Modelling the Spatio-Temporal Concentration of Diesel Particulate Matter in an Underground Mine

Abstract

Diesel Particulate Matter (DPM) is an important pollutant, both in industrial areas and cities, and also in underground mines. DPM is essentially the carbonaceous aerosol emitted by diesel engines, with a primary particle size of 10-30 nm, though which rapidly agglomerates to form 100-300 nm aerosols. Most guidelines limit occupational exposure to DPM (measured as elemental carbon) to 100 μg/m3, on an 8-hr averaged basis. However directly assessing worker exposure is both time consuming and expensive. Apart from sampling the exposure of each individual worker, or conducting continuous (and expensive) monitoring, it is difficult to determine if the DPM levels in a workplace will be sufficient to cause DPM exposures above guideline levels. This work has developed a combined particle dynamics and Bayesian regression model, which allows the DPM levels in an underground mine to be predicted both spatially and temporally. The model incorporates known physical effects, (airflow conditions, dispersion, agglomeration), vehicle movement and vehicle emission rates. This enables the model to account for changing (increased) levels of productivity in the mine, a change in the vehicle fleet, or other such factors. The model has been validated against a monitoring study performed in the mine