Forward research on transmission characteristics of near-surface particulate-matter-polluted atmosphere in mining area combined with CFD method

Wenzheng Wang; Yanming Wang; Guoqing Shi

doi:10.1364/OE.23.0A1010

1. Introduction

Atmospheric particulate matter (PM) has received much attention in recent years because of its adverse effects on human health and its influence on the climate and environment [1,2 ]. Based on aerodynamic diameter, particulate matters are classified into TSP (total suspended particles), PM10 (PM with an aerodynamic diameter <10μm) and PM2.5 (PM with an aerodynamic diameter <2.5μm) [3]. Relevant studies have shown that inhaling PM10 particles can introduce toxic substances into the human body via deposition in the lungs, which are closely linked to some respiratory system illnesses (e.g., asthma, lung cancer, and bronchitis) and cardiovascular diseases [4]. Furthermore, the ability of PM to absorb and scatter solar radiation affects the atmospheric radiation balance, cloud development, and the local environment [5,6 ].

PM is emitted into the atmosphere mainly because of anthropogenic activities and natural processes. Anthropogenic activities such as motor vehicle emissions, power generation, and industrial processes are the principal cause of atmospheric PM pollution [7]. Meanwhile, some gases such as sulfur dioxide, nitrogen oxide, and ammonia, which are emitted from power plants, industrial units, and automobiles, can form secondary particles through complex chemical reactions [8]. In addition, natural factors such as soil dust caused by sandstorms, and maritime salts and volcanic dust also contribute to atmospheric PM pollution [9]. Because of the heavy atmospheric PM pollution, a large number of studies have been conducted to investigate the physical and chemical properties of PM in order to achieve the accurate monitoring of PM [10,11 ]. In recent years, many satellite and ground-based optical remote sensing detection technologies (e.g., MODIS, HIRDLS, AVHRR, MISR, and the AERONET sun-photometer global detection network) have been developed to obtain the optical characteristics of atmospheric aerosols [12,13 ]. Based on the optical characteristics of atmospheric aerosols, especially the aerosol optical depth (AOD), certain retrieval methods such as the damped Gauss–Newton iteration algorithm, moment method, genetic algorithm, and stochastic particle swarm optimization algorithm, have been used to retrieve the distributions of aerosol particles [14,15 ]. Before performing an inverse calculation, the AOD or aerosol optical transmission characteristics should be calculated based on Mie scattering theory and the radiative transfer model and then used as an input for the inverse analysis [16,17 ]. The AOD calculated in the forward problem is the base of the inverse problem which is intended to obtain the optimum solution [18].

China is a country greatly affected by PM pollution that is largely emitted from eastern, central and, southwestern areas. In recent years, studies of PM pollution have focused mainly on heavily polluted cities, where many effective networks for monitoring atmospheric aerosols have been established [19,20 ]. Conversely, few studies have considered air quality in the coal mining areas of northwest China, which lack routine environmental monitoring. In mining areas, large numbers of dust particles emitted into the atmosphere from coal mines combined with sand particles entrained during sandstorms [21,22 ]. Meanwhile, coal mining areas tend to be densely populated and the threat to the local population from PM pollution is as serious as that in cities. Therefore, it is urgently required that basic optical research is conducted in such areas in order to realize the effective monitoring of PM pollution.

To effectively and accurately monitor and assess PM pollution, the spatial distribution and optical radiation transfer characteristics of the PM must be investigated thoroughly. Relevant researches have shown that airborne particles represented by PM10 are the main dust particles emitted from mine ventilation air shaft and arid areas which are the primary cause for some human diseases [23,24 ]. Therefore, with a focus on PM10 (represented by aerodynamic diameters of 5μm), the CFD method was adopted to simulate and analyze the spatial distribution of hybrid particles (sand and coal particles) considering the local desertification and dust emission from mining activities. In combination with the Mie scattering and discrete ordinates method, the radiation properties and transmission characteristics of the PM were obtained, providing theoretical guidance for the monitoring of atmospheric PM pollution in the mining area of northwest China.

2. Model and formulas

2.1 Dust particles migration model in near-surface atmosphere

To study the regularity of the distribution and migration of sand and coal particles in the near-surface atmosphere, an air-dust coupled flow mathematical description was established considering the local desertification and dust emission from mining activities. The 3-D steady-state incompressible Navier–Stokes equations were adopted to describe the airflow in the open atmosphere. The k-ε model was applied to describe turbulence effects and heat transfer was ignored [25]. Consequently, the two-phase fluid model can be written in the form of the following equations.

Conservation equations for mass and momentum:

\nabla \cdot ρ U = 0

ρ U \cdot \nabla U = \nabla \cdot σ + ρ g

where $ρ$ is the fluid density, $U$ is the fluid velocity, $σ$ is the viscous stress tensor, and $g$ is gravitational acceleration.

Continuity equation:

\frac{\partial u_{i}}{\partial x_{i}} = 0

Average time equation:

\frac{\partial (ρ u_{i} u_{j})}{\partial x_{j}} = - \frac{\partial p}{\partial x_{i}} + \frac{\partial}{\partial x_{j}} [μ (\frac{\partial u_{j}}{\partial x_{j}} + \frac{\partial u_{j}}{\partial x_{i}} - \frac{2}{3} δ_{i j} \frac{\partial u_{i}}{\partial x_{i}})] + \frac{\partial}{\partial x_{j}} (- ρ \bar{{u^{'}}_{i}} \bar{{u^{'}}_{j}})

Turbulent equation (k equation):

\frac{\partial}{\partial x_{i}} (ρ k u_{i}) = \frac{\partial}{\partial x_{i}} [(μ + \frac{μ_{t}}{σ_{k}}) \frac{\partial k}{\partial x_{j}}] + G_{k} - ρ ε

Dissipation equation ( $ε$ equation):

\frac{\partial}{\partial x_{i}} (ρ ε u_{i}) = \frac{\partial}{\partial x_{j}} [(μ + \frac{μ_{t}}{σ_{ε}}) \frac{\partial ε}{\partial x_{j}}] + ρ C_{1} S ε - ρ C_{2} \frac{ε^{2}}{k + \sqrt{υ ε}}

Equation of balance of forces between particles:

\frac{d u_{p}}{d t} = F_{D} (u - u_{p}) + \frac{g_{x} (ρ_{p} - ρ)}{ρ_{p}} + F_{x}

where u is the velocity of the continuous phase; u_p is the particle velocity; $μ$ is the dynamic viscosity of fluid; $ρ$ is the density of the continuous phase; $ρ_{p}$ is the particle density; G_k is the term for kinetic energy for turbulence; i, j = 1,2,3; and C ₁, C ₂, $σ_{k}$ , and $σ_{ε}$ are constants whose values are 1.44, 1.92, 1.0, and 1.2, respectively. $F_{D} (u - u_{p})$ is the drag force of the unit mass between particles.

2.2 Scattering model of dust particles

The regularity of the distribution and migration of dust particles in the near-surface atmosphere can be obtained by establishing, calculating, and solving the air–dust coupled flow mathematical model. Mie scattering model was adopted for the simulation and calculation [26,27 ]. The spectral attenuation and scattering factors of single dust particle are given below:

Q_{e} (m, χ) = \frac{C_{e}}{G} = \frac{2}{χ^{2}} \sum_{n = 1}^{\infty} (2 n + 1) Re (a_{n} + b_{n}) = \frac{4}{χ^{2}} Re {S_{0}}

Q_{s} (m, χ) = \frac{C_{s}}{G} = \frac{2}{χ^{2}} \sum_{n = 1}^{\infty} (2 n + 1) [{| a_{n} |}^{2} + {| b_{n} |}^{2}]

where m is the particle optical constant $m = n - i k$ ; n and k are the refractive index (single refractive index) and absorption index, respectively; $χ$ is the size parameter $χ = π D / λ$ ; G is the geometric projection area of the spherical particle, $G = π D^{2} / 4$ , μm²; Re is the real component of plural; $a_{n}$ and $b_{n}$ are the Mie scattering coefficients; $S_{1}$ and $S_{2}$ are the complex amplitude functions; $S_{0}$ , $S_{1} (0)$ , and $S_{2} (0)$ are the prior amplitude functions $S_{0} = S_{1} (0) = S_{2} (0)$ .

Then, the scattering phase function of single particle $Φ_{p}$ which involved in radiative transfer simulation could be obtained:

Φ (θ) = \frac{1}{Q_{s} χ^{2}} [{| S_{1} |}^{2} + {| S_{2} |}^{2}]

where $θ$ is the scattering angle. Aiming at the particle system of dust with uniform and the number density is defined as N, the attenuation coefficient of particles can be given as:

β = Q_{e} \cdot N \cdot G

2.3 Radiative transfer model of polluted atmospheric medium

Based on the simulation results of CFD method and Mie scattering model, the optical scattering and attenuation properties of dust can be obtained. In order to study the transmission characteristics of the near-surface atmosphere polluted by mixed dust, the discrete ordinates method was applied to solve the radiative transfer equation (RTE) [28,29 ]. For a non-gray emitting–absorbing–scattering medium, the RTE can be presented as follows:

(Ω \cdot \nabla) I (r, Ω) = - (κ_{a} + κ_{s}) I (r, Ω) + κ_{a} I_{b} (r) + \frac{κ_{s}}{4 π} \int_{Ω^{'} = 4 π} Φ (Ω^{'}, Ω) I (r, Ω^{'}) d Ω^{'}

If the surface medium of the dust particles is assumed non-gray, it emits and reflects diffusely and then, the radiative boundary condition can be given by:

I (r, Ω) = ε I_{b} (r) + \frac{ρ}{π} \int_{n \cdot Ω^{'} < 0} | n \cdot Ω | I (r, Ω^{'}) d Ω^{'}

where $I (r, Ω)$ is the spectral radiative intensity of the dust particles in direction $Ω$ ; $I_{b} (r)$ is the intensity of blackbody radiation; $κ_{a}$ and $κ_{s}$ are the absorption and scattering coefficients of dust particles obtained by the Mie scattering model, respectively; $(Ω \cdot \nabla) I (r, Ω)$ is the gradient of the intensity in the specific direction $Ω$ ; $Φ (Ω^{'}, Ω) I (r, Ω^{'}) d Ω^{'}$ is the part of radiative energy scattered into the outgoing direction of dust particles; $ε$ and $ρ$ are the surface emissivity and reflectivity of the dust particles, respectively; and $n$ is the unit normal vector at the boundary location.

The RTE can be replaced by a discrete set of equations for a finite number of ordinate directions, as follows:

μ_{m} \frac{\partial I_{m}}{\partial x} + ξ_{m} \frac{\partial I_{m}}{\partial y} + η_{m} \frac{\partial I_{m}}{\partial z} = - (κ_{a} + κ_{s}) I_{m} + κ_{a} I_{b} + \frac{κ_{s}}{4 π} \sum_{m^{'} = 1}^{M} Φ (m^{'}, m) I_{m^{'}} ω_{m^{'}}

For simplicity, the subscript $λ$ representing the wavelength in the RTE is neglected. $μ$ , $ξ$ and $η$ are the direction cosines of x, y and z, respectively. $ω$ is the integral coefficient in the radiative transfer direction.

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 incident solar energy and radiation reflecting from the surface, the emissivity and reflectivity at the boundaries are set to zero.

The Sn method was used in the formation method of the discrete ordinates, which ensured these coordinates were symmetric and remained constant in any rotation of 90°. The value of n ensured the points on the spherical surface to be arranged symmetrically. The phase function can be defined by the Legendre polynomial:

Φ (θ) = \sum_{n = 0}^{N} (2 n + 1) a_{n} P_{n} (\cos ψ)

\cos ψ = μ μ^{'} + ξ ξ^{'} + η η^{'}

3. Dust distribution in the near-surface atmosphere

3.1 Geometry model and parameter configuration

The CFD method was adopted in this work to create a numerical simulation of the regularity of the distribution and migration of dust particles in the near-surface atmosphere of the mining area of northwest China. The mine dust dispersion field is an open atmosphere space and the terrain fluctuation is relatively small. Therefore, a simplified cuboid calculation area of 1000 × 400 × 200 m was established, in which the dust source was simplified to be 20 × 20 × 2 m, as shown in Fig. 1 .

Fig. 1 Schematic of dust dispersion simulation field.

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Previous study on field measurement of dust emission have shown the particles emitted from coal mine and the arid areas are mainly airborne particles which can be represented by 5μm [23], the total release intensity (flow rate in DPM model) is determined to be 0.02 g/(m²·s) among which sand particles and coal particles account for 80% and 20%, respectively [24,30 ]. Based on the field measurement data, combined with geometric model, the main parameters and boundary conditions of the numerical simulation were determined, as shown in Table 1 .

Table 1. Boundary conditions and parameter settings of dust source

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3.2 Simulation results

According to the established model, combined with the CFD method, 3-D simulations of dust particle diffusion were performed. The flow laws of the atmospheric wind field and dust concentration distribution in the 2-D vertical direction were obtained.

As shown in Figs. 2 and 3 , the dust particles gradually spread outward as the wind flows in the open atmosphere. The dust particles are not fully diffused at the dust source, but as the wind turbulence increases and gradually reaches a maximum, the dust particles gradually spread out completely. When the dust diffusion distance comes to 600 m, the dust particles gradually settle down under the influence of gravity. In the vertical direction, dust particle diffusion is relatively low near the dust source. As the distance from the dust source increases, the expanding range of dust particles in the vertical direction is enhanced by upstream turbulence. The maximum distance of vertical diffusion reaches 40 m at the distance of 400 m from the dust source. The concentration distribution of dust particles in the vertical direction at different points from dust source is shown in Table 2 .

Fig. 2 Diagram of velocity vector.

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Fig. 3 Diagram of dust concentration distribution.

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Table 2. Concentration distribution of dust particles in the vertical direction

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Apart from the simulation presented above, the distribution of dust particles in the vertical direction under different wind speeds (2, 5, and 8 m/s), different dust release intensities (0.004, 0.02, and 0.04 g/(m²·s)), and different compositions (coal/sand particles account for 20:80%, 10:90%, and 0:100%) were calculated and analyzed based on the same geometry model.

4. Optical constants of hybrid particles

The mining area of northwest China was considered as the research object for this paper, where the local atmospheric PM is composed of coal and sand particles in the proportions of 20% and 80%, respectively. Therefore, the optical constants of hybrid particles should be determined first before the optical radiation transfer characteristics of the hybrid dust particles are calculated. Coal particles are typically lean coal dust particles released from mining activities in the area and the sand particles are typically dust particles derived from sandstorms. The optical constants of these two types of dust particles were obtained from previous study [31,32 ], as shown in Fig. 4 .

Fig. 4 Complex refractive index of coal and sand particles: (a) The n value of the complex refractive index; (b) The k value of the complex refractive index.

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The optical constants of the hybrid particles can be obtained by adopting the effective medium theories based on the share and optical constants of each component. Coal and sand particles account for 20% and 80%, respectively, of the hybrid particles in this work, coal particles can be assumed to be buried within the matrix of sand particles. Thus, the widely used Maxwell–Garnett form was adopted for the calculation [33]. The calculation considered two cases: (a) coal and sand particles account for 20% and 80%, respectively; and (b) coal and sand particles account for 10% and 90%, respectively. Based on the complex refractive index of hybrid particles and Mie scattering method, the spectral attenuation factor of hybrid particles were simulated, as shown in Fig. 5 .

Fig. 5 Spectral attenuation factor of hybrid particles.

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As can be seen from Fig. 6 , the n value of the hybrid particles’ complex refractive index in case (b) is obviously enhanced compared with case (a). This indicates stronger scattering due to the increase in the proportion of sand particles among the hybrid particles. The k value in case (b) is obviously reduced compared with case (a). This indicates weaker absorption due to the increase in the proportion of sand particles among the hybrid particles. The complex refractive index of the hybrid particles shows an overall upward trend with increasing wavelength.

Fig. 6 Complex refractive index of hybrid particles.

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Figure 5 presents that the attenuation factor of hybrid particles in case (a) and (b) have similar spectral variation rule. The attenuation factor is relatively greater under short waveband but drops sharply with the increase of wavelength before 6μm. Then, an obviously peak at 9μm is observed indicating the strongest attenuation characteristics. Meanwhile, the attenuation factor of hybrid particle in case (a) is slightly greater than that in case (b) under short and long waveband but a little smaller under medium waveband.

5. Results and discussion

According to local meteorological data and based on the CFD method, the distributions of dust particles in the vertical direction were obtained under different wind speeds (2, 5, and 8 m/s), different dust release intensities (0.004, 0.02, and 0.04 g/(m²·s)), and different compositions (coal/sand particles account for 20:80%, 10:90%, and 0:100%). Based on the simulation results of dust distribution in the direction perpendicular to the ground, combined with the optical constants of the hybrid particles, the attenuation characteristics and radiation transfer characteristics of the dispersion particles in the vertical direction, under different influencing factors, were simulated and analyzed. In the simulation, the 1-D RTE solution based on the discrete ordinates method with S6 coordinates for radiative transfer in the vertical atmospheric medium was implemented. Here, the attenuation properties of diffusion solid aerosols are evaluated and compared with spectral variables of medium transmittance and aerosol optical depth (AOD).

γ_{λ} = I_{λ} (L) / I_{λ} (0)

A O D = \int_{0}^{L} β (x) d x

where, $I_{λ} (0)$ is a given spectral incidence intensity into the particles medium at the top boundary. $I_{λ} (L)$ is the transmitted intensity at the surface. L is the length of PM-polluted atmosphere.

5.1 Optical radiation characteristics of dust particles

As the wind flows in the open atmosphere, dust particles gradually flow outward and then settle under the action of gravity, resulting in a variation of dust concentration. Based on the simulation results of dust dispersion, combined with the Mie scattering method, for short (2μm), medium (5μm), and long (20μm) wavelengths, the attenuation characteristics of the dispersion particles were calculated for different locations, as shown in Fig. 7 .

Fig. 7 Attenuation coefficient variation of dust particles in the vertical direction: (a) 200 m from the dust source; (b) 400 m from the dust source; (c) 600 m from the dust source.

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As can be seen from Fig. 7, the attenuation coefficient of hybrid particles are obviously decreased with the increase of wavelength, which is closely linked to its spectral attenuation factor in Fig. 5.

The attenuation coefficient of hybrid particles shows an overall downward trend with the increase of height. At the point 200 m from the dust source, the attenuation coefficient increases slightly with the increase of height and then remains largely stable from 5 to 19 m, following which the attenuation coefficient of dust particles drops sharply by an order of magnitude. At the point 600 m from the dust source, the attenuation coefficient shows an approximately linear downward trend with the increase of height, the degree of decline is by an order of magnitude at the height of 35 m. This is because dust particles disperse in the horizontal and vertical directions at the same time under the action of the airflow and gravity. Relatively close to the dust source (i.e., 200 m), dust particles are not fully dispersed in the vertical direction, which results in the corresponding rising and then dropping trend of dust concentration in the vertical direction, similar to the variation law of the attenuation coefficient of the hybrid particles. With the increase of diffusion distance in the horizontal direction, the distribution of dust concentration in the vertical direction decreases with an index variation rule under the combined effect of gravity and airflow, causing the same variation law of attenuation coefficient in the vertical direction.

5.2 Aerosol optical depth (AOD) under different wind speeds

The outward spread of dust particles is caused mainly by the effects of the atmospheric airflow. Therefore, the dust concentration distribution is directly affected by the wind speed, which further affects the spectral AOD of the dispersion particles. This section considers three sampling sites (200, 400, and 600 m from the dust source, respectively) for which the calculation of the spectral AOD of the dispersion particles was performed under wind speeds of 2, 5, and 8 m/s, as shown in Fig. 8 .

Fig. 8 Spectral aerosol optical depth (AOD) versus wind speed: (a) 200 m from the dust source; (b) 400 m from the dust source; (c) 600 m from the dust source.

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As can be seen from Fig. 8, the AOD of the dust particles drops sharply with the increase of wavelength, reaching a minimum point for the wavelength of 6μm. Following this, it rises sharply and erratically to a maximum point for the wavelength of 9μm, finally the AOD decreases gently with an index variation rule and keeps stable when it comes to 20μm. The AOD of dust particles is closely linked to its attenuation characteristics, an obvious trough and peak of attenuation factor are observed at 6μm and 9μm, respectively in Fig. 5, resulting in the corresponding minimum and maximum point of AOD.

The AOD of the dust particles decreases obviously with increasing wind speed. At the point 200 m from the dust source, the AOD under the wind speed of 2 m/s is twice that compared with the wind speeds of 5 and 8 m/s. However, the difference in AOD between 5 and 8 m/s is not obvious. After the point 400 m from the dust source, there is a clear downward trend with increasing wind speed. This is because the AOD is defined as the integral of the attenuation coefficient of the dust particles through an atmospheric column from the ground to the top of the atmosphere. Therefore, the variation law of the AOD is closely linked to the distribution of dust particles. Dust particles are not fully dispersed at the point 200 m from the dust source, and the dust-settling effect of the airflow does not function under the turbulent conditions of the atmosphere especially when the wind speed is greater than 5 m/s. As can be seen in Table 3 , the overall dust concentration distribution under 5 m/s is significantly decreased compared to 2 m/s but only a little greater than 8m/s. At the point 400 m from the dust source, the distribution of dust particles gradually becomes stable, and greater wind speed contributes more to the dispersion and deposition of the dust particles, resulting in the obvious decrease of the atmospheric AOD.

Table 3. Dust concentration distribution under different wind speeds at the point 200 m

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5.3 Transmission characteristics under different dust release intensities

The distribution of dust concentration is greatly affected by dust source intensity, which has much to do with the transmission characteristics of the dispersion particles. Three sites (200, 400, and 600 m from the dust source, respectively) were selected as sampling points, and the simulation calculation was performed on the spectral transmission of the dust particles under dust release intensities of 0.004, 0.02, and 0.04 g/(m²·s), respectively, as shown in Fig. 9 .

Fig. 9 Spectral transmittance versus dust source intensity:(a) 200 m from the dust source; (b) 400 m from the dust source; (c) 600 m from the dust source.

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As can be seen from Fig. 9, the spectral transmittance of dust particles rises sharply with the increase of wavelength, reaching a maximum for the wavelength of 6μm. Following this, it drops sharply to a minimum for the wavelength of 9μm, finally the transmittance increases gently with an index variation rule and keeps stable when it comes to 14μm. Contrary to AOD, the trough of attenuation factor at 6μm and the peak at 9μm cause the corresponding maximum and minimum of transmittance at 6μm and 9μm, respectively.

Figure 9 presents that: the transmittance of the dispersion particles decrease considerably with the increase of dust source intensity. The dust concentration in the atmosphere is clearly enhanced by the increasing dust source intensity, which results in the enhanced absorption and scattering characteristics, as well as the decreased transmission characteristics. The rate of decline of transmittance with increasing dust source intensity is relatively higher in the 1–6 and 9–25μm wavebands. The spectral transmittance variation of the dispersion particles is not that obvious for low dust release intensity but do become more distinct as the intensity of dust release increases.

5.4 Transmission characteristics under different compositions

Dust particles suspended in the near-surface atmosphere comprise mainly coal and sand particles. When the composition of hybrid particles varies, the transmission characteristics of the dispersion particle system is affected because of the variation of dust concentration distribution, as well as the variation of the optical constants of the hybrid particles. Calculations were performed on the spectral transmittance of the dispersion particles for three groups (a–c) in which the coal/sand particles accounted for 20:80%, 10:90%, and 0:100%, respectively, as shown in Fig. 10 .

Fig. 10 Spectral transmittance versus composition of hybrid particles: (a) 200 m from the dust source; (b) 400 m from the dust source; (c) 600 m from the dust source.

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As can be seen from Fig. 10, the transmission characteristics of the dispersion particle system are generally enhanced with the increase of the proportion of sand particles among the hybrid particles. In the short and medium wavebands, the transmittance of the dispersion particles are enhanced with the increase of the proportion of sand particles at the sampling point 200 m from the dust source; however, the variation law is not obvious at the sampling point 400 m from the source and even decreases at certain wavelengths. At the point 600 m from the source, the trend of decrease is more evident. In the waveband after 10μm, the transmittance of the dispersion particles are obviously enhanced with the increase of the proportion of sand particles among the hybrid particles, however, the trend of increase gradually slows with the increasing distance of sampling distance.

Sand particles are more prone to settle compared with coal particles because of the larger density of sand particles. Meanwhile, the number of mixture particle is reduced with the increase of the proportion of sand particles because of the larger density of sand particle compared to coal particle and the constant dust release source. Thus, the sedimentation rate of the hybrid particles is accelerated to a certain extent with the increase of the proportion of sand particles, causing the enhancement of the transmission characteristics. With the increase of the distance from dust source, the difference of dust concentration distribution under different compositions is gradually weakened resulting in the weakened variation of transmission characteristics under different compositions. Meanwhile, sand particles have weaker attenuation characteristics and better transmission characteristics compared with coal particles in the same situation and thus, the increase in the proportion of sand particles among the hybrid particles also leads to the enhancement of transmission characteristics of the dispersion particles.

6. Conclusions

In this study, the CFD method was applied to simulate the law of hybrid dust migration in the near-surface atmosphere of the mining area in northwest China, based on a two-phase fluid model, the 2-D distribution of dust concentration in the vertical direction were obtained. The optical constants of hybrid dust particles were obtained by adopting the effective medium theories based on the share and optical constants of each component. Combined with the Mie scattering and discrete ordinates methods, the simulation model of the radiation transfer characteristics of particles in the near-surface atmosphere was established, the variation laws of the AOD and transmission characteristics of the dust particles in the near-surface atmosphere were analyzed and summarized.

The spectral transmission characteristics of dust particles in the near-surface atmosphere are mainly affected by the distribution of dust concentration and optical properties of the dust particles. Because of the scattering and attenuation effect of dust particles, the AOD and transmission characteristics of the polluted atmosphere are directly affected by the distribution of dust concentration in the near-surface atmosphere. The simulation results show that: (a) the attenuation characteristics of dust particles are obviously decreased with height. (b) Aerosol optical depth (AOD) of dispersion particles is obviously decreased with the increase of wind speeds. The transmittance of dispersion particles are obviously decreased with the increase of dust release intensity yet enhanced to some certain extent with the increase of sand particles’ proportion among hybrid particles. (c) The transmittance of dispersion particles are relatively weak and volatile under short wave and medium wave, but enhanced obviously when the wavelength comes to 10μm and stays stable eventually.

Acknowledgments

This research was supported by the Fundamental Research Funds for the Central Universities (2014QNB02), the National Natural Science Foundation of China (Grant no. 51134020) and A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Model	Definition	Model	Definition
Solver	Segregated	Diameter Distribution	Uniform
Viscous Model	k-epsilon	Diameter	5μm
Energy	Off	Flow Rate	0.02 g/(m2·s)
Discrete Phase Model	On	Turbulent Dispersion	Stochastic tracking
Drag Law	Spherical	Inlet Boundary Type	Velocity-inlet
Material	Hybrid particles	Inlet Velocity Magnitude	5 m/s
Injection Type	Surface	Outlet Boundary Type	Outflow
Wall	DPM Condition		Trap
Wall	Shear Condition		No slip

Height/m	Dust concentration/(kg/m³)			Height/m	Dust concentration/(kg/m³)
Height/m	200 m	400 m	600 m	Height/m	200 m	400 m	600 m
0	4.59E-06	1.03E-05	2.07E-05	21	1.58E-05	7.25E-06	1.01E-05
1	8.25E-06	1.01E-05	2.06E-05	22	1.23E-05	5.99E-06	9.11E-06
2	1.19E-05	9.85E-06	2.08E-05	23	8.99E-06	9.03E-06	8.06E-06
3	1.55E-05	8.65E-06	2.16E-05	24	6.35E-06	1.10E-05	7.90E-06
4	1.67E-05	7.88E-06	2.22E-05	25	4.05E-06	1.05E-05	8.73E-06
5	1.58E-05	8.46E-06	2.23E-05	26	2.55E-06	9.34E-06	8.22E-06
6	1.66E-05	8.43E-06	2.04E-05	27	1.50E-06	6.60E-06	6.19E-06
7	1.76E-05	7.31E-06	1.70E-05	28	4.42E-07	4.71E-06	4.37E-06
8	1.61E-05	6.75E-06	1.38E-05	29	0	5.11E-06	2.80E-06
9	1.40E-05	7.27E-06	1.08E-05	30	0	5.49E-06	2.16E-06
10	1.18E-05	8.52E-06	9.69E-06	31	0	5.85E-06	2.67E-06
11	9.75E-06	1.12E-05	1.00E-05	32	0	5.87E-06	2.78E-06
12	1.08E-05	1.28E-05	1.20E-05	33	0	4.92E-06	2.34E-06
13	1.27E-05	1.21E-05	1.51E-05	34	0	4.19E-06	1.65E-06
14	1.27E-05	1.16E-05	1.61E-05	35	0	4.14E-06	6.18E-07
15	1.20E-05	1.14E-05	1.51E-05	36	0	4.12E-06	0
16	1.42E-05	1.18E-05	1.29E-05	37	0	4.17E-06	0
17	1.75E-05	1.35E-05	9.74E-06	38	0	3.83E-06	0
18	1.97E-05	1.41E-05	9.09E-06	39	0	2.19E-06	0
19	2.16E-05	1.24E-05	1.08E-05	40	0	5.58E-07	0
20	1.95E-05	1.03E-05	1.12E-05

Height/m	Dust concentration/(kg/m³)			Height/m	Dust concentration/(kg/m³)
Height/m	2 m/s	5 m/s	8 m/s	Height/m	2 m/s	5 m/s	8 m/s
0	0	4.59E-06	2.84E-05	22	4.69E-05	1.23E-05	6.58E-07
2	7.38E-06	1.19E-05	2.80E-05	24	5.24E-05	6.35E-06	7.93E-08
4	1.29E-05	1.67E-05	2.33E-05	26	4.02E-05	2.55E-06	0
6	9.98E-06	1.66E-05	2.00E-05	28	2.10E-05	4.42E-07	0
8	1.25E-05	1.61E-05	1.52E-05	30	1.38E-05	0	0
10	1.25E-05	1.18E-05	1.23E-05	32	2.42E-05	0	0
12	1.22E-05	1.08E-05	1.17E-05	34	2.64E-05	0	0
14	2.26E-05	1.27E-05	7.78E-06	36	1.82E-05	0	0
16	3.46E-05	1.42E-05	4.71E-06	38	1.21E-05	0	0
18	3.58E-05	1.97E-05	4.26E-06	40	5.33E-06	0	0
20	3.45E-05	1.95E-05	2.91E-06

Model	Definition	Model	Definition
Solver	Segregated	Diameter Distribution	Uniform
Viscous Model	k-epsilon	Diameter	5μm
Energy	Off	Flow Rate	0.02 g/(m2·s)
Discrete Phase Model	On	Turbulent Dispersion	Stochastic tracking
Drag Law	Spherical	Inlet Boundary Type	Velocity-inlet
Material	Hybrid particles	Inlet Velocity Magnitude	5 m/s
Injection Type	Surface	Outlet Boundary Type	Outflow
Wall	DPM Condition		Trap
Wall	Shear Condition		No slip

Height/m	Dust concentration/(kg/m³)			Height/m	Dust concentration/(kg/m³)
Height/m	200 m	400 m	600 m	Height/m	200 m	400 m	600 m
0	4.59E-06	1.03E-05	2.07E-05	21	1.58E-05	7.25E-06	1.01E-05
1	8.25E-06	1.01E-05	2.06E-05	22	1.23E-05	5.99E-06	9.11E-06
2	1.19E-05	9.85E-06	2.08E-05	23	8.99E-06	9.03E-06	8.06E-06
3	1.55E-05	8.65E-06	2.16E-05	24	6.35E-06	1.10E-05	7.90E-06
4	1.67E-05	7.88E-06	2.22E-05	25	4.05E-06	1.05E-05	8.73E-06
5	1.58E-05	8.46E-06	2.23E-05	26	2.55E-06	9.34E-06	8.22E-06
6	1.66E-05	8.43E-06	2.04E-05	27	1.50E-06	6.60E-06	6.19E-06
7	1.76E-05	7.31E-06	1.70E-05	28	4.42E-07	4.71E-06	4.37E-06
8	1.61E-05	6.75E-06	1.38E-05	29	0	5.11E-06	2.80E-06
9	1.40E-05	7.27E-06	1.08E-05	30	0	5.49E-06	2.16E-06
10	1.18E-05	8.52E-06	9.69E-06	31	0	5.85E-06	2.67E-06
11	9.75E-06	1.12E-05	1.00E-05	32	0	5.87E-06	2.78E-06
12	1.08E-05	1.28E-05	1.20E-05	33	0	4.92E-06	2.34E-06
13	1.27E-05	1.21E-05	1.51E-05	34	0	4.19E-06	1.65E-06
14	1.27E-05	1.16E-05	1.61E-05	35	0	4.14E-06	6.18E-07
15	1.20E-05	1.14E-05	1.51E-05	36	0	4.12E-06	0
16	1.42E-05	1.18E-05	1.29E-05	37	0	4.17E-06	0
17	1.75E-05	1.35E-05	9.74E-06	38	0	3.83E-06	0
18	1.97E-05	1.41E-05	9.09E-06	39	0	2.19E-06	0
19	2.16E-05	1.24E-05	1.08E-05	40	0	5.58E-07	0
20	1.95E-05	1.03E-05	1.12E-05

Forward research on transmission characteristics of near-surface particulate-matter-polluted atmosphere in mining area combined with CFD method

Abstract

1. Introduction

2. Model and formulas

2.1 Dust particles migration model in near-surface atmosphere

2.2 Scattering model of dust particles

2.3 Radiative transfer model of polluted atmospheric medium

3. Dust distribution in the near-surface atmosphere

3.1 Geometry model and parameter configuration

3.2 Simulation results

4. Optical constants of hybrid particles

5. Results and discussion

5.1 Optical radiation characteristics of dust particles

5.2 Aerosol optical depth (AOD) under different wind speeds

5.3 Transmission characteristics under different dust release intensities

5.4 Transmission characteristics under different compositions

6. Conclusions

Acknowledgments

References and links

Cited By

Figures (10)

Tables (3)

Equations (18)

Optics Express