Real-time breath gas analysis coupled to gas exchange modeling is emerging as promising strategy to enhance the information gained from breath tests. It is shown for exhaled breath carbon monoxide (eCO), a potential biomarker for oxidative stress and respiratory diseases, that a weighted, nonlinear least-squares fit of simulated to measured expirograms can be used to extract physiological parameters, such as airway and alveolar concentrations and diffusing capacities. Experimental CO exhalation profiles are acquired with high time-resolution and precision using mid-infrared tunable diode laser absorption spectroscopy and online breath sampling. A trumpet model with axial diffusion is employed to generate eCO profiles based on measured exhalation flow rates and volumes. The concept is demonstrated on two healthy non-smokers exhaling at a flow rate of 250 ml s-1 during normal breathing and at 120 ml s-1 after 10 s of breath-holding. The obtained gas exchange parameters of the two subjects are in a similar range, but clearly distinguishable. Over a series of twenty consecutive expirograms, the intra-individual variation in the alveolar parameters is less than 6%. After a 2 h exposure to 10 ± 2 ppm CO, end-tidal and alveolar CO concentrations are significantly increased (by factors of 2.7 and 4.9 for the two subjects) and the airway CO concentration is slightly higher, while the alveolar diffusing capacity is unchanged compared to before exposure. Using model simulations, it is found that a three-fold increase in maximum airway CO flux and a reduction in alveolar diffusing capacity by 60% lead to clearly distinguishable changes in the exhalation profile shape. This suggests that extended breath CO analysis has clinical relevance in assessing airway inflammation and chronic obstructive pulmonary disease. Moreover, the novel methodology contributes to the standardization of real-time breath gas analysis.