Comprehensive experimental research on the fundamental optical properties of dust pollution in a coal mine is presented. Rock dust generated in a tunneling roadway was sampled and the spectral complex refractive index within an infrared range of 2.5–25 μm was obtained by Fourier transform infrared spectroscopy measurement and Kramers–Kronig relation. Experimental results were validated to be consistent with equivalent optical constants simulated by effective medium theory based on component analysis of x-ray fluorescence, which illustrates that the top three mineral components are (62.06%), (21.26%), and (4.27%). The complex refractive index and the spatial distribution tested by a filter dust and particle size analyzer were involved in the simulation of extinction properties of rock dust along the tunneling roadway solved by the discrete ordinates method and Mie scattering model. The compared results illustrate that transmission is obviously enhanced with the increase of height from the floor but weakened with increasing horizontal distance from the air duct.
© 2015 Optical Society of America
Dust is generated and dispersed into mine air through rock breakage, loading, conveying, and transportation with the flow of the mine ventilation system [1,2]. The tunneling face in a coal mine is a quite serious dust generating location especially when advancing the roadheader causing coal miners to be exposed to rock dust . Epidemiological studies have demonstrated that silicosis, coal workers’ pneumoconiosis (CWP), and bronchitis symptoms are closely linked to coal miners’ exposure to dust and its quartz content [4,5]. The tunneling face has been proved to be one of the most serious dust polluted workplaces of coal mines and most rock dust generated here is of high quartz content . Thus, coal workers who are seriously exposed to rock dust in the tunneling roadway are very prone to occupational lung disease, such as silicosis, CWP, and bronchitis symptoms. It is urgent and necessary to strengthen theoretical and experimental research on the fundamental physical behavior of rock dust to achieve the real-time monitoring of rock dust pollution.
In recent years, much research has been carried out to achieve the accurate and real-time monitoring of particulate matter (PM) especially for atmospheric PM pollution [7,8]. Various satellite and ground-based optical remote sensing detection technologies have been applied to obtain the radiative transfer properties of PM, especially the transmission property and aerosol optical depth. They have been combined with certain retrieval methods, such as the damped Gauss–Newton iteration algorithm, genetic algorithm, support vector machine, and stochastic particle swarm optimization algorithm [9 –11], so the distribution of PMs can be obtained. Rock dust is the typical PM suspended in the mine atmosphere which can be detected and monitored with the modified optical method used in atmospheric PM monitoring. Before conducting the inverse calculation for retrieving dust distribution, the basic optical properties and transmission characteristics of rock dust must be determined and obtained, which serve as the base for inverse calculation intending to obtain the optimum solution .
The complex refractive index is required for determining the optical properties of rock dust in the tunneling roadway of coal mines [12,13]. The complex refractive index of rock dust is not directly measureable but can be determined from certain measurable quantities (e.g., transmission, reflection, and scattering) combined with the appropriate theoretical model [e.g., Fresnel formulas, Mie scattering method, and Kramers–Kronig (K–K) relation] [14,15]. Thus, a large number of studies have been performed by relevant investigators on the complex refractive index of metals  (e.g., Al, Cu, and Fe), semiconductors  (e.g., Si, ZnS, Ge, and SiC), and insulators [18 –22] (e.g., , of crystalline, of glass, , and ). Meanwhile, relevant studies have been widely conducted on the mineral component analysis of rock dust generated in underground coal mines, which illustrate that rock dust particle is the complex mixture of various components mainly including , , , MgO, etc. [23,24]. The complex refractive index of rock dust in the tunneling roadway of coal mines is different but the combined joint effect of the complex refractive index of all mineral components in rock dust. Two approaches were mainly adopted to obtain the complex refractive index of such mixture dust: (a) directly measured from experiment combined with certain retrieval methods (e.g., K–K relation), and (b) calculated based on the complex refractive index of its components combined with the appropriate method (e.g., effective medium method). Nowadays, little research focuses on the optical properties of rock dust generated in underground coal mines since most current studies are about the optical properties of pure mineral substance [16,22] or the dust in open atmosphere and outer space . Thus, special attention should be given to such mixture rock dust on its basic optical properties in order to provide a theoretical foundation for the further forward and inverse simulation.
In this study, a comprehensively experimental research on fundamental infrared properties of dust pollution in coal mines was presented. First, enough raw rock dust particles were sampled and measured on dust concentration in the tunneling roadway of the coal mine for further analysis. Then, a particle size analyzer was adopted to test the size distribution of rock dust, which was validated on its accuracy by a scanning electron microscopy (SEM) image. The spatial distribution of rock dust in the tunneling roadway can be determined based on the dust concentration and size distribution of rock dust. Meanwhile, the spectral transmittance of rock dust was then measured by Fourier transform infrared spectroscopy (FT-IR), which was then adopted to retrieve the complex refractive index of rock dust by the K–K relation and Mie scattering model. The retrieved complex refractive index of rock dust was validated and proved to be acceptable for further simulation by the effective medium method based on the mineral components of rock dust tested by x-ray fluorescence (XRF) and their complex refractive index in previous literature. Eventually, based on the obtained spatial distribution of rock dust in the tunneling roadway and the complex refractive index rock dust, combined with Mie scattering and discrete ordinates methods, a typical forward research was conducted in the tunneling roadway of the coal mine which can serve as the basis for the further inverse simulation for the real-time monitoring of rock dust pollution in the tunneling roadway of the coal mine. The whole technical route of this study was depicted in Fig. 1.
2. EXPERIMENTAL SETUP AND RESULTS
A large amount of rock dust is generated and dispersed into the mine atmosphere through rock breakage, loading, and transportation in the tunneling face of the coal mine, which greatly threatens the occupational health of coal miners. The real-time monitoring of rock dust pollution is necessary and urgent, and requires the forward study on the transmission properties of rock dust. Thus, the spatial distribution of rock dust and the complex refractive index should be determined first for further forward calculation. Rock dust particles were sampled in the tunneling roadway at certain evenly spaced sampling points. Thus, the spatial concentration distribution and size distribution of rock dust were determined by a filter dust analyzer, particle size distribution, and SEM, respectively. Aiming at the spectral transmittance for retrieving complex refractive index and micromineral components of rock dust, FT-IR and XRF were subsequently applied for the optical properties of rock dust.
A. Dust Sampling
The sampling coal mine is situated at , in the Wuda Coal District of Inner Mongolia Autonomous Region at the northern end of Helan Mountain, southern edge of Ulan Buh Desert, adjacent to the Ningxia Hui Autonomous Region, and with the Yellow River crossing through from south to north at its eastern edge . The whole coal field is an area of almost . The climate of Wuda Coal District is middle latitude, strongly continental, fully arid climate with only 7–20 precipitation days per year . The Wuda District holds a large amount of coal reserve which consists of coal-bearing strata of Pennsylvanian and Permian ages. Approximately 80% of the coal mined in China comes from Pennsylvanian coal seams including Pennsylvanian Benxi Formation, the Pennsylvanian Taiyuan Formation, the Early Permian Shanxi Formation and Xiashihezi Formation, and the Late Permian Shangshihezi Formation. The Taiyuan Formation is the major coal-bearing formation, which consists of sandstone, limestone, mudstone, and mineral coal bed such as No. 9, No. 10, No. 12, No. 13, and No. 15 coal seams, with a thickness between 70 and 140 m decreasing from the center toward both the south and north . Nowadays, the mining operation is located at the No. 15 coal seam of Pennsylvanian Taiyuan Formation. The roof and floor of the mining coal seam are mainly composed of sandy shale, sandstone, mudstone, and carbon shale [29,30].
Rock dust is generated in the tunneling face when the roadheader works on the tunneling face. Meanwhile, fresh air flows into the tunneling roadway through an air duct, which provides oxygen for coal workers and dilutes dust particles in the tunneling roadway. Thus, the generated rock dust particles are carried out of the tunneling roadway with the airflow to reduce the rock dust pollution.
According to the GB5748-85 (determination method of dust in the air of the workplace) jointly issued by the Ministry of Health and Ministry of Labor and Personnel of China , the dust filter measurement method is adopted in our study among which the main instrument is the filter dust analyzer. This method is the standard method for dust measurement in China and widely used in the field workplace, especially in underground coal mines, and has high accuracy. The rock dust was collected by the filter dust analyzer, which was placed in the middle of the tunneling roadway at the breathing level of workers. The filter dust analyzer is powered by a suction pump which can draw dust-polluted air into the sampler, then the rock dust in the air can be retained on the membrane surface of the sampler. Evenly spaced sampling points were placed in the central axis of the tunneling face with the interval of 2.5 m from 0 to 30 m, and the other four points in the vertical direction (0.2 m above/below central axis) and the horizontal direction (0.5 m left/right to central axis) along the tunneling roadway were placed with the interval of 5 m from 0 to 25 m, as can be seen in Fig. 2. The dust sampling line is also the optical calculation line (optical depth) adopted in the further forward transmission simulation.
B. Spatial Distribution of Rock Dust in Concentration and Diameter
On the basis of GB5748-85 of China for the dust measurement method used in our study, the concentration of rock dust can be determined when rock dust is sampled based on the weight gain of membrane and the intake air volume into the filter dust analyzer as follows: , where is the concentration of rock dust, and are the weight of membrane before and after sampling, is the intake air volume into the filter dust analyzer. Thus, the concentration of rock dust at different sampling points can be determined, as shown in Fig. 3.
After the dust concentration was determined, the SEM experiment was conducted to qualitatively analyze the microstructure of rock dust. The SEM experiment was conducted on the rock dust that was sampled 20 m from the dust source along the central axis under high vacuum and nonconductive mode. The resolution under high vacuum mode is that: . The microscopic structure of rock dust was then obtained, as presented in Fig. 4.
As can be seen from the SEM image of the rock dust, the rock dust collected at the breathing level of workers is mainly respirable dust which can suspend in the mine atmosphere for a long time. This is due to the fact that the overall rock dust generated in the tunneling face disperses outward and is deposited under the combined effect of airflow and gravity. The settlement of rock dust particles is obviously enhanced with the increase of its diameter leading to that most larger dust particles are gradually deposited to the ground with the dispersion but smaller dust particles remain in the air under the turbulence effect of airflow. Meanwhile, the respirable rock dust presented in the SEM image collected in the middle of the tunneling roadway at the breathing level of workers is the main cause of CWP; thus, it is urgent and necessary to conduct the monitoring of rock dust pollution levels in the tunneling face of coal mines.
Hence, in order to quantitatively study the size distribution of rock dust, the rock dust sampled at different sampling sites was further tested on untreated rock dust by the particle size analyzer (Bettersize2000). The main performance parameters of the particle size analyzer are as follows: test range, 0.02–2000 um; laser light source, fiber semiconductor laser; measurement principle, Mie scattering theory; repeatability error, less than 1% (D50); accuracy error, less than 1% (D50); photoelectric detector, 90, 80 forward, 10 backward; test time, 2–3 min. Then, the size distribution of rock dust was obtained and shown by Rosin–Ramller function and histogram, as presented in Fig. 5. The Rosin–Rammler function represents the cumulative distribution of the mass of rock dust greater than certain diameter ; and are the coefficients related to the diameter distribution of coal particles. is the correlation coefficient of Rosin–Rammler function . The histogram is the mass distribution of rock dust under certain diameter range.
As can be seen from Fig. 5, the size distribution of rock dust was obviously moved to smaller particles from larger particles with the increase of sampling distance from the dust source. Larger particles are more prone to settle down compared to smaller particles under the combined effect of gravity and airflow turbulence.
C. Spectral Transmittance and Component Analysis of Rock Dust
There are many methods to obtain the complex refractive index by experimental results, such as transmittance, reflectivity, angular scattering, etc. Commonly, the reflectivity method is mainly applied to bulk material but not suitable for dust particles, which are very different from those of the same materials in bulk form. The angular scattering method can perfectly keep the nature status but demands at least three different experimental results at the same wavelength. This method can hardly obtain the spectral distribution since the laser device just emits a certain wavelength. The transmittance method can directly measure and then retrieve the complex refractive index of dust particles at a certain wavelength range. Meanwhile, this method can keep its nature status, and the measuring device is also simple. Thus, the transmittance method is commonly employed to obtain the complex refractive index of particles .
Then, FT-IR was applied to test the transmittance of rock dust particles, which was then used to obtain the complex refractive index. The rock dust was pulverized to less than 200 mesh (74 μm) and dried at 105°C for 4 h. Then rock dust was suspended and pressed in a KBr matrix with the thickness of 1 mm and the proportion of 1:100 under the pressure of for 3 min to measure its spectral transmittance within the spectral range of , as shown in Fig. 6.
Meanwhile, XRF was conducted to obtain the mineral compositions of the rock dust. The rock dust was pulverized to less than 200 mesh (74 μm) and dried at 105°C for 2 h. The detailed performance index of XRF is as follows: scan mode: sequential scanning; hardware indicators: power 4kw, maximum voltage 60 kv, maximum current 170 mA; element detection range: Be (4)–U (92); detection limit: PPM . The XRF results are shown in Table 1.
3. COMPLEX REFRACTIVE INDEX OF ROCK DUST
Based on the spectral transmittance of rock dust particles, combined with the properties of KBr matrix preparation, the K–K relation was adopted to retrieve the complex refractive index of rock index . The extinction factor of rock dust can be obtained based on the transmittance of rock dust as follows,
In the FT-IR test, all dust samples were pulverized to less than 200 mesh (74 μm). For simplicity, the particle size in the dust cloud is assumed as uniform with a diameter of 50 μm. Based on the reduced particle dispersion, the extinction factor of rock dust could be given,
The extinction factor can also be calculated if the complex refractive index of the rock dust is determined combined with the Mie scattering model ,
The real part of the complex refractive index at the wavelength of can be determined if was given for calculation by the optical dispersion theory. The obtained complex refractive index [including and ] was then adopted to calculate the extinction factor of rock dust, which was compared to the extinction factor measured from the transmittance experiment. When the calculated spectral extinction factor is appropriately the same as that of the measured extinction factor within acceptable error, the complex refractive index used can be considered as the reliable complex refractive index with high accuracy. The detailed inverse calculation process can be found in . The real part and imaginary part of complex refractive index can be described with the K–K relation as15],
Thus, the complex refractive index of rock dust can be obtained based on the spectral transmittance properties of rock dust from the FT-IR experiment combined with the K–K relation and Mie scattering within the wavelength of 2.5–25 μm, as shown in Table 2.
The rock dust is a mixture of different mineral compositions mainly including (62.06%), (21.26%), (4.27%), etc., as can be seen in Table 1. Thus, the complex refractive index of rock dust can be approximately determined by the complex refractive index of these mineral compositions. In order to validate the accuracy and effectiveness of the measured complex refractive index of rock dust, the complex refractive index of three main compositions in previous literature ( in , in , in ) whose component share is over 3% are adopted to obtain the approximately theoretical complex refractive index of the mixture rock dust by the effective medium theory based on the shared and optical constants of each composition .
Several models of the effective medium theory were widely used to calculate the complex refractive index and dielectric constant of mixture dust particles, mainly including the Maxwell–Garnett model and the Bruggeman model. The equivalent complex refractive index of the mixture dust is a function of the complex refractive index, proportion, and microstructure parameters of its components. Considering the proportion of each component, the Maxwell–Garnett model is applicable to the case where there exists a major component which is considered as the matrix and the other remaining components with small share are buried in the matrix. While, when the several components are of similar proportion and randomly distributed and mixed with each other, the Bruggeman model is more adopted. In this study, accounts for 62.06%, which can be considered as the matrix; meanwhile, the proportion of the other components is relatively small, which can be considered as adulterants buried in the matrix. Thus, the Maxwell–Garnett model was applied for the simulation in this study ,
For nonmagnetic material, the relationship between the dielectric constant and optical constants can be shown as . Combined with and , the equivalent optical constants of mixture rock dust can be determined and obtained by Eqs. (8) and (9):
Then, the complex refractive index of rock dust obtained from the FT-IR experiment, the equivalent complex refractive index of mixture rock dust, and the complex refractive index of three major mineral components (, , ) in rock dust were depicted in Fig. 7. Meanwhile, the whole waveband of 2–25 μm is divided into three partial wavebands (8–11, 16–19, 19–25 μm) considering the peak of and of the complex refractive index. The wavelength of the peak point under each waveband is listed in Table 3.
Figure 7 presents that the spectral complex refractive index of the three main components (, , and ) vary obviously with different peaks and troughs. Further quantitative comparison of the experimental and simulated peaking wavelength of and of the complex refractive index in Table 3 illustrates that the equivalent complex refractive index of the mixture dust obtained agrees quite well with the complex refractive index of rock dust in variation and magnitude. The peaking wavelength error from the experiment and simulation are all within 0.3 μm, as can be seen in Table 3, which indicates the similar variation law. A slight difference in magnitude of the experimental and simulated and of the complex refractive index can be obviously observed in Fig. 7. The relatively slight error and difference are mainly attributed to the neglect of materials with small component and trace elements. Thus, the complex refractive index of rock dust can be accepted for the further numerical simulation.
4. SIMULATION OF EXTINCTION PROPERTIES IN TUNNELING ROADWAY
Forward research on the radiative transfer characteristics is the fundamental model for inverse problem in dust pollution monitoring. Based on all the experiments operated in the above sections and the technical route in this study, the forward simulation of the extinction properties in the tunneling roadway could be implemented.
At first, according to the measurement data of sampling dust concentrations, statistical dispersion functions, and complex refractive index of rock dust, the optical properties of the local dust cloud in the tunneling roadway, including scattering and extinction coefficients, were calculated by the Mie scattering model. And then, the one-dimension radiative transfer model was involved to simulate the extinction effect of rock dust along the full tunneling roadway. Here, the corresponding conditions at the boundary are supposed to be semitransparent medium involved in the simulation setup in the calculation field. In this case study, without consideration of incidence and radiation reflecting from the surface, the emissivity and reflectivity at the boundaries are set to zero.
Considering isotropic scattering, the discrete ordinates method [12,37] was applied to solve the radiative transfer equation and the transmission properties of rock dust along the tunneling roadway at breathing level with three heights [1.3 m (line 3 in Fig. 2), 1.5 m (line 1 in Fig. 2), 1.7 m (line 2 in Fig. 2)] and at the same level of 1.5 m with three widths [ (line 5 in Fig. 2), 0 m (line 1 in Fig. 2), 0.5 m (line 4 in Fig. 2)] were separately compared and analyzed, as shown in Fig. 8.
Figure 8 demonstrates that the spectral transmission of rock dust varies obviously within the spectral range 2.5–25 μm. Two peaks for transmittance at around 6.7 and 15 μm can be clearly observed, which can be considered as reference spectrum. Meanwhile, in the vertical direction, the transmission properties of rock dust are clearly enhanced with the increase of height, as can be observed in Fig. 8(a). This is because the rock dust would gradually fall down to the floor under the effect of gravity as it dispersed outward with airflow; thus, the overall distribution of rock dust in the lower position is relatively more serious than that of the higher. The transmission properties of rock dust on the right side of the central axis are stronger than that of the left side. This is due to the fact that the air duct (airflow inlet) is placed on the right side of the tunneling roadway; rock dust particles generated in the tunneling face are mainly blown to the exhaust side (left side) near the roadheader. After that, the rock dust gradually disperses evenly to the whole roadway. Thus, the overall distribution of rock dust on the right side is slighter compared to the left side causing the stronger transmission on the right side.
In this study, the rock dust was sampled at evenly spaced sampling points in the tunneling face of the coal mine and the distribution of rock dust in concentration and diameter were obtained by testing the sampled rock dust with a filter dust analyzer and particle size analyzer. Then, the SEM was conducted to analyze the microdiameter of rock dust, which validated the accuracy of diameter distribution by the particle size analyzer. The spectral transmittance of rock dust within the waveband of in a KBr matrix was tested by FT-IR, which was subsequently used to simulate the complex refractive index of rock dust combined with the K–K relation. XRF was performed to obtain the mineral components of rock dust, which showed that the main components of rock dust are (62.06%), (21.26%), and (4.27%). Then, based on the complex refractive index of the three major components (, , and ) in [17 –19], combined with the component share obtained in XRF and the effective medium theory, the complex refractive index of rock dust was validated and proved to be accurate and effective despite the deviation of small components.
The proved complex refractive index of rock dust was then adopted to perform the forward simulation on the spectral transmission characteristics of rock dust in the tunneling roadway based on the spatial distribution of rock dust. The discrete ordinates method combined with the Mie scattering model were solved within the waveband of 2.5–25 μm to obtain the spectral transmittance of rock dust at different detection positions. The simulation results illustrate that the spectral transmission properties of rock dust vary obviously among which exists two peaks for transmittance at around 6.7 and 15 μm. Meanwhile, the transmission of rock dust is clearly enhanced with the increase of height but weakened with the increasing horizontal distance from the air duct on the right side of the tunneling roadway.
Priority Academic Program Development of Jiangsu Higher Education Institutions; Fundamental Research Funds for the Central Universities (2014QNB02).
1. K. J. Candra, S. A. Pulung, and M. A. Sadashiv, “Dust dispersion and management in underground mining faces,” Int. J. Min. Sci. Technol. 24, 39–44 (2014).
2. M. Onder and S. Onder, “Evaluation of occupational exposures to respirable dust in underground coal mines,” Ind. Health. 47, 43–49 (2009).
3. E. Petavratzi, S. Kingman, and I. Lowndes, “Particulates from mining operations: a review of sources, effects and regulations,” Miner. Eng. 18, 1183–1199 (2005).
4. V. Castranova and V. Vallyathan, “Silicosis and coal workers’ pneumoconiosis,” Environ. Health Perspect. 108, 675–684 (2000).
5. S. D. Mamuya, M. Bratveit, J. Mwaiselage, Y. S. Mashalla, and B. Moen, “High exposure to respirable dust and quartz in a labour-intensive coal mine in Tanzania,” Ann. Occup. Hyg. 50, 197–204 (2006).
6. W. Chen, Z. Zhuang, M. D. Attfield, B. T. Chen, P. Gao, J. C. Harrison, C. Fu, J. Q. Chen, and W. E. Wallace, “Exposure to silica and silicosis among tin miners in China: exposure-response analyses and risk assessment,” Occup. Environ. Med. 58, 31–37 (2001). [CrossRef]
7. P. Rai, B. Chutia, and S. Patil, “Monitoring of spatial variations of particulate matter (PM) pollution through bio-magnetic aspects of roadside plant leaves in an Indo-Burma hot spot region,” Urban Forestry Urban Greening 13, 761–770 (2014).
8. Y. Yuan, H. Yi, Y. Shuai, B. Liu, and H. Tan, “Inverse problem for aerosol particle size distribution using SPSO associated with multi-lognormal distribution model,” Atmos. Environ. 45, 4892–4897 (2011). [CrossRef]
9. H. Zuo, Q. Liu, J. Wang, L. Yang, and S. Luo, “Selecting appropriate wavelengths to improve the precision of retrieving the aerosol size-distribution,” J. Quant. Spectrosc. Radiat. Transfer 111, 205–213 (2010). [CrossRef]
10. Y. Yuan, H. Yi, Y. Shuai, F. Wang, and H. Tan, “Inverse problem for particle size distributions of atmospheric aerosols using stochastic particle swarm optimization,” J. Quant. Spectrosc. Radiat. Transfer 111, 2106–2114 (2010). [CrossRef]
11. L. X. Ma, F. Q. Wang, C. A. Wang, C. C. Wang, and J. Y. Tan, “Monte Carlo simulation of spectral reflectance and BRDF of the bubble layer in the upper ocean,” Opt. Express 23, 24274–24289 (2015). [CrossRef]
12. W. Z. Wang, Y. M. Wang, and G. Q. Shi, “Forward research on transmission characteristics of near-surface particulate-matter-polluted atmosphere in mining area combined with CFD method,” Opt. Express 23, A1010–A1023 (2015). [CrossRef]
13. W. Z. Wang, Y. M. Wang, G. Q. Shi, and D. M. Wang, “Numerical study on infrared optical property of diffuse coal particles in mine fully mechanized working combined with CFD method,” Math. Probl. Eng. 2015, 501401 (2015).
14. M. P. Menguc, S. Manickavasagam, and D. A. D’sa, “Determination of radiative properties of pulverized coal particles from experiments,” Fuel 73, 613–625 (1994). [CrossRef]
15. C. F. Bohren and D. R. Huffman, Absorption and Scattering of Light by Small Particles (Wiley, 1998).
16. H. Fukutani and O. Sueoka, Optical Properties and Electronic Structure of Metals and Alloys (North-Holland, 1966).
17. E. D. Palik, Handbook of Optical Constants of Solids (Academic, 1998).
18. W. G. Spitzer and D. A. Kleinman, “Infrared lattice bands of quartz,” Phys. Rev. 121, 1324–1335 (1961). [CrossRef]
19. S. Onari, T. Arai, and K. Kudo, “Infrared lattice vibrations and dielectric dispersion in α-Fe2O3,” Phys. Rev. B 16, 1717–1721 (1977).
20. J. Lee, “Estimation of emission properties for silica particles using thermal radiation spectroscopy,” Appl. Opt. 50, 4262–4267 (2011). [CrossRef]
21. M. Q. Brewster and T. Kunitomo, “The optical constants of coal, char and limestone,” J. Heat Transfer 106, 678–683 (1984). [CrossRef]
22. G. N. Plass, “Mie scattering and absorption cross sections for aluminum oxide and magnesium oxide,” Appl. Opt. 3, 867–872 (1964). [CrossRef]
23. S. J. Schatzel, “Identifying sources of respirable quartz and silica dust in underground coal mines in southern West Virginia, western Virginia, and eastern Kentucky,” Int. J. Coal Geol. 78, 110–118 (2009). [CrossRef]
24. S. J. Schatzel and B. W. Stewart, “A provenance study of mineral matter in coal from Appalachian Basin coal mining regions and implications regarding the respirable health of underground coal workers: a geochemical and Nd isotope investigation,” Int. J. Coal Geol. 94, 123–136 (2012). [CrossRef]
25. M. T. Lemmon, M. J. Wolff, J. F. Bell III, M. D. Smith, B. A. Cantor, and P. H. Smith, “Dust aerosol, clouds, and the atmospheric optical depth record over 5 Mars years of the Mars Exploration Rover mission,” Icarus 251, 96–111 (2015). [CrossRef]
26. Y. Liang, H. Liang, and S. Zhu, “Mercury emission from coal seam fire at Wuda, Inner Mongolia, China,” Atmos. Environ. 83, 176–184 (2014). [CrossRef]
27. C. Kuenzer, J. Zhang, Y. Sun, Y. Jia, and S. Dech, “Coal fires revisited: the Wuda coal field in the aftermath of extensive coal fire research and accelerating extinguishing activities,” Int. J. Coal Geol. 102, 75–86 (2012). [CrossRef]
28. S. Dai, D. Ren, Y. Tang, L. Shao, and S. Li, “Distribution, isotopic variation and origin of sulfur in coals in the Wuda coalfield, Inner Mongolia, China,” Int. J. Coal Geol. 51, 237–250 (2002). [CrossRef]
29. H. W. Pfefferkorn and J. Wang, “Early Permian coal-forming floras preserved as compressions from the Wuda District (Inner Mongolia, China),” Int. J. Coal Geol. 69, 90–102 (2007). [CrossRef]
30. Z. Song, C. Kuenzer, H. Zhu, Z. Zhang, Y. Jia, Y. Sun, and J. Zhang, “Analysis of coal fire dynamics in the Wuda syncline impacted by fire-fighting activities based on in-situ observations and Landsat-8 remote sensing data,” Int. J. Coal Geol. 141–142, 91–102 (2015). [CrossRef]
31. H. Liu, Coal Mine Safety Regulation, Expert Interpretation (China University of Mining and Technology, 2006) (In Chinese).
32. S. E. Allaire and L. E. Parent, “Size guide number and Rosin-Rammler approaches to describe particle size distribution of granular organic-based fertilisers,” Biosystems Eng. 86, 503–509 (2003).
33. L. Rouleau, J.-F. Deü, A. Legay, and F. Le Lay, “Application of Kramers–Kronig relations to time-temperature superposition for viscoelastic materials,” Mech. Mater. 65, 66–75 (2013). [CrossRef]
34. H. Tan, X. Xia, L. Liu, and L. Ruan, Numerical Calculation of Infrared Radiation Properties and Transfer (Harbin Institute of Technology, 2006) (In Chinese).
35. L. M. Ruan, H. Qi, W. An, and H. P. Tan, “Inverse radiation problem for determination of optical constants of fly-ash particles,” Int. J. Thermophys. 28, 1322–1341 (2007). [CrossRef]
36. R. Ruppin, “Evaluation of extended Maxwell-Garnett theories,” Opt. Commun. 182, 273–279 (2000). [CrossRef]
37. L. H. Liu, L. M. Ruan, and H. P. Tan, “On the discrete ordinates method for radiative heat transfer in anisotropically scattering media,” Int. J. Heat Mass Transfer 45, 3259–3262 (2002). [CrossRef]