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Microplastics, as a new type of water pollutant, are receiving increasing attention. Due to their widespread distribution, being difficult to degrade, and toxic properties to organisms, they pose a serious threat to the health of freshwater ecosystems. This paper reviews the definition, classification, sources, as well as distribution and migration patterns of microplastics in the freshwater environment. It also discusses the toxicological effects of microplastics on freshwater organisms and the application of physical, chemical, and biotechnological technologies in the removal of microplastics. Microplastics can be categorized into primary and secondary microplastics based on types, sizes, and sources. Comprehensive analysis indicates that selecting appropriate microplastic collection and pretreatment methods is fundamental for quantitative and qualitative analysis and other research. The distribution and migration of microplastics are influenced by factors such as geographical locations, wind, water flow, and flow rate. Studies have shown that microplastics exert toxic effects on the growth and reproduction of freshwater organisms by adsorbing toxic chemicals and microbes on the surfaces, and that microplastics even affect the food chain and the health of freshwater ecosystems. To address these issues, this paper reviews the current applications of physical technologies such as adsorption, membrane filtration and sedimentation, chemical technologies such as coagulation and flocculation processes, oxidation, polymerization and photocatalytic degradation, and biological technologies such as microbial degradation. The paper concludes by proposing research prospects in four aspects: analytical methods for microplastics, environmental toxicity studies, removal technologies, and policies and regulations. This paper aims to provide ideas and references for incorporating microplastic indicators into the health assessment system of rivers and lakes and to offer management strategies for the river and lake health evaluation system.

[Objective] The extensive development of biological soil crusts driven by the project of “Grain for Green” on the Loess Plateau can significantly inhibit soil erosion, but the characteristics of soil erosion resistance and the response mechanism with the growth and development of biological soil crusts are still unclear. [Methods] Algal crusts, moss crusts, and natural succession crusts were cultivated for 176 days to systematically study the effects of different developmental stages and types of biological soil crusts on loessial soil erosion resistance. [Results] (1)The coverage, thickness, biomass, chlorophyll and roughness of the biological soil crusts increased significantly with growth time, and the moss crusts were higher at the beginning of cultivation and the natural succession crusts were highest at the end of cultivation. (2) With the development of biological soil crusts, the cohesion increased as a power function, which increased by 39.8%~60.3% compared with the initial period; the mass loss rate decreased exponentially, which decreased by 45.6%~57.3% compared with the initial period; and the saturated hydraulic conductivity (Ks) was more complex, but the Ks was lowest at the end of cultivation (0.08~0.12 mm/min). (3) Soil cohesion increased as a power function with the increase of coverage, thickness, chlorophyll, biomass and roughness, and the mass loss rate decreased exponentially with the increase of coverage, thickness, chlorophyll, biomass and roughness. (4) The comprehensive index of soil erosion resistance (Cser) constructed based on soil properties can effectively show the effect of biological soil crusts development on soil erosion resistance. And the Cser can be expressed as a power function of coverage (Cov), thickness (T) and biomass (B) (Cser=0.279 Cov0.194T0.188B0.119, R2=0.73, p

Graph neural networks (GNNs) have evolved many variants for predicting the properties of crystal materials. While most networks within this family focus on improving model structures, the significance of atomistic features has not received adequate attention. In this study, we constructed an atomistic line GNN model using compositionally restricted atomistic representations which are more elaborate set of descriptors compared to previous GNN models, and employing unit graph representations that account for all symmetries. The developed model, named as CraLiGNN, outperforms previous representative GNN models in predicting the Seebeck coefficient, electrical conductivity, and electronic thermal conductivity that are recorded in a widely used thermoelectric properties database, confirming the importance of atomistic representations. The CraLiGNN model allows optional inclusion of additional features. The supplement of bandgap significantly enhances the model performance, for example, more than 35% reduction of mean absolute error in the case of 600 K and 10 19 cm − 3 concentration. We applied CraLiGNN to predict the unrecorded thermoelectric transport properties of 14 half-Heusler and 52 perovskite compounds, and compared the results with first-principles calculations, showing that the model has extrapolation ability to identify the thermoelectric potential of materials. [ABSTRACT FROM AUTHOR]