Low-Cost Radon Detector with Low-Voltage Air-Ionization Chamber

Development of multi-detector soil radon measurement system based on IoT

A multi-detector soil radon measurement system based on IoT (the Internet of Things) has been developed for the specific application of long-term monitoring of soil radon concentration in remote mining areas. The system utilizes the scintillation chamber method to measure radon concentration, with SiPM (Silicon photomultiplier) for photoelectric conversion. This is combined with temperature compensation technology and 'triple-proof' protection measures to enhance the anti-interference capability of the instrument, thereby indirectly ensuring the accuracy of the measurement results. To address the issue of inconvenient data networking in the field, a complementary 'NB-IoT (Narrow Band Internet of Things) + Bluetooth' dual wireless network transmission method is employed. Additionally, the online monitoring and management platform for soil radon concentration on the cloud server enables online monitoring and management of data.The developed system demonstrated a sensitivity of 1.56 cph/(Bq/m³), a relative error of ≤10%, and an relative standard deviation (RSD) of ≤5.59%. Additionally, the system exhibited an endurance of 53 h when powered by a 12Ah battery and connected to three measurement nodes. The calibrated system has conducted long-term monitoring of radon concentration in a uranium mining area. The test and practical application demonstrate that the developed system meets the requirements of field data networking and the expansion of multiple detection nodes, operates reliably, and enables long-term continuous online monitoring of radon concentration at multiple depths of a single measuring point and multiple measuring points in a region. This provides effective data support for soil radon-related research.

Small ion pulse ionization chamber for radon measurement in underground space

Radon, prevalent in underground spaces, requires continuous monitoring due to health risks. Traditional detectors are often expensive, bulky, and ill-suited for humid environments in underground spaces. This study presents a compact, cost-effective radon detector designed for long-term, online monitoring. It uses a small ionization chamber with natural airflow, avoiding the need for fans or pumps, and includes noise filtering and humidity mitigation. Featuring multi-point networking and easy integration capabilities, this detector significantly enhances radon monitoring in challenging, underground conditions.

Radon in Indoor Air: Towards Continuous Monitoring

Radon poses significant health risks. Thus, the continuous monitoring of radon concentrations in buildings’ indoor air is relevant, particularly in schools. Low-cost sensors devices are emerging as promising technologies, although their reliability is still unknown. Therefore, this is the first study aiming to evaluate the performance of low-cost sensors devices for short-term continuous radon monitoring in the indoor air of nursery and primary school buildings. Five classrooms of different age groups (infants, pre-schoolers and primary school children) were selected from one nursery and one primary school in Porto (Portugal). Radon indoor concentrations were continuously monitored using one reference instrument (Radim 5B) and three commercially available low-cost sensors devices (Airthings Wave and RandonEye: RD200 and RD200P2) for short-term sampling (2–4 consecutive days) in each studied classroom. Radon concentrations were in accordance with the typical profiles found in other studies (higher on weekends and non-occupancy periods than on occupancy). Both RadonEye low-cost sensors devices presented similar profiles with Radim 5B and good performance indices (R2 reaching 0.961), while the Airthings Wave behavior was quite different. These results seem to indicate that the RadonEye low-cost sensors devices studied can be used in short-term radon monitoring, being promising tools for actively reducing indoor radon concentrations.

Novel Algorithm for Radon Real-Time Measurements with a Pixelated Detector

Nowadays, radon gas exposure is considered one of the main health concerns for the population because, by carrying about half the total dose due to environmental radioactivity, it is the second cause of lung cancer after smoking. Due to a relatively long half-life of 3.82 days, the chemical inertia and since its parent Ra-226 is largely diffuse on the earth’s crust and especially in the building materials, radon can diffuse and potentially saturate human habitats, with a concentration that can suddenly change during the 24 h day depending on temperature, pressure, and relative humidity. For such reasons, ‘real-time’ measurements performed by an active detector, possibly of small dimensions and a handy configuration, can play an important role in evaluating the risk and taking the appropriate countermeasures to mitigate it. In this work, a novel algorithm for pattern recognition was developed to exploit the potentialities of silicon active detectors with a pixel matrix structure to measure radon through the α emission, in a simple measurement configuration, where the device is placed directly in air with no holder, no collection filter or electrostatic field to drift the radon progenies towards the detector active area. This particular measurement configuration (dubbed as bare) requires an α/β-discrimination method that is not based on spectroscopic analysis: as the gas surrounds the detector the α particles are emitted at different distances from it, so they lose variable energy amount in air depending on the traveled path-length which implies a variable deposited energy in the active area. The pixels matrix structure allows overcoming this issue because the interaction of α, β and γ particles generate in the active area of the detector clusters (group of pixels where a signal is read) of different shape and energy dispersion. The novel algorithm that exploits such a phenomenon was developed using a pixelated silicon detector of the TimePix family with a compact design. An α (Am-241) and a β (Sr-90) source were used to calibrate the algorithm and to evaluate its performances in terms of β rejection capability and α recognition efficiency. Successively, the detector was exposed to different radon concentrations at the ENEA-INMRI radon facility in ‘bare’ configuration, in order to check the linearity of the device response over a radon concentration range. The results for this technique are presented and discussed, highlighting the potential applications especially the possibility to exploit small and handy detectors to perform radon active measurements in the simplest configuration.