# Low-Cost CO2 Sensors: On-Site Performance Evaluation and Co-Location Correction Procedure for Reliable Ventilation Assessments in Schools

## TL;DR

This paper evaluates low-cost CO2 sensors for school ventilation monitoring and introduces a method to improve their accuracy without lab equipment.

## Contribution

A practical on-site calibration method for low-cost CO2 sensors using co-location and normalization is introduced.

## Key findings

- Regression-based correction reduced sensor error by 16% during validation.
- Final correction factors reduced RMSE by 27% and out-of-range measurements by 43%.
- Corrected sensors showed RMSE of 7.4 ppm at ambient CO2 levels and 11.9 ppm below 1500 ppm.

## Abstract

Adequate ventilation is essential for maintaining indoor environmental quality in schools, where ventilation standards are often based on an indoor concentration of human-generated carbon dioxide (CO2) above ambient levels. Low-cost non-dispersive infrared (NDIR) CO2 sensors offer a practical solution for ventilation monitoring, yet variability between sensors can compromise accuracy, particularly when applications depend on the determination of precise concentration differences. This study evaluates the performance of twenty-three low-cost CO2 sensors, developing normalisation functions to improve comparability across sensors, introducing an accessible methodology for on-site sensor calibration without the need for laboratory-grade reference equipment. The sensors were co-located for three independent test periods in 2025 representing typical school internal conditions in Ireland. Pre-normalisation analysis showed strong linearity (coefficient of determination (R2) = 0.999) but notable variability, with a mean root mean square error (RMSE) of 18.3 ppm and 0.45% of measurements outside manufacturers stated accuracy. Normalisation models were trained and validated using a leave-one-period-out approach. Regression-based correction yielded the greatest improvement, reducing RMSE by 16%. When applied to the full dataset, final correction factors reduced RMSE by 27%, out-of-range measurements by 43%, and proportional bias by 31%. Corrected sensors demonstrated highly consistent performance, particularly within the CO2 ranges most relevant for classroom ventilation assessment, with an RMSE = 7.4 parts per million (ppm) at ambient concentrations and 11.9 ppm at concentrations below 1500 ppm. Field-based co-location in the deployment environment across full CO2 cycles, combined with a network-derived global reference, produced effective correction factors. Performance declined marginally above 1500 ppm and during dynamic occupancy, while overall accuracy remained strong. The study presents a practical and accessible methodology for evaluating and normalising low-cost CO2 sensors without specialised laboratory equipment, supporting reliable ventilation assessments in schools.