Modeling dispersion of dust caused by tailings and mining activities at Jajarm based on gradation analysis

This study presents a novel application of source-specific particle size distribution (PSD) and moisture content-derived from laser gradation analysis of mining wastes-as explicit inputs to the AERMOD dispersion model, significantly improving its predictive realism for PM₁₀ from complex mining operations at the Jajarm Alumina Plant in Iran. Sampling targeted key waste types, including bauxite crusher slime, red mud, and lime tailings, with laser diffraction (HORIBA LA-950) used to quantify PSD and ASTM D2216 for moisture determination. Emission rates were calculated using AP-42 methodologies and integrated into AERMOD with seasonal surface parameters and high-resolution meteorological data. Model validation against field measurements on 21 June 2018 showed excellent agreement (R² = 0.88, RMSE = 127 µg/m³), confirming that incorporation of gradation-specific data enhances prediction accuracy compared to generic assumptions. The highest 24-h PM₁₀ concentration reached 2087 µg/m³ near crushers, with an annual average of 542 µg/m³, far exceeding the air standards for particulate matter set by the EPA and the air quality standard in Iran (150 μg/m³). Results identify bauxite crusher slime (60% PM₁₀) and red mud (50% PM₁₀, 15.4% clay) as high-dispersion-potential wastes, and lime loading/unloading as the largest emission source (104 t/a). We recommend prioritizing dust suppression at lime/bauxite handling operations and surface stabilization of fine-grained tailings to mitigate air quality and public health impacts.