Development and validation of models to predict personal ventilation rate for air pollution research

Air pollution intake represents the amount of pollution inhaled into the body and may be calculated by multiplying an individual’s ventilation rate with the concentration of pollutant present in their breathing zone. Ventilation rate is difficult to measure directly, and methods for estimating ventilation rate (and intake) are lacking. Therefore, the goal of this work was to examine how well linear models using heart rate and other basic physiologic data can predict personal ventilation rate. We measured personal ventilation and heart rate among a panel of subjects (n = 36) while they conducted a series of specified routine tasks of varying exertion levels. From these data, 136 candidate models were identified using a series of variable transformation and selection algorithms. A second “free-living” validation study (n = 26) served as an independent validation dataset for these candidate models. The top-performing model, which included heart rate (H(r)), resting heart rate (H(rest)), age, sex, and hip circumference and interactions between sex with H(r), H(rest), age, and hip predicted ventilation rate (V(e)) to within 11% and 33% for moderate (V(e) = 45 L/min) and low (V(e)= 15 L/min) intensity activities, respectively, based on the validation study. Many of the promising candidate models performed substantially worse under independent validation. Our results indicate that while measures of air pollution exposure and intake are highly correlated within tasks for a given individual, this correlation decreases substantially across tasks (i.e., as individuals go about a series of typical daily activities). This discordance between exposure and intake may influence exposure-response estimates in epidemiological studies. New air pollution studies should consider the trade-offs between the predictive ability of intake models and the error potentially introduced by not accounting for ventilation rate.