Artificial intelligence applications for sustainable solid waste management practices in Australia: A systematic review

Developing environmental, social and governance (ESG) strategies on evaluation of municipal waste disposal centers: A case of Mexico

Waste disposal systems are crucial components of environmental management, and focusing on this sector can contribute to the development of various other sectors and improve social welfare. Urban waste is no longer solely an environmental issue; it now plays a significant role in the economy, energy, and value creation, with waste disposal centers (WDCs) being a key manifestation. The purpose of this study is to measure the performance of WDCs in the state of Nuevo León, Mexico, with the aim of developing environmental, social, and governance (ESG) strategies to strengthen and prepare the WDCs for the industrial developments in this state. By identifying environmental variables and undesirable factors, the efficiency and managerial capacity of 32 WDCs were assessed. The analysis revealed that 9 out of the 32 WDCs are technically efficient, while the remaining 23 require significant improvements. Using the Data Envelopment Analysis (DEA) technique, an average efficiency score of 0.91 was found, with a standard deviation of 0.08. The managerial capacity analysis indicated that the highest-ranked WDC achieved an efficiency score of 1, whereas the lowest-ranked WDC scored 0.67. Finally, an operational map of development strategies was developed using the Interpretive Structural Modeling (ISM) and Matrix Impact Cross-Reference Multiplication Applied to a Classification (MICMAC) approach. The results indicate that four phases of development should be followed for real development and maturity of development in these WDCs, including Groundwork, Structuring, Development and Growth, and Smart Maturity.

Municipal solid waste management challenges in developing regions: A comprehensive review and future perspectives for Asia and Africa

The rapid urbanization witnessed in developing countries in Asia and Africa has led to a substantial increase in municipal solid waste (MSW) generation. However, the corresponding disposal strategies, along with constraints in land resources and finances, compounded by unorganized public behaviour, have resulted in ineffective policy implementation and monitoring. This lack of systematic and targeted orientation, combined with blind mapping, has led to inefficient development in many areas. This review examines the key challenges of MSW management in developing countries in Asia and Africa from 2013 to 2023, drawing insights from 170 academic papers. Rather than solely focusing on recycling, the study proposes waste sorting at the source, optimization of landfill practices, thermal treatment measures, and strategies to capitalize on the value of waste as more pertinent solutions aligned with local realities. Barriers to optimizing management systems arise from socio-economic factors, infrastructural limitations, and cultural considerations. The review emphasizes the importance of integrating the study area into the circular economy framework, with a focus on enhancing citizen participation in solid waste reduction and promoting recycling initiatives, along with seeking economic assistance from international organizations.

Living review framework for better policy design and management of hazardous waste in Australia

The significant increase in hazardous waste generation in Australia has led to the discussion over the incorporation of artificial intelligence into the hazardous waste management system. Recent studies explored the potential applications of artificial intelligence in various processes of managing waste. However, no study has examined the use of text mining in the hazardous waste management sector for the purpose of informing policymakers. This study developed a living review framework which applied supervised text classification and text mining techniques to extract knowledge using the domain literature data between 2022 and 2023. The framework employed statistical classification models trained using iterative training and the best model XGBoost achieved an F1 score of 0.87. Using a small set of 126 manually labelled global articles, XGBoost automatically predicted the labels of 678 Australian articles with high confidence. Then, keyword extraction and unsupervised topic modelling with Latent Dirichlet Allocation (LDA) were performed. Results indicated that there were 2 main research themes in Australian literature: (1) the key waste streams and (2) the resource recovery and recycling of waste. The implication of this framework would benefit the policymakers, researchers, and hazardous waste management organisations by serving as a real time guideline of the current key waste streams and research themes in the literature which allow robust knowledge to be applied to waste management and highlight where the gap in research remains.

Agro-industrial waste management employing benefits of artificial intelligence

By 2050, the world's population is predicted to reach over 9 billion, which requires 70% increased production in agriculture and food industries to meet demand. This presents a significant challenge for the agri-food sector in all aspects. Agro-industrial wastes are rich in bioactive substances and other medicinal properties. They can be used as a different source for manufacturing products like biogas, biofuels, mushrooms, and tempeh, the primary ingredients in various studies and businesses. Increased importance is placed on resource recovery, recycling, and reusing (RRR) any waste using advanced technology like IoT and artificial intelligence. AI algorithms offer alternate, creative methods for managing agro-industrial waste management (AIWM). There are contradictions and a need to understand how AI technologies work regarding their application to AIWM. This research studies the application of AI-based technology for the various areas of AIWM. The current work aims to discover AI-based models for forecasting the generation and recycling of AIWM waste. Research shows that agro-industrial waste generation has increased worldwide. Infrastructure needs to be upgraded and improved by adapting AI technology to maintain a balance between socioeconomic structures. The study focused on AI's social and economic impacts and the benefits, challenges, and future work in AIWM. The present research will increase recycling and reproduction with a balance of cost, efficiency, and human resources consumption in agro-industrial waste management.

Volume Determination Challenges in Waste Sorting Facilities: Observations and Strategies

In this case study on volume determination in waste sorting facilities, we evaluate the effectiveness of ultrasonic sensors and address waste-material-specific challenges. Although ultrasonic sensors offer a cost-effective automation solution, their accuracy is affected by irregular waste shapes, varied compositions, and environmental factors. Notable inconsistencies in volume measurements between storage bunkers and conveyor belts underscore the need for a comprehensive approach to standardize bale production. With prediction reliability being constrained by limited datasets, undocumented modifications to machine settings, and sensor failures, this task renders a challenging application area for machine learning. We explore related research and present dataset analyses from three distinct waste sorting facilities in Europe, addressing issues such as sensor usability, data quality, and material specifics. Our analysis suggests promising strategies and future directions for enhancing waste volume measurement accuracy, ultimately aiming to advance sustainable waste management.

Tackling municipal solid waste crisis in India: Insights into cutting-edge technologies and risk assessment

Municipal Solid Waste (MSW) management is a pressing global concern, with increasing interest in Waste-to-Energy Technologies (WTE-T) to divert waste from landfills. However, WTE-T adoption is hindered by financial uncertainties. The economic benefits of MSW treatment and energy generation must be balanced against environmental impact. Integrating cutting-edge technologies like Artificial Intelligence (AI) can enhance MSW management strategies and facilitate WTE-T adoption. This review paper explores waste classification, generation, and disposal methods, emphasizing public awareness to reduce waste. It discusses AI's role in waste management, including route optimization, waste composition forecasting, and process parameter optimization for energy generation. Various energy production techniques from MSW, such as high-solids anaerobic digestion, torrefaction, plasma pyrolysis, incineration, gasification, biodegradation, and hydrothermal carbonization, are examined for their advantages and challenges. The paper emphasizes risk assessment in MSW management, covering chemical, mechanical, biological, and health-related risks, aiming to identify and mitigate potential adverse effects. Electronic waste (E-waste) impact on human health and the environment is thoroughly discussed, highlighting the release of hazardous substances and their contribution to air, soil, and water pollution. The paper advocates for circular economy (CE) principles and waste-to-energy solutions to achieve sustainable waste management. It also addresses complexities and constraints faced by developing nations and proposes strategies to overcome them. In conclusion, this comprehensive review underscores the importance of risk assessment, the potential of AI and waste-to-energy solutions, and the need for sustainable waste management to safeguard public health and the environment.

Solid waste management in the context of the waste hierarchy and circular economy frameworks: An international critical review

Growing populations and consumption drive the challenges of solid waste management (SWM); globalization of transport, food production, and trade, including waste trading, distributes risks worldwide. Using waste hierarchy (WH; reduce, reuse, and recycle) and circular economy (CE) concepts, we updated a conceptual waste framework used by international organizations to evaluate SWM practices. We identified the key steps and the important factors, as well as stakeholders, which are essential features for effective SWM. Within this updated conceptual framework, we qualitatively evaluated global SWM strategies and practices, identifying opportunities, barriers, and best practices. We find that, although a few exceptional countries exhibit zero-waste compliance, most fare poorly, as exhibited by the high waste generation, incineration, and disposal (open dumping, landfilling) volumes. In the Global North, SWM strategies and practices rely heavily on technologies, economic tools, regulatory frameworks, education, and social engagement to raise stakeholder awareness and enhance inclusion and participation; in the Global South, however, many governments take sole legal responsibility for SWM, seeking to eliminate waste as a public "nuisance." Separation and recycling in the Global South are implemented mainly by "informal" economies in which subsistence needs drive recyclable material retrieval. Imported, regionally inappropriate tools, economic constraints, weak policies and governance, waste trading, noninclusive stakeholder participation, data limitations, and limited public awareness continue to pose major waste and environmental management challenges across nations. In the context of the framework, we conclude that best practices from around the world can be used to guide decision-making, globally. Despite variations in drivers and needs across regions, nations in both the Global North and South need to improve WH and CE compliance, and enhance stakeholder partnership, awareness, and participation throughout the SWM process. Partnerships between the Global North and South could better manage traded wastes, reduce adverse impacts, and enhance global environmental sustainability and equity, supporting UN Sustainable Development Goals. Integr Environ Assess Manag 2024;20:9-35. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Recent applications of AI to environmental disciplines: A review

The rapid development and efficiency of Artificial Intelligence (AI) tools have made them increasingly popular in various fields and research domains. The environmental discipline is now experiencing an exponential interest in harnessing the potential of AI over the past decade. We have reviewed the latest applications of AI tools in the environmental disciplines, highlighting the opportunities they present and discussing their advantages and disadvantages in this field. After the emergence of deep learning algorithms in 2010, interest in using AI tools for environmental tasks has grown exponentially. Among the studied articles, over 65 % of environmental tasks that demonstrate interest in using AI tools initially relied on conventional statistical and mathematical models. Using AI tools can greatly benefit the areas of environmental science and engineering. One of the main advantages of utilizing AI tools is their ability to analyze and process large amounts of data efficiently. Recently, the European Union established a European supercomputing ecosystem program to advance science and enhance the quality of life for its citizens. Nine of these projects prioritize environmental and sustainable goals. Despite the benefits of AI, it is still in its early stages of development, which comes with environmental concerns. The amount of power consumed and the time required to train an AI model can greatly affect the carbon emissions it produces, exacerbating the challenges posed by climate change. Efforts are currently underway to develop AI technology that is environmentally sustainable, minimizes energy consumption, and has a low carbon footprint. Selecting the appropriate AI model architecture can reduce energy consumption by almost 90 %. The main finding suggests that collaboration between environmental and AI professionals becomes crucial in leveraging the full potential of AI in addressing pressing environmental challenges.

A comprehensive review of micro- and nano-plastics in the atmosphere: Occurrence, fate, toxicity, and strategies for risk reduction

Micro- and nano-plastics (MNPs) have received considerable attention over the past 10 years due to their environmental prevalence and potential toxic effects. With the increase in global plastic production and disposal, MNP pollution has become a topic of emerging concern. In this review, we describe MNPs in the atmospheric environment, and potential toxicological effects of exposure to MNPs. Studies have reported the occurrence of MNPs in outdoor and indoor air at concentrations ranging from 0.0065 items m-3 to 1583 items m-3. Findings have identified plastic fragments, fibers, and films in sizes predominantly <1000 μm with polyamide (PA), polyester (PES), polyethylene terephthalate (PET), polypropylene (PP), rayon, polyethylene (PE), polystyrene (PS), polyvinyl chloride (PVC), polyacrylonitrile (PAN), and ethyl vinyl acetate (EVA) as the major compounds. Exposure through indoor air and dust is an important pathway for humans. Airborne MNPs pose health risks to plants, animals, and humans. Atmospheric MNPs can enter organism bodies via inhalation and subsequent deposition in the lungs, which triggers inflammation and other adverse health effects. MNPs could be eliminated through source reduction, policy/regulation, environmental awareness and education, biodegradable materials, bioremediation, and efficient air-filtration systems. To achieve a sustainable society, it is crucial to implement effective strategies for reducing the usage of single-use plastics (SUPs). Further, governments play a pivotal role in addressing the pressing issue of MNPs pollution and must establish viable solutions to tackle this significant challenge.

Advanced IoT Pressure Monitoring System for Real-Time Landfill Gas Management

This research presents a novel stand-alone device for the autonomous measurement of gas pressure levels on an active landfill site, which enables the real-time monitoring of gas dynamics and supports the early detection of critical events. The developed device employs advanced sensing technologies and wireless communication capabilities, enabling remote data transmission and access via the Internet. Through extensive field experiments, we demonstrate the high sampling rate of the device and its ability to detect significant events related to gas generation dynamics in landfills, such as flare shutdowns or blockages that could lead to hazardous conditions. The validation of the device's performance against a high-end analytical system provides further evidence of its reliability and accuracy. The developed technology herein offers a cost-effective and scalable solution for environmental landfill gas monitoring and management. We expect that this research will contribute to the advancement of environmental monitoring technologies and facilitate better decision-making processes for sustainable waste management.

Machine learning models for estimating contamination across different curbside collection strategies

Contaminated recyclables, which are frequently discarded as waste, pose a significant challenge to the implementation of a circular economy. These contaminated recyclables impede the circulation of resources, resulting in higher processing costs at material recovery facilities (MRFs). Over the past few decades, machine learning (ML) models such as linear regression (LR), support vector machine (SVM), and random forest (RF) have evolved to provide new methods for predicting inbound contamination rates in addition to traditional statistical models. In this study, we applied ML models to predict inbound contamination rates using demographic features from 15 counties in the U.S. with different curbside collection strategies. In general, we found that ML models outperformed linear mixed models. Specifically, SVM models had the highest performance (R² = 0.75; mean absolute error (MAE) = 0.06), which may be due to their ability to model nonlinear relationships between features and inbound contamination rates. The key predictor was population, with poverty rate being positively correlated and median age negatively correlated with inbound contamination rates. To improve the management of contamination and enhance the implementation of a circular economy, better models are needed to understand and estimate inbound contamination rates as well as identify critical factors in the present and future.

A strategic review on sustainable approaches in municipal solid waste management andenergy recovery: Role of artificial intelligence,economic stability andlife cycle assessment

The consumption of energy levels has increased in association with economic growth and concurrently increased the energy demand from renewable sources. The need under Sustainable Development Goals (SDG) intends to explore various technological advancements for the utilization of waste to energy. Municipal Solid Waste (MSW) has been reported as constructive feedstock to produce biofuels, biofuel carriers and biochemicals using energy-efficient technologies in risk freeways. The present review contemplates risk assessment and challenges in sorting and transportation of MSW and different aspects of conversion of MSW into energy are critically analysed. The circular bioeconomy of energy production strategies and management of waste are also analysed. The current scenario on MSW and its impacts on the environment are elucidated in conjunction with various policies and amendments equipped for the competent management of MSW in order to fabricate a sustained environment.

Artificial intelligence for waste management in smart cities: a review

The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.

Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning

The immense growth of the population generates a polluted environment that must be managed to ensure environmental sustainability, versatility and efficiency in our everyday lives. Particularly, the municipality is unable to cope with the increase in garbage, and many urban areas are becoming increasingly difficult to manage. The advancement of technology allows researchers to transmit data from municipal bins using smart IoT (Internet of Things) devices. These bin data can contribute to a compelling analysis of waste management instead of depending on the historical dataset. Thus, this study proposes forecasting models comprising of 1D CNN (Convolutional Neural Networks) long short-term memory (LSTM), gated recurrent units (GRU) and bidirectional long short-term memory (Bi-LSTM) for time series prediction of public bins. The execution of the models is evaluated by Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient determination (R²) and Root Mean Squared Error (RMSE). For different numbers of epochs, hidden layers, dense layers, and different units in hidden layers, the RSME values measured for 1D CNN, LSTM, GRU and Bi-LSTM models are 1.12, 1.57, 1.69 and 1.54, respectively. The best MAPE value is 1.855, which is found for the LSTM model. Therefore, our findings indicate that LSTM can be used for bin emptiness or fullness prediction for improved planning and management due to its proven resilience and increased forecast accuracy.

Challenges and Priorities of Municipal Solid Waste Management in Cambodia

Municipal solid waste (MSW) management is one of the utmost challenges for Cambodia’s city and district centers. The unsound management of MSW has detrimentally affected the environment and human health. In the present study, an attempt has been made to provide a comprehensive insight into the generation and characteristics, policies and legislation frameworks, management arrangement, collection, treatment, and disposal of MSW. The experience of developed and developing countries and the challenges and priorities of MSW management in Cambodia are also highlighted. In Cambodia, about 4.78 million tons of MSW were generated in 2020, with a 0.78 kg/capita/day generation rate. Only 86% of cities and districts have access to MSW collection services. The current practice of MSW management is reliance on landfill (44%). There are 164 landfills operating countrywide, receiving about 5749 tons of MSW per day. Recycling, incineration, and composting share 4%, 4%, and 2% of MSW generation, respectively. In 2021, the total revenue that was recovered from recyclables was USD 56M. The study concludes several major challenges and proposes valuable suggestions, which may be beneficial for the improvement of the current system to support the sustainable management of MSW in Cambodia.