Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units

Kropat, Georg; Bochud, François; Jaboyedoff, Michel; Laedermann, Jean-Pascal; Murith, Christophe; Palacios, Martha; Baechler, Sébastien

doi:10.1016/j.jenvrad.2015.05.006

Cited by 35 publications

(22 citation statements)

References 55 publications

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“…The geogenic radon potential plays the main role for radon risk maps, although this approach implies an extensive description of the local geology, uranium and radium contents, soil-gas radon, and permeability [15]. Currently, there is no consensus on a generally accepted method for geogenic, indoor and groundwater radon risk mapping [16][17][18][19][20][21][22][23][24][25], but there is an enormous effort by the scientific community to develop a new methodology for a conceptual geogenic radon hazard index that explains all the complex system of radon production and transport into different anthropogenic compartments and natural systems [17,21,26]. Despite the reduced contribution of radon contamination in groundwater on the overall radon risk, the groundwater recharge coefficient was included as a geogenic factor in the geogenic radon hazard index proposed by Bossew et al [26].…”

Section: Introductionmentioning

confidence: 99%

An Assessment of Groundwater Contamination Risk with Radon Based on Clustering and Structural Models

Martins

Pereira

Oliveira

et al. 2019

Water

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There is currently some controversy in the scientific community regarding the efficiency of the water–rock interaction process in the contamination of radon in groundwater. In this study, some difficulties were found in the sampling phase. Many of the water collection points are used for human consumption. As such, some municipalities did not want to collaborate. When this natural contaminant is undetectable to the human sense and may cause pulmonary neoplasms in the long term, it is difficult to obtain collaboration from the municipalities concerned. To overcome this controversy, it is important to understand that geogenic, climatic, hydrological, and topographic features may contribute to the effective transfer of radon from rocks to groundwater. In brief, this new approach combines the radon transfer from the geological substrate to the groundwater circulation through hierarchic agglomerative clustering (HAC) and partial least squares-path modeling (PLS-PM) methods. The results show that some lithologies with higher radon production may not always contribute to noticeable groundwater contamination. In this group, the high-fracturing density confirms the recharge efficiency, and the physical-chemical properties of the hydraulic environment (electric conductivity) plays the main role of radon unavailability in the water intended for human consumption. Besides, the hydraulic turnover time of the springs can be considered an excellent radiological indicator in groundwater. In the absence of an anomalous radioactive source near the surface, it means that the high-turnover time of the springs leads to a low-radon concentration in the water. Besides linking high-risk areas with a short period required to free local flow discharges, this study exposes the virtues of a new perspective of a groundwater contamination risk modeling.

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Section: Introductionmentioning

confidence: 99%

An Assessment of Groundwater Contamination Risk with Radon Based on Clustering and Structural Models

Martins

Pereira

Oliveira

et al. 2019

Water

View full text Add to dashboard Cite

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“…The application of an explorative statistical technique as performed via a principal component analysis (PCA) on several covariates was developed by [31], thus using the first PC as GRHI. Recent attempts ( [32,33,52,53]) utilized machine learning (ML) methods, which are considered particularly powerful for "high dimensional" multivariate settings and in particular, also for confirmative statistical techniques such as spatial regression (i.e., statistical approaches with many predictors).…”

Section: History Of the Geogenic Radon Hazard Indexmentioning

confidence: 99%

Development of a Geogenic Radon Hazard Index—Concept, History, Experiences

Bossew

Cinelli

Ciotoli

et al. 2020

IJERPH

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Exposure to indoor radon at home and in workplaces constitutes a serious public health risk and is the second most prevalent cause of lung cancer after tobacco smoking. Indoor radon concentration is to a large extent controlled by so-called geogenic radon, which is radon generated in the ground. While indoor radon has been mapped in many parts of Europe, this is not the case for its geogenic control, which has been surveyed exhaustively in only a few countries or regions. Since geogenic radon is an important predictor of indoor radon, knowing the local potential of geogenic radon can assist radon mitigation policy in allocating resources and tuning regulations to focus on where it needs to be prioritized. The contribution of geogenic to indoor radon can be quantified in different ways: the geogenic radon potential (GRP) and the geogenic radon hazard index (GRHI). Both are constructed from geogenic quantities, with their differences tending to be, but not always, their type of geographical support and optimality as indoor radon predictors. An important feature of the GRHI is consistency across borders between regions with different data availability and Rn survey policies, which has so far impeded the creation of a European map of geogenic radon. The GRHI can be understood as a generalization or extension of the GRP. In this paper, the concepts of GRP and GRHI are discussed and a review of previous GRHI approaches is presented, including methods of GRHI estimation and some preliminary results. A methodology to create GRHI maps that cover most of Europe appears at hand and appropriate; however, further fine tuning and validation remains on the agenda.

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“…58 Compared to MLR, PLS can reduce the dimension of the original variables while maintaining important information. 59 Cross-validation is used to determine the optimal number of components.…”

Section: Regressionsmentioning

confidence: 99%

“…Therefore, RFR as an ensemble of regression trees is the most frequently used decision tree-based model. 59 (1)…”

Section: Decision Treesmentioning

confidence: 99%

Machine learning and statistical models for predicting indoor air quality

et al. 2019

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Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically based mechanistic models or statistical models that are driven by measured data. In comparison with mechanistic models mostly used in unoccupied or scenario-based environments, statistical models have great potential to explore IAQ captured in large measurement campaigns or in real occupied environments. The present study carried out the first literature review of the use of statistical models to predict IAQ. The most commonly used statistical modeling methods were reviewed and their strengths and weaknesses discussed.Thirty-seven publications, in which statistical models were applied to predict IAQ, were identified. These studies were all published in the past decade, indicating the emergence of the awareness and application of machine learning and statistical modeling in the field of IAQ. The concentrations of indoor particulate matter (PM 2.5 and PM 10 ) were the most frequently studied parameters, followed by carbon dioxide and radon. The most popular statistical models applied to IAQ were artificial neural networks, multiple linear regression, partial least squares, and decision trees. K E Y W O R D Sartificial neural networks, data mining, IAQ, partial least squares, particulate matter, regression | 705 WEI Et al.

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Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units

Cited by 35 publications

References 55 publications

An Assessment of Groundwater Contamination Risk with Radon Based on Clustering and Structural Models

An Assessment of Groundwater Contamination Risk with Radon Based on Clustering and Structural Models

Development of a Geogenic Radon Hazard Index—Concept, History, Experiences

Machine learning and statistical models for predicting indoor air quality

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