Revealing Microplastic risks in stratified water columns of the East China Sea offshore

IF 12.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2025-03-01 Epub Date: 2024-12-01 DOI:10.1016/j.watres.2024.122900
Jiawei Li , Yang Liu , Qiqi Chen , Yixuan Cai , Yixin Liao , Lili Liu , Yuanyuan Tang
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Abstract

Microplastics have been proven to impact a broad range of marine species significantly. This study investigated the vertical distribution characteristics of microplastics (MPs) to verify their potential toxicity, distribution patterns, and affecting probability on organisms offshore of the East China Sea (ECS), China. Significant variations in MP characteristics across stratified water layers were identified and corroborated through artificial neural network (ANN) analysis. By a combination of species sensitivity distribution (SSD), risk quotient (RQ) and joint probability curves (JPC) method, this study gave the regional risk thresholds and current risk distributions. Based on SSD, the derived predicted no-effect concentration for the ecosystem was 52.0 items/L (95 % confidence interval: 13.7–262.8 items/L), with the 5 % species hazardous concentration at 103.6 items/L. The RQ assessment results indicated varying ecological risk levels across different water layers, with the highest risks transitioning from north to south and from surface to bottom layers. Most sites exhibited a moderate risk level, with the highest risks identified in surface water near the Yangtze River Estuary, China. Conversely, the JPC analysis suggested a minimal ecological risk across the study area, emphasizing variable ecological risk contingent on species presence. This study underscores the importance of examining surface and intermediate water layers for marine habitats and organisms, highlighting the necessity of prioritizing investigations into the distribution of MPs across different water layers in the ECS, particularly focusing on buoyant polyester fibers present in the upper water column and the layers beneath the offshore surface.

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揭示东海近海分层水柱微塑料风险
微塑料已被证明会对许多海洋物种产生重大影响。本文研究了微塑料(MPs)的垂直分布特征,以验证其对中国东海近海生物的潜在毒性、分布模式和影响概率。通过人工神经网络(ANN)分析,确定并证实了不同分层水层间MP特征的显著变化。结合物种敏感性分布(SSD)、风险商(RQ)和联合可倾性曲线(JPC)方法,给出了区域风险阈值和当前风险分布。基于SSD的预测值为:无影响浓度为52.0种/L(95%可信区间为13.7 ~ 262.8种/L), 5%种有害浓度为103.6种/L。RQ评价结果表明,不同水层的生态风险水平不同,风险最高的水层由北向南、由表层向底层过渡。大部分站点的风险等级为中等,其中长江口附近的地表水风险最高。相反,JPC分析表明,整个研究区域的生态风险最小,强调生态风险随物种的存在而变化。这项研究强调了检查海洋栖息地和生物的表层和中间水层的重要性,强调了优先调查MPs在ECS不同水层分布的必要性,特别是关注存在于上层水柱和近海表面以下层的浮力聚酯纤维。
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来源期刊
Water Research
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.
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