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People living around surface mines are exposed to enhanced respirable particle level. Not many studies are available that evaluated the contribution of mining to it. The paper presented the result of study that investigated spatio-temporal variability of particle concentrations around a surface coal mine in eastern part of India. Particle concentra...
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Background
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Citations
... shows the exposure of the HEMM operators is dominated by coarse particles. Previous studies384 have reported that mining predominantly produces coarse particles (PM10)(Sahu et al., 2018).385 Very fine particles like PM1 are usually emitted from the combustion source (Lim et al., 2012; Li 386 and Lin, 2002). ...
As a part of their occupation, the operators of heavy earth moving machineries (HEMM) working in opencast mines are exposed to several air pollutants, prominent one being the airborne particulate matter. The paper presents the findings from a study that was aimed at investigating the particulate matter exposure of heavy earth moving machineries (HEMM’s) operators in highly mechanized opencast mines. The HEMMs included in the study are dumper, shovel and drill. The mean in-cabin PM 10 concentrations of dumper and shovel, which had an air-conditioning system in the cabin, were in the range of 600–650 µg m − 3 . Operators inside the drill cabin that did not have air conditioning systems were exposed to as high as 1992 µg m − 3 However, cabins were effective in preventing up to 20% particulate matter exposure by the operators in comparison to persons working in an open mine atmosphere. The main haul road in mines contributed ~ 50% higher exposure than the internal haul roads. Coarse particles (PM 10 ) are retained in upper respiratory tracts while the fines (PM 2.5 and PM 1 ) travel via tracheobronchial region and reach the alveolar region. Occupational exposure of HEMM operators to different pollutants in a mine is at present very limited and therefore more studies need to be conducted.
... Wuhai, an open-pit mining coal resource-based city, is located in the arid desert area of Northwest China, where has an extremely fragile ecosystem 26,27 . Previous researchs have investigated the temporospatial distribution of dust concentration and the characteristics of heavy metal pollution of atmospheric dustfall, as well as using principal component analysis to analyze the pollution sources and their contribution 28,29 . ...
Atmospheric dustfall is solid air pollutant, has a major impact on the environment and human health. The objective of this study was to investigate the microscopic characteristics and sources of atmospheric dustfall in open-pit mining coal resource-based city in the arid desert area of Northwest China. The characteristics of size and shape factors, variation of shape factors with size distribution, types of individual particles, and sources of atmospheric dustfall, which were collected in the open-pit mining area and surrounding areas, were analyzed by X-ray diffraction (XRD) and scanning electron microscopy coupled with an energy dispersive spectrometer (SEM–EDS) combined with graphical method and shape factors. The results showed that the atmospheric dustfall in all functional areas was dominated by coarse-grained particles. The shape of the atmospheric dustfall deviated from spherical shape, and with decreasing particle size, the difference in shape factors increased in each functional area. The EDS and XRD analyses indicated the presence of 13 types of particles. The sources were mainly local and included soil dust from each functional area; industrial dust, construction dust, biogenic impurities, fossil fuel combustion, wear products of motor vehicle parts, motor vehicle exhaust emissions, and emission and excreta from biological activities in each functional area except the desert area; emissions from a steel plant in the industrial area; coal-associated ore, coal dust, coal gangue emissions, and emissions from the spontaneous combustion of coal gangue in the open-pit mining area; secondary chemical crystallization products in the industrial area and the open-pit mining area; dust generated by vehicles abrading the surface of the off-mine coal road and in the open-pit mining area.
... Rapid industrialization, construction, transportation utilization, urbanization, economic development, and energy demands have severely deteriorated the air quality in industrial areas, especially PM (particulate matter). Currently, ~ 90% of India's total coal production is through open-cast mines (Sahu et al. 2018). Many inorganic compounds containing trace metals are emitted by natural and anthropogenic sources such as coal and oil combustion, crustaceous materials, construction activities, incineration, and road dust (Sahoo et al. 2016, Yadav 2015, Yadav and Jamal 2018a. ...
Singrauli coal fields are air polluted in an industrial town in India. The contribution of anthropogenic activities to increased particulate matter (PM) in the study area has been calculated. The size-segregated PM was collected in 2016 and 2017 and carried out for morphological and composition analysis. PCA (principal component analysis) and PMF (positive matrix factorization) were applied to quantify for identification and apportionment of sources. Based on the study, biomass burning and vehicular emission were the primary source of particulate matter; and PCA and PMF identify the contribution of biomass burning, vehicular emission, mining activity, resuspended dust, secondary inorganic aerosols, and traffic-related emission as the major sources of particulate matter in Singrauli coalfield.
... These results may be important for areas with similar climatic conditions in the world [58]. Earlier work has focused on the evaluation of larger fractions, which have less impact on human health and have less transport capacity [59]. Figure 1 shows a geographical map indicating the anthropogenic sources of coal-rock dust, including our research area (open-pit coal mines and coal-fired power station). ...
... This statement is also applicable to the gradual decrease in the amount of dust from PM10 to PM0.1. Thus, according to the analysis of snow, we did not see a significant decrease in pollution with distance from the source of dust, which is typical in the study of dust air pollution [59]. This result needs an additional study with a more regular network of observations in different wind directions. ...
... This statement is also applicable to the gradual decrease in the amount of dust from PM 10 to PM 0.1 . Thus, according to the analysis of snow, we did not see a significant decrease in pollution with distance from the source of dust, which is typical in the study of dust air pollution [59]. This result needs an additional study with a more regular network of observations in different wind directions. ...
This study is aimed at elucidating the fractional composition, volume and toxicity of dust that is deposited in the snow cover for the period of snow accumulation at different distances from coal mines and a coal-fired power station in the Kemerovo region (Russia). During the filtration process, fractions of 10–0.1 µm and less than 0.1 µm were isolated and weighed. Light microscopy was used to estimate the size of dust particles in the 10–0.1 µm fraction. We found that the total volume and fractional composition of dust has no significant trend to change in the research space. The dust contamination is associated mainly with PM2 particles. Genotoxic tests on cell lines A549 and MRC-5 with different concentrations of dust showed high toxicity (including control points). Taking into account the fact that an increase in the concentration of PM leads to intensification in the toxicity of dust, we can determine that the territory within the studied boundaries is dangerous for the population. Our study is important for understanding the processes of formation, toxicity, transport and sedimentation in the snow cover from dust generated in the process of coal mining and the operation of a coal-fired power station.
... The weather station with a combination of internal and external sensors records and stores the meteorological data that are subsequently downloaded. Collection of meteorological data in opencast mines using a portable weather has been practiced in several air quality studies (Gautam & Patra, 2015;Sahu & Patra, 2020;Sahu et al., 2018aSahu et al., , 2018b. The weather station was also factory calibrated before the start of the study. ...
... Previous studies in opencast mines focused on estimation of PM generation from various mining activities such top soil removal, drilling, loading, unloading, etc. (Nagesha et al., 2019;Onder & Yigit, 2009;Mandal et al., 2012;Chakraborty et al., 2002), in-pit PM emission from different unit operations and the assessment of PM concentration in mine surroundings, typically at a distance ~ 300-500 m (Kundu & Pal, 2015;Espitia-Pérez et al., 2018;Sahu et al., 2018a;Sahu & Patra, 2020;Patra et al., 2020). Information on PM concentrations at pit boundary is a new input to the present literature as we did not find pertinent results from previous study corresponding to the pit boundary. ...
This study gives insight into the spatiotemporal variability of respirable PM concentrations around typically highly mechanized opencast coal mines in India and its influence on ambient air quality, which at present is scant in the literature. The results suggest that at a distance of 500 m from the pit boundary, the respirable PM concentrations were found higher than the background concentration (1.52, 1.80 and 1.89 times for PM10, PM2.5 and PM1 at Mine 1 and 1.4, 1.35 and 1.35 times for PM10, PM2.5 and PM1 at Mine 2) which suggests that residents up to and beyond 500 m from the mine are exposed to the PM emitted from mining activities. For PM2.5 and PM1 concentrations, RH was the most important determinant (PM2.5: 24.8%; PM1: 30.1%). Pit boundary PM concentration was the weakest determinant (7%) for the PM2.5 concentration in mine surroundings, and wind speed was found as the weakest determinant (6.6%) for PM1 concentration. Conversely, distance (20.8%) was the important determinant of PM10 concentrations. All the four predictors (RH, wind speed, distance and Pit boundary PM concentration) could explain 46–54% variability in the PM concentrations. Inclusion of pit boundary PM concentration as a predictor increased the prediction capability of the models (all the developed models have R² > 0.40 at significance level p < 0.05). PM prediction models can be used by the mining and regulatory authorities to assess the respirable PM level in mine surroundings.
... Contrary to diarrhoea, there was a sharp increase in the risk for cough episodes in impacted areas over the study period, particularly later in the advanced extraction phase, although not statistically significant. These results are similar to those of previous studies reporting increased likelihood for respiratory-related diseases in children living in mining communities [48,49] and may reflect the increased levels of air pollution around active mines found in other studies [50][51][52][53]. Additionally, mining-related and other environmental pollution have been associated with poor child health outcomes, including respiratory diseases [23,54,55]. ...
Background
The African continent hosts many industrial mining projects, and many more are planned due to recent prospecting discoveries and increasing demand for various minerals to promote a low-carbon future. The extraction of natural resources in sub-Saharan Africa (SSA) represents an opportunity for economic development but also poses a threat to population health through rapid urbanisation and environmental degradation. Children could benefit from improved economic growth through various channels such as access to high-quality food, better sanitation, and clean water. However, mining can increase food insecurity and trigger local competition over safe drinking water. Child health can be threatened by exposure to mining-related air, noise, and water pollution. To assess the impact of mines on child health, we analyse socio-demographic, health, and mining data before and after several mining projects were commissioned in SSA.
Results
Data of 90,951 children living around 81 mining sites in 23 countries in SSA were analysed for child mortality indicators, and 79,962 children from 59 mining areas in 18 SSA countries were analysed for diarrhoea, cough, and anthropometric indicators. No effects of the launch of new mining projects on overall under-five mortality were found (adjusted Odds Ratio (aOR): 0.88; 95% Confidence Interval (CI): 0.68–1.14). However, activation of mining projects reduced the mortality risk among neonates (0–30 days) by 45% (aOR: 0.55; 95% CI: 0.37–0.83) and risk for a child to develop diarrhoeal diseases by 32% (aOR: 0.68; 95% CI: 0,51–0.90). The timing analysis of observed changes showed that there is a significant decline in the risk for childhood diarrhoea (aOR: 0.69; 95% CI: 0.49–0.97), and the mean height-for-age z-scores by 28 percentage points, during the prospection and construction phase; i.e., within four years to the initiation of extraction activity. No effects were found for cough and weight-for-height.
Conclusion
The results presented suggest that the impacts of mining on child health vary throughout the mine’s life cycle. Mining development likely contributes positively to the income and livelihoods of the impacted communities in the initial years of mining operations, particularly the prospection and construction phase; these potential benefits are likely to be at least partially offset by food insecurity and environmental pollution during early and later mining stages, respectively. Further research is warranted to better understand these health impacts and to identify policies that can help sustain the positive initial health impacts of mining projects in the long term.
... An aerosol spectrometer (Model 1.108; GRIMM Aerosol Technik, Germany) was used as a reference instrument in this study to evaluate the performance of the sensors. The instrument is widely accepted as a standard instrument and has been used in the past for occupational health monitoring (Gautam et al., 2016;Stroszejn-Mrowca and sahumarcekl-Stańczyk, 2003), indoor air quality assessment (Tang et al., 2012), aerosol dispersion research (Sahu et al., 2018) and inhalation toxicology estimation (Int Panis et al., 2010). The instrument has an in-built pump that draws air at a constant rate of 1.2 l min − 1 . ...
Low-cost sensors have the potential to revolutionize air pollution research by providing high spatial resolution data. However, data accuracy from the low-cost sensors remains a major concern. Therefore, it is necessary to evaluate the performance of the low-cost sensors. The current study evaluated the performance of two such low cost PM (particulate matter) sensors viz. OPC N2 (∼400 USD) and PM Nova sensor (∼20 USD) for four different uncontrolled test environments i.e. (a) Indoor (b) Indoor with incense stick (c) Ambient air (outdoor) and (d) Traffic, using an aerosol spectrometer (GRIMM 1.108) as the reference instrument. The results showed that both OPC N2 and PM Nova can record the airborne PM concentration with good accuracy as demonstrated through linear regression with Grimm (OPC N2, R² = 0.954–0.987 and PM Nova, R² = 0.872–0.981). The performance of the sensors was better for the finer particles (PM1 and PM2.5) than coarser particles (PM10). Meteorological parameters such as wind speed, temperature and RH did not have any significant impact on the sensor performance. Using a sampling tube did not have an impact on sensor performance. A modified python script for data retrieval from the sensors in a user-friendly format has been developed. More evaluations of used sensors and in diverse environmental settings can further increase the understanding about the sensor performance.
... There are many opencast and underground coal mines in the region, with a total production capacity exceeding 270 million t·y -1 . However, large amounts of atmospheric PM are generated in coal mines during blasting, mining, transportation, loading and unloading operations (Sahu et al., 2018;Zhai et al., 2019). In addition, the temperate, dry continental climate in this area, with low precipitation and strong evaporation all year round, increases the likelihood of dust pollution. ...
The surface operations area of an underground coal mine near Lingwu in Ningxia Hui Autonomous Region was selected for this study. Particulate matter (PM) was sampled in the coal screening plant during the day and night in Spring and Winter, 2019. Twelve trace metals and eight water-soluble ions in particulate matter up to 10 μm in diameter (PM10) and total suspended particles (TSP) were analyzed using ICP-OES and ion chromatography, respectively. The enrichment factor (EF) and positive matrix fraction (PMF) were used to identify potential sources of particulate metals. The forward trajectory model was used to analyze the main migration pathways of particles. Results showed that higher concentrations of PM10 and TSP were found in Spring than in Winter; the concentration of PM at night was lower than during the day. Most of the trace metals in TSP were greater than in PM10, while the content of most water-soluble ions in TSP was lower than in PM10. The EF analysis confirmed that particulate metals were attributable to anthropogenic emission. PMF model results further demonstrated that the main sources of PM in both seasons were regional suspended dust, traffic emission, industrial emission and coal burning. Air mass dispersion analysis showed that PM generated by these operations may spread to eastern and southeastern China within 72 h in Spring, while mainly to southeastern and southern China in Winter. These suggest a need for greater focus on strengthening the monitoring and early warning of the presence of atmospheric PM in southern Shanxi, China. Because of the risks that particulate metals pose to human health, the protection of children should be strengthened around the surface operation area of an underground coal mine. Moreover, monitoring of the concentrations of Cr in PM10 and Mn in TSP in Spring should be strengthened, and the opposite procedure should be adopted in Winter. These findings are useful for providing a theoretical basis for the prevention and control of pollutants in underground mining areas and the construction of cleaner production lines.
... Beyond its part in the climate crisis, the coal complex in India has significant health impactsmainly through local air pollutionincluding premature mortality, ranging from 80,000 to 115,000 premature deaths per year in the local population living around coal-fired power plants (Guttikunda and Jawahar, 2014). Coal-mine workers and communities around coal mines face many adverse diseases, prominent among them pneumoconiosis (commonly known as black lung disease) due to inhalation of coal dust as well as diseases due to polluted drinking water (Sahu et al., 2018;Mishra, 2015). Next to the slow violence of pollution, mining accidents are a persistent hazard (Maiti et al., 2009) with usually fatal consequences. ...
Coal is on the rise in India: despite the devasting impacts of the climate crisis, the awareness for land and forest rights, and political talk of a coal phase-out. In this article, we demonstrate that despite the renewables-led rhetoric, India is in the midst of a transition to (not away from) greater use of coal in its fossil energy system and in the electricity system in particular. We investigate this paradox by combining socio-metabolic and political ecological analysis of the Indian coal complex. Our framework integrates material and energy flow data as characterizing the Indian fossil energy transition, indicators on the development and structure of the coal industry , and studies of ecological distribution conflicts around coal. The dominant claim to expansive use of coal and the competing counterclaims are indicative of underlying power relations which can also be witnessed in other countries. In India, they extend into the conflicted development of renewable energy including hydropower, in which the land dispossession, exclusion, and injustices associated with the expansion of the coal complex are reproduced. We conclude that the current energy transition-in which coal continues to play a dominant role-is neither sustainable nor just.
... The Pearson correlation coefficient (R) has been estimated with the aid of the SPSS package (IBM SPPS Statistics 20) to evaluate the association between individual independent and dependent variables (Sahu et al. 2018a;Manju et al. 2018). R is estimated using the following expression: ...
The findings presented in this paper are based on the study conducted in a large opencast coal project in India. As a part of this study, particulate matter (PM) levels at different distances from the pit boundary were measured to understand dispersion of particles of different sizes emitted from the mine. Portable spectrometers and weather stations were deployed for measurement of PM concentration and meteorological parameters, respectively. The objectives were to quantify PM (PM10, PM2.5, and PM1) escaping from an active mine, estimate spatial variations of PM concentration in mine surrounding, evaluate role of meteorology, distance from the mine on PM concentration in the mining locality, and develop a model for prediction of PM10, PM2.5, and PM1 levels in the surroundings of a mine. A test of equality of means, correlation analysis, bivariate regression analysis, stepwise regression analysis, and the general linear model univariate procedure (analysis of covariance (ANCOVA)) were used for data analysis. At pit boundary, the average PM10, PM2.5, and PM1 concentrations were 2.75, 2.44, and 2.58 times the corresponding background level, and it reduced to 1.51, 1.79, and 1.90 times at a distance 500 m from the mine. Stepwise regression analysis revealed that between temperature and RH, which are highly correlated, RH is a better predictor of PM concentration. Although the proposed models had moderate R2 values (AdjR2: PM10 = 0.35; PM2.5 = 0.43; PM1 = 0.42), the models were reasonably good in predicting PM concentration (index of agreement = 0.80–0.89), with better prediction for fine particles (R2 = 0.48 for PM10, 0.62 for PM2.5, and 0.66 for PM1). The general linear model results revealed that distance is the largest predictor (16%) for PM10, but RH explains the highest variability for PM2.5 (28.2%) and PM1 (31.4%) concentrations. Wind speed was the least powerful determinant (4.5–9.9%). Using meteorological parameters (RH, temperature, and wind speed) and distance as input neurons, a feed-forward back-propagation artificial neural network model has been developed for prediction of particulate matter concentration.
