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Carbon monoxide (CO) poisoning is an important public health issue globally. Several CO exposure models have been built to predict the kinetics of CO uptake and elimination in CO poisoning but based on a limited dataset confined to a handful of young, healthy male volunteers. It is important to improve these models by using a wider range of individuals to seek an optimal performance across a realistic range of characteristics of the general population. Therefore, to expand the validity and practical applicability of CO exposure models to people with different characteristics, such as age, sex, height, weight, smoking status and ethnicity, we carried out three studies to test the relationship between these factors and pulmonary function, represented by the Diffusion capacity of the Lung for CO (DLCO), as well as the rate of CO uptake and CO elimination. Specifically, we first explored how demographic, physiological and behavioural factors affect CO uptake and CO elimination among young volunteers. The experiment indicated that smoking status did not influence CO uptake and elimination in this group. Then, we collected pulmonary function test (PFT) data from two hospitals, one in the UK and one in Taiwan. Given the exceptional circumstances of the COVID-19 pandemic in the UK, obtaining ethics approval for hospital data was challenging, however with great effort, PFT data were collected from both hospitals. We found that males had a higher value of DLCO than females, and DLCO was positively associated with height and weight, and negatively associated with age. Later, the estimated DLCO results from PFT in relatively healthy individuals were used to update established Coburn-Forster-Kane (CFK) models. These showed that, for example, when the CO exposure scenario was the same, CO uptake and elimination rate were higher in a younger male than an older male. In conclusion, the updated model has the advantage of being able to predict CO uptake and elimination for a wider range of individuals compared to previous CFK models, factoring in their age, sex, height, weight, ethnic group, and smoking status. This could help estimate past CO exposure, and help medical staff recognize CO exposure and design optimal treatments for CO exposure victims.