Determinants of diesel particulate matter (DPM) concentration in underground metalliferous mines using multivariate regression analysis
Keywords:Diesel particulate matter, diesel-powered equipment, exhaust gases, health effects.
AbstractThe use of diesel engine powered equipment in underground mines across the globe has been increased considerably in the recent past to enhance the productivity and safety standards. However, the extensive use of diesel engine powered equipment caused severe health hazards because of the exposure to diesel particulate matter (DPM) and toxic gases discharged from the exhausts of these equipment. NIOH and IARC, USA, reported diesel exhausts including DPM are suspected as human carcinogen. The number of diesel equipment deployed in Indian underground mine has also increased exponentially, which resulted into significant level of DPM exposure. There have not been any comprehensive study on the exposure of DPM as well as any stipulation in the current mine safety legislation in India so far except a guideline from DGMS. The concentration of DPM depends upon many factors such as ventilation, engine designs, maintenance, types of fuel, condition of roadways, exhaust treatment arrangements etc. In the present study, field investigations were accomplished in the three underground metalliferous mines. Different parameters like quantity of air, power and life of engine and gradient of roadways were taken as independent variables to predict the concentration of DPM. Multivariate regression analysis was carried out for establishing the relation between DPM and these independent parameters, and a significant empirical equation has been derived. Thereafter, DPM values were measured by Airtec DPM monitoring instrument and the values predicted from the multivariate regression model and measured values from the instrument were validated through chi square test.
Mcginn, S. (1960): Controlling Diesel Emissions in Underground Mining within an Evolving Regulatory Structure in Canada and the United States of America. IntegrVlsi J, 1–11.
Kumar MS, Dash AK, Bhattcharjee RM, Panigrahi DC (2019): Diesel Exhaust and Diesel Particulate Matter (DPM) in Underground Mines of India.In Proceedings of the 11th International Mine Ventilation Congress, Springer, Singapore, 483-492.
Grenier, M (2000): Evaluation of the Contribution of Light-Duty Vehicles to the Underground Atmosphere Diesel Emissions Burden, DEEP .
Salvi S, Blomberg A, Rudell B, Kelly F, Sandstrom T, Holgate ST, Frew A. Acute (1999): Inflammatory responses in the airways and peripheral blood after short-term exposure to diesel exhaust in healthy human volunteers. American journal of respiratory and critical care medicine.159 (3):
Report on Carcinogens (2000): Fourteenth Edition, National Toxicology Program, Department of Health and Human Services US : http://ntp.niehs.nih.gov/go/ roc 2000
Chang P. Xu G. (2017): A review of the health effects and exposure-responsible relationship of diesel particulate matter for underground mines. Int J Min Sci Technol, 27: 831–8. https://doi.org/10.1016/ j.ijmst.2017.07.020.
WHO (2012): IARC Press Release N° 213, 12,.
Haney RA, Saseen GP. (2000): Estimation of diesel particulate concentrations in underground mines. Mining engineering.
Zheng, Y, Tien JC. (2008): DPM dispersion study using CFD for underground metal/nonmetal mines. In Proceedings of the 12th US/North America mine ventilation symposium, Reno, 487-493.
Mischler SE, Colinet JF. (2009): Controlling and monitoring underground mines in the United States. Natl Inst Occup Saf Heal.
Bugarski AD, Cauda EG, Janisko SJ, Mischler SE, Noll JD.(2011): Diesel Aerosols and Gases in Underground Mines/: Guide to Exposure Assessment and Control. Natl Inst Occup Saf Heal, 1–150.
MSHA Report. (2014): MSHA diesel Inventor. 13. Diesel Emissions Evaluation Program (DEEP) Report (2001): Sampling for Diesel Particulate Matter in Mines, 26.
Silverman DT, Samanic CM, Lubin JH, Blair AE, Stewart PA, Vermeulen R, (2012): The diesel exhaust in miners study: A nested case-control study of lung cancer and diesel exhaust. J Natl Cancer Inst 104:855– 68. https://doi.org/10.1093/jnci/djs034.
DGMS. (2018): Standards and Safety Provisions of Diesel Equipment for using in belowground coal and metalliferous mines, 1-24.
Kurnia JC, Sasmito AP, Wong WY, Mujumdar AS. (2014): Prediction and innovative control strategies for oxygen and hazardous gases from diesel emission in underground mines. Sci Total Environ 481:317–34. https://doi.org/10.1016/j.scitotenv.2014.02.058.
Noll JD, Janisko S. (2013): Evaluation of a wearable monitor for measuring real-time diesel particulate matter concentrations in several underground mines. J Occup Environ Hyg 2013;10:716–22. https://doi.org/ 10.1080/15459624.2013.821575.
Baxter CW, Smith DW, Stanley SJ. (2004): A comparison of artificial neural networks and multiple regression methods for the analysis of pilot-scale data. J Environ Eng Sci ;3:S45–58. https://doi.org/10.1139/ s03-081.
Chandrakar S, Paul PS, Sawmliana C. (2021): Influence of void ratio on "Blast Pull” for different confinement factors of development headings in underground metalliferous mines. Tunnelling and Underground Space Technology.
Lokhande RD, Murthy VMSR, Singh KB. (2014): Predictive models for pot-hole depth in underground coal mining” some Indian experiences. Arab J Geosci, 7: 4697–705. https://doi.org/10.1007/s12517-013-1077-0.
Roy S, Adhikari GR, Renaldy TA, Jha AK. (2011): Development of multiple regression and neural network models for assessment of blasting dust at a large surface coal mine. J Environ Sci Technol, 3(4), 284–301.