● Methods for estimating the aging of environmental micro-plastics were highlighted. ● Aging pathways & characterization methods of microplastics were related and reviewed. ● Possible approaches to reduce the contamination of microplastics were proposed. ● The prospect and deficiency of degradable plastics were analyzed. With the increasing production of petroleum-based plastics, the problem of environmental pollution caused by plastics has aroused widespread concern. Microplastics, which are formed by the fragmentation of macro plastics, are bio-accumulate easily due to their small size and slow degradation under natural conditions. The aging of plastics is an inevitable process for their degradation and enhancement of adsorption performance toward pollutants due to a series of changes in their physiochemical properties, which significantly increase the toxicity and harm of plastics. Therefore, studies should focus on the aging process of microplastics through reasonable characterization methods to promote the aging process and prevent white pollution. This review summarizes the latest progress in natural aging process and characterization methods to determine the natural aging mechanism of microplastics. In addition, recent advances in the artificial aging of microplastic pollutants are reviewed. The degradation status and by-products of biodegradable plastics in the natural environment and whether they can truly solve the plastic pollution problem have been discussed. Findings from the literature pointed out that the aging process of microplastics lacks professional and exclusive characterization methods, which include qualitative and quantitative analyses. To lessen the toxicity of microplastics in the environment, future research directions have been suggested based on existing problems in the current research. This review could provide a systematic reference for in-depth exploration of the aging mechanism and behavior of microplastics in natural and artificial systems. 相似文献
● MSWNet was proposed to classify municipal solid waste.● Transfer learning could promote the performance of MSWNet.● Cyclical learning rate was adopted to quickly tune hyperparameters. An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions. 相似文献