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Papers
61,005 resultsShowing papers similar to Source separation, transportation, pretreatment, and valorization of municipal solid waste: a critical review
ClearArtificial 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.
A Systematic Review of Solid Waste Management (SWM) and Artificial Intelligence approach
This systematic review found that artificial intelligence and machine learning are increasingly being applied to solid waste management for tasks like waste classification, collection route optimization, and landfill monitoring. AI-based approaches showed significant improvements over traditional methods in sorting accuracy and operational efficiency.
Nanotechnology and AI Impact on Waste Management
This review examines how artificial intelligence and nanotechnology are being combined to transform solid waste management, offering more efficient and sustainable approaches to one of the world's most pressing environmental and public health challenges.
AI 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.
Autonomous Waste Classification Using Multi-Agent Systems and Blockchain: A Low-Cost Intelligent Approach
This study developed and tested a prototype smart waste bin using multi-agent systems and blockchain to automatically sort waste (organic, plastic, paper) in real time, demonstrating a low-cost approach for intelligent municipal waste management.
AI-based wastewater treatment for a circular economy and sustainable management of PFAS, heavy metals, microplastics, and antibiotics
This review examined how artificial intelligence can be integrated into wastewater treatment systems to improve removal of emerging contaminants including PFAS, heavy metals, microplastics, and antibiotics. The authors conclude that AI-driven optimization offers significant potential for a circular economy approach to water treatment.
The Use of Artificial Intelligence and Machine Learning in Creating a Roadmap Towards a Circular Economy for Plastics
This paper examines how artificial intelligence and machine learning can help transition the plastics industry toward a circular economy. AI tools can optimize recycling processes, predict material degradation, and identify opportunities to reduce plastic waste before it enters the environment.
Leveraging Municipal Solid Waste Management with Plasma Pyrolysis and IoT: Strategies for Energy Byproducts and Resource Recovery
This review examines how plasma pyrolysis technology, combined with Internet of Things monitoring, can improve the treatment of municipal solid waste that contains hazardous materials including microplastics. The approach converts waste into valuable energy products like syngas and bio-oil while significantly reducing waste volume. The integration of real-time sensor data and machine learning could optimize operational conditions and improve treatment efficiency.
A hybrid machine learning-mathematical programming optimization approach for municipal solid waste management during the pandemic
Researchers combined machine learning forecasting with mathematical supply-chain optimization to model municipal solid waste management in New York City under COVID-19 conditions, revealing trade-offs between economic efficiency and landfill diversion that could inform planning for future pandemic scenarios.
AI Applied to the Circular Economy: An Approach in the Wastewater Sector
This review explored how artificial intelligence can be applied to circular economy objectives in the wastewater sector, examining AI-driven approaches for optimizing water treatment, resource recovery, and system efficiency. The paper identified opportunities and challenges for integrating machine learning into water utility operations within an ecological transition framework.
A Smart Garbage Classification based on Deep Learning
Researchers developed an AI-powered garbage classification system using deep learning to automatically sort waste categories. Accurate automated waste sorting could improve plastic recycling rates, reducing the amount of plastic that eventually breaks down into environmental microplastics.
Application of AI-Enabled Computer Vision Technology for Segregation of Industrial Plastic Wastes
Researchers developed an AI-powered computer vision system to segregate mixed industrial plastic wastes by polymer type, addressing a key barrier to effective plastic recycling. The system achieved high classification accuracy across common plastic categories, demonstrating that machine vision can improve sorting efficiency and recycled plastic quality.
Municipal solid waste management challenges in developing regions: A comprehensive review and future perspectives for Asia and Africa
Researchers reviewed a decade of municipal solid waste challenges in developing countries across Asia and Africa, finding that inadequate infrastructure, cultural barriers, and poor policy enforcement are driving waste crises. The study recommends source-level sorting, improved landfill practices, and stronger community involvement rather than focusing solely on recycling.
GRUBin: Time-Series Forecasting-Based Efficient Garbage Monitoring and Management System for Smart Cities
Researchers developed GRUBin, a smart waste monitoring system using time-series forecasting with GRU neural networks to predict bin fill levels and optimize collection schedules, outperforming IoT-only approaches in reducing unnecessary waste collection trips in smart city environments.
Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management
This review explores how artificial intelligence and Internet of Things sensors can be used to detect and monitor environmental pollutants, including microplastics, in air, water, and soil. Machine learning methods show promise for improving pollution tracking and prediction, but challenges remain around data sharing and model reliability. Advanced monitoring technology could play a key role in identifying and managing microplastic contamination in the environment.
Smart Bin and IoT: A Sustainable Future for Waste Management System in Nigeria
Researchers proposed a smart waste bin system using Internet of Things technology to improve waste management in Nigerian cities. The system uses sensors and Wi-Fi connectivity to monitor bin fill levels remotely, enabling more efficient waste collection routes. The study highlights how affordable IoT-based solutions could help developing nations reduce plastic waste accumulation and environmental pollution.
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.
Machine learning and circular bioeconomy: Building new resource efficiency from diverse waste streams
Researchers reviewed how machine learning is being applied across four key biorefinery systems — composting, fermentation, anaerobic digestion, and thermochemical conversion — finding that AI-based models consistently outperform traditional mechanistic approaches in handling the high-dimensional complexity of circular bioeconomy processes.
An Overview of the Latest Developments and Potential Paths for Artificial Intelligence in Wastewater Treatment Systems
This review surveys recent developments in applying artificial intelligence to wastewater treatment, including models for water quality prediction, process optimization, and contaminant removal. The study covers neural networks, support vector machines, decision trees, and deep learning approaches used to improve efficiency and reduce costs. The authors highlight that AI-driven methods show promise for optimizing the removal of emerging contaminants, including microplastics, from wastewater systems.
Plastic waste recycling: existing Indian scenario and future opportunities
Researchers mapped plastic waste recycling infrastructure across Indian states, finding that PP and PE are most commonly reprocessed, and outlined key opportunities in mechanical recycling, chemical recycling, and waste-to-energy approaches—along with AI and blockchain tools—needed to build a circular plastic economy in India.
Global Plastic Waste Management: Analyzing Trends, Economic and Social Implications, and Predictive Modeling Using Artificial Intelligence
This study analyzed global plastic waste management practices and used artificial intelligence models to predict future waste trends. The researchers found that current waste management systems are struggling to keep up with rising plastic production, posing threats to ecosystems, human health, and the economy. The AI models help forecast where waste generation is headed, which could inform better policy decisions.
Design and Fabrication of Material Separation Machine for Sustainable Development
This paper is not relevant to microplastics research — it describes the design and fabrication of a robotic material separation machine intended to sort recyclable waste more efficiently using AI-inspired engineering principles.
COVID-19 and waste management in Indian scenario: challenges and possible solutions
Researchers review how COVID-19 dramatically amplified India's already-strained biomedical waste management challenges, warning that improper disposal of pandemic-associated waste risks food chain contamination and a secondary 'waste disaster,' and calling for automated, mechanized waste management systems to handle current and future health emergencies.
Sensor-based and Robot Sorting Processes and their Role in Achieving European Recycling Goals - A Review
This review covers sensor-based and robotic sorting technologies for waste management, assessing how they can help achieve European recycling targets. Improved sorting is critical for increasing plastic recycling rates and reducing the amount of plastic that enters the environment as pollution.