Abstract
Existing equations to estimate ventilation (VE) may not represent the Chinese population. The objective is to develop regression equations to predict the basal metabolic rate (BMR) for ventilation estimation. 80 participants underwent the incremental tests on a bicycle ergometer, wearing a fitted facial mask with an airflow sensor connected to the cardiopulmonary gas analyzer, where the energy expenditure, metabolic factors and VE were monitored simultaneously. Linear regression models were established between BMR and body weight, which were used to estimate energy expenditure and VE. Extrapolation of the regression model was evaluated by the five-fold cross-validation. And we also assessed the inhaled load of air pollutants in subgroups at the same exposure levels. Regression models for males and females were BMR (kJ/d) = 107.58 × weight (kg)-172.61 and BMR (kJ/d) = 105.61 × weight (kg)-26.94, respectively. The model showed good fitness between the measured and predicted VE. Differences between the measured and predicted VE of this model are smaller than that of other models. There were significant differences in inhaled load participants in the same exposure concentrations. The regression model showed that weight and BMR are highly correlated and can be used to estimate individual VE.
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Funding
This work was supported by the National Natural Science Foundation [grant numbers 41977374].
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Yuchen Zhao: Formal analysis, Methodology, Investigation, Data Writing—original draft, Visualization. Qian Guo: Methodology, Investigation, Writing—review & editing. Jing Shao: Methodology, Writing—review & editing. Ping Liu: Methodology, Writing—review & editing. Qirong Wang: Methodology, Writing—review & editing. Zongshuang Wang: Writing—review & editing. Xiaoli Duan: Conceptualization, Methodology, Investigation, Data curation, Writing—review & editing, Supervision, Project administration, Funding acquisition.
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Zhao, Y., Guo, Q., Shao, J. et al. Using energy expenditure to estimate the minute ventilation and inhaled load of air pollutants: a pilot survey in young Chinese adults. Environ Sci Pollut Res 30, 93892–93899 (2023). https://doi.org/10.1007/s11356-023-28038-2
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DOI: https://doi.org/10.1007/s11356-023-28038-2