Spray polyurethane foam insulation is a widely used insulation material in both new construction and building renovations. To better understand its impact on indoor air quality in addition to establishing consensus standards for measuring chemical emissions experimentally, a recently developed mathematical model and software tool by the U.S. Environmental Protection Agency, i-SVOC, was applied to estimate a semivolatile organic compound (SVOC) flame retardant (tris[2-chloro-1-methylethyl] phosphate [TCPP]) emitted from spray foam and its fate and transport in a modeled indoor environment. With limited literature data, we first estimated the key parameters required by i-SVOC software, including the solid/air partition coefficient (kma), solid-phase diffusion coefficient (Ds), and gas-phase mass transfer coefficient (hg) of TCPP, and then included indoor sinks such as dry wall and flooring as well as the interaction between TCPP and airborne particles. Finally, i-SVOC was used to calculate the airborne TCPP concentration and source emissions/sink sorption factor using a hypothetical room condition. Additional simulations were conducted using the microchamber conditions and compared to the experimental results. In addition, a sensitivity analysis was performed to study the impact of key parameters on modeling results. Comparing between modeling and experimental results, we found that at 23°C the emissions factors derived from the microchamber study were very close to the high-end modeling results, whereas at higher temperatures, i-SVOC predicted lower emissions factors with current inputs. In the sensitivity analysis, kma was found to be the most important parameter to explain such differences between modeling and experimental results. The hg contributed to the differences as well with a higher impact within the first few hours. In addition, it is important to include indoor SVOC sinks, which are ubiquitous in indoor environments. Although i-SVOC is a promising tool for studying SVOC emissions from building materials, experimental measurements are essential for validating input parameter estimation and modeling results.