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

IF 11.4 1区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research Pub Date : 2025-03-28 DOI:10.1016/j.watres.2025.123568

Xiaowei Ding , Binyan Zhang , Chensi Shen , Rundong Wang , Shanshan Yin , Fang Li , Chenye Xu

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Abstract

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.

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来源期刊

Water Research 环境科学-工程：环境

CiteScore

20.80

自引率

9.40%

发文量

1307

审稿时长

38 days

期刊介绍： Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.