a waste management performance assessment protocol that captures all relevant components of waste management processes. The methodology is based on emission as the indicator of choice for assessing the performance of a city-wide or regional waste management system. The study developed a framework, Comprehensive Emission Quantification Model (CEQ-Model), for measuring the sustainability of the MSWM system hinged on process-based emission modelling. This framework incorporated aspects of the Intergovernmental Panel on Climate Change (IPCC) Guidelines and Life Cycle Thinking (LCT), framed using carefully selected MSWM scenarios in New Zealand. In a ranked cumulative emission quantification assessment of four Territorial Authorities (TA), the research determined the performance level of each TA relative to others. Auckland Council, with a total emission of 4.272x1012 KgCO2-e generated from disposing of 2,425,022 tonnes of waste, was the most unsustainable territory, followed by Rotorua District with total emission of 6.666x10 10 KgCO2-e from 45,000 tonnes of waste, and Waikato District with emission of 5.380x10 10 KgCO2-e from 29,796 tonnes. In comparison, the most sustainable TA is Opotiki District with total emission of 2.314x10 9 KgCO2-e from 1,450 tonnes of waste, giving emission per tonne of waste per year as 1.762 GgCO2-e, 1.806 GgCO2-e, 1.6 GgCO2-e, and 1.481 GgCO2-e for Auckland Council, Waikato District, Opotiki District, and Rotorua District respectively.

Waste management is a relevant challenge for cities around the world to ensure citizens' life quality, and to significantly contribute to environmental protection.Hence, inefficiencies in waste management thwarts the sustainable development of cities. Smart waste management can help improving the waste management services of cities by pursuing strategic goals, including waste reduction, waste collection and transport optimization, and waste treatment optimization.However, current research insufficiently addresses the contribution of information systems used in the context of smart waste management to sustainable development.Based on a literature review, we show how different systems in the smart waste management context contribute to strategic goals and, thereby, to sustainable development.We call for more research on smart waste management that accounts for the information systems' contribution to sustainable development.

Background: Healthcare waste management (HCWM) system in healthcare facilities is essential in dealing with the spread of infectious diseases, especially during an outbreak period such as the COVID-19 pandemic. The study assessed health care waste management situation in selected healthcare facilities in the greater Accra and Ashanti regions of Ghana during the COVID-19 pandemic.Methods: This was a multi-facility-based cross-sectional study that used a monitoring tool of the health facilities regulatory agency of Ghana to collect information on health care waste management practices at the peak of the COVID-19 pandemic. Data was gathered from 501 healthcare facilities in the greater Accra (335) and Ashanti (151) regions. Descriptive, Chi-square and multiple logistic regression were performed. All statistical analyses were considered significant at an alpha level of 0.05.Results: Less than half (45.7%) of the health facilities were assessed as having HCWM systems with majority (54.3%) having effective infectious waste management system. The health facilities in the greater Accra region (38.8%) and Ashanti region (60.9%) were categorized as poor on healthcare waste management system. The assessment levels of governance/leadership, management, quality assurance system, human resource, infection prevention and control equipment and water management were all significantly associated with the adherence to good HCWM systems.Conclusions: Health facilities were assessed as having good healthcare waste management systems, especially in the greater Accra region compared to the Ashanti, however treatment and safe disposal should be improved.

Improper disposal of solid waste that impacts human health and pollutes the environment, arising a need for successful and necessary collection of waste materials. However, most trash bins placed in cities can be seen overflowing due to traditional or inefficient waste management approaches. Therefore, a real-time remote monitoring system is needed to alert the level of garbage in bins to the relevant authority for immediate waste clearance. This paper presents the development and validation of a self-powered, simple connect, IoT solution to monitor the unfilled level of trash bins from a central monitoring station. The end sensor nodes of the developed IoT system are called Bin Level Monitoring Unit (BLMU) which are installed in every trash bin where the unfilled level needs to be monitored. Every BLMU measures the unfilled level of the trash bins and transmits it to a wireless access point unit (WAPU). Each WAPU receives the unfilled level data from several BLMUs and uploads it to the central server for storage and analysis. The waste collection authority can view and analyze the unfilled level of each bin using a smart graphical user interface. The following important experiments were carried out to validate the developed system: (1) the developed bin level monitoring system was tested by filling a trash bin with solid waste at various levels, and the corresponding unfilled level of the trash bin was monitored using the smart graphical user interface. (2) The life expectancy of the BLMU was evaluated as approximately 434 days. (3) The maximum transmission distance between a BLMU and a WAPU is 119 m. (4) The cost of a developed trash bin is 107 USD. Based on the results achieved, the developed trash bins can be suitable for smart cities.

Urban waste management poses significant challenges due to the growing volume and complexity of waste generated in cities. Effective classification of waste into degradable and non-degradable categories is critical for optimizing waste disposal, promoting recycling, and mitigating environmental impacts. This report presents a comprehensive exploration of an adaptive machine learning algorithm for image-based waste classification, designed to enhance municipal waste management systems. The proposed approach integrates advanced image processing techniques with machine learning models, such as Convolutional Neural Networks (CNNs) and their efficient variants, to classify waste with high accuracy. A citizen reporting interface allows users to capture and upload waste images, which are processed in real-time by a centralized system. The adaptive algorithm incorporates preprocessing steps, feature extraction, and model inference to deliver actionable insights, enabling municipal authorities to deploy appropriate waste collection mechanisms promptly. Through a detailed literature review, the report examines existing methodologies, benchmarks their performance, and identifies key challenges, including data quality, scalability, and integration with urban infrastructure. Case studies from global implementations illustrate the transformative potential of AI-driven waste management systems. Furthermore, the report outlines future directions, such as leveraging advanced deep learning architectures, real-time deployment using edge computing, and expanding classification to include hazardous and recyclable waste categories. This work contributes to the growing field of smart waste management by demonstrating how machine learning can address critical urban challenges. By fostering collaboration between technology and sustainability, the proposed framework lays the foundation for cleaner and more sustainable cities.

The paper presents advanced computational solutions for selected sectors in the context of the optimization of technology processes as an innovation and progress in improving energy efficiency of smart cities. The main emphasis was placed on the sectors of critical urban infrastructure, including in particular the use of algorithmic models based on artificial intelligence implemented in supervisory control systems (SCADA-type, including Virtual SCADA) of technological processes involving the sewage treatment systems (including in particular wastewater treatment systems) and waste management systems. The novelty of the presented solution involves the use of predictive diagnostic tools, based on multi-threaded polymorphic models supporting decision making processes during the control of a complex technological process and objects of distributed network systems (smart water grid, smart sewage system, smart waste management system) and solving problems of optimal control for smart dynamic objects with logical representation of knowledge about the process, the control object and the control itself, for which the learning process consists of successive validation and updating of knowledge and the use of the results of this updating to make control decisions. The advantage of the proposed solution in relation to the existing ones lies in the use of advanced models of predictive diagnostics, validation and reconstruction of data, implemented in functional tools, allowing the stabilization of the work of technological objects through the use of FTC technology (fault tolerant control) and soft sensors, predictive measurement path diagnostics (sensors, transducers), validation and reconstruction of measurement data from sensors in the measuring paths in real time. The dedicated tools (Intelligent Real Time Diagnostic System − iRTDS) built into the system of a hierarchical, multi-threaded control optimizing system of SCADA system allow to obtain advanced diagnostics of technological processes in real time using HPC technology. In effect of the application of the proprietary iRTDS tool, we obtain a significant rise of energy efficiency of technological processes in key sectors of the economy, which in global terms, e.g., urban agglomeration, increases the economic efficiency.

Waste segregation is the process of sorting and separating different types of waste to permit recycling and proper disposal. Technology businesses are attempting to address the garbage problem from a variety of angles, including improving recycling systems and developing novel materials for compostable single-use items. However, many people today do not adequately separate their waste, particularly in congested areas where people and transactions move at a rapid pace, such as malls, airports, local supermarkets, etc. The researchers propose a project termed A.T.O.M.S., or Automated Trash Organizing Management System, which is a Multi-sensorial Self Segregating Trash Bin that will be made up of several electronic components, sensors, and computer languages, and automatically classifies the waste being disposed of into specific classifications. As part of this ongoing study, the initial testing of the prototype manages to perform its initial function by detecting the type of trash and segregating it accordingly and appropriately. The results will positively impact society since the waste management system is improved. Recommendations to modify the dimensions of some parts of the device and usage of alternative power were made. The implication of this research project is the contribution of innovative solutions toward waste management process enhancement. Keywords: Waste management, segregation, innovation, experimental research.