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Papers
61,005 resultsShowing papers similar to Machine learning approach to uncover customer plastic bag usage patterns in a grocery store
ClearMachine Learning Methods Analysis of Preceding Factors Affecting Behavioral Intentions to Purchase Reduced Plastic Products
Researchers applied machine learning to analyze factors preceding behavioral intentions related to environmental sustainability, using survey data and ML models to identify the most predictive variables. The ML approach outperformed conventional regression in capturing non-linear relationships between attitudes, norms, and behavioral intent toward pro-environmental actions.
Environmental degradation of consumer plastics into microplastics and nanoplastics and their classification using machine learning
Researchers studied the environmental degradation of three common consumer plastics — low-density polyethylene shopping bags, polyethylene terephthalate water bottles, and take-food trays — under UV radiation, abrasion, atmospheric oxidation, and freeze-thaw cycles, then applied machine learning algorithms to classify the resulting microplastic and nanoplastic particles.
Environmental degradation of consumer plastics into microplastics and nanoplastics and their classification using machine learning
Researchers studied the environmental degradation of consumer plastics — LDPE shopping bags, PET water bottles, and take-food trays — under UV radiation, abrasion, atmospheric oxidation, and freeze-thaw cycles, then trained and applied machine learning classifiers to identify and categorize the resulting microplastic and nanoplastic particles.
Machine learning-driven analysis of soil microplastic distribution in the Bang Pakong Watershed, Thailand
Researchers used machine learning techniques to analyze the distribution and influencing factors of soil microplastic contamination in the Bang Pakong Watershed in Thailand. The study identified key environmental and land-use variables that predict microplastic occurrence, providing a data-driven approach for understanding how microplastics distribute across agricultural and urban landscapes.
Behavioral insights into reusable bag adoption: Evaluating the effectiveness of the theory of planned behavior in Lahore
Researchers investigated the behavioral determinants of reusable bag adoption over single-use plastic bags, using behavioral insights frameworks to evaluate the effectiveness of policy interventions and identify factors that drive sustained reuse rather than one-time uptake.
Prediction of microplastic abundance in surface water of the ocean and influencing factors based on ensemble learning
Researchers used machine learning to predict microplastic levels in ocean surface waters and identify the key factors driving contamination. Their models found that geographic location, ocean currents, and proximity to populated coastlines were major predictors of microplastic abundance. This approach could help scientists map pollution hotspots without costly and time-consuming physical sampling.
Development of a New Conceptual Model: Consumers’ Purchase Intention towards Eco-friendly Bags
This paper is not about microplastics; it proposes a consumer behavior model to understand factors influencing purchase intentions toward eco-friendly bags as a plastic reduction strategy.
Predicting green product consumption using theory of planned behavior and reasoned action
Researchers applied the theory of planned behavior to investigate how environmental awareness and social influence predict consumer intentions to use reusable bags, finding that these factors significantly shape green purchasing behavior in a plastic waste reduction context.
Application of machine learning in assessing spatial distribution patterns of soil microplastics: a case study of the Bang Pakong Watershed, Thailand
Machine learning models were applied to predict spatial distribution patterns of microplastics in soils across a Thai watershed, identifying land use types and proximity to water bodies as key factors driving contamination levels.
Uporaba plastičnih vrećica pri kupnji hrane - anketa potrošača
This Croatian-language consumer survey examined patterns of plastic bag use when purchasing food, in the context of growing awareness about plastic pollution. It provides insight into consumer attitudes and behavior regarding single-use plastic packaging.
Predictive modeling of microplastic adsorption in aquatic environments using advanced machine learning models
Scientists used advanced machine learning models to predict how microplastics interact with and absorb organic pollutants in water. The results showed that microplastics with certain chemical properties attract more toxic compounds, which matters because contaminated microplastics in waterways can concentrate harmful chemicals that may eventually reach humans through drinking water and seafood.
A Systematic Review of Consumer Perception: Factors Affecting Green Shopping Bags
This systematic review examines what drives consumers to choose reusable shopping bags over plastic ones. Understanding these factors helps reduce plastic bag waste at the source, which is important because plastic bags are a major contributor to microplastic pollution as they break down in the environment.
Integrating Genomic and Proteomic Data Using Machine Learning for Plastic Biodegradation: A Systematic Review
This systematic review summarizes how machine learning and genomic data are being used to identify microbes and enzymes that can break down plastic waste. The research is significant for microplastic concerns because discovering more effective biological degradation pathways could provide a natural solution for reducing the microplastic pollution that accumulates in our environment and bodies.
Machine Learning to Predict the Adsorption Capacity of Microplastics
Researchers developed machine learning models to predict the adsorption capacity of microplastics for chemical pollutants, providing a computational tool to better understand how microplastics act as vectors for contaminant dispersal in aquatic environments.
Detection of Microplastics Using Machine Learning
Researchers reviewed and demonstrated machine learning approaches for detecting and classifying microplastics in environmental samples, finding that automated image analysis and spectral classification methods can improve the speed and accuracy of microplastic monitoring compared to manual methods.
What Drives Microplastic Exposure in Human Blood and Feces? Machine Learning Reveals Potential Key Influencing Factors
Researchers analyzed 229 blood and 227 fecal samples for microplastics using pyrolysis-GC-MS and applied machine learning to identify the strongest predictors of microplastic body burden. The model identified diet, packaging use, and indoor environment as key drivers of microplastic levels in human blood and feces, highlighting lifestyle factors as modifiable exposure determinants.
Exploring the Research on Utilizing Machine Learning in E-Learning Systems
Not relevant to microplastics — this systematic literature review surveys how machine learning techniques are applied in e-learning systems to improve educational outcomes and predict student performance.
Decoding the Plastic Patch: Exploring the Global Microplastic Distribution in the Surface Layers of Marine Regions with Interpretable Machine Learning
Researchers used interpretable machine learning algorithms to predict global marine microplastic distribution patterns based on calibrated field data. The study found that biogeochemical and human activity factors had the greatest influence on microplastic concentrations, which ranged from about 0.2 to 27 particles per cubic meter across the world's oceans, providing a framework for pollution management and decision-making.
Assessment of machine learning-based methods predictive suitability for migration pollutants from microplastics degradation
Researchers assessed the usefulness of machine learning methods for predicting the migration of chemical pollutants from microplastics. The study found that artificial neural networks and support vector methods showed strong potential for modeling and predicting the leaching of plasticizers and other contaminants, which could reduce the need for extensive laboratory analyses.
Current applications and future impact of machine learning in emerging contaminants: A review
This review examines how machine learning is being applied to emerging contaminant research including microplastics, covering identification, environmental behavior prediction, bioeffect assessment, and removal optimization of these pollutants.
[Overview of the Application of Machine Learning for Identification and Environmental Risk Assessment of Microplastics].
This review examines the application of machine learning (ML) methods for identifying microplastics and assessing their environmental risks, covering techniques for improving the accuracy and reliability of microplastic detection across different environmental media. Researchers highlight how ML can systematically analyse pollution characteristics and support ecological risk evaluation of microplastic contamination.
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.
Rapid and Nondestructive On-Site Classification Method for Consumer-Grade Plastics Based on Portable NIR Spectrometer and Machine Learning
Researchers used a portable near-infrared spectrometer combined with machine learning to rapidly identify and classify seven types of consumer plastic waste on-site without damaging the samples. Faster and cheaper plastic identification tools are important for improving plastic recycling efficiency and ultimately reducing the amount of plastic that ends up as microplastic pollution.
Factors Related to Reducing The Use of Plastic Bags in Kabupaten Bekasi
This Indonesian survey study examined the factors that influence whether people in Bekasi Regency reduce their use of plastic bags, finding that knowledge, attitudes, and access to alternatives were key predictors. Reducing single-use plastic bag consumption is important for limiting the amount of plastic that fragments into microplastics in the environment. The paper provides insights for designing behavior-change interventions aimed at plastic pollution reduction.