Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084, PR China.
Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084, PR China.
J Hazard Mater. 2014 Sep 15;280:143-55. doi: 10.1016/j.jhazmat.2014.07.064. Epub 2014 Aug 10.
Atmospheric dispersion models play an important role in nuclear power plant accident management. A reliable estimation of radioactive material distribution in short range (about 50 km) is in urgent need for population sheltering and evacuation planning. However, the meteorological data and the source term which greatly influence the accuracy of the atmospheric dispersion models are usually poorly known at the early phase of the emergency. In this study, a modified ensemble Kalman filter data assimilation method in conjunction with a Lagrangian puff-model is proposed to simultaneously improve the model prediction and reconstruct the source terms for short range atmospheric dispersion using the off-site environmental monitoring data. Four main uncertainty parameters are considered: source release rate, plume rise height, wind speed and wind direction. Twin experiments show that the method effectively improves the predicted concentration distribution, and the temporal profiles of source release rate and plume rise height are also successfully reconstructed. Moreover, the time lag in the response of ensemble Kalman filter is shortened. The method proposed here can be a useful tool not only in the nuclear power plant accident emergency management but also in other similar situation where hazardous material is released into the atmosphere.
大气扩散模型在核电厂事故管理中起着重要作用。为了进行人口掩蔽和疏散规划，迫切需要对短距离（约 50 公里）内的放射性物质分布进行可靠估计。然而，在紧急情况的早期阶段，对大气扩散模型的准确性有很大影响的气象数据和源项通常知之甚少。在本研究中，提出了一种改进的集合卡尔曼滤波数据同化方法，结合拉格朗日烟羽模型，利用场外环境监测数据，同时改进模型预测并重建短距离大气扩散的源项。考虑了四个主要的不确定性参数：源释放率、烟羽抬升高度、风速和风向。双试验表明，该方法有效地改善了预测浓度分布，并且成功地重建了源释放率和烟羽抬升高度的时间分布。此外，集合卡尔曼滤波的响应时间延迟也缩短了。该方法不仅可以在核电厂事故应急管理中，而且在其他类似的大气中释放有害物质的情况下，都可以作为一种有用的工具。
