We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Machine learning may accelerate the recognition and control of microplastic pollution: Future prospects
Summary
This review examines how machine learning techniques including neural networks and random forests are being applied to microplastic detection, classification, and ecological risk assessment, demonstrating faster and more accurate results than traditional analytical methods. The authors identify data standardization and model interpretability as key challenges for broader adoption.
Microplastics (MPs, sizes <5 mm) have been found to be widely distributed in various environments, such as marine, freshwater, terrestrial and atmospheric systems. Machine learning provides a potential solution for evaluating the ecological risks of MPs based on big data. Compared with traditional models, data-driven machine learning can accelerate the realization of the control of hazardous MPs and reduce the impact of MPs at both local and global scales. However, there are some urgent issues that should be resolved. For example, lack of MP databases and incomparable literatures causing the current MP data cannot fully support big data research. Therefore, it is imperative to formulate a set of standard and universal MP collection and testing protocols. For machine learning, predictions of large-scale MP distribution and the corresponding environmental risks remain lacking. To accelerate studies of MPs in the future, the methods and theories achieved for other particle pollutants, such as nanomaterials and aerosols, can be referenced. Beyond predication alone, the improvement of causality and interpretability of machine learning deserves attention in the studies of MP risks. Overall, this perspective paper provides insights for the development of machine learning methods in research on the environmental risks of MPs.
Sign in to start a discussion.
More Papers Like This
Advances in machine learning for the detection and characterization of microplastics in the environment
This review examines how machine learning and artificial intelligence are being used to speed up and improve the detection of microplastics in the environment. Techniques like neural networks and computer vision can now automatically identify plastic types and count particles much faster than traditional manual methods, though challenges remain in standardizing these approaches.
[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 methodology for the fast identification and monitoring of microplastics in environmental samples using random decision forest classifiers
Researchers developed a methodology using random decision forest classifiers for the fast identification and monitoring of microplastics in environmental samples. The approach provides a machine learning-based tool to accelerate microplastic detection and reduce the analytical burden of characterising particles across diverse environmental matrices.
Recent advances in the application of machine learning methods to improve identification of the microplastics in environment
This review examined a decade of progress in applying machine learning algorithms to microplastic identification, finding that support vector machines and artificial neural networks significantly improve detection accuracy and efficiency when combined with spectroscopic techniques like FTIR and Raman.
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.