Understanding the Sources of Heavy Metal Pollution in Ambient Air of Neighboring a Solid Waste Landfill Site

Mehta, Umangi; Kaul, D. S.; Westerdahl, Dane; Ning, Zhi; Zhang, Kai; Sun, Li; Wei, Ping; Gajjar, Hardik V.; Jeyaraman, J.D.; Patel, Mansi; Joshi, Rutu R.

doi:10.1007/s41810-022-00131-y

Cited by 6 publications

(5 citation statements)

References 71 publications

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“…The results might be related to the heterogeneous distribution of construction sites and landfill sites in the area [ 18 ]. Additionally, the emission of HMs in such sites was generally influenced by the construction materials and landfill waste, which might have resulted in the difference HM concentrations from site to site [ 40 ]. The CV values of As, Co, Cr, Mn, and Fe in the DD and RD were lower than 48.5%.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, mining and metallurgy activities always result in significant amounts of tailings and numerous waste dumps, which increase the accumulation of HMs in LD [ 17 ]. However, construction activity is more likely to be nonrecurring, indicating that it would be more difficult for HMs to amass in CD [ 40 ].…”

Section: Resultsmentioning

confidence: 99%

“…Guan et al has already revealed that Ni is the marker of heavy oil combustion [ 63 ]. Mehta et al believe that Co originates from nonferrous or ferrous industries [ 40 ]. Furthermore, Ni and Co are widely used in the smelting of ores, alloy manufacturing, and steel production [ 16 ].…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, the average concentrations of these HMs in DD and LD were relatively higher than in other types of fugitive dust ( Table 1 ). The results also indicated that these HMs exist in the local atmosphere and in tailing wastes, which are related to mining activities [ 40 ].…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

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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“…The Co and Zn concentrations could be related to mining and industrial operations [38]. Co is thought to come from the nonferrous or ferrous industries [39 ].The Co may have been released into the air and then redeposited on the surface from various manufacturers or plants [40 ].…”

Section: Principle Components Analysis (Pca)mentioning

confidence: 99%

Pollution Level and Sources of Heavy Metals in Indoor Dust from College of Science, University of Anbar Campus, Iraq

Al-Rawi,

Aljumialy,

Saod

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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This research aims to evaluate the level of heavy metal pollution [Cadmium (Cd), Cobalt (Co), Chromium (Cr), Copper (Cu), Lead (Pb) and Zinc (Zn)] in indoor dust samples taken from the College of Science at the University of Anbar in Iraq and their likely sources. Atomic absorption spectrophotometer was used to evaluate the dust samples that were taken from 39 locations (classrooms, offices, and laboratories). Zn > Pb > Cr > Co > Cu > Cd was the order in which the heavy metal concentrations were found. The findings showed that all metal concentrations at various times exceeded background values. Using the geo-accumulation index (Igeo) and contamination (CF), the pollution level was calculated. The values of Igeo and CF indicate that the indoor dust in the College of Science is heavily polluted with Pb and Cd and is unpolluted to moderate polluted with the other metals. Pb, Cd, and Zn may be released from anthropogenic sources, while Co, Cr, and Cu may come from a combination of sources, according to the coefficient of variation (CV) comparison of the heavy metal concentrations with background values, Pearson’s correlation, cluster analysis (CA), and principle component analysis (PCA).

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Spatial Distribution of Heavy Metal Contamination in Road Dust Samples from an Urban Environment in Samsun, Türkiye

Istanbullu

Şevik

Işınkaralar

et al. 2023

Bull Environ Contam Toxicol

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Understanding the Sources of Heavy Metal Pollution in Ambient Air of Neighboring a Solid Waste Landfill Site

Cited by 6 publications

References 71 publications

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

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

Pollution Level and Sources of Heavy Metals in Indoor Dust from College of Science, University of Anbar Campus, Iraq

Spatial Distribution of Heavy Metal Contamination in Road Dust Samples from an Urban Environment in Samsun, Türkiye

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