Artificial intelligence applications in solid waste management: A systematic research review

Machine learning based prediction for China's municipal solid waste under the shared socioeconomic pathways

Reliable forecast of municipal solid waste (MSW) generation is crucial for sustainable and efficient waste management. Big data analysis is a novel method to forecast MSW more accurately. Thus, this study employs five kinds of supervised machine learning approaches including linear regression, polynomial regression, support vector machine, random forest, and extreme gradient boosting (XGBoost) to examine their forecast performances. China's MSW generation from 2020 to 2060 under five shared socioeconomic pathways (SSPs) is further predicted and the mechanisms between MSW generation and socioeconomic features are explored. Results show that population and GDP are two dominant indicators in MSW prediction, and XGBoost model is proved to be effective in MSW forecast. MSW generation of China in 2060 is estimated to be 464-688 megatons under different SSPs scenarios, about four to six times of that in 2000. SSP3 that has the most population, least GDP and the highest climate change challenges is the only scenario showing a potential of MSW peak during the study period. The key for MSW increase is mainly the increase of per capita MSW caused by GDP. Finally, several policy recommendations are raised to reduce the overall MSW generation.

Household waste management in Singapore and Shanghai: Experiences, challenges and opportunities from the perspective of emerging megacities

Due to rapid economic development and urbanisation, emerging megacities with dense populations have witnessed a significant increase in waste generation. Megacities face challenges in developing sustainable waste management systems. Considerable heterogeneity exists across megacities in management strategies. The two selected emerging megacities, Singapore (a city-state) and Shanghai, have similar developmental characteristics, but their waste management modes differ strikingly. This study assessed the two modes in terms of management strategies, environmental effects, economic costs, and social outcomes. Environmental footprint analysis and cost quantification were employed for the assessment based on public data. The research results would permit a deeper understanding of the long-term sustainability of each mode while considering the feasibility of implementation across different contexts. It was found that the waste management system in Singapore had a relatively lower environmental impact than Shanghai before Shanghai's new waste segregation and recycling policy in 2019. However, when the effect of fossil fuel substitution is taken into account, the environmental burden in Shanghai can be lowered more substantially than the one in Singapore. Although Shanghai had more economic burden for the waste segregation at source, it tended to implement the circular economy principles (e.g., reduce, reuse, and recycling) better and improve its sense of community significantly. Based on the practical experiences from the two representative megacities, suggestions for better waste management practices were provided for Singapore, Shanghai, and other emerging megacities with similar circumstances. In addition, challenges and opportunities related to household waste segregation and recycling were identified to guide future practices in emerging megacities.

Building demolition waste management through smart BIM: A case study in Hong Kong

Hong Kong's construction industry, known for its massive building infrastructure, produces an enormous amount of waste every year, the vast majority of which is disposed for landfills. Therefore, some effective operational measures and waste management policies have been implemented. However, enormous waste remains a concern for stakeholders and exert pressure on the limited capacity of Hong Kong's landfills. Though previous research discusses Building Information Modelling (BIM) application for construction waste management enhancement, the BIM model has not been widely implemented for building demolition with waste management. Hence, as a response to the aforementioned shortcomings, this paper develops a conceptual framework that allows collecting, maintaining, and analyzing comprehensive information through Smart BIM that uses advanced technologies such as Internet of Things (IoT) and capable of reacting to user activities such as waste quantitative assessment, demolition process planning, optimal disposal route selection, and waste management strategy are executed. The advantages of the proposed framework are shown in a case study benefit-cost analysis based on three planned reuse and recycling-rate scenarios that explain on- and off-site recycling methods. The results show that the proposed framework will pave the way for generating sustainable waste disposal practices by providing technical and decision-making support functionalities to engineers and planners in the construction industry.

Prediction of China’s Industrial Solid Waste Generation Based on the PCA-NARBP Model

Industrial solid waste (ISW) accounts for the most significant proportion of solid waste in China. Improper treatment of ISW will cause significant environmental pollution. As the basis of decision-making and the management of solid waste resource utilization, the accurate prediction of industrial solid waste generation (ISWG) is crucial. Therefore, combined with China’s national conditions, this paper selects 14 influential factors in four aspects: society, economy, environment and technology, and then proposes a new prediction model called the principal component analysis nonlinear autoregressive back propagation (PCA-NARBP) neural network model. Compared with the back propagation (BP) neural network model and nonlinear autoregressive back propagation (NARBP) neural network model, the mean absolute percentage error (MAPE) of this model reaches 1.25%, which shows that it is more accurate, includes fewer errors and is more generalizable. An example is given to verify the effectiveness, feasibility and stability of the model. The forecast results show that the output of ISW in China will still show an upward trend in the next decade, and limit the total amount to about 4.6 billion tons. This can not only provide data support for decision-makers, but also put forward targeted suggestions on the current management situation in China.

Computer vision for solid waste sorting: A critical review of academic research

Waste sorting is highly recommended for municipal solid waste (MSW) management. Increasingly, computer vision (CV), robotics, and other smart technologies are used for MSW sorting. Particularly, the field of CV-enabled waste sorting is experiencing an unprecedented explosion of academic research. However, little attention has been paid to understanding its evolvement path, status quo, and prospects and challenges ahead. To address the knowledge gap, this paper provides a critical review of academic research that focuses on CV-enabled MSW sorting. Prevalent CV algorithms, in particular their technical rationales and prediction performance, are introduced and compared. The distribution of academic research outputs is also examined from the aspects of waste sources, task objectives, application domains, and dataset accessibility. The review discovers a trend of shifting from traditional machine learning to deep learning algorithms. The robustness of CV for waste sorting is increasingly enhanced owing to the improved computation powers and algorithms. Academic studies were unevenly distributed in different sectors such as household, commerce and institution, and construction. Too often, researchers reported some preliminary studies using simplified environments and artificially collected data. Future research efforts are encouraged to consider the complexities of real-world scenarios and implement CV in industrial waste sorting practice. This paper also calls for open sharing of waste image datasets for interested researchers to train and evaluate their CV algorithms.

ANN Hybrid Model for Forecasting Landfill Waste Potential in Lithuania

Waste management is currently a fast-growing environmental business and one of solutions to manage the huge amount of waste being generated on landfills is to use the disposed waste as an energy source. There is a major focus on energy forecasting, highlighting the importance of having reliable data on the volume and composition of municipal solid waste in landfills. However, the lack of historical data is forcing the development of machine-learning based models. This study contributes to this field by proposing a hybrid ANN-based model to forecast the total amount of landfill waste, different waste fraction and the potential for energy recovery. The proposed model includes an adaptive number of inputs adjusted to the relevant waste fraction and to the specific landfill. The obtained results substantiated that the proposed model allows for stable and accurate forecasting of recovered energy potential in cases where there is insufficient historical data. The experiments showed that the model with 12 inputs (meaning the forecast of the future value takes into account the last 12 months of data) was the most accurate in the energy forecasting task, with the lowest forecasting error in terms of mean absolute error −8.9878 gigawatt hours per year.

Analysis of input set characteristics and variances on k-fold cross validation for a Recurrent Neural Network model on waste disposal rate estimation

The use of machine learning techniques in waste management studies is increasingly popular. Recent literature suggests k-fold cross validation may reduce input dataset partition uncertainties and minimize overfitting issues. The objectives are to quantify the benefits of k-fold cross validation for municipal waste disposal prediction and to identify the relationship of testing dataset variance on predictive neural network model performance. It is hypothesized that the dataset characteristics and variances may dictate the necessity of k-fold cross validation on neural network waste model construction. Seven RNN-LSTM predictive models were developed using historical landfill waste records and climatic and socio-economic data. The performance of all trials was acceptable in the training and validation stages, with MAPE all less than 10%. In this study, the 7-fold cross validation reduced the bias in selection of testing sets as it helps to reduce MAPE by up to 44.57%, MSE by up to 54.15%, and increased R value by up to 8.33%. Correlation analysis suggests that fewer outliers and less variance of the testing dataset correlated well with lower modeling error. The length of the continuous high waste season and length of total high waste period appear not important to the model performance. The result suggests that k-fold cross validation should be applied to testing datasets with higher variances. The use of MSE as an evaluation index is recommended.

Waste Management 4.0: An Application of a Machine Learning Model to Identify and Measure Household Waste Contamination—A Case Study in Australia

Waste management directly and indirectly contributes to all sustainable development goals. Hence, the modernisation of the current ineffective management system through Industry 4.0-compatible technologies is urgently needed. Inspired by the fourth industrial revaluation, this study explores the potential application of waste management 4.0 in a local government area in Perth, Western Australia. The study considers a systematic literature review as part of an exploratory investigation of the current applications and practices of Industry 4.0 in the waste industry. Moreover, the study develops and tests a machine learning model to identify and measure household waste contamination as a waste management 4.0 case study application. The study reveals that waste management 4.0 offers various opportunities and sustainability benefits in reducing costs, improving efficiency in the supply chain and material flow, and reducing as well as eliminating waste by achieving holistic circular economy goals. The significant barriers and challenges involve initial investments in developing and maintaining waste management 4.0 technology, platform and data acquisition. The proof-of-concept case study on the machine learning model detects selected waste with considerable precision (over 70% for selected items). The number and quality of the labelled data significantly influences the model’s accuracy. The data on waste contamination are essential for local governments to explore household waste recycling practices besides developing effective waste education and communication methods. The study concludes that waste management 4.0 can be an effective tool for acquiring real-time data; however, overcoming the current limitations needs to be addressed before applying waste management 4.0 into practice.

Forecasting municipal solid waste in Lithuania by incorporating socioeconomic and geographical factors

Forecasting municipal solid waste (MSW) generation and composition plays an essential role in effective waste management, policy decision-making and the MSW treatment process. An intelligent forecasting system could be used for short-term and long-term waste handling, ensuring a circular economy and a sustainable use of resources. This study contributes to the field by proposing a hybrid k-nearest neighbours (H-kNN) approach to forecasting municipal solid waste and its composition in the regions that experience data incompleteness and inaccessibility, as is the case for Lithuania and many other countries. For this purpose, the average MSW generation of neighbouring municipalities, as a geographical factor, was used to impute missing values, and socioeconomic factors together with demographic indicator affecting waste collected in municipalities were identified and quantified using correlation analysis. Among them, the most influential factors, such as population density, GDP per capita, private property, foreign investment per capita, and tourism, were then incorporated in the hierarchical setting of the H-kNN approach. The results showed that, in forecasting MSW generation, H-kNN achieved MAPE of 11.05%, on average, including all Lithuanian municipalities, which is by 7.17 percentage points lower than obtained using kNN. This implies that by finding relevant factors at the municipal level, we can compensate for the data incompleteness and enhance the forecasting results of MSW generation and composition.

Neuro-Particle Swarm Optimization Based In-Situ Prediction Model for Heavy Metals Concentration in Groundwater and Surface Water

Limited monitoring activities to assess data on heavy metal (HM) concentration contribute to worldwide concern for the environmental quality and the degree of toxicants in areas where there are elevated metals concentrations. Hence, this study used in-situ physicochemical parameters to the limited data on HM concentration in SW and GW. The site of the study was Marinduque Island Province in the Philippines, which experienced two mining disasters. Prediction model results showed that the SW models during the dry and wet seasons recorded a mean squared error (MSE) ranging from 6 × 10-7 to 0.070276. The GW models recorded a range from 5 × 10-8 to 0.045373, all of which were approaching the ideal MSE value of 0. Kling-Gupta efficiency values of developed models were all greater than 0.95. The developed neural network-particle swarm optimization (NN-PSO) models for SW and GW were compared to linear and support vector machine (SVM) models and previously published deterministic and artificial intelligence (AI) models. The findings indicated that the developed NN-PSO models are superior to the developed linear and SVM models, up to 1.60 and 1.40 times greater than the best model observed created by linear and SVM models for SW and GW, respectively. The developed models were also on par with previously published deterministic and AI-based models considering their prediction capability. Sensitivity analysis using Olden's connection weights approach showed that pH influenced the concentration of HM significantly. Established on the research findings, it can be stated that the NN-PSO is an effective and practical approach in the prediction of HM concentration in water resources that contributes a solution to the limited HM concentration monitored data.

Forecasting plastic waste generation and interventions for environmental hazard mitigation

Plastic waste and its environmental hazards have been attracting public attention as a global sustainability issue. This study builds a neural network model to forecast plastic waste generation of the EU-27 in 2030 and evaluates how the interventions could mitigate the adverse impact of plastic waste on the environment. The black-box model is interpreted using SHapley Additive exPlanations (SHAP) for managerial insights. The dependence on predictors (i.e., energy consumption, circular material use rate, economic complexity index, population, and real gross domestic product) and their interactions are discussed. The projected plastic waste generation of the EU-27 is estimated to reach 17 Mt/y in 2030. With an EU targeted recycling rate (55%) in 2030, the environmental impacts would still be higher than in 2018, especially global warming potential and plastic marine pollution. This result highlights the importance of plastic waste reduction, especially for the clustering algorithm-based grouped countries with a high amount of untreated plastic waste per capita. Compared to the other assessed scenarios, Scenario 4 with waste reduction (50% recycling, 47.6% energy recovery, 2.4% landfill) shows the lowest impact in acidification, eutrophication, marine aquatic toxicity, plastic marine pollution, and abiotic depletion. However, the global warming potential (8.78 Gt CO₂eq) is higher than that in 2018, while Scenario 3 (55% recycling, 42.6% energy recovery, 2.4% landfill) is better in this aspect than Scenario 4. This comprehensive analysis provides pertinent insights into policy interventions towards environmental hazard mitigation.

State of science: models and methods for understanding and enhancing teams and teamwork in complex sociotechnical systems

This state of the science review brings together the disparate literature of effective strategies for enhancing and accelerating team performance. The review evaluates and synthesises models and proposes recommended avenues for future research. The two major models of the Input-Mediator-Output-Input (IMOI) framework and the Big Five dimensions of teamwork were reviewed and both will need significant development for application to future teams comprising non-human agents. Research suggests that a multi-method approach is appropriate for team measurements, such as the integration of methods from self-report, observer ratings, event-based measurement and automated recordings. Simulations are recommended as the most effective team-based training interventions. The impact of new technology and autonomous agents is discussed with respect to the changing nature of teamwork. In particular, whether existing teamwork models and measures are suitable to support the design, operation and evaluation of human-nonhuman teams of the future. Practitioner summary: This review recommends a multi-method approach to the measurement and evaluation of teamwork. Team models will need to be adapted to describe interaction with non-human agents, which is what the future is most likely to hold. The most effective team training interventions use simulation-based approaches.

Comparative performance analysis of support vector regression and artificial neural network for prediction of municipal solid waste generation

The evolution of machine learning (ML) algorithms provides researchers and engineers with state-of-the-art tools to dynamically model complex relationships. The design and operation of municipal solid waste (MSW) management systems require accurate estimation of generation rates. In this study, we applied rapid, non-linear and non-parametric data driven ML algorithms independently, multi-layer perceptron artificial neural network (MLP-ANN) and support vector regression (SVR) models to predict annual MSW generation rates in Bahrain. Models were trained and tested with MSW generation data for period of 1997-2019. The population, gross domestic product, annual tourist numbers, annual electricity consumption and total annual CO₂ emissions were selected as explanatory variables and incorporated into developed models. The zero score normalization (ZSN) and minimum maximum normalization (MMN) methods were utilized to improve the quality of data and subsequently enhances the performance of ML algorithms. Statistical metrics were employed to discriminate performance of MLP-ANN and SVR models. The linear, polynomial, radial basis function (RBF) and sigmoid kernel functions were investigated to find the optimal SVR model. Results showed that RBF-SVR model with R² value of 0.97% and 4.82% and absolute forecasting error (AFE) for the period of 2008 and 2019 exhibits superior prediction and robustness in comparison to MLP-ANN. The efficacy of MLP-ANN model was also reasonably successful with R² value of 0.94. It was shown that MMN pre-processing generated optimal MLP-ANN model while ZSN pre-processing produced optimal RBF-SVR model. This work also highlights the importance of application of ML modelling approaches to plan and implement their roadmap for waste management systems by policymakers.

Solid Waste Analysis Using Open-Access Socio-Economic Data

Nowadays, problems related with solid waste management become a challenge for most countries due to the rising generation of waste, related environmental issues, and associated costs of produced wastes. Effective waste management systems at different geographic levels require accurate forecasting of future waste generation. In this work, we investigate how open-access data, such as provided from the Organisation for Economic Co-operation and Development (OECD), can be used for the analysis of waste data. The main idea of this study is finding the links between socio-economic and demographic variables that determine the amounts of types of solid wastes produced by countries. This would make it possible to accurately predict at the country level the waste production and determine the requirements for the development of effective waste management strategies. In particular, we use several machine learning data regression (Support Vector, Gradient Boosting, and Random Forest) and clustering models (k-means) to respectively predict waste production for OECD countries along years and also to perform clustering among these countries according to similar characteristics. The main contributions of our work are: (1) waste analysis at the OECD country-level to compare and cluster countries according to similar waste features predicted; (2) the detection of most relevant features for prediction models; and (3) the comparison between several regression models with respect to accuracy in predictions. Coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), respectively, are used as indices of the efficiency of the developed models. Our experiments have shown that some data pre-processings on the OECD data are an essential stage required in the analysis; that Random Forest Regressor (RFR) produced the best prediction results over the dataset; and that these results are highly influenced by the quality of available socio-economic data. In particular, the RFR model exhibited the highest accuracy in predictions for most waste types. For example, for “municipal” waste, it produced, respectively, R2 = 1 and MAPE=4.31 global error values for the test set; and for “household” waste, it, respectively, produced R2 = 1 and MAPE=3.03. Our results indicate that the considered models (and specially RFR) all are effective in predicting the amount of produced wastes derived from input data for the considered countries.

Measuring Waste Recyclability Level Using Convolutional Neural Network and Fuzzy Inference System

This paper presents a hybrid model that is used to measure the waste recyclability level using a convolutional neural network (CNN) and fuzzy inference system (FIS; WRL-CNNFIS). The proposed system uses waste images to train a multilayer convolutional neural network to extract the most relevant features that were used in a rule-based fuzzy system to give an accurate percentage of the recyclability level of these images. The proposed model did overcome many challenges in transfer learning models alone, like overfitting and low accuracy. The use of fuzzy rules, improved the performance even with a small data set, Results have shown the effectiveness of the proposed model in terms of all four metrics: accuracy, precision, recall, and F1 score. The performance was measured under two testing scenarios. For all evaluation measurement, in all experiments the validation was conducted using the cross validation in the last step. The proposed approach is a robust and consistent approach for classifying organic and recyclable waste types. WRL-CNNFIS has achieved accuracy rate of more than 98%.

Yield prediction of "Thermal-dissolution based carbon enrichment" treatment on biomass wastes through coupled model of artificial neural network and AdaBoost

The "Thermal-dissolution based carbon enrichment" was proven as an efficient and homogenizing treatment method in converting biomass wastes into similar high-quality carbon materials. However, their yields varied significantly with respect to the different experimental parameters employed. It is therefore imperative to establish the correlation between product yield and experimental parameters for material selection and condition optimization. In this study, Adaboost was coupled with an artificial neural network algorithm to precisely describe the abovementioned correlation. The results demonstrated the effectiveness of this model through its outstanding predicting performance for all the products, especially, the coefficient of determination in predicting the yield of Residue was as high as 0.97. Additionally, the coupling effect of temperature and time was observed. This study not only validates a close correlation between selected experimental parameters and product yields, but also provides a quick and reliable way for material selection and condition optimization.

Source separation, transportation, pretreatment, and valorization of municipal solid waste: a critical review

Waste sorting is an effective means of enhancing resource or energy recovery from municipal solid waste (MSW). Waste sorting management system is not limited to source separation, but also involves at least three stages, i.e., collection and transportation (C&T), pretreatment, and resource utilization. This review focuses on the whole process of MSW management strategy based on the waste sorting perspective. Firstly, as the sources of MSW play an essential role in the means of subsequent valorization, the factors affecting the generation of MSW and its prediction methods are introduced. Secondly, a detailed comparison of approaches to source separation across countries is presented. Constructing a top-down management system and incentivizing or constraining residents' sorting behavior from the bottom up is believed to be a practical approach to promote source separation. Then, the current state of C&T techniques and its network optimization are reviewed, facilitated by artificial intelligence (AI) and the Internet of Things technologies. Furthermore, the advances in pretreatment strategies for enhanced sorting and resource recovery are introduced briefly. Finally, appropriate methods to valorize different MSW are proposed. It is worth noting that new technologies, such as AI, show high application potential in waste management. The sharing of (intermediate) products or energy of varying processing units will inject vitality into the waste management network and achieve sustainable development.

Blockchain Technology for Governance of Plastic Waste Management: Where Are We?

Blockchain technology is emerging as a plausible disruptor of waste management practices that influence the governance of plastics. The interest among the waste management community in the potential and fundamental changes to complex resource management associated with blockchain adoption parallels recent research in other sectors, such as finance, health, public administration, etc. During any comparable period characterized by a step-change in positive coverage of an early-stage technology, it can be challenging for actors to access a grounded, evidence-based oversight of the current state of practice and make informed decisions about whether or how to adopt blockchain technology. The current absence of such a systematic overview of recent experiences with blockchain initiatives disrupting waste practices not only limits the visibility of these experimental efforts, but also limits the learning that can be shared across waste plastics researcher and practitioner communities. This paper contributes with a current overview of blockchain technology adoption in the waste management sector, giving particular attention to implications for the governance of plastics. Our study draws on both primary interview data and secondary documentation data to map the landscape of current blockchain initiatives in the global waste sector. We identify four areas of blockchain use that are beginning to change waste management practices (payment, recycling and reuse rewards, monitoring and tracking of waste, and smart contracts). We conclude by outlining five areas of significant blockchain uses, implications, and influences of relevance to the development of circular plastic waste governance in both research and practice.

Data mining and knowledge discovery in databases for urban solid waste management: A scientific literature review

The processes related to solid waste management (SWM) are being revised as new technologies emerge and are applied in the area to achieve greater environmental, social and economic sustainability for society. To achieve our goal, two robust review protocols (Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)) were used to systematically analyze 62 documents extracted from the Web of Science database to identify the main techniques and tools for Knowledge Discovery in Databases (KDD) and Data Mining (DM) as applied to SWM and explore the technological potential to optimize the stages of collecting and transporting waste. Moreover, it was possible to analyze the main challenges and opportunities of KDD and DM for SWM. The results show that the most used tools for SWM are MATLAB (29.7%) and GIS (13.5%), whereas the most used techniques are Artificial Neural Networks (35.8%), Linear Regression (16.0%) and Support Vector Machine (12.3%). In addition, 15.3% of the studies were conducted with data from China, 11.1% from India and 9.7% of the studies analyzed and compared data from several other countries. Furthermore, the research showed that the main challenges in the field of study are related to the collection and treatment of data, whereas the opportunities appear to be linked mainly to the impact on the pillars of sustainable development. Thus, this study portrays important issues associated with the use of KDD and DM for optimal SWM and has the potential to assist and direct researchers and field professionals in future studies.

Estimation of municipal solid waste amount based on one-dimension convolutional neural network and long short-term memory with attention mechanism model: A case study of Shanghai

Municipal solid waste (MSW) amount has direct influence on MSW management, policy-decision making, and MSW treatment methods. Machine learning has great potential for prediction, but few studies apply the approaches of deep learning to forecast the quantity of MSW. Therefore, the aim of this study is to evaluate the feasibility and practicability of employing the methods of supervised learning, including Attention, one-dimension Convolutional Neural Network (1D-CNN) and Long Short-Term Memory (LSTM) to predict the MSW Amount in Shanghai. Integrated 1D-CNN and LSTM with Attention model, the new structure model (1D-CNN-LSTM-Attention, 1D-CLA), is designed to forecast MSW amount. In addition, the influence of socioeconomic factors on MSW amount, the structure and layers distribution of Attention, 1D-CNN, LSTM and 1D-CLA are also discussed. The results indicate that the correlation coefficients of Attention, one-dimension CNN, LSTM, and proposed 1D-CLA model to predict the MSW in Shanghai are 78%, 86.6%, 90%, and 95.3%, respectively, suggesting the feasible and practicable. The values of 24, 0.01, 50 and 25 for the number of neurons, dropout, the value of epoch number and Batch size best fit 1D-CLA to predict the amount of MSW in Shanghai. Furthermore, the performance of 1D-CLA is better than any single model or two model's combination (R² is 95.3%) and the mechanism of 1D-CLA is contributed by three former models following the order: LSTM>CNN>Attention.

A web based methane emissions modelling platform: Models and software development

This study developed a platform using a modelling and web technology approach to estimate methane emissions from landfills to assess methane emissions across the region. The web technology-based software EnLandFill, which was developed, allows users to log in, interact with landfill databases, and document and extract information regarding landfill emissions. Models that integrate web technology with databases and geographic information systems (GIS) are described. One of the achievements of this study was the development of an inverse algorithm to determine the waste source capacity according to a dispersion model, accounting for complex terrain and meteorological time-series data extracted from the Weather Research and Forecasting (WRF) model. EnLandFill software was applied to quantify CH₄ emissions for key developing regions, predicting approximately 158,977 tonnes, equivalent to 167,786,878 m³ of CH₄ for the period of 2019 - 2030. The software also allows the evaluation of the scope and level of impacts of landfill emissions under given meteorological conditions.

Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables

Construction and demolition waste (DW) generation information has been recognized as a tool for providing useful information for waste management. Recently, numerous researchers have actively utilized artificial intelligence technology to establish accurate waste generation information. This study investigated the development of machine learning predictive models that can achieve predictive performance on small datasets composed of categorical variables. To this end, the random forest (RF) and gradient boosting machine (GBM) algorithms were adopted. To develop the models, 690 building datasets were established using data preprocessing and standardization. Hyperparameter tuning was performed to develop the RF and GBM models. The model performances were evaluated using the leave-one-out cross-validation technique. The study demonstrated that, for small datasets comprising mainly categorical variables, the bagging technique (RF) predictions were more stable and accurate than those of the boosting technique (GBM). However, GBM models demonstrated excellent predictive performance in some DW predictive models. Furthermore, the RF and GBM predictive models demonstrated significantly differing performance across different types of DW. Certain RF and GBM models demonstrated relatively low predictive performance. However, the remaining predictive models all demonstrated excellent predictive performance at R2 values > 0.6, and R values > 0.8. Such differences are mainly because of the characteristics of features applied to model development; we expect the application of additional features to improve the performance of the predictive models. The 11 DW predictive models developed in this study will be useful for establishing detailed DW management strategies.

Application of artificial neural networks for predicting the physical composition of municipal solid waste: An assessment of the impact of seasonal variation

Sustainable planning of waste management is contingent on reliable data on waste characteristics and their variation across the seasons owing to the consequential environmental impact of such variation. Traditional waste characterization techniques in most developing countries are time-consuming and expensive; hence the need to address the issue from a modelling approach arises. In modelling the complexity within the system, a paradigm shift from the classical models to the intelligent models has been observed. The application of artificial intelligence models in waste management is gaining traction; however its application in predicting the physical composition of waste is still lacking. This study aims at investigating the optimal combinations of network architecture, training algorithm and activation functions that accurately predict the fraction of physical waste streams from meteorological parameters using artificial neural networks. The city of Johannesburg was used as a case study. Maximum temperature, minimum temperature, wind speed and humidity were used as input variables to predict the percentage composition of organic, paper, plastics and textile waste streams. Several sub-models were stimulated with combination of nine training algorithms and four activation functions in each single hidden layer topology with a range of 1-15 neurons. Performance metrics used to evaluate the accuracy of the system are, root mean square error, mean absolute deviation, mean absolute percentage error and correlation coefficient (R). Optimal architectures in the order of input layer-number of neurons in the hidden layer-output layer for predicting organic, paper, plastics and textile waste were 4-10-1, 4-14-1, 4-5-1 and 4-8-1 with R-values of 0.916, 0.862, 0.834 and 0.826, respectively at the testing phase. The result of the study verifies that waste composition prediction can be done in a single hidden-layer satisfactorily.

Estimating marine plastic pollution from COVID-19 face masks in coastal regions

Face masks are playing an essential role in preventing the spread of COVID-19. Face masks such as N95, and surgical masks, contain a considerable portion of non-recyclable plastic material. Marine plastic pollution is likely to increase due to the rapid use and improper dispensing of face masks, but until now, no extensive quantitative estimation exists for coastal regions. Linking behaviour dataset on face mask usage and solid waste management dataset, this study estimates annual face mask utilization and plastic pollution from mismanaged face masks in coastal regions of 46 countries. It is estimated that approximately 0.15 million tons to 0.39 million tons of plastic debris could end up in global oceans within a year. With lower waste management facilities, the number of plastic debris entering the ocean will rise. Significant investments are required from global communities in improving the waste management facilities for better disposal of masks and solid waste.

SARS-CoV-2 pandemic-induced PPE and single-use plastic waste generation scenario

The SARS-CoV-2 pandemic has demonstrated both positive and negative effects on the environment. Major concerns over personal hygiene, mandated and ease in lockdown actions and slackening of some policy measures have led to a massive surge in the use of disposable personal protective equipment (PPE) and other single-use plastic items. This generated an enormous amount of plastic waste from both healthcare and household units, and will continue to do so for the foreseeable future. Apart from the healthcare workers, the general public have become accustomed to using PPE. These habits are threatening the land and marine environment with immense loads of plastic waste, due to improper disposal practices across the world, especially in developing nations. Contaminated PPE has already made its way to the oceans which will inevitably produce plastic particles alongside other pathogen-driven diseases. This study provided an estimation-based approach in quantifying the amount of contaminated plastic waste that can be expected daily from the massive usage of PPE (e.g. facemasks) because of the countrywide mandated regulations on PPE usage. The situation of Bangladesh has been analysed and projections revealed that a total of 3.4 billion pieces of single-use facemask, hand sanitizer bottles, hand gloves and disposable polyethylene bags will be produced monthly, which will give rise to 472.30 t of disposable plastic waste per day. The equations provided for the quantification of waste from used single-use plastic and PPE can be used for other countries for rough estimations. Then, the discussed recommendations will help concerned authorities and policy makers to design effective response plans. Sustainable plastic waste management for the current and post-pandemic period can be imagined and acted upon.

A societal transition of MSW management in Xiamen (China) toward a circular economy through integrated waste recycling and technological digitization

Recently Xiamen (China) has encountered various challenges of municipal solid waste management (MSWM) such as lack of a complete garbage sorting and recycling system, the absence of waste segregation between organic and dry waste at source, and a shortage of complete and clear information about the MSW generated. This article critically analyzes the existing bottlenecks in its waste management system and discusses the way forward for the city to enhance its MSWM by drawing lessons from Hong Kong's effectiveness in dealing with the same problems over the past decades. Solutions to the MSWM problem are not only limited to technological options, but also integrate environmental, legal, and institutional perspectives. The solutions include (1) enhancing source separation and improving recycling system; (2) improving the legislation system of the MSWM; (3) improvement of terminal disposal facilities in the city; (4) incorporating digitization into MSWM; and (5) establishing standards and definitions for recycled products and/or recyclable materials. We also evaluate and compare different aspects of MSWM in Xiamen and Hong Kong SAR (special administrative region) under the framework of 'One Country, Two Systems' concerning environmental policies, generation, composition, characteristics, treatment, and disposal of their MSW. The nexus of society, economics of the MSW, and the environment in the sustainability sphere are established by promoting local recycling industries and the standardization of recycled products and/or recyclable materials. The roles of digitization technologies in the 4^th Industrial Revolution for waste reduction in the framework of circular economy (CE) are also elaborated. This technological solution may improve the city's MSWM in terms of public participation in MSW separation through reduction, recycle, reuse, recovery, and repair (5Rs) schemes. To meet top-down policy goals such as a 35% recycling rate for the generated waste by 2030, incorporating digitization into the MSWM provides the city with technology-driven waste solutions.

The “ReWaste4.0” Project—A Review

ReWaste4.0 is an innovative and cooperative K-Project in the period 2017–2021. Through ReWaste4.0 the transformation of the non-hazardous mixed municipal and commercial waste treatment industry towards a circular economy has started by investigating and applying the new approaches of the Industry 4.0. Vision of the ReWaste4.0 is, among others, the development of treatment plants for non-hazardous waste into a “Smart Waste Factory” in which a digital communication and interconnection between material quality and machine as well as plant performance is reached. After four years of research and development, various results have been gained and the present review article summarizes, links and discuss the outputs (especially from peer-reviewed papers) of seven sub-projects, in total, within the K-project and discusses the main findings and their relevance and importance for further development of the waste treatment sector. Results are allocated into three areas, namely: contaminants in mixed waste and technical possibilities for their reduction as well as removal; secondary raw and energy materials in mixed waste and digitalization in waste characterization and treatment processes for mixed waste. The research conducted in ReWaste4.0 will be continued in ReWaste F for further development towards a particle-, sensor- and data-based circular economy in the period 2021–2025.

Applying artificial neural networks (ANNs) to solve solid waste-related issues: A critical review

Artificial neural networks (ANNs) have recently attracted significant attention in environmental areas because of their great self-learning capability and good accuracy in mapping complex nonlinear relationships. These properties of ANNs benefit their application in solving different solid waste-related issues. However, the configurations, including ANN framework, algorithm, data set partition, input parameters, hidden layer, and performance evaluation, vary and have not reached a consensus among relevant studies. To address the current state of the art of ANN application in the solid waste field and identify the commonalities of ANNs, this critical review was conducted by focusing on a modeling perspective and using 177 relevant papers published over the last decade (2010-2020). We classified the reviewed studies into four categories in terms of research scales. ANNs were found to be applied widely in waste generation and technological parameter prediction and proven effective in solving meso-microscale and microscale issues, including waste conversion, emissions, and microbial and dynamic processes. Given the difficulty of data collection in many solid waste-related issues, most studies included a data size of 101-150. For mathematical optimization, dividing the data into training-validation-test sets is preferable, and the training set is supposed to account for ~70%. A single hidden layer is usually sufficient, and the optimal numbers of hidden layer nodes most likely range from 4 to 20. This review is supposed to contribute basic and comprehensive knowledge to the researchers in general waste management and specialized ANN study on solid waste-related issues.

A Review of Composting Process Models of Organic Solid Waste with a Focus on the Fates of C, N, P, and K

To foster a circular economy in line with compost quality assessment, a deep understanding of the fates of nutrients and carbon in the composting process is essential to achieve the co-benefits of value-added and environmentally friendly objectives. This paper is a review aiming to fill in the knowledge gap about the composting process. Firstly, a systematic screening search and a descriptive analysis were conducted on composting models involving the fates of Carbon (C), Nitrogen (N), Phosphorus (P) and Potassium (K) over the past decade, followed by the development of a checklist to define the gap between the existing models and target models. A review of 22 models in total led to the results that the mainstream models involved the fates of C and N, while only a few models involved P and K as target variables. Most of the models described the laboratory-scale composting process. Mechanism-derived models were relatively complex; however, the application of the fractionation of substrates could contribute to reducing the complexity. Alternatively, data-driven models can help us obtain more accurate predictions and involve the fates of more nutrients, depending on the data volume. Finally, the perspective of developing composting models for the fates of C, N, P, and K was proposed.

Consumer Intention to Participate in E-Waste Collection Programs: A Study of Smartphone Waste in Indonesia

Indonesia is a developing country with a low-level e-waste management system based on a limited number of informal initiatives. E-waste requires proper management procedures, which involve the design of a reverse logistics management network. Consumers play a critical role in such a network, because the network runs when they willingly participate as suppliers of waste. This paper applies the Theory of Planned Behavior framework and extends it using Reverse Logistics drivers, the Value Belief Norm Theory, and facility accessibility to explain consumer intention to participate in e-waste collection programs. A survey was conducted on smartphone users in Indonesia, with a total of 324 valid questionnaires. The results showed that government drivers, facility accessibility, and personal attitudes significantly influence consumer intentions. Environmental concern has a positive influence on consumer intentions through the variables of the Theory of Planned Behavior and perceived behavioral control through government drivers. This study shows the need for integration, because the variables reinforce each other. However, neither economic drivers nor subjective norms significantly influence consumer intentions. This finding distinguishes Indonesia from other countries, especially developed countries, in that e-waste collection programs have not become part of the culture in Indonesia. For this reason, Indonesia needs regulations, as the most influential variable, to regulate the implementation of such a program.

Cost analysis and optimization of Blockchain-based solid waste management traceability system

As global concerns over End-of-Life (EoL) wastes released to the environment is rising, the need for enhancing the transparency of recycling systems is growing. To address the waste traceability issue, technologies such as Blockchain can be instrumental in the proper disposal and handling of wastes. In this paper, we propose a Blockchain-based Solid Waste Management (SWM) model that can help municipalities enhance the efficiency of their waste management efforts. A Blockchain framework owned and controlled by a municipality is proposed in which customer companies pay to join the platform to avail services from the suppliers managed by the municipality. The cost burdens to both supplier and consumer companies have been discussed. In addition, an optimization model is developed to determine the optimal quantity of waste that can be traded between supplier and consumer companies in order to maximize their profit based on parameters such as the number of suppliers, consumer companies, and the processing capacity of customer companies and several constraints including maximum storing capacity, storage, and transportation constraints. Further, the cost aspects associated with Blockchain implementation are estimated from several use cases obtained from companies providing Blockchain solutions.

More Is Not Enough: A Deeper Understanding of the COVID-19 Impacts on Healthcare, Energy and Environment Is Crucial

The coronavirus disease 2019 (COVID-19) pandemic has magnified the insufficient readiness of humans in dealing with such an unexpected occurrence. During the pandemic, sustainable development goals have been hindered severely. Various observations and lessons have been highlighted to emphasise local impacts on a single region or single sector, whilst the holistic and coupling impacts are rarely investigated. This study overviews the structural changes and spatial heterogeneities of changes in healthcare, energy and environment, and offers perspectives for the in-depth understanding of the COVID-19 impacts on the three sectors, in particular the cross-sections of them. Practical observations are summarised through the broad overview. A novel concept of the healthcare-energy-environment nexus under climate change constraints is proposed and discussed, to illustrate the relationships amongst the three sectors and further analyse the dynamics of the attention to healthcare, energy and environment in view of decision-makers. The society is still on the way to understanding the impacts of the whole episode of COVID-19 on healthcare, energy, environment and beyond. The raised nexus thinking could contribute to understanding the complicated COVID-19 impacts and guiding sustainable future planning.

Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review

Conventional treatment and recycling methods of organic solid waste contain inherent flaws, such as low efficiency, low accuracy, high cost, and potential environmental risks. In the past decade, machine learning has gradually attracted increasing attention in solving the complex problems of organic solid waste treatment. Although significant research has been carried out, there is a lack of a systematic review of the research findings in this field. This study sorts the research studies published between 2003 and 2020, summarizes the specific application fields, characteristics, and suitability of different machine learning models, and discusses the relevant application limitations and future prospects. It can be concluded that studies mostly focused on municipal solid waste management, followed by anaerobic digestion, thermal treatment, composting, and landfill. The most widely used model is the artificial neural network, which has been successfully applied to various complicated non-linear organic solid waste related problems.

Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets

Recently, artificial intelligence (AI) technologies have been employed to predict construction and demolition (C&D) waste generation. However, most studies have used machine learning models with continuous data input variables, applying algorithms, such as artificial neural networks, adaptive neuro-fuzzy inference systems, support vector machines, linear regression analysis, decision trees, and genetic algorithms. Therefore, machine learning algorithms may not perform as well when applied to categorical data. This article uses machine learning algorithms to predict C&D waste generation from a dataset, as a way to improve the accuracy of waste management in C&D facilities. These datasets include categorical (e.g., region, building structure, building use, wall material, and roofing material), and continuous data (particularly, gloss floor area), and a random forest (RF) algorithm was used. Results indicate that RF is an adequate machine learning algorithm for a small dataset consisting of categorical data, and even with a small dataset, an adequate prediction model can be developed. Despite the small dataset, the predictive performance according to the demolition waste (DW) type was R (Pearson's correlation coefficient) = 0.691-0.871, R2 (coefficient of determination) = 0.554-0.800, showing stable prediction performance. High prediction performance was observed using three (for mortar), five (for other DW types), or six (for concrete) input variables. This study is significant because the proposed RF model can predict DW generation using a small amount of data. Additionally, it demonstrates the possibility of applying AI to multi-purpose DW management.

A Thematic Network-Based Methodology for the Research Trend Identification in Building Energy Management

The rapid increase in the number of online resources and academic articles has created great challenges for researchers and practitioners to efficiently grasp the status quo of building energy-related research. Rather than relying on manual inspections, advanced data analytics (such as text mining) can be used to enhance the efficiency and effectiveness in literature reviews. This article proposes a text mining-based approach for the automatic identification of major research trends in the field of building energy management. In total, 5712 articles (from 1972 to 2019) are analyzed. The word2vec model is used to optimize the latent Dirichlet allocation (LDA) results, and social networks are adopted to visualize the inter-topic relationships. The results are presented using the Gephi visualization platform. Based on inter-topic relevance and topic evolutions, in-depth analysis has been conducted to reveal research trends and hot topics in the field of building energy management. The research results indicate that heating, ventilation, and air conditioning (HVAC) is one of the most essential topics. The thermal environment, indoor illumination, and residential building occupant behaviors are important factors affecting building energy consumption. In addition, building energy-saving renovations, green buildings, and intelligent buildings are research hotspots, and potential future directions. The method developed in this article serves as an effective alternative for researchers and practitioners to extract useful insights from massive text data. It provides a prototype for the automatic identification of research trends based on text mining techniques.