The identification of atmospheric dust sources represents a critical challenge in environmental monitoring and climate research. The potential application of the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) methodology, implemented through the generic open-source software package genES-MDA, to address the inverse problem of dust source identification in atmospheric systems is presented. Building upon successful applications in groundwater contaminant source identification and hydrological inverse modeling, we discuss how the same theoretical framework and computational tools can be adapted to atmospheric dust source identification by appropriately modifying the state equations and numerical codes. The genES-MDA package provides a model-independent framework that requires only access to a Python interface or command-line interface to the forward model, making it suitable for integration with atmospheric transport models. This approach offers a systematic methodology for uncertainty quantification and parameter estimation in atmospheric dust source identification problems. It extends the proven capabilities of ensemble-based data assimilation methods to atmospheric environmental applications, maintaining their fundamental advantages while addressing the specific characteristics of atmospheric transport processes.