A methodological approach for context-based CO₂ assessment practices in dynamic indoor environments

This paper systematically reviews CO₂ sensor deployment strategies to capture spatial heterogeneity in classrooms and other complex indoor environments. While single-point monitoring may be sufficient in small, well-mixed spaces, it often produces non-representative measurements in larger areas where airflow dynamics and occupant distribution create uneven concentration patterns. The aim is to synthesize empirical evidence from prior studies and propose recommendations for minimum sensor density under different ventilation and occupancy conditions, addressing limitations in current monitoring practices that can bias exposure assessments.

A systematic scoping review synthesized 23 peer-reviewed studies encompassing 176 case studies across classrooms, offices, laboratories and residential settings, incorporating both experimental measurements and computational fluid dynamics simulations. Data extraction focused on space typology, occupant density, ventilation rates, sensor configurations and observed spatial variability. To enable cross-study comparisons, measurements with disparate temporal averaging windows were normalized to a standardized 15-min interval using a scaling methodology derived from enhanced form of Fick's law. This synthesis facilitated development of a predictive formula for sensor density requirements and a classification framework characterizing spatial CO₂ heterogeneity across diverse indoor environments.

Analysis of 176 case studies demonstrated that spatial CO₂ heterogeneity correlates strongly with ventilation regime and occupant density, with natural ventilation systems exhibiting severe non-uniformity (>300 ppm differentials) in 47.1% of cases compared to 36.2% for mechanical systems. Balanced mechanical ventilation achieved optimal performance, with 60% of measurements maintaining moderate spatial gradients (=200 ppm). A predictive algorithm was derived establishing minimum sensor requirements as N = [11.4D_occ/(0.35ACH + 0.7)], augmented by a 10% occupancy-to-sensor density threshold for educational facilities. Temporal normalization via enhanced Fickian diffusion modeling enabled standardization to 15-min averaging intervals, facilitating development of a five-tier classification schema delineating measurement uncertainty from ventilation inadequacy and identifying critical thresholds requiring immediate remediation.

The findings are derived solely from published literature and have not yet been validated across a wider spectrum of building types or with pollutants beyond CO₂. Broader field studies, integration with intelligent sensor networks and comprehensive cost–benefit assessments are required to confirm applicability and support large-scale adoption.

The study offers evidence-based guidance for CO₂ sensor deployment in classrooms and other complex indoor environments. The predictive equation and 10% occupancy-to-sensor rule provide practical tools for determining minimum sensor density, while the five-tier classification identifies when variability reflects inadequate ventilation rather than sensor error. Applying these strategies enables more reliable monitoring, supports informed ventilation management, and prevents biased assessments based on single-point measurements. Improved deployment practices not only reduce costs and operational inefficiencies but also help maintain indoor air quality at levels that protect health and sustain occupants' cognitive performance.

Ensuring accurate CO₂ monitoring has direct societal benefits, particularly in educational and workplace settings where air quality influences health, attendance and cognitive performance. Reliable sensor deployment helps identify ventilation shortcomings that may otherwise compromise learning outcomes, productivity and well-being. By supporting more equitable access to healthy indoor environments, these strategies contribute to reducing exposure risks, limiting airborne disease transmission and balancing energy use with occupant needs. The outcomes of this research therefore extend beyond technical applications, promoting healthier, safer and more efficient buildings that positively impact individuals and communities alike.

Despite extensive indoor air quality research, no comprehensive framework has systematically addressed CO₂ spatial variability across diverse ventilation regimes and occupancy conditions. Previous investigations remained methodologically fragmented, with only one study examining sampling location while neglecting temporal resolution and sensor density requirements. This investigation synthesizes 176 case studies to establish the first integrated assessment framework, contributing: (1) a predictive sensor density algorithm incorporating occupant loading and ventilation effectiveness, (2) temporal normalization methodology enabling cross-study comparability and (3) a five-tier classification schema distinguishing instrumental uncertainty from ventilation inadequacy. These advances provide foundational evidence-based guidelines for spatially representative CO₂ monitoring protocols.