Measures based on concurrent alterations of an environment’s physical, chemical, and biological factors are commonly adopted to control harmful algal blooms (HABs). It was postulated that the combinations and interactions of multiple measures could exert cumulative effects (as the overall effect may or may not be equal to the additive sum for each measure individually). However, few studies have further assessed whether the cumulative effect is synergistic, additive, or antagonistic. This study proposed a framework to distinguish and quantify the cumulative effects. We also designed an experiment to investigate the cumulative effect of the combined utilization of physical (flow velocity), chemical (copper), and biological (propionamide) measures on algae growth inhibition. The results show that the cumulative effect of physical and chemical measures on algae growth inhibition was antagonistic; the cumulative effect of physical and biological measures was antagonistic; the cumulative effect of chemical and biological measures was synergistic, and the cumulative effect of all the measures together tended to be antagonistic. These results showed that the synergistic interactions between chemical and biological measures produced antagonistic effects when physical measures were added. Through response surface methodology analysis, we also found that the physical factor was the most significant factor affecting the cumulative effect, followed by the chemical factor and then the biological factor. Our results provide a more detailed understanding of the interaction patterns among multiple measures that affect algal growth. Importantly, this understanding can be further integrated into future strategy development to fully exploit the potential of the cumulative effect at its maximum performance.

As an important primary producer in aquatic ecosystems, the various parameters within the mathematical models are used to describe the growth of microalgae and need to be estimated by carefully designed experiments. Non-uniform sampling has proved to generate a deliberately optimized sampling temporal schedule that can benefit parameter estimation. However, the current non-uniform sampling method depends on prior knowledge of the nominal values of the model parameters. It also largely ignores the uncertainty associated with the nominal values, thus inducing unacceptable parameter estimates. This study focuses on the uncertainty problem and describes a new sampling design that couples the traditional uniform and non-uniform sampling schedules to benefit from the merits of both methods. Based on D-optimal design, we first derive the non-uniform optimal sampling points by maximizing the determinant of the Fisher information matrix. Then the confidence interval around the non-uniform sampling points is determined by Monte Carlo simulations based on the prior knowledge of parameter distribution. Finally, we wrap the non-uniform sampling points with the uniform sampling points within the confidence interval to obtain the ultimate optimal experimental design. Scenedesmus obliquus, whose growth curve follows a four-parameter model, was used as a case study. Compared with the traditional sampling design, the simulation results show that our proposed coupled sampling schedule can partly eliminate the uncertainty in parameter estimates caused by fixed systematic errors in observations. Our coupled sampling can also retain some advantages belonging to non-uniform sampling, in exploiting information maximization and managing the cost of sampling.

Sediment indices usually set heavy-metal priorities in mariculture bays, but uptake and regulation vary among taxa, so seafood priorities may differ. In a single-season survey of the Maowei Sea (Beibu Gulf) in April 2022, seven metals were measured in water (27 stations), sediment (15 stations) and seven commercial taxa, each represented by the mean of 12 separately analysed station composites. We compared their rankings across sediment risk, bioaccumulation and dietary hazard. Cd and Hg contributed 85% of the sediment ecological risk index. Biota-sediment accumulation factors were highest for Cd in clams, Zn in oyster, Cu in crustaceans and Hg in fish. At an assumed 10% inorganic-As fraction, dietary hazard indices exceeded 1 for hard clam (1.44) and mitten crab (1.04). At 5%, no taxon exceeded 1. Under both fractions, the leading dietary metal in most taxa was As or Cu, not Cd or Hg. Cd or Hg took over in clams and fish at lower fractions, but not in crustaceans or oyster. In this survey, sediment rankings were a weak guide to seafood priorities, which argues for monitoring by taxon and metal. Measuring tissue As speciation should come first, since the dietary ranking depends on it.

Multiple environmental stressors can directly or indirectly affect zooplankton community structure in marine coastal waters. However, quantifying the relative importance of eutrophication-mediated pathways remains challenging. In this study, we surveyed zooplankton, phytoplankton, and water conditions at 24 stations in Qinzhou Bay during spring and used correlation analysis and structural equation modelling (SEM) to examine the association between eutrophication and zooplankton biomass. The results showed that zooplankton biomass increased from the Inner Bay to the Outer Bay; whereas, zooplankton evenness decreased. Biomass was significantly negatively correlated with both the eutrophication index and zooplankton evenness; a single small copepod numerically dominated the assemblage, so high biomass and low evenness co-occurred as expressions of dominance. The SEM showed an acceptable fit and linked eutrophication to lower zooplankton biomass mainly through heavy-metal enrichment, community dominance (low evenness), and reduced phytoplankton density. The total association between eutrophication and biomass was −0.40, primarily indirect rather than through a significant direct path (−0.13, p = 0.51), with heavy-metal enrichment and community dominance (low evenness) as the main statistical mediators. Because the data come from a single spring survey of 24 stations, these path coefficients should be interpreted as correlational associations requiring multi-season confirmation.

Sediment pollution indices convert metal concentrations into contamination classes, hotspot rankings, and dominant-element assignments (the metal with the largest single-element risk factor, E_r). For seven metals at 49 stations in three northern Beibu Gulf bays, we examined sensitivity across nine geochemical backgrounds, three potential ecological risk index (RI) cut-point conventions, and a threshold effect level (TEL) toxicity-normalised denominator. First, for within-source envelope variation (varying one regional background across its published lower, central, and upper bounds), 62.9% of 294 station–metal geoaccumulation (I_geo) classes and 38.8% of station RI classes were unchanged; moving from lower to upper bounds reduced stations with pollution load index (PLI) > 1 from 31 to 12, reversing the pooled majority verdict without changing reference sources. Second, across four regional backgrounds at central values, agreement was 59.2% for I_geo class, 79.6% for PLI > 1, and 87.8% for RI class at fixed cut-points (42.9% across the 12 reference-by-cut-point combinations). Station rankings were robust across geochemical backgrounds (Spearman ρ = 0.895–1.000), whereas switching to the TEL-normalised construct changed the dominant element at 42 of 49 stations. Assessments should report benchmark sources, denominator meanings, cut-point conventions, and sensitivity across defensible choices.