Study the effect of different shapes of plume rise on Gaussian plume models and its maximum in unstable conditions

Essa Khaled SM, El Saied Sawsan EM, Wheida Aa, Naze Mustafa El
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Abstract

Exposure to radioactive pollutants such as Iodine-135 I135 seriously threatens public health and environmental balance. Monitoring and managing these pollutants require expensive economic equipment that is not suitable for low-income countries such as Egypt. Therefore, trying to derive a mathematical model that estimates the concentrations of these radioactive pollutants with high accuracy and a low relative error coefficient compared to the actually measured values ​​is very important. Therefore a mathematical Gaussian model was received to estimate the concentrations of I135 emitted from the research nuclear reactor in the Inshas region in Egypt using different shapes of plume rise in unstable conditions. A comparison between the derived model and its maximum values with observed concentrations data measuring on Egyptian Atomic Energy Authority for I135 in unstable conditions has been found. The derived model may be applied to estimate and predict the emissions of any radioactive pollutant for any similar area and similar type of used reactor, which provides high-precision technology with zero economic cost. The statistical technique shows that the entire Gaussian model and its maximum inside a factor of two with observed concentration data achieved 98%. In addition, the statistics show that all the Gaussian plume models and their maximum have a correlation coefficient of about 0.95%. Also, the normalized mean square error. And the fraction bias. are near-zero values in all Gaussian models and their maximum.
研究了不同羽流上升形状对高斯羽流模型的影响及其在不稳定条件下的最大值
暴露于碘- 135i135等放射性污染物严重威胁公众健康和环境平衡。监测和管理这些污染物需要昂贵的经济设备,而这些设备不适合埃及等低收入国家。因此,试图推导出一种数学模型,以高精度和较低的相对误差系数来估计这些放射性污染物的浓度,与实际测量值相比是非常重要的。因此,采用数学高斯模型,利用不稳定条件下不同形状的羽流上升,估计了埃及因沙斯地区研究用核反应堆排放的I135浓度。导出的模型及其最大值与埃及原子能机构在不稳定条件下测量的I135的观测浓度数据进行了比较。所导出的模型可用于估算和预测任何类似地区和类似类型的乏反应堆的任何放射性污染物的排放量,提供了零经济成本的高精度技术。统计技术表明,整个高斯模型及其在两个因子内的最大值与观测浓度数据达到98%。此外,统计表明,所有高斯羽流模型及其最大值的相关系数约为0.95%。同样,归一化均方误差。分数偏差。在所有高斯模型及其最大值中都接近于零。
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