Exposure Assessment to Respirable Free Silica in Coal Mining Areas: A Health Risk Assessment Approach

Exposure Assessment to Respirable Free Silica in Coal Mining Areas: A Health Risk Assessment Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exposure Assessment to Respirable Free Silica in Coal Mining Areas: A Health Risk Assessment Approach Manish Yadav, Nitin Kumar Singh, Sumit Saha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7308663/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Environmental Geochemistry and Health → Version 1 posted 11 You are reading this latest preprint version Abstract The International Agency for Research on Cancer (IARC) has classified free silica/quartz as a Group 1 carcinogen, indicating sufficient evidence of its carcinogenicity in humans. In the present study, suspended particulate matter (SPM), respirable dust (PM 10 ), and free silica content in dust were assessed to determine the associated exposure risk in three mega coal mines (Bharatpur, Kaniha, and Lingaraj OCP) of the Talcher Coalfield located in Odisha, India. The respirable dust samples collected on the filter paper were analyzed using Fourier transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and Energy Dispersive Spectrometer (EDS) to characterize their composition and morphology. The highest concentrations of SPM and PM 10 were observed at Bharatpur OCP, with a mean value of SPM and PM 10 of 394 µg/m 3 and 136 µg/m 3 , respectively. In contrast, Kaniha OCP exhibited slightly lower concentrations for SPM and higher concentrations of PM 10 , with mean values of 230 µg/m 3 and 193 µg/m 3 , respectively. When compared with Bharatpur OCP, the highest concentration of free silica was observed at Kaniha OCP, with values ranging from 5.94 µg/m 3 to 114.89 µg/m 3 and a mean concentration of 41.59 µg/m 3 . The health risk assessment, conducted using USEPA methodology, indicates that Kaniha OCP poses the highest risks of exposure to respirable silica, with both non-carcinogenic and carcinogenic outcomes, followed by Bharatpur OCP. In contrast, the Lingaraj OCP exhibited comparatively lower health risk levels. The SEM/EDS analysis showed clear evidence of the presence of respirable free silica particles across all three mining sites. Quartz Free silica Coal mining Respirable dust Health risk assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Coal continues to serve as a vital foundation for global energy and industrial development, playing a central role in meeting primary energy demands and supporting human progress with global industrialization ( Azam et al., 2024 ; Wang et al., 2023 ). As a key component of the global energy mix, coal remains indispensable, fulfilling over half of the world’s primary energy demand and sustaining several core industrial sectors. In 2023, the global coal demand surged to an all-time high of 8.68 billion tonnes, primarily driven by increased consumption in China and India, as well as a decline in hydropower output that intensified dependency on coal-based electricity. Of the total demand, 5.85 billion tonnes were consumed by the power sector, while 2.83 billion tonnes were utilized in non-power sectors. This upward trend persisted in 2024, with global demand for coal increasing by 1.2%, primarily fueled by continuous growth in the electricity sector ( IEA 2025 ) . Notably, China and India have set new consumption records, with respective increases of 1.2% and 5.5%, supported by robust economic and industrial expansion ( Mastoi et al., 2022 ). The continued increase in coal production reflects not only growing demand for energy but also underscores an increasingly alarming environmental scenario ( Yadav et al., 2019 ). Coal mining involves a range of intensive activities such as excavation, drilling and blasting, loading and unloading of coal and overburden, operation of heavy vehicles on haul roads, dragline operations, coal crushing in feeder breakers, mine fires, exposure of pit faces, wind erosion, and emissions from heavy earthmoving machinery ( Nair et al., 2023 ; Yadav et al., 2025 ). These activities, followed by the combustion of coal in power plants, release substantial amounts of carbon, thereby contributing to climate change and the emission of hazardous pollutants such as PM 10 , PM 2.5 , NO x , and SO x ( Asif et al., 2022 ). Collectively, these impacts pose serious threats to the environment, diminishing coal’s appeal and prompting many countries to pursue cleaner and more sustainable energy alternatives ( Ma et al., 2021 ). Dust pollution is one of the most pressing environmental concerns associated with coal mining, predominantly manifesting as suspended particulate matter (SPM) and particulate matter with a diameter of 10 microns or less (PM 10 ) (Sharma et al., 2023) . The size of particulate matter is a critical factor in determining its site of deposition within the human respiratory tract and its resulting health impacts. While larger particles (SPM) are generally trapped in the nasal passages and upper respiratory tract, finer particles, PM 10 , can reach deeper regions of the lungs, posing more serious health risks ( Kumar et al., 2024 ). In open-pit coal mines, airborne particulate matter constitutes a complex mixture with varying size, morphology, and composition. Its physical, chemical, and biological characteristics are largely determined by the nature of the coal and the associated geological strata ( Liu et al., 2024 ). The coal mining dust often contains elevated concentrations of toxic elements such as chromium (Cr), cadmium (Cd), manganese (Mn), nickel (Ni), lead (Pb), arsenic (As), zinc (Zn), mercury (Hg), and iron (Fe), thereby amplifying its potential health hazards ( Zhang et al., 2021 ; Gopinathan et al., 2023 ). In addition to particle size, the specific chemical composition of dust plays a crucial role in determining its toxicity. Thus, it is insufficient to regard coal dust as a singular harmful agent. Notably, coal dust is frequently accompanied by crystalline silica (quartz), which is released during the cutting and handling of rock materials ( Yang et al., 2022 ). These fine quartz particles are especially concerning due to their well-established link to severe respiratory illness and their contribution to the explosibility of dust mixtures in mining environments ( Hoy & Chambers, 2020 ) . Prolonged exposure to such dusty conditions significantly increases occupational health risks ( Andraos et al., 2018 ; O’Day et al., 2022 ). Coal mining dust is known to cause chronic respiratory conditions such as chronic bronchitis, emphysema, and pulmonary fibrosis ( Hnizdo & Vallyathan, 2003 ) . More critically, it is a leading cause of occupational diseases such as coal workers’ pneumoconiosis (CWP) and silicosis, both of which have garnered growing global concerns ( Castranova & Vallyathan, 2000 ) . For example, a study reported that by the end of 2021, China alone had recorded 1.026 million cases of occupational diseases had been reported, of which 916,000 were pneumoconiosis cases ( Wang et al., 2023 ). Notably, around 60% of these were identified as CWP, highlighting the severe health burden associated with dust exposure in coal mining operations ( Chen et al., 2024 ). Apart from pneumoconiosis and silicosis, exposure to crystalline silica is also linked to lung cancer ( Yang et al., 2024 ). The International Agency for Research on Cancer (IARC) has classified crystalline silica and cristobalite (a polymorph of crystalline silica) as Group 1 carcinogens, indicating that there is sufficient evidence to confirm that crystalline silica can cause cancer in humans (IARC 1995) . This highlights the importance of assessing free silica concentration across mining areas with varying geological formations of coal and overburden. However, limited research has been conducted in large-scale mega mines with production capacities exceeding 15 million tonnes per year. Additionally, a human health risk assessment has been carried out alongside the study of free silica morphology, which could be further explored in terms of surface chemistry and associated health effects. Coal dust from various parts of the different mines, including coal seams, rock dust, floors, and roofs, may exhibit distinct surface areas, physicochemical characteristics, and varying toxicity levels. With the above background, it is important to conduct extensive monitoring of respirable quartz levels in heavily operated mega mines. In the present study, a comprehensive analysis of free silica (quartz) concentrations was performed using Fourier transform Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM), and Energy Dispersive Spectroscopy (EDS) across coal mines in Odisha, India, with production capacities exceeding 14 million tonnes per year (MTY). To the best of our knowledge, this study represents the first large-scale field investigation conducted in the high-capacity coal mining environments. Furthermore, through this study, we particularly aim to conduct an in-depth monitoring campaign to measure free silica concentration in the coal mining industry, with a particular focus on the health risk assessment. The findings are intended to provide a baseline for future studies aimed at safeguarding coal miners’ health and fostering sustainable mining practices. This effort also opens new avenues for understanding the interactions between free silica-containing coal dust and the surrounding environment. 2. Materials and methods 2.1. Sampling site description For this study, the sampling was carried out at three open-cast coal mines, namely Kaniha OCP (Open Cast Project) with a capacity of 14 MTY, Lingaraj OCP (20 MTY), and Bharatpur OCP (20 MTY), located in the Talcher coalfield, Angul, Odisha (India), as shown in Fig. 1 . The Talcher coalfield forms the southeastern part of the Lower Gondwana basin within the Mahanadi Valley Group of coalfields in Odisha. It extends approximately 80 km along strike (east-west) and 26 km along the dip rise (north-south), covering a coal-bearing area of about 1860 sq. km. According to estimates of the Geological Survey of India (GSI), it is one of the richest coalfields in the country, with a coal reserve of approximately 42.54 billion tonnes (MoC 2025) . The Mahanadi Coalfield Limited (MCL), a state-owned company, is the primary producer in the region, and all three mines studied in this research are under its management. The Talcher region holds great importance for India and is often referred to as the powerhouse of Odisha. Several mega coal mines in this region supply coal to key industries in the vicinity for electricity generation, aluminium production, heavy water plants, and sponge Iron units. Additionally, the region is densely populated, with large numbers of villages located near mining areas. 2.2. Sample collection and analysis In this study, the air sampling at Kaniha OCP, Lingaraj OCP, and Bharatpur OCP was conducted using high-volume respirable dust samplers (RDS). The details of monitoring locations are provided in supplementary material ( Fig. S1 to S3) . The sampling was performed for a 24-hour duration, twice a month, during the following periods: October 2023 to January 2024 for Kaniha OCP, February 2024 to April 2024 for Lingaraj OCP, and March 2024 to May 2024 for Bharatpur OCP. A total of 70 samples were collected during the study period. The Envirotech APM 460 model respirable dust sampler was employed for the sampling and measurements. The ambient air was drawn through a size-selective inlet measuring 20.3 × 25.4 cm at a flow rate of 1132 L/min. Particles with aerodynamic diameter 10 µm were deposited in a separate collection cup (US EPA 1999) . The difference between the initial and final filter weight, divided by the volume of air sampled, was used to determine the PM 10 concentration. The respirable dust collected on the filter paper was analyzed using the Fourier transform Infrared (FTIR) following the direct-on-filter (DOF) method as outlined in the HSE 2005; MDHS101 guidelines. FTIR spectra were recorded using a Thermo Scientific Nicolet iS20 Smart iTX-Diamond ATR-IR spectrometer, scanning in the range of 4000 − 400 cm − 1 with a spectral resolution of 0.25 cm − 1 . The dust-laden filter paper was placed directly in the sample compartment of the FTIR unit for DOF analysis (HSE 2005) . The quartz doublet peaks were observed at 799 cm − 1 ( Pandey et al., 2017 ; Dash et al., 2022) , confirming the presence of crystalline silica. For each sample, the peak height measured was converted to the amount of quartz content (in mg) in the respirable dust deposited on filter paper using the calibration curve established from peak height versus standard quartz (Sikron F600, HSE Standard Quartz). Some of the FTIR peaks have been shown in the supplementary material, Fig. S4-S6 . To ensure the reliability of the experimental analysis, the samples were also analyzed using Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) analysis. The compositional analysis (semi-quantitative) and morphology of compounds were examined using a Zeiss Evo 18 Scanning Electron Microscopy (SEM) with EDX (EDAX Octane Elect EDS) system. 2.3. Risk characterization Risk assessment of toxic metals involves four key steps: hazard identification, exposure assessment, dose-response assessment, and risk characterization. This approach has been widely applied to evaluate the risks posed by toxic metals in soils, water, and air. In this study, risk characterization for both carcinogenic and non-carcinogenic endpoints was performed based on the levels of free silica observed in the collected samples (US EPA 2009) . 2.3.1. Non-cancer risk characterization Non-cancer risks are assessed based on the theory that a threshold level of exposure exists, below which adverse health effects are unlikely. This evaluation involves the use of factors such as the reference dose (RfD), reference concentration (RfC), inhalation reference exposure concentration, or acceptable daily intake (ADI). These values represent the estimated daily exposure levels for the general population, including sensitive subgroups, below which no appreciable risk of adverse health effects is expected over a lifetime. For this study, a reference concentration (RfC) of 3 µg·m⁻³, as recommended by the Office of Environmental Health Hazard Assessment (OEHHA), was used for PM 4 crystalline silica. Further, inhalation risks expressed as hazard quotient (HQ), were estimated by comparing this R f C value with the actual exposure concentration (EC) (OEHHA 2005). The EC in µg·m − 3 was calculated using the following Eq. ( 1 ): $$\:\varvec{E}\varvec{C}=\frac{C\times\:ET\times\:EF\times\:ED}{AT\times\:24}$$ 1 where C is the concentration of silica in air in µg/m 3 , ET is the exposure time in hours/day, EF is the exposure frequency in days/year, ED is the exposure duration in years and AT is the averaging time, which was calculated as exposure duration in years × 306 days/year × 8 h/day. ET and ED were set as 8 h/day and 60 years, respectively. It has been assumed that a person is working for 30 years in a service and is exposed to an 8-hour shift. For the C, two scenarios were anticipated: one is at peak concentration and one is at mean concentration: The risk, expressed as a hazard quotient (HQ), was then calculated using the following Eq. ( 2 ): $$\:\varvec{H}\varvec{Q}=\frac{EC}{RfC}$$ 2 where an HQ value of less than 1 would indicate no possible risk to developing a non-cancer adverse health effect, and an HQ value higher than 1 would indicate a possible risk ( Oosthuizen et al., 2015 ). 2.3.2. Cancer risk characterization The aim of cancer risk characterization is to quantify the excess lifetime cancer risk resulting from inhalation exposure to crystalline silica present in coal mine dust. Excess or incremental lifetime risk is defined as the enhanced probability of cancer occurrence, above background levels, resulting from continuous occupational exposure from an individual’s service period. For inhalation-related carcinogenic effects, risk estimation is based on inhalation unit risk (IUR) values that represent the estimated risk per unit of pollutant concentration. These IURs, derived from toxicological data, are applied to the measured airborne silica concentrations to calculate site-specific cancer risks. Consequently, incremental cancer risks associated with both peak concentration and mean concentration exposure scenarios can be determined using Eq. ( 3 ): $$\:\varvec{C}\varvec{R}\:=\:EC\:\times\:IUR$$ 3 where CR is cancer risk, IUR is expressed in 1/µg·m − 3 (In this case 1.85 x 10 − 5 ), EC is the exposure concentration in µg/m 3 , as calculated from Eq. ( 1 ) using EF (308 days/year) and AT (10950 days). ET and ED were set as 8 h/day and 30 years, respectively. For EF, the two scenarios anticipated in the non-cancer risk calculations were also used for cancer risk calculations. 2.4. HYSPLIT model The transport trajectories of air parcels at the sampling site were computed using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, developed by the United States National Oceanic and Atmospheric Administration (NOAA) ( Rolph et al., 2017 ). This analysis aimed to assess the atmospheric dispersion of silica dust, including its potential travel distance and impact on sensitive receptors. Twenty-four-hour forward air mass trajectories were generated using the HYSPLIT 4 model for each of the three sampling sites. Trajectories were calculated at an initial height of 100 m, 500 m, and 1000 m above ground level to capture vertical variation in dispersion patterns. 3. Results and discussion 3.1. Variation of SPM and PM 10 The particulate matter, namely suspended particulate matter (SPM) and PM 10 , has been collected from the three major surface coal mining projects, which are Bharatpur OCP, Kaniha OCP, and Lingaraj OCP. The project-wise box plot illustrated the distribution of particulate matter concentration, both Suspended Particulate Matter (SPM) and PM₁₀. Figure 2 reveals the significant variability in particulate pollution levels through the box plot. The variability can be linked to the operational practice, production intensity, and micro-meteorology of the area. The present production capacity of Bharatpur OCP, Kaniha OCP, and Lingaraj OCP is 20 million tonnes per year, 14 million tonnes per year, and 20 million tonnes per year, respectively. Among all three mines, Bhratapur OCP shows the highest particulate pollution level as evidenced by the highest maximum and mean values for both SPM and PM₁₀. At Bharatpur OCP, SPM varies from 156 µg/m³ to 927 µg/m³ with a mean value of 394 µg/m³, and PM 10 varies from 136 µg/m³ to 822 µg/m³ with a mean value of 333 µg/m³. In contrast, Kaniha OCP exhibits a notably different plot compared to the Bharatpur OCP. Both SPM and PM₁₀ concentration levels are remarkably lower and more tightly clustered. At Kaniha OCP, SPM varies from 74 µg/m³ to 447 µg/m³ with a mean value of 230 µg/m³, and PM 10 varies from 62 µg/m³ to 405 µg/m³ with a mean value of 193 µg/m³. However, Lingaraj OCP displays a mixed trend. While PM₁₀ concentrations remain moderate and comparable to those observed at Kaniha, as they vary from 106 µg/m³ to 361 µg/m³, with a mean value of 205 µg/m³. The SPM levels are noticeably elevated compared to the Kaniha OCP, as they vary from 165 µg/m³ to 550 µg/m³ with a mean value of 301 µg/m³. The interquartile range for SPM and PM 10 at Bharatpur is notably wide, reflecting highly unstable air quality conditions. This suggests episodic spikes in emissions, potentially attributable to activities such as coal excavation, overburden removal, blasting, unpaved haul road traffic, and wind erosion from exposed surfaces. Although the interquartile range for SPM and PM 10 at Kaniha OCP is very compressed, indicating relatively stable air quality conditions. While some lower outliers exist—particularly for SPM—there is an absence of upper extreme values, reflecting better dust control management by the project authority. This suggests that the emission sources are better managed. While in Lingaraj OCP, the presence of mild outliers further suggests intermittent high-emission events. The discrepancy between PM₁₀ and SPM in Lingaraj OCP shows that a larger fraction of the particulate load is dominated by coarse particles (> 10 µm), likely from ground-level activities such as mechanical excavation, loading, and vehicular movement on unpaved surfaces. Although high value at particular mines can not only be attributed to the mismanagement, other operational factors can also affect the emissions. For instance, in a mine where only coal excavation is carried out, the dust levels are likely to be lower compared to a site where both coal and overburden removal activities are simultaneously conducted. Season also plays an important role in determining the pollutant level ( Shelton et al., 2022 ). In winters, inversion affects the dispersion of the air pollutants, resulting in higher ground-level concentrations ( Largeron & Staquet, 2016 ) . From a regulatory perspective, the reported values, especially at Bharatpur and Lingaraj, often exceed the coal mine standards for PM₁₀ (300 µg/m³, 24-hour average), raising concerns about occupational and public health risks in surrounding communities (MoEF 2000) . Therefore, the results underscore the necessity of targeted pollution mitigation strategies, including water spraying, dust suppressants, green belt development, and the adoption of enclosed material handling systems. Notably, Kaniha OCP—with its relatively controlled pollutant levels—may serve as a benchmark for best practices in emission control within coal mining operations. 3.2. Spatio-temporal variation of PM 10 and SPM The spatiotemporal distribution of PM₁₀ concentrations across various monitoring locations in Kaniha OCP, Lingaraj OCP, and Bharatpur OCP highlights substantial heterogeneity in PM 10 concentration levels over the study period. In Kaniha OCP, a total of five locations, marked in green color, have been selected for monitoring during the study period ( Fig. 3 and supplementary material, Fig. S7) . As per coal mining standards, the permissible limit for PM₁₀ is 300 µg/m³. Two monitoring locations, namely Near Canteen View Point (309 µg/m³) and Near DG generator (406 µg/m³), recorded concentrations exceeding this CPCB-prescribed limit. Both the high values were recorded during the winter season, which is also considered the worst season for air dispersion due to the occurrence of inversion conditions ( Xu et al., 2019 ). Similarly, in Lingaraj OCP, a total of four locations, marked in blue, have been selected for monitoring during the study period. The total three times recorded concentration exceeded the limit for PM 10 . One at Near Sub-station (362 µg/m³) and two times at Weigh bridge (312 & 309 µg/m³). All exceedance occurs during the month of March. The pressure of coal production and its dispatch is highest during the month of March due to the financial ending month, which might have resulted in the high values. In Bharatpur OCP, a total of four locations, marked in red color, have been selected for monitoring during the study period. The values of PM 10 remain under control during the initial study period, then they suddenly rose upto 822 µg/m³ at the BLA camp location. An increase in PM₁₀ levels was observed across all four monitoring locations of Bharatpur OCP during this period. The concentrations recorded were unusually high, even by typical mining area standards. Such elevated levels are likely associated with episodic activities such as large-scale overburden removal or a temporary lapse in dust control measures. A total of 8 times of exceedance of the standard is recorded. The spatio-temporal analysis of SPM concentrations across various monitoring locations in Bharatpur, Kaniha, and Lingaraj OCPs from November 2023 to May 2024 reveals substantial spatial and temporal variability, highlighting the complex dynamics of dust generation and dispersion in open-cast coal mining environments ( Fig. 4 and Fig. S8) . The trends of SPM are similar to the PM 10, but interestingly, there has been 100% compliance with the CPCB coal mine standards at all the locations of Kaniha OCP and Lingaraj OCP. As per coal mining standards, the permissible limit for PM₁₀ is 500 µg/m³. In Bharatpur OCP, the SPM concentrations remained high, similar to PM 10 for May-24 month, where it exceeded the limits across all locations. A dramatic spike was observed in May 2024 at Near Coal Stock (927 µg/m³), Near View Point (907 µg/m³), Near Pump House (859 µg/m³), and BLA Camp (688 µg/m³). These peak values, far exceeding the CPCB’s 24-hour coal mine standards for SPM, signal episodes of intense dust generation likely associated with peak summer operations, heavy vehicular activity, and failure of adequate dust suppression during that period. 3.3. Spatio-temporal variation of free silica (Quartz) Particles from PM 10 samples were used for the determination of free silica, also known as crystalline silica or α-quartz. The box plot shown in the Fig. 5 explains the distribution of free silica concentration for the project, namely Bharatpur OCP, Kaniha OCP, and Lingaraj OCP. Notably, Kaniha OCP shows the highest value and variation of the concentration of free silica. The concentration at Kaniha OCP varies from 5.94 µg/m³ to 114.89 µg/m³ having mean value of 41.59 µg/m³ µg/m³. The value above 75 µg/m³ can be considered an outlier value. This indicates the presence of episodic exposure spikes of free silica due to the failure of control measures like water sprinkling, intense material handling, or the production of coal. The temporal and spatial distribution of free silica (measured in µg/m³) across various monitoring locations in Kaniha OCP reveals significant fluctuations in concentration levels shown in green color ( Fig. 6 ) . In Kaniha OCP, prominent peaks are observed at locations such as Near the DG Generator, Near Canteen View Point, near Z patch-2 ( Fig. S9 ), and Near Stockyard with concentrations reaching as high as 114.89 µg/m³, 92.74 µg/m³, 73.44 µg/m³, and 67.87 µg/m³, respectively, in different periods of time. These spikes may be attributed to increased operations such as production, drilling, material handling, or a lack of effective dust suppression. In contrast, Bharatpur OCP reflects a relatively lower dispersion of free silica values. The means are substantially lower than those in Kaniha, with the majority of data points ranging from 3.68 µg/m³ to 33.38 µg/m³, having a mean value of 9.68 µg/m³. The temporal and spatial distribution of Bharatpur OCP has been shown in the figure with red color. The values at Bharatpur OCP are comparatively very low compared to Kaniha OCP. The only value a little bit high was observed at the Near Pump house, having 33.35 µg/m³. Lingaraj OCP, on the other hand, demonstrates the most compact and least variable free silica distribution. The free silica value varies from 5.54 µg/m³ to 14.57 µg/m³, with the mean value of 10.10 µg/m³, and since the interquartile range is narrow, it indicates a consistently low exposure profile across sampling points. The temporal and spatial distribution of Lingaraj OCP has been shown in Fig. 6 with blue color. These values are very low compared to the Kaniha OCP. The highest value observed was near the sub-station, i.e, 14.25 µg/m³. Interestingly, while SPM and PM₁₀ levels were higher at Bharatpur OCP and Lingaraj OCP compared to Kaniha OCP, the highest concentration of free silica was observed at Kaniha OCP, despite its relatively lower SPM and PM₁₀ levels. This indicates that the elevated silica content at Kaniha is likely due to the geogenic characteristics of the area, which naturally contribute to higher silica dust emissions. Kaniha OCP is located at an aerial distance of approximately 15 to 18 kilometers from Bharatpur and Lingaraj OCPs, which accounts for the variation in mineral formations across these sites. In contrast, Bharatpur and Lingaraj OCPs are only about 2.5 kilometers apart, resulting in similar geological formations between them. Detailed mineralogical composition of one coal sample from all three mines is listed in Table S1 . In India, the maximum exposure limit for respirable dust is set at 3000 µg/m³ for an 8-hour time-weighted average (TWA), provided the free silica content is below 5%. If the silica content exceeds 5%, the MEL is calculated using the formula: 15 divided by the percentage of silica in the respirable dust. In many other countries, the MEL for crystalline silica in respirable airborne dust is significantly stricter, typically 300 µg/m³ (8-hour TWA). However, it is generally considered reasonably practicable to control exposure to 100 µg/m³ or lower using engineering or process controls. DGMS (Director General of Mines Safety) strongly recommends that mine management ensure workers are not exposed to respirable crystalline silica levels exceeding 100 µg/m³ (8-hour TWA) (DGMS 2010; Singh et al., 2022 ). If this level cannot be consistently maintained, additional protective measures should be implemented. Although there are currently no prescribed standards for 24-hour ambient air sampling of free silica, existing limits are primarily designed for occupational exposure. However, in India, many villages are situated in close proximity to active mining areas, often due to challenges related to rehabilitation and resettlement. As a result, the general population—including vulnerable groups—is continuously exposed to potentially hazardous air quality conditions. Free silica is a predominant mineral found in coal dust, and as of this date, it is a well-established fact that prolonged exposure to free silica dust causes serious respiratory and autoimmune diseases, such as silicosis, rheumatoid arthritis, and even cancer. Notably, excessive quartz exposure has been recognized as a key factor in the recent resurgence of pneumoconiosis cases in countries like China. While a comparison with occupational standards shows that free silica concentrations at most monitoring sites remain within the permissible limit of 100 µg/m³, one exception was observed at Kaniha OCP, where the levels exceeded this threshold. Despite this, there is an urgent need for regulatory authorities to establish specific ambient air quality standards for free silica to ensure the health and safety of communities living near mining operations. 3.4. Risk characterization Based on the updated Health Risk Assessment results for inhalation exposure to respirable free silica across three mining sites — Kaniha OCP, Bharatpur OCP, and Lingaraj OCP — the findings indicate varying degrees of non-carcinogenic and carcinogenic risks. The potential non-cancer and cancer risks were assessed for workers and persons living nearby for each of the three projects based on the concentration of free silica in two possible scenarios: For scenario 1, the worst-case was anticipated, i.e., exposure at maximum observed concentration, whereas for scenario 2, exposure only at the mean concentration was anticipated. For worst-case scenarios, all HQ values were well above 1, indicating that all projects, under maximum concentration, carry the potential for developing non-cancer adverse health effects, as mentioned in Table 1 . The greater the HQ value, the greater the level of concern. At Kaniha OCP, the maximum exposure concentration (EC) reached 0.032 mg/m³, resulting in a hazard quotient (HQ) of 10.773, followed by Bharatpur OCP, where the peak EC of 0.009 mg/m³ leads to an HQ of 3.132. In the case of Lingaraj OCP, the risk is relatively lower but not negligible. The maximum HQ at Lingaraj OCP is 1.369, slightly above the safe limit. However, for exposure to mean values, the hazard quotient of Kaniha OCP is above 1, i.e, 3.9, posing a risk for developing adverse health impacts. For the other two projects—Bharatpur OCP and Lingaraj OCP, the HQ values are below 1, showing no significant health impacts. For the cancer risk characterization, cancer risk estimates higher than 1 in 10,000 individuals (10 –4 ) potentially developing cancer in their lifetime are considered to be of regulatory concern. At Kaniha OCP, the cancer risk (CR) at maximum exposure is 5.98x10 − 4 , exceeding the upper acceptable USEPA range of 1x10 − 4 , suggesting elevated carcinogenic risk ( Megido et al., 2017 ). Even under average conditions (EC = 0.012 mg/m³), the CR is 2.16x10 − 4 , still surpassing acceptable limits, reinforcing the need for exposure mitigation. At Bharatpur OCP, the CR of 1.74x10 − 4 has been assessed for the worst-case scenario. While the mean exposure (EC = 0.003 mg/m³) yields a CR of 5.04x10 − 5 , indicating an elevated cancer risk during peak operation, but acceptable during the business-as-usual days. For Lingaraj OCP, CR over the worst-case scenario is 7.6x10 − 5 , which is within the upper bounds of acceptable risk. Over the mean concentration exposure, the CR is 5.26x10 − 5 , indicating acceptable long-term cancer risk. Kaniha OCP presents the highest health risks from respirable silica exposure, both for non-carcinogenic and carcinogenic outcomes, followed by Bharatpur, while Lingaraj OCP poses a comparatively lower but still noteworthy risk. These results strongly support the need for enhanced dust control measures, health surveillance, and personal protective equipment (PPE) deployment, especially at Kaniha OCPs, to mitigate long-term health impacts on workers and nearby communities. Table 1 Potential non-cancer and cancer risk due to crystalline silica at the different mining locations at maximum concentration and at mean concentration. Mine Parameter Max Value Mean Value Interpretation Kaniha OCP Inhalation Exposure Concentration (EC) 0.032 0.012 Exposure concentration is above screening level Hazard Quotient (HQ) 10.773 3.9 Non-cancer risk > 1 – Potential concern Cancer Risk (CR) 0.000598 0.0002164 Acceptable ( 1 → Health concern at peak Cancer Risk (CR) 0.000174 0.0000504 Negligible cancer risk Lingaraj OCP Inhalation Exposure Concentration (EC) 0.004 0.003 Lowest among all Hazard Quotient (HQ) 1.369 0.947 Max HQ > 1 → Slight concern Cancer Risk (CR) 0.000076 0.0000526 No significant cancer risk 3.5. HYSPLIT forward trajectory analysis The forward trajectory plot generated using the NOAA model represents the potential pollutant transport from the mining region comprising Kaniha OCP, Bharatpur OCP, and Lingaraj OCP, using a source location (Fig. 7 ). The trajectories are simulated at three different altitudes—100 m (green), 500 m (red), and 1000 m (blue) above ground level—for a 24-hour period starting on 22 January 2024. The dispersion pattern clearly indicates that pollutants emitted from these mines can travel toward the northeast and west directions, covering significant distances, especially at higher altitudes. The 1000 m trajectory shows far-reaching transport, indicating that fine particulates like PM 10 and associated free silica in respirable dust can reach downwind receptors upto the Similpal national park (Odisha), an eco-sensitive zone. The mid-level (500 m) and surface-level (100 m) trajectories indicate westward dispersion, extending up to parts of Chhattisgarh. Specifically, the 100-meter trajectories are reaching as far as the Barnawapara forest range in Kasdol (Chhattisgarh), while several 500 m trajectories extend up to Nawagarh Tehsil (Chhattisgarh). As previously discussed, elevated levels of free silica near mining areas pose significant health risks to individuals residing nearby. However, the forward trajectory analysis further reveals that pollutant transport is not limited to the immediate vicinity of the mines. Instead, airborne contaminants have the potential to affect distant villages, agricultural zones, and ecologically sensitive areas along the dispersion path, raising broader environmental and public health concerns ( Liu et al., 2022 ). Given the persistence of air parcel movement in these directions, communities along the predicted paths may face increased exposure to hazardous pollutants, which could lead to respiratory ailments, cardiovascular issues, or other long-term health effects. These results highlight the need for strategic planning in emission control and continuous air quality monitoring at the sites to protect public health in downwind receptor zones. 3.6. Morphological assessment The SEM-EDS micrographs and corresponding EDS of suspended particulate matter (SPM) samples collected from three coal mines: Kaniha OCP (A), Lingaraj OCP (B), and Bharatpur OCP (C) are shown in Fig. 8 . The SEM/EDS is helpful to characterize and confirm the presence of free silica (quartz) particles in the mining environment ( Azam et al., 2024 ; Trechera et al., 2020 ). In Kaniha OCP, the SEM image Fig. 8 A, collected from the Near Z Patch site, reveals angular and irregular-shaped particles, indicating their mechanical origin, possibly from coal cutting and overburden handling. The EDS spectrum from Spot-2 shows a dominant silicon (Si) peak, along with oxygen (O) and minor carbon (C) peaks. The high intensity of the Si peak confirms the presence of silicates or quartz (SiO 2 ), likely from surrounding rock or dust mixed with the coal. In Lingaraj OCP (Fig. 8 B), at the near Sump No. 4 site, the micrograph also shows coarse, fragmented particles with varying textures, suggesting a mixed origin of coal cutting and overburden removal. At EDS Spot-3, the spectrum again exhibits a strong Si signal, accompanied by peaks of O and C. This confirms the presence of free silica, likely in the form of quartz, consistent with mining dust. At Bharatpur OCP’s (Fig. 8 C) near the View Point site, the SEM image shows larger particles with sharper edges, similar to earlier images, which may be derived from active coal cutting and overburden removal through blasting activities. The EDS spectrum at Spot-4 confirms the presence of Si and O as dominant elements, reinforcing the identification of quartz particles in the sample. The presence of significant carbon in all the EDS shows that there is likely a mix of coal dust and mineral fragments. All three mining sites show clear evidence of respirable free silica particles in the SPM, confirmed through EDS peaks primarily consisting of silicon and oxygen, which are characteristic of quartz (SiO₂). Some other SEM-EDS images have also been included in the supplementary material, Fig. S10-S12 . The variation in particle morphology and size suggests differences in source material or activity intensity. This highlights the potential health risk due to silica exposure, especially in high-production opencast coal mines, and emphasizes the importance of continuous monitoring and mitigation strategies to be adopted. 4. Conclusion The interquartile range for SPM and PM 10 was observed to be the highest at Bharatpur OCP, followed by Lingaraj OCP, and the minimum at Kaniha OCP. Interestingly, in the case of free silica, the highest concentration of free silica was observed at Kaniha OCP, having mean values of 41.59 µg/m³, despite its relatively lower SPM and PM₁₀ levels, followed by Lingaraj OCP (10.10 µg/m³) and Bharatpur OCP (9.68 µg/m³). This indicates that the elevated silica content at Kaniha is likely due to the geogenic characteristics of the area, which naturally contribute to higher silica dust emissions. Based on health risk assessment over two scenarios, one is at the worst case, where non-cancer risk was observed at the maximum free silica concentration. However, at mean values, the non-cancer risk is observed only at Kaniha OCP. In both scenarios, during the assessment of cancer risk, Kaniha OCP is susceptible to cancer risk. However, Bhartapur OCP is susceptible only during the worst-case scenario and acceptable during usual days. Lingaraj OCP is free from any cancer risk under both scenarios. The forward trajectory of the air mass shows that people residing far from the mining site are also susceptible to the concentration of free silica. The SEM-EDS images show larger particles with sharper edges and confirm the presence of Si and O as dominant elements, reinforcing the identification of quartz particles in the samples. The presence of significant carbon in all the EDS analyses shows that there is likely a mix of coal dust and mineral fragments. Declarations Acknowledgement M.Y. and S.S. acknowledge the Central Mine Planning & Design Institute Limited, Bhubaneswar, India, for financial support under Grant No.: SSP-506. The authors gratefully acknowledge the Environment Department of the Central Mine Planning & Design Institute Limited for providing financial support. The authors also thank Mahanadi Coalfields Limited, Odisha, India, for facilitating access to the mine sites and CSIR-Institute of Minerals and Materials Technology, Bhubaneswar, India, for extending infrastructural support for sample analysis. All authors have read, understood, and have complied with the statement on "Ethical responsibilities of Authors" as found in the Instructions for Authors. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Financial interest declaration The authors declare no competing financial interests. Credit authorship contribution statement Manish Yadav: Data curation, Writing– original draft, Methodology, Software, Writing– review & editing. Nitin Kumar Singh: Writing– review & editing, Supervision, Validation. Sumit Saha: Conceptualization, Methodology, Supervision, Writing– review & editing, Validation. Data Availability The datasets generated during and/or analysed during the current study will made available on request. Ethics approval No ethical approval is required. Consent to participate Not required Consent to publish Not required References Andraos, C., Utembe, W., & Gulumian, M. (2018). Exceedance of environmental exposure limits to crystalline silica in communities surrounding gold mine tailings storage facilities in South Africa. Science of the total environment, 619, 504–516. Asif, Z., Chen, Z., Wang, H., & Zhu, Y. (2022). Update on air pollution control strategies for coal-fired power plants. Clean technologies and environmental policy, 24(8), 2329–2347. Azam, S., Liu, S., Bhattacharyya, S., & Mishra, D. P. (2024). Prevalence of nano-sized coal mine dust in North and Central Appalachian coal mines–Insights from SEM-EDS imaging. Journal of Hazardous Materials, 476, 135226. Castranova, V., & Vallyathan, V. (2000). Silicosis and coal workers' pneumoconiosis. Environmental health perspectives, 108(suppl 4), 675–684. Chen, H. L., Li, C. H., Zhai, P. Y., Zhuang, X., Lian, Y. L., Qiao, X., Feng, J., Qian, Z. S., & Qin, G. (2024). Survival and disease burden analyses of occupational pneumoconiosis during 1958–2021 in Huangshi city, China: a retrospective cohort study. BMC public health, 24(1), 1437. https://doi.org/10.1186/s12889-024-18847-6 Dash, T. R., Tripathy, D. P., & Pandey, J. K. (2020). Chemical characterization of PM10 and evaluation of health risk for the people residing around a highly mechanized opencast coal mine using FTIR spectroscopy. Arabian Journal of Geosciences, 13(4), 175. Directorate General of Mines Safety (DGMS). (2010, January 21). Respirable dust measurements and control to prevent pneumoconiosis in mines (DGMS (Tech.) (S&T) Circular No. 01). Ministry of Labour & Employment, Government of India. Gopinathan, P., Subramani, T., Barbosa, S., & Yuvaraj, D. (2023). Environmental impact and health risk assessment due to coal mining and utilization. Environmental Geochemistry and Health, 45(10), 6915–6922. Health and Safety Executive (HSE). (2005). MDHS 101: Crystalline silica in respirable airborne dusts – Direct-on-filter analyses by infrared spectroscopy and X-ray diffraction. Methods for the Determination of Hazardous Substances. Health and Safety Laboratory. https://www.hse.gov.uk/pubns/mdhs/index.htm Hnizdo, E., & Vallyathan, V. (2003). Chronic obstructive pulmonary disease due to occupational exposure to silica dust: a review of epidemiological and pathological evidence. Occupational and environmental medicine, 60(4), 237–243. Hoy, R. F., & Chambers, D. C. (2020). Silica-related diseases in the modern world. Allergy, 75(11), 2805–2817. IEA (2025), Global Energy Review 2025, IEA, Paris https://www.iea.org/reports/global-energy-review-2025 , Licence: CC BY 4.0 International Agency for Research on Cancer. (1995). IARC monographs on the evaluation of carcinogenic risks to humans (Vol. 48, p. 691). Lyon, France: International Agency for Research on Cancer. Kumar, A., Kumar, A., Singh, P., & Elumalai, S. P. (2024). Trace element profile of TSPM with depth within opencast coal mines for risk assessment and their source apportionment. Air Quality, Atmosphere & Health, 17(12), 2949–2965. Largeron, Y., & Staquet, C. (2016). Persistent inversion dynamics and wintertime PM10 air pollution in Alpine valleys. Atmospheric Environment, 135, 92–108. Liu, H., Wang, X., Talifu, D., Ding, X., Abulizi, A., Tursun, Y., … Xie, X. (2022). Distribution and sources of PM2. 5-bound free silica in the atmosphere of hyper-arid regions in Hotan, North-West China. Science of The Total Environment, 810, 152368. Liu, L., Ge, Z., Zhou, Z., Li, Z., & Deng, Q. (2024). Mineral composition, pore structure and mechanical properties of coal measure strata rocks: a case study of Pingdingshan Coalfield. Science of The Total Environment, 952, 175944. Ma, Q., Wu, J., He, C., & Fang, X. (2021). The speed, scale, and environmental and economic impacts of surface coal mining in the Mongolian Plateau. Resources, Conservation and Recycling, 173, 105730. Mastoi, M. S., Munir, H. M., Zhuang, S., Hassan, M., Usman, M., Alahmadi, A., & Alamri, B. (2022). A critical analysis of the impact of pandemic on China’s electricity usage patterns and the global development of renewable energy. International Journal of Environmental Research and Public Health, 19(8), 4608. Megido, L., Suárez-Peña, B., Negral, L., Castrillón, L., & Fernández-Nava, Y. (2017). Suburban air quality: Human health hazard assessment of potentially toxic elements in PM10. Chemosphere, 177, 284–291. Ministry of Coal, Government of India. (n.d.). Coal reserves. Retrieved July 13, 2025, from https://www.coal.gov.in/en/major-statistics/coal-reserves Ministry of Environment and Forests (MoEF). (2000, September 25). Standards for coal mines (Notification No. GSR 742(E)) [Government notification]. Government of India, New Delhi. Nair, A. S., Soni, K., Singh, P., Meena, K., & Sharma, R. (2023). Assessment of spatio-temporal variation in AOD over four coal mine sites of Assam through satellite observation. Journal of Environmental Studies and Sciences, 13(4), 629–642. O’Day, P. A., Pattammattel, A., Aronstein, P., Leppert, V. J., & Forman, H. J. (2022). Iron speciation in respirable particulate matter and implications for human health. Environmental Science & Technology, 56(11), 7006–7016. Office of Environmental Health Hazard Assessment. (2005). Adoption of chronic reference exposure levels for silica (crystalline, respirable). California Environmental Protection Agency. Oosthuizen, M. A., Wright, C. Y., Matooane, M., & Phala, N. (2015). Human health risk assessment of airborne metals to a potentially exposed community: a screening exercise. Pandey, J. K., Agarwal, D., Gorain, S., Dubey, R. K., Vishwakarma, M. K., Mishra, K. K., & Pal, A. K. (2017). Characterization of respirable dust exposure of different category of workers in Jharia Coalfields. Arabian Journal of Geosciences, 10, 1–10. Rolph, G., Stein, A., & Stunder, B. (2017). Real-time Environmental Applications and Display System: READY. Environmental Modelling & Software, 95, 210–228. https://doi.org/10.1016/j.envsoft.2017.06.025 Sharma, R., & Kumar, A. (2023). Analysis of seasonal and spatial distribution of particulate matters and gaseous pollutants around an open cast coal mining area of Odisha, India. Environmental Science and Pollution Research, 30(14), 39842–39856. Shelton, S., Liyanage, G., Jayasekara, S., Pushpawela, B., Rathnayake, U., Jayasundara, A., & Jayasooriya, L. D. (2022). Seasonal variability of air pollutants and their relationships to meteorological parameters in an urban environment. Advances in Meteorology, 2022(1), 5628911. Singh, S. K., Singh, R. K., Singh, K. K., Singh, R. K., & Singh, S. (2022). Concentration, sources and health effects of silica in ambient respirable dust of Jharia coalfields region, India. En-viron Sci Eur 34 (1). Trechera, P., Moreno, T., Córdoba, P., Moreno, N., Zhuang, X., Li, B., … Querol, X. (2020). Mineralogy, geochemistry and toxicity of size-segregated respirable deposited dust in underground coal mines. Journal of Hazardous Materials, 399, 122935. U.S. Environmental Protection Agency. (1999). Compendium method IO-2.1: Sampling of ambient air for total suspended particulate matter (SPM) and PM₁₀ using high volume (HV) sampler (EPA/625/R-96/010a). Center for Environmental Research Information, Office of Research and Development. U.S. Environmental Protection Agency. (2009). Risk assessment guidance for Superfund Volume I: Human health evaluation manual (Part F, Supplemental guidance for inhalation risk assessment) (EPA/540/R-070/002). Washington, DC: U.S. Environmental Protection Agency. Wang, H., & Zhang, D. (2023). Examining the interplay between fossil fuel mining, sustainable growth, and economic prosperity. Resources Policy, 87, 104324. Wang, H., Ye, Q., Zhang, H., Sun, X., & Li, T. (2023). Prevention and Treatment of Pneumoconiosis in the Context of Healthy China 2030. China CDC weekly, 5(41), 927–932. https://doi.org/10.46234/ccdcw2023.162 Xu, T., Song, Y., Liu, M., Cai, X., Zhang, H., Guo, J., & Zhu, T. (2019). Temperature inversions in severe polluted days derived from radiosonde data in North China from 2011 to 2016. Science of the Total Environment, 647, 1011–1020. Yadav, M., Sahu, S. P., & Singh, N. K. (2019). Multivariate statistical assessment of ambient air pollution in two coalfields having different coal transportation strategy: a comparative study in Eastern India. Journal of Cleaner Production, 207, 97–110. Yadav, M., Singh, N. K., Sahu, S. P., Garnayak, D., & Sahu, S. K. (2025). Impact of Mining and Allied Activities on Air Quality of a Mega Coal Mining Zone in India. Environmental Quality Management, 34(4), e70089. Yang, Q., Liu, G., Yang, L., Yun, J., Zhang, X., Zhao, C., … Jiang, G. (2024). Atmospheric emissions of respirable quartz from industrial activities in China. Nature Sustainability, 7(9), 1120–1127. Yang, X., Lu, D., Zhu, B., Sun, Z., Li, G., Li, J., … Jiang, G. (2022). Phase transformation of silica particles in coal and biomass combustion processes. Environmental Pollution, 292, 118312. Zhang, H., Zhang, F., Song, J., Tan, M. L., Kung, H. T., & Johnson, V. C. (2021). Pollutant source, ecological and human health risks assessment of heavy metals in soils from coal mining areas in Xinjiang, China. Environmental Research, 202, 111702. Additional Declarations No competing interests reported. Supplementary Files SI.pdf GraphicalAbstract.png Cite Share Download PDF Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Environmental Geochemistry and Health → Version 1 posted Editorial decision: Revision requested 24 Nov, 2025 Reviews received at journal 21 Nov, 2025 Reviews received at journal 12 Sep, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviewers agreed at journal 08 Aug, 2025 Reviewers agreed at journal 08 Aug, 2025 Reviewers invited by journal 08 Aug, 2025 Editor assigned by journal 07 Aug, 2025 Submission checks completed at journal 07 Aug, 2025 First submitted to journal 06 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7308663","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498347923,"identity":"ccc0aad3-d092-44a5-ae4a-6f244130184b","order_by":0,"name":"Manish Yadav","email":"","orcid":"","institution":"Coal India (India)","correspondingAuthor":false,"prefix":"","firstName":"Manish","middleName":"","lastName":"Yadav","suffix":""},{"id":498347924,"identity":"d3e36cd0-5401-4dc2-a2bb-36afcc0fbcb0","order_by":1,"name":"Nitin Kumar Singh","email":"","orcid":"","institution":"Marwadi 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sites.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7308663/v1/f8c03ab09079c44d02cf2158.png"},{"id":89006611,"identity":"f6067755-a900-45ce-9199-f3859407e149","added_by":"auto","created_at":"2025-08-13 16:27:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1575967,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of free silica concentration (µg/m³) from three mining sites.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7308663/v1/be15ad290131d134fa21d8d8.png"},{"id":89006614,"identity":"7b4b0223-32bc-417b-8472-abb530514541","added_by":"auto","created_at":"2025-08-13 16:27:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":597620,"visible":true,"origin":"","legend":"\u003cp\u003eForward trajectory of the air mass on 22\u003csup\u003end\u003c/sup\u003e Jan. 2024 at three altitudes, 100, 500, and 1000 m.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7308663/v1/c874974cfc455365cc8c10a6.png"},{"id":89006615,"identity":"805e4082-1e20-4ce9-8961-2a6456d543d9","added_by":"auto","created_at":"2025-08-13 16:27:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1448855,"visible":true,"origin":"","legend":"\u003cp\u003eSEM and corresponding EDS image of three locations: (A) Kaniha OCP, (B) Lingaraj OCP, and (C) Bharatpur OCP.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7308663/v1/ddf547740f64ae2f7a3a11e8.png"},{"id":103251212,"identity":"c8ad7138-e51c-4810-b367-3b0d3aeebbb6","added_by":"auto","created_at":"2026-02-23 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16:27:43","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5142964,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-7308663/v1/031a5ac002214e62a7cc420e.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exposure Assessment to Respirable Free Silica in Coal Mining Areas: A Health Risk Assessment Approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCoal continues to serve as a vital foundation for global energy and industrial development, playing a central role in meeting primary energy demands and supporting human progress with global industrialization \u003cb\u003e(\u003c/b\u003eAzam et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As a key component of the global energy mix, coal remains indispensable, fulfilling over half of the world\u0026rsquo;s primary energy demand and sustaining several core industrial sectors. In 2023, the global coal demand surged to an all-time high of 8.68\u0026nbsp;billion tonnes, primarily driven by increased consumption in China and India, as well as a decline in hydropower output that intensified dependency on coal-based electricity. Of the total demand, 5.85\u0026nbsp;billion tonnes were consumed by the power sector, while 2.83\u0026nbsp;billion tonnes were utilized in non-power sectors. This upward trend persisted in 2024, with global demand for coal increasing by 1.2%, primarily fueled by continuous growth in the electricity sector \u003cb\u003e(\u003c/b\u003eIEA \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Notably, China and India have set new consumption records, with respective increases of 1.2% and 5.5%, supported by robust economic and industrial expansion \u003cb\u003e(\u003c/b\u003eMastoi et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe continued increase in coal production reflects not only growing demand for energy but also underscores an increasingly alarming environmental scenario \u003cb\u003e(\u003c/b\u003eYadav et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Coal mining involves a range of intensive activities such as excavation, drilling and blasting, loading and unloading of coal and overburden, operation of heavy vehicles on haul roads, dragline operations, coal crushing in feeder breakers, mine fires, exposure of pit faces, wind erosion, and emissions from heavy earthmoving machinery \u003cb\u003e(\u003c/b\u003eNair et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yadav et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These activities, followed by the combustion of coal in power plants, release substantial amounts of carbon, thereby contributing to climate change and the emission of hazardous pollutants such as PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, NO\u003csub\u003ex\u003c/sub\u003e, and SO\u003csub\u003ex\u003c/sub\u003e \u003cb\u003e(\u003c/b\u003eAsif et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Collectively, these impacts pose serious threats to the environment, diminishing coal\u0026rsquo;s appeal and prompting many countries to pursue cleaner and more sustainable energy alternatives \u003cb\u003e(\u003c/b\u003eMa et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDust pollution is one of the most pressing environmental concerns associated with coal mining, predominantly manifesting as suspended particulate matter (SPM) and particulate matter with a diameter of 10 microns or less (PM\u003csub\u003e10\u003c/sub\u003e) \u003cb\u003e(Sharma et al., 2023)\u003c/b\u003e. The size of particulate matter is a critical factor in determining its site of deposition within the human respiratory tract and its resulting health impacts. While larger particles (SPM) are generally trapped in the nasal passages and upper respiratory tract, finer particles, PM\u003csub\u003e10\u003c/sub\u003e, can reach deeper regions of the lungs, posing more serious health risks \u003cb\u003e(\u003c/b\u003eKumar et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In open-pit coal mines, airborne particulate matter constitutes a complex mixture with varying size, morphology, and composition. Its physical, chemical, and biological characteristics are largely determined by the nature of the coal and the associated geological strata \u003cb\u003e(\u003c/b\u003eLiu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe coal mining dust often contains elevated concentrations of toxic elements such as chromium (Cr), cadmium (Cd), manganese (Mn), nickel (Ni), lead (Pb), arsenic (As), zinc (Zn), mercury (Hg), and iron (Fe), thereby amplifying its potential health hazards \u003cb\u003e(\u003c/b\u003eZhang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gopinathan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition to particle size, the specific chemical composition of dust plays a crucial role in determining its toxicity. Thus, it is insufficient to regard coal dust as a singular harmful agent. Notably, coal dust is frequently accompanied by crystalline silica (quartz), which is released during the cutting and handling of rock materials \u003cb\u003e(\u003c/b\u003eYang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These fine quartz particles are especially concerning due to their well-established link to severe respiratory illness and their contribution to the explosibility of dust mixtures in mining environments \u003cb\u003e(\u003c/b\u003eHoy \u0026amp; Chambers, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Prolonged exposure to such dusty conditions significantly increases occupational health risks \u003cb\u003e(\u003c/b\u003eAndraos et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; O\u0026rsquo;Day et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Coal mining dust is known to cause chronic respiratory conditions such as chronic bronchitis, emphysema, and pulmonary fibrosis \u003cb\u003e(\u003c/b\u003eHnizdo \u0026amp; Vallyathan, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2003\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. More critically, it is a leading cause of occupational diseases such as coal workers\u0026rsquo; pneumoconiosis (CWP) and silicosis, both of which have garnered growing global concerns \u003cb\u003e(\u003c/b\u003eCastranova \u0026amp; Vallyathan, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2000\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. For example, a study reported that by the end of 2021, China alone had recorded 1.026\u0026nbsp;million cases of occupational diseases had been reported, of which 916,000 were pneumoconiosis cases \u003cb\u003e(\u003c/b\u003eWang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, around 60% of these were identified as CWP, highlighting the severe health burden associated with dust exposure in coal mining operations \u003cb\u003e(\u003c/b\u003eChen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eApart from pneumoconiosis and silicosis, exposure to crystalline silica is also linked to lung cancer \u003cb\u003e(\u003c/b\u003eYang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The International Agency for Research on Cancer (IARC) has classified crystalline silica and cristobalite (a polymorph of crystalline silica) as Group 1 carcinogens, indicating that there is sufficient evidence to confirm that crystalline silica can cause cancer in humans \u003cb\u003e(IARC 1995)\u003c/b\u003e. This highlights the importance of assessing free silica concentration across mining areas with varying geological formations of coal and overburden. However, limited research has been conducted in large-scale mega mines with production capacities exceeding 15\u0026nbsp;million tonnes per year. Additionally, a human health risk assessment has been carried out alongside the study of free silica morphology, which could be further explored in terms of surface chemistry and associated health effects. Coal dust from various parts of the different mines, including coal seams, rock dust, floors, and roofs, may exhibit distinct surface areas, physicochemical characteristics, and varying toxicity levels.\u003c/p\u003e\u003cp\u003eWith the above background, it is important to conduct extensive monitoring of respirable quartz levels in heavily operated mega mines. In the present study, a comprehensive analysis of free silica (quartz) concentrations was performed using Fourier transform Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM), and Energy Dispersive Spectroscopy (EDS) across coal mines in Odisha, India, with production capacities exceeding 14\u0026nbsp;million tonnes per year (MTY). To the best of our knowledge, this study represents the first large-scale field investigation conducted in the high-capacity coal mining environments. Furthermore, through this study, we particularly aim to conduct an in-depth monitoring campaign to measure free silica concentration in the coal mining industry, with a particular focus on the health risk assessment. The findings are intended to provide a baseline for future studies aimed at safeguarding coal miners\u0026rsquo; health and fostering sustainable mining practices. This effort also opens new avenues for understanding the interactions between free silica-containing coal dust and the surrounding environment.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Sampling site description\u003c/h2\u003e\u003cp\u003eFor this study, the sampling was carried out at three open-cast coal mines, namely Kaniha OCP (Open Cast Project) with a capacity of 14 MTY, Lingaraj OCP (20 MTY), and Bharatpur OCP (20 MTY), located in the Talcher coalfield, Angul, Odisha (India), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The Talcher coalfield forms the southeastern part of the Lower Gondwana basin within the Mahanadi Valley Group of coalfields in Odisha. It extends approximately 80 km along strike (east-west) and 26 km along the dip rise (north-south), covering a coal-bearing area of about 1860 sq. km. According to estimates of the Geological Survey of India (GSI), it is one of the richest coalfields in the country, with a coal reserve of approximately 42.54\u0026nbsp;billion tonnes \u003cb\u003e(MoC 2025)\u003c/b\u003e. The Mahanadi Coalfield Limited (MCL), a state-owned company, is the primary producer in the region, and all three mines studied in this research are under its management. The Talcher region holds great importance for India and is often referred to as the powerhouse of Odisha. Several mega coal mines in this region supply coal to key industries in the vicinity for electricity generation, aluminium production, heavy water plants, and sponge Iron units. Additionally, the region is densely populated, with large numbers of villages located near mining areas.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Sample collection and analysis\u003c/h2\u003e\u003cp\u003eIn this study, the air sampling at Kaniha OCP, Lingaraj OCP, and Bharatpur OCP was conducted using high-volume respirable dust samplers (RDS). The details of monitoring locations are provided in supplementary material (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e to S3)\u003c/b\u003e. The sampling was performed for a 24-hour duration, twice a month, during the following periods: October 2023 to January 2024 for Kaniha OCP, February 2024 to April 2024 for Lingaraj OCP, and March 2024 to May 2024 for Bharatpur OCP. A total of 70 samples were collected during the study period. The Envirotech APM 460 model respirable dust sampler was employed for the sampling and measurements. The ambient air was drawn through a size-selective inlet measuring 20.3 \u0026times; 25.4 cm at a flow rate of 1132 L/min. Particles with aerodynamic diameter\u0026thinsp;\u0026lt;\u0026thinsp;10 \u0026micro;m were collected on a filter paper, and particles with aerodynamic diameter\u0026thinsp;\u0026gt;\u0026thinsp;10 \u0026micro;m were deposited in a separate collection cup \u003cb\u003e(US EPA 1999)\u003c/b\u003e. The difference between the initial and final filter weight, divided by the volume of air sampled, was used to determine the PM\u003csub\u003e10\u003c/sub\u003e concentration. The respirable dust collected on the filter paper was analyzed using the Fourier transform Infrared (FTIR) following the direct-on-filter (DOF) method as outlined in the \u003cb\u003eHSE 2005; MDHS101\u003c/b\u003e guidelines. FTIR spectra were recorded using a Thermo Scientific Nicolet iS20 Smart iTX-Diamond ATR-IR spectrometer, scanning in the range of 4000\u0026thinsp;\u0026minus;\u0026thinsp;400 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e with a spectral resolution of 0.25 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The dust-laden filter paper was placed directly in the sample compartment of the FTIR unit for DOF analysis \u003cb\u003e(HSE 2005)\u003c/b\u003e. The quartz doublet peaks were observed at 799 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e \u003cb\u003e(\u003c/b\u003ePandey et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; \u003cb\u003eDash et al., 2022)\u003c/b\u003e, confirming the presence of crystalline silica. For each sample, the peak height measured was converted to the amount of quartz content (in mg) in the respirable dust deposited on filter paper using the calibration curve established from peak height versus standard quartz (Sikron F600, HSE Standard Quartz). Some of the FTIR peaks have been shown in the supplementary material, \u003cb\u003eFig. S4-S6\u003c/b\u003e. To ensure the reliability of the experimental analysis, the samples were also analyzed using Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) analysis. The compositional analysis (semi-quantitative) and morphology of compounds were examined using a Zeiss Evo 18 Scanning Electron Microscopy (SEM) with EDX (EDAX Octane Elect EDS) system.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Risk characterization\u003c/h2\u003e\u003cp\u003eRisk assessment of toxic metals involves four key steps: hazard identification, exposure assessment, dose-response assessment, and risk characterization. This approach has been widely applied to evaluate the risks posed by toxic metals in soils, water, and air. In this study, risk characterization for both carcinogenic and non-carcinogenic endpoints was performed based on the levels of free silica observed in the collected samples \u003cb\u003e(US EPA 2009)\u003c/b\u003e.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1. Non-cancer risk characterization\u003c/h2\u003e\u003cp\u003eNon-cancer risks are assessed based on the theory that a threshold level of exposure exists, below which adverse health effects are unlikely. This evaluation involves the use of factors such as the reference dose (RfD), reference concentration (RfC), inhalation reference exposure concentration, or acceptable daily intake (ADI). These values represent the estimated daily exposure levels for the general population, including sensitive subgroups, below which no appreciable risk of adverse health effects is expected over a lifetime. For this study, a reference concentration (RfC) of 3 \u0026micro;g\u0026middot;m⁻\u0026sup3;, as recommended by the Office of Environmental Health Hazard Assessment (OEHHA), was used for PM\u003csub\u003e4\u003c/sub\u003e crystalline silica. Further, inhalation risks expressed as hazard quotient (HQ), were estimated by comparing this R\u003csub\u003ef\u003c/sub\u003eC value with the actual exposure concentration (EC) \u003cb\u003e(OEHHA 2005).\u003c/b\u003e The EC in \u0026micro;g\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e was calculated using the following Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{E}\\varvec{C}=\\frac{C\\times\\:ET\\times\\:EF\\times\\:ED}{AT\\times\\:24}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere C is the concentration of silica in air in \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, ET is the exposure time in hours/day, EF is the exposure frequency in days/year, ED is the exposure duration in years and AT is the averaging time, which was calculated as exposure duration in years \u0026times; 306 days/year \u0026times; 8 h/day. ET and ED were set as 8 h/day and 60 years, respectively. It has been assumed that a person is working for 30 years in a service and is exposed to an 8-hour shift. For the C, two scenarios were anticipated: one is at peak concentration and one is at mean concentration:\u003c/p\u003e\u003cp\u003eThe risk, expressed as a hazard quotient (HQ), was then calculated using the following Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{H}\\varvec{Q}=\\frac{EC}{RfC}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere an HQ value of less than 1 would indicate no possible risk to developing a non-cancer adverse health effect, and an HQ value higher than 1 would indicate a possible risk \u003cb\u003e(\u003c/b\u003eOosthuizen et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2. Cancer risk characterization\u003c/h2\u003e\u003cp\u003eThe aim of cancer risk characterization is to quantify the excess lifetime cancer risk resulting from inhalation exposure to crystalline silica present in coal mine dust. Excess or incremental lifetime risk is defined as the enhanced probability of cancer occurrence, above background levels, resulting from continuous occupational exposure from an individual\u0026rsquo;s service period. For inhalation-related carcinogenic effects, risk estimation is based on inhalation unit risk (IUR) values that represent the estimated risk per unit of pollutant concentration. These IURs, derived from toxicological data, are applied to the measured airborne silica concentrations to calculate site-specific cancer risks. Consequently, incremental cancer risks associated with both peak concentration and mean concentration exposure scenarios can be determined using Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e):\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{C}\\varvec{R}\\:=\\:EC\\:\\times\\:IUR$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere CR is cancer risk, IUR is expressed in 1/\u0026micro;g\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e (In this case 1.85 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), EC is the exposure concentration in \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, as calculated from Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) using EF (308 days/year) and AT (10950 days). ET and ED were set as 8 h/day and 30 years, respectively. For EF, the two scenarios anticipated in the non-cancer risk calculations were also used for cancer risk calculations.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.4. HYSPLIT model\u003c/h2\u003e\u003cp\u003eThe transport trajectories of air parcels at the sampling site were computed using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, developed by the United States National Oceanic and Atmospheric Administration (NOAA) \u003cb\u003e(\u003c/b\u003eRolph et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This analysis aimed to assess the atmospheric dispersion of silica dust, including its potential travel distance and impact on sensitive receptors. Twenty-four-hour forward air mass trajectories were generated using the HYSPLIT 4 model for each of the three sampling sites. Trajectories were calculated at an initial height of 100 m, 500 m, and 1000 m above ground level to capture vertical variation in dispersion patterns.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Variation of SPM and PM\u003csub\u003e10\u003c/sub\u003e\u003c/h2\u003e\u003cp\u003eThe particulate matter, namely suspended particulate matter (SPM) and PM\u003csub\u003e10\u003c/sub\u003e, has been collected from the three major surface coal mining projects, which are Bharatpur OCP, Kaniha OCP, and Lingaraj OCP. The project-wise box plot illustrated the distribution of particulate matter concentration, both Suspended Particulate Matter (SPM) and PM₁₀. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reveals the significant variability in particulate pollution levels through the box plot. The variability can be linked to the operational practice, production intensity, and micro-meteorology of the area. The present production capacity of Bharatpur OCP, Kaniha OCP, and Lingaraj OCP is 20\u0026nbsp;million tonnes per year, 14\u0026nbsp;million tonnes per year, and 20\u0026nbsp;million tonnes per year, respectively. Among all three mines, Bhratapur OCP shows the highest particulate pollution level as evidenced by the highest maximum and mean values for both SPM and PM₁₀. At Bharatpur OCP, SPM varies from 156 \u0026micro;g/m\u0026sup3; to 927 \u0026micro;g/m\u0026sup3; with a mean value of 394 \u0026micro;g/m\u0026sup3;, and PM\u003csub\u003e10\u003c/sub\u003e varies from 136 \u0026micro;g/m\u0026sup3; to 822 \u0026micro;g/m\u0026sup3; with a mean value of 333 \u0026micro;g/m\u0026sup3;. In contrast, Kaniha OCP exhibits a notably different plot compared to the Bharatpur OCP. Both SPM and PM₁₀ concentration levels are remarkably lower and more tightly clustered. At Kaniha OCP, SPM varies from 74 \u0026micro;g/m\u0026sup3; to 447 \u0026micro;g/m\u0026sup3; with a mean value of 230 \u0026micro;g/m\u0026sup3;, and PM\u003csub\u003e10\u003c/sub\u003e varies from 62 \u0026micro;g/m\u0026sup3; to 405 \u0026micro;g/m\u0026sup3; with a mean value of 193 \u0026micro;g/m\u0026sup3;. However, Lingaraj OCP displays a mixed trend. While PM₁₀ concentrations remain moderate and comparable to those observed at Kaniha, as they vary from 106 \u0026micro;g/m\u0026sup3; to 361 \u0026micro;g/m\u0026sup3;, with a mean value of 205 \u0026micro;g/m\u0026sup3;. The SPM levels are noticeably elevated compared to the Kaniha OCP, as they vary from 165 \u0026micro;g/m\u0026sup3; to 550 \u0026micro;g/m\u0026sup3; with a mean value of 301 \u0026micro;g/m\u0026sup3;.\u003c/p\u003e\u003cp\u003eThe interquartile range for SPM and PM\u003csub\u003e10\u003c/sub\u003e at Bharatpur is notably wide, reflecting highly unstable air quality conditions. This suggests episodic spikes in emissions, potentially attributable to activities such as coal excavation, overburden removal, blasting, unpaved haul road traffic, and wind erosion from exposed surfaces. Although the interquartile range for SPM and PM\u003csub\u003e10\u003c/sub\u003e at Kaniha OCP is very compressed, indicating relatively stable air quality conditions. While some lower outliers exist\u0026mdash;particularly for SPM\u0026mdash;there is an absence of upper extreme values, reflecting better dust control management by the project authority. This suggests that the emission sources are better managed. While in Lingaraj OCP, the presence of mild outliers further suggests intermittent high-emission events. The discrepancy between PM₁₀ and SPM in Lingaraj OCP shows that a larger fraction of the particulate load is dominated by coarse particles (\u0026gt;\u0026thinsp;10 \u0026micro;m), likely from ground-level activities such as mechanical excavation, loading, and vehicular movement on unpaved surfaces. Although high value at particular mines can not only be attributed to the mismanagement, other operational factors can also affect the emissions. For instance, in a mine where only coal excavation is carried out, the dust levels are likely to be lower compared to a site where both coal and overburden removal activities are simultaneously conducted. Season also plays an important role in determining the pollutant level \u003cb\u003e(\u003c/b\u003eShelton et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In winters, inversion affects the dispersion of the air pollutants, resulting in higher ground-level concentrations \u003cb\u003e(\u003c/b\u003eLargeron \u0026amp; Staquet, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eFrom a regulatory perspective, the reported values, especially at Bharatpur and Lingaraj, often exceed the coal mine standards for PM₁₀ (300 \u0026micro;g/m\u0026sup3;, 24-hour average), raising concerns about occupational and public health risks in surrounding communities \u003cb\u003e(MoEF 2000)\u003c/b\u003e. Therefore, the results underscore the necessity of targeted pollution mitigation strategies, including water spraying, dust suppressants, green belt development, and the adoption of enclosed material handling systems. Notably, Kaniha OCP\u0026mdash;with its relatively controlled pollutant levels\u0026mdash;may serve as a benchmark for best practices in emission control within coal mining operations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Spatio-temporal variation of PM\u003csub\u003e10\u003c/sub\u003e and SPM\u003c/h2\u003e\u003cp\u003eThe spatiotemporal distribution of PM₁₀ concentrations across various monitoring locations in Kaniha OCP, Lingaraj OCP, and Bharatpur OCP highlights substantial heterogeneity in PM\u003csub\u003e10\u003c/sub\u003e concentration levels over the study period. In Kaniha OCP, a total of five locations, marked in green color, have been selected for monitoring during the study period \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003eand supplementary material, Fig. S7)\u003c/b\u003e. As per coal mining standards, the permissible limit for PM₁₀ is 300 \u0026micro;g/m\u0026sup3;. Two monitoring locations, namely Near Canteen View Point (309 \u0026micro;g/m\u0026sup3;) and Near DG generator (406 \u0026micro;g/m\u0026sup3;), recorded concentrations exceeding this CPCB-prescribed limit. Both the high values were recorded during the winter season, which is also considered the worst season for air dispersion due to the occurrence of inversion conditions \u003cb\u003e(\u003c/b\u003eXu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, in Lingaraj OCP, a total of four locations, marked in blue, have been selected for monitoring during the study period. The total three times recorded concentration exceeded the limit for PM\u003csub\u003e10\u003c/sub\u003e. One at Near Sub-station (362 \u0026micro;g/m\u0026sup3;) and two times at Weigh bridge (312 \u0026amp; 309 \u0026micro;g/m\u0026sup3;). All exceedance occurs during the month of March. The pressure of coal production and its dispatch is highest during the month of March due to the financial ending month, which might have resulted in the high values. In Bharatpur OCP, a total of four locations, marked in red color, have been selected for monitoring during the study period. The values of PM\u003csub\u003e10\u003c/sub\u003e remain under control during the initial study period, then they suddenly rose upto 822 \u0026micro;g/m\u0026sup3; at the BLA camp location. An increase in PM₁₀ levels was observed across all four monitoring locations of Bharatpur OCP during this period. The concentrations recorded were unusually high, even by typical mining area standards. Such elevated levels are likely associated with episodic activities such as large-scale overburden removal or a temporary lapse in dust control measures. A total of 8 times of exceedance of the standard is recorded.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe spatio-temporal analysis of SPM concentrations across various monitoring locations in Bharatpur, Kaniha, and Lingaraj OCPs from November 2023 to May 2024 reveals substantial spatial and temporal variability, highlighting the complex dynamics of dust generation and dispersion in open-cast coal mining environments \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cb\u003eFig. S8)\u003c/b\u003e. The trends of SPM are similar to the PM\u003csub\u003e10,\u003c/sub\u003e but interestingly, there has been 100% compliance with the CPCB coal mine standards at all the locations of Kaniha OCP and Lingaraj OCP. As per coal mining standards, the permissible limit for PM₁₀ is 500 \u0026micro;g/m\u0026sup3;. In Bharatpur OCP, the SPM concentrations remained high, similar to PM\u003csub\u003e10\u003c/sub\u003e for May-24 month, where it exceeded the limits across all locations. A dramatic spike was observed in May 2024 at Near Coal Stock (927 \u0026micro;g/m\u0026sup3;), Near View Point (907 \u0026micro;g/m\u0026sup3;), Near Pump House (859 \u0026micro;g/m\u0026sup3;), and BLA Camp (688 \u0026micro;g/m\u0026sup3;). These peak values, far exceeding the CPCB\u0026rsquo;s 24-hour coal mine standards for SPM, signal episodes of intense dust generation likely associated with peak summer operations, heavy vehicular activity, and failure of adequate dust suppression during that period.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Spatio-temporal variation of free silica (Quartz)\u003c/h2\u003e\u003cp\u003eParticles from PM\u003csub\u003e10\u003c/sub\u003e samples were used for the determination of free silica, also known as crystalline silica or α-quartz. The box plot shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e explains the distribution of free silica concentration for the project, namely Bharatpur OCP, Kaniha OCP, and Lingaraj OCP. Notably, Kaniha OCP shows the highest value and variation of the concentration of free silica. The concentration at Kaniha OCP varies from 5.94 \u0026micro;g/m\u0026sup3; to 114.89 \u0026micro;g/m\u0026sup3; having mean value of 41.59 \u0026micro;g/m\u0026sup3; \u0026micro;g/m\u0026sup3;. The value above 75 \u0026micro;g/m\u0026sup3; can be considered an outlier value. This indicates the presence of episodic exposure spikes of free silica due to the failure of control measures like water sprinkling, intense material handling, or the production of coal. The temporal and spatial distribution of free silica (measured in \u0026micro;g/m\u0026sup3;) across various monitoring locations in Kaniha OCP reveals significant fluctuations in concentration levels shown in green color \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. In Kaniha OCP, prominent peaks are observed at locations such as Near the DG Generator, Near Canteen View Point, near Z patch-2 (\u003cb\u003eFig. S9\u003c/b\u003e), and Near Stockyard with concentrations reaching as high as 114.89 \u0026micro;g/m\u0026sup3;, 92.74 \u0026micro;g/m\u0026sup3;, 73.44 \u0026micro;g/m\u0026sup3;, and 67.87 \u0026micro;g/m\u0026sup3;, respectively, in different periods of time. These spikes may be attributed to increased operations such as production, drilling, material handling, or a lack of effective dust suppression.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn contrast, Bharatpur OCP reflects a relatively lower dispersion of free silica values. The means are substantially lower than those in Kaniha, with the majority of data points ranging from 3.68 \u0026micro;g/m\u0026sup3; to 33.38 \u0026micro;g/m\u0026sup3;, having a mean value of 9.68 \u0026micro;g/m\u0026sup3;. The temporal and spatial distribution of Bharatpur OCP has been shown in the figure with red color. The values at Bharatpur OCP are comparatively very low compared to Kaniha OCP. The only value a little bit high was observed at the Near Pump house, having 33.35 \u0026micro;g/m\u0026sup3;. Lingaraj OCP, on the other hand, demonstrates the most compact and least variable free silica distribution. The free silica value varies from 5.54 \u0026micro;g/m\u0026sup3; to 14.57 \u0026micro;g/m\u0026sup3;, with the mean value of 10.10 \u0026micro;g/m\u0026sup3;, and since the interquartile range is narrow, it indicates a consistently low exposure profile across sampling points. The temporal and spatial distribution of Lingaraj OCP has been shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e with blue color. These values are very low compared to the Kaniha OCP. The highest value observed was near the sub-station, i.e, 14.25 \u0026micro;g/m\u0026sup3;.\u003c/p\u003e\u003cp\u003eInterestingly, while SPM and PM₁₀ levels were higher at Bharatpur OCP and Lingaraj OCP compared to Kaniha OCP, the highest concentration of free silica was observed at Kaniha OCP, despite its relatively lower SPM and PM₁₀ levels. This indicates that the elevated silica content at Kaniha is likely due to the geogenic characteristics of the area, which naturally contribute to higher silica dust emissions. Kaniha OCP is located at an aerial distance of approximately 15 to 18 kilometers from Bharatpur and Lingaraj OCPs, which accounts for the variation in mineral formations across these sites. In contrast, Bharatpur and Lingaraj OCPs are only about 2.5 kilometers apart, resulting in similar geological formations between them. Detailed mineralogical composition of one coal sample from all three mines is listed in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn India, the maximum exposure limit for respirable dust is set at 3000 \u0026micro;g/m\u0026sup3; for an 8-hour time-weighted average (TWA), provided the free silica content is below 5%. If the silica content exceeds 5%, the MEL is calculated using the formula: 15 divided by the percentage of silica in the respirable dust. In many other countries, the MEL for crystalline silica in respirable airborne dust is significantly stricter, typically 300 \u0026micro;g/m\u0026sup3; (8-hour TWA). However, it is generally considered reasonably practicable to control exposure to 100 \u0026micro;g/m\u0026sup3; or lower using engineering or process controls. DGMS (Director General of Mines Safety) strongly recommends that mine management ensure workers are not exposed to respirable crystalline silica levels exceeding 100 \u0026micro;g/m\u0026sup3; (8-hour TWA) \u003cb\u003e(DGMS 2010;\u003c/b\u003e Singh et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). If this level cannot be consistently maintained, additional protective measures should be implemented. Although there are currently no prescribed standards for 24-hour ambient air sampling of free silica, existing limits are primarily designed for occupational exposure. However, in India, many villages are situated in close proximity to active mining areas, often due to challenges related to rehabilitation and resettlement. As a result, the general population\u0026mdash;including vulnerable groups\u0026mdash;is continuously exposed to potentially hazardous air quality conditions. Free silica is a predominant mineral found in coal dust, and as of this date, it is a well-established fact that prolonged exposure to free silica dust causes serious respiratory and autoimmune diseases, such as silicosis, rheumatoid arthritis, and even cancer. Notably, excessive quartz exposure has been recognized as a key factor in the recent resurgence of pneumoconiosis cases in countries like China. While a comparison with occupational standards shows that free silica concentrations at most monitoring sites remain within the permissible limit of 100 \u0026micro;g/m\u0026sup3;, one exception was observed at Kaniha OCP, where the levels exceeded this threshold. Despite this, there is an urgent need for regulatory authorities to establish specific ambient air quality standards for free silica to ensure the health and safety of communities living near mining operations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Risk characterization\u003c/h2\u003e\u003cp\u003eBased on the updated Health Risk Assessment results for inhalation exposure to respirable free silica across three mining sites \u0026mdash; Kaniha OCP, Bharatpur OCP, and Lingaraj OCP \u0026mdash; the findings indicate varying degrees of non-carcinogenic and carcinogenic risks. The potential non-cancer and cancer risks were assessed for workers and persons living nearby for each of the three projects based on the concentration of free silica in two possible scenarios: For scenario 1, the worst-case was anticipated, i.e., exposure at maximum observed concentration, whereas for scenario 2, exposure only at the mean concentration was anticipated. For worst-case scenarios, all HQ values were well above 1, indicating that all projects, under maximum concentration, carry the potential for developing non-cancer adverse health effects, as mentioned in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The greater the HQ value, the greater the level of concern. At Kaniha OCP, the maximum exposure concentration (EC) reached 0.032 mg/m\u0026sup3;, resulting in a hazard quotient (HQ) of 10.773, followed by Bharatpur OCP, where the peak EC of 0.009 mg/m\u0026sup3; leads to an HQ of 3.132. In the case of Lingaraj OCP, the risk is relatively lower but not negligible. The maximum HQ at Lingaraj OCP is 1.369, slightly above the safe limit. However, for exposure to mean values, the hazard quotient of Kaniha OCP is above 1, i.e, 3.9, posing a risk for developing adverse health impacts. For the other two projects\u0026mdash;Bharatpur OCP and Lingaraj OCP, the HQ values are below 1, showing no significant health impacts. For the cancer risk characterization, cancer risk estimates higher than 1 in 10,000 individuals (10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e) potentially developing cancer in their lifetime are considered to be of regulatory concern. At Kaniha OCP, the cancer risk (CR) at maximum exposure is 5.98x10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, exceeding the upper acceptable USEPA range of 1x10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, suggesting elevated carcinogenic risk \u003cb\u003e(\u003c/b\u003eMegido et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Even under average conditions (EC\u0026thinsp;=\u0026thinsp;0.012 mg/m\u0026sup3;), the CR is 2.16x10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, still surpassing acceptable limits, reinforcing the need for exposure mitigation. At Bharatpur OCP, the CR of 1.74x10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e has been assessed for the worst-case scenario. While the mean exposure (EC\u0026thinsp;=\u0026thinsp;0.003 mg/m\u0026sup3;) yields a CR of 5.04x10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, indicating an elevated cancer risk during peak operation, but acceptable during the business-as-usual days. For Lingaraj OCP, CR over the worst-case scenario is 7.6x10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, which is within the upper bounds of acceptable risk. Over the mean concentration exposure, the CR is 5.26x10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, indicating acceptable long-term cancer risk. Kaniha OCP presents the highest health risks from respirable silica exposure, both for non-carcinogenic and carcinogenic outcomes, followed by Bharatpur, while Lingaraj OCP poses a comparatively lower but still noteworthy risk. These results strongly support the need for enhanced dust control measures, health surveillance, and personal protective equipment (PPE) deployment, especially at Kaniha OCPs, to mitigate long-term health impacts on workers and nearby communities.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePotential non-cancer and cancer risk due to crystalline silica at the different mining locations at maximum concentration and at mean concentration.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMine\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMax Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eKaniha OCP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInhalation Exposure Concentration (EC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExposure concentration is above screening level\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHazard Quotient (HQ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e10.773\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e3.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eNon-cancer risk\u0026thinsp;\u0026gt;\u0026thinsp;1\u003c/b\u003e \u0026ndash; Potential concern\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCancer Risk (CR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0002164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAcceptable (\u0026lt;\u0026thinsp;1\u0026times;10⁻⁴)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eBharatpur OCP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInhalation Exposure Concentration (EC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate exposure level\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHazard Quotient (HQ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.132\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax HQ\u0026thinsp;\u0026gt;\u0026thinsp;1 \u0026rarr; Health concern at peak\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCancer Risk (CR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0000504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNegligible cancer risk\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eLingaraj OCP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInhalation Exposure Concentration (EC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLowest among all\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHazard Quotient (HQ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.369\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax HQ\u0026thinsp;\u0026gt;\u0026thinsp;1 \u0026rarr; Slight concern\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCancer Risk (CR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0000526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo significant cancer risk\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5. HYSPLIT forward trajectory analysis\u003c/h2\u003e\u003cp\u003eThe forward trajectory plot generated using the NOAA model represents the potential pollutant transport from the mining region comprising Kaniha OCP, Bharatpur OCP, and Lingaraj OCP, using a source location (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The trajectories are simulated at three different altitudes\u0026mdash;100 m (green), 500 m (red), and 1000 m (blue) above ground level\u0026mdash;for a 24-hour period starting on 22 January 2024. The dispersion pattern clearly indicates that pollutants emitted from these mines can travel toward the northeast and west directions, covering significant distances, especially at higher altitudes. The 1000 m trajectory shows far-reaching transport, indicating that fine particulates like PM\u003csub\u003e10\u003c/sub\u003e and associated free silica in respirable dust can reach downwind receptors upto the Similpal national park (Odisha), an eco-sensitive zone. The mid-level (500 m) and surface-level (100 m) trajectories indicate westward dispersion, extending up to parts of Chhattisgarh. Specifically, the 100-meter trajectories are reaching as far as the Barnawapara forest range in Kasdol (Chhattisgarh), while several 500 m trajectories extend up to Nawagarh Tehsil (Chhattisgarh). As previously discussed, elevated levels of free silica near mining areas pose significant health risks to individuals residing nearby. However, the forward trajectory analysis further reveals that pollutant transport is not limited to the immediate vicinity of the mines. Instead, airborne contaminants have the potential to affect distant villages, agricultural zones, and ecologically sensitive areas along the dispersion path, raising broader environmental and public health concerns \u003cb\u003e(\u003c/b\u003eLiu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Given the persistence of air parcel movement in these directions, communities along the predicted paths may face increased exposure to hazardous pollutants, which could lead to respiratory ailments, cardiovascular issues, or other long-term health effects. These results highlight the need for strategic planning in emission control and continuous air quality monitoring at the sites to protect public health in downwind receptor zones.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Morphological assessment\u003c/h2\u003e\u003cp\u003eThe SEM-EDS micrographs and corresponding EDS of suspended particulate matter (SPM) samples collected from three coal mines: Kaniha OCP (A), Lingaraj OCP (B), and Bharatpur OCP (C) are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The SEM/EDS is helpful to characterize and confirm the presence of free silica (quartz) particles in the mining environment \u003cb\u003e(\u003c/b\u003eAzam et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Trechera et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In Kaniha OCP, the SEM image Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, collected from the Near Z Patch site, reveals angular and irregular-shaped particles, indicating their mechanical origin, possibly from coal cutting and overburden handling. The EDS spectrum from Spot-2 shows a dominant silicon (Si) peak, along with oxygen (O) and minor carbon (C) peaks. The high intensity of the Si peak confirms the presence of silicates or quartz (SiO\u003csub\u003e2\u003c/sub\u003e), likely from surrounding rock or dust mixed with the coal. In Lingaraj OCP (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB), at the near Sump No. 4 site, the micrograph also shows coarse, fragmented particles with varying textures, suggesting a mixed origin of coal cutting and overburden removal. At EDS Spot-3, the spectrum again exhibits a strong Si signal, accompanied by peaks of O and C. This confirms the presence of free silica, likely in the form of quartz, consistent with mining dust. At Bharatpur OCP\u0026rsquo;s (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC) near the View Point site, the SEM image shows larger particles with sharper edges, similar to earlier images, which may be derived from active coal cutting and overburden removal through blasting activities. The EDS spectrum at Spot-4 confirms the presence of Si and O as dominant elements, reinforcing the identification of quartz particles in the sample. The presence of significant carbon in all the EDS shows that there is likely a mix of coal dust and mineral fragments. All three mining sites show clear evidence of respirable free silica particles in the SPM, confirmed through EDS peaks primarily consisting of silicon and oxygen, which are characteristic of quartz (SiO₂). Some other SEM-EDS images have also been included in the supplementary material, \u003cb\u003eFig. S10-S12\u003c/b\u003e. The variation in particle morphology and size suggests differences in source material or activity intensity. This highlights the potential health risk due to silica exposure, especially in high-production opencast coal mines, and emphasizes the importance of continuous monitoring and mitigation strategies to be adopted.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe interquartile range for SPM and PM\u003csub\u003e10\u003c/sub\u003e was observed to be the highest at Bharatpur OCP, followed by Lingaraj OCP, and the minimum at Kaniha OCP. Interestingly, in the case of free silica, the highest concentration of free silica was observed at Kaniha OCP, having mean values of 41.59 \u0026micro;g/m\u0026sup3;, despite its relatively lower SPM and PM₁₀ levels, followed by Lingaraj OCP (10.10 \u0026micro;g/m\u0026sup3;) and Bharatpur OCP (9.68 \u0026micro;g/m\u0026sup3;). This indicates that the elevated silica content at Kaniha is likely due to the geogenic characteristics of the area, which naturally contribute to higher silica dust emissions. Based on health risk assessment over two scenarios, one is at the worst case, where non-cancer risk was observed at the maximum free silica concentration. However, at mean values, the non-cancer risk is observed only at Kaniha OCP. In both scenarios, during the assessment of cancer risk, Kaniha OCP is susceptible to cancer risk. However, Bhartapur OCP is susceptible only during the worst-case scenario and acceptable during usual days. Lingaraj OCP is free from any cancer risk under both scenarios. The forward trajectory of the air mass shows that people residing far from the mining site are also susceptible to the concentration of free silica. The SEM-EDS images show larger particles with sharper edges and confirm the presence of Si and O as dominant elements, reinforcing the identification of quartz particles in the samples. The presence of significant carbon in all the EDS analyses shows that there is likely a mix of coal dust and mineral fragments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.Y. and S.S. acknowledge the Central Mine Planning \u0026amp; Design Institute Limited, Bhubaneswar, India, for financial support under Grant No.: SSP-506. The authors gratefully acknowledge the Environment Department of the Central Mine Planning \u0026amp; Design Institute Limited for providing financial support. The authors also thank Mahanadi Coalfields Limited, Odisha, India, for facilitating access to the mine sites and CSIR-Institute of Minerals and Materials Technology, Bhubaneswar, India, for extending infrastructural support for sample analysis.\u003c/p\u003e\n\u003cp\u003eAll authors have read, understood, and have complied with the statement on \u0026quot;Ethical responsibilities of Authors\u0026quot; as found in the Instructions for Authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial interest declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCredit authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eManish Yadav:\u003c/strong\u003e Data curation, Writing\u0026ndash; original draft, Methodology, Software, Writing\u0026ndash; review \u0026amp; editing. \u003cstrong\u003eNitin Kumar Singh:\u003c/strong\u003e Writing\u0026ndash; review \u0026amp; editing, Supervision, Validation. \u003cstrong\u003eSumit Saha:\u003c/strong\u003e Conceptualization, Methodology, Supervision, Writing\u0026ndash; review \u0026amp; editing, Validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study will made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo ethical approval is required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot required\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot required\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndraos, C., Utembe, W., \u0026amp; Gulumian, M. (2018). Exceedance of environmental exposure limits to crystalline silica in communities surrounding gold mine tailings storage facilities in South Africa. Science of the total environment, 619, 504\u0026ndash;516.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAsif, Z., Chen, Z., Wang, H., \u0026amp; Zhu, Y. (2022). Update on air pollution control strategies for coal-fired power plants. Clean technologies and environmental policy, 24(8), 2329\u0026ndash;2347.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAzam, S., Liu, S., Bhattacharyya, S., \u0026amp; Mishra, D. P. (2024). Prevalence of nano-sized coal mine dust in North and Central Appalachian coal mines\u0026ndash;Insights from SEM-EDS imaging. Journal of Hazardous Materials, 476, 135226.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCastranova, V., \u0026amp; Vallyathan, V. (2000). Silicosis and coal workers' pneumoconiosis. Environmental health perspectives, 108(suppl 4), 675\u0026ndash;684.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen, H. L., Li, C. H., Zhai, P. Y., Zhuang, X., Lian, Y. L., Qiao, X., Feng, J., Qian, Z. S., \u0026amp; Qin, G. (2024). Survival and disease burden analyses of occupational pneumoconiosis during 1958\u0026ndash;2021 in Huangshi city, China: a retrospective cohort study. BMC public health, 24(1), 1437. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-024-18847-6\u003c/span\u003e\u003cspan address=\"10.1186/s12889-024-18847-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDash, T. R., Tripathy, D. P., \u0026amp; Pandey, J. K. (2020). Chemical characterization of PM10 and evaluation of health risk for the people residing around a highly mechanized opencast coal mine using FTIR spectroscopy. Arabian Journal of Geosciences, 13(4), 175.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDirectorate General of Mines Safety (DGMS). (2010, January 21). Respirable dust measurements and control to prevent pneumoconiosis in mines (DGMS (Tech.) (S\u0026amp;T) Circular No. 01). Ministry of Labour \u0026amp; Employment, Government of India.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGopinathan, P., Subramani, T., Barbosa, S., \u0026amp; Yuvaraj, D. (2023). Environmental impact and health risk assessment due to coal mining and utilization. Environmental Geochemistry and Health, 45(10), 6915\u0026ndash;6922.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHealth and Safety Executive (HSE). (2005). MDHS 101: Crystalline silica in respirable airborne dusts \u0026ndash; Direct-on-filter analyses by infrared spectroscopy and X-ray diffraction. Methods for the Determination of Hazardous Substances. Health and Safety Laboratory. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.hse.gov.uk/pubns/mdhs/index.htm\u003c/span\u003e\u003cspan address=\"https://www.hse.gov.uk/pubns/mdhs/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHnizdo, E., \u0026amp; Vallyathan, V. (2003). Chronic obstructive pulmonary disease due to occupational exposure to silica dust: a review of epidemiological and pathological evidence. Occupational and environmental medicine, 60(4), 237\u0026ndash;243.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoy, R. F., \u0026amp; Chambers, D. C. (2020). Silica-related diseases in the modern world. Allergy, 75(11), 2805\u0026ndash;2817.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIEA (2025), Global Energy Review 2025, IEA, Paris \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iea.org/reports/global-energy-review-2025\u003c/span\u003e\u003cspan address=\"https://www.iea.org/reports/global-energy-review-2025\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Licence: CC BY 4.0\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInternational Agency for Research on Cancer. (1995). IARC monographs on the evaluation of carcinogenic risks to humans (Vol. 48, p. 691). Lyon, France: International Agency for Research on Cancer.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumar, A., Kumar, A., Singh, P., \u0026amp; Elumalai, S. P. (2024). Trace element profile of TSPM with depth within opencast coal mines for risk assessment and their source apportionment. Air Quality, Atmosphere \u0026amp; Health, 17(12), 2949\u0026ndash;2965.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLargeron, Y., \u0026amp; Staquet, C. (2016). Persistent inversion dynamics and wintertime PM10 air pollution in Alpine valleys. Atmospheric Environment, 135, 92\u0026ndash;108.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, H., Wang, X., Talifu, D., Ding, X., Abulizi, A., Tursun, Y., \u0026hellip; Xie, X. (2022). Distribution and sources of PM2. 5-bound free silica in the atmosphere of hyper-arid regions in Hotan, North-West China. Science of The Total Environment, 810, 152368.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, L., Ge, Z., Zhou, Z., Li, Z., \u0026amp; Deng, Q. (2024). Mineral composition, pore structure and mechanical properties of coal measure strata rocks: a case study of Pingdingshan Coalfield. Science of The Total Environment, 952, 175944.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa, Q., Wu, J., He, C., \u0026amp; Fang, X. (2021). The speed, scale, and environmental and economic impacts of surface coal mining in the Mongolian Plateau. Resources, Conservation and Recycling, 173, 105730.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMastoi, M. S., Munir, H. M., Zhuang, S., Hassan, M., Usman, M., Alahmadi, A., \u0026amp; Alamri, B. (2022). A critical analysis of the impact of pandemic on China\u0026rsquo;s electricity usage patterns and the global development of renewable energy. International Journal of Environmental Research and Public Health, 19(8), 4608.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMegido, L., Su\u0026aacute;rez-Pe\u0026ntilde;a, B., Negral, L., Castrill\u0026oacute;n, L., \u0026amp; Fern\u0026aacute;ndez-Nava, Y. (2017). Suburban air quality: Human health hazard assessment of potentially toxic elements in PM10. Chemosphere, 177, 284\u0026ndash;291.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinistry of Coal, Government of India. (n.d.). Coal reserves. Retrieved July 13, 2025, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.coal.gov.in/en/major-statistics/coal-reserves\u003c/span\u003e\u003cspan address=\"https://www.coal.gov.in/en/major-statistics/coal-reserves\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinistry of Environment and Forests (MoEF). (2000, September 25). Standards for coal mines (Notification No. GSR 742(E)) [Government notification]. Government of India, New Delhi.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNair, A. S., Soni, K., Singh, P., Meena, K., \u0026amp; Sharma, R. (2023). Assessment of spatio-temporal variation in AOD over four coal mine sites of Assam through satellite observation. Journal of Environmental Studies and Sciences, 13(4), 629\u0026ndash;642.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO\u0026rsquo;Day, P. A., Pattammattel, A., Aronstein, P., Leppert, V. J., \u0026amp; Forman, H. J. (2022). Iron speciation in respirable particulate matter and implications for human health. Environmental Science \u0026amp; Technology, 56(11), 7006\u0026ndash;7016.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOffice of Environmental Health Hazard Assessment. (2005). Adoption of chronic reference exposure levels for silica (crystalline, respirable). California Environmental Protection Agency.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOosthuizen, M. A., Wright, C. Y., Matooane, M., \u0026amp; Phala, N. (2015). Human health risk assessment of airborne metals to a potentially exposed community: a screening exercise.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePandey, J. K., Agarwal, D., Gorain, S., Dubey, R. K., Vishwakarma, M. K., Mishra, K. K., \u0026amp; Pal, A. K. (2017). Characterization of respirable dust exposure of different category of workers in Jharia Coalfields. Arabian Journal of Geosciences, 10, 1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRolph, G., Stein, A., \u0026amp; Stunder, B. (2017). Real-time Environmental Applications and Display System: READY. Environmental Modelling \u0026amp; Software, 95, 210\u0026ndash;228. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envsoft.2017.06.025\u003c/span\u003e\u003cspan address=\"10.1016/j.envsoft.2017.06.025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSharma, R., \u0026amp; Kumar, A. (2023). Analysis of seasonal and spatial distribution of particulate matters and gaseous pollutants around an open cast coal mining area of Odisha, India. Environmental Science and Pollution Research, 30(14), 39842\u0026ndash;39856.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShelton, S., Liyanage, G., Jayasekara, S., Pushpawela, B., Rathnayake, U., Jayasundara, A., \u0026amp; Jayasooriya, L. D. (2022). Seasonal variability of air pollutants and their relationships to meteorological parameters in an urban environment. Advances in Meteorology, 2022(1), 5628911.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh, S. K., Singh, R. K., Singh, K. K., Singh, R. K., \u0026amp; Singh, S. (2022). Concentration, sources and health effects of silica in ambient respirable dust of Jharia coalfields region, India. En-viron Sci Eur 34 (1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTrechera, P., Moreno, T., C\u0026oacute;rdoba, P., Moreno, N., Zhuang, X., Li, B., \u0026hellip; Querol, X. (2020). Mineralogy, geochemistry and toxicity of size-segregated respirable deposited dust in underground coal mines. Journal of Hazardous Materials, 399, 122935.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eU.S. Environmental Protection Agency. (1999). Compendium method IO-2.1: Sampling of ambient air for total suspended particulate matter (SPM) and PM₁₀ using high volume (HV) sampler (EPA/625/R-96/010a). Center for Environmental Research Information, Office of Research and Development.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eU.S. Environmental Protection Agency. (2009). Risk assessment guidance for Superfund Volume I: Human health evaluation manual (Part F, Supplemental guidance for inhalation risk assessment) (EPA/540/R-070/002). Washington, DC: U.S. Environmental Protection Agency.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, H., \u0026amp; Zhang, D. (2023). Examining the interplay between fossil fuel mining, sustainable growth, and economic prosperity. Resources Policy, 87, 104324.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, H., Ye, Q., Zhang, H., Sun, X., \u0026amp; Li, T. (2023). Prevention and Treatment of Pneumoconiosis in the Context of Healthy China 2030. China CDC weekly, 5(41), 927\u0026ndash;932. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.46234/ccdcw2023.162\u003c/span\u003e\u003cspan address=\"10.46234/ccdcw2023.162\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu, T., Song, Y., Liu, M., Cai, X., Zhang, H., Guo, J., \u0026amp; Zhu, T. (2019). Temperature inversions in severe polluted days derived from radiosonde data in North China from 2011 to 2016. Science of the Total Environment, 647, 1011\u0026ndash;1020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYadav, M., Sahu, S. P., \u0026amp; Singh, N. K. (2019). Multivariate statistical assessment of ambient air pollution in two coalfields having different coal transportation strategy: a comparative study in Eastern India. Journal of Cleaner Production, 207, 97\u0026ndash;110.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYadav, M., Singh, N. K., Sahu, S. P., Garnayak, D., \u0026amp; Sahu, S. K. (2025). Impact of Mining and Allied Activities on Air Quality of a Mega Coal Mining Zone in India. Environmental Quality Management, 34(4), e70089.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang, Q., Liu, G., Yang, L., Yun, J., Zhang, X., Zhao, C., \u0026hellip; Jiang, G. (2024). Atmospheric emissions of respirable quartz from industrial activities in China. Nature Sustainability, 7(9), 1120\u0026ndash;1127.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang, X., Lu, D., Zhu, B., Sun, Z., Li, G., Li, J., \u0026hellip; Jiang, G. (2022). Phase transformation of silica particles in coal and biomass combustion processes. Environmental Pollution, 292, 118312.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, H., Zhang, F., Song, J., Tan, M. L., Kung, H. T., \u0026amp; Johnson, V. C. (2021). Pollutant source, ecological and human health risks assessment of heavy metals in soils from coal mining areas in Xinjiang, China. Environmental Research, 202, 111702.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-geochemistry-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"egah","sideBox":"Learn more about [Environmental Geochemistry and Health](https://www.springer.com/journal/10653)","snPcode":"10653","submissionUrl":"https://submission.nature.com/new-submission/10653/3","title":"Environmental Geochemistry and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Quartz, Free silica, Coal mining, Respirable dust, Health risk assessment","lastPublishedDoi":"10.21203/rs.3.rs-7308663/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7308663/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe International Agency for Research on Cancer (IARC) has classified free silica/quartz as a Group 1 carcinogen, indicating sufficient evidence of its carcinogenicity in humans. In the present study, suspended particulate matter (SPM), respirable dust (PM\u003csub\u003e10\u003c/sub\u003e), and free silica content in dust were assessed to determine the associated exposure risk in three mega coal mines (Bharatpur, Kaniha, and Lingaraj OCP) of the Talcher Coalfield located in Odisha, India. The respirable dust samples collected on the filter paper were analyzed using Fourier transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and Energy Dispersive Spectrometer (EDS) to characterize their composition and morphology. The highest concentrations of SPM and PM\u003csub\u003e10\u003c/sub\u003e were observed at Bharatpur OCP, with a mean value of SPM and PM\u003csub\u003e10\u003c/sub\u003e of 394 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e and 136 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, respectively. In contrast, Kaniha OCP exhibited slightly lower concentrations for SPM and higher concentrations of PM\u003csub\u003e10\u003c/sub\u003e, with mean values of 230 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e and 193 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, respectively. When compared with Bharatpur OCP, the highest concentration of free silica was observed at Kaniha OCP, with values ranging from 5.94 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e to 114.89 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e and a mean concentration of 41.59 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e. The health risk assessment, conducted using USEPA methodology, indicates that Kaniha OCP poses the highest risks of exposure to respirable silica, with both non-carcinogenic and carcinogenic outcomes, followed by Bharatpur OCP. In contrast, the Lingaraj OCP exhibited comparatively lower health risk levels. The SEM/EDS analysis showed clear evidence of the presence of respirable free silica particles across all three mining sites.\u003c/p\u003e","manuscriptTitle":"Exposure Assessment to Respirable Free Silica in Coal Mining Areas: A Health Risk Assessment Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-13 16:27:38","doi":"10.21203/rs.3.rs-7308663/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-24T09:01:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-21T05:32:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-12T13:03:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89347070084966354827579544313343403517","date":"2025-08-16T08:23:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172040120966584192536278788914339221921","date":"2025-08-13T03:30:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142678923923609026788720962153855002448","date":"2025-08-08T12:01:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109088263666135886560736372356366883481","date":"2025-08-08T09:12:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-08T06:24:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-07T21:14:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-07T18:29:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Geochemistry and Health","date":"2025-08-06T10:13:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-geochemistry-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"egah","sideBox":"Learn more about [Environmental Geochemistry and Health](https://www.springer.com/journal/10653)","snPcode":"10653","submissionUrl":"https://submission.nature.com/new-submission/10653/3","title":"Environmental Geochemistry and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"08ab85d4-d5f2-42d7-a77d-e89f64055c1c","owner":[],"postedDate":"August 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:02:25+00:00","versionOfRecord":{"articleIdentity":"rs-7308663","link":"https://doi.org/10.1007/s10653-026-03071-5","journal":{"identity":"environmental-geochemistry-and-health","isVorOnly":false,"title":"Environmental Geochemistry and Health"},"publishedOn":"2026-02-20 15:57:50","publishedOnDateReadable":"February 20th, 2026"},"versionCreatedAt":"2025-08-13 16:27:38","video":"","vorDoi":"10.1007/s10653-026-03071-5","vorDoiUrl":"https://doi.org/10.1007/s10653-026-03071-5","workflowStages":[]},"version":"v1","identity":"rs-7308663","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7308663","identity":"rs-7308663","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}