Study region Aerosol dispersion is investigated in this study at the basin of Lake Urmia in Iran following its disappearance in 2023, a disaster triggered by mismanagement and the absence of effective planning. Study focus The study introduces the STOP-SaltWind framework composed of six consensually-selected data layers processed by a scoring system of rates and weights, including: Temperature, Precipitation, Salt (Normalised Difference Salinity Index) and Wind speed. Their information content is assessed through correlations; although the scores are subjective, their quality can be enhanced by methods similar to deep neural networks (DNN) using aerosol absorption index as a label dataset. New hydrological insights Basic framework results show that correlation in the data layers are non-random signal, achieving 41–60 % ‘overall accuracy’ in confusion matrix across three major salt-wind events (2021/2022/2023), and hence proof-of-concept for STOP-SaltWind. A supervised clustering DNN further enhanced overall accuracy to 80 % with consistently high Area Under Curve (AUC) values exceeding 0.9. These findings confirm that the information content of the framework is significant and inherent subjectivity reducible by advanced techniques, making it applicable to similar study areas. The desiccated lakebed exposes the basin to chronic aerosol dispersion risks, particularly at five hotspots, impacting health, the environment, agriculture, flora and fauna. Basin-wide risk exposures can be reduced by effective planning and governance, including measures to restore inflows and cover the exposed saltpan.
