Experimental and Numerical Assessment of Multi- Source PM Emissions during Ore Mucking in a Polymetallic Underground Mine Environment Abdullah Rasheed Qureshi Lead Supervisor: Dr. Sergei Sabanov Internal Co-Supervisor: Dr. Emil Bayramov External Co-Supervisor: Prof. Jürgen Brune A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy Department of Mining Engineering School of Mining and Geosciences 2025 i Originality Statement I affirm that the work submitted is wholly my own and contains no materials previously published or authored by others, save when proper acknowledgment is provided in the thesis. I thus declare that the intellectual substance of this thesis is the result of my own work, and no significant portions of this material have been utilized to meet the requirements for any other degree or diploma at Nazarbayev University or any other educational institution. Abdullah Rasheed Qureshi Date: 7th May 2025 ii Dedication I completely dedicate my thesis to my respected parents, my cherished wife Faiza Qureshi, my beloved daughter Amna Rasheed Qureshi, and my son Abdul Majid Qureshi, whose steadfast love, support, and understanding have illuminated my path during this arduous academic endeavor. To my parents, whose sacrifices and faith in me have been the cornerstone of my resolve; to my wife, your support and companionship have infused joy into the most challenging times; and to my kids, your innocent and delightful smiles have ignited my ambition to strive for greater accomplishments. Additionally, I would like to dedicate this thesis to the memory of my friend Abdul Sami Gahejo Abro (Late). He has been a steadfast partner throughout my early career and academic journey. He consistently strived to investigate new academic avenues, and his encouragement motivated me to excel in my endeavors. My dedication serves as a modest expression of my thanks and a monument to the significant influence they have had on my life and my academic accomplishment. With my utmost affection and commitment, iii Acknowledgment As I conclude this thesis, I am profoundly grateful to those whose support and guidance have been instrumental throughout this academic journey. I extend my sincere gratitude to my supervisor, Dr. Sergei Sabanov, for his steadfast support, expert advice, and extensive knowledge. His mentorship has significantly refined my research abilities and motivated me to explore new avenues in academia. I am truly privileged to have worked under his guidance. I am equally thankful to my co-supervisors, Dr. Emil Bayramov and Prof. Jürgen Brune, for their essential contributions and unwavering support. Their insightful feedback and dedication have been pivotal in shaping this thesis. I am very appreciative of Prof. Fidelis Suorineni and Prof. Ali Mortazavi, who served as internal examiners, for their constructive feedback on my research. Their guidance has greatly enhanced the quality of my work, and I am grateful for their expertise and support. I express my profound gratitude to Prof. Shimin Liu, Professor of Energy and Mineral Engineering at Penn State University, USA, for serving as an external examiner. His feedback was instrumental in improving the overall quality of my research. I also extend my sincere thanks to Prof. Peyman Pourafshary, Acting Vice Dean for Academic Affairs and Research, for his efforts in managing and coordinating the smooth conduct of my PhD thesis defense. I acknowledge the continuous support of Dr. Izhar Mithal Jiskani, Post.Doc, Department of Sustainability and Planning, Aalborg University, Denmark, for his tireless assistance in reviewing and suggesting improvements throughout the research process. I am grateful to Nursultan Magauiya, Nursultan Kuzembayev, and Gulim Kurmangazy for their contributions to fieldwork experimentation and their unwavering support. I am thankful to Nazarbayev University for providing funding for this project under Research Grant #0122022FD4128. I extend my thanks to all the faculty members and administrative staff of the School of Mining and Geosciences for their continuous support and assistance. Finally, I appreciate everyone who has been part of this remarkable journey, contributing to my pursuit of knowledge and making it a gratifying and enriching experience. iv Abstract Particulate matter (PM) emissions during load-haul-dump (LHD) operations in underground polymetallic mines pose significant health risks to miners and contribute to environmental pollution. This study investigates the PM dispersion from various emission sources during ore handling in a polymetallic underground mine. A combined approach of field experimentation and numerical simulation was employed to evaluate the airflow dynamics and PM diffusion characteristics in the operational drift of mine, situated in East Kazakhstan. For numerical simulation, ANSYS Fluent was used to model and analyze the scenarios. Two primary operating conditions (OC-1 and OC-2) were examined. In OC-1 the airflow and PM2.5 dispersion characteristics were evaluated based on existing and simulating volumetric airflows during LHD handling ore, including condition 1 (C1) loading at the working face, condition 2 (C2) dumping at a temporary dumpsite, and condition (C3) dumping into an underground mine truck (UMT). The existing volumetric airflow in the mine drift was Q = 13 m3/s, and the simulated volumetric airflows were Q = 15, 17, and 20 m3/s to assess the impact of increased ventilation rate on PM dispersion. In OC-2 airflow patterns and PM mitigation strategies were investigated under four auxiliary ventilation system (AVS) designs. The four AVS designs were categorized based on the positioning of AVS ducts’ outlets and all four AVS designs were assessed under scenario 1 (S1) loading near the working face, and scenario 2 (S2) unloading inside the temporary dumpsite. The numerical simulation was compared with the data collected during the field experimentation to validate the results. The comparative analysis revealed the difference between experimental and simulation results for both airflow and PM was less than 10% in OC-1 and OC-2. The research delineates complex airflow patterns characterized by backflow, vortex, unsteady, and steady flow regions. Findings indicate that C2 exposes LHD operators to the highest PM concentrations, followed by C1, while C3 results in greater PM exposure for UMT operators compared to LHD operators. The study further evaluates PM diffusion characteristics across the experimental and simulated volumetric airflows and AVS designs in both OC-1 and OC-2. The analysis of the novel combination of spatial-temporal model and multiple sources of PM revealed that Q = 17 m3/s was able to reduce PM concentrations to 28%, 29%, and 20% in C1, C2, and C3, respectively. Similarly, AVS 2 showed 15% and 27% reduction of PM concentrations in S1 and S2, respectively. Moreover, correlation equations, with high coefficient values (R2), were proposed between the PM concentrations and length of the mine drift to predict the PM concentrations for similar operating conditions in underground mines. These findings provide valuable insights for developing strategies to reduce elevated PM concentrations in the mine drifts. Implementing the recommended AVS designs, and volumetric airflows can significantly enhance air quality management in underground mining environments, promoting miner health and operational efficiency. Keywords: Auxiliary Ventilation System, Diesel-powered equipment, Dual Duct Forced, Field experimentation, Numerical simulation, Particulate matter, Pollution risks, Underground mine environment, Volumetric airflows v Contents Originality Statement ......................................................................................................................................... i Dedication......................................................................................................................................................... ii Acknowledgment ............................................................................................................................................. iii Abstract ........................................................................................................................................................... iv Contents ............................................................................................................................................................ v List of Figures................................................................................................................................................ viii List of Tables .................................................................................................................................................... x 1. Introduction .................................................................................................................................................. 1 1.1. Background ............................................................................................................................................ 1 1.2. Problem statement ................................................................................................................................. 3 1.3. Aim and objectives ................................................................................................................................ 4 1.4. Research hypotheses .............................................................................................................................. 4 1.5. Proposed methodology and research framework ................................................................................... 4 1.6. Practical significance ............................................................................................................................. 5 1.7. Structure of the thesis ............................................................................................................................ 6 2. Literature Review ......................................................................................................................................... 7 2.1. Introduction to PM ................................................................................................................................. 7 2.2 PM and health effects ............................................................................................................................. 7 2.2.1 Toxicological studies ............................................................................................................................ 8 2.2.2. Epidemiological studies ...................................................................................................................... 9 2.3. Sources and monitoring of PM ............................................................................................................. 11 2.3.1. Shift-average monitoring technique ................................................................................................... 11 2.3.2. Real-time monitoring technique ......................................................................................................... 11 2.3.3. CFD analysis of PM .......................................................................................................................... 14 3. Methods and materials ................................................................................................................................ 18 3.1 Introduction .......................................................................................................................................... 18 3.2 Mine site description............................................................................................................................. 18 3.3 Computational model description ......................................................................................................... 19 3.4. Methodology for operating condition 1(OC1) ..................................................................................... 19 vi 3.5. Methodology for operating condition 2 (OC2) .................................................................................... 20 3.5.1. Assessment of the DDF-AVS designs ............................................................................................... 20 3.6 Numerical models ................................................................................................................................. 22 3.6.1. Fluid flow model ............................................................................................................................... 22 3.6.2. Particle flow model ........................................................................................................................... 23 3.7 Mesh generation and sensitivity analysis.............................................................................................. 23 3.8 Input Parameter setup ........................................................................................................................... 24 3.9. Layout of measuring points in OC1 and OC2 ..................................................................................... 25 3.10. Real-time monitoring ......................................................................................................................... 27 3.10.1. DustTrak filter kit ............................................................................................................................ 27 3.11. Assumptions ....................................................................................................................................... 28 4. Results and discussion ................................................................................................................................ 29 4.1. Results ................................................................................................................................................. 29 4.1.1. The airflow distribution analysis in OC1 .......................................................................................... 29 4.1.2. PM analysis in OC1 .......................................................................................................................... 31 4.1.2.1. PM migration under C1 ................................................................................................................. 31 4.1.2.2. PM migration under C2 ................................................................................................................. 32 4.1.2.3. PM migration under C3 ................................................................................................................. 34 4.1.3. Effect of varying volumetric airflows to dilute PM concertation ..................................................... 35 4.1.3.1. Effect of varying volumetric airflows in C1 .................................................................................. 36 4.1.3.2. Effect of varying volumetric airflows in C2 .................................................................................. 37 4.1.3.3. Effect of varying volumetric airflows in C3 .................................................................................. 38 4.1.4. Comparative analysis of PM diffusion rate under varying volumetric airflows. .............................. 39 4.1.4.1. PM diffusion rate in C1 ................................................................................................................. 39 4.1.4.2. PM diffusion rate in C2 ................................................................................................................. 40 4.1.4.3. PM diffusion rate in C3 ................................................................................................................. 40 4.1.5. Proposed volumetric airflow ............................................................................................................. 42 4.1.6. The airflow distribution analysis in OC 2 ......................................................................................... 43 4.1.6.1. Airflow distribution in x, y, and z coordinates ............................................................................... 43 4.1.6.2. Airflow velocity vectors distribution on monitoring planes .......................................................... 45 4.1.6.3. Analysis of PM contours ................................................................................................................ 47 vii 4.1.6.4. Spatial-temporal distribution characteristics of PM ...................................................................... 49 4.1.6.5. Comparative analysis of PM diffusion rate under all AVS-Designs 1 – 4 ..................................... 52 4.1.6.5.1. PM diffusion rate in S1 ........................................................................................................... 52 4.1.6.5.2. PM diffusion rate in S2 ........................................................................................................... 52 4.1.7. Filter Analysis by SEM/EDS (Jeol JSM-IT200) ............................................................................... 54 4.1.8. Numerical simulation validation with field experimentation ............................................................ 55 4.2. Discussion ............................................................................................................................................ 57 5. Applications and Limitations ...................................................................................................................... 59 5.1. Applications of current research work ................................................................................................. 59 5.2. Limitations of current research work ................................................................................................... 59 6. Conclusion and Recommendations ............................................................................................................ 61 6.1. Conclusions ......................................................................................................................................... 61 6.2. Recommendations................................................................................................................................ 62 References ...................................................................................................................................................... 63 viii List of Figures Fig. 1. Research framework overview ..............................................................................................................5 Fig. 2. The approximate location of the polymetallic underground mine in East-Kazakhstan .......................18 Fig. 3. (a) Computational model of underground mine drift, (b) C1, (b) C2, and (c) C3................................20 Fig. 4. (a) Geometric model of underground mine drift, (b) S1, and (c) S2 ....................................................21 Fig. 5. (a) AVS-1, (b) AVS-2, (c) AVS-3, and (d) AVS-4 for S1 and S2..........................................................21 Fig. 6. (a) Mesh independence test, (b) surface mesh, and (c) volumetric mesh ............................................24 Fig. 7. Distribution of monitoring points in (a) Mine drift, (b) Haulage drift, and (c) layout of the cross .....26 Fig. 8. Data monitoring planes layout in (a) S1, and (b) S2 ............................................................................26 Fig. 9. Real-time field data monitoring instruments (a) Leica Disto D2, (b) Alnor RVA501, and (c) DustTrakTM monitor ............................................................................................................................................................27 Fig. 10. (a). Internal filter kir, (b) mesh filter and monitoring points, and (c) SEM/EDS Jeol JSM-IT200 ....28 Fig. 11. Airflow streamlines distribution in: (a, b) condition 1 at breathing zones 1.5 m and 2.2 m, (c, d) condition 2 at breathing zones 1.5 m and 2.2 m, and (e, f) condition 3 at breathing zones 1.5 m and 2.2 m .31 Fig. 12. (I-a) 3D distribution of PM, (I-b) PM distribution at monitoring planes, (II-a) airflow vectors and PM distribution at breathing zone 1.5 m, (II-b) PM concentration at breathing zone 1.5 m, (II-c) airflow vectors and PM distribution at breathing zone 22.2 m, (II-d) PM concentration at breathing zone 2.2 m, in C1 .......32 Fig. 13. (I-a) 3D distribution of PM, (I-b) PM distribution at monitoring planes, (II-a) airflow vectors and PM distribution at breathing zone 1.5 m, (II-b) PM concentration at breathing zone 1.5 m, (II-c) airflow vectors and PM distribution at breathing zone 2.2 m, (II-d) PM concentration at breathing zone 2.2 m in C2 ..........34 Fig. 14. (I-a) 3D distribution of PM, (I-b) PM distribution at monitoring planes, (II-a) airflow vectors and PM distribution at breathing zone 1.5, (II-b) PM concentration at breathing zone 1.5 m, (II-c) airflow vectors and PM distribution at breathing zone 2.2, (II-b) PM concentration at breathing zone 2.2 m in C3.....................35 Fig. 15. PM concentration distribution at breathing zones (i) 1.5 m and (ii) 2.2 m, under varying supplied volumetric airflows in C1: (a) Q = 13 m3/s, (b) Q = 15 m3/s, (c) Q = 17 m3/s, (d) Q = 20 m3/s ..................37 Fig. 16. PM concentration distribution at breathing zones (i) 1.5 m and (ii) 2.2 m, under varying supplied volumetric airflows in C2: (a) Q = 13 m3/s, (b) Q = 15 m3/s, (c) Q = 17 m3/s, (d) Q = 20 m3/s ..................38 Fig. 17. PM concentration distribution at breathing zones (i) 1.5 m and (ii) 2.2 m, under varying supplied volumetric airflows in C3: (a) Q = 13 m3/s, (b) Q = 15 m3/s, (c) Q = 17 m3/s, (d) Q = 20 m3/s ..................39 Fig. 18. PM diffusion rate under different volumetric airflows (a) C1, (b) C2, and (c) C3 ............................42 Fig. 19. Airflow velocity (Vx, Vy, Vz) in x, y, and z dimensions on the line monitors (a) S1 and (b) S2 ......45 ix Fig. 20. Airflow vectors representation on monitoring planes (a) S1 and (b) S2 ............................................47 Fig. 21. 20. PM concentration on monitoring planes (a) S1 and (b) S2 ..........................................................49 Fig. 22. Spatiotemporal PM distribution in (a) S1 and (b) S2 .........................................................................51 Fig. 23. Comparative analysis of PM diffusion under all AVS-Designs 1 – 4 ................................................53 Fig. 24. SEM/EDS analysis of mesh filter (a) elemental mass concentration peaks, (b) morphological distribution at point 1, (c) Carbon particles, (d) Floride particles, and (e) all elemental particles ..................55 Fig. 25. Comparison of experimental and simulation results along with absolute error:(a) airflow in C1, (b) PM in C2, (e) airflow in C3, (f) PM in C3 ......................................................................................................57 x List of Tables Table 1. Input parameters and corresponding values of simulation model .....................................................25 Table 2. Elemental mass concentration on mesh filter ....................................................................................54 Page 1 of 82 1. Introduction 1.1. Background The mining industry produces raw materials to meet global industrial demands. The mining system consists of sub-systems, which involve excavation, transportation and processing. However, these mining subsystems contribute to the generation of hazardous materials (PM and gases), posing a threat to the health of underground miners and the mine environment [1-3]. Diesel-powered equipment (DPE) is essential to the mining transportation sector due to its operational versatility and accessibility. The variable working conditions of the DPE produce diesel particulate matter (DPM), and the generation of other hazardous aerosols in underground mines [4, 5]. Airborne dust generation and migration in underground mines are affected by dust particle size, shape, density, ventilation system, mine opening size and shape, and ongoing mining operations [6, 7]. Thus, airborne dust particle size distribution affects dust dispersion within mining systems [8]. PM sizes vary from coarse particles ≤ 10 µm, fine particles ≤ 2.5 µm, and ultra-fine particles ≤ 1 µm [9]. PM may remain suspended for extended periods owing to their small size. The human body can inhale an excessive amount of suspended PM after prolonged exposure. PM can bypass human body defenses and cause damage in the lungs by transporting additional hazardous substances to the alveoli. Particles from the alveolar epithelium can spread to connective tissue, blood, and lymph, causing respiratory and blood system diseases and possibly cancer [10-13]. Recent research at Harvard's T. H. Chan School of Public Health found that polluted air containing excessively small particles increases early mortality [14]. Air pollution causes 4.2 million casualties worldwide, making it the fifth biggest cause of death, according to the World Health Organization (WHO) [15-18]. The National Institute for Occupational Safety and Health (NIOSH) specifies PM as a potential occupational carcinogen, while the International Agency for Research on Cancer (IARC) classifies it as a Group 1 human carcinogen [19, 20]. Underground mining environments, characterized by narrow roadways, restricted cross-sectional areas, and poor ventilation, are prone to the prolonged suspension of air contaminants, hindering their effective dissipation. Furthermore, these conditions substantially elevate the susceptibility of these contaminants to human health relative to non-mining areas[21-23]. Therefore, it is crucial to examine the attributes and dispersion of PM generated during material transport by LHD under varying operational situations to formulate solutions aimed at mitigating miners' exposure to potential hazardous materials. Presently, many methods have been employed in underground mines to mitigate PM risks including the utilization of biodiesel, the implementation of diesel filters, and the deployment of environmental cabs and ventilation systems [24] . The AVS is the primary technology for diluting PM in underground mines. For this reason, an effective and economic AVS design is necessary, which contains both airflow quantity and the length and position of the outlets of ducts. Usually, a single duct forced (SDF), dual duct hybrid (DDH), Page 2 of 82 or a dual duct forced (DDF) AVS is adopted to ventilate and control PM in mine working faces or roadways with a dead end. Additionally, Computational Fluid Dynamics (CFD) can be used for this purpose to visualize the intricacies related to airflow dynamics and PM dilution by changing the AVS characteristics. CFD has been effectively utilized in mining engineering research to address diverse issues connected to air and particle flow. Chang et al. [24] employed CFD to analyze the concentration distribution and diffusion patterns of DPM at various positions of SDF-AVS at the mine working face. Their findings indicated that an AVS with a pipe length exceeding the actual length by 5 m exhibited higher dilution efficacy for DPM. Duan et al. [25] utilized CFD to investigate the dispersion patterns of DPM under SDF-AVS in an underground mine. The excavation working face indicated that DPM accumulated on both sides of the monorail crane and within the airflow's blind zone. Liu et al. [26] adopted CFD to investigate the distribution of PM generated by trackless rubber wheel vehicles (TRTVs) at idling speeds under various operational situations and recommended appropriate ventilation strategies. The mine was equipped with SDF-AVS. Xu et al. [27] employed an isolation zone of an underground mine as an operational model and utilized CFD to predict the concentration distribution of DPM within this isolation area of an underground coal mine. 2D and 3D models were developed to thoroughly examine the airflow and particle concentration distribution features within the roadway. The roadway contained two dump/load sites but was not equipped with any AVS. These studies adopted SDF-AVS to mitigate the DPM in a working face or near a dead-end inside an underground coal mine. I.M. Konduri et al. [28] investigated airflow migration and dust diffusion at the working face under DDHAVS by a combination of numerical simulation and experimental testing, thereby elucidating the principles governing airflow migration and dust diffusion. Liu et al. [29] examined airflow dynamics and dust dispersion in the roadway utilizing the DDH-AVS by integrating numerical modeling with field measurements. The findings indicate that an increased distance between the air pressure outlet and the head enhances the reduction and regulation of dust dispersion in the roadway, with optimal dust removal efficacy occurring at 35 m. In another study. Cheng et al. [30] used CFD to simulate the effects of various ventilation parameters on the airflow and dust flow fields under DDH-AVS. The results indicate that the dust suppression efficacy of the air curtain is optimal when the air inlet-outlet ratio is 0.75 and the distance between the air curtain generator and the working face is 20 m. Zhou et al. [31] examine PM and tail gas flow affected by DDH-AVS in a fully mechanized working face. The research additionally investigated the influence of air suction volume and the distance between the DPE and the heading face on the diffusion dynamics of particulate matter and exhaust gases. The findings indicated that a reduced distance between the DPE and the heading face necessitated an increased volumetric airflow to effectively dilute the harmful particles. Conversely, reduced volumetric airflow adequately dilutes the DPM and tail gas when the distance between the heading face and DPE is extended. In another study, Zhou et al. [32] examined the pollution for dust from the heading face and DPM under DDH-AVS in a mechanized excavation face. Several operational scenarios were developed utilizing CFD, Page 3 of 82 focusing on the air intake volumes of the dust removal (exhausted) fan and the distance from the fan's outlet to the heading face. The findings indicated that optimal management of dust and DPM is attained with varying volumetric air intakes when the distance from the dust removal fan outlet to the heading face is 5 m. Wang et al. [33] utilized CFD to suggest a ventilation and dust control strategy employing DDF-AVS at the tunnel excavation face, with the objective of optimizing dust control and reduction methods in the underground coal mining environment. The ideal configuration stipulates a distance of 5 m between the DDF-AVS outlet on the driver's side and the excavation face, a distance of 5 m between the DDF-AVS on the return air side and the excavation face, and an airflow ratio of 6:4 between the DDF-AVS. 1.2. Problem statement Despite these numerous studies conducted in underground coal mines to address the substantial PM exposure and potential health impact on the human and environment, there are still challenges that complicate the understanding of PM sources and behavior under different AVS in underground mines. Firstly, a significant literature review reveals that most studies explicitly refer to "PM" in the context of surface mines [34-38], while "DPM" is specifically linked to underground mines [25, 39-42]. This distinction is likely due to the elemental composition of DPM which poses greater health risks than PM. Thus, significant attention has been given to DPM in underground mines, rather than other potential sources of PM in underground mines. Moreover, the necessary airflow quantity in an underground mine is generally calculated by multiplying the engine power of the diesel vehicles operating within the mine by the unit airflow requirement. This requirement typically ranges from 0.05 to 0.06 cubic meters per second kilowatt of engine power (m³/kWs) in Australia, 0.047 to 0.092 m³/kWs in Canada, 0.067 m³/kWs in China, and 0.063 m³/kWs in South Africa [43, 44]. Usually, the AVS supply a constant airflow quantity at the working face, while the mining operations are dynamic. The diverse operating conditions contribute to various scenarios and sources of PM generation. These include the operation of multiple DPEs, performing the loading and dumping of ore, and idling at a constant speed. It remains unclear how these volumetric airflows or AVS designs influence the effectiveness of the PM diffusion with varying operating conditions. This study conducts field work in a polymetallic underground mine in Kazakhstan. Currently, no policy specifies the required airflow quantities for effective dilution of PM in underground mines in the region. As global demand for minerals continues to rise, the depths of mines have significantly increased, presenting new challenges for mine ventilation systems. To ensure adequate airflow under these conditions, the SDF-AVS may not suffice, making the installation of the DDF-AVS necessary. Furthermore, operating parameters such as airflow quantity and outlet position are typically determined based on practical experience [33]. To maximize the potential of the DDF-AVS, it is crucial to explore the AVS airflow quantities and outlet positions in relation to PM generation from various sources and dispersion in underground mines. Therefore, a thorough investigation into the characteristics and optimal parameters of the DDF-AVS is imperative. Page 4 of 82 Addressing these gaps is crucial for developing effective ventilation strategies and technical measures to reduce PM exposure. There is an urgent need for research that integrates field experiments with numerical simulations to evaluate and optimize DDF-AVS supplied volumetric airflows and outlets position under various operational conditions, including different DPE operations and the influence of LHD activities on airflow dynamics and PM dispersion. The insights acquired will be essential for enhancing health and safety of miners, improving air quality, and promoting sustainable practices in underground mining environments. 1.3. Aim and objectives To study the multi-source PM generation and transposition under dynamic airflow velocity and AVS design characteristics, the aim of this thesis is to analyze a novel combination of spatial-temporal modelling and PM emitted from multiple sources to propose a PM diffusion model for dynamic operating conditions in the operational drift of an underground mine. The primary objectives are as follows: ➢ Determine the existing airflow and PM concentrations by experimental measurement in the mine operational drifts and produce the numerical simulation to further analyze different aspects of airflow and PM generation. ➢ Evaluate the influence of different volumetric airflows and elaborate a correlation between the airflow and PM dispersion. ➢ Evaluate the effectiveness of existing and simulated DDF-AVS designs over PM dispersion and analyze a relationship between AVS design and PM dispersion. 1.4. Research hypotheses PM2.5 emissions produced during LHD operations in underground polymetallic mines pose considerable occupational health hazards and jeopardize the efficacy of typical ventilation systems. Maintaining appropriate air quality standards necessitates a comprehensive understanding of airflow dynamics and pollutants dispersion under diverse operational circumstances. While previous studies have examined general ventilation practices, there is a lack of research dedicated to assessing specific design configurations and airflow modifications through integrated experimental and simulation methodologies. This study hypothesizes optimizing ventilation parameters, particularly by enhancing volumetric airflow rates and strategically positioning AVS outlets will substantially decrease PM2.5 concentrations during LHD operations in underground polymetallic mines, thus reducing miners’ exposure and enhancing overall air quality. 1.5. Proposed methodology and research framework To achieve the research objectives, the research framework adopted in this research study is shown in Fig. 1. A mine drift with a working face and a temporary dumpsite, and a haulage drift considered inside a Page 5 of 82 polymetallic underground mine situated in East Kazakhstan. Several monitoring points at different cross- sectional planes in the mine drift and haulage drift were chosen to record the data. The airflow velocities were recorded by using a hand-held anemometer and a real-time PM concentration monitor was used to record the data on the monitoring points. Afterwards, Ansys-Fluent was selected to conduct numerical simulation. A computational geometry domain of the mine drift and haulage drift was constructed based on the dimensions measured during the field visit. Subsequently, the distribution of computing domain in small finite volume cells (mesh) was carried out and the boundary conditions were assigned. The Eulerian- Lagragian model was selected to simulate the airflow velocities and particle trajectories in the computational domain. Following that, the numerical simulation model was validated by comparing the results with field experiment. An allowable limit of less than 15 % of relevant error was selected to validate the numerical model. After achieving the validation criteria, the numerical simulation was evaluated, and conclusions were drawn as per the objectives of this study. Fig. 1. Research framework overview 1.6. Practical significance The practical significance of this research study can be expressed as follows: ➢ This research will assist in identifying the importance of different airflow regions developed in the underground mine. These airflow regions play a pivotal role in the transposition of PM concentrations. The mining professionals will gain valuable insights into the development of airflow regions at different locations and operating conditions to estimate the behavior of PM migration in those regions of the underground polymetallic mines. ➢ This investigation will enhance the understanding of the regular PM levels found in conventional underground metal mining by offering a thorough database of transient PM concentrations at the mine drift and haulage drift of the underground mine. This information will help in properly interpreting and controlling PM levels. Page 6 of 82 ➢ This research is pioneering effort to determine the PM concentration produced from multiple sources in the underground metallic mine. Previously, researchers considered DPE exhaust as the only source of PM produced in the underground coal mines. Considering the correct sources of PM generation will reveal the actual PM concentrations threaten the miners’ health in underground polymetallic mine. ➢ This study is the first attempt to adopt DDF-AVS in the mine drift to analyze the behavior of PM migration and dilution under different operating conditions. ➢ This research is pioneering in assessing whether the volumetric airflow in underground mines selected based on the number of diesel engines working in the mine is sufficient to ventilate the mine drift while PM is produced from multiple sources in the underground polymetallic mine. ➢ Finally, this research study proposes a novel PM ventilation model, based on spatial-temporal diffusion modelling of PM emitted from multiple sources that will be useful for professionals to integrate with mining operations schedules to minimize the PM exposure to miners. 1.7. Structure of the thesis This dissertation is structured into six sections to facilitate understanding. Section 1 presents the research background and articulates the problem statement, highlighting the anticipated scientific and industrial contributions. Section 2 offers a critical review of the literature relevant to this research. Section 3 elaborates on the methods and materials employed in conducting the research. Section 4 analyzes the impact of airflow quantity and the influence of DDF-AVS design on PM diffusion. A detailed discussion is conducted in section 5. Section 6 discusses the limitations of this work and recommendations for future work. Finally, Section 7 summarizes the conclusions drawn from this research. Page 7 of 82 2. Literature Review 2.1. Introduction to PM Air pollution associated with PM is an escalating global concern [45]. The amalgamation of airborne PM and gaseous pollutants can directly affect human beings, influencing both living conditions and health. Human activities, such as mining, result in health and safety concerns for workers, along with environmental challenges [46, 47]. Dust and PM are generated at several stages of mining activities, including excavation, transportation, and mineral processing [48]. These processes emit particles of varying sizes, from coarse to ultrafine, which can remain airborne for prolonged durations, contingent upon their size and mass. Exposure to such PM can result in respiratory issues, including intensified asthma, bronchitis, and other respiratory illnesses. The tiny particles, specifically PM2.5 (PM with an aerodynamic diameter of ≤2.5 µm), can infiltrate the lungs and potentially reach the circulation, leading to cardiovascular complications and aggravating pre-existing health disorders. Furthermore, the amalgamation of PM with gaseous contaminants such as sulfur dioxide (SO2), nitrogen oxides (NOx), and volatile organic compounds (VOCs) can lead to the generation of secondary pollutants, including ozone and smog [49]. These secondary pollutants enhance health concerns, leading to breathing problems and cardiovascular disease among populations living near mining sites or in regions with elevated human activities. The mineralogical and chemical characteristics, along with the mass and surface area of dust particles, directly influence health, resulting in conditions such as lung cancer, bronchial asthma, chronic bronchitis, pneumoconiosis, pulmonary tuberculosis, occupational asthma, chronic obstructive pulmonary disease, dust-related fibrosis, cardiovascular disease, cerebrovascular disease, and gastric cancer [50]. Thus, to control the PM health impact on humans, Occupational Safety and Health Administration (OSHA) proposed 10 mg/m3 and Mine Safety and Health Administration (MSHA) established DPM exposure threshold of 0.16 mg/m3 (quantified as total carbon) for underground metal and nonmetal mines, as a Time Weighted Average (TWA) for Personal Exposure Limit (PEL) to PM under a single 8-hour shift [51-54]. 2.2 PM and health effects Since the research conducted by Dockery et al. [46], PM has been strongly associated with negative health outcomes, revealing a significant correlation between PM levels and fatalities across six U.S. cities with varying average pollution levels. In multiple ways the PM has impacted human health. For instance, PM has been associated with cardiovascular diseases, pulmonary illness, diabetes, and neurological disorders [55, 56]. Despite the undeniable facts about the adverse health impacts of fine particle pollution, the specific characteristics of the particles responsible for these implications remain ambiguous. The adverse health impacts are typically associated with PM mass concentration [57, 58]. Current guidelines concerning PM emissions predominantly rely on the mass concentration of particles [59]. The relationship between early mortality and PM concentration differs by location, equivalent PM concentrations result in distinctly fewer Page 8 of 82 premature fatalities in China compared to Europe [60]. This development underscores the necessity of determining which sources of PM emissions are more detrimental than others. The health implications of particles can generally be assessed with two distinct methodologies. Epidemiological studies examine the correlations between fine particle concentrations and health effects at the population level. Toxicological investigations examine the direct results of particle exposure either in animal subjects or in cell exposure. 2.2.1 Toxicological studies The toxicological investigations underscore the significance of aerosol composition and origins in the health impacts of particles. Park et al. [61] conducted a study that ranked the toxicity of PM from various sources based on oxidative potential measurements and in vitro cellular exposures. Research indicated that PM originating from combustion sources exhibited greater toxicity compared to that from non-combustion sources, particularly emphasizing the harmfulness of traffic-related aerosols, as diesel and gasoline engine emissions were identified as the most toxic contributors. Additional examined sources included biomass and coal combustion, as well as road dust comprising ammonium sulfate, ammonium nitrate, sea spray, and secondary organic aerosols. Therefore, the PM emitted from diesel engines has significance as compared to the other sources of PM to have higher worsen health effects. A substantial number of animal experiments have been conducted to assess the potential health impacts of prolonged DPM exposure. Heinrich et al. [62] performed a series of tests with rats, mice, and hamsters subjected to both unfiltered and filtered diesel particulate matter to investigate its carcinogenic potential. All the test subjects were 8 to 10 weeks old prior to exposure. The exposure duration was 19 hours per day, five days per week. The longest exposed durations for mice, rats, and hamsters were 120, 140, and 120 weeks, respectively. The amount of unfiltered DPM in this investigation was around 4 mg/m3. Each batch comprised 96 animals. A clean air exposure chamber was utilized for the control groups, which had an equivalent sample size. A significant incidence of lung tumors in rats (18%, 17/95) was detected following prolonged exposure to DPM, in contrast to the control group (0%, 0/96). Similarly, Mauderly et al. [63] performed a cancer prevention investigation on rats exposed to soot (a principal component of DPM) at high, moderate, and low concentrations (0.35, 3.5, 7.0 mg/m3, respectively) for a duration of up to 30 months (7 hours per day, 5 days per week). The findings indicated that the incidence of lung tumors in the high and intermediate exposure groups was 13% and 4%, respectively, surpassing the control group's rate of 1%. Animal research has produced significant findings and data from experiments supporting the positive association of DPM exposition and detrimental health effects. While prolonged exposure to DPM may lead to lung tumors in humans, this does not imply that the dose-response data from rat carcinoma research is relevant to humans. Numerous investigations indicated that lung overload resulted in an elevated incidence of lung tumors in rats [64-66]. The clearance and load function of lungs in humans much exceeds that found in rats. The deposition of DPM in the lungs varies across animals and people, even when exposed to the Page 9 of 82 same dosage of DPM. Therefore, using the DPM exposure dose from laboratory animals as a benchmark for human DPM exposure is unsuitable [67]. Consequently, several occupational investigations have been undertaken, yielding epidemiological information pertinent to the correlation between DPM exposure and lung cancer risk. 2.2.2. Epidemiological studies In 1986, NIOSH released a report that encompassed several animal experiments and epidemiological investigations about the health risks of prolonged exposure to DPM. In 1988, NIOSH re-evaluated the data from the 1986 report and determined that prolonged exposure to elevated quantities (exceeding 4 mg/m3) of diesel exhaust could substantially heighten the risk of lung tumors in the tested animals. Nevertheless, only two epidemiological research referred to in the work demonstrated that the lung cancer mortality among train workers escalated with prolonged exposure to DPM emissions [68, 69]. NIOSH recommended that DPM may have carcinogenic effects on humans, based on ample animal research and scant epidemiological evidence [70]. In 1988, the IARC convened a review conference with a panel of specialists to assess the health implications of DPM exposure. Consistent with the NIOSH guideline, the IARC designated DPM as a potential human carcinogen (category 2A) [71]. The review primarily assessed over ten study groups pertaining to various industries (railroad workers, drivers, and miners) and case-control studies associated with many diseases (lung cancer, bladder cancer, etc.). However, the correlation between prolonged DPM exposure and lung cancer incidence could not be established due to insufficient evidence from epidemiological research. In 2012, IARC conducted a subsequent study after 24 years, following the initial review in 1988. A significant outcome of this research was the reclassification of DPM as carcinogenic to humans (Group 1), as documented in a report published in 2013 [72]. Several epidemiological studies involving various work titles, as examined in the IARC assessment, presented substantial proof of the cancer risk of DPM [72]. Garshick et al. [73] determined that the relative risk (RR) for lung tumor fatality among long-term exposed railroad workers was 1.40 (95% CI: 1.30–1.51) in comparison to personnel without frequent exposition to DPM emissions. This study did not account for smoking history, a potential confounding variable affecting the results. Consequently, Garshick et al. [74] performed an additional trial with adjustments for smoking history. The study results indicated that the RR of lung cancer was 1.22 (95% CI: 1.12–1.32) when adjusted for smoking history and 1.35 (95% CI: 1.24– 1.46) while not adjusted. The findings indicated a minor variation in the risk of lung cancer death after accounting for smoking history. The substantial sample size and extended duration of this investigation facilitated the derivation of credible results. Comparable findings were also observed in epidemiological investigations including workers in the trucking industry, construction sector, and other domains associated with DPM exposure [75-79]. Compared to other industries, underground miners encounter elevated amounts of DPM due to confined operational spaces and inadequate ventilation. In the previous decade, only three epidemiological studies on underground mining have been undertaken, all indicating a significant correlation between prolonged exposure to DPM and an increased risk of lung cancer. A cohort mortality study [80] analyzed 12,315 Page 10 of 82 miners from eight non-metal mines in the U.S. where all the miners worked for over a year during the utilization of diesel equipment. Mortality statistics were monitored until 1997, employing respirable elemental carbon (EC) as a substitute for DPM. Exposure estimates were derived from personal respirable elemental carbon (REC) measurements collected between 1998 and 2001, with historical REC concentrations extrapolated from this data. The study considered characteristics including sex, work titles, and birth dates, but did not include data of smoking history. The findings revealed average DPM values of 1.7 μg/m³ for surface miners and 128.2 μg/m³ for underground miners. The RR of cancer-related fatality was 1.21 (95% CI: 1.01–1.45) for underground miners and 1.33 for surface miners. When average DPM being exposed above 946 μg/m³ yearly, the RR for underground miners elevated to 2.21 (95% CI: 1.19– 4.09). The data indicates a greater lung cancer fatality risk in underground miners relative to surface miners, along with a trend of escalating risk associated with extended exposure to DPM. Silverman et al. [81] expanded upon the previous cohort study by conducting a nested case-control study utilizing the same group of miners as the research sample. This study included additional variables, such as smoking history and prior respiratory disease, while still supporting the findings of Attfield et al. [80]. The findings demonstrated that the risk of lung cancer mortality escalated with extended exposure to DPM (15year lag), independent of smoking status. Underground miners with extended exposure to high levels of diesel particulate matter (≥15 years) exhibited an exponential rise in the risk of lung cancer mortality relative to surface miners exposed to lower concentrations. Another cohort study [82] involving 5,862 German potash miners, conducted from 1970 to 2001, investigated the association with DPM exposition and lung cancer fatality. Total carbon (TC) served as a substitute for DPM, with cumulative diesel exposure assessed by multiplying TC concentrations by the duration of miners' exposure. Smoking history was considered an influencing factor. The standardized mortality ratio (SMR) for lung cancer in the entire cohort was 0.73 (95% CI: 0.57–0.93). In sub-cohorts with a cumulative DPM exposure of 4.9 mg/m³-years, the SMRs were 1.28 (95% CI: 0.61–2.71) and 1.50 (95% CI: 0.66–3.43), respectively, when compared to the low-exposure group, with adjustments made for smoking. The results indicated a positive correlation between DPM exposition and lung cancer fatalities, with risk escalating over time. Consequently, both toxicological and epidemiological studies clearly indicate a positive correlation between prolonged DPM exposure and an elevated risk of lung cancer. Despite minimal research explicitly targeting underground miners, a substantial association has been demonstrated between extended exposure to elevated DPM concentrations and increased lung cancer mortality. Considering that underground DPM levels surpass those in other work environments, additional research is required to investigate this occupational group more thoroughly. Furthermore, the possible interaction between DPM and other airborne pollutants, such as dust, must be recognized, as these contaminants may exacerbate negative health outcomes. Page 11 of 82 2.3. Sources and monitoring of PM Prior research on PM in underground mines has concentrated on mining operations producing mechanical dust, which can result in elevated mass concentrations in areas close to working face and processing plant [83-86]. The impact of DPM on mine environment, fine particulate concentrations, and occupational exposures has been extensively studied [87-90]. A prior investigation in an underground gold mine demonstrated that DPE exhaust accounts for 78%–98% of the PM2.5 mass and over 90% of the PM2.5 carbon concentration [7]. It has been proposed that DPM concentrations in deep mines can be diminished by up to 95% with the utilization of contemporary exhaust after-treatment systems [91]. 2.3.1. Shift-average monitoring technique Earlier in underground mines, the NIOSH 5040 method was used to identify and control the DPM levels. This method is the recognized standard and is regarded as the most precise approach for assessing miners' DPM exposure [92]. This method is extensively employed to assess DPM exposure due to its reduced susceptibility to influence from mineral sources or other combustibles, and its ability to distinguish between organic and elemental carbon (OC and EC) content. Nonetheless, the NIOSH 5040 method is predicated on shift averages and, like other shift-average measurement techniques, it possesses certain limitations. A shortcoming of the approach is the delay between measurement and result reporting, necessitated by the requirement for processing in a specialized laboratory. This delay may extend beyond two weeks in certain instances, potentially leading to excessive exposure of the miners to DPM. Another limitation is its ability to identify increased DPM levels during brief yet critical intervals. The approach is essentially deficient in assessing DPM transients during measurement. This method complicates the assessment of the impact of variations in DPM levels in the mine environment due to alterations in mining activities, as it may necessitate the collection of multiple air samples from mine. This escalates the time and expenses associated with the DPM monitoring procedure [93]. 2.3.2. Real-time monitoring technique Currently, PM and EC concentrations in underground mines are being monitored using real-time measuring equipment [94, 95]. Mischler et al. [96] examined many DPM measurement techniques and determined that the TEOM monitor produced elevated EC concentrations compared to the usual sampling procedure. Arnott et al. [97] quantified DPM concentrations in Nevada gold mines using the NIOSH 5040 method and real-time monitoring techniques. A photoacoustic monitor was employed to quantify Black Carbon (BC), while a DustTrak nephelometer quantified total scattering PM with submicron sizes (dPM1). Based on the NIOSH 5040 approach, BC (photoacoustic monitor detection) and dPM1 (DustTrak nephelometer measurement) represent EC and TC, respectively. The TC and EC values derived from the NIOSH 5040 technique were approximately 50% of the corresponding values obtained from real-time DustTrak nephelometers and photoacoustic monitors. Arnott et al. proposed that in mine analysis of real-time BC and dPM1 measurements utilizing photoacoustic and DustTrak sensors should be calibrated to yield findings consistent with equivalent EC and TC measurements as per the NIOSH 5040 standard for compliance purposes. Page 12 of 82 Numerous researchers [98-101] documented assessments of DPM in various Australian coal mines utilizing a real-time-modified personal dust monitor alongside the NIOSH 5040 approach. The real-time DPM monitor (D-PDM) was created based on the Personal Dust Monitor (PDM). “Thermo Fisher Scientific” structurally modified the PDM monitor to make it a submicron (under 1 micron) real-time DPM monitor. A cap light, sample intake, belt-mounted enclosure with respirable dust cyclone, sampling and mass measurement instruments, and charging and communication module for monitor-to-computer data transfer are PDM's main components. PDM data is immediate and quantifies particle mass. PDM findings are less sensitive to water spray droplets than optical approaches. [98]. The Pittsburgh Research Laboratories of NIOSH have conducted a laboratory test of the proposal to convert PDM into D-PDM. The chosen submicron size-selective inlet for the prospective field D-PDM instrument was the BGI 1 μm sharp-cut cyclone. The D-PDM instrument was in the prototype phase. Gillies and Wu [98] established a link between D-PDM mass concentration and TC and EC derived from the NIOSH 5040 technique, identifying a unique correlation equation for each mine. The discrepancies in correlation equations were presumed to arise from inter-mine variations in factors such as atmospheric contamination levels (dust and DPM), vehicle fleet composition, fuel type, engine maintenance, combustion efficiency, engine performance, and interference from other submicron aerosols. Multiple mine-specific and combined linear correlations with zero intercept and strong correlation coefficients were identified. Takiff and Aiken [102] employed an ICx real-time DPM monitor to assess DPM on a vehicle. This monitor was created by the Respiratory Hazards Control Branch at the NIOSH Pittsburgh Research Laboratory [103, 104] and was later licensed to ICx Technologies. The ICx real-time DPM monitor was employed to assess the EC component of the DPM. Takiff and Aiken elucidated the operational principle of the licensed iteration of a beta prototype of the real-time EC monitor, provided by ICx Technologies, and conducted DPM measurements in a commercial mine. They concluded that the real-time ICx DPM monitor accurately mirrors the results of the NIOSH 5040 method in underground mines and is capable of effectively determining real-time DPM concentrations in such environments. Noll et al. [105] evaluated the efficacy of three portable devices: a PDM produced by Thermo Scientific, a prototype EC monitor (Airtec) developed by FLIR, and a prototype AE91 instrument from Magee Scientific. The instruments were assessed for their capacity to deliver direct reading tailpipe analysis for DPM. The average biases of the tailpipe results from the PDM and Airtec were discovered to be 3 ± 12% and 4 ± 20%, respectively. In comparison to the conventional approach of measuring tailpipe particle concentrations from diluted exhaust. The AE91 data exhibited a robust correlation with the reference technique. Their findings indicated that these measures would allow mine operators to assess tailpipe concentrations at any site within the mine, facilitating the quantification of engine repairs, modifications, and the identification of cars with the highest DPM emissions. Nonetheless, there are certain limitations to using these technologies for tailpipe concentrations, such as the need that sample collection by Airtec to be undertaken for only 30 seconds when the engine operates at low loads. The PDM offers total DPM mass, whereas the AE91 and Airtec are limited to providing EC concentrations exclusively. A significant Page 13 of 82 disadvantage of this study was the restricted amount of data points and engines utilized; hence, further data encompassing a greater variety of engines and testing facilities are required for enhanced certainty and confidence. More substantial engines must additionally undergo testing due to their potential to generate distinct airflow in the exhaust. Noll and Janisko [106] assessed the possible disruptions of dust, humidity, and oil mist on the accuracy of the FLIR Airtec monitor in limestone and granite mines. They determined that when accounting for spatial variability, the FLIR Airtec measurements were comparable to the NIOSH 5040 technique values. The findings of the research conducted by Noll and Janisko indicated that dust and elevated humidity did not influence the FLIR Airtec readings when an impactor was employed. They determined that, in addition to the recognized potential interferences, the existence of certain submicron particles in the monitoring environment could introduce a bias in the FLIR Airtec readings. Gillies et al. [107] examined several ambient monitoring approaches employed in underground mines and determined that real-time DPM ambient monitoring is increasingly recognized as an engineering tool for optimizing DPM management tactics. Khan and Gillies [108] employed the commercially available FLIR Airtec in conjunction with the NIOSH 5040 technique to ascertain DPM concentrations in both metal and non-metal mines, subsequently identifying significant DPM sources. In metal mines, front-end loaders and dump trucks were the primary generators of diesel particulate matter (DPM), while in non-metal mines, load-haul dumps were the principal contributors to DPM [108]. Khan and Gillies [109] reported preliminary results from a study conducted in a mine utilizing high-percentage biodiesel, observing that the TWA EC values recorded by the FLIR Airtec monitor were typically greater than those obtained through the NIOSH 5040 method. According to the first results, researchers established a correlation equation for TWA EC readings taken using the FLIR Airtec and NIOSH 5040 instruments [109]. While the initial research indicated a potential bias in the TWA EC readings of the FLIR Airtec, the findings of that study were derived from insufficient evidence. Farzaneh et al. [110] analyzed the amounts of diesel particulates at five locations during the operational periods of an underground mine in Kazakhstan. Real-time monitoring of particle number concentration (PNC), lung deposited surface area (LDSA), and concentrations of PM1, PM2.5, and PM10 were performed in the mining operational area and the loader driver's breathing zone within the cabin. The findings indicated that most particles fall within the sub-100 nm range. The concentration levels of PM1 and PM2.5 in the loader cabin area (LA) and operating area (OA) of the mine were comparable due to the uniform dispersion of particulate matter during the mine's operational phase. The primary source of PM1 and PM2.5 was the diesel engine, whereas the low LA/OA ratio for PM10 indicated that the source of the coarse particles was dust resuspension in the vicinity of the loader cabin. Sabanov et al. [86] analyzed PM and LDSA concentrations in oil shale underground mine operations. A real-time particulate matter monitor (DustTrak DRX) and a multimeric fine particle detector (Naneous Partector 2) were utilized for field data collecting during loading and dumping operations of a diesel engine Page 14 of 82 loader. The investigation of PM, LDSA, particle surface area concentration (SA), average particle diameter (d), PNC, and PM0.3 produced some useful association factors. Diesel exhaust emissions caused an average increase in LDSA concentration during loading, which reduced during dumping. The concentration of PM1 was lower during loading and increased during the dumping procedure. Similarly, while loading, an average larger particle diameter was identified, whereas dumping, an average smaller one, was observed. The relationship between PNC and particle diameter shows an approximate split between DPM and oil shale dust sizes. These experimental research studies provided insight into the composition, characteristics, and concentrations of hazardous materials. However, the scope of these studies is limited in the visualization of PM migration, dispersion, and dilution. To visualize PM transition, dispersal, and dilution and to reduce environmental intricacies and measurement limitations, the computational fluid dynamics (CFD) modelling has been employed in many studies. The use of CFD can assist in realizing the new designs of airflow ducts, mine tunnels and mine ventilation systems. 2.3.3. CFD analysis of PM CFD simulation has been effectively employed in mining investigation to identify spontaneous combustion and implement inert gas in gob regions [111, 112], examine gas concentrations and airflow dynamics in continuous mining operations [113-116], analyze dust issues and devise dust management systems for underground and open-pit mines [117-120], and enhance the design characteristics for mineral processing [121]. CFD modeling has emerged as a potent instrument for comprehending airflow dynamics, gas behavior, and dust behavior within a complex three-dimensional environment. It can also furnish valuable insights for preliminary proposal assessment and regulatory appraisal. A major limitation associated with CFD is the validation of the simulation with experimentation. The following research studies adopted a combination of experimental and numerical simulation methodologies. Ping et al. [24] addressed the DPM pollution issues and improved the auxiliary ventilation system at a development face in an underground mine in Western Australia, using it as a physical model, while CFD was performed to assess airflow characteristics and DPM concentration distributions in the development face. The acquired simulation findings were verified using real-time measured data. The variations of DPM concentrations across three scenarios, featuring varying duct lengths, were subsequently compared to the AIOH standard for DPM (0.1 mg/m3). The findings indicated that the existing auxiliary ventilation system was ineffective in significantly reducing DPM concentration, but a ventilation system with a duct length 5 m longer than the current design demonstrated superior DPM dilution efficacy. In another study, Zeng et al. [122] developed 3D numerical model of DPM dispersion within a single straight entrance for LHD performing loading and hauling operations utilizing ANSYS FLUENT. The loading procedure was conducted for a duration of 3 minutes. The dynamic mesh method in FLUENT was employed to analyze the effect of truck movement on DPM distribution. The resultant DPM distributions are provided for the scenarios in which the vehicle was moving upstream and downstream of the loading Page 15 of 82 face. The analysis showed intriguing phenomena, including the piston effect, stratification of DPM in the roof area, and the recirculating of diesel exhaust against ventilation. The modeling results can ascertain whether the regions within the face area and straight entry above the existing U.S. regulatory threshold for DPM concentration (>160 µg/m3). Xu et al. [87] conducted a research study in an isolated area of an underground mine in the U.S., which served as the physical model for their investigation. CFD was employed to analyze the dispersion of DPM under two different operating conditions. The researchers utilized a discrete phase model to represent DPM, which demonstrated a superior alignment with experimental data compared to studies that treat DPM as a continuous phase. Elevated concentrations of DPM were observed in both scenarios. The authors suggested that this methodology could be effectively used to optimize the ventilation system. Liu et al. [26] adopted an integrated approach of numerical simulations and field measurements to investigate the displacement patterns of PM emitted by a trackless rubber-tyred vehicle (TRTV) at idle speed for 60 seconds under varying movement conditions, as well as the dilution effects of the ventilation rate on PM. The results indicated that under varying mobility conditions, the PM predominantly transmitted along the roadway floor, although exhibited overall upward diffusion patterns, signifying that the chambers are situated in high-risk zones. Additionally, the dilution effects of the elevated ventilation rate on PM were examined. The ideal dilution ventilation rate was determined to be 4600 m³/min for condition 1 and 2800 m³/min for condition 2. Duan et al. [25] investigates the diffusion and distribution of DPM by CFD numerical simulation, conducting a numerical modeling of airflow and the DPM field at the heading face of an underground coal mine. The findings indicate that between 15 s and 60 s, DPM consistently builds in the blind zone of airflow and on either side of the monorail locomotive, with concentrations exceeding 1.2 × 10−7 kg/m3. The DPM cloud at that moment extends 60 m horizontally and has a distribution pattern characterized by elevated levels in the center and diminished levels on either side. At 90 s, a portion of DPM dissipates to the roadway outlet. At that time, along the entire roadway, starting 23 m from the heading face, the concentration of DPM significantly increases, reaching a peak of 2.91 × 10-7 kg/m³ at 33 m. Following this peak, the concentration begins to decline gradually. Zhou et al. [31] employed a numerical modeling approach to examine the effects of air suction volume (Q) and the distance (L) between diesel vehicles and head-faces on the diffusion patterns of DPM, CO, and NOx during long suction and short pressure ventilation. The results indicated that for L = 20 m, the diesel vehicle is positioned nearer to the suction air duct. At this stage, when Q = 600 m³/min, the tail gas control efficacy in the roadway is at its peak. Furthermore, with L = 40 m, the diesel vehicle is positioned centrally on the roadway. At this point, when Q = 300 m³/min, the tail gas control efficacy in the roadway is at its peak. When L = 60 m and Q = 200 m³/min, the predominant ventilation type in the roadway is pressure-in ventilation. The higher and medium NOx concentration zones within this air volume are minimal. Page 16 of 82 Liu et al. [123] investigated the transportation of CO and PM released by diesel vehicles under three operational scenarios was examined using CFD numerical modeling and field measurements. The concentrations of CO and PM varied with alterations in the airflow field across different operational conditions, exhibiting an overall consistent distribution. Despite the differing migration patterns of CO and PM under various operational settings, CO at elevated concentrations (C ≥ 44.74 ppm) and PM at high concentrations (C ≥ 89.47 mg/m³) were predominantly located in proximity to the exhaust pipe of the diesel vehicle. Liu et al. [124] investigated the spatiotemporal distribution of PM in the exhaust of two diesel vehicles. The authors used a combination of CFD numerical simulations and field data collection. The investigation contained two operating scenarios (OS): both vehicles moving into the wind (OS-1) and moving with the wind (OS-2). The results demonstrated that in both circumstances, the airflow velocity between the two vehicles displayed a circumferential distribution, and an airflow vortex was generated in the chamber due to wind coupling. When vehicles traveled in the same direction against the wind, PM with a concentration range of 15.79–26.32 mg/m3 could ascend to the height of the human respiratory zone and was predominantly dispersed on the eastern side of the roadway. Furthermore, the PM concentration in the vicinity of the driver's position exceeded the human exposure limit, necessitating personal protective equipment for the drivers. As vehicles traveled in the same direction as the wind, the concentration of PM on the airflow outlet side significantly increased over time, particularly for PM within the concentration range of 21.05–31.58 mg/m3, in comparison to the airflow inlet side. A comprehensive literature review in this chapter reveals that PM from the tailpipe exhaust has been a major concern in underground mine environments. Most studies adopted either field experimentation or combining it with numerical simulation as a methodology to understand the PM emission and migration characteristics. To control PM migration, the literature review highlights different control techniques by modifying operating conditions or the existing ventilation system in the mine. The extensive literature review revealed that existing studies primarily focused on DPM in underground coal mines, overlooked PM generated by other mining operations, and did not consider non-coal mines. In most studies, the DPE were idling at a constant speed on the roadway or inside a chamber with or without an AVS system. However, the emission of DPM and PM both depends upon the engine load and mining operation. Usually there is more than one DPE operating in mining areas, but most studies considered only one DPE while exploring the DPM emission and dispersion. The mining areas with dead-ends are equipped with SDF-AVS or DDH-AVS in the existing studies, which are considered insufficient in many cases. This study addresses these research gaps by considering the emission of both DPM from the exhaust tailpipe and PM during loading and unloading of muck in an underground polymetallic mine. There were more than one DPE performing loading and unloading operations in the mining area, depicting the enhanced emissions of DPM and PM concentrations. The impact of existing volumetric airflow and DDF-AVS design on the transposition of DPM and PM was evaluated, and an optimized volumetric airflow and strategic positioning Page 17 of 82 of AVS outlets of DDF-AVS will be proposed to decrease the residence time of the pollutants in the mining area. Page 18 of 82 3. Methods and materials 3.1 Introduction This research study employed a combination of real-time monitoring and numerical simulations to assess airflow characteristics and the distribution of PM. The real-time monitoring conducted in the mine during DPE handling transportation provided crucial information about fluctuations in airflow velocity and PM concentration across various operational scenarios. However, several challenges arose with real-time monitoring, including the necessity to maintain a safe distance between the DPE and data recording personnel to prevent accidents. Additionally, identifying and recording field data required the DPE to remain idle for specific durations, which led to unwanted delays in transportation and subsequent operations. Furthermore, the numerical data collected from the field lacked the ability to visualize the actual mine site, its operating conditions, airflow characteristics, and PM migration patterns. To address the limitations of real-time monitoring, numerical simulations emerged as a valuable tool, capable of not only recreating actual mining scenarios but also illustrating airflow characteristics and the generation, emission, and transposition of PM within the underground mine section. However, the accuracy of the simulation model is paramount; it must be validated against real-time monitoring data to ensure its authenticity. Once validated, modifications to the operating scenarios can further enhance the evaluation of airflow characteristics and PM migration, all while mitigating the in-situ risks associated with field operations. This presents an additional advantage of utilizing numerical simulations. 3.2 Mine site description In this study, the airflow velocity and real-time PM sampling were carried out in an underground polymetallic mine located in the East-Kazakhstan, as shown in Fig. 2. The mine was producing 300,000 tons/year of ore. This study was conducted based on the data collected during a filed visit. Fig. 2. The approximate location of the polymetallic underground mine in East-Kazakhstan Page 19 of 82 3.3 Computational model description The computational model was developed based on field observations. The operating scenarios of DPE inside a mine drift near a working face and a temporary dumpsite, and at a haulage drift were observed, and similar working conditions were developed to simulate the actual underground mine operating conditions. A 3D computational model was constructed by using Ansys Geometry Space-Claim. The mine drift was 70 m long and featured a rectangular cross-section measuring 4.4 m in width and 4.3 m in height. Additionally, a temporary dumpsite, measuring 12 m in length, 5 m in width, and 4.3 m in height, was situated 20 m from the entrance of the mine drift. The mine drift was equipped with a forced dual duct auxiliary ventilation system. The outlets of ventilation ducts, with a diameter of 0.8 m, were positioned 10 m away from the working face. Two types of DPE were considered in this study, LHD Cat R1700 and UMT CatAD45. The LHD had dimensions of 10 m in length, 2.9 m in width, and 2.5 m in height, with a maximum power capacity of 361hp/269 kW. The exhaust gas outlet of the LHD was at the lower left of the back. On the other hand, the UMT featured dimensions of 11.5 m in length, 3.02 m in width, and 2.8 m in height, with a maximum power capacity was 581hp/433 kW. Mainly, the LHD was operating inside the mine drift, and the UMT was hauling at the cross-section of mine drift and haulage drift. The emissions from both LHD and UMT engines correspond to the Tier 3 engine equivalent. The engine utilizes diesel fuel DT-L-K2 (standard GOST 305–82), which is the first approximation aligning with the Euro 3 standard, differing only in the cetane number. 3.4. Methodology for operating condition 1(OC1) In operating condition (OC) 1, the airflow distribution and PM dispersion were examined within mine drift and haulage drift during the muck loading and unloading operations of the LHD, without considering the vehicle load. Fig. 3. (a) illustrates the computational model specifications and boundary conditions, which closely resemble experimental field observations. Fig. 3. (b) shows condition 1 (C1) where the LHD was loading muck at the active working face, positioned facing into the wind. Fig. 3. (c) illustrates condition 2 (C2) where the LHD was working perpendicular to the wind to unload muck inside the temporary dumpsite. Fig. 3. (d) shows condition 3 (C3) where the LHD was dumping muck onto the UMT while hauled in the haulage drift, at the entrance of the mine drift, similarly positioned facing against the wind (condition 3). Page 20 of 82 Fig. 3. (a) Computational model of underground mine drift, (b) C1, (b) C2, and (c) C3 3.5. Methodology for operating condition 2 (OC2) In OC 2, the impact of different DDF-AVS designs was evaluated on the PM dispersion. Fig. 4. (a) shows the geometric model of the underground mine drift and associated characteristics. Fig. 4. (b) depicts the scenario 1 (S1) in which LHD was loading ore near the working face. Fig. 4. (c) highlights scenario 2 (S2) in which LHD was dumping inside the temporary dumpsite. In OC2, the dimensions of the mine drift and temporary dumpsite and the positions of the LHD were similar like OC1, however, only change was between the actual positions and simulated positions of the DDF-AVS outlets. 3.5.1. Assessment of the DDF-AVS designs The spacing between the center axes of the AVS outlets and their positioning relative to the central axis of the mine drift along the z-axis are two design parameters incorporated in the DDF-AVS system. Figure 4 illustrates various design configurations created to assess the impact of these parameters on the airflow field and the transposition of PM to enhance the DDF-AVS. In AVS 1, Fig 5. (a), the outputs are positioned 1 m apart, with Vent 1 located 2.4 m from the left wall and Vent 2 located 1 m from the right wall. Fig. 5. (b) illustrates AVS 2, indicating that the outlets are positioned 1.1 m apart, with both vents situated 1.65 m from the corresponding right and left walls. In AVS 3, Fig. 5. (c), the outlets are 2 m apart, with both vents located 1.2 m from the side walls. In AVS 4 Fig. 5. (d), the outlets are located 3 m apart, with each vent located 0.7 m from the side walls. Page 21 of 82 Fig. 4. (a) Geometric model of underground mine drift, (b) S1, and (c) S2 Fig. 5. (a) AVS-1, (b) AVS-2, (c) AVS-3, and (d) AVS-4 for S1 and S2 Page 22 of 82 3.6 Numerical models In general, two CFD numerical models are available to simulate two-phase flow (airflow and PM) [125]. The selection of numerical scheme usually depends upon the operating environmental conditions and boundary conditions of the computational domain. The first model is referred to as Species Transport, which utilizes a Eulerian-Eulerian (fluid-fluid) methodology. In this approach, both the PM and the airflow are regarded as a continuous phase [126, 127]. The second model is the Discrete Phase Model, which employs the Eulerian-Lagrangian method (particle-fluid) and considers PM as solid particles and airflow as fluid [128-130]. Several simulation-based investigations were undertaken to compare the outcomes of the CFD continuous phase model and the discrete phase model. The authors determined that both models are viable for simulating a solid-gas environment. Nonetheless, discrete phase model exhibits superior concordance with experimental outcomes in comparison to the continuous model [131-133]. Regarding PM, numerical research investigations were undertaken, employing both continuous and discrete phase models to analyze the distribution of diesel PM in the underground mine. They asserted that a superior correlation was established between the experimental results and the continuous phase model. Nonetheless, the computational expense associated with the continuous phase is relatively lower than that of the discrete phase models [27, 134] due to the Species Transport Model's handling of continuous fields. Conversely, the discrete phase models entail the tracking of particle motion and its interaction with fluid and other particles. Consequently, this research employed the Eulerian-Lagrangian methodology to simulate airflow and particulate matter in the subterranean mine. 3.6.1. Fluid flow model The airflow was regarded as a continuous phase and incompressible fluid. The equations for mass conservation and momentum [135, 136] can be expressed as equations 1 and 2. (1) (2) Where, p, 𝜏̿ , ρ𝑔⃗ , 𝑣⃗ , Sm, 𝐹⃗ are the static pressure, stress tensor, gravitational body force, velocity vector, added mass form dispersed phase to the continuous phase and external body forces, respectively. (3) Where 𝜇 and I are molecular viscosity, and unit tensor. The standard model of turbulent kinetic energy (k) and dissipation rate (𝜀) are usually considered to simulate the turbulent flow [137-140]. The k-equation: (4) Page 23 of 82 The turbulent viscosity 𝜇𝑡 can be determined as follows: (5) The 𝜀 equation: (6) Where 𝜌, 𝑘, 𝜀, 𝜇, 𝜇𝑡, 𝐺𝑘, are the density, turbulent kinetic energy, turbulent energy dissipation rate, laminar viscosity coefficient, turbulence viscosity coefficient, and average velocity gradient, respectively. The constant values of terms 𝐶1𝜀, 𝐶2𝜀, 𝐶𝜇, 𝜎𝑘, 𝜎𝜀 are 1.44, 1.92, 0.09, 1.00, and 1.30, respectively. 3.6.2. Particle flow model ANSYS Fluent discrete phase model (Lagrangian) predicts the trajectory of particles by integrating the balance force on the particle. The particles are injected as surface source. The DPM is injected from the exhaust tailpipe and the PM is injected from the LHD’s bucket surface. This force equates the particle inertia with the forces acting on the particle and can be written as [140, 141]: (7) where , and 𝜏̿𝑟 are the particle mass, fluid phase velocity, particle velocity, fluid density, density of the particle, an additional force, drag force, and droplet or particle relaxation time, respectively. The 𝜏̿𝑟 can be calculated by: (8) Here, 𝜇, 𝑑𝑝, and Re are molecular viscosity of the fluid, particle diameter, and relative Reynolds number, which can be numerically defined as: (9) 3.7 Mesh generation and sensitivity analysis The accuracy of the mesh greatly affects the outcomes of the simulation. As a rule of thumb, the simulation's accuracy improves as the mesh density increases. However, the time and resources needed for computing will increase. As a result, this study split the mesh into three groups with varying quantities, and each group's skewness was set to less than or equal to 0.3 [24, 25, 134]. In both, OC 1 and 2 three mesh groups (fine, medium, and coarse) were created to distribute the domain in the mesh elements. In OC 1 the number of elements in each mesh category was around 2.3 million, 1.8 million, and 1.4 million, respectively. In OC 2 the number of elements was around 1.9 million, 1.5 million, and 1.1 million, respectively. Page 24 of 82 The mesh sensitivity analysis was performed with a mesh ratio maintained at no less than 1.26 [142]. The height of the initial mesh cell was set at 0.02 m, determined by the calculation of dimensionless y+ = 30 [143]. The overall number of prism layers was determined to be 5, with an inflation ratio of 1.2 for each layer. Ansys-Fluent was employed to simulate the airflow in the drift using multiple mesh schemes. CFDPost was utilized to employ line monitors for the calculation of airflow velocity. The line monitors have been defined on the x, y, and z coordinates in the following sequence: L1(x, 1.5, 3.4), L2(x, 1.5, 2.2), L3(x, 1.5, 1), L4(x, 2.2, 1), L5(x, 2.2, 2.2), and L6(x, 2.2, 3.4). Fig. 6. (a) illustrates the outcomes of the mesh independence test, with the blue, red, and black lines denoting airflow velocity in Coarse, Medium, and Fine Meshes, respectively. The brown, green, and purple bars illustrate the relative error [144] between Coarse and Medium Meshes, Coarse and Fine Meshes, and Medium and Fine Meshes, respectively. Fig. 6. (b and c) show the appearance of surface and volumetric meshes (Medium Mesh) of the computational domain. Fig. 6. (a) Mesh independence test, (b) surface mesh, and (c) volumetric mesh The figure depicts that the variation trend of the airflow field obtained from the 3 meshes is consistent with each other. The simulation results obtained from medium and fine mesh have the same variation trend and the data are nearly similar. Comparatively, the data obtained by the coarse mesh has a considerable deviation. Thus, to gain more accurate simulation results and save computational cost, medium mesh was chosen for simulation. 3.8 Input Parameter setup After meshing the geometric model, the mesh file was imported into Ansys-Fluent for parameter setting. The details of each parameter and corresponding values are detailed in Table 1. Page 25 of 82 Table 1. Input parameters and corresponding values of simulation model Parameter Value Parameter Value Gravity (m/s2) 9.81 Minimum particle diameter (m) 1e-6 Air inlet (m/s) 1.2 Median particle diameter (m) 1.75e-6 AVS-DDF 1 and 2 (m/s) 13 Mass flow rate UMT (kg/s) 4.57e-6 Exit boundary type Pressure outlet Mass flow rate LHD (kg/s) 3.11e-6 Maximum particle diameter (m) 2.5e-6 Mass flow rate PM (kg/s) 5.67e-5 Solver Type Pressure-Based Boundary type of airflow inlet Velocity-inlet Boundary type of PM inlet Mass flow-inlet Diameter distribution of PM Rosin-Rammler Initialization Method Standard Initialization Scheme COUPLE Turbulent Kinetic Energy Second Order Upwind Turbulent Dissipation Rate Second Order Upwind Near-wall treatment Standard wall function Pressure outlet (kPa) 101.325 As the airflow in the mine drift is low-speed incompressible flow, the solver type in this study was set as Pressure-based, while the scheme of the solution method was set as Coupled. To improve the accuracy of simulation results, the discretization of the momentum, turbulent kinetic energy, and turbulent dissipation rate equations were all set as Second order upwind. 3.9. Layout of measuring points in OC1 and OC2 In all three conditions of OC1, each measuring point was denoted by a number (1 to 6) and corresponding coordinates (x, y, z), as shown in Fig. 7. The measuring points 1, 2, and 3 were aligned with breathing zone at 1.5 m and the measuring points 4, 5, and 6 were established on breathing zone 2.2 m. The measuring points in the mine drift on each cross section were located at 1 (x, 1.5, 3.4), 2 (x, 1.5, 2.2), 3 (x, 1.5, 1), 4 (x, 2.2, 1), 5 (x, 2.2, 2.2), and 6 (x, 2.2, 3.4), respectively. Page 26 of 82 Similarly, in the haulage drift measuring points distributed as 1 (71, 1.5, z), 2 (72.5, 1.5, z), 3 (74, 1.5, z), 4 (74, 2.2, z), 5 (72.5, 2.2, z), and 6 (71, 2.2, z), respectively. Moreover, the cross-sections selected in C1 were at x = 20, 30, 40, 50, 60, and 70 m, respectively. In C2, the selected cross sections were at x = 10, 20, 30, 40, 50, 60, and 70 m, and in C3, the cross sections were selected at x = 10, 20, 30, 40, 50, and 60 m, respectively. While the selected cross-sections in haulage drift were constant in all three conditions and situated at z = 10, 15, and 20 m, respectively. As mentioned earlier, OC2 is limited to mine drift only and lacks the haulage drift, so the layout of monitoring planes in both scenarios of OC2, Fig. 8. (a and b), follows the similar pattern which was discussed above for OC1. Fig. 7. Distribution of monitoring points in (a) Mine drift, (b) Haulage drift, and (c) layout of the cross sections Fig. 8. Data monitoring planes layout in (a) S1, and (b) S2 Page 27 of 82 3.10. Real-time monitoring Real-time data monitoring in the mine was conducted using various instruments. To capture field data at specific points and cross-sections, the Leica Disto D2 laser distance meter, as shown in Fig. 9. (a), was utilized for accurate distance measurements. Fig. 9. (b) shows the Alnor RVA501 Rotating Vane Anemometer which was used to measure the airflow velocity in the mine. Fig. 9. (c) shows the DustTrak™ DRX Aerosol Monitor 8533 which was used to capture the PM concentrations in the mine. It is capable of simultaneously measuring both mass and size fractions. This multi-channel, battery-operated, data-logging, light-scattering laser photometer provides real-time aerosol mass readings while also collecting gravimetric samples. It is suitable for a range of applications, including indoor and outdoor settings, industrial and occupational hygiene, baseline screening, remote monitoring, and research studies. The device employs a sheath air system that isolates the aerosol within the optics chamber, ensuring cleaner optics for enhanced reliability and reduced maintenance. The flow rate of the DustTrak™ pump was calibrated to 1.7 liters per minute, allowing for simultaneous measurement of size-segregated mass fraction concentrations corresponding to PM1, PM2.5, PM10, and total PM size fractions [145, 146]. Fig. 9. Real-time field data monitoring instruments (a) Leica Disto D2, (b) Alnor RVA501, and (c) DustTrakTM monitor 3.10.1. DustTrak filter kit The DustTrak was fitted with a 37 mm replaceable mesh filter housed within a concealed filter kit in its body. The airflow passes through the air inlet and outlet cavities of the kit, as shown in Fig. 10. (a). A mesh filter is inserted in between the internal filter kit to trap the particles on the mesh filter. Five points were selected on the mesh filter for further analysis of the elemental composition of the particles under scanning electron microscope (SEM), as shown in Fig. 10. (b). This study utilized the SEM ‘Jeol JSM-IT200’ (JEOL, Freising, Germany), which was integrated with energy dispersive X- ray spectroscopy (EDS), as shown in Fig. 10 (c). A sputter coating layer, measuring 5 mm in thickness, was Page 28 of 82 applied to the sample filter before it was placed inside the SEM for analysis. The desktop computer connected to the SEM ran the application ‘SEM Operation’, which was responsible for controlling all aspects of the equipment. Fig. 10. (a). Internal filter kir, (b) mesh filter and monitoring points, and (c) SEM/EDS Jeol JSM-IT200 3.11. Assumptions In this study, the exhaust pipe of DPE and muck loading and unloading operation of LHD were considered as the main sources of the PM generation. However, the resuspension of the PM in the breathing zone due to DPE maneuvering was neglected. The PM particles consisted of very small size and volume fraction, and the velocity of PM production due to muck loading and unloading was considered similar to the regional airflow velocity. Because of the higher airflow velocity near the working face, neither the drag force nor the gravitational force was effective enough to control particles movement. On the other hand, PM released from the DPE exhaust tail pipe bears higher velocity. Most of the produced PM quickly followed the airflow direction after being discharged. However, a significant amount of PM in C1 and C2 showed prolonged unpredictable movements before eventually being carried out of the mine drift. However, the small size of PM particles compared to the intense airflow velocity, the Brownian force acting on PM was considered insignificant [147]. Additionally, the thermal impacts of PM emissions were considered minor due to the large cross-sectional area of mine, leading to lower temperatures and reduced thermal conductivity of the released PM [24, 134, 148]. Thus, solely the impact of gravity and drag force on PM was considered [53]. In addition, the PM was considered as discrete phase and the relevant parameters were set according to the actual condition of the experimental field and the characteristics of the PM [149, 150]. Page 29 of 82 4. Results and discussion 4.1. Results 4.1.1. The airflow distribution analysis in OC1 The airflow velocity vectors were selected to represent the airflow distribution and direction at breathing zones 1.5 m and 2.2 m above the ground [124, 151-153]. During field experiment in the mine drift, the supplied volumetric airflow from the outlet of both ducts was 13 m3/s (v = 13 m/s), the temperature, pressure and relative humidity in the mine drift were 22.85 ℃, 968 hPa, and 68%, respectively. Based on the supplied airflow distributions, the mine drift and haulage drift were partitioned into separate regions at each of the breathing zones: backflow region, vortex region, unsteady flow region, and steady flow region. The defined regions were visually depicted using dashed-line quadrilateral and circular shapes in Fig. 10. The initiation of the backflow region began near the working face because the supplied airflow struck the working face, causing a rebound action that directed the airflow towards the exit of the mine drift. The backflow region covered nearly 13 m from the working face. Following that, a vortex region developed, characterized by the interaction between high-velocity and low-velocity airflow components. The vortex region lasts between 13 m and 20 m. Afterwards, an unstable flow region was developed because of further decrease in airflow velocity. This region exists between 20 m and a little over 52 m. Additionally, between 52 m and 68 m is a relatively stable flow region developed in the mine drift. Near the end of the mine drift the airflow mixed with the existing airflow in the haulage drift. Due to the amalgamation of two airflows an unstable flow region developed in the haulage drift. The airflow distrib