Quantile regression and Bayesian cluster detection to identify radon prone areas

Sarra, Annalina; Fontanella, Lara; Valentini, Pasquale; Palermi, Sergio

doi:10.1016/j.jenvrad.2016.06.014

Cited by 13 publications

(4 citation statements)

References 53 publications

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“…Ver sions that are more elab o rate at tempt to de velop quan ti ta tive mod els of the spa tial struc ture us ing geostatistical tools. This in cludes ma chine learn ing [7,8], quantile re gres sion [9] and lo cal regres sion [10]. A hi er ar chi cal re gres sion model where spa tial de pend ence en ters via li thol ogy as a pre dic tor has been pro posed in [11] and fur ther de vel oped as a generalized ad di tive mixed model in Aus tria ( [12], not yet fully pub lished).…”

Section: Es Ti Ma Tion Of Ra Don Pri or Ity Ar Eassupporting

confidence: 56%

Radon priority areas - definition, estimation and uncertainty

Bossew

2018

Nucl Technol Radiat Prot

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Pos ing a sub stan tial risk to hu man health, in door ra don has in creas ingly been sub ject to reg ula tion. One key con cept is the one of ra don prone or pri or ity ar eas, which are un der stood as re gions where pre ven tion, mit i ga tion or remediation ac tion should be taken with pri or ity. Ra don pri or ity ar eas must be de fined, and once de fined, es ti mated from data. Ra don pri or ity area es ti ma tion or de lin ea tion amounts to a clas si fi ca tion prob lem, as a do main (a coun try) has to be di vided into mu tu ally ex clu sive zones of dif fer ent "priorityness". Clas si fy ing ar eas into pri or ity zones en tails de ci sions about ac tion to be im ple mented. Touch ing stake holder in ter ests, may prove eco nom i cally and po lit i cally costly. There fore, de ci sions should be jus ti fiable, which im plies, among other, re li abil ity of the ra don pri or ity area es ti mate, mean ing statis ti cally, con trol ling for es ti ma tion un cer tainty. Some as pects of ra don pri or ity area def i nition, es ti ma tion, and un cer tainty will be dis cussed in this pa per.

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Section: Es Ti Ma Tion Of Ra Don Pri or Ity Ar Eassupporting

confidence: 56%

Radon priority areas - definition, estimation and uncertainty

Bossew

2018

Nucl Technol Radiat Prot

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“…Some regression models, including MLR and stepwise regression models, have been applied to indoor PM and NO 2 in three types of buildings: schools, dwellings, and subway stations . Other regression models, including kernel regression and Bayesian spatial quantile regression, have been developed to predict indoor radon concentration at large scales in Switzerland and Italy . For a given environment, the model performance depends largely on the selection of inputs.…”

Section: Resultsmentioning

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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“…This is appropriate for any type of spatial study aimed at modelling the trend in variability and developing appropriate spatial models. Several studies have established that geological data and indoor radon levels are sufficient factors for obtaining indoor radon maps [29,31].…”

Section: State Of the Artmentioning

confidence: 99%

Sorrentina Peninsula: Geographical Distribution of the Indoor Radon Concentrations in Dwellings—Gini Index Application

2021

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The radon isotope (222Rn, half-life 3.8 days) is a radioactive byproduct of the 238U decay chain. Because radon is the second biggest cause of lung cancer after smoking, dense maps of indoor radon concentration are required to implement effective locally based risk reduction strategies. In this regard, we present an innovative method for the construction of interpolated maps (kriging) based on the Gini index computation to characterize the distribution of Rn concentration. The Gini coefficient variogram has been shown to be an effective predictor of radon concentration inhomogeneity. It allows for a better constraint of the critical distance below which the radon geological source can be considered uniform, at least for the investigated length scales of variability; it also better distinguishes fluctuations due to environmental predisposing factors from those due to random spatially uncorrelated noise. This method has been shown to be effective in finding larger-scale geographical connections that can subsequently be connected to geological characteristics. It was tested using real dataset derived from indoor radon measurements conducted in the Sorrentina Peninsula in Campania, Italy. The measurement was carried out in different residences using passive detectors (CR-39) for two consecutive semesters, beginning in September–November 2019 and ending in September–November 2020, to estimate the yearly mean radon concentration. The measurements and analysis were conducted in accordance with the quality control plan. Radon concentrations ranged from 25 to 722 Bq/m3 before being normalized to ground level, and from 23 to 933 Bq/m3 after being normalized, with a geometric mean of 120 Bq/m3 and a geometric standard deviation of 1.35 before data normalization, and 139 Bq/m3 and a geometric standard deviation of 1.36 after data normalization. Approximately 13% of the tests conducted exceeded the 300 Bq/m3 reference level set by Italian Legislative Decree 101/2020. The data show that the municipalities under investigation had no influence on indoor radon levels. The geology of the monitored location is interesting, and because soil is the primary source of Rn, risk assessment and mitigation for radon exposure cannot be undertaken without first analyzing the local geology. This research examines the spatial link among radon readings using the mapping based on the Gini method (kriging).

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Quantile regression and Bayesian cluster detection to identify radon prone areas

Cited by 13 publications

References 53 publications

Radon priority areas - definition, estimation and uncertainty

Radon priority areas - definition, estimation and uncertainty

Machine learning and statistical models for predicting indoor air quality

Sorrentina Peninsula: Geographical Distribution of the Indoor Radon Concentrations in Dwellings—Gini Index Application

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