PM<sub>10</sub>emissions due to storage in coal piles in a mining industrial area

Rojano, Roberto; Arregocés, Heli A.; Angulo, Luis C.; Restrepo, Gloria

doi:10.2495/air160091

Cited by 6 publications

(5 citation statements)

References 14 publications

(4 reference statements)

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“…To identify possible emission sources at the sampling sites, all maps figures show spatial location of pits, disposal, and storing areas inside the mining areas. Coal storage piles (A1, A2, and A3) were identified from a previous report (Rojano et al, 2016). A1 and A2 are located in the north of the mining zone.…”

Section: Resultsmentioning

confidence: 99%

“…To locate the possible emission source responsible for high EF values in the sampling sites, we decided to perform an identification of pits, disposal and storing areas inside the mining areas. In a previous report by Rojano et al (2016), were identified three areas for coal storage A1, A2, and A3. We only considered A1 and A3, as they were located near our sampling sites.…”

Section: Demographic Characteristics Groupmentioning

confidence: 99%

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Geospatial analysis of residential proximity to open-pit coal mining areas in relation to micronuclei frequency, particulate matter concentration, and elemental enrichment factors

et al. 2018

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During coal surface mining, several activities such as drilling, blasting, loading, and transport produce large quantities of particulate matter (PM) that is directly emitted into the atmosphere. Occupational exposure to this PM has been associated with an increase of DNA damage, but there is a scarcity of data examining the impact of these industrial operations in cytogenetic endpoints frequency and cancer risk of potentially exposed surrounding populations. In this study, we used a Geographic Information Systems (GIS) approach and Inverse Distance Weighting (IDW) methods to perform a spatial and statistical analysis to explore whether exposure to PM and PM pollution, and additional factors, including the enrichment of the PM with inorganic elements, contribute to cytogenetic damage in residents living in proximity to an open-pit coal mining area. Results showed a spatial relationship between exposure to elevated concentrations of PM PM and micronuclei frequency in binucleated (MNBN) and mononucleated (MNMONO) cells. Active pits, disposal, and storage areas could be identified as the possible emission sources of combustion elements. Mining activities were also correlated with increased concentrations of highly enriched elements like S, Cu and Cr in the atmosphere, corroborating its role in the inorganic elements pollution around coal mines. Elements enriched in the PM fraction contributed to increasing of MNBN but seems to be more related to increased MNMONO frequencies and DNA damage accumulated in vivo. The combined use of GIS and IDW methods could represent an important tool for monitoring potential cancer risk associated to dynamically distributed variables like the PM.

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Section: Resultsmentioning

confidence: 99%

Section: Demographic Characteristics Groupmentioning

confidence: 99%

Geospatial analysis of residential proximity to open-pit coal mining areas in relation to micronuclei frequency, particulate matter concentration, and elemental enrichment factors

et al. 2018

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“…The highest contribution came from non-paved roads, which is similar to previous studies. Huertas et al [14,38,39] all showed that unpaved roads were the primary source of emissions, with PM 2.5 emissions accounting for 78.4%, 34%, and 60% of the total emissions, respectively, followed by transmission, loading and unloading, wind erosion, and other activities. The second major contribution source in this study was wind erosion on the surface of the coal piles.…”

Section: Pm 25 Emission Inventory Calculation and Spatial Distributionmentioning

confidence: 99%

Environmental Risk Assessment for PM2.5 Pollution from Non-Point Sources in the Mining Area Based on Multi-Source Superposition and Diffusion

Zhou

Meng

et al. 2021

Sustainability

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To identify high-concentration contributing sources and highly dispersive pollution sources of fine particulate matter, analyze the relationship between the location and distribution shape of emission sources and the concentration contribution and dispersion of particulate matter, and realize the atmospheric environment risk simulation and the differential control of non-point sources in the mining area, taking a large mining area in Inner Mongolia as an example, we classified the emission sources of PM2.5 (particulate matter less than 2.5 μm) and complied with the emission inventory. A CALPUFF model was used to simulate the contribution for the PM2.5 concentration of six types of emission sources and a multi-source superposition. Through scenario simulation, we analyzed the relationship between the spatial distribution of emission sources and the emission concentration and diffusion in a large mining area. We analyzed the relative risks of six types of sources under the influence of other superimposed sources and the change of emission concentration during transmission. We established a three-dimensional evaluation model to assess the atmospheric environmental risk of PM2.5 non-point sources in the mining area, considering the change rate of PM2.5 concentration with migration, the relative contribution ratio of superimposed sources, and the equal contribution index of the standard concentration. The results show that the maximum equal contribution index of standard concentration of multi-source superposition was 4.40. Among them, non-paved roads, exposed surface sources of coal piles, and exposed surface sources of mine pits and dumps were the top three pollution contributors, and their maximum equal contribution indexes of standard concentration were 2.40, 2.21, and 2.10, respectively. The effect of superimposed pollution sources was affected by the wind field and the spatial distribution density of emission sources, while the dispersion was affected by the wind direction and the distribution direction of pollution sources. In the case of the same source intensity and emission area, the denser the source distribution was, the higher the emission concentration was, the smaller the contribution ratio of superimposed sources was, and the greater the relative pollution risk was. When the angle between the direction of dispersed linear pollution sources and the dominant wind direction was smaller, the emission concentration was higher, but the diffusion surface was smaller. When the angle with the direction of wind direction was larger, the emission concentration was lower, but the diffusion surface was larger. Concentrated pollution sources had the highest concentration and the diffusion surface was in the middle. Non-paved roads had the highest environmental risk, with an average risk of 5.61 × 10−2, followed by coal piles with an average value of 2.06 × 10−2, followed by pits and dumps with an average value of 1.89 × 10−2; the environmental risk of loading and unloading sources was the lowest. Unpaved roads were pollution sources with high relative pollution risk and diffusion risk, and their average relative pollution risk and diffusion risk were 2.34 × 10−2 and 3.28 × 10−2, respectively. In the case of multi-source superposition, the high-risk areas were mainly heavily polluted areas with intensive emission sources, while the medium-risk areas were moderately polluted areas with scattered pollution sources, and the diffusion risk was high. This research concludes that the dispersed distribution of pollution sources can reduce pollution risk, and the smaller the angle is between the linear distribution direction of pollution sources and the dominant wind direction, the smaller the diffusion risk is. Therefore, differentiated control can be carried out according to the characteristics of pollution sources. The conclusions can provide methods and theoretical support for the control of atmospheric environment risk, pollution prevention, and control planning in mining areas.

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“…La Guajira es de clima cálido catalogada como la zona más seca de Colombia. La Tabla 1 presenta una descripción de los promedios, desviaciones, valores máximos y mínimos de los parámetros meteorológicos entre enero y junio de 2012 (Rojano et al, 2016).…”

Section: Metodologíaunclassified

Análisis y Origen de las Concentraciones de TSP y PM10 en Minería de Carbón a Cielo Abierto usando Gráficos Polares

et al. 2018

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ResumenSe estudiaron la variabilidad espacial y temporal de TSP y PM10 durante el período comprendido entre 2012 y 2016 en una de las minas de carbón a cielo abierto más grandes del mundo (El Cerrejón) ubicada en el noreste de Colombia. Los resultados mostraron concentraciones promedio de TSP y PM10 de 86 µgm -3 (CI95% 84-88 µgm -3 ) y 34 µgm -3 (CI95% 33-35 µgm -3 ), respectivamente. Se observó que las condiciones meteorológicas asociadas con los valores máximos de TSP y PM10, fueron vientos con velocidad mayor a 2 ms -1 y con dirección predominante NNE -NEE. Las gráficas polares y el análisis de función de probabilidad condicional estimado con el paquete openair de programa estadístico R, mostraron que los aportes de material particulado TSP y PM10, se relaciona directamente, con las operaciones mineras desarrolladas en los pit de explotación, aunque se registran aportes significativos de PM10 de fuentes regionales, no asociadas a la explotación de carbón. AbstractThe spatial and temporal variability of TSP and PM10 were studied during the period from 2012 to 2016 in one of the largest opencast coal mines in the world (El Cerrejón) located in northeastern Colombia. The results showed average concentrations of TSP and PM10 of 86 µgm -3 (CI95% 84-88 µgm-3) and 34 µgm -3 (CI95% 33-35 µgm -3 ), respectively. It was observed that the meteorological conditions associated with the maximum values of TSP and PM10, were winds with velocity greater than 2 ms -1 and predominant direction NNE -NEE. Polar plots and conditional probability function analysis showed that the contributions of particulate material TSP and PM10, is directly related to the mining operations developed in the pit, although there are significant contributions of PM10 from regional sources not associated with the open-pit coal mine.

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PM₁₀emissions due to storage in coal piles in a mining industrial area

Cited by 6 publications

References 14 publications

Geospatial analysis of residential proximity to open-pit coal mining areas in relation to micronuclei frequency, particulate matter concentration, and elemental enrichment factors

Geospatial analysis of residential proximity to open-pit coal mining areas in relation to micronuclei frequency, particulate matter concentration, and elemental enrichment factors

Environmental Risk Assessment for PM2.5 Pollution from Non-Point Sources in the Mining Area Based on Multi-Source Superposition and Diffusion

Análisis y Origen de las Concentraciones de TSP y PM10 en Minería de Carbón a Cielo Abierto usando Gráficos Polares

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PM10emissions due to storage in coal piles in a mining industrial area

Cited by 6 publications

References 14 publications

Geospatial analysis of residential proximity to open-pit coal mining areas in relation to micronuclei frequency, particulate matter concentration, and elemental enrichment factors

Geospatial analysis of residential proximity to open-pit coal mining areas in relation to micronuclei frequency, particulate matter concentration, and elemental enrichment factors

Environmental Risk Assessment for PM2.5 Pollution from Non-Point Sources in the Mining Area Based on Multi-Source Superposition and Diffusion

Análisis y Origen de las Concentraciones de TSP y PM10 en Minería de Carbón a Cielo Abierto usando Gráficos Polares

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PM₁₀emissions due to storage in coal piles in a mining industrial area