Seasonal effect and source apportionment of polycyclic aromatic hydrocarbons in PM2.5

Khan, Firoz; Latif, Mohd Talib; Lim, Chee Hou; Amil, Norhaniza; Jaafar, Shoffian Amin; Dominick, Doreena; Nadzir, Mohd Shahrul Mohd; Sahani, Maneesh; Tahir, Norhayati Mohd

doi:10.1016/j.atmosenv.2015.01.077

Cited by 159 publications

(80 citation statements)

References 69 publications

(92 reference statements)

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“…The average concentrations of total PAHs ( P PAHs) and 15 USEPA priority PAHs ( P 15 PAHs, excluding NaP due to its high volatility) were 13.04 (0.5e62.08) ng/m 3 and 9.72 (0.29e56.97) ng/m 3 , respectively. The PAH concentrations in this study were consistent with previously reported values by Li et al (2006) (23.7 ± 18.4 ng/m 3 ) and Gao et al (2012) (17.1 ng/m 3 ) in Guangzhou, lower than that measured in Beijing (116 ng/m 3 ) (Zhou et al, 2005), but higher than those measured in many other cities, such as Houston, USA (0.78 ng/m 3 ) (Fraser et al, 2002), Lumpur, Malaysia (2.79 ng/m 3 ) (Khan et al, 2015), Mount Taishan, China (6.88 ng/m 3 ) , Rio de Janeiro, Brazil (3.80 ± 2.88 ng/ m 3 ) (Oliveira et al, 2014). P PAHs and P 15 PAHs at rural JL (17.61 ± 3.19 ng/m 3 and 13.14 ± 2.35 ng/m 3 ) showed significantly higher levels (p < 0.05) than those at urban SZ (11.56 ± 2.04 ng/m 3 and 8.54 ± 1.46 ng/m 3 ) and YJ (9.26 ± 1.21 ng/m 3 and 7.04 ± 0.83 ng/m 3 ) but had no significant difference when compared to another rural site WQS (13.69 ± 2.86 ng/m 3 and 10.14 ± 2.13 ng/m 3 ) ( Table 1).…”

Section: Source Apportionment With Positive Matrix Factorizationmentioning

confidence: 67%

Attributing risk burden of PM2.5-bound polycyclic aromatic hydrocarbons to major emission sources: Case study in Guangzhou, south China

Gao

et al. 2016

Atmospheric Environment

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Section: Source Apportionment With Positive Matrix Factorizationmentioning

confidence: 67%

Attributing risk burden of PM2.5-bound polycyclic aromatic hydrocarbons to major emission sources: Case study in Guangzhou, south China

Gao

et al. 2016

Atmospheric Environment

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“…Multivariate receptor models are very useful in the environmental studies of source apportionment of pollutants. Among them, PMF, simply requiring PM speculated data, is the most preferred and trusted one (Khan et al, 2015). In this model, any data matrix X with dimension n row by m columns, where n and m are the number of samples and the number of species, can be expressed as following equations.…”

Section: Positive Matrix Factorizationmentioning

confidence: 99%

Characteristics and Source Analysis of Trace Elements in PM2.5 in the Urban Atmosphere of Wuhan in Spring

Acciai

Zhang

Wang

et al. 2017

Aerosol Air Qual. Res.

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The concentrations of PM 2.5 and trace elements with hourly resolution were measured during May 2014 in urban residential area of Wuhan, the biggest city in central China. During the period of measurement, the average temperature was approximate 25°C without domestic heating and cooling. The average concentration of PM 2.5 was 95.53 µg m -3 , which was higher than the limit of Ambient Air Quality Standard of China GB3095-2012 (75 µg m -3 , Level 2). A sand storm original from Northwestern China was also recorded. Concentrations of major trace elements (K, Ca, V, Cr, Mn, Fe, Ni, Cu, Zn, Ga, As, Se, Pd, Ag, Cd, Au, Hg, Pb, Co, Sn, Sb, Tl) were comparable to previous studies, except for Ba and Ca with more than doubled concentrations (103 ng m -3 , 1,792.49 ng m -3 ) due to the storm. Enrichment Factor (EF) and Positive Matrix Factorization (PMF) were employed to characterize the emission sources. The computation of EF showed that Zincs was highly enriched. Four sources, biomass burning (63.2%) might mainly related to power plants using biowaste burning and bio-related cooking activities, metallurgical and steel industries (14%), dust crustal (12.5%) and dust associated with vehicular traffic (10.4%), were identified in decreasing order of average percentage contribution to the PM 2.5 mass with the aid of trace elements. The orientations of emission sources were also addressed.

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“…Unfortunately, RMAAQS for PM 2.5 are not established and chemical characterization of ambient PM 2.5 is necessary for evaluating human health effects and source apportionment. Recently, PM 2.5 concentrations have been reported (Tahir et al, 2013;Fujii et al, 2014b;EeLing et al, 2015;Khan et al, 2015), however, the identified components are limited such as inorganic ions and metals.…”

Section: Introductionmentioning

confidence: 99%

A Case Study of PM2.5 Characterization in Bangi, Selangor, Malaysia during the Southwest Monsoon Season

Fujii

Mahmud

Tohno

et al. 2016

Aerosol Air Qual. Res.

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A case study was carried out to characterize the ambient PM 2.5 based on ground-based sampling in Bangi, Selangor, Malaysia in September, 2013 during the southwest monsoon season. We determined the total mass concentration, organic carbon, elemental carbon (EC), and metals in PM 2.5 samples. The mean PM 2.5 mass concentration was 44.5 µg m -3 , showing that it exceeded the national air quality standard of 35 µg m -3 for 24-hour PM 2.5 by the U.S. Environmental Protection Agency. Relatively high OC and EC concentrations of this study compared to those of other Southeast Asian countries were observed, which indicate that significant sources of OC and EC exist. The results of char-EC/soot-EC ratios strongly suggest that biomass burning is the main contributor to ambient EC concentrations compared to coal combustion and motor vehicle emissions. From calculations using the mass closure model, organic matter was the most abundant component in PM 2.5 mass at 22.4 ± 6.65 µg m -3 , followed by nss-sulfate at 4.84 ± 2.49 µg m -3 , and EC at 4.11 ± 0.916 µg m -3 . This result indicates that targeting the sources of carbonaceous PM 2.5 is a crucial step to improve the air quality in this study field.

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Seasonal effect and source apportionment of polycyclic aromatic hydrocarbons in PM2.5

Cited by 159 publications

References 69 publications

Attributing risk burden of PM2.5-bound polycyclic aromatic hydrocarbons to major emission sources: Case study in Guangzhou, south China

Attributing risk burden of PM2.5-bound polycyclic aromatic hydrocarbons to major emission sources: Case study in Guangzhou, south China

Characteristics and Source Analysis of Trace Elements in PM2.5 in the Urban Atmosphere of Wuhan in Spring

A Case Study of PM2.5 Characterization in Bangi, Selangor, Malaysia during the Southwest Monsoon Season

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