Novel knowledge for identifying point pollution sources in watershed environmental management

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2025-01-20 DOI:10.1016/j.watres.2025.123168
Yuqing Tian, Zongguo Wen, Yanhui Zhao
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Abstract

Identifying point pollution sources (PPSs) is essential for enforcing penalties against illegal discharge behaviours that violate acceptable water quality (WQ) standards. However, there are existing knowledge gaps in understanding the association between the pollutants in water bodies and the pollutants emitted by PPSs, as well as how to utilize the knowledge to identify PPSs in water pollution accidents. This study developed a novel framework for identifying PPSs based on the conventional chemical pollutants and matrix calculations model (CCI-MCM). A two-step statistical analysis and correlation analysis extracted pollutant information in sewage wastewater from 256,025 PPSs and further developed the similarity matrix of industrial sewage wastewater indicators (SM-ISWI) and the correlation matrix of industrial sewage wastewater indicators (CM-ISWI). The SM-ISWI and CM-ISWI comprised 820 and 7790 pollution units, which could distinguish 41 industries and further identify the PPSs in these industries. Single factor index analysis and Pearson correlation analysis developed the WQ concentration matrix (WQ-CM) and WQ concentration correlation matrix (WQ-CCM), highlighting concentration anomalies of conventional chemical pollutants in natural water bodies and supply data for matrix calculation model to identify PPSs. The matrix calculation model with the Zf, Zc and Zf-c scores indicated the relative probability of each PPS responsible for the water pollution. Four publicly reported water pollution incidents in China were selected as case studies to validate the effectiveness of the CCI-MCM in PPSs identification. The TE values in four case areas ranged from 25.0% to 53.9%, demonstrating a practical enhancement in identifying PPSs relative to random sampling identifying PPSs methods. The proposed CCI-MCM method provided specialized knowledge in understanding the association between the pollutants in water bodies and the pollutants emitted by PPSs, as well as how to utilize the knowledge to identify PPSs in water pollution accidents.

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流域环境管理中点源识别的新知识
识别点污染源对于惩罚违反可接受水质标准的非法排放行为至关重要。然而,在了解水体中污染物与PPSs排放污染物之间的关系,以及如何利用这些知识识别水污染事故中的PPSs方面存在知识空白。本研究开发了一个基于传统化学污染物和矩阵计算模型(CCI-MCM)的识别pps的新框架。通过两步统计分析和相关分析,提取了256025个pps的污水中污染物信息,进一步建立了工业污水指标相似矩阵(SM-ISWI)和工业污水指标相关矩阵(CM-ISWI)。SM-ISWI和CM-ISWI分别包含820和7790个污染单元,可以区分41个行业,并进一步识别这些行业的pps。单因素指数分析和Pearson相关分析建立了WQ浓度矩阵(WQ- cm)和WQ浓度相关矩阵(WQ- ccm),突出了天然水体中常规化学污染物的浓度异常,为矩阵计算模型识别PPSs提供数据。基于Zf、Zc和Zf-c得分的矩阵计算模型表示各PPS对水污染负责的相对概率。选取中国4起公开报道的水污染事件作为案例研究,验证CCI-MCM在pps识别中的有效性。四个病例区的TE值从25.0%到53.9%不等,表明相对于随机抽样识别pps方法,在识别pps方面有了实际的增强。提出的CCI-MCM方法为了解水体中污染物与PPSs排放污染物之间的关系以及如何利用这些知识识别水污染事故中的PPSs提供了专业知识。
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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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