基于使用LASSO回归和KURAMA数据的生态半衰期概况的环境剂量等效率分布的预测模型的开发
Yoshiaki Shikaze1, Kimiaki Saito2, Naoki Tanimura3
1Nuclear Emergency Assistance and Training Center, Nuclear Safety and Emergency Preparedness Institute, Japan Atomic Energy Agency, 178-4-4 Wakashiba, Kashiwa, Chiba 227-0871, Japan.
Radiation protection dosimetry
|July 24, 2025
概括
两组件模型通常很适合福岛空气剂量速率衰变,但更简单的模型在约20%的情况下更好. 生态半衰期因土地使用和初始剂量率而异.
科学领域:
- 环境科学 环境科学
- 辐射保护 辐射保护
- 核安全问题 核安全问题
背景情况:
- 两组件模型通常用于描述核事故后空气剂量速率 (ADR) 衰减.
- 验证该模型的充分性和了解生态半衰期对于准确的污染评估至关重要.
研究的目的:
- 评估福岛ADR衰变的两个组件模型的充分性.
- 为了研究放射性在不同土地使用类型中的生态半衰期概况.
- 评估初始ADR对衰变速率的影响.
主要方法:
- 从福岛第一核电站事故 (2011-2016) 获得的广泛的汽车运输调查数据的分析.
- 应用最小绝对收缩和选择操作员 (LASSO) 回归与高自由度模型.
- 对ADR衰变配件的一组件和两组件模型的比较.
- 为未来的ADR分配开发和验证一个预测模型.
主要成果:
- 在大多数福岛病例中,双组件模型充分近似了ADR衰变,但在约20%的病例中,单组件模型优越.
- 快速衰变组件的生态半衰期达到1年 (0.3-0.4年) 以下,而缓慢衰变组件的峰值更为广泛 (几年至50年).
- 城市地区的ADR降低速度最快,森林的ADR降低速度最慢,由于快速衰变成分的影响,更快的衰变与更高的初始ADR相关.
结论:
- 两组件模型通常适合,但对于福岛的ADR衰变分析并不普遍最佳.
- 生态半衰期的概况取决于土地使用情况,并受到初始污染水平的影响.
- 结合这些因素的预测模型显示出很好的准确性,但突出了道路通道区域和森林之间的衰变率差异.
更多相关视频
相关概念视频
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
127
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
127
Mechanistic Models: Compartment Models in Individual and Population Analysis
87
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
87
Residuals and Least-Squares Property
7.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.8K
Regression Analysis
6.0K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.0K
Multiple Regression
3.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.2K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
717
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
717


