Deep learning models have been widely used in time-series numerical prediction of atmospheric environmental quality. The fundamental feature of this application is to discover the correlation between influencing factors and target parameters through a deep network structure. These relationships in original data are affected by several different frequency factors. If the deep network is adopted without guidance, these correlations may be masked by entangled multifrequency data, which will cause the problem of insufficient correlation feature extraction and difficult model interpretation. Because the wavelet transform has the ability to separate these entangled multifrequency data, and these correlations can be extracted by deep learning methods, a hybrid model combining wavelet transform and transformer-like (WTformer) was designed to extract time–frequency domain features and prediction of air quality. The 2018–2021 hourly data in Guilin was used as the benchmark training dataset. Pollutants and meteorological variables in the local dataset are decomposed into five frequency bands by wavelet. The analysis of the WTformer model showed that particulate matter (PM_2.5 and PM₁₀) had an obvious correlation in the low-frequency band and a low correlation in the high-frequency band. PM_2.5 and temperature had a negative correlation in the high-frequency band and an obvious positive correlation in the low-frequency band. PM_2.5 and wind speed had a low correlation in the high-frequency band and an obvious negative correlation in the low-frequency band. These results showed that the laws of variables in the time–frequency domain could be found by the model, which made it possible to explain the model. The experimental results show that the prediction performance of the established model was better than that of multilayer perceptron (MLP), one-dimensional convolutional neural network (1D-CNN), gate recurrent unit (GRU), long short-term memory (LSTM) and Transformer, in all time steps (1, 4, 8, 24 and 48 h).

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.