Management of Radon Data in the State of Ohio, U.S.A.

Kumar, Ashok

doi:10.2174/1875040001104010057

Cited by 5 publications

(7 citation statements)

References 6 publications

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“…The system collates radon level data for locations in 1,496 zip codes and encompasses all the 88 counties in Ohio (14). The radon databases posted on the ORIS website consist of five distinct databases: homes database, school database, water database, mitigation database, and tester database (1, 15).…”

Section: Methods Of Approachmentioning

confidence: 99%

Impact of the Hydraulic Fracturing on Indoor Radon Concentrations in Ohio: A Multilevel Modeling Approach

Sajja

Kumar

2019

Front. Public Health

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According to the United States Environmental Protection Agency (U.S. EPA), exposure to radon gas is the second leading cause of lung cancer after smoking. Extant research that has reported that fracking activity increases the radon levels. “Fracking” also known as hydraulic fracturing, which is a technology that is used to extract naturally occurring shale gas from the Marcellus and the Utica shales. Based on the data from the Ohio Radon Information System (ORIS) from 2007 to 2014 in Ohio, this research uses multilevel modeling (MLM) to examine the association between the incidences of hydraulic fracturing and elevated airborne radon levels. The ORIS data include information on 118,421 individual records of households geocoded to zip code areas. Individual records include radon concentrations, device types of the test, and seasons. Euclidean distances between zip code centroid to the 1,162 fracking wells are measured at the zip code level. Two additional zip code variables, namely the population density and urbanicity, are also included as control variables. Multilevel modeling results show that at the zip code level, distance to fracking wells and population density are significant and negative covariate of the radon concentration. By comparing with urban areas, urban clusters, and rural areas are significant which linked to higher radon concentrations. These findings lend support to the effect of hydraulic fracturing in influencing radon concentrations, and promote public policies that need to be geographically adaptable.

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Section: Methods Of Approachmentioning

confidence: 99%

Impact of the Hydraulic Fracturing on Indoor Radon Concentrations in Ohio: A Multilevel Modeling Approach

Sajja

Kumar

2019

Front. Public Health

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“…The statistics for radon concentrations in Ohio is provided by Kumar et al [31]. Of a total of 1492 zip codes across Ohio, radon and uranium concentration data are available for 1261 zip codes.…”

Section: Data Preparationmentioning

confidence: 99%

Knowledge‐based neural network approaches for modeling and estimating radon concentrations

Akkala

Bhatt

Devabhaktuni

et al. 2012

Env Prog and Sustain Energy

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Radon is a chemically inert, tasteless, and odorless gas, which causes lung cancer in people who are exposed to higher concentrations for extended periods of time. It is a byproduct of the decay of uranium in the soil. High concentrations are present in closed units, like houses, schools, etc. To identify houses with unacceptably high radon levels, the radon concentration for each zip code in Ohio needs to be measured. However, not all of the zip codes are surveyed, owing to reasons such as inapproachability. In places where data is unavailable, the concentrations need to be estimated using interpolation techniques. This article proposes the application of knowledge‐based neural network approaches, namely prior knowledge input method, source difference method, and space‐mapped neural network method for modeling and predicting radon concentrations. To this end, knowledge available in the form of uranium concentration data is exploited. Modeling accuracies of the proposed techniques are compared with those of the commonly used multilayer perceptron networks (employed in one of our recent work) and conventional interpolation techniques. © 2012 American Institute of Chemical Engineers Environ Prog, 32: 355‐364, 2013

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“…Since 1989, to help mitigators in identifying the buildings with high radon concentration levels and implement a cost‐effective radon mitigation plan, the Ohio Department of Health (ODH) with radon testing laboratories, local health departments, and university researchers has collected radon information for more than 200,000 homes and schools across Ohio . The data collected by these organizations are being managed by different database management systems . Most of the Ohio indoor radon data are contained in the Ohio Radon Information System (ORIS) developed by The University of Toledo in association with Ohio Air Quality Development Authority and Ohio Department of Health (ODH) .…”

Section: Introductionmentioning

confidence: 99%

“…The data collected by these organizations are being managed by different database management systems . Most of the Ohio indoor radon data are contained in the Ohio Radon Information System (ORIS) developed by The University of Toledo in association with Ohio Air Quality Development Authority and Ohio Department of Health (ODH) .…”

Section: Introductionmentioning

confidence: 99%

Interpolation techniques for modeling and estimating indoor radon concentrations in Ohio: Comparative study

Gummadi

Bhatt

Adusumilli

et al. 2014

Env Prog and Sustain Energy

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Radon‐222 and its parent, radium‐226, is a naturally occurring radioactive decay product of uranium‐238. The US Environmental Protection Agency (USEPA) states that 10% of total lung cancer cases in the United States are directly attributable to indoor radon. The USEPA has categorized Ohio as a Zone 1 state (i.e., the average indoor radon screening level is greater than 4 pCi/L). To implement preventive measures, it is necessary to know radon concentration levels in all the zip codes of a geographic area. However, it is not possible to survey all the zip codes, owing to reasons such as inapproachability. In such places where radon data are unavailable, several interpolation techniques are used to estimate the radon concentrations. This article presents a comparison between recently developed interpolation techniques such as neural network techniques, support vector regression (SVR), random forest regression (RFR), and the conventional interpolation techniques. The performance of all the techniques has been assessed using 10‐fold cross‐validation data. This study confirmed that the correction‐based artificial neural networks have performed better over SVR and RFR with least validation error of 3.63. © 2014 American Institute of Chemical Engineers Environ Prog, 34: 169–177, 2015

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Management of Radon Data in the State of Ohio, U.S.A.

Cited by 5 publications

References 6 publications

Impact of the Hydraulic Fracturing on Indoor Radon Concentrations in Ohio: A Multilevel Modeling Approach

Impact of the Hydraulic Fracturing on Indoor Radon Concentrations in Ohio: A Multilevel Modeling Approach

Knowledge‐based neural network approaches for modeling and estimating radon concentrations

Interpolation techniques for modeling and estimating indoor radon concentrations in Ohio: Comparative study

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