Tritium plume observations in Lake Huron: measurements of dispersion and comparison of lateral plume structure with the normal distribution of the Gaussian Plume Model

Q2 Environmental Science
Volodymyr Korolevych, Dan Festarini
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引用次数: 0

Abstract

This study describes observations of the near-shore transport and dispersion of routine but intermittent tritium emissions from the Bruce Nuclear Generating Station into Lake Huron, which are used as a tracer for inferring dispersion parameters. Study region covers 45 km along the shore northward, 38 km southward, and 18 km off the shore. Currents were recorded by GPS drifters deployed in the study region, and tritium concentrations in the effluent plume were collected both along drifter trajectories and across them. Two episodes were analysed: one with the plume going into the headwind and one with a regular wind-driven plume. In situ dispersion was calculated from tritium plume observations. The highest concentrations encountered at each distance from the source were combined to locate the plume centreline and deduce scaling (dependence on the distance to the source) according to a power-law fit to these concentrations. Lateral distribution of concentration in the plume was Gaussian Plume Model parameterised with this in situ dispersion and then applied to remaining sampling locations, that is, to all off-centreline observations to model predictions.
The headwind episode yielded a scaling exponent of dispersion α = 0.711 and magnitude Sy = 11.3, and the episode of a regular wind-driven plume yielded α = 0.411 and Sy = 25.9. Gaussian Plume Model from SRS-19 was deployed with these parameters. Across the analyzed region model predictions were characterized by R2 = 0.34 and Pearson r = 0.36 during the headwind episode and R2 = 0.72 with r = 0.71 during the regular wind-driven episode.
休伦湖氚羽流观测:分散的测量和与高斯羽流模型正态分布的横向羽流结构的比较
本研究描述了从布鲁斯核电站向休伦湖排放的常规但间歇的氚的近岸运输和扩散的观测结果,这些氚被用作推断扩散参数的示踪剂。研究区域沿海岸向北45公里,向南38公里,离海岸18公里。部署在研究区域的GPS漂浮器记录了洋流,并沿漂浮器轨迹和漂浮器轨迹收集了污水羽流中的氚浓度。他们分析了两个事件:一个是羽流进入逆风,另一个是常规的风驱动羽流。根据氚羽流观测计算了原位弥散。在距离源的每个距离处遇到的最高浓度被结合起来,以确定羽流中心线的位置,并根据与这些浓度相符的幂律推导出尺度(取决于与源的距离)。羽流中浓度的横向分布是高斯羽流模型,用这种原位分散参数化,然后应用于剩余的采样位置,即所有离中心线观测到的模型预测。逆风期的尺度指数为α = 0.711,震级为Sy = 11.3;规则风羽期的尺度指数为α = 0.411,震级为Sy = 25.9。利用这些参数部署了SRS-19的高斯羽流模型。逆风期模型预测R2 = 0.34, Pearson r = 0.36;常规风期模型预测R2 = 0.72, r = 0.71。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Environmental Challenges
Environmental Challenges Environmental Science-Environmental Engineering
CiteScore
8.00
自引率
0.00%
发文量
249
审稿时长
8 weeks
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