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Article

Analysis of Experimental Measurements of Particulate Matter (PM) and Lung Deposition Surface Area (LDSA) in Operational Faces of an Oil Shale Underground Mine

by
Sergei Sabanov
*,
Abdullah Rasheed Qureshi
,
Ruslana Korshunova
and
Gulim Kurmangazy
School of Mining and Geosciences, Nazarbayev University, Astana 010000, Kazakhstan
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(2), 200; https://doi.org/10.3390/atmos15020200
Submission received: 18 December 2023 / Revised: 23 January 2024 / Accepted: 30 January 2024 / Published: 5 February 2024
(This article belongs to the Special Issue Measurement, Evaluation and Modeling of Particulate Matter and Air Quality Index)
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Abstract

:
Particulate matter (PM) in the context of underground mining results from various operations such as rock drilling and blasting, ore loading, hauling, crushing, dumping, and from diesel exhaust gases as well. These operations result in the formation of fine particles that can accumulate in the lungs of mineworkers. The lung deposited surface area (LDSA) concentration is a variant solution to evaluate potential health impacts. The aim of this study is to analyse PM and LDSA concentrations in the operational workings of the oil shale underground mine. Experimental measurements were carried out by a direct-reading real-time PM monitor, Dusttrak DRX, and a multimetric fine particle detector, Naneous Partector 2, during the loading and dumping processes using the diesel engine loader. Consequently, the analysis was conducted on PM, LDSA, particle surface area concentration (SA), average particle diameter (d), particle number concentration (PNC), and particle mass (PM0.3), producing a few valuable correlation factors. Averaged LDSA was around 1433 μm2/cm3 and reached maximum peaks of 2140 μm2/cm3 during the loading, which was mostly related to diesel exhaust emissions, and within the dumping 730 μm2/cm3 and 1840 μm2/cm3, respectively. At the same time, average PM1 was about 300 μg/ m3 during the loading, but within the dumping peaks, it reached up to 10,900 μg/ m3. During the loading phase, particle diameter ranged from 30 to 90 nm, while during the dumping phase peaks, it varied from 90 to 160 nm. On this basis, a relationship between PNC and particle diameter has been produced to demonstrate an approximate split between diesel particulate matter (DPM) and oil shale dust diameters. This study offers important data on PM and LDSA concentration that can be used for estimating potential exposure to miners at various working operations in the oil shale underground mines, and will be used for air quality control in accordance with establishing toxic aerosol health effects.

1. Introduction

1.1. Air Quality in Underground Mines

The origins of particle emissions in underground mines are mining activities, diesel engine vehicles, and blasting operations [1,2,3,4]. Dust concentrations depend on mining activities [5], and can contain crystalline silica that can cause silicosis, lung cancer, and other diseases of the respiratory system [6,7]. The issue of air quality has consistently been a matter of great concern because the dust generated during mining seriously affects the quality of underground air and worker health [8,9]. The authors state that most of the studies prefer to neglect the assessment of air quality in underground mines, confining themselves to the analysis and forecasting. However, the unique conditions of underground mines, with their increased concentration of gases and solid particles, make it urgent to apply the air quality index (AQI) for effective monitoring and maintenance of safe working conditions.
Significant emphasis should also be placed on the assessment of PM concentration in underground mining operations [10]. A number of studies used various measurement tools and methodologies to determine the concentration of PM in underground mines. In a study conducted by Saarikoski et al. (2019) [11], an underground chrome mine revealed a significant influence of the location and time intervals of measurements on the number and mass concentration of particle matter. For measurements, an optical particle counter was used in various parts of the mine. It indicated that PM10 varied from 22 to 1100 μg/m3, the total number of concentrations varied from 1.7 × 103 to 2.3 × 105 cm−3, and the ore crushing process creates dust with particle sizes over 2.5 μm. At the same time, in the Saarikoski et al. (2018) [3] study, the average concentrations of particles in a chrome mine were measured, and this value was 2.3 ± 1.4 × 104 cm−3. The distribution of the number of particles by size varied in the range from 30 to 200 nm, but the most common sizes were less than 30 nm. As an alternative for assessment, the properties of solid particles and aerosols in underground mining environments could be assessed by scanning electron microscopy and energy dispersive X-ray spectroscopy (SEM-EDS) analysis [12,13,14].

1.2. Lung Deposited Surface Area

The combustion of diesel is the main source of the toxic gases and particles in underground mines. When underground miners, particularly truck drivers, continuously breathe contaminated air for a prolonged period of time, severe occupational disorders may develop. In addition to toxic gases, diesel engines also release ultrafine particles (UFPs) with a size of less than 0.1 µm. Their accumulation in the respiratory system is hazardous and can result in severe respiratory illnesses [15]. When evaluated in various environmental circumstances, the lung deposited surface area concentration (LDSA) approach was successful in recognizing UFP [16,17,18,19,20,21]. Salo et al. (2021) [22], using ELPI+ in a chrome underground mine, found that LDSA concentrations vary from 137 to 405 μm2/cm3. The optimal particle size to estimate the LDSA distribution was no more than 100 nm, except at the blasting site, where the particle sizes were closer to 700 nm. Jafarigo et al. (2023) [23] showed that the average concentration of LDSA was relatively high (4 × 103 μm2/cm3), while the majority of particles had a size of less than 100 nm. Afshar-Mohajer et al. (2020) [16] monitored the concentration of inhaled particles in the four main technological divisions of a taconite mine, crushing, dry and wet milling, and granulation, to measure particles smaller than 300 nm. The results showed that the average LDSA concentration was highest during the granulation process, at 199 ± 48 μm2/cm3. In other mining areas, the concentration ranged from 80 to 200 μm2/cm3. In addition, the taconite mine LDSAs in crushing and pelletizing operations were 142 ± 52 μm2/cm3 and 200 ± 48 μm2/cm3, respectively.
UFPs can carry a high concentration of toxic substances, and therefore, they can cause inflammation through oxidative stress responses, atherosclerosis, an increase in blood pressure, and even myocardial infraction [24]. The LDSA is a different method of describing particle toxicity. The challenge with current mass-based exposure limits is that they fail to account for ultrafine particles (less than 100 nm), which have negligible contributions to particle mass, but can penetrate deep into human airways, lodge in lung alveoli, and have high toxicity contributions. This is especially concerning because most diesel engine particle emissions (measured by number) fall into the ultrafine range [17]. In distinct metropolitan contexts, LDSA concentrations, size distributions, and height profiles have been measured in ambient conditions [18,19,21]. LDSA concentrations have been connected to emissions from combustion sources. Afshar-Mohajer et al. (2020) [16] used a variety of measures, including LDSA, to investigate the variability of aerosol concentrations in different processing zones of a taconite mine. Huynh et al. (2018) [17] used numerous particle concentration measures, including LDSA, to investigate fine particle concentrations in six taconite mines. LDSA concentrations in their investigation ranged from 50 to 300 µm2/cm3 depending on the processing region. Although LDSA levels varied from station to station, Afshar-Mohajer et al. (2020) [18] also noticed the remarkable repeatability of concentration each day. The aim of this experimental sampling was to determine the exposure of the cabin operator to diesel exhaust nanoparticles.
Benedetto et al., in their study, were the first to characterize and determine the behavior of UFPs in synthetic lung fluids. The authors found a large variability in the hydrodynamic diameter, with values less than 1 nm and greater than 5 µm, and recognized aggregation and disaggregation processes in Gamble solution and artificial lysosomal fluid using dynamic light scattering. The results of their study proposed an interaction between nanoparticles and lung fluids, particularly within the alveolar macrophage region. [25]. Kalaiarasan at al. concluded that respiratory deposition doses and particle number concentrations can be applicable to investigate relationships between particle diameter and deposition in the different regions of the lungs, considering the impact of the UFP deposition in the deeper region of the lungs [26].
A notable Issue arises from the observation that a majority of the particles generated in coal and oil shale mines have a finely dispersed nature, enabling their infiltration into the respiratory system [27,28]. Thus, the LDSA indicator can be used to determine the potential places of deposition of particles in the lungs and to study the impact of inhaled particles on humans [29]. A number of researchers measured the LDSA in underground mines using a variety of measuring instruments in their research. Salo et al. (2021, 2023) [22,30] studied the distribution and concentration of LDSA in various places of a chrome underground mine using an electrical low-pressure impactor (ELPI+), and evaluated the possibility of using sensors to measure this indicator. The author stated that the environment around the sensors was complex because the particle sizes often exceeded the optimal range (20–300 nm), and dust accumulated inside the devices. Studies conducted in different underground mines confirm the high concentration of fine particles. These results highlight the need for enhanced monitoring of mine air quality to protect workers’ health and ensure safe working conditions.

1.3. Oil Shale Particulates

According to Wang, Liu, and Gratt (1985) [27,28,31,32], coal and oil shale mines are significant sources of dust particles in the mining environment, and their detailed study in the context of composition, size, and monitoring methods is of particular interest. In underground coal mines, dust includes the smallest particles, and this dust consists mainly of particles containing carbon, heavy metals (arsenic, mercury, lead, etc.), and other pollutants that can lead to serious diseases of the respiratory tract of mine workers [32]. Chang and Xu (2017) [33] and Widodo et al. (2023) [34] claimed that in coal mines, especially with improper ventilation, the formation of explosive mixtures of dust and gas is possible, which increases the risk to workers. A study on the measurement of dust concentration in a coal underground mine was conducted by Jin et al. (2023) [35] and showed that it ranges from 3.4 to 106.2 mg/m3.
In comparison with coal mines, oil shale mines produce dust with a different composition [27,28]. According to the authors’ study, oil shale dust contains minerals such as quartz, gypsum, and mica, as well as various chemical compounds associated with oil and gas production processes (kerogen, lead, cadmium, etc.). Typical oil shale mineral parts chemical composition consists of SiO2, Al2O3, Fe2O3, TiO2, CaO, MgO, SO3, K2O, Na2O, P2O5, and kerogen oil with an elemental composition H, C, S, N, and O [36,37]. As an unconventional resource, oil shale is widely distributed around the world and has a very high potential; however, industrial mining produces it only in China, Estonia, and Brazil [36,37]. For this reason, there are only a few studies related to oil shale dust compounds, and none about particulate matter or aerosol concentration and distribution in oil shale underground mines. Wang et al. (2019) [27], in their study about the explosibility of oil shale dust, argued that dust particles in oil shale have an extremely small size and fine dispersion. In the study of kinetic analysis on the deflagration characteristics of oil shale dust conducted by Meng et al. (2022) [38], the distribution of particles of oil shale dust was determined using a laser particle size analyzer called ‘Mastersizer 2000’. The average particle size of two different oil shales was 15 µm and 60 µm, respectively. In studies conducted by Yu et al. (2017) [39], the particle sizes in the oil shale dust were measured using a laser diffraction analyzer with particle sizes 68–80 µm. Teinemaa et al. (2002) [40] investigated oil shale combustion fly-ash aerosols and stated the bimodal composition first maximum at 0.1 μm (fine particles) and the second maximum around 3.5 μm (coarse particles).
However, all of these studies on oil shale were limited to measurements, monitoring, and analysis of PM and LDSA in underground oil shale mines, and therefore focused on the air quality problems in operational faces. The aim of this study is to analyse PM and LDSA concentrations in the operational workings of the oil shale underground mine. Producing relationships between currently used PM indicators and more descriptive and innovative indicators (LDSA and PNC) determines the need to study these components. The investigation of the correlation between LDSA and PM1 and PM2.5 is an important part of this study because these indicators have more diverse sources, including diesel exhaust emissions and secondary aerosol formation, which might be anthropogenic or biogenic in origin [41]. Kuula et al. (2020) and Luoma et al. (2021) stated in their studies that PM2.5 has been shown to highly correlate with BC and LDSA [18,42]. PM10 concentrations are dominated by noncombustion sources, e.g., dust.
This paper begins with a brief overview of the mine site and a description of measuring instruments, introduces results, and leads to discussion. Measurement results include filter analysis by SEM/EDS and derived relationships between LDSA and PM.
This study offers important data on PM and LDSA exposure to miners at various working operations in the oil shale mines, and therefore can be used to ptimize auxiliary ventilation to dilute toxic concentrations. Such experimental measurements of PM and LDSA have never been conducted at any underground oil shale mines; thus, the produced analysis and received results will considerably contribute to the development of wide global knowledge in this field. These findings enhance our understanding of the different particle concentrations and should help devise ways of reducing exposure to miners, as well as estimating the risks associated with the health of workers in hazardous areas. From an innovation perspective, the exposure limits should be represented by nanoparticles, and LDSA can be an option to use as a measure to demonstrate health relevance.

2. Materials and Methods

2.1. Study Mine Site

The study mine employs the room-and-pillar mining with drilling and blasting method followed by mucking to conveyors, which transport the oil shale to a beneficiation plant with an annual production of 6 million tons. The studied mining block dimensions are about 300 m in width and around 800 m in length, located at a depth of 50 m (Figure 1). The dimensions of the pillars are about 50 m2. The room’s size is about 7 m by 7 m. In this study, samplings were produced at three location points: Zone 1(black star) is on the incoming fresh airflow, Zone 2 (purple star) is on the operational face, and Zone 3 (red star) is on the dumping point (8-point green star) to the conveyor belt (Figure 1). In Figure 1, blue arrows show fresh air and red arrows present exhausted air; the green seven-point star is a dumping station; and the gray line in the middle of the mining block is the belt conveyor.
The studied oil shale is made of organically rich stratified sedimentary rock (15–46% kerogen, 26–57% carbonates, and 18–42% clastic minerals). The karst clay composition is approximately 10–15% within tectonic displacement zones. The fractured zones’ rock is dolomitized and contains calcite and pyrite veins, as well as marcasite, galena, sphalerite, and barite. The mined oil shale seam comprises thin layers of oil shale together with thin intervening layers of limestone. Oil shale uniaxial compressive strength is 18–40 MPa, and limestone is 65–82 MPa; the volume density varies from 1.2 to 1.7 t/m3 and from 2.1 to 2.5 t/m3, respectively [37,43]. The average chemical composition of the oil shale mineral part is presented in Figure 2.
The entire operation of the mine’s ventilation system is facilitated by the utilization of intake ventilation fans, each with a power output of 500 kW and an airflow rate of 140 m3/s. These fans are situated on the surface and are responsible for supplying fresh air to the operational sections of the mine. Additionally, booster fans are installed underground to assist in this process. The polluted air is circulated through exhaust ventilation tunnels and released to the surface through return ventilation shafts. [44].
The sampling instruments were placed 160 cm above the floor to ensure effective collection of samples near the mineworker’s respiratory level. The experimental measurements were conducted in Zone 2 and Zone 3 (Figure 1), requiring the use of the LHD loader ‘Liebher LH410’ with a bucket size of 6.5 m3 and equipped with the diesel engine model ‘Volvo TAD872VE, 210 kW, Tier 4f’. The loader exploits high-quality diesel fuel to meet Euro 3 standards, and at the moment of measurements, the engine hours were 15, 835 eng/h. Samplings were conducted at a working engine temperature of 90 °C, which was in operation for 2 h without stopping. In Zone 2, the sampling was produced about 2 m away from the loader exhaust tailpipe to consider diesel particulate matter (DPM) emissions. In Zone 3, samplings considered a process of oil shale dumping from the loader to the conveyor, which is associated with a high dust environment where diesel emissions are also present. In Zone 3, the sampling point was about 8 m away from the loader. All measurements were produced on the downstream air, whose quantity was about 20 m3/s. The mine environment conditions obtained from the instruments showed that the air temperature was 12 °C, the humidity was 87%, and the atmospheric pressure was 1024 hPa.

2.2. Measuring Instruments

The ‘Naneos Partector 2’ (Naneos Particle Solutions gmbh, Alte Spinnerei 9, CH-5210 Windisch, Switzerland) multimetric particle detector uses dual noncontact detection stages to measure the LDSA, PNC, and average particle diameter (d). Additionally, it calculates the particle surface area concentration (SA) and the particle mass < 0.3 μm (PM0.3). The manufacturer provides the following measurements and accuracy ranges for each variable of Partector 2: LDSA = 0–12,000 ± 30% μm2/cm3, SA = 0–50,000 ± 30% μm2/cm3, d = 10–300 ± 30% nm, PNC = 0–106 ± 30% pt/cm3, and PM0.3 = 0–1000 ± 50% μg/m3. The displayed LDSA value is only accurate in the size range of 20–400 nm; however, the instrument can be used to measure micron-sized particles too. The 20–150 nm uses a fixed deposition voltage, and 10–300 nm uses an adaptive deposition voltage. The particle size range of LDSA is from 10 nm to 10 μm, and for size distribution, it has 8 channels between 10 and 300 nm. The noise floor is about 0.5 μm2/cm3 in particle-free air for LDSA.
Partector 2 uses diffusion charging of airborne particles, and the resulting measurement of acquired particle charge is used to compute LDSA and from this metric, to estimate particle number and particle size. When there is a temporal charge gradient in the induction stage, a current spike is induced with a magnitude proportional to the charge gradient. The total current induced by particle-borne charges is determined from the current peaks in the induction stage, and the alveolar LDSA concentration is determined by applying a corresponding calibration factor [22,45,46]. The instrument achieves a resolution of 1 s for particles larger than 10 nm. Partector 2 comprises a unipolar emission charger, an ion trap, and an induction stage for detecting the particle charge. The flow rate of the sample was 0.5 L per minute.
For PM concentrations, the ‘DustTrak™ DRX Aerosol Monitor 8533’ (TSI Incorporated, 500 Cardigan Road, Shoreview, MN, USA) was used to simultaneously measure both mass and size fractions of aerosols. The DustTrak is a 90° light scattering instrument that measures both particulate size fraction (PM1, PM2.5, PM4, PM10) and mass concentration, and allows real-time aerosol monitoring. The mass concentration range is from 0.001 to 150 mg/m3, with an accuracy of ±5%, and the particle size range is 0.1–15 μm [47]. During the sampling, the DustTrak pump flow rate was set at 1.7 L per minute (lpm). The DustTrak was equipped with a 37 mm replaceable mesh filter inside the concealed filter kit within the body. The mesh filter sample was used to analyze the morphology and elemental characteristics of airborne particles under the scanning electron microscope (SEM) ‘Jeol JSM-IT200’ (JEOL, Freising, Germany) integrated with energy dispersive X-ray spectroscopy (EDS). The sputter coating layer of 5 mm thickness was applied to the sample filter and then placed inside the SEM for analysis. The desktop computer connected to the SEM contained the application ‘SEM Operation’. This computer program was used to control all aspects of the equipment. Moreover, the experimental data collected from the field and the laboratory experiments were analyzed using a single variable regression approach. The R-squared value was what determined the strength of the correlations between the variables. The SEM/EDS examination of individual particles yields crucial data regarding the size, shape, quantity, elemental composition, and surface area of the particles. This information is valuable for conducting a comprehensive physicochemical characterization of PM in order to investigate particle deposition and toxicity. The JSM-IT200 is equipped with functionalities that allow for efficient and effortless SEM investigation. With an accelerating voltage of 30 kV, the electron optics system can reach a resolution of 3.0 nm. It is very flexible and can be used for everything from high-resolution observation to EDS analysis. The Specimen Exchange Navi is a user-friendly feature that provides guidance for tasks such as sample loading, area search, and SEM image viewing. The EDS system is fully integrated and consists of a silicon-drift detector with a resolution of 130 eV. It also contains a feature called “live EDS Analysis”, which allows for the simultaneous presentation of the chemical composition of the specimen while imaging. The “Zeromag” function simplifies sample navigation, making it easier to focus on research points on the sample. The length, width, and height of the sample size are limited to 3 cm × 3 cm × 2 cm, respectively [48,49].

2.3. Data Analysis Tools

Regression analysis is employed to examine the impact of independent variables on dependent variables, and to compute the numerical values of the dependent variables [50]. Based on the quantity of independent variables and the data analysis conducted in this experiment, single variable linear regression and nonlinear power regression models were adopted. The mathematical representation of the linear regression model is as follows:
y = mx + b
where y is the dependent variable, x is the independent variable, m is the estimated slope, and b is the estimated intercept. The mathematical expression of the nonlinear power regression model is as follows:
Y = axb
where y is the dependent variable, x is the independent variable, and a and b are constants determined by the regression analysis.
The coefficient of determination R2 is used to measure the quality of the fit in regression analysis. The number of variables refers to the proportion of the dependent variables’ variability that is explained by the regression equation. It is one of the measures used to assess the adequacy of the fit [51]. The coefficient of determination, R2, varies between 0 and 1, with a higher R2 number indicating a stronger correlation. The R2 can be calculated as follows:
R2 = [1 − SSR/SST]
where SSR is the sum of squared regression, and SST is the total sum of squares, the distance of the data away from mean all squared [52]. Monte Carlo simulation is used for building models demonstrating possible outcomes of LDSA and PM1 at Zone 2 and Zone 3 by considering the instrument’s accuracy. The distribution of probability, which is a range of values, replaces any uncertainty factor when producing the models. After that, numerous computations of the outcomes are produced, at every turn establishing a variety of stochastic variables for the probability functions. Monte Carlo modeling helps to determine the value distribution of the probable numbers with several likely outcome values. For this study, Monte Carlo modeling uses input results from program evaluation and review technique (PERT) distributions [53,54,55]. Palisade@Risk 8.5 software is used for Monte Carlo modeling. The Weibull distribution for fit comparison of LDSA and PM values for each location of the measurements is proposed [56].

3. Results

3.1. Measurements Results

Figure 3 demonstrates the measurement results of PM, LDSA, SA, PNC, and diameter in three zones.
In Zone 1, measurements were produced over 217 min with a sampling interval of 1 min. All PM1 and PM2.5 showed nearly close values; however, PM10 and PMtotal have peaks because of some disturbance from other mine activities (opening and closing air doors, diesel vehicles, etc.). PM0.3 measured by the Partector is similar to averaged PM1, but the curve has some drops contradictory to the peaks of PMtotal. However, LDSA and PNC were stable apart from those external disturbances. Surface area concentration has similar drops compared to PM0.3 readings, as the same instrument ‘Partector 2’ produced it.
In Zone 2, the instruments were placed in the operational room (Figure 1) for a total sampling time of 356 s, but only for 150 s did the loader produce its work in the face. This was done for safety reasons: leave instruments alone in the mine’s working face and do not restrict the loader’s maneuvers and work. PM has peaks at the time of 80 s and at 210 s when the loader entered and exited the room. These peaks can be interpreted as oil shale dust. Then, the loader’s diesel engine was running at its nominal working rate of 2300 RPM (revolutions per minute) to produce the highest diesel emission concentration that can be used to estimate DPM. This can be observed in Figure 3, Zone 2, where peaks of LDSA and PNC appeared on the interval from 95 to 203 s. At the same time, the particle diameter dropped.
In Zone 3, the instruments were allocated to the downstream air, about 8 m away from the dumping point where the oil shale material was dumped on the conveyor. The total sampling duration was 424 s with four dumping cycles, whose peaks (about 2–7 s per dump) can be seen in Figure 1, Zone 3. Dumping was carried out on the time interval of 62–67 s, 115–118 s, 178–181 s, and 337–341 s. Between the peaks, the LDSA and the PNC were not too different from the regular mine air atmosphere in comparison with the incoming fresh air measurements in Zone 1, thanks to the sufficient mine ventilation.
Table 1 and Table 2 below present measurement data statistics showing minimum, mean, and maximum values. Full descriptive statistics are presented in Appendix A. In Zone 3, all PM is very high compared to Zone 2. Zone 3 minimum PM numbers are very close to each other; however, maximum values differ considerably due to dumping peaks associated with high dust dispersions. However, Zone 2 has the highest maximum values for the LDSA, the surface area, and the PNC.
From Figure 3, Zone 2, it can be observed that the data obtained from the loader diesel engine emissions created particles with a diameter of 30–70 nm. Particle diameters over 90 nm are mostly received from the dumping peaks, and are mostly associated with oil shale dust, which consists of over 80% of carbon particles, as per the results obtained from the filter SEM/EDS analysis.

3.2. Filter Analysis by SEM/EDS (Jeol JSM-IT200)

The SEM/EDS apparatus tested the mesh filter used in the DustTrak instrument. The results showed that the mesh filter contained four elements in total. The mesh filter morphology image was observed at a scale of 10 µm (Figure 4a) and showed the carbon particles. These particles are mostly clustered and irregular in symmetry. Most of the particles are spherical in shape and agglomerated. Assuming that the DPM particle size is smaller than one micrometer, most of the particles in Figure 4a can be related to oil shale dust particles. Figure 4b displays the elemental mass concentration and highlights the SEM/EDS peaks for each element, along with associated intensity counts and energy dispersive X-ray.
The SEM/EDS analysis of the filter revealed a diverse elemental composition, with four elements identified. Carbon and oxygen emerged as the predominant elements, constituting the major elemental composition. Notably, gold (Au) and copper (Cu) were also detected, albeit in traces, each contributing to less than 2% of the total mass concentration. Consequently, these two elements were excluded from further analysis due to their minimal impact on the overall composition. This elemental distribution pattern remained consistent across various monitoring points on the sample filter.

3.3. Data Analysis

Relationships between PM and LDSA were produced using the linear regression model with a single explanatory variable. Results for Zone 2 are presented in Figure 5, and demonstrate the best R2 = 0.52 for PM1 compared to other PM. For this relationship in Zone 2, the data used only the diesel engine exhaust, when it worked on its nominal RPM.
However, from Figure 6, it can be observed that the highest R2 = 0.46 is for PM10, and PM1 has R2 = 0.37.
This happened in Zone 3 due to some high peaks associated with the dumping emissions; thus, the relationship can be better presented through nonlinear trend lines. These results can be found in Appendix A.

3.4. Particle Diameter Analysis

The time-series trends of the PM concentration in Zone 3 demonstrated spikes considerably higher than the baseline PM concentration in Zone 2, which measured diesel exhaust gases related to DPM emissions. Particle diameter ranged from 30 to 90 nm (Zone 2), while during the dumping peaks it varied from 90 to 160 nm (Zone 3). Figure 7 provides particle number concentration at particle diameter distributions measured at Zone 2 and Zone 3. Colored areas show different locations (Zone 1 and Zone 2) and estimated sources of emission (purple—DPM, orange—oil shale dust particles).
Figure 8 demonstrates results obtained from the Monte Carlo simulation to show possible outcomes of particle diameters 34–70 nm for DPM and 92–142 nm for oil shale particulates.
For Monte Carlo simulation of LDSA and PM1 value distribution by considering the instrument’s accuracy propagation as a main variability, a PERT distribution for min, max, and weighted average values were utilised. Fit comparisons with the Weibull distribution were produced. LDSA outcomes for Zone 2 are presented in Figure 8, where values are estimated in a range of 1154–1700 µm2/cm3, and PM1 in Figure 9 in a range of 198–408 μg/m3. For Zone 3, LDSA estimated in a range of 440–1097 µm2/cm3 (Figure 10), and PM1 in a range of 7147–17,142 μg/m3 (Figure 11).
Zone 2 is characterized by more stable data, and is therefore pretty close to a normal distribution. A probability bin limit of 0.001 was chosen to fit the Partector 2 instrument accuracy interval to obtain at least 70% confidence. For DustTrak, a 90% confidence level has been used.
Zone 3 has spikes associated with dumping operations, and therefore values have higher deviations.
For Zone 3, a probability bin limit of 0.0008 was chosen for LDSA (Figure 12).

4. Discussion

4.1. Compliance of the Results Obtained with the Research Hypothesis

The relationship between PM and LDSA concentrations measured by two different instruments does not have a great R2 factor, and it might not be recommended to use one instrument separately for measurements. Partector 2 demonstrated its reliability to be used for measurements of gaseous aerosols, i.e., DPM, and DustTrak is more suitable for solid particles, i.e., oil shale dust particulates. This can be applied to coal particulates as well. By these experimental measurements, particle diameter distributions in relation to PNC have been produced, and showed that a particle diameter of 30–90 nm is related to DPM, and a diameter of 90 to 160 nm is associated with oil shale dust particulates.
Because the diffusion charger utilised by Partector 2 is not a reference method to calculate PNC, the Condensation Particle Counter (CPC) model 3007, TSI, USA, was used as a reference monitor for collation measurements to obtain a calibration factor [23]. The diffusion charging method showed a reasonable correlation with the reference CPC if solid particles exist. However, the weakest part of this method is the assumption that solid and volatile particles are in agglomeration mode. To prove this, the particle size distribution will be required [23].
Braakhuis et al. (2014), Kuuluvainen et al. (2016), Kuula et al. (2020), Tran et al. (2020), Afshar-Mohajer et al. (2020), and Huynh et al. (2018) [16,17,18,19,20,21] derived that diesel particulates occur in two size modes, where <1.0 μm single particles are dominant, and about 10% are >1.0 μm due to agglomeration. Therefore, it is difficult to determine the total amount present if mixed with coal, as it is combustible carbon-based. Thus, based on the above-mentioned research studies’ outcomes, 0.8 μm can provide an approximate split between DPM and carbon dust [16,17,18,19,20,21]. According to our measurements, the split is close to 0.01 μm, which is relevant for the nuclei mode of diesel engine exhaust gases. To confirm these hypotheses, the elemental or total carbon filter measurements need to be produced, and mass size distributions should be calculated.
LDSA in Zone 2 during the high RPM of the diesel loader engine (DPM stage) reached an average concentration of 1455 μm2/cm3, and in Zone 3 within the dumping peaks, it reached an average concentration of about 830 μm2/cm3. However, in Zone 3, excluding the dumping peaks, the averaged LDSA was around 370 μm2/cm3. Compared to other studies of LDSA values, this study’s results highlight the high emissions associated with DPM at high diesel engine RPM during the loading process, and the high dump peaks (oil shale dust) in the dumping process.

4.2. Limitations of this Study and Generalization of Its Results

This investigation was restricted to the quantification of black carbon, elemental or total carbon, particle size distribution, and time duration; therefore, it is not feasible to apply these results to establish standard exposure limits. Gaining insight into particulate matter morphology is valuable due to its direct correlation with elemental composition [57]. The morphology of particles, which significantly affects toxicity due to the increase in the sorption potential of dust particles with increasing surface area, has not been discussed in this study, due to limited experimental measurements. The toxicity of dust particles depends on their origin, shape, reactivity, surface chemistry, and charge. If the shape of a particle changes, affecting the diffusion speed, then the hydrodynamic size will also change. Anthropogenic particles are characterized by more spherical shapes due to combustion processes [13,58,59,60,61]. Particle size and shape of samples could result from the presence of different mineralogical phases [25]. Many researchers have stated that particle shape may have great impact on its toxicity [13,58,59,60,61]. Filter sampling duration was produced in a short time interval, and therefore did not show the entire chemical composition. This could be one of the primary reasons for the chemical composition’s homogeneity in carbon and oxygen elements. Particle size distribution analysis by selecting representative images from SEM should be performed. Proper sampling of DPM will require PTFE filters (25 mm or 37 mm) to collect elemental composition, and quartz filters (37 mm) to collect carbonaceous components.
The complexity of determining UFPs could result in greater errors in measurements. The unknown shape of the particle size distribution plays a significant role in determining the measurement uncertainty of Partector 2. It is calibrated for lognormal particle size distributions with a geometric standard deviation of 1.9, which is a reasonable assumption for many environments. In the case of measurements of particles of only a specific diameter (monodisperse particles), the Partector 2 diameter determination will be inaccurate, as will the number, surface, and mass determination.
The accuracy and precision of the measured and corrected DustTrak measurements were not evaluated against the reference measurements due to the absence of gravimetric analysis. Thus, correction factors need to be determined and applied to the results. Li et al. (2019) reported correction factors (0.31–0.43) for DustTrak 30-min PM2.5 at RH conditions (50–90%) [62]. DustTrak underestimated PM10 mass concentrations (24 h average) by about 20% compared to the reference gravimetric method. DustTrak PM10 measurements have better accuracy (19.83%) but lower precision (R2 = 0.73) than PM2.5 measurements. Particularly, the gravimetric method’s measurement of DustTrak PM10 readings at higher PM10 levels (>200 g m−3) reveals relatively low precision [63].

4.3. Proposals for Practical Application

LDSA, as a rather new metric, is still rarely included in long-term air quality monitoring networks. LDSA can be alternatively calculated by lung deposition models and particle size distributions [18,56]; however, measurements of the full spectrum of particle size distributions are not common either. Increased peak concentrations during the dumping process and personal exposure to toxic aerosols are, therefore, miners’ health concerns. Based on the study outcomes, it is proposed to use direct-reading aerosol instruments to monitor real-time exposures to miners and for air quality control. A better design and planning of auxiliary ventilation and the relocation of miners cleaning the conveyor belt away from the dumping point may reduce personal exposure to freshly emitted airborne particles. Carrying out a working shift assessment of LDSA exposure to miners by recording individual time–activity exercise can be recommended. Such a universally applicable personal exposure assessment will help with mine site measurements in the context of a miner’s health risk assessment and miners’ well-being improvement. Electric loaders can be proposed to be used to improve mine air quality with miners’ health benefits. Global ramifications for occupational safety regulations and public health policies can result from a more detailed study of LDSA in terms of cross-industry applications.

4.4. Suggestions for Future Research Directions

Future studies on particle matter in oil shale mines can be focused on exposure time, particle nuclei mode and agglomeration mode, and long-term impact estimation on the human body. Exposure limits should be elaborated and represented for nanoparticles, and therefore LDSA can be an option to use as a measure to demonstrate health relevance. Work out methodologies to represent LDSA as an indicator for air quality monitoring in relation to nanoparticles, and recommend setting up exposure standards for miners.

5. Conclusions

Analyses of PM and LDSA concentrations in the operational workings of the oil shale underground mine revealed that PM and LDSA levels fluctuated significantly during loading and unloading. Variations of particle diameter for DPM and oil shale and LDSA and PM1 values distribution for consideration of the instrument’s accuracy variability have been run with the help of Monte Carlo simulation to demonstrate possible outcomes using a PERT distribution. A relationship between PNC and particle diameter distribution was produced, and demonstrated an approximate split between DPM and oil shale dust. This study offers important data on PM and LDSA concentrations that can be used to estimate potential exposure to miners at various working operations in the oil shale underground mines, and will be used for air quality control in accordance with establishing toxic aerosol health effects. Exposure limits to nanoparticles and LDSA can be an option to use as a measure to demonstrate health relevance.

Author Contributions

Conceptualization, S.S.; methodology, S.S.; software, S.S.; validation, S.S.; formal analysis, S.S.; investigation, S.S.; resources, S.S.; data curation, S.S.; writing—original draft preparation, S.S., R.K., A.R.Q. and G.K.; writing—review and editing, S.S.; visualization, A.R.Q.; supervision, S.S.; project administration, S.S.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Nazarbayev University Grant Programs: Research Grant #20122022FD4128 and Collaborative Research Project #091019CRP2104.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

Acknowledgments

This study is supported by Nazarbayev University Grant Programs.

Conflicts of Interest

The authors declare no conflict of interest.

Correction Statement

This article has been republished with a minor correction to the Funding statement. This change does not affect the scientific content of the article.

Appendix A

The relationships between PNC, PM1, PM10, and PM0.3 were examined through best fit trend lines. In Zone 1, results revealed low correlations with R2 = 0.0004 for PNC and PM1 and R2 = 3 × 10−5 for PM1 and PM0.3. Conversely, a notable correlation R2 = 0.84 between PNC and PM0.3 was observed. It should be noted that PNC and PM0.3 data were received from the same instrument (Partector 2) and demonstrated its reliability for such measurements in the oil shale underground mine atmosphere. In Figure A1, Zone 2 showed better correlations for PNC and PM1 with R2 = 0.57, and for PM1 and PM0.3 with R2 = 0.55; however, there are some deviations to R2 = 0.62 for PNC and PM0.3 compared to Zone 1. Zone 3 demonstrated correlations for PNC and PM1 with R2 = 0.32, some decreases of R2 = 0.12 for PM1 and PM0.3, and R2 = 0.31 for PNC and PM0.3 (Figure A1).
In addition, LDSA was compared to PNC, PM0.3, PM1, and PMtotal showed a good correlation in Zone 2 for LDSA/PNC with R2 = 0.95, LDSA/PMtotal with R2 = 0.49, LDSA/PM0.3 with R2 = 0.77, and LDSA/PM1 with R2 = 0.64. Zone 3 LDSA/PNC has R2 = 0.72, LDSA/PMtotal has R2 = 0.49, and LDSA/PM0.3 has R2 = 0.55. LDSA/PM1 has R2 = 0.45 (Figure A2).
Considering that in Zone 2, measurements were mostly related to DPM, and the Partector instrument was more sensitive for sampling fine particles, the PNC and LDSA demonstrated the best relationships. In Zone 3, dumping created dust particles, thus the LDSA/PMtotal has a better correlation than in Zone 2.
Atmosphere 15 00200 g0a1
Figure A1. Relationships between PNC, PM1, and PM0.3 in Zone 2 and Zone 3.
Figure A1. Relationships between PNC, PM1, and PM0.3 in Zone 2 and Zone 3.
Atmosphere 15 00200 g0a1
Atmosphere 15 00200 g0a2
Figure A2. Relationships between LDSA, PNC, PM1, PM10, PM0.3, and PM total in Zone 2 and Zone 3.
Figure A2. Relationships between LDSA, PNC, PM1, PM10, PM0.3, and PM total in Zone 2 and Zone 3.
Atmosphere 15 00200 g0a2
Table A1. Descriptive statistic for DustTrak (Zone 1).
Table A1. Descriptive statistic for DustTrak (Zone 1).
ParametersPM1 (µg/m3)PM2.5 (µg/m3)PM4 (µg/m3)PM10 (µg/m3)PMTotal (µg/m3)
Mean47.8703703749.5555555650.671296353.4953703757.46296296
Standard Error0.2470484850.8073111940.8048044530.8742413391.414774566
Median4748495151
Mode4647484848
Standard Deviation3.63085637311.8650029311.8281615112.8486711620.79285473
Sample Variance13.183118140.7782946139.9054048165.0883506432.3428079
Kurtosis16.2949093986.1918811985.5386059965.3004717.3808661
Skewness3.6244681798.9720577928.9135459657.3942315363.954185538
Range28124125128140
Minimum4445454545
Maximum72169170173185
Sum10,34010,70410,94511,55512,412
Count216216216216216
Largest (1)72169170173185
Smallest (1)4445454545
Table A2. Descriptive statistic for Partector 2 (Zone 1).
Table A2. Descriptive statistic for Partector 2 (Zone 1).
ParametersSurface (µm2/cm3)Mass Conc (µg/m3) PartectorNumber (cm−3)Diam (nm)LDSA (µm2/cm3)
Mean1027.95839437.9726146834,358.178965.30733945124.9018349
Standard Error7.0670478950.471652806413.23874660.4944286110.300598315
Median1053.40539.73532,711.567124.8
Mode940.9139.383495268122.4
Standard Deviation104.34371176.9638702266101.3969757.3001509544.438280932
Sample Variance10,887.6101848.4954885237,227045.0453.2922039519.69833763
Kurtosis7.3378481443.86926244710.535568755.228288191−0.26471414
Skewness−2.39287987−1.747719232.84285011−1.999678410.132200729
Range689.3641.68426404524.2
Minimum501.418.9526,16932112
Maximum1190.7750.6368,80977136.2
Sum22,4094.938278.0374,9008314,23727,228.6
Count218218218218218
Largest (1)1190.7750.6368,80977136.2
Smallest (1)501.418.9526,16932112
Table A3. Descriptive statistic for DustTrak (Zone 2).
Table A3. Descriptive statistic for DustTrak (Zone 2).
ParametersPM1 (µg/m3)PM2.5 (µg/m3)PM4 (µg/m3)PM10 (µg/m3)PMTotal (µg/m3)
Mean189.6207865194.7949438199.6404494212.9747191230.0786517
Standard Error4.3769272034.8908484995.0416307535.1791231655.614386065
Median147149153.5173.5200.5
Mode123139142135147
Standard Deviation82.583697392.2803449295.125298897.71950045105.9320244
Sample Variance6820.067068515.6620599048.8224729549.10076811,221.5938
Kurtosis3.6022276187.3621663168.2117202327.6017609266.375944446
Skewness1.5721356062.1463322412.2331595532.1300413141.943837711
Range539601654659671
Minimum95111112112112
Maximum634712766771783
Sum67,50569,34771,07275,81981,908
Count356356356356356
Largest (1)634712766771783
Smallest (1)95111112112112
Table A4. Descriptive statistic for Partector 2 (Zone 2).
Table A4. Descriptive statistic for Partector 2 (Zone 2).
ParametersSurface (µm2/cm3)Mass Conc (µg/m3) PartectorNumber (cm−3)Diam (nm)LDSA (µm2/cm3)
Mean3246.36615288.8163202224,7692.44159.13202247554.3589888
Standard Error152.92253953.2368440618011.336890.84270415932.60363849
Median1512.56559.99548,678.565173.95
Mode891.8420.5643,90765159.6
Standard Deviation2885.33670561.0726515833,9837.224815.90011028615.1642207
Sample Variance83,25167.9033729.8687711.15489 × 1011252.813506937,8427.0184
Kurtosis−0.3099725034.7340852820.7667231281.734794534−0.369654767
Skewness1.1187055131.8375985281.4303838470.058309851.14080232
Range13,432.3459.9413,405911222109.2
Minimum14,176.08475.4122,57028141.5
Maximum11,55706.3531,618.6113,631611502250.7
Sum35635688,17850921,05119,7351.8
Count14,176.08475.41356356356
Largest (1)743.7815.4713,631611502250.7
Smallest (1)3246.36615288.816320222257028141.5
Table A5. Descriptive statistic for DustTrak (Zone 3).
Table A5. Descriptive statistic for DustTrak (Zone 3).
ParametersPM1 (µg/m3)PM2.5 (µg/m3)PM4 (µg/m3)PM10 (µg/m3)PMTotal (µg/m3)
Mean3823.0566044060.3867924895.50943410040.7169820846.24764
Standard Error191.9699323200.8198256238.8996956500.87347281003.648002
Median262528153365666514,700
Mode101010,800290013,20011,700
Standard Deviation3952.9028434135.1332984919.24581310,313.6160520,666.37723
Sample Variance15,625440.8917,099327.3924,198979.3710,6370675.942,7099147.9
Kurtosis2.8591768232.5250488271.9461137321.6721602481.133770927
Skewness1.5905618131.5219513291.4163974521.3955089471.249761376
Range22,37622,97125,96250,91810,0637
Minimum124129138182363
Maximum22,50023,10026,10051,10010,1000
Sum16,2097617,2160420,7569642,5726488,38809
Count424424424424424
Largest (1)22,50023,10026,10051,10010,1000
Smallest (1)124129138182363
Table A6. Descriptive statistic for Partector 2 (Zone 3).
Table A6. Descriptive statistic for Partector 2 (Zone 3).
ParametersSurface (µm2/cm3)Mass Conc (µg/m3) PartectorNumber (cm−3)Diam (nm)LDSA (µm2/cm3)
Mean2425.99856169.5740801913,9306.113253.70990566372.1228774
Standard Error72.409128731.9783016615669.5396880.61368359612.11371387
Median1895.29556.34598,647.552289.95
Mode#N/A55.7259,56146179.1
Standard Deviation1490.99521640.7357244211,6742.967412.63651866249.4366353
Sample Variance22,23066.7351659.39924413,628920438159.681603862,218.63505
Kurtosis10.6032332914.54372913.2388214220.305374567.931571367
Skewness2.8065572213.2276107232.8111892113.0783817822.383864586
Range10,709.14343.4999,99131201687.3
Minimum10362524,91830160.7
Maximum11,745.4368.5310,248311501848
Sum10,28623.3929,499.4159,06579222,77315,7780.1
Count424424424424424
Largest (1)11,745.4368.5310,248311501848
Smallest (1)1036.2625.0424,91830160.7

References

  1. McDonald, J.D.; Zielinska, B.; Sagebiel, J.C.; McDaniel, M.R.; Mousset-Jones, P. Source apportionment of airborne fine particulate matter in an underground mine. J. Air Waste Manag. Assoc. 2003, 53, 386–395. [Google Scholar] [CrossRef]
  2. Ghose, M.K. Generation and quantification of hazardous dusts from coal mining in the indian context. Environ. Monit. Assess. 2007, 130, 35–45. [Google Scholar] [CrossRef]
  3. Saarikoski, S.; Teinilä, K.; Timonen, H.; Aurela, M.; Laaksovirta, T.; Reyes, F.; Hillamo, R. Particulate matter characteristics, dynamics, and sources in an underground mine. Aerosol Sci. Technol. 2018, 52, 114–122. [Google Scholar] [CrossRef]
  4. Timonen, H.; Teinilä, K.; Aurela, M.; Reyes, F.; Vásquez, Y.; Bloss, M.; Oyola, P.; Hillamo, R.; Asmi, E.; Saarikoski, S. Sources and composition of particulate matter in boreal arctic environment next to an active mining area. Boreal Environ. Res. 2018, 23, 105–125. [Google Scholar]
  5. McDonald, J.D.; Zielinska, B.; Sagebiel, J.C.; McDaniel, M.R. Characterization of fine particle material in ambient air and personal samples from an underground mine. Aerosol Sci. Technol. 2002, 36, 1033–1044. [Google Scholar] [CrossRef]
  6. Straif, K.; Benbrahim-Tallaa, L.; Baan, R.; Grosse, Y.; Secretan, B.; El Ghissassi, F.; Bouvard, V.; Guha, N.; Freeman, C.; Galichet, L.; et al. A review of human carcinogens-Part C: Metals, arsenic, dusts, and fibres. Lancet Oncol. 2009, 10, 453–454. [Google Scholar] [CrossRef] [PubMed]
  7. Leung, C.C.; Yu IT, S.; Chen, W. Silicosis. Lancet 2012, 379, 2008–2018. [Google Scholar] [CrossRef] [PubMed]
  8. Li, S.; Chen, X.; Peng, G.; Han, M.; Guo, Q.; Hou, J.; Gao, B. Research on the Evaluation of Air Quality in Underground Coal Mines Based on a Generalized Contrastive Weighted Comprehensive Scale Index Method. Atmosphere 2023, 14, 1021. [Google Scholar] [CrossRef]
  9. Liu, W.; Zhou, Y. Study of comprehensive evaluation on air quality in mine based on matter element model. Miner. Resour. Dev. 2015, 35, 61–63. (In Chinese) [Google Scholar]
  10. Patra, A.K.; Gautam, S.; Kumar, P. Emissions and human health impact of particulate matter from surface mining operation—A review. Environ. Technol. Innov. 2016, 5, 233–249. [Google Scholar] [CrossRef]
  11. Saarikoski, S.; Salo, L.; Bloss, M.; Alanen, J.; Teinilä, K.; Reyes, F.; Timonen, H. Sources and characteristics of particulate matter at five locations in an underground mine. Aerosol Air Qual. Res. 2019, 19, 2613–2624. [Google Scholar] [CrossRef]
  12. Roy, D.; Singh, G.; Gosai, N. Identification of possible sources of atmospheric PM 10 using particle size, SEM-EDS and XRD analysis, Jharia Coalfield Dhanbad, India. Environ. Monit. Assess. 2015, 187, 680. [Google Scholar] [CrossRef]
  13. Puławska, A.; Manecki, M.; Flasza, M.; Styszko, K. Origin, distribution, and perspective health benefits of particulate matter in the air of underground salt mine: A case study from Bochnia, Poland. Environ. Geochem. Health 2021, 43, 3533–3556. [Google Scholar] [CrossRef] [PubMed]
  14. Akinyemi, S.A.; Oliveira, M.L.; Nyakuma, B.B.; Dotto, G.L. Geochemical and morphological evaluations of organic and mineral aerosols in coal mining areas: A case study of Santa Catarina, Brazil. Sustainability 2022, 14, 3847. [Google Scholar] [CrossRef]
  15. Diesel Particulate Matter Exposure of Underground Coal Miners. (2001, January 19). Federal Register. Available online: https://www.federalregister.gov/documents/2001/01/19/01-995/diesel-particulate-matter-exposure-of-underground-coal-miners (accessed on 17 December 2023).
  16. Afshar-Mohajer, N.; Foos, R.; Volckens, J.; Ramachandran, G. Variability of aerosol mass and number concentrations during taconite mining operations. J. Occup. Environ. Hyg. 2020, 17, 1–14. [Google Scholar] [CrossRef] [PubMed]
  17. Braakhuis, H.M.; Park, M.V.; Gosens, I.; De Jong, W.H.; Cassee, F.R. Physicochemical characteristics of nanomaterials that affect pulmonary inflammation. Part. Fibre Toxicol. 2014, 11, 18. [Google Scholar] [CrossRef]
  18. Kuula, J.; Kuuluvainen, H.; Niemi, J.V.; Saukko, E.; Portin, H.; Kousa, A.; Aurela, M.; Rönkkö, T.; Timonen, H. Long-term sensor measurements of lung deposited surface area of particulate matter emitted from local vehicular and residential wood combustion sources. Aerosol Sci. Technol. 2020, 54, 190–202. [Google Scholar] [CrossRef]
  19. Kuuluvainen, H.; Rönkkö, T.; Järvinen, A.; Saari, S.; Karjalainen, P.; Lähde, T.; Pirjola, L.; Niemi, J.V.; Hillamo, R.; Keskinen, J. Lung deposited surface area size distributions of particulate matter in different urban areas. Atmos. Environ. 2016, 136, 105–113. [Google Scholar] [CrossRef]
  20. Huynh, T.; Ramachandran, G.; Quick, H.; Hwang, J.; Raynor, P.C.; Alexander, B.H.; Mandel, J.H. Ambient fine aerosol concentrations in multiple metrics in taconite mining operations. Ann. Work. Expo. Health 2018, 63, 77–90. [Google Scholar] [CrossRef] [PubMed]
  21. Tran, P.T.M.; Ngoh, J.R.; Balasubramanian, R. Assessment of the integrated personal exposure to particulate emissions in urban micro-environments: A pilot study. Aerosol Air Qual. Res. 2020, 20, 341–357. [Google Scholar] [CrossRef]
  22. Salo, L.; Rönkkö, T.; Saarikoski, S.; Teinilä, K.; Kuula, J.; Alanen, J.; Keskinen, J. Concentrations and size distributions of particle lung-deposited surface area (LDSA) in an underground mine. Aerosol Air Qual. Res. 2021, 21, 200660. [Google Scholar] [CrossRef]
  23. Jafarigol, F.; Sabanov, S.; Magauiya, N.; Dauitbay, Z.; Qureshi, A.R.; Adotey, E.; Salmanimojaveri, M.; Torkmahalleh, M.A. Particle number, mass, and surface area concentrations inside an underground metalliferous mine in Kazakhstan. Atmos. Pollut. Res. 2023, 14, 101871. [Google Scholar] [CrossRef]
  24. Delfino, R.J.; Sioutas, C.; Malik, S. Potential role of ultrafine particles in associations between airborne particle mass and cardiovascular health. Environ. Health Perspect. 2005, 113, 934–946. [Google Scholar] [CrossRef]
  25. Schiavo, B.; Morton-Bermea, O.; Meza-Figueroa, D.; Acosta-Elías, M.; González-Grijalva, B.; Armienta-Hernández, M.A.; Claudio Inguaggiato, C.; Valera-Fernández, D. Characterization and Polydispersity of Volcanic Ash Nanoparticles in Synthetic Lung Fluid. Toxics 2023, 11, 624. [Google Scholar] [CrossRef]
  26. Kalaiarasan, G.; Kumar, P.; Tomson, M.; Zavala-Reyes, J.C.; Porter, A.E.; Young, G.; Sephton, M.A.; Abubakar-Waziri, H.; Pain, C.C.; Adcock, I.M.; et al. Particle Number Size Distribution in Three Different Microenvironments of London. Atmosphere 2024, 15, 45. [Google Scholar] [CrossRef]
  27. Wang, J.; Meng, X.; Ma, X.; Xiao, Q.; Liu, B.; Zhang, G. Experimental study on whether and how particle size affects the flame propagation and explosibility of oil shale dust. Process Saf. Prog. 2019, 38, e12075. [Google Scholar] [CrossRef]
  28. Wang, Z.; Li, S.; Ren, T.; Wu, J.; Lin, H.; Shuang, H. Respirable dust pollution characteristics within an underground heading face driven with continuous miner–A CFD modelling approach. J. Clean. Prod. 2019, 217, 267–283. [Google Scholar] [CrossRef]
  29. Lepistö, T.; Kuuluvainen, H.; Lintusaari, H.; Kuittinen, N.; Salo, L.; Helin, A.; Niemi, J.V.; Manninen, H.E.; Timonen, H.; Jalava, P.; et al. Connection between lung deposited surface area (LDSA) and black carbon (BC) concentrations in road traffic and harbour environments. Atmos. Environ. 2022, 272, 118931. [Google Scholar] [CrossRef]
  30. Salo, L. Characterization and Development of Electrical Methods for Aerosol Measurement. 2023. Available online: http://urn.fi/URN:ISBN:978-952-03-2930-3 (accessed on 16 June 2023).
  31. Gratt, L.B. Risk analysis of hazardous materials in oil shale. J. Hazard. Mater. 1985, 10, 317–350. [Google Scholar] [CrossRef]
  32. Liu, T.; Liu, S. The impacts of coal dust on miners’ health: A review. Environ. Res. 2020, 190, 109849. [Google Scholar] [CrossRef]
  33. Chang, P.; Xu, G. A review of the health effects and exposure-responsible relationship of diesel particulate matter for underground mines. Int. J. Min. Sci. Technol. 2017, 27, 831–838. [Google Scholar] [CrossRef]
  34. Widodo, N.P.; Putra, S.H.; Ihsan, A.; Gautama, R.S.; Cheng, J.; Waly, F.Z.; Permadi, D.A. The study of coal dust minimum explosion concentration of subbituminous coal. Process Saf. Environ. Prot. 2023, 177, 1387–1392. [Google Scholar] [CrossRef]
  35. Jin, H.; Li, S.; Yu, H.; Yuan, L.; Zhou, F.; Tan, Z. Filtration of dust particles in underground coal mines. Powder Technol. 2023, 423, 118506. [Google Scholar] [CrossRef]
  36. Dyni, J.R. Geology and Resources of Some World Oil-Shale Deposits. Scientific Investigations Report 2005–5294, USGS. 2003. Available online: https://pubs.usgs.gov/sir/2005/5294/ (accessed on 1 June 2006).
  37. Sabanov, S.; Qureshi, A.R.; Dauitbay, Z.; Kurmangazy, G. A Method for the Modified Estimation of Oil Shale Mineable Reserves for Shale Oil Projects: A Case Study. Energies 2023, 16, 5853. [Google Scholar] [CrossRef]
  38. Meng, X.; Liu, Y.; Wang, J.; Wang, Z.; Yan, K. Experimental study and kinetic analysis on the deflagration characteristics of oil shale dust. Process Saf. Environ. Prot. 2022, 159, 830–841. [Google Scholar] [CrossRef]
  39. Yu, L.; Li, G.; Liu, W.; Yu, J.; Yuan, C. Experimental investigations on ignition sensitivity of hybrid mixtures of oil shale dust and syngas. Fuel 2017, 210, 1–7. [Google Scholar] [CrossRef]
  40. Teinemaa, E.; Kirso, U.; Strommen, M.; Kamens, R. Atmospheric behaviour of oil-shale combustion fly ash in a chamber study. Atmos. Environ. 2002, 36, 813–824. [Google Scholar] [CrossRef]
  41. Sillanpää, S.; Fung, P.L.; Niemi, J.V.; Kousa, A.; Kangas, L.; Zaidan, M.A.; Timonen, H.; Petäjä, T.; Kulmala, M.; Hussein, T. Long-term air quality trends of regulated pollutants in Helsinki metropolitan area in 1994–2019 and implications to air quality index. Boreal Environ. Res. 2022, 27, 61–79. Available online: http://hdl.handle.net/10138/343590 (accessed on 17 December 2023).
  42. Luoma, K.; Niemi, J.V.; Aurela, M.; Fung, P.L.; Helin, A.; Hussein, T.; Kangas, L.; Kousa, A.; Rönkkö, T.; Timonen, H.; et al. Spatiotemporal variation and trends in equivalent black carbon in the Helsinki metropolitan area in Finland. Atmos. Chem. Phys. 2021, 21, 1173–1189. [Google Scholar] [CrossRef]
  43. Sabanov, S.; Nikitin, O.; Qureshi, A.; Dauitbay, Z. A case study to improve blasting efficiency by the use of emulsion explosives. Oil Shale 2023, 40, 244–259. [Google Scholar] [CrossRef]
  44. Sabanov, S. Analysis of field test work results for the oil shale mine ventilation simulation. Oil Shale 2018, 35, 356–364. [Google Scholar] [CrossRef]
  45. Todea, A.M.; Beckmann, S.; Kaminski, H.; Asbach, C. Accuracy of electrical aerosol sensors measuring lung deposited surface area concentrations. J. Aerosol Sci. 2015, 89, 96–109. [Google Scholar] [CrossRef]
  46. Fierz, M.; Meier, D.; Steigmeier, P.; Burtscher, H. Aerosol measurement by induced currents. Aerosol Sci. Technol. 2014, 48, 350–357. [Google Scholar] [CrossRef]
  47. Kappelt, N.; Russell, H.S.; Fessa, D.; Van Ryswyk, K.; Hertel, O.; Johnson, M.S. Particulate air pollution in the Copenhagen metro part 1: Mass concentrations and ventilation. Environ. Int. 2023, 171, 107621. [Google Scholar] [CrossRef] [PubMed]
  48. Balasurya, S.; Syed, A.; Thomas, A.M.; Marraiki, N.; Elgorban, A.M.; Raju, L.L.; Das, A.; Khan, S.S. Rapid colorimetric detection of mercury using silver nanoparticles in the presence of methionine. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 2020, 228, 117712. [Google Scholar] [CrossRef] [PubMed]
  49. Fatima, S.; Mishra, S.K.; Kumar, U.; Ahlawat, A.; Dabodiya, T.S.; Khosla, D. Role of morphology and chemical composition of PM for particle deposition in human respiratory system: A case study over megacity-Delhi. Urban Clim. 2023, 47, 101344. [Google Scholar] [CrossRef]
  50. Duan, M.; Wang, Y.; Ren, X.; Qu, X.; Cao, Y.; Yang, Y.; Nian, L. Correlation analysis of three influencing factors and the dust production rate for a free-falling particle stream. Particuology 2017, 34, 126–133. [Google Scholar] [CrossRef]
  51. Jose, J.; Patel, J.N.; Patnaik, S. Application of regression analysis in K-SVD dictionary learning. Optik 2015, 126, 2295–2299. [Google Scholar] [CrossRef]
  52. Galoie, M.; Motamedi, A.; Fan, J.; Moudi, M. Prediction of water quality under the impacts of fine dust and sand storm events using an experimental model and multivariate regression analysis. Environ. Pollut. 2023, 336, 122462. [Google Scholar] [CrossRef]
  53. Clark, C.E. The PERT model for the distribution of an activity. Oper. Res. 1962, 10, 405–406. [Google Scholar] [CrossRef]
  54. Lambrigger, D.D.; Shevchenko, P.V.; Wüthrich, M.V. The quantification of operational risk using internal data, relevant external data and expert opinion. J. Oper. Risk 2007, 2, 3–27. [Google Scholar] [CrossRef]
  55. Karwanski, M.; Grzybowska, U. Modeling Correlations in Operational Risk. Acta Physica Polonica A 2018, 133, 1402–1407. [Google Scholar] [CrossRef]
  56. Fung, P.L.; Sillanpää, S.; Niemi, J.V.; Kousa, A.; Timonend, H.; Zaidan, M.A.; Saukko, E.; Kulmala, M.; Petäjä, T.; Hussein, T. Improving the current air quality index with new particulate indicators using a robust statistical approach. Sci. Total Environ. 2022, 844, 157099. [Google Scholar] [CrossRef] [PubMed]
  57. Panda, S.; Nagendra, S.S. Chemical and morphological characterization of respirable suspended particulate matter (PM10) and associated heath risk at a critically polluted industrial cluster. Atmos. Pollut. Res. 2018, 9, 791–803. [Google Scholar] [CrossRef]
  58. Sarver, E.; Keles, C.; Rezaee, M. Beyond conventional metrics: Comprehensive characterization of respirable coal mine dust. Int. J. Coal Geol. 2019, 207, 84–95. [Google Scholar] [CrossRef]
  59. Trecheraa, P.; Morenoa, T.; Córdobaa, P.; Morenoa, N.; Zhuangc, X.; Lic, B.; Lic, J.; Shangguanc, Y.; Kandlerd, K.; Domingueze, A.O.; et al. Mineralogy, geochemistry and toxicity of size-segregated respirable deposited dust in underground coal mines. J. Hazard. Mater. 2020, 399, 122935. [Google Scholar] [CrossRef]
  60. Schriefl, M.A.; Nishida, R.T.; Knoll, M.; Boies, A.M. Characterization of particle number counters based on pulsed-mode diffusion charging. Aerosol Sci. Technol. 2020, 54, 772–789. [Google Scholar] [CrossRef]
  61. Chen, Y.-H.; Nguyen, D.; Brindley, S.; Ma, T.; Xia, T.; Brune, J.; Brown, J.M.; Tsai, C.S.-J. The dependence of particle size on cell toxicity for modern mining dust. Sci. Rep. 2023, 13, 5101. [Google Scholar] [CrossRef]
  62. Li, Z.; Che, W.; Lau, A.K.H.; Fung, J.C.H.; Lin, C.; Lu, X. A feasible experimental framework for field calibration of portable light-scattering aerosol monitors: Case of TSI DustTrak. Environ. Pollut. 2019, 255, 113136. [Google Scholar] [CrossRef]
  63. Javed, W.; Guo, B. Performance Evaluation of Real-time DustTrak Monitors for Outdoor Particulate Mass Measurements in a Desert Environment. Aerosol Phys. Instrum. 2021, 21, 200631. [Google Scholar] [CrossRef]
Atmosphere 15 00200 g001
Figure 1. Schematic layout of the measurement area in the underground mine using the room-and-pillar mining method, and photos from the dumping station with the loader bucket taken by the authors.
Figure 1. Schematic layout of the measurement area in the underground mine using the room-and-pillar mining method, and photos from the dumping station with the loader bucket taken by the authors.
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Figure 2. Chemical composition of oil shale mineral part.
Figure 2. Chemical composition of oil shale mineral part.
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Figure 3. Results of measurements in three zones.
Figure 3. Results of measurements in three zones.
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Figure 4. (a) Morphology; (b) elemental mass concentration.
Figure 4. (a) Morphology; (b) elemental mass concentration.
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Figure 5. Linear relationships of LDSA and PM for Zone 2.
Figure 5. Linear relationships of LDSA and PM for Zone 2.
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Figure 6. Linear relationships of LDSA and PM for Zone 3.
Figure 6. Linear relationships of LDSA and PM for Zone 3.
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Figure 7. Particle number concentration and particle diameter distributions (purple—DPM from Zone 2, orange—carbon particles from oil shale from Zone 3).
Figure 7. Particle number concentration and particle diameter distributions (purple—DPM from Zone 2, orange—carbon particles from oil shale from Zone 3).
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Figure 8. Variations of particle diameter for DPM and oil shale.
Figure 8. Variations of particle diameter for DPM and oil shale.
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Figure 9. Fit comparison of LDSA in Zone 2.
Figure 9. Fit comparison of LDSA in Zone 2.
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Figure 10. Fit comparison of PM1 in Zone 2.
Figure 10. Fit comparison of PM1 in Zone 2.
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Figure 11. Fit comparison of LDSA in Zone 3.
Figure 11. Fit comparison of LDSA in Zone 3.
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Figure 12. Fit comparison of PM1 in Zone 3.
Figure 12. Fit comparison of PM1 in Zone 3.
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Table 1. Measurements data basic statistics (Partector 2).
Table 1. Measurements data basic statistics (Partector 2).
NameValueZone 1Zone 2Zone 3
LDSA (µm2/cm3)min112142161
mean125554372
max13622511848
STDV0.332.60312.113
Surface area (µm2/cm3)min5017441036
mean102832462426
max119114,17611,745
STDV7.06152.92272.409
PNC (pt/cm3)min26,16922,57024,918
mean34,355247,673139,306
max68,809963,161924,831
STDV41318,0115669
Diameter (nm)min322830
mean655954
max77150150
STDV0.4940.8420.613
PM0.3 (µg/m3)min91525
mean388970
max51475369
STDV0.4713.2361.978
Table 2. Measurements data basic statistics (DustTrak DRX).
Table 2. Measurements data basic statistics (DustTrak DRX).
NameValueZone 1Zone 2Zone 3
PM1
(µg/m3)		min4495124
mean481903823
max7263422,500
STDV0.2474.376192
PM2.5
(µg/m3)		min45111129
mean501954060
max16971223,100
STDV0.8074.890201
PM4
(µg/m3)		min45112138
mean512004896
max17076626,100
STDV0.8045.041239
PM10
(µg/m3)		min45112182
mean5421310,041
max17377151,100
STDV0.8745.179501
PMtotal
(µg/m3)		min45112363
mean5723020,846
max185783101,000
STDV1.4145.6141004
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MDPI and ACS Style

Sabanov, S.; Qureshi, A.R.; Korshunova, R.; Kurmangazy, G. Analysis of Experimental Measurements of Particulate Matter (PM) and Lung Deposition Surface Area (LDSA) in Operational Faces of an Oil Shale Underground Mine. Atmosphere 2024, 15, 200. https://doi.org/10.3390/atmos15020200

AMA Style

Sabanov S, Qureshi AR, Korshunova R, Kurmangazy G. Analysis of Experimental Measurements of Particulate Matter (PM) and Lung Deposition Surface Area (LDSA) in Operational Faces of an Oil Shale Underground Mine. Atmosphere. 2024; 15(2):200. https://doi.org/10.3390/atmos15020200

Chicago/Turabian Style

Sabanov, Sergei, Abdullah Rasheed Qureshi, Ruslana Korshunova, and Gulim Kurmangazy. 2024. "Analysis of Experimental Measurements of Particulate Matter (PM) and Lung Deposition Surface Area (LDSA) in Operational Faces of an Oil Shale Underground Mine" Atmosphere 15, no. 2: 200. https://doi.org/10.3390/atmos15020200

APA Style

Sabanov, S., Qureshi, A. R., Korshunova, R., & Kurmangazy, G. (2024). Analysis of Experimental Measurements of Particulate Matter (PM) and Lung Deposition Surface Area (LDSA) in Operational Faces of an Oil Shale Underground Mine. Atmosphere, 15(2), 200. https://doi.org/10.3390/atmos15020200

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