4.7 Article

Inversion of the Degradation Coefficient of Petroleum Hydrocarbon Pollutants in Laizhou Bay

Journal

Publisher

MDPI
DOI: 10.3390/jmse9060655

Keywords

petroleum hydrocarbon pollutants; degradation coefficient; Laizhou Bay; adjoint method

Funding

  1. National key research and development program [2018YFC1407602, 2019YFC1408405]
  2. National Natural Science Foundation of China [42076011, U1806214]
  3. Shandong Provincial Natural Science Foundation, China [ZR2019MD010]
  4. Innovation and Strengthen Project of Guangdong Province [2018KQNCX082]

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This paper investigates the degradation characteristics of petroleum hydrocarbon pollutants in the ocean using a model of transport and degradation, and validates the feasibility and accuracy of the model through experiments. The findings provide important reference for studying pollutant degradation.
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.

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