逸度模型在新污染物多介质归趋研究中的应用

Application of fugacity model in the study of multi-media fate of emerging contaminants

  • 摘要: 基于逸度方法的逸度模型不仅适用于预测新污染物在环境各介质中的浓度水平,还可以揭示区域内污染的空间分布特征,是新污染物管理中的重要工具。总结分析了逸度模型的发展历程及模型分级,将常用逸度模型依据不同区域尺度进行划分,归纳了其研究应用,探讨了逸度模型的优缺点并展望了未来的改进方向。结果显示,逸度模型在模拟新污染物归趋方面具有良好适用性,其应用范围广泛,涵盖了从湖泊河流至区域、大陆及全球范围内新污染物的多介质归趋,学者们通过改进开发出适用特定区域或具有不同输出结果的逸度模型,将其应用于新污染物浓度模拟、长期停留时间模拟、远距离迁移模拟等方面,其预测结果较为可靠,为新污染的风险评估和控制管理提供了可靠的决策支持。当前逸度模型存在的问题包括输出结果高度依赖输入数据的可靠性、对环境条件的动态变化考虑不充足、性能验证过于依赖监测数据以及新污染物理化性质带来的较大不确定性等。未来改进需要提高污染物排放参数的准确性,并在模型参数及结构方面进行优化,完善相关的质量保证数据以提高模型的可靠性,通过模型的耦合或联用以提升模型性能。

     

    Abstract: The fugacity model based on the fugacity method is not only applicable to predicting the concentration levels of emerging pollutants in various environmental media, but also can reveal the spatial distribution characteristics of pollution within a region. It is an important tool in the management of emerging pollutants. We summarizes and analyzes the development history and classification of fugacity model, divide the common fugacity models according to different regional scales, and discuss their current research and applications. We also discuss the advantages and disadvantages of the models and propose the improvement directions. The results indicate that the fugacity model has good applicability in simulating the fate of emerging pollutants. Its application scope is broad, covering the multi-media fate of emerging pollutants from lakes and rivers to regional and global scales. Different versions of models have been developed to simulate emerging pollutant concentration, long-term residence, and long-distance migration, with relatively reliable predictive results that provide dependable decision support for risk assessment and management of emerging pollutants. The problems of current fugacity models include high dependence of output on reliability of input data, insufficient consideration of dynamic changes of environmental conditions, excessive dependence of performance verification on monitoring data and large uncertainty caused by physical and chemical properties of emerging pollutants. In the future, it is necessary to improve the accuracy of pollutant emission parameters, optimize the model parameters and structure, improve the reliability of the model by improving the relevant quality assurance data, and improve the model performance by coupling or combining the models.

     

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