Source Identification of Heavy Metals in Particulate Matter (PM10) in a Malaysian Traffic Area Using Multivariate Techniques

Elhadi, Rasheida E.; Abdullah, Ahmad Makmom; Abdullah, Abdul Halim; Ash’aari, Zulfa Hanan; Kura, Nura Umar; Gumel, Danladi Yusuf; Adamu, A.K.

doi:10.15244/pjoes/69941

Cited by 14 publications

(9 citation statements)

References 30 publications

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“…Principal component analysis was used to establish the probable sources and explain the relationship between heavy metals in PM 10 , PM 2.5 and road dust in the study area. 46 , 47 Principal component analysis with varimax rotation and Kaiser normalization was performed using the Statistical Package for the Social Sciences (SPSS) software (version 21.0). In the present study, principal components having the eigenvalues greater than 1 were extracted 48 using the heavy metal concentration of all 112 samples (56 samples from each type; PM 10 and PM 2.5 ).…”

Section: Methodsmentioning

confidence: 99%

Assessment of Pollution and Health Risks of Heavy Metals in Particulate Matter and Road Dust Along the Road Network of Dhanbad, India

Kumari

Jain

Elumalai

2021

Journal of Health and Pollution

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Background. The rise in particulate matter (PM) concentrations is a serious problem for the environment. Heavy metals associated with PM10, PM2.5, and road dust adversely affect human health. Different methods have been used to assess heavy metal contamination in PM10, PM2.5, and road dust and source apportionment of these heavy metals. These assessment tools utilize pollution indices and health risk assessment models. Objectives. The present study evaluates the total mass and average concentrations of heavy metals in PM10, PM2.5, and road dust along selected road networks in Dhanbad, India, analyzes the source apportionment of heavy metals, and assesses associated human health risks. Methods. A total of 112 PM samples and 21 road dust samples were collected from six stations and one background site in Dhanbad, India from December 2015 to February 2016, and were analyzed for heavy metals (iron (Fe), lead (Pb), cadmium (Cd), nickel (Ni), copper (Cu), chromium (Cr), and zinc (Zn)) using atomic absorption spectrophotometry. Source apportionment was determined using principal component analysis. A health risk assessment of heavy metal concentrations in PM10, PM2.5, and road dust was also performed. Results. The average mass concentration was found to be 229.54±118.40 μg m−3 for PM10 and 129.73 ±61.74 μg m−3 for PM2.5. The average concentration of heavy metals was found to be higher in PM2.5 than PM10. The pollution load index value of PM10 and PM2.5 road dust was found to be in the deteriorating category. Vehicles were the major source of pollution. The non-carcinogenic effects on children and adults were found to be within acceptable limits. The heavy metals present in PM and road dust posed a health risk in the order of road dust> PM10> and PM2.5. Particulate matter posed higher health risks than road dust due to particle size. Conclusions. The mass concentration analysis indicates serious PM10 and PM2.5 contamination in the study area. Vehicle traffic was the major source of heavy metals in PM10, PM2.5, and road dust. In terms of non-carcinogenic risks posed by heavy metals in the present study, children were more affected than adults. The carcinogenic risk posed by the heavy metals was negligible. Competing Interests. The authors declare no competing financial interests

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Section: Methodsmentioning

confidence: 99%

Assessment of Pollution and Health Risks of Heavy Metals in Particulate Matter and Road Dust Along the Road Network of Dhanbad, India

Kumari

Jain

Elumalai

2021

Journal of Health and Pollution

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“…There are also many modes of public transportation, such as buses and private vehicles. In March 2020, the concentrations of PM concentrations around main roads with tra c and construction activities were found to be quite high, namely 62.16 ± 39.37 µg/m 3 [24,25]. PM 2.5 and PM 10 concentrations are also affected by wind direction and wind speed.…”

Section: Conditional Probability Functionmentioning

confidence: 99%

Identification of Metal Pollutants in Ambient Air (Case Study: West Surabaya, East Java, Indonesia)

Ciptaningayu¹,

Syafei²,

Santoso³

et al. 2020

Preprint

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Surabaya City, especially the western area of Surabaya, is utilized for industrial space, warehousing, final disposal sites (FDS), and port areas. Activities in these areas produce emissions, particularly PM2.5 (particulate matter smaller than 2.5 µm) and PM10 (particulate matter smaller than 10 µm). PM2.5 and PM2.5−10 contain metals and can cause respiratory disorders and death. Therefore, it is necessary to determine the concentrations of PM2.5 and PM2.5−10 and the heavy metals contained in the PMs. Sampling for PM2.5 and PM2.5−10 was conducted using a Gent Stacked Filter Unit (Gent SFU) every six (6) days between October 2019–April 2020. The samples were then analyzed using gravimetry to determine mass concentrations of PM2.5 and PM10 and analyzed using XRF (X-Ray Fluorescence) to determine the elements contained within PM2.5 and PM2.5−10 and their concentrations. The elemental concentrations were used as Positive Matrix Factorization (PMF) inputs to identify potential pollutant sources. The results of the PMF analysis were then used to determine the source locations of pollutants by using a Conditional Probability Function (CPF). The observed concentrations of PM2.5 and PM10 are 11.47 µg/m3 and 27.49 µg/m3, or 11.45 µg/Nm3 and 26.98 µg/Nm3. 18 elements are identified in the PM, namely Na, Mg, Al, Si, S, K, Cl, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Br, and Pb. Potential sources of PM2.5 pollutants are a combination of Cu industries, biomass burning, Ni industries, non-ferrous metal industries, transportation activities, iron and steel industries, construction activities, dust, port activities, and Pb industries. Potential sources of PM2.5−10 pollutants are construction activities, dust, transportation activities, non- ferrous metals industries, and Ni industries. The estimated source locations of the pollutants contributing to PM2.5 and PM2.5−10 are spread out from north to southwest. Based on this analysis, the concentrations of PM2.5, PM2.5−10, and metal elements met air quality standards.

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“…Road dust resuspended by vehicular activity could be enriched in Ni, Cr, andFe due to activities like wear and tear of tires, oil burning, abrasion of mechanical parts of vehicles, and oil lubricants (Pandey et al 2012;Pachauri et al 2013;Geng et al 2014;Das et al 2015;Elhadi et al 2017;Sah et al 2019). Cu is associated with the wearing of brakes, the automobile's oil pump, and corrosion of metal parts coming in contact with oil (Sah et al 2019).…”

Section: 2)mentioning

confidence: 99%

Characterisation of particulate matter and identification of emission sources in Greater Caracas, Venezuela

Morantes

González

Rincón

2021

Air Qual Atmos Health

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Between June and September 2018, particulate matter (PM) samples were taken in the Sartenejas Valley, southeast of Greater Caracas, Venezuela. The aim was to evaluate the morphology and the elemental chemical composition of particulate matter and establish possible emission sources during the rainy season. Functional groups were identified by FTIR spectroscopic analysis, and morphology and elemental composition were obtained by SEM–EDX analysis. The sampling period coincided with a Sahara dust storm. The SEM–EDX and FTIR analyses found evidence of mineral elements related to soil and crustal origins. The presence of C-rich or C-containing aerosols is related to biological sources or mineral carbon. SEM–EDX analysis of PM revealed the following particle groups: geogenic, metallic, C-rich, and secondary aerosols. Quantitative source appointments through principal component analysis (PCA) corroborated PM sources, including soil dust, sea salts, and reacted aerosols. According to the authors’ knowledge, this study represents the first report to indicate that an episode of African dust could influence the particles collected in an intertropical continental sector in Venezuela, South America.

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Source Identification of Heavy Metals in Particulate Matter (PM10) in a Malaysian Traffic Area Using Multivariate Techniques

Cited by 14 publications

References 30 publications

Assessment of Pollution and Health Risks of Heavy Metals in Particulate Matter and Road Dust Along the Road Network of Dhanbad, India

Assessment of Pollution and Health Risks of Heavy Metals in Particulate Matter and Road Dust Along the Road Network of Dhanbad, India

Identification of Metal Pollutants in Ambient Air (Case Study: West Surabaya, East Java, Indonesia)

Characterisation of particulate matter and identification of emission sources in Greater Caracas, Venezuela

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