Abstract

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.

Highlights

  • There are many large-scale mining areas in northern China, and the emissions of various sources of particulate matter in open-pit mining areas has a complex impact on the formation of haze and dust in Beijing, Tianjin, Hebei, and other central regions [1,2].In particular, non-point sources are difficult to control, with low control efficiency, high pollution risk, and great health hazard [3,4,5]

  • Calculating the relative contribution and dispersion of PM2.5 among multiple types and quantities of emission sources, and analyzing the relationship between the spatial distribution of pollution sources and the emission concentration and dispersion of PM2.5 will improve the accuracy of atmospheric environmental risk assessments, and provide a basis for differentiated management of pollution sources

  • PM2.5 emission intensity, the emission sources were sorted as non-paved roads > coal piles

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Summary

Introduction

Non-point sources are difficult to control, with low control efficiency, high pollution risk, and great health hazard [3,4,5]. All types of non-point sources in the study area have been used as a whole for simulation, which fails to consider the differences in the migration and diffusion rules of particles emitted by different non-point sources [11,12,13], or only the pollution sources within the study area are taken as the object, ignoring the overlapping risks that are caused by scattered and non-point sources around the research object. Calculating the relative contribution and dispersion of PM2.5 (particulate matter less than 2.5 μm) among multiple types and quantities of emission sources, and analyzing the relationship between the spatial distribution of pollution sources and the emission concentration and dispersion of PM2.5 will improve the accuracy of atmospheric environmental risk assessments, and provide a basis for differentiated management of pollution sources

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