Multivariate statistical analysis of heavy metals contamination in atmospheric dust of Kermanshah province, western Iran, during the spring and summer 2013

Doabi, Shahab Ahmadi; Afyuni, Majid; Karami, Mahin

doi:10.1016/j.gexplo.2017.06.007

Cited by 49 publications

(23 citation statements)

References 60 publications

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“…The regional mean precipitation, wind speed and maximum wind speed are also shown in Figure 3(a) to illustrate the reasons for the seasonal variations in the DDF. The observations show that 75% of yearly dust events occur in the months of March to August when the monthly DDF is above 15%, which is consistent with previous studies (Alizadeh-Choobari et al, 2016;Doabi, Afyuni, & Karami, 2017;Nabavi et al, 2016;Sarraf, Rasouli, Mohammadi, & Sadr, 2016). The DDF is lowest in December (4%), and after January, it begins to increase and peaks in June and July (33%), followed by a decrease from August to December.…”

Section: Reanalysis Datasupporting

confidence: 91%

Interannual variations of dust activity in western Iran and their possible mechanisms

Kamal

Lin

2019

Big Earth Data

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Through an analysis of station observations and reanalysis data, in this study, we investigate the variations of dust activity during 1979-2015 in western and southwestern Iran and their associated mechanisms. The dust day frequency (DDF) is used to identify dust activities. The results show larger interannual variabilities in the DDF in spring and summer, with standard deviations (σ) of 10% and 13%, respectively. Correlation analyses reveal that the interannual variability of DDF in western Iran is largely regulated by wind speed, precipitation and soil moisture in the region. The regional mean spring (March-May) DDF shows strong negative correlations with spring precipitation (correlation coefficient R = −0.5) and soil moisture (R = −0.6) over western to southwestern Iran, but strong positive correlations between the springtime DDF and surface winds can be found with the R reaching 0.4-0.6. Dust activity in the summer dry season (June-August) is mainly associated with surface winds over southwestern Iran, especially in regions near the Persian Gulf, where the R between the DDF and surface winds reaches 0.5. Meanwhile, the summer DDF also shows strong negative correlations (R < −0.4) with soil moisture of the top layer in the middle to southern part of the region. These results highlight the key roles of wind speed, precipitation and soil moisture in determining the interannual variation of dust activities in western and southwestern Iran.

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Section: Reanalysis Datasupporting

confidence: 91%

Interannual variations of dust activity in western Iran and their possible mechanisms

Kamal

Lin

2019

Big Earth Data

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“…Factor analysis (FA) is a conventional multivariate statistical method that determines the general relationship between measured variables by revealing multidimensional patterns that may be useful for classifying the original data [ 30 , 31 , 32 , 33 ]. FA is used to explore the possible sources and the hydrogeochemical processes affecting HMs by reducing the dimensionality of the dataset to several influencing factors.…”

Section: Methodsmentioning

confidence: 99%

Effects of Mining Activities on the Release of Heavy Metals (HMs) in a Typical Mountain Headwater Region, the Qinghai-Tibet Plateau in China

Wei

Sun

et al. 2018

IJERPH

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Understanding the heavy metal (HM) contamination in alpine mountain headwaters regions is important to maintaining the ecosystem stability of the basin. A total of 119 water samples and 104 sediment samples were collected along tributaries and the main course of Heihe River. The concentrations of eight heavy metals (As, Cd, Cr, Cu, Mn, Ni, Pb, and Zn) in water and sediment were measured to describe their spatial variability and to assess water quality. To identify the origins and pathways of HMs, anions, cations, and trace elements, as well as δD/δ18O stable isotopes in water samples were also measured. The results of water quality assessment suggested that tributaries were affected by local mining activity. Factor analysis in sediments showed that all HMs in sediments were inherited from the parent bedrock. Both natural weathering and mining contribute HMs. Cr and Ni were homologous with a source from the weathering of basic gabbro and serpentine at Yushigou. Mn appeared to be influenced more by artificial activities such as agriculture and grazing. Depending on the mining technique involved, two pathways for the release of HMs were distinguished in this area. For open-pit mining, mining promoted the release of HMs primarily via enhanced weathering. For underground mining, HMs might have contributed to greater acid mine discharge at high elevations due to the weak weathering processes. As the elevation decreases, precipitation increases, and a series of complex hydrological factor significantly affect leaching and runoff. The study results can be applied to improve water management efficiency.

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“…where C i is the concentration of the measured heavy metal i in the particulates or soil (mg/kg), C r is the concentration of the reference element (Al), and P and S represent PM 2.5 and Jiangsu background soil, respectively [23,24]. There is no enrichment if the EF < 10 in PM 2.5 and the main source may be natural, such as rock weathering or soil, but the primary source is anthropogenic when EF > 10 [25].…”

Section: Calculation Of the Enrichment Factors (Efs) For Source Identmentioning

confidence: 99%

Seasonal Levels, Sources, and Health Risks of Heavy Metals in Atmospheric PM2.5 from Four Functional Areas of Nanjing City, Eastern China

Luo

et al. 2019

Atmosphere

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Aerosol pollution is a serious environmental issue, especially in China where there has been rapid urbanization. To identify the intra-annual and regional distributions of health risks and potential sources of heavy metals in atmospheric particles with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5), this work collected monthly PM2.5 samples from urban, industrial, suburban, and rural areas in Nanjing city during 2016 and analyzed the heavy metal compositions (Cu, Pb, Cd, Co, V, Sr, Mn, Ti, and Sb). Enrichment factors (EFs) and principal component analysis (PCA) were applied to investigate the sources. The atmospheric PM2.5 pollution level was highest in the industrial area, followed by the urban and suburban areas, and was the lowest in the rural area. Seasonally, the concentrations of PM2.5 and associated heavy metals in spring and winter were higher than those in summer and autumn. Besides natural sources, heavy metal pollution in PM2.5 might come from metallurgical dust in the industrial area, while it mainly comes from automobile exhaust in urban and suburban areas. Health risk assessments revealed that noncancerous hazards of heavy metals in PM2.5 were low, while the lifetime cancer risks obviously exceeded the threshold. The airborne metal pollution in various functional areas of the city impacted human health differently.

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Multivariate statistical analysis of heavy metals contamination in atmospheric dust of Kermanshah province, western Iran, during the spring and summer 2013

Cited by 49 publications

References 60 publications

Interannual variations of dust activity in western Iran and their possible mechanisms

Interannual variations of dust activity in western Iran and their possible mechanisms

Effects of Mining Activities on the Release of Heavy Metals (HMs) in a Typical Mountain Headwater Region, the Qinghai-Tibet Plateau in China

Seasonal Levels, Sources, and Health Risks of Heavy Metals in Atmospheric PM2.5 from Four Functional Areas of Nanjing City, Eastern China

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