Analyzing mixtures of air pollutants is crucial for identifying major emission sources and developing effective mitigation strategies. This study introduces a Bayesian spatiotemporal multivariate receptor model, reformulating the classical source apportionment problem within a functional framework. The model incorporates a hierarchical structure including spatial dependency while estimating the optimal number of sources. The proposed approach is validated using simulated data, illustrating its ability to reconstruct source emissions and source profiles.