A novel physics-informed AI framework for the assessment and prediction of indoor radon concentration and risk classification

IF 1.8 3区工程技术 Q3 CHEMISTRY, INORGANIC & NUCLEAR

Applied Radiation and Isotopes Pub Date : 2026-06-01 Epub Date: 2026-02-27 DOI:10.1016/j.apradiso.2026.112533

Mutlu Zeybek

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引用次数: 0

Abstract

Indoor radon gas is a leading environmental cause of lung cancer, yet accurate risk assessment remains challenging due to the practical difficulties of direct measurement. This study introduces a novel Physics-Informed Neural Network (PINN) framework that integrates physical laws of radon transport with machine learning to predict indoor radon concentrations (Qt). Our Geologically-Informed Radon Assessment (GIRA) model incorporates radon contributions from geological foundations (Qg), faults (Qf), and building materials (Qb), while accounting for building porosity. When validated against a dataset of 957 structures in Western Türkiye, the PINN model significantly outperformed conventional machine learning approaches, achieving a Mean Absolute Error of 52 Bq/m³ and R² of 0.96. The framework successfully identified 15.3% of structures as high-risk (>300 Bq/m³), demonstrating its capability for automated radon risk classification. This physics-informed approach provides a robust, interpretable, and cost-effective tool for proactive public health planning and targeted radon mitigation strategies, establishing a new paradigm in environmental hazard assessment.

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一种新的基于物理的人工智能框架，用于评估和预测室内氡浓度和风险分类。

室内氡气是肺癌的主要环境原因，但由于直接测量的实际困难，准确的风险评估仍然具有挑战性。本研究引入了一种新的物理信息神经网络（PINN）框架，该框架将氡传输的物理定律与机器学习相结合，以预测室内氡浓度（Qt）。我们的地质信息氡评估（GIRA）模型结合了地质基础（Qg）、断层（Qf）和建筑材料（Qb）的氡贡献，同时考虑了建筑孔隙度。当在Western trkiye中对957个结构的数据集进行验证时，PINN模型显著优于传统的机器学习方法，平均绝对误差为52 Bq/m3， R2为0.96。该框架成功地将15.3%的结构识别为高风险（bbb300 Bq/m3），证明了其自动氡风险分类的能力。这种了解物理的方法为积极主动的公共卫生规划和有针对性的氡缓解战略提供了一种强有力的、可解释的和具有成本效益的工具，建立了环境危害评估的新范式。

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

Applied Radiation and Isotopes 工程技术-核科学技术

CiteScore

3.00

自引率

12.50%

发文量

406

审稿时长

13.5 months

期刊介绍： Applied Radiation and Isotopes provides a high quality medium for the publication of substantial, original and scientific and technological papers on the development and peaceful application of nuclear, radiation and radionuclide techniques in chemistry, physics, biochemistry, biology, medicine, security, engineering and in the earth, planetary and environmental sciences, all including dosimetry. Nuclear techniques are defined in the broadest sense and both experimental and theoretical papers are welcome. They include the development and use of α- and β-particles, X-rays and γ-rays, neutrons and other nuclear particles and radiations from all sources, including radionuclides, synchrotron sources, cyclotrons and reactors and from the natural environment. The journal aims to publish papers with significance to an international audience, containing substantial novelty and scientific impact. The Editors reserve the rights to reject, with or without external review, papers that do not meet these criteria. Papers dealing with radiation processing, i.e., where radiation is used to bring about a biological, chemical or physical change in a material, should be directed to our sister journal Radiation Physics and Chemistry.