A genetic algorithm is used to couple a dispersion and transport model with a pollution receptor model for the purpose of assimilating sensor data to characterize emission sources. This coupling allows the use of the backward (receptor) model to calibrate the forward (dispersion) model, potentially across a wide range of meteorological conditions. The genetic algorithm optimizes the source calibration factors that connect the two models. This methodology is demonstrated for a basic Gaussian plume dispersion model, then progresses to incorporating an operational transport and dispersion model. It is verified in the context of both synthetic data and actual monitored data from field tests with known release amounts. Its error bounds are set using Monte Carlo techniques and robustness assessed through the addition of white noise. The impact of varying the genetic algorithm parameters is assessed.