In the quest to improve indoor air quality (IAQ) and optimize building ventilation systems, researchers have long relied on predicting human CO2 generation rates. However, a recent study led by Oluwatobi Oke of the National Institute of Standards and Technology (NIST) in Gaithersburg, MD, has shed new light on the accuracy of these predictions, with significant implications for the energy sector.
The study, published in the journal *Indoor Environments* (translated to English as “Indoor Environments”), evaluated two prominent methods for estimating CO2 emission rates from building occupants: the ASHRAE approach and the Persily and de Jonge method. These estimates are crucial for determining ventilation rates and assessing IAQ, but they often rely on assumptions about occupants’ activities and characteristics.
“Many applications in building ventilation and indoor air quality use indoor CO2 concentrations as indicators, but these applications often involve technical misinterpretations,” Oke explained. “Our study aimed to assess the accuracy of these predictions, as accurate data is essential for energy-efficient and healthy building design.”
The research team utilized data from experiments conducted by four research laboratories, involving healthy adults performing various activities, from sleeping to cycling and sedentary tasks like reading. They compared the predicted CO2 emission rates using two types of input values: measured data from the experiments and data derived from the literature.
The findings revealed that the ASHRAE approach consistently underestimated CO2 emission rates, with absolute mean prediction errors ranging from 29% to 58%. In contrast, the Persily and de Jonge method showed lower prediction errors, particularly when measured inputs were used, with absolute mean differences ranging from 6% to 21%.
“Our results highlight the critical importance of accurate input data,” Oke stated. “When measured inputs are unavailable, literature-derived values should be used with an understanding of their uncertainty.”
These findings have significant commercial impacts for the energy sector. Accurate CO2 emission rate predictions are essential for designing energy-efficient ventilation systems that maintain good IAQ. Overestimating or underestimating these rates can lead to inefficient energy use, increased costs, and potential health issues for building occupants.
The study suggests that the Persily and de Jonge method, when used with measured inputs, provides a more accurate estimate of CO2 emission rates. However, when measured data is unavailable, practitioners should be cautious when using literature-derived values and consider the associated uncertainties.
As the building industry continues to prioritize energy efficiency and occupant health, this research underscores the need for more accurate and reliable methods for predicting human CO2 generation rates. Future developments in this field may focus on improving measurement techniques and refining prediction models to better account for the variability in occupant activities and characteristics.
In the meantime, building designers, HVAC engineers, and energy consultants can use these findings to make more informed decisions about ventilation system design and operation, ultimately leading to more sustainable and healthier buildings.