Spatial modeling of geogenic indoor radon distribution in Chungcheongnam-do, South Korea using enhanced machine learning algorithms

Rezaie, Fatemeh; Panahi, Mahdi; Bateni, Sayed M.; Kim, Seonhong; Lee, Jongchun; Lee, Jungsub; Yoo, Juhee; Kim, Hyesu; Kim, Sung Won; Lee, Saro

doi:10.1016/j.envint.2022.107724

Cited by 11 publications

(2 citation statements)

References 117 publications

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“…Based on part of the data, Li et al developed an ensemble learning model to estimate monthly ZCTA-level radon concentrations for ZCTAs in Greater Boston, a densely populated fraction of this study region . Multiple studies used other classic statistical learning methods to model the spatial distributions of radon concentrations in different parts of the world. ,− In this study, we developed an innovative geographical machine learning method to model the complex relationships between ZCTA-level radon concentrations and various predictors. This approach was particularly useful for studying large and heterogeneous areas, where the relationships between radon and predictors may vary significantly across space.…”

Section: Introductionmentioning

confidence: 99%

Predicting Monthly Community-Level Radon Concentrations with Spatial Random Forest in the Northeastern and Midwestern United States

Li,

Stern,

Garshick

et al. 2023

Environ. Sci. Technol.

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In 1987, the United States Environmental Protection Agency recommended installing a mitigation system when the indoor concentration of radon, a well-known carcinogenic radioactive gas, is at or above 148 Bq/m 3 . In response, tens of millions of short-term radon measurements have been conducted in residential buildings over the past three decades either for disclosure or to initially evaluate the need for mitigation. These measurements, however, are currently underutilized to assess population radon exposure in epidemiological studies. Based on two relatively small radon surveys, Lawrence Berkeley National Laboratory developed a state-of-the-art national radon model. However, this model only provides coarse and invariant radon estimations, which limits the ability of epidemiological studies to accurately investigate the health effects of radon, particularly the effects of acute exposure. This study involved obtaining over 2.8 million historical short-term radon measurements from independent laboratories. With the use of these measurements, an innovative spatial random forest (SRF) model was developed based on geological, architectural, socioeconomical, and meteorological predictors. The model was used to estimate monthly community-level radon concentrations for ZIP Code Tabulation Areas (ZCTAs) in the northeastern and midwestern regions of the United States from 2001 to 2020. Via cross-validation, we found that our ZCTA-level predictions were highly correlated with observations. The prediction errors declined quickly as the number of radon measurements in a ZCTA increased. When ≥15 measurements existed, the mean absolute error was 24.6 Bq/m 3 , or 26.5% of the observed concentrations (R 2 = 0.70). Our study demonstrates the potential of the large amount of historical short-term radon measurements that have been obtained to accurately estimate longitudinal ZCTA-level radon exposures at unprecedented levels of resolutions and accuracy.

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Section: Introductionmentioning

confidence: 99%

Predicting Monthly Community-Level Radon Concentrations with Spatial Random Forest in the Northeastern and Midwestern United States

Li,

Stern,

Garshick

et al. 2023

Environ. Sci. Technol.

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“…CaO and Sr are pertinent indicators in the geochemical composition of the soil (Liu et al 2013; Wen et al 2020) and their consideration is imperative for analyzing IRC maps (Cho et al 2015).Topographic factors (Fig.5) are another facet of IRC analysis. Slope, the topographic wetness index (TWI), wind exposition, valley depth, and slope length and the steepness (LS) factor collectively contribute to how the surrounding landscape in uences radon entry into buildings, in uencing radon migration pathways and concentration variation(Cho et al 2015;Rezaie et al 2023). …”

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confidence: 99%

Mapping Indoor Radon Concentrations in Chungcheongbuk-do, South Korea: A Geospatial Analysis using Machine Learning Models

Widya,

Rezaie,

Lee

et al. 2024

Preprint

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Radon is a naturally occurring radioactive gas found in many terrestrial materials. Due to the potential health risks linked to persistent exposure to high radon concentrations, it is essential to investigate indoor radon accumulation. This study generated indoor radon index maps for Chungcheongbuk-do, South Korea, selected factors with frequency ratios (FRs) and validated them using the FR, convolutional neural network, long short-term memory, and group method of data handling machine learning models. The establishment of a geospatial database provided a basis for the integration and analysis of indoor radon concentrations (IRCs), along with relevant geological, soil, topographical, and geochemical data. The study calculated the correlations between IRC and diverse factors statistically. The IRC potential was mapped for Chungcheongbuk-do by applying the above techniques, to assess the potential radon distribution. The robustness of the validated model was assessed using the area under the receiver operating curve.

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Machine Learning for Indoor Air Quality Assessment: A Systematic Review and Analysis

Saini,

Dutta,

Marques

2024

Environ Model Assess

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Spatial modeling of geogenic indoor radon distribution in Chungcheongnam-do, South Korea using enhanced machine learning algorithms

Cited by 11 publications

References 117 publications

Predicting Monthly Community-Level Radon Concentrations with Spatial Random Forest in the Northeastern and Midwestern United States

Predicting Monthly Community-Level Radon Concentrations with Spatial Random Forest in the Northeastern and Midwestern United States

Mapping Indoor Radon Concentrations in Chungcheongbuk-do, South Korea: A Geospatial Analysis using Machine Learning Models

Machine Learning for Indoor Air Quality Assessment: A Systematic Review and Analysis

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