As the global-nuclear-capacity-tripling plan is implemented, reconstruction of the source locations and release rates of atmospheric radionuclides becomes increasingly important for the environment and human health. However, such reconstruction is vulnerable to unrealistic solutions because it is ill-posed. This study proposed a spatiotemporally constrained reconstruction method that excludes false estimates and achieves high accuracy. It uses the Spearman’s correlation coefficient to constrain the spatial search range and applies the L2 cost-function within this range to retrieve the source location. Using this location, time-varying release rates are estimated with non-smooth constraints, which simultaneously reconstructs the peak releases and removes unrealistic oscillations. Validation against both field experiments and real-world events demonstrated that this method effectively excludes false source locations. The estimated location is up to 96.27%, 98.31%, and 96.48% closer to the reported sources than those of the L2 cost-function, Pearson-correlation-constrained L2 cost-function, and Bayesian methods, respectively. The estimated release rates matched the reported time windows and total amounts, avoiding the unrealistic oscillations in other estimation methods. The proposed method exhibited superior performance and speed over the L2 cost-function method under different station layouts and numbers. Furthermore, it could improve other methods using different cost functions, indicating its potential for various applications.

Environmental Implication

Source reconstruction of atmospheric radionuclide releases is becoming increasingly important for the environment and human health because of the persistent leakage risk and increased demands following the global-nuclear-capacity-tripling announcement. We propose a spatiotemporally constrained reconstruction method that adaptively excludes false source locations and unrealistic temporal variations, both of which are long-standing problems in reconstruction of real events, and achieves robust performance under scenarios with different observation sparsity and station layouts. Additionally, the proposed approach can improve existing methods, especially with limited observations, thereby representing a versatile tool both for global radioactivity surveillance and environmental process investigation based on radioactivity tracers.

中文翻译：

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基于空间预选和非平滑约束的不同稀疏度观测对大气放射性核素的鲁棒源重建


随着全球核能容量三倍计划的实施，重建大气放射性核素的源位置和释放速率对环境和人类健康变得越来越重要。然而，这种重建很容易受到不切实际的解决方案的影响，因为它是错误的。本研究提出了一种排除错误估计并实现高精度的时空约束重建方法。它使用 Spearman 相关系数来约束空间搜索范围，并在此范围内应用 L2 成本函数来检索源位置。使用这个位置，使用非平滑约束估计时变释放速率，同时重建峰值释放并消除不切实际的振荡。针对现场实验和真实世界事件的验证表明，该方法有效地排除了错误的源位置。与 L2 成本函数、Pearson 相关约束的 L2 成本函数和贝叶斯方法相比，估计位置分别接近报告的来源 96.27%、98.31% 和 96.48%。估计的释放速率与报告的时间窗口和总量相匹配，避免了其他估计方法中不切实际的振荡。在不同站点布局和数量下，所提出的方法表现出优于 L2 成本函数方法的性能和速度。此外，它可以使用不同的成本函数改进其他方法，表明其在各种应用中的潜力。

环境影响

由于持续的泄漏风险和全球核能能力三倍宣布后需求增加，大气放射性核素释放的源重建对环境和人类健康变得越来越重要。我们提出了一种时空约束重建方法，该方法自适应地排除了虚假源位置和不切实际的时间变化，这两者都是真实事件重建中长期存在的问题，并在不同观测稀疏性和站布局的情景下实现了鲁棒性能。此外，所提出的方法可以改进现有方法，尤其是在观测有限的情况下，从而代表了全球放射性监测和基于放射性示踪剂的环境过程调查的通用工具。