The Timtumili Minanya/Derwent Estuary has elevated zinc, lead, cadmium and mercury concentrations in sediments, reflecting historical and ongoing industrial inputs. Biomonitoring data collected by Nyrstar were analysed to assess seafood metal concentrations, evaluate spatial and temporal patterns, and inform the Department of Health, which has the statutory role of issuing seafood safety advice. Monitoring comprised biennial sampling of Southern Sand Flathead (Platycephalus bassensis), six-week deployments of Pacific Oysters (Crassostrea gigas), and triennial sampling of wild oysters and Blue Mussels (Mytilus edulis). Concentrations were compared with Food Standards Australia New Zealand guidelines. Trends from 2002 to 2024 were assessed using HARSAT linear mixed models.
Metal concentrations were higher in Derwent biota than at the Mickeys Bay control. In Southern Sand Flathead, mercury exceeded the 0.5 mg/kg maximum level in 61% of legal-size fish sampled during 2020 to 2024, particularly in the middle and lower estuary. Only 8% of captured flathead were legal size. Flathead mercury showed no statistically significant change from 2002 to 2024 despite documented reductions in anthropogenic inputs. Shellfish concentrations were greatest near Nyrstar Wharf and in Ralphs Bay. During 2020 to 2025, lead exceeded the maximum level in 73% of surface-deployed oyster samples, 77% of wild mussel samples and 29% of wild oyster samples. Cadmium exceedances were less frequent, while mercury remained below the maximum level in all shellfish. However, from 2002 to 2024, mercury increased significantly in deployed oysters at four of nine estuary sites while remaining below the maximum level.
These findings demonstrate the persistent influence of legacy contamination on metal bioavailability. This monitoring provides evidence for Department of Health advice to avoid Derwent shellfish and bream and limit consumption of other fish. Monitoring, arsenic speciation, fish-length standardisation, investigation of selenium–mercury interactions and predictive modelling are recommended to strengthen risk assessment and management.
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