Benà Eleonora, Ciotoli Giancarlo, Petermann Eric, Bossew Peter, Ruggiero Livio, Verdi Luca, Huber Paul, Mori Federico, Mazzoli Claudio, Sassi Raffaele
Dipartimento di Geoscienze, Università di Padova, Padova, Italy.
Istituto di Geologia Ambientale e Geoingegneria (IGAG), Consiglio Nazionale delle Ricerche (CNR), Roma, Italy; Istituto Nazionale di Geofisica e Vulcanologia (INGV), Roma, Italy.
Sci Total Environ. 2024 Feb 20;912:169569. doi: 10.1016/j.scitotenv.2023.169569. Epub 2023 Dec 27.
Radon is a radioactive gas and a major source of ionizing radiation exposure for humans. Consequently, it can pose serious health threats when it accumulates in confined environments. In Europe, recent legislation has been adopted to address radon exposure in dwellings; this law establishes national reference levels and guidelines for defining Radon Priority Areas (RPAs). This study focuses on mapping the Geogenic Radon Potential (GRP) as a foundation for identifying RPAs and, consequently, assessing radon risk in indoor environments. Here, GRP is proposed as a hazard indicator, indicating the potential for radon to enter buildings from geological sources. Various approaches, including multivariate geospatial analysis and the application of artificial intelligence algorithms, have been utilised to generate continuous spatial maps of GRP based on point measurements. In this study, we employed a robust multivariate machine learning algorithm (Random Forest) to create the GRP map of the central sector of the Pusteria Valley, incorporating other variables from census tracts such as land use as a vulnerability factor, and population as an exposure factor to create the risk map. The Pusteria Valley in northern Italy was chosen as the pilot site due to its well-known geological, structural, and geochemical features. The results indicate that high Rn risk areas are associated with high GRP values, as well as residential areas and high population density. Starting with the GRP map (e.g., Rn hazard), a new geological-based definition of the RPAs is proposed as fundamental tool for mapping Collective Radon Risk Areas in line with the main objective of European regulations, which is to differentiate them from Individual Risk Areas.
氡是一种放射性气体,是人类电离辐射暴露的主要来源。因此,当它在密闭环境中积聚时,会对健康构成严重威胁。在欧洲,最近已通过立法来解决住宅中的氡暴露问题;该法律规定了国家参考水平以及定义氡优先区域(RPA)的指南。本研究着重绘制地质成因氡潜力(GRP)图,作为识别RPA的基础,并据此评估室内环境中的氡风险。在此,GRP被提议作为一种危害指标,表明氡从地质源进入建筑物的可能性。已采用多种方法,包括多元地理空间分析和人工智能算法的应用,基于点测量生成GRP的连续空间图。在本研究中,我们使用了一种强大的多元机器学习算法(随机森林)来创建普斯特里亚山谷中部地区的GRP图,纳入了来自人口普查区的其他变量,如作为脆弱性因素的土地利用以及作为暴露因素的人口,以创建风险图。意大利北部的普斯特里亚山谷因其众所周知的地质、构造和地球化学特征而被选为试点地区。结果表明,高氡风险区域与高GRP值以及居民区和高人口密度相关。从GRP图(例如,氡危害)出发,提出了一种基于地质的RPA新定义,作为绘制集体氡风险区域的基本工具,这与欧洲法规的主要目标一致,即把它们与个体风险区域区分开来。