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Chinese Journal of Ecology ›› 2025, Vol. 44 ›› Issue (1): 175-184.doi: 10.13292/j.1000-4890.202501.034

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Distribution characteristics and risk assessment of heavy metals in river sediments of the Pearl River delta.

SUN Kuokai1, HOU Qingye1*, YANG Zhongfang1, YU Tao2, WANG Jiaxin1   

  1. (1School of Earth Science and Resources, China University of Geosciences (Beijing), Beijing 100083, China; 2School of Mathematics and Physics, China University of Geosciences (Beijing), Beijing 100083, China).

  • Online:2025-01-10 Published:2025-01-15

Abstract: We analyzed the contents of eight heavy metals (As, Cd, Cu, Pb, Zn, Ni, Hg and Cr), pH, organic carbon content, and oxide content in river sediments of the Pearl River Basin, aiming to clarify the content and distribution characteristics of heavy metals. Heavy metal content was classified and a spatial distribution map was drawn, based on the background values, median values, threshold effect concentrations (TECs), and possible effect concentrations (PECs) of national river sediments. The pollution level and ecological risk of heavy metals were evaluated using sediment quality guideline (SQG) and sediment toxicity index (STd) methods. The positive matrix factorization (PMF) model was used to trace the source of heavy metals. The results showed that heavy metals in the sediments of Pearl River system as a whole had a moderate ecological risk, and the risk of heavy metal pollution was high near Beijiang River, Zhaoqing City and the downstream river network area. The main sources of heavy metals in river sediments include natural and anthropogenic sources. The natural sources mainly include carbonate rocks, acid igneous rock and weathering erosion of various metal deposits in the Pearl River basin. Human sources mainly include agricultural sewage sludge, urban sewage, industrial emissions, and the combustion of fossil fuels. Among the eight heavy metal elements, As and Cd contribute the most to pollution factors.


Key words: stream sediment, Pearl River, heavy metal, ecological risk assessment, positive matrix factorization