Modeling of human exposure to aqueous algal odorants geosmin (earthy), 2-methylisoborneol (musty), and (trans,cis)-2,6-nonadienal (cucumber, fishy), and the solvent trichloroethylene (sweet chemical), was investigated to improve the understanding of water-air transfer by including humans as sensors to detect contaminants. A mass-transfer model was employed to determine indoor air concentrations when water was used for showering under varying conditions (shower stall volume, water and air flow rate, temperature, aqueous odorant concentration, shower duration). Statistical application of multiple linear regression and tree regression were employed to determine critical model parameters. The model predicted that concentrations detectable to the human senses were controlled by temperature, odor threshold, and aqueous concentration for the steady-state model, whereas shower volume, air flow, and water flow are also important for the dynamic model and initial detection of the odorant immediately after the showering is started. There was excellent agreement of model predictions with literature data for human perception of algal odorants in their homes and complaints to water utilities. TCE performed differently than the algal odorants due to its higher Henry's law constant, in spite of similar gas and liquid diffusivities. The use of nontoxic odorants offers an efficient tool to calibrate indoor air/water shower models.