The aim of this work package is to devise novel concepts and methodologies for sensors that will detect and measure radon activity concentration in indoor air with improved response time, heightened sensitivity, and decreased measurement uncertainty. The new high-precision measurement sensors will be calibrated and tested for linearity and time response in WP2, used in sensor networks developed in WP3, and offered to SMEs as cost-effective and material-conserving option, facilitating industrial-scale production.

Three detection concepts will be studied in this work package. Each of these concepts has its own advantages and drawbacks, requiring in-depth optimisation, and a combination of these technologies is assumed to be optimal.

Task 1.1 will study the first approach that uses semiconductors for detection of radon and its progeny. Silicon-type diodes offer high-quality alpha spectrometry with simple acquisition electronics. Their excellent energy resolution (down to 10 keV full-width half maximum (FWHM)) allows to identify and distinguish radon isotopes, their decay products, and other alpha-emitting radionuclides in air in case of radiological incidents. This makes it also a highly promising tool for time response measurement instruments developed in WP2. Although cost-effective diodes suitable for radon detection already exist, their parameters must be optimised, electronics miniaturised, and software improved for inclusion into a radon sensor network. Their low power consumption and operating voltage are beneficial for cost-effective miniaturisation.

The second approach, involving ionisation chambers, will be studied in Task 1.2. This solution can be scaled up to larger volumes (up to several litres), allowing decreased detection limits necessary for high-precision measurements at very low activity levels. However, their spectral resolution for alpha particles is much lower than that of silicon sensors, around 250 keV FHWM. They have several drawbacks, including sensitivity to microphonics due to mechanical vibrations of the wires due to ambient sounds. Other challenges for use in-situ would be the potential dependency on environment variables such as pressure, humidity, CO2 content – all of which can affect ionisation yields and ion drift in the chamber. Developing solutions to correct these effects will contribute to advancing this technology for low-level radon concentration measurement and therefore usage for metrological traceability in WP2.

Task 1.3 aims at investigating the third concept that is based on scintillators. This project intends to explore a recent innovation for pure beta-emitting gases, utilising porous inorganic scintillators. The feasibility of using this technology for the detection of alpha radiation from radon and its progeny as well as its suitability for inclusion into a radon sensor network will be investigated. Porous inorganic scintillators combine excellent sensitivity, offering a significant surface-to-volume ratio with a detection efficiency close to unity, and the potential to discriminate between various beta and alpha emitters. With the ability to detect radon and all its descendants, they can achieve even greater sensitivity than ionisation chambers, which are limited to alpha emitters.

Task 1.4 aims at transferring the new technological developments for radon concentration measurements to industrial partners. Whereas in the case of semiconductor and ionisation chamber, it involves assembling materials and sensors, (i.e., mostly mechanics and electronics), the scintillator process is twofold: (1) the production of scintillation material by a chemistry lab on a large scale, and (2) the packaging of the scintillator and its associated electronics to construct the sensor. These two competences are distinctly different and, therefore, are kept separate as a first approach of commercial product.

This work package aims to develop traceable, in-situ operando calibration procedures for ²²²Rn activity concentration sensors, achieving less than 10 % calibration relative standard uncertainty at an activity concentration level down to 50 Bq·m^-3 while considering response time, linearity and dynamic range testing. The methods developed in this task will be significantly improved by the technology obtained from WP1 such as time response measurement from A1.1.2 and radon measurement obtained with ionisation chamber from A1.2.5. To avoid delays in establishing the new methods due to the development time within WP1, a comprehensive literature review will be conducted to select representative commercial devices and the method will be established based on the various findings and development paths established in WP1. This progress in the work on existing instruments will allow to quickly apply techniques with the new developments from WP1 and limit the risks that could arise in the development of new sensors within the project.

While the development of in-situ calibration techniques in radon metrology is a major challenge, the knowledge gained and the techniques developed will address other critical metrological questions. Recent studies have highlighted the importance of time response and linearity for radon sensors, and therefore, this work package will not only enhance radon metrology in general (uncertainty and activity concentration limits) but also extend the impact beyond the project’s primary goal of building a radon activity concentration detector network.

Task 2.1 will develop traceable laboratory calibration procedures for the existing commercially available and newly developed sensors from WP1. This task will have the dual purpose of selecting commercial instruments as test models and, more importantly, ensuring good calibration practices for various measurement technologies for the production of the good practice guide for traceable laboratory calibration procedures.

Task 2.2 will investigate the influence of environmental parameters on the calibration of existing commercially available and the new sensors from WP1. Evaluating these parameters on typical instruments will allow to properly identify the necessary parameters in radon sensor networks. This identification will lead to the development of real-time correction techniques, particularly for WP3 and WP4

Task 2.3 aims to determine the response characteristics of radon sensors that are already available and then on the sensors developed in WP1. This task addresses critical points characterising radon instruments, which are absent in current calibration procedures. It will enable the proposal of methodologies suitable for the proper comprehensive calibration of radon measurement instruments. Additionally, it will provide essential parameters for on-site calibration.

Task 2.4 will develop a method for traceable, in-situ operando calibration of 222Rn activity concentration detector networks.

The aim of this work package is to facilitate the future development of quality assured, fit-for-purpose radon, sensor networks. A test network consisting of commercial sensors and the sensors developed and tested in WP1 for large buildings in future cities, integrated with data analysis framework suitable for real-time monitoring for anomalies, will be developed. The project will make use of emerging technologies such as edge computing, including techniques of machine learning and artificial intelligence. Digital SI to facilitate collaborative research, development, as well as self-sustaining expandability, will be implemented. This work package comprises both ionising radiation metrology and sensor network metrology and data science. As a result, it will establish links with other existing European metrology networks compatible with the environmental parameters that were identified within WP2.

The work package will focus on the development of a testbed, established within a large building or campus of buildings, that will be available for realistic trials of new radon networks. In parallel, existing radon and comparable radiation sensor networks will be reviewed to identify existing best practise (e.g. covering data collection, analysis, QA/QC, calibration etc). This best practise will be used as a starting point for further development (e.g. though implementation of AI methods to automate analysis). Technical solutions will be developed for the interfacing with radon sensors. A test network will be established at the testbed that will be type-tested against appropriate ISO and IEC standards to determine its metrological validity.

Task 3.1 will review existing networks in order to determine suitable methods for the radon sensor network. A radon network testbed at a large building or campus of buildings will be developed within which a test network will be established. 

Task 3.2 will leverage existing network data within the consortium, as well as data from the literature, to lay the groundwork and guide the necessary developments for on-site calibration, particularly in the operation of radon sensor networks.

Task 3.3 will develop universal interfaces for the radon sensor network. This task will establish a universal interface following current standards for the connectivity of radon instruments (and in general for IoT devices), with the MQTT protocol already identified as the best candidate.

In Task 3.4 data from radon sensors will be investigated and analysis methods will be developed based on AI and ML to increase time response of the device and to make the background correction more efficient. The methods will be tested on the radon sensor network used in the testbed.

This work package will lay the foundations for the seamless integration of the radon network testbed developed in WP3 into various pre-existing sensor networks. The result of this integration will be the establishment of a framework that facilitates the development and implementation of intelligent and holistic data analysis and integration methods.

By successfully integrating the radon network testbed with other sensor networks, the ultimate goal is to enable a harmonious synergy that improves the efficiency of energy consumption, air quality management and radiation protection within building. This synergy would enable to strategically optimise the use of resources, leading to more sustainable and environmentally conscious practices.

In essence, this work package serves as a crucial stepping stone towards the realisation of a cohesive and intelligent network that goes beyond the capabilities of individual sensors. This will include recommendations for how AI/ML can be utilised for intelligent analysis, and how to prepare the network for implementation of a digital twin.

Task 4.1 will collect and analyse technical information on different sensors and environmental monitoring technologies commonly used in large buildings and other relevant environments. This will be done through a literature review on the main pollutants monitored in existing IAQ networks, an analysis of existing European air quality networks and the solutions they have enabled, and through a review of European legal requirements and national recommendations.

The aim of Task 4.2 is to produce a good practice guide for the development of an extension of the sensor network to include other existing and potential building sensor networks (i.e., the extension goes larger than radon). The work within this task will include identifying gaps in existing radiation networks, proposing suitable parameters for the networks, and investigating possible inclusion of other radiation sensors.

Task 4.3 will perform measurements of radon and other parameters in existing sensor networks and on this basis a model for integrating data from the above networks will be developed. In addition, AI/ML methods in data analysis of air quality sensor networks will be investigated. Recommendations for comprehensive air quality measurements, including indoor radon, will be produced.