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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引用次数: 0

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 Z_f, Z_c and Z_f-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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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.