Assessment of Elemental Components in Atmospheric Particulate Matter from a Typical Mining City, Central China: Size Distribution, Source Characterization and Health Risk

Liu, Hongxia; Xiang, Jueyi; Zheng, Jingru; Yao, Ruizhen; Liu, Shan; Liu, Ting; Zhang, Jiaquan; Zhan, Chuanlang; Xiao, Wensheng; Cao, Junji

doi:10.1007/s00128-020-03039-w

Cited by 8 publications

(6 citation statements)

References 41 publications

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“…The average daily dose ( ADDinh ) (mg kg −1 day −1 ) for elements via inhalation was calculated as [ 27 ]:

where the ADDinh is the average daily dose (mg kg −1 day −1 ) of exposure to elements through inhalation; C is the concentration of elements, (mg m −3 ); InhR is the inhalation rate of PM 2.5 ; EF is exposure frequency; ED is exposure duration; BW is average body weight; AT is average time with AT non-car . A detailed description of the values of exposure factors for children and adults applied to the Equation (1) is given in Table 2 .…”

Section: Methodsmentioning

confidence: 99%

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Elemental Composition of PM2.5 and PM10 and Health Risks Assessment in the Industrial Districts of Chelyabinsk, South Ural Region, Russia

Krupnova

Rakova

Bondarenko

et al. 2021

IJERPH

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Air pollution impacts all populations globally, indiscriminately and has site-specific variation and characteristics. Airborne particulate matter (PM) levels were monitored in a typical industrial Russian city, Chelyabinsk in three destinations, one characterized by high traffic volumes and two by industrial zone emissions. The mass concentration and trace metal content of PM2.5 and PM10 were obtained from samples collected during four distinct seasons of 2020. The mean 24-h PM10 ranged between 6 and 64 μg/m3. 24-h PM2.5 levels were reported from 5 to 56 μg/m3. About half of the 24-h PM10 and most of the PM2.5 values in Chelyabinsk were higher than the WHO recommendations. The mean PM2.5/PM10 ratio was measured at 0.85, indicative of anthropogenic input. To evaluate the Al, Fe, As, Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn concentration in PM2.5 and PM10, inductively coupled plasma mass spectrometry (ICP-MS) was used. Fe (337–732 ng/m3) was the most abundant component in PM2.5 and PM10 samples while Zn (77–206 ng/m3), Mn (10–96 ng/m3), and Pb (11–41 ng/m3) had the highest concentrations among trace elements. Total non-carcinogenic risks for children were found higher than 1, indicating possible health hazards. This study also presents that the carcinogenic risk for As, Cr, Co, Cd, Ni, and Pb were observed higher than the acceptable limit (1 × 10−6).

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“…The average daily dose ( ADDinh ) (mg kg −1 day −1 ) for elements via inhalation was calculated as [ 27 ]:

Section: Methodsmentioning

confidence: 99%

“…The average daily dose (ADDinh) (mg kg −1 day −1 ) for elements via inhalation was calculated as [27]:…”

Section: Exposure Assessment and Risk Calculationmentioning

confidence: 99%

Elemental Composition of PM2.5 and PM10 and Health Risks Assessment in the Industrial Districts of Chelyabinsk, South Ural Region, Russia

Krupnova

Rakova

Bondarenko

et al. 2021

IJERPH

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“…Much research, especially in large or industrial cities, has discussed fugitive dust from different sources, such as motor vehicles, fuel burning, construction activities, industrial activities, agricultural activities, and so on, [ 9 , 10 ]. Unfortunately, fewer studies have been undertaken to distinguish the sources of heavy metals (HMs) in mining and metallurgy areas, where fugitive dust may bind with relatively high heavy metal contaminants [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…With thousands of years of mining and metallurgy history, southeast Hubei is the largest polymetallic deposit in central China [ 12 , 13 , 14 ]. Due to the long-term extensive mining activities, former studies have revealed that HMs in local fugitive dust, such as Cu, Zn, As, Pb, Cd, and so on, can cause potential cancer risks in citizens [ 11 , 15 ]. Besides contributing to the understanding of the health risks from fugitive dust, local governments should be interested in finding out where the HMs in fugitive dust may be from to develop accurate atmospheric pollution control policies [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

The Predominant Sources of Heavy Metals in Different Types of Fugitive Dust Determined by Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) Modeling in Southeast Hubei: A Typical Mining and Metallurgy Area in Central China

Chen

Wen

Wang

et al. 2022

IJERPH

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To develop accurate air pollution control policies, it is necessary to determine the sources of different types of fugitive dust in mining and metallurgy areas. A method integrating principal component analysis and a positive matrix factorization model was used to identify the potential sources of heavy metals (HMs) in five different types of fugitive dust. The results showed accumulation of Mn, Fe, and Cu can be caused by natural geological processes, which contributed 38.55% of HMs. The Ni and Co can be released from multiple transport pathways and accumulated through local deposition, which contributed 29.27%. Mining-related activities contributed 20.11% of the HMs and showed a relatively high accumulation of As, Sn, Zn, and Cr, while traffic-related emissions contributed the rest of the HMs and were responsible for the enrichment in Pb and Cd. The co-applied source-identification models improved the precision of the identification of sources, which revealed that the local geological background and mining-related activities were mainly responsible for the accumulation of HMs in the area. The findings can help the government develop targeted control strategies for HM dispersion efficiency.

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“…At present, a "one-size-fits-all" control approach is mostly adopted. The existing particulate matter risk assessment mostly takes the particulate matter concentration as the risk source and the population as the risk receptor to evaluate the exposure health risks [6][7][8][9][10]. In traditional risk assessments, 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.…”

Section: Introductionmentioning

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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Assessment of Elemental Components in Atmospheric Particulate Matter from a Typical Mining City, Central China: Size Distribution, Source Characterization and Health Risk

Cited by 8 publications

References 41 publications

Elemental Composition of PM2.5 and PM10 and Health Risks Assessment in the Industrial Districts of Chelyabinsk, South Ural Region, Russia

Elemental Composition of PM2.5 and PM10 and Health Risks Assessment in the Industrial Districts of Chelyabinsk, South Ural Region, Russia

The Predominant Sources of Heavy Metals in Different Types of Fugitive Dust Determined by Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) Modeling in Southeast Hubei: A Typical Mining and Metallurgy Area in Central China

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

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