Radon concentration levels in ground water from Toluca, Mexico

Olguín, M. T.; Segovia, N.; Tamez, Elsa; Alcántara, M.T.; Bulbulian, S.

doi:10.1016/0048-9697(93)90058-e

Cited by 11 publications

(5 citation statements)

References 2 publications

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“…These values are significantly higher than the results reported in other sites in Mexico. Olguin et al (1993) reported 11.3 Bq/L as highest value in the city of Toluca. However, much higher values have been reported over the world, like 122.2 Bq/L in Brazil (Bonotto and Caprioglio, 2002), 3735 Bq/L in China (Zhou et al, 2001) and 3470 Bq/L in Sweden (Salih et al, 2004).…”

Section: Ground-watermentioning

confidence: 94%

Radon concentrations in ground and drinking water in the state of Chihuahua, Mexico

Villalba¹,

Sujo²,

Montero-Cabrera³

et al. 2005

Journal of Environmental Radioactivity

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Section: Ground-watermentioning

confidence: 94%

Radon concentrations in ground and drinking water in the state of Chihuahua, Mexico

Villalba¹,

Sujo²,

Montero-Cabrera³

et al. 2005

Journal of Environmental Radioactivity

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“…Radon content varied depending on the sample source, reaching a maximum of 11.3 Bq L -1 at Toluca City (Segovia, & Bulbulian, 1992). Fifty-eight percent of the wells sampled contained less than 2.4 Bq L -1 , confirming that water samples from alluvial and basaltic rocks usually contain low levels of radon (Olguin et al, 1993). Natural radionuclides and physicoEnviron Geochem Health (2007) 29: 143-153 147 chemical properties have also been determined for groundwater samples from boreholes used to supply drinking water to Toluca City.…”

Section: Radon In Groundwater and Drinking Water Supply Systemsmentioning

confidence: 95%

“…Concentrations of 222 Rn, natural radionuclides, and physicochemical conditions were determined in groundwater samples from wells and boreholes of the drinking water supply system of Toluca City (Olguin, Segovia, Tamez, Alcantara, & Bulbulian, 1993;Segovia et al, 1999a). In other places, for example Mexico and Michoacan states, chemical analysis of major and trace elements, and determination of radon were also performed in springs and wells from urban and agricultural zones in the Lerma river basin Alfaro et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Environmental radon studies in Mexico

Segovia

Gaso²,

Armienta

2007

Environ Geochem Health

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Radon has been determined in soil, groundwater, and air in Mexico, both indoors and outdoors, as part of geophysical studies and to estimate effective doses as a result of radon exposure. Detection of radon has mainly been performed with solid-state nuclear track detectors (SSNTD) and, occasionally, with active detection devices based on silicon detectors or ionization chambers. The liquid scintillation technique, also, has been used for determination of radon in groundwater. The adjusted geometric mean indoor radon concentration (74 Bq m-3) in urban developments, for example Mexico City, is higher than the worldwide median concentration of radon in dwellings. In some regions, particularly hilly regions of Mexico where air pollution is high, radon concentrations are higher than action levels and the effective dose for the general population has increased. Higher soil radon levels have been found in the uranium mining areas in the northern part of the country. Groundwater radon levels are, in general, low. Soil-air radon contributing to indoor atmospheres and air pollution is the main source of increased exposure of the population.

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“…Radon ( 222 Rn) gas is ubiquitous in the environment. It is found in air, water, and soil, and concentrates in the environment and buildings in a complex manner dependent upon geological, chemical, meteorological, and other temporally variant parameters [1][2][3][4][5][6][7][8][9][10]. While the bulk of knowledge about the adverse health effects has resulted from studies of lung cancer in uranium miners, radon health effects are an active area of epidemiological work involving indoor domestic radon gas concentrations [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Imputation by feature importance (IBFI): A methodology to envelop machine learning method for imputing missing patterns in time series data

2022

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A new methodology, imputation by feature importance (IBFI), is studied that can be applied to any machine learning method to efficiently fill in any missing or irregularly sampled data. It applies to data missing completely at random (MCAR), missing not at random (MNAR), and missing at random (MAR). IBFI utilizes the feature importance and iteratively imputes missing values using any base learning algorithm. For this work, IBFI is tested on soil radon gas concentration (SRGC) data. XGBoost is used as the learning algorithm and missing data are simulated using R for different missingness scenarios. IBFI is based on the physically meaningful assumption that SRGC depends upon environmental parameters such as temperature and relative humidity. This assumption leads to a model obtained from the complete multivariate series where the controls are available by taking the attribute of interest as a response variable. IBFI is tested against other frequently used imputation methods, namely mean, median, mode, predictive mean matching (PMM), and hot-deck procedures. The performance of the different imputation methods was assessed using root mean squared error (RMSE), mean squared log error (MSLE), mean absolute percentage error (MAPE), percent bias (PB), and mean squared error (MSE) statistics. The imputation process requires more attention when multiple variables are missing in different samples, resulting in challenges to machine learning methods because some controls are missing. IBFI appears to have an advantage in such circumstances. For testing IBFI, Radon Time Series Data (RTS) has been used and data was collected from 1st March 2017 to the 11th of May 2018, including 4 seismic activities that have taken place during the data collection time.

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Radon concentration levels in ground water from Toluca, Mexico

Cited by 11 publications

References 2 publications

Radon concentrations in ground and drinking water in the state of Chihuahua, Mexico

Radon concentrations in ground and drinking water in the state of Chihuahua, Mexico

Environmental radon studies in Mexico

Imputation by feature importance (IBFI): A methodology to envelop machine learning method for imputing missing patterns in time series data

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