Changes in characteristics and risk of freshwater microplastics under global warming

Enhanced ecological risk of microplastic ingestion by fish due to fragmentation and deposition in heavily sediment-laden river

The widespread occurrence of microplastics (MPs) in rivers has aroused increasing concerns. However, there remains a significant gap about its effect on fish with different species, especially in highly-sediment-laden rivers. Here, through a large-scale investigation of microplastics in the Yellow River, our research highlighted effects of heavily sediments on MPs contamination in fish gut. MPs were 100 % tested in water, sediment and fish gut samples, with MPs in the lower reach 2∼3 times larger than that of the upper reach. Most of the microplastics were small (<1 mm), fibrous and blue fragments, composed of polyethylene, polypropylene, and polyethylene terephthalate. Feeding habitat and environment significantly controlled MPs ingestion by fish (p < 0.05), of which filter feeders and species with broader dietary preferences exhibited higher ingestion abundance, omnivorous fish abundance up to 24.9 items/individual. Heavily sediment load accelerated the fragmentation and deposition of MPs (p < 0.05), leading to the generation of more and smaller MPs particles, increasing ecological risks to aquatic organisms. Downstream, smaller sediment size and higher organic matter content also facilitated microplastic accumulation. The prevalence of highly toxic polyvinyl chloride polymers was emerged as the major contributor to environmental risks. Our results suggested that the contribution and ecological risks of small microplastics are worth attention in the mid and lower reaches of the Yellow River.

Correcting microplastic pollution and risk assessment in Chinese watersheds

Microplastics (MPs) are emerging pollutants that are attracting attention because of their potential threats posed and their widespread presence in the environment. MP pollution in Chinese watersheds requires assessment; however, existing risk models face data-scale biases. By compiling 2,474 samples from 165 articles, we constructed a national dataset on MPs and propose a novel framework that integrates rescaled MP concentrations with MP characteristics to recalibrate MP pollution and ecological risks. The results showed that MP concentrations show substantial variability across seven orders of magnitude, and corrected data offered a more accurate representation of environmental concentrations. MP shapes, polymers, and colors differed among river basins, and population density and precipitation were important drivers of variations in MP concentrations. MP shapes, colors, and sizes that were not previously considered are now included in the risk assessment of MPs. Furthermore, 50% of the sampling sites were in the dangerous and extremely dangerous ecological risk classes. The concentrations measured at 16.98% of the sampling sites exceeded the risk threshold, therefore posing ecological and toxicological risks. The assessment framework may provide overall insights into the differences in MP pollution in river basins.

Are we underestimating the driving factors and potential risks of freshwater microplastics from in situ and in silico perspective?

The high loads of heterogeneous microplastics (MPs) in water system sparked the exploration of MPs source and impact in the environment. However, the contributions of driving factors to MPs contamination and the potential risks posed by multidimensional characteristics are still poorly understood. By incorporating in situ investigation with machine learning predictions, this study reported widespread MPs contamination in both textile upstream and receiving watershed in the Yangtze River Delta. The dominant MPs categories were fibers (0.1-0.5 mm in size), transparent in color, and composed of polyethylene terephthalate. These morphological characteristics indicated a conditional fragmentation process, suggesting that larger MPs are more prone to fragmentation. Multivariable analysis revealed significant correlations between MPs occurrence and factors of metal concentrations, geographic locations, and water qualities, highlighting the roles of textile production and automotive tire wear in determining MPs abundance. Among five machine learning models, Random Forest outperformed others in predicting MPs abundance. The interpretable analysis indicated that longitude (35.3 %), TN (13.8 %) and Sb (13.4 %) were pivotal nodes in shaping the MPs abundance. Emission point sources from express, autotire and textile yield feature importance from 6.60 % to 7.88 %. A total 12.39 % of the predicted variability can be further explained by interaction effects. Besides, MPERI and MultiMP indices based on abundance, size, color, shape, and polymer distributions suggested that most sampling sites fell within moderate to high-risk categories. Artificial neural network-based assessment results are suitable for explaining the MPs induced risks and polymer type was the most influential variable in determining the risk values. These quantitative insights into the driving factors and potential risks behind MPs occurrence improve our knowledge to manage MPs pollution in large-scale watersheds, providing crucial information for the development of effective mitigation strategies.