Environ Monit Assess. 2026 May 17;198(6):598. doi: 10.1007/s10661-026-15421-y.
ABSTRACT
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.
PMID:42143632 | DOI:10.1007/s10661-026-15421-y
