Les contributions de croqAIR à la CISBAT 2025
- joanfredericrey
- 25 juin
- 4 min de lecture
Dernière mise à jour : 3 sept.
En cette année 2025, le Centre Romand de la Qualité de l'Air Intérieur et du Radon (croqAIR) contribue à la conférence sur l'environnement bâti, la CISBAT 2025, se tenant à Lausanne entre le 3 et le 5 septembre 2025.
Impact of CO₂ visualization on classroom air quality
Classroom air quality is crucial for children’s health, comfort, and cognitive performance. This study examines whether educational CO2 sensors displaying concentration levels and risk indicators encourage air renewal adjustments. CO2 monitors were installed in 48 classrooms across 24 schools, with continuous year-round monitoring with discrete sensors (no display) and two one-week periods of educational sensor use (with display) in summer and winter. Results show no significant CO2 levels during summer periods, likely due to naturally low CO2 levels (565 ppm on average) from frequent window openings. In winter, however, CO2 levels dropped within four weeks of educational sensor installation – from 1025 ppm to 880 ppm in naturally ventilated classrooms, and from 710 to 530 ppm in mechanically ventilated ones – suggesting increased awareness of ventilation needs. These findings indicate that educational CO2 sensors are associated with lower winter-time CO2 concentrations, and consequently, they have the potential to drive positive changes and have an effective role in indoor air quality management.
Considering the presence of users while assessing indoor radon levels in public schools on short-term periods of investigation
In Switzerland, official radon measurements in schools require either a yearlong passive measurement or a minimum three-month passive measurement during the heating season. For cases where these durations are impractical, such as before renovations, short-term methodologies have been developed to estimate radon levels. Since 2018, the Federal Office of Public Health (FOPH) has proposed a short-term real-time measurement procedure to estimate the probability of exceeding 300 Bq/m3 under pressurized winter-like conditions, using a 168-hour protocol. An alternative methodology, adapted from Norway’s 2015 protocol, considers occupancy periods and uses a 120-hour investigation window. This study aims at implementing and comparing these two approaches in schools in the Canton of Fribourg, where previous dosimetric radon measurements were available. Results reveal that both methodologies yielded comparable outcomes, identifying radon concentrations exceeding reference levels when appropriate. However, the protocol considering occupancy offers a more targeted approach, particularly when high radon levels occur outside of occupation hours. This finding emphasizes the importance of accounting for occupancy when assessing radon risks, as it optimizes remediation efforts and ensures measures are proportional to the actual health risk. Both methodologies have practical applications, but incorporating occupancy schedules can better balance safety, cost, and comfort for building occupants.
Comparative evaluation of predictive models for identifying radon-prone areas and buildings in Switzerland
Radon, a naturally occurring gas originating from the ground, varies in concentration depending on geological and environmental factors. Radon-prone areas, as defined by the International Commission on Radiological Protection, exhibit significantly higher radon levels compared to other regions. Since measuring radon in every building is economically and logistically infeasible, predictive models offer a valuable alternative for assessing indoor radon levels. Using radon database provided by the Swiss Federal Office of Public Health (FOPH), extended with other data, this study evaluated four predictive methods: multiple linear regression, logistic regression, random forest regression, and random forest classification. These models incorporated diverse datasets, including geological, climatological, and building characteristics. Results revealed that random forest classification was the most effective, correctly predicting indoor radon levels above or below the 300 Bq/m3 reference threshold in 85% of cases. Random forest regression and logistic regression performed moderately, explaining 32% and 20% of variance, respectively, while multiple linear regression explained only 16% of the variance. Significant predictors included geology, building age, floor level, and foundation type, were consistent across methods, but also the previous literature. Predicting binary variables (above or below the reference level) proved more accurate than continuous radon level predictions. This study highlights the potential of machine learning methods, particularly random forest classification, to inform radon-prone area identification and guide public health interventions.
Assessing the annual representativeness of a 120-hour radon measurement methodology in Switzerland
In Switzerland, official radon measurements typically require yearlong or at least three-month passive dosimetric measurements during the heating season. For faster results, the Swiss Federal Office of Public Health (FOPH) introduced a 120-hour measurement protocol in 2018. This method aims to replicate winter conditions year-round to estimate the probability of exceeding the reference value of 300 Bq/m3. While widely used, its representativeness and the necessity of depressurization of a building have not been thoroughly evaluated. This study compares the 120-hour methodology, carried both in summer and winter, with yearlong and three-month measurements and assesses the impact of summer depressurization. Measurements were conducted in five homes during winter without depressurization, and summer with and without depressurization. Results showed good consistency between 120-hour and official measurements in three out of five homes, both during winter and summer periods, validating the method’s general reliability. However, in two homes, the 120-hour approach overestimated radon risk. No significant differences were observed between summer measurements with and without depressurization, supporting the simplification of the protocol by omitting depressurization in non-heating seasons. However, our results indicated that the 120-hour methodology may not be able to replace long-term measurements as it requires broader validation to confirm its representativeness.
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