Dust distribution in open-pit mines based on monitoring data and fluent simulation
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To understand the concentration and distribution of PM2.5 and PM10 in open-pit mines, a beta-ray particle monitor and some laser monitors were arranged in Haerwusu Surface Coal Mine (HSCM), Inner Mongolia, China. A fluent simulation was made to study the dust move in the pit and escape rate and time out of the pit. The main conclusions include (1) in HSCM, the concentration of PM10 changes with that of PM2.5, meeting the power function PM10 = 2.548 × PM2.50.993. The dust concentration around the working mining equipment is very high. For example, around a working drill, the PM2.5 can be up to 426 μg/m3, and around a working power shovel, the PM2.5 can be up to 352 μg/m3. (2) At the same time, the PM2.5 concentration is nearly equal throughout the pit, away from the operating equipment, with a confidence level of 95%. The mean dust concentration away from the equipment is 76.7 μg/m3 when this mining equipment is working. So, the number of monitors in the pit can be decreased without affecting the quality of dust monitoring, which means that the cost of monitoring can be cut down. (3) Base on Fluent simulation results, the average escape time of dust particles with different diameters is similar, but the maximum escape time decreases as the particle diameter increases, which means that most dust moves with the air swirl, but some smaller dust particles can hang in the pit for a longer time. Also, the escape rate decreases rapidly as the diameter of the dust increases. (4) Dust is rotated and diffused evenly in the pit under the action of the eddy current in the pit. Finally, when the dust is swirled to a higher level than that of the pit head, the dust can escape out of the pit.
KeywordsPM2.5 PM10 Open-pit mine Particle monitoring Fluent simulation Haerwusu Surface Coal Mine
Project is supported by National Key Technology R&D Program (2016YFC0501100) and the National Natural Science Foundation of China (51034005) which is greatly appreciated.
- Bonifacio, H. F., Maghirang, R. G., Trabue, S. L., McConnell, L. L., Prueger, J. H., & Bonifacio, E. R. (2015). TSP, PM10, and PM2.5 emissions from a beef cattle feedlot using the flux-gradient technique. Atmospheric Environment, 101, 49–57. https://doi.org/10.1016/j.atmosenv.2014.11.017.CrossRefGoogle Scholar
- Evagelopoulos, V., Zoras, S., Triantafyllou, A. G., & Albanis, T. A. (2005). PM10-PM2.5 time series and fractal analysis. In T. D. Lekkas (Ed.), Proceeding of the 9th International Conference on Environmental Science and Technology Vol B - Poster Presentations (pp. B169-B174). Proceedings of the International Conference on Environmental Science and Technology.Google Scholar
- Gao, J., Tian, H., Cheng, K., Lu, L., Zheng, M., Wang, S., Hao, J., Wang, K., Hua, S., Zhu, C., & Wang, Y. (2015). The variation of chemical characteristics of PM2.5 and PM10 and formation causes during two haze pollution events in urban Beijing, China. Atmospheric Environment, 107, 1–8. https://doi.org/10.1016/j.atmosenv.2015.02.022.CrossRefGoogle Scholar
- Kok, J. F. (2015). An improved model for mineral dust emission (invited presentation). Seventh Symposium on Aerosol-Cloud-Climate Interactions.Google Scholar
- Kok, J. F., Mahowald, N. M., Albani, S., Fratini, G., Gillies, J. A., Ishizuka, M., Leys, J. F., Mikami, M., Park, M. S., Park, S. U., van Pelt, R. S., Ward, D. S., & Zobeck, T. M. (2014). An improved dust emission model with insights into the global dust cycle’s climate sensitivity. Atmospheric Chemistry and Physics Discussions, 14(5), 6361–6425.CrossRefGoogle Scholar
- Li, Z., Sjoedin, A., Romanoff, L. C., Horton, K., Fitzgerald, C. L., Eppler, A., et al. (2011). Evaluation of exposure reduction to indoor air pollution in stove intervention projects in Peru by urinary biomonitoring of polycyclic aromatic hydrocarbon metabolites. Environment International, 37(7), 1157–1163. https://doi.org/10.1016/j.envint.2011.03.024.CrossRefGoogle Scholar
- Romualdo, L. L., Santos, R. S., Lima, F. C., Andrade, L. S., Ferreira, I. M., & Pozza, S. A. (2015). Environmental impact monitoring of a minero-chemical complex in Catalao urban area of PTS, PM10 and PM2.5 by EDX characterization. Icheap12: 12th International Conference on Chemical & Process Engineering, 43, 1909–1914. https://doi.org/10.3303/cet1543319.
- Sosa, G., Vega, E., Gonzálezavalos, E., Mora, V., & Lópezveneroni, D. (2013). Air pollutant characterization in Tula industrial corridor, central Mexico, during the MILAGRO study. BioMed Research International, 2013(10), 521728.Google Scholar
- Tiwari, S., Srivastava, A. K., Bisht, D. S., Bano, T., Singh, S., Behura, S., Srivastava, M. K., Chate, D. M., & Padmanabhamurty, B. (2009). Black carbon and chemical characteristics of PM10 and PM2.5 at an urban site of North India. Journal of Atmospheric Chemistry, 62(3), 193–209. https://doi.org/10.1007/s10874-010-9148-z.CrossRefGoogle Scholar
- Tiwari, S., Hopke, P. K., Pipal, A. S., Srivastava, A. K., Bisht, D. S., Tiwari, S., Singh, A. K., Soni, V. K., & Attri, S. D. (2015). Intra-urban variability of particulate matter (PM2.5 and PM10) and its relationship with optical properties of aerosols over Delhi, India. Atmospheric Research, 166, 223–232. https://doi.org/10.1016/j.atmosres.2015.07.007.CrossRefGoogle Scholar
- Vaupel, K., Klenk, U., & Schmidt, E. (2016). Emissions from open pit mines - a challenge for air dispersion modeling. Gefahrstoffe Reinhaltung der Luft, 76(1–2), 14–18.Google Scholar
- Yokelson, R. J., Burling, I. R., Gilman, J. B., Warneke, C., Stockwell, C. E., de Gouw, J., Akagi, S. K., Urbanski, S. P., Veres, P., Roberts, J. M., Kuster, W. C., Reardon, J., Griffith, D. W. T., Johnson, T. J., Hosseini, S., Miller, J. W., Cocker III, D. R., Jung, H., & Weise, D. R. (2013). Coupling field and laboratory measurements to estimate the emission factors of identified and unidentified trace gases for prescribed fires. Atmospheric Chemistry and Physics, 13(1), 89–116. https://doi.org/10.5194/acp-13-89-2013.CrossRefGoogle Scholar