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Papers
61,005 resultsShowing papers similar to Development of a Decision Support System Prototype for Time and Cost Reduction in Collecting Recyclable Waste
ClearAI Techniques Aid for Optimizing the Collection System of Industrial Plastic Waste
This study applied artificial intelligence techniques to optimize collection routes and predict demand for industrial plastic waste pickup. AI methods outperformed traditional statistical approaches in accuracy and route efficiency. Smarter collection systems could significantly reduce costs and improve recovery rates for industrial plastic waste.
Leveraging ICT in Karachi’s Solid Waste Management System by Involving Waste Pickers, Recyclers, and Community—An Integrated Approach
This study proposes integrating informal waste pickers and recyclers into Karachi's formal solid waste management system using information and communication technology (ICT). Better waste collection infrastructure, including for plastic waste, could reduce the amount of plastic that ends up in rivers and eventually the ocean.
Informal recycling sector contribution to plastic pollution mitigation: A systematic scoping review and quantitative analysis of prevalence and productivity
This systematic review quantifies the role of informal waste pickers in reducing plastic pollution worldwide. The findings highlight that these workers prevent significant amounts of plastic from entering the environment, making their contributions essential to global efforts to reduce the microplastic contamination that ultimately affects human health.
Optimizing plastics recycling networks
Researchers developed mathematical optimization models — including linear programming tools — to help plan efficient plastic recycling networks that can tolerate some contamination from mixed plastic waste streams. These models could help overcome a key barrier to large-scale recycling by intelligently matching waste sources with the plants best equipped to handle them.
Challenges to Increase Plastic Sorting Efficiency
This paper reviews the current challenges and future opportunities for improving plastic sorting rates in waste management systems. Higher sorting rates are essential for increasing plastic recycling and reducing the amount of plastic that ends up fragmented into microplastics in the environment.
Enhancing Waste Management with a Deep Learning-based Automatic Garbage Classifier
This paper is not about microplastics; it presents a deep learning convolutional neural network system for automatically classifying garbage by material type to improve waste sorting efficiency and reduce the labor burden of manual waste management.
Socio-Economic Contributions of Informal Waste Pickers to Urban Waste Management
This study examines the socio-economic contributions of informal waste pickers to urban waste management systems, finding they prevent significant recyclable materials from reaching landfills, reduce greenhouse gas emissions, and sustain livelihoods for millions of urban poor. The authors argue for formalization pathways that integrate waste pickers into municipal systems while preserving their economic autonomy.
Decision Analysis Approaches on the Collection Methods of Polyethylene Terephthalate Waste
This study compared three decision-analysis methods (AHP, TOPSIS, and VIKOR) to rank PET plastic bottle cap collection strategies and found that threshold-based collection (collecting caps only above a weight or volume threshold) is the most cost-effective and practical approach. Although this paper focuses on PET waste management strategy rather than microplastic contamination directly, improving PET collection efficiency is an important upstream step for reducing the plastic debris that ultimately fragments into microplastics in the environment.
Reduction of cost and emissions by using recycling and waste management system
An optimization model for integrated waste management systems was developed with dual objectives of minimizing cost and greenhouse gas emissions, demonstrating that simultaneous economic and environmental goals can be achieved through system-level design. The model provides a tool for sustainable waste management planning.
A Machine Arm to Assist in Trash Sorting using machine Learning and Object Detection
Not relevant to microplastics — this paper describes a robotic arm system that uses machine learning and computer vision to sort recyclable waste materials, focused on automation of waste sorting processes.
A Reliable and Robust Deep Learning Model for Effective Recyclable Waste Classification
Researchers developed a deep learning computer model that can sort waste into six categories, including plastic, with 95% accuracy. While this is a waste management technology rather than a health study, better automated waste sorting could help keep more plastics out of the environment where they break down into microplastics. Improved recycling through AI-powered sorting is one practical step toward reducing the microplastic pollution that eventually reaches people.
Developing Traceability Systems for Effective Circular Economy of Plastic: A Systematic Review and Meta-Analysis
This meta-analysis pools data from multiple studies on traceability systems that track plastic through its lifecycle, from production to recycling. Better tracking of plastics could reduce waste and microplastic pollution by improving recycling quality and keeping more plastic out of the environment.
Artificial intelligence for waste management in smart cities: a review
Researchers reviewed how artificial intelligence (AI) is being applied to nearly every aspect of waste management, from sorting recyclables with up to 99.95% accuracy to cutting transportation costs by over 36%. Their findings show AI could dramatically improve how cities handle plastic and other waste, reducing pollution and public health burdens.
Increasing the Efficiency of Recoveryand Finalization of Plastic Wastein a Company
This study evaluated and proposed improvements to plastic waste recovery and finishing processes in a manufacturing company, using continuous improvement methodologies. Efficient plastic waste recovery in manufacturing reduces both costs and the environmental release of plastic material that can eventually become microplastic pollution.
An Automatic Garbage Classification System Based on Deep Learning
Researchers developed an automated garbage classification system using a deep learning algorithm based on ResNet-34, achieving 99% classification accuracy with a processing time of under one second per item. Automated waste sorting technology like this could improve the efficiency of plastic waste recovery and reduce mismanaged plastic that eventually becomes environmental pollution.
Source separation, transportation, pretreatment, and valorization of municipal solid waste: a critical review
Researchers reviewed the full chain of municipal solid waste management — from source separation through collection, pretreatment, and valorization — finding that AI and the Internet of Things are emerging as powerful tools for optimizing collection routing and sorting efficiency within circular waste management systems.
Analyzing Productivity and Behavior of Plastic Drop-Off Points: A Case Study of Send Plastic Home Project in Plastic Waste Recycling during COVID-19 Outbreak
This study evaluated the productivity of plastic drop-off collection points in Bangkok during the COVID-19 pandemic, finding that visibility and convenience are key factors affecting participation rates. Effective plastic collection infrastructure is important for keeping plastic waste out of the environment.
Global Plastic Pollution and Informal Waste Pickers
This review examines how plastic pollution disproportionately affects communities in the Global South, where plastic consumption has risen sharply while waste management infrastructure remains underfunded or nonexistent. The study highlights the role of approximately 20 million informal waste workers who already recover and recycle significant quantities of plastic but often work under hazardous conditions without recognition or support. Evidence indicates that integrating these informal workers into formal waste management systems could be an effective strategy for reducing plastic pollution while improving livelihoods.
Particle Swarm Optimization Based Efficient Path Planning in Autonomous Marine Trash Collection
Researchers developed a marine trash-collecting robot guided by Particle Swarm Optimization (PSO) and GPS, which uses a conveyor-based collection mechanism and sensor input to navigate waterways and efficiently collect floating plastic debris.
Exploring the Plastic Collection and Recycling Trends in Sri Lanka
This paper is not about microplastics; it surveys plastic waste collection and recycling infrastructure in Sri Lanka, documenting correlations between tourism, rainfall, and recycling rates for different polymer types.
Sustainable Plastic Waste Management Using a System Dynamics Approach
This study used system dynamics modeling to analyze municipal solid plastic waste management, simulating how different policy interventions affect waste generation, recycling, and environmental leakage over time. Understanding the dynamics of plastic waste systems helps identify the most effective points for intervention to reduce microplastic pollution.
Improvement of Human and Environmental Health Through Waste Management in Antigua and Barbuda
This paper described community-led waste management improvements in Antigua and Barbuda that contributed to biodiversity conservation and reduced land-based pollution. Reducing solid waste and improving waste sorting directly decreases the plastic that eventually fragments into microplastics in coastal and marine environments.
The Analyzing of Social Economic Impacted By Optimalization Of Recycling Waste As Supported For Circular Economy On Community-Based Tourism In Pasaran Island
Researchers investigated how waste recycling initiatives on Pasaran Island, Indonesia, created alternative livelihoods — particularly for women — finding that optimizing organic and plastic waste management generated approximately 50% profit margins and contributed to improved social and environmental conditions.
Efficient algorithmic coupling technique for precision recycling of seven types of mixed plastic waste
Researchers developed a two-step machine learning coupling technique combining Linear Support Vector Classification (Linear-SVC) with a Multi-layer Perceptron (MLP) to improve the precision of sorting seven types of mixed plastic waste. The coupling technique raised overall plastic identification accuracy from 94.7% to 97.7% and substantially improved classification accuracy for HDPE and LDPE from 79-94%, while also reducing computation time compared to the single-step MLP approach.