Modelling the Spatio-Temporal Concentration of Diesel Particulate Matter in an Underground Mine
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 aerosol...
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| Format: | Conference Paper |
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Modelling and Simulation Society of Australia and New Zealand Inc
2011
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| Online Access: | http://www.mssanz.org.au/modsim2011/A10/mullins.pdf http://hdl.handle.net/20.500.11937/40555 |
| _version_ | 1848755903023546368 |
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| author | Mullins, Benjamin O'Leary, R. King, Andrew Rumchev, Krassi Bertolatti, Dean |
| author2 | F. Chan |
| author_facet | F. Chan Mullins, Benjamin O'Leary, R. King, Andrew Rumchev, Krassi Bertolatti, Dean |
| author_sort | Mullins, Benjamin |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T09:03:41Z |
| format | Conference Paper |
| id | curtin-20.500.11937-40555 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:03:41Z |
| publishDate | 2011 |
| publisher | Modelling and Simulation Society of Australia and New Zealand Inc |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-405552023-01-27T05:52:11Z Modelling the Spatio-Temporal Concentration of Diesel Particulate Matter in an Underground Mine Mullins, Benjamin O'Leary, R. King, Andrew Rumchev, Krassi Bertolatti, Dean F. Chan D. Marinova R.S. Anderssen 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. 2011 Conference Paper http://hdl.handle.net/20.500.11937/40555 http://www.mssanz.org.au/modsim2011/A10/mullins.pdf Modelling and Simulation Society of Australia and New Zealand Inc fulltext |
| spellingShingle | Mullins, Benjamin O'Leary, R. King, Andrew Rumchev, Krassi Bertolatti, Dean Modelling the Spatio-Temporal Concentration of Diesel Particulate Matter in an Underground Mine |
| title | Modelling the Spatio-Temporal Concentration of Diesel Particulate Matter in an Underground Mine |
| title_full | Modelling the Spatio-Temporal Concentration of Diesel Particulate Matter in an Underground Mine |
| title_fullStr | Modelling the Spatio-Temporal Concentration of Diesel Particulate Matter in an Underground Mine |
| title_full_unstemmed | Modelling the Spatio-Temporal Concentration of Diesel Particulate Matter in an Underground Mine |
| title_short | Modelling the Spatio-Temporal Concentration of Diesel Particulate Matter in an Underground Mine |
| title_sort | modelling the spatio-temporal concentration of diesel particulate matter in an underground mine |
| url | http://www.mssanz.org.au/modsim2011/A10/mullins.pdf http://hdl.handle.net/20.500.11937/40555 |