Modeling multi-source plastic pollution yield and transport driven by catchment hydrometeorological processes

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL
Yongyong Zhang , Ming Dou , Xueliang Cai , Bing Han , Zhen Wang , Xiaoyu Niu , Lihui An , Jianxiong Kang , Lijun Zhou
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Abstract

Plastic pollution has emerged as a global environmental concern, impacting both terrestrial and marine ecosystems. However, understanding of plastic sources and transport mechanism at the catchment scale remains limited. This study introduces a multi-source plastic yield and transport model, which integrates catchment economic activities, climate data, and hydrological processes. Model parameters were calibrated using a combination of field observations, existing literature, and statistical random sampling techniques. The model demonstrated robust performance in simulating both plastic yield and transport from 2010 to 2020 in the upper and middle Mulan River Catchment, located in southeast China. The annual average yield coefficients were found to closely align with existing estimations, and the riverine outflow exhibited a high correlation coefficient of 0.97, with biases ranging from -63.0 % to -21.4 % across all monitoring stations. The analysis reveals that, on average, 12.5 ± 2.5 % of the total plastic yield is transported to rivers annually, with solid waste identified as the primary source, accounting for 37.8 ± 20.7 % of the total load to rivers, followed by agricultural film (26.4 ± 9.8 %), impermeable surfaces (21.5 ± 10.3 %), urban and rural sewage (10.4 ± 5.0 % and 3.0 ± 1.5 %, respectively), and industrial wastewater (0.9 ± 0.7 %). The annual average outflow was estimated to between 9.3 and 43.0 ton/year (median: 23.1) at a 95 % confidence level. This study not only provides insights into the primary sources and transport pathways of plastic pollution at the catchment scale, but also offers a valuable tool for informing effective plastic pollution mitigation strategies.

Abstract Image

集水区水文气象过程驱动的多源塑料污染产量和迁移建模
塑料污染已成为全球关注的环境问题,对陆地和海洋生态系统都造成了影响。然而,人们对集水区规模的塑料来源和迁移机制的了解仍然有限。本研究引入了一个多源塑料产量和迁移模型,该模型整合了流域经济活动、气候数据和水文过程。模型参数的校准结合了实地观测、现有文献和统计随机抽样技术。该模型在模拟 2010 年至 2020 年中国东南部木兰溪中上游流域的塑性产流和输移方面表现出色。研究发现,年平均产量系数与现有估算值非常接近,河流流出量的相关系数高达 0.97,所有监测站的偏差在 -63.0 % 到 -21.4 % 之间。分析表明,平均每年有 12.5 ± 2.5 % 的塑料总产量流入河流,其中固体废物是主要来源,占河流总负荷的 37.8 ± 20.7 %,其次是农膜(26.4 ± 9.8 %)、不透水地面(21.5 ± 10.3 %)、城市和农村污水(分别为 10.4 ± 5.0 % 和 3.0 ± 1.5 %)以及工业废水(0.9 ± 0.7 %)。在 95% 的置信水平下,年平均流出量估计为 9.3 至 43.0 吨/年(中位数:23.1 吨/年)。这项研究不仅有助于深入了解集水区塑料污染的主要来源和迁移途径,还为制定有效的塑料污染减缓战略提供了宝贵的工具。
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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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