Active Monitoring and Statistical Prediction of Indoor Radon Concentration May Reduce Lung Cancer Risk

January was National Radon Action Month, a time when the US Environmental Protection Agency (EPA) encourages homeowners to test and mitigate the deadly effects of radon gas. While modern radon monitoring devices can detect the presence of radon quickly and accurately, reports reveal that too few people are taking advantage of this technology. In response, scientists are turning to statistical prediction methodologies to help prevent radon-related deaths.

The dangers of radon gas

Radon exposure is the leading cause of lung cancer in non-smokers According to the EPA, radon-related lung cancer deaths total 21,000 deaths per year in the United States alone. Exposure occurs when uranium and radium in the ground break down over time and release colorless, odorless and tasteless radon. Outdoors, radon dissipates quickly. However, when it enters buildings through cracks or other holes in the foundation, it can accumulate to dangerous levels. The EPA has found that 1 in 15 homes in the United States have high levels above the recommended threshold for taking mitigation action. .

Detect the presence of radon

There are a wide variety of solutions for detecting the presence of radon in homes and other buildings. Basic radon detectors, sometimes called passive detectors, expose special materials to the air in a building for a period ranging from days to months. The detection materials are then sent to a laboratory to be analyzed and assessed for radon levels.

Radon monitors, sometimes called active detectors, use more advanced electronic technology to continuously measure radon levels in the air over long periods of time. Since a wide variety of factors can affect the level of radon in the air, continuous radon monitoring is generally considered a better method of protection against radon hazards, especially since radon levels are known to fluctuate with atmospheric pressure and temperature conditions.

Predict Radon Levels with Geoengineering Studies

To help identify areas where radon is most likely to be at dangerous levels, scientists have recently started using machine learning statistical methodologies. Although radon can be present in well water and natural gas, it primarily enters homes and other buildings through the ground on which a building is built. When radon detection data is combined with local soil composition data, machine learning algorithms can help identify the environmental conditions most likely to result in elevated radon levels.

In a recent study in South Korea, scientists combined radon test data with environmental data, including soil mineral composition, elevation, water table and rock lithology, enabling probabilistic and deep learning algorithms to determine which combination of factors led to the highest radon levels.

In other study in the United States, researchers have even taken into account other factors such as atmospheric variables, urbanization, community economic well-being, and monthly and annual variations to assess spatial and temporal variations in the level of radon. The data they collected was used to develop better prevention and mitigation control strategies aimed at mitigating and reducing occupant exposure to radon in urban areas.

Electronic radon detectors enabled by active Wi-Fi, which continuously monitor radon levels, are available with geolocation. With the appropriate authorizations, their capabilities will allow the construction over time of increasingly detailed maps of the areas most at risk. With the integration or creation of other databases, it will potentially one day be possible to issue alerts to certain types of home construction that have the potential for high radon risks that are most likely to occur during certain seasons or under certain weather conditions. ,

The dangers of radon are obvious. Unfortunately, too few people know about them. With the help of active monitoring and machine learning techniques, future researchers could for the first time have the tools to focus actions where they are needed most. These may involve future prediction-based public policy measures to target awareness, subsidize testing and perhaps even mitigate, on a regional and/or income level basis. Such measures could eventually target homes, schools and offices, to make the biggest difference in health outcomes for the largest number of people given the funding available. Those involved in public policy, radon advocacy, and public health may want to take note.

About the Author

Insoo Park is CEO of Ecosens Inc., an innovator in the radon gas monitoring industry offering people peace of mind with its intelligent, highly accurate radon detectors for homes, educational campuses, assisted living facilities, community centers and commercial buildings. The company’s real-time smart radon detectors use patented ionization chamber detection technology with high-accuracy performance capable of delivering the first radon result in minutes, not days.

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