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Microplastics have been widely detected in the natural water environment, which brings inevitable risks to the water ecosystem and human health. However, the understanding of the potential impact of microplastics on aquatic animals, plants, and human health is still limited, and technical methods to control microplastic pollution in natural water are still rare. Hence, this paper summarizes the progress of research on microplastic pollution in water systems in terms of microplastic source, attributes, distribution characteristics, environmental effects, and prevention and control methods according to the relevant research reports on water microplastic pollution. It also expounds the basic ways for the prevention, control, and treatment of water microplastics, and looks forward to the research direction of water microplastic pollution in the future. The results show that the abundance of fresh water microplastics in China is higher than that in other regions, but the pollution level of marine microplastics is at the middle level. Compared with other countries, the pollution degree of microplastics in aquatic organisms in China is at the middle and lower level, but the spatial heterogeneity is more obvious. Through hydraulic control and the substitution of degradable plastic products, water microplastic pollution can be greatly reduced. This paper can provide a reference basis for the formulation of microplastic pollution prevention and control in China. [ABSTRACT FROM AUTHOR]

With the rapid development of planting techniques using plastic film mulching, the content of residual plastic film in soil increases year by year, which pollutes the soil and water, endangers the growth of crops and reduces the quality and yield of agricultural products. Therefore, in order to solve the problem that plastic film with a thickness of 0.008 mm, commonly used in China, is difficult to recycle, this study designed the residue film recycling machine based on the existing research results. After harvesting cotton in Xinjiang, the working performance of the residue film recovery machine of plough layer was measured. Through theoretical optimization and field experiments, the effects of conveyor speed, distance between elastic teeth and type of elastic teeth on residual film recovery were studied. The relationship between the parameters of the residual film recovery machine and the recovery effect of the residual film was analyzed. When the rotational speed and inclination angle of the conveying and separating device were 74 rpm, 35° and 120 rpm, 45°, respectively, under the condition that the distance between C-shaped elastic teeth was 59 mm, the recovery and separation rate of residual plastic film were 88.12% and 83.27%, respectively. Based on the study results, it is recommended to accelerate the development of naturally degradable agricultural plastic film or to popularize and apply thickened film. Relevant local standards and policies have been formulated to address the problem of residual film pollution, and a governance system has been established for the benefit of the government, scientific research institutions, enterprises and farmers. This study provides a reference for mechanized recovery of residual plastic film in soil and treatment of soil pollution. [ABSTRACT FROM AUTHOR]

With the rapid process of both urban sprawl and urban renewal, large numbers of old buildings have been demolished in China, leading to wide spread construction sites, which could cause severe dust contamination. To alleviate the accompanied dust pollution, green plastic mulch has been widely used by local governments of China. Therefore, timely and accurate mapping of urban green plastic covered regions is of great significance to both urban environmental management and the understanding of urban growth status. However, the complex spatial patterns of the urban landscape make it challenging to accurately identify these areas of green plastic cover. To tackle this issue, we propose a deep semi-supervised learning framework for green plastic cover mapping using very high resolution (VHR) remote sensing imagery. Specifically, a multi-scale deformable convolution neural network (CNN) was exploited to learn representative and discriminative features under complex urban landscapes. Afterwards, a semi-supervised learning strategy was proposed to integrate the limited labeled data and massive unlabeled data for model co-training. Experimental results indicate that the proposed method could accurately identify green plastic-covered regions in Jinan with an overall accuracy (OA) of 91.63%. An ablation study indicated that, compared with supervised learning, the semi-supervised learning strategy in this study could increase the OA by 6.38%. Moreover, the multi-scale deformable CNN outperforms several classic CNN models in the computer vision field. The proposed method is the first attempt to map urban green plastic-covered regions based on deep learning, which could serve as a baseline and useful reference for future research. [ABSTRACT FROM AUTHOR]