IAQ Assessment Index for Smart Ventilation Systems

Introduction to Smart Ventilation

• Smart ventilation continuously adjusts airflow based on real-time air quality and occupancy to enhance IAQ and reduce energy costs.

• The assessment of these systems is complicated by the lack of standardized metrics and varying priorities, such as occupant health, comfort, and building preservation.

• The study aims to address these gaps by evaluating smart ventilation strategies through literature review and numerical simulations.

Methodology 

• Multizone numerical simulations were conducted with MATHIS (Demouge, F. and Piriou, J., 2018) in a 106 m² apartment (Figure 1 left), modeling different ventilation systems: Mechanical Heat Recovery Ventilation (MHRV), Mechanical Extract Ventilation (MEV), and Positive Input Ventilation (PIV).

• Scenarios included fixed occupancy patterns for weekdays and weekends across four seasonal weather conditions. 

• Four pollutants (CO₂, humidity, PM2.5, and total VOCs) were analyzed using two IAQ indices to compare system performance.

More information on the ventilation systems and control strategies can be found in (Golaz, B., Robitu, M. and Lefebvre, C., 2025). 

Ventilation Systems and Control Strategies

• Three ventilation systems were assessed with both conventional and demand-controlled strategies, focusing on CO₂, humidity, and multi-pollutant indices.

• MHRV strategies included constant airflow and CO₂-boosted configurations, while MEV strategies utilized humidity-sensitive controls and CO₂ adjustments. 

• PIV systems were evaluated with constant airflow and CO₂-based control strategies.

IAQ Assessment Indices

• Two indices were used: IAQ VS developed by Eurovent Certita Certification (2021), calculated per zone, which combines sub-indices for CO₂, humidity, and TVOC, and the MOPA index developed by Poirier, B. (2023), which calculates per occupant or per room depending on pollutant and evaluates health protection, comfort, and building durability.

• Both indices provide a comparative analysis of ventilation performance, highlighting the importance of considering multiple pollutants and their effects on health and building integrity.

Results and Discussion 

• Smart ventilation strategies demonstrated the ability to adapt airflow based on occupancy, significantly reducing ventilation demand during low-occupancy periods while ensuring adequate ventilation during peak occupancy, as illustrated in Figure 2. For CO₂ based demand-controlled strategies in MHRV and PIV systems, weekday average air change rates (ACH) were reduced to 88% of the reference strategy, indicating potential energy savings during low occupancy. Conversely, during weekends, when occupancy is higher, the same strategies increased ventilation rates up to 141% of the reference ACH, ensuring sufficient air renewal during peak use. 

• The analysis revealed that no single strategy performed optimally across all indoor air quality assessment indices, highlighting the need for tailored ventilation control approaches depending on occupancy patterns and dominant pollutants (Figure 3). Across both IAQ VS and MOPA indices, none of the investigated strategies simultaneously minimized all pollutant-related and moisture-related sub-indices. While consistent trends were observed for CO₂ and TVOC between the two indices, reflecting the convergence between occupant exposure (MOPA) and room-based concentration assessment (IAQ VS), performance reversals were frequently observed between weekdays and weekends, particularly for constant air volume (CAV) strategies. These systems generally performed better during low-occupancy periods but showed degraded performance under higher occupancy, underlining the importance of occupancy-responsive control.

• The choice of control indicators (e.g., CO₂ vs. humidity) significantly influenced both energy efficiency and pollutant exposure, emphasizing the importance of selecting appropriate metrics for smart ventilation control and assessment (Figure 3). For instance, in the MEV system, the transition from the reference humidity-based configuration (Hygro B) to configurations including CO₂ sensors in bedrooms or living areas substantially reduced moisture-related risks, with the MOPA RH 70 sub-index decreasing from 11.09 to 5.50–5.40 on weekdays. At the same time, CO₂ sensing improved occupant-related exposure, as reflected by the decrease in the MOPA CO₂ indicator from 0.48 to 0.41–0.34. Conversely, in PIV systems, CO₂-based control strategies effectively reduced occupant exposure (MOPA CO₂ decreased to 0.30) but could allow moisture accumulation in under-ventilated technical spaces, with RH 70 values remaining as high as 7.52 during weekends. These findings underscore the dissociation between occupant-related health risks (CO₂, TVOC, PM2.5) and building-related risks associated with high humidity, emphasizing the need for multi-pollutant or differentiated control strategies that simultaneously address both types of risk. 

Key Findings 

• Strategies that modulate airflow based on real-time occupancy can achieve substantial energy savings without compromising IAQ.

• Systems relying solely on humidity may overlook CO₂ levels, while those focused on CO₂ may neglect humidity control, highlighting the need for a balanced approach.

• The MOPA index provides a more comprehensive evaluation by distinguishing between occupant health risks and building preservation needs.

Conclusion 

• The study underscores the importance of dynamic ventilation strategies that adapt to real-life occupancy patterns while maintaining minimum airflow to protect building integrity. Results show that demand-controlled ventilation can reduce airflow during low-occupancy periods without compromising IAQ, provided that a base ventilation rate is maintained to limit moisture-related risks and ensure building durability. 

• Effective smart ventilation must balance occupant comfort, building preservation, and energy efficiency, with careful consideration of assessment indices and their thresholds. IAQ assessment should distinguish between occupant-related indicators (e.g. CO₂) and building-related indicators (e.g. humidity), as threshold selection directly influences ventilation rates and energy demand. 

• Future assessments should integrate energy performance metrics alongside IAQ indices to optimize ventilation strategies for sustainability and health. Airflow rates and indoor temperature affect both comfort and energy use and should therefore be jointly considered when selecting and optimising smart ventilation solutions. 

Acknowledgments 

• The research was supported by French manufacturers of heating and ventilation systems, with contributions from various organizations and individuals involved in the project.

This summary encapsulates the essential findings and methodologies of the study, emphasizing the significance of smart ventilation systems in enhancing indoor air quality while addressing energy efficiency and occupant health.

References 

Demouge, F. and Piriou, J. 2018. From Technical Appraisal of Demand-Controlled Ventilation Systems to Indoor Air Quality Assessment Using the Thermo-Hygro-Aeraulic code MATHIS. 39th Air Infiltration and Ventilation Centre Conference (AIVC 2018), 7th TightVent, (p. 5). 

Golaz, B., Robitu, M. and Lefebvre, C., 2025, IAQ Assessment Index For Smart Ventilation Systems,

Eurovent Certita Certification, 2021, Technical Certification Rules Of The Eurovent Certified Performance Mark - Indoor Air Quality and Energy Efficiency of Ventilation Systems. ECP-28-IAQVS. 

Poirier, B., 2023, Evaluation of the overall performance of smart ventilation in low-energy housing. PhD Thesis, Université Savoie Mont Blanc.