Radon in Indoor Air: Towards Continuous Monitoring

Short-Term vs. Long-Term: A Critical Review of Indoor Radon Measurement Techniques

Radon is a known carcinogen, and the accurate assessment of indoor levels is essential for effective mitigation strategies. While long-term testing provides the most reliable data, short-term testing (STT) offers a quicker and more cost-effective alternative. This review evaluated the accuracy of STT in predicting annual radon averages and compared testing strategies in Europe (where long-term measurements are common) and the United States (where STT is prevalent). Twenty (20) studies were systematically identified through searches in scientific databases and the grey literature, focusing on STT accuracy and radon management. This review revealed several factors that influence the accuracy of STT. Most studies recommended a minimum four-day test for initial screening, but accuracy varied with radon levels. For low levels (<75 Bq/m3), a one-week STT achieved high confidence (>95%) in predicting annual averages. However, accuracy decreased for moderate levels (approximately 50% success rate), necessitating confirmation with longer testing periods (3 months). High radon levels made STT unsuitable due to significant fluctuations. Seasonality also played a role, with winter months providing a more representative picture of annual radon averages. STT was found to be a useful method for screening low-risk areas with low radon concentrations. However, its limitations were evident in moderate- and high-level scenarios. While a minimum of four days was recommended, longer testing periods (3 months or more) were crucial for achieving reliable results, particularly in areas with potential for elevated radon exposure. This review suggests the need for further research to explore the possibility of harmonizing radon testing protocols between Europe and the United States.

Radon exposure risks among residents proximal to gold mine tailings in Gauteng Province, South Africa: a cross-sectional preliminary study protocol

Gold mine tailings, a legacy of the mining industry, harbors significant amount of radon gas, a classified human carcinogen. Radon exposure, especially near tailings, is a significant public health threat, potentially leading to increased risk of lung cancer, leukemia, and chronic obstructive pulmonary disease (COPD). These health problems are often associated with lower survival rates and significant financial burdens. This ongoing research aim to evaluating the relationship between indoor radon exposure and lung cancer, leukemia, and COPD risks among residents proximal to gold mine tailings in Gauteng Province, South Africa. This cross-sectional preliminary study focus on two distinct groups: Riverlea (exposed group, <2 km to Gold mine tailings) and Orlando East (unexposed group, >2 km to Gold mine tailings). Indoor radon levels is measured using AlphaE monitors, while health risks (lung cancer, leukemia, and COPD) linked to exposure are evaluated through interview-administered questionnaire and secondary data from Gauteng Health Department. Of the 476 residents randomly selected for this study, 300 have already participated, with balanced representation from both the exposed and unexposed groups. The study will compare indoor radon levels and health outcomes between the two groups. This study's results could aid in creating targeted interventions and policies to mitigate indoor radon exposure risks and safeguard vulnerable communities from this significant public health hazard.

Radon Exposure Concentrations in Finnish Workplaces

ABSTRACT

The aim of this study was to obtain information on the radon concentrations to which Finnish workers are exposed. Radon measurements were conducted as integrated measurements in 700 workplaces, supplemented by continuous radon measurements in 334 workplaces. The occupational radon concentration was calculated by multiplying the result of the integrated measurements by the seasonal correction factor and the ventilation correction factor (ratio between the working time and the full-time radon concentration obtained from continuous measurement). The annual radon concentration to which workers are exposed was weighted by the actual number of workers in each province. In addition, workers were divided into three main occupational categories (working mainly outdoors, underground, or indoors above ground). Probability distribution of the parameters affecting radon concentration levels were generated to calculate a probabilistic estimate of the number of workers exposed to excessive radon levels. With deterministic methods, the geometric and arithmetic mean radon concentrations in conventional, above-ground workplaces were 41 and 91 Bq m -3 , respectively. The estimated geometric and arithmetic mean annual radon concentrations that Finnish workers are exposed to were assessed as 19 and 33 Bq m -3 , respectively. The generic ventilation correction factor for workplaces was calculated as 0.87. Assessed with probabilistic methods, there are approximately 34,000 workers in Finland whose exposure to radon exceeds the reference level of 300 Bq m -3 . Although radon concentrations are generally low in Finnish workplaces, many workers are exposed to high levels of radon. Radon exposure in the workplace is the most common source of occupational radiation exposure in Finland.

Quantifying indoor radon levels and determinants in schools: A case study in the radon-prone area Galicia-Norte de Portugal Euroregion

Radon is a carcinogenic compound, and is particularly concerning in the education sector, where children and teachers may be exposed even longer than at home. Thus, this study intended to characterise radon in the indoor air of scholar environments in different provinces/districts of the Euroregion Galicia-Norte de Portugal. With a pioneering approach, this study evaluated the influence of specific factors/characteristics (location, type of management, construction material, season and floor within the building) and quantified their relative contribution to indoor radon levels. Radon was continuously monitored in 416 classrooms from school buildings located in urban and rural sites from different provinces/districts both in the regions of Galicia (A Coruña and Lugo provinces) and Portugal (Porto and Bragança districts), considering rooms for different age groups (from nursery schools to universities). Single and multivariate linear regression models were built considering the radon concentrations as the outcome variable and different room/building characteristics as predictor variables. Mean and median radon concentrations were 332 Bq·m^-3 and 181 Bq·m^-3, respectively. The radon concentrations observed are a public health concern, as almost 1/3 of the places monitored exceeded the reference limit value of the European legislation (300 Bq·m^-3). Moreover, around 50 % of the indoor levels measured could be attributed to room/building characteristics: the building's location and the main construction material, as well as the occupants' age group, the floor within the building and the school's type of management (public/private). This study concluded that radon testing is needed in all school buildings and classrooms without exceptions. Thus, public administrations are urged to dedicate funds for testing, mitigation and public dissemination initiatives in schools. A special protocol for radon sampling in school buildings should also be developed.

Machine Learning-Based Radon Monitoring System

Radon (Rn) is a biological threat to cells due to its radioactivity. It is capable of penetrating the human body and damaging cellular DNA, causing mutations and interfering with cellular dynamics. Human exposure to high concentrations of Rn should, therefore, be minimized. The concentration of radon in a room depends on numerous factors, such as room temperature, humidity level, existence of air currents, natural grounds of the buildings, building structure, etc. It is not always possible to change these factors. In this paper we propose a corrective measure for reducing indoor radon concentrations by introducing clean air into the room through forced ventilation. This cannot be maintained continuously because it generates excessive noise (and costs). Therefore, a system for predicting radon concentrations based on Machine Learning has been developed. Its output activates the fan control system when certain thresholds are reached.