Evaluation of the combined toxicity of copper and benzo[a]pyrene to phytoplankton in the Jiaozhou Bay based on mesocosm experiment.

Coastal ecosystems are frequently subjected to complex mixtures of pollutants. However, the combined ecological effects of different pollutant types, such as heavy metals and persistent organic compounds, remain poorly understood in natural environments. To investigate the combined ecological effects of copper (Cu) and benzo[a]pyrene (BaP) on phytoplankton in natural marine environments, a mesocosm experiment was conducted in the Jiaozhou Bay in autumn 2024. Changes in phytoplankton biomass (chlorophyll a, Chl-a), community structure, and nutrients were monitored in response to exposure to Cu and BaP, both individually and in combination. The results showed that Cu exhibited a strong dose-dependent inhibition effect: at low concentration level, the highly tolerant phytoplankton species Amphora sp. became dominant (Y = 1.000, where Y represents the dominance index) while at high concentration, the growth of phytoplankton was completely suppressed. In comparison to Cu, BaP showed relatively weaker inhibition effect on total biomass but markedly altered the community composition of phytoplankton, with Leptocylindrus danicus becoming dominant at high concentration (Y = 0.636). Cu and BaP showed joint toxic effects at both the low-concentration and high-concentration treatments. An antagonistic interaction was observed at the low-concentration treatment, with the measured inhibition rate (82.37 ± 1.40%) lower than the predicted value from the independent action model (92.19 ± 0.84%). In contrast, an additive effect was observed at the high-concentration treatment, implied by the consistency between the measured (98.78 ± 0.88%) and predicted (99.84 ± 0.02%) inhibition rates. This study demonstrated that in natural marine environments, combined Cu and BaP pollution can influence phytoplankton communities through either antagonistic or additive interactions, depending on their concentrations. These findings offer critical empirical evidence and a framework for developing more accurate risk assessment models for multi-pollutant scenarios in coastal ecosystems.