Your browser doesn't support javascript.
loading
Driving factor, source identification, and health risk of PFAS contamination in groundwater based on the self-organizing map.
Zeng, Jingwen; Liu, Kai; Liu, Xiao; Tang, Zhongen; Wang, Xiujuan; Fu, Renchuan; Lin, Xiaojun; Liu, Na; Qiu, Jinrong.
Afiliación
  • Zeng J; South China Institute of Environmental Sciences, Ministry of Ecology and Environment (MEE), Guangzhou 510655, Guangdong, PR China.
  • Liu K; College of Life Science and Technology, Jinan University, Guangzhou 510632, Guangdong, PR China.
  • Liu X; College of Life Science and Technology, Jinan University, Guangzhou 510632, Guangdong, PR China.
  • Tang Z; Anew Global Consulting Limited, Guangzhou 510075, Guangdong, PR China.
  • Wang X; South China Institute of Environmental Sciences, Ministry of Ecology and Environment (MEE), Guangzhou 510655, Guangdong, PR China.
  • Fu R; College of Environment and Climate, Jinan University, Guangzhou 510632, Guangdong, PR China.
  • Lin X; South China Institute of Environmental Sciences, Ministry of Ecology and Environment (MEE), Guangzhou 510655, Guangdong, PR China.
  • Liu N; College of Life Science and Technology, Jinan University, Guangzhou 510632, Guangdong, PR China. Electronic address: liuna@jnu.edu.cn.
  • Qiu J; South China Institute of Environmental Sciences, Ministry of Ecology and Environment (MEE), Guangzhou 510655, Guangdong, PR China. Electronic address: qiujinrong@scies.org.
Water Res ; 267: 122458, 2024 Sep 15.
Article en En | MEDLINE | ID: mdl-39303575
ABSTRACT
The complex interactions between groundwater chemical environments and PFAS present challenges for data analysis and factor assessment of the spatial distribution and source attribution of PFAS in groundwater. This study employed spatial response analysis combining self-organizing maps (SOM), K-means clustering, Spearman correlation, positive matrix factorization (PMF) and risk quotient (RQ), to uncover the spatial characteristics, driving factors, sources, and human health risks of groundwater PFAS in the Pearl River Basin. The results indicated that the characteristics of PFAS in groundwater were classified into 16 neurons, which were further divided into 6 clusters (I-VI). This division was due to the contribution of industrial pollution (33.2 %) and domestic pollution (31.5 %) to the composition of PFAS in groundwater. In addition, the hydrochemical indicators such as pH, dissolved organic carbon (DOC), chloride (Cl-), and calcium ions (Ca2+) might also affect the distribution pattern of PFAS. The potential human health risk in the area was minimal, with cluster Ⅱ presenting the highest risk (RQ value 0.25) which is closely related to PFOA emissions from fluoropolymer industry. This study provides a theoretical basis and data support for applying of SOM to the visualization and control of PFAS contamination in groundwater.
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Water Res Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Water Res Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido