Spatial-temporal variations and trends predication of trace metals in oysters from the Pearl River Estuary of China during 2011–2018

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Article number114812
Journal / PublicationEnvironmental Pollution
Online published20 May 2020
Publication statusPublished - Sep 2020


Estuaries are often considered to be the filters of pollutants from the land-derived outflows of freshwater to open seawater. Oysters are efficient bioaccumulators of metals in the estuarine environment, however, little information is available on the long-term tissue variability of metals in a large dynamic estuary under complex urbanized and anthropogenic impacts. Thus, an eight-year biomonitoring study of metals (Ag, Cd, Cr, Cu, Ni, Pb, and Zn) in the oysters from 10 sites were carried out to reveal the highly spatial-temporal variations in the Pearl River Estuary (PRE) of China during 2011–2018. Cd, Cu, and Zn in oysters were significantly correlated with the dissolved metals in seawater. Geographically, Ag, Cd, and Cr were higher in the western sites, and Cu, Ni, and Zn were higher in the eastern sites. High seasonal variations of Ag, Cu, and Zn were found in the wet season. The calculated annual change rates (vc) of Cd, Cu, Zn, Ag, Pb, Ni, and Cr in the oysters were −1.1, −45, −48, 0.338, −0.216, −2.2, and −2.8 μg/g/y, respectively. If such decreasing rates of vc (or natural logarithm rates v) were maintained, Cd, Cu, Zn, Pb, and Ni in oysters from PRE would be expected to recover to the national 50% concentrations in years 2022 (2024), 2045 (2079), 2073 (2110), 2021 (2023), and 2019 (2020), respectively. Long-term series observations of metals in organisms reflected the real bioavailability of metals, pollution status, and trends for environmental management and control in a large dynamic and contaminated estuary.

Research Area(s)

  • Metal pollution, Oyster biomonitors, Pearl river estuary (PRE), Spatial-temporal variations, Trends prediction