Oil spills have immediate adverse effects on marine ecological functions. Accurate assessment of the damage caused by the oil spill is of great significance for the protection of marine ecosystems. In this study the observation data of Chaetoceros and shellfish before and after the Penglai 19-3 oil spill in the Bohai Sea were analyzed by the least-squares fitting method and radial basis function (RBF) interpolation. Besides, an oil transport model is provided which considers both the hydrodynamic mechanism and monitoring data to accurately simulate the spatial and temporal distribution of total petroleum hydrocarbons (TPH) in the Bohai Sea. It was found that the abundance of Chaetoceros and shellfish exposed to the oil spill decreased rapidly. The biomass loss of Chaetoceros and shellfish are 7.25×1014~7.28×1014 ind and 2.30×1012~2.51×1012 ind in the area with TPH over 50 mg/m3 during the observation period, respectively. This study highlights the evaluation of ecological resource loss caused by the oil spill, which is useful for the protection and restoration of the biological resources following the oil spill.

When petroleum hydrocarbon pollutants enter the ocean, besides the migration under hydrodynamic constraints, their degradation due to environmental conditions also occurs. However, available observations are usually spatiotemporally disperse, which makes it difficult to study the degradation characteristics of pollutants. In this paper, a model of transport and degradation is used to estimate the degradation coefficient of petroleum hydrocarbon pollutants with the adjoint method. Firstly, the results of a comprehensive physical–chemical–biological test of the degradation of petroleum hydrocarbon pollutants in Laizhou Bay provide a reference for setting the degradation coefficient on the time scale. In ideal twin experiments, the mean absolute errors between observations and simulation results obtain an obvious reduction, and the given distributions can be inverted effectively, demonstrating the feasibility of the model. In a practical experiment, the actual distribution of petroleum hydrocarbon pollutants in Laizhou Bay is simulated, and the simulation results are in good agreement with the observed ones. Meanwhile, the spatial distribution of the degradation coefficient is inverted, making the simulation results closer to the actual observations.

The setting of initial values is one of the key problems in ocean numerical prediction, with the accuracy of sea water temperature (SWT) simulation and prediction greatly affected by the initial field quality. In this paper, we describe the development of an adjoint assimilation model of temperature transport used to invert the initial temperature field by assimilating the observed data of sea surface temperature (SST) and vertical temperature. Two ideal experiments were conducted to verify the feasibility and validity of this method. By assimilating the “observed data”, the mean absolute error (MAE) between the simulated temperature data and the “observed data” decreased from 1.74 °C and 1.87 °C to 0.13 °C and 0.14 °C, respectively. The spatial distribution of SST difference and the comparison of vertical data also indicate that the regional error of vertical data assimilation is smaller. In the practical experiment, the monthly average temperature field provided by World Ocean Atlas 2018 was selected as background filed and optimized by assimilating the SST data and Argo vertical temperature observation data, to invert the temperature field at 0 a.m. on 1 December 2014 in the South China Sea. Through data assimilation, MAE was reduced from 1.29 °C to 0.65 °C. In terms of vertical observations data comparison and SST spatial distribution, the temperature field obtained by inversion is in good agreement with SST and Argo observations.