A hybrid source apportionment strategy using positive matrix factorization (PMF) and molecular marker chemical mass balance (MM-CMB) models

Lu, Zhaojie; Liu, Qingyang; Ying, Xiong; Fan, Hongsong; Zhou, Jiabin; Schauer, James J.

doi:10.1016/j.envpol.2018.02.091

Cited by 62 publications

(37 citation statements)

References 47 publications

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“…We measured organic source tracers including alkanes, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(e) pyrene, benzo(ghi)perylene, picene, 17a(H)-22,29,30trisnorhopane, levoglucosan and cholesterol by GC-MS 5890-5972 on the basis of methods described in prior work [7,19,20]. Briefly, 0.635 cm 2 of the quartz filter was extracted using the mixtures of dichloromethane and methanol for three times at the room temperature.…”

Section: Chemical Compositionsmentioning

confidence: 99%

“…The CMB model is able to apportion the contributions of primary source to OC concentration using source-specific organic components [7]. Secondary organic carbon (SOC) could be estimated from the differences between OC concentrations and contributions of primary emission sources to OC concentrations.…”

Section: Cmb Modelmentioning

confidence: 99%

“…High PM 2.5 concentrations in the atmosphere could lead to form the haze days, which result in a diversity of respiratory illnesses [3][4][5][6]. Therefore, understanding emissions sources responsible for high PM 2.5 concentration is important for air quality management to abate specific emission sources [6][7][8][9]. Source apportionment studies are able to provide useful information to policymakers on source estimations of ambient PM 2.5 concentrations [1,9].…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, receptor models are widely used for source apportionment studies worldwide [7,9,10]. Positive matrix factorization (PMF) is one of the receptor models for apportioning PM 2.5 sources.…”

Section: Introductionmentioning

confidence: 99%

“…CMB model could apportion PM 2.5 sources with the input of local profile of specific sources [3,12]. However, it has no requirement on the minimum size of ambient PM 2.5 sample [7,13]. Because of this advantage, CMB is suitable for understanding source contributions of PM 2.5 concentration in the areas with polluted air during short periods [12].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Assessment of source contributions to organic carbon in ambient fine particle using receptor model with inorganic and organic source tracers at an urban site of Beijing

Liu

Gong

2020

SN Appl. Sci.

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The collections of ambient fine particles were carried out in the period of January 16 to 31, 2013, in Beijing. The levels of carbonaceous aerosols (i.e., organic carbon and elemental carbon) in fine particles were determined. The chemical compositions of primary source tracers including alkanes, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(e)pyrene, benzo(ghi)perylene, picene, 17a(H)-22,29,30-trisnorhopane, levoglucosan, Al and Fe in fine particles were analyzed. Chemical mass balance (CMB) model coupled with inorganic and organic source tracers was utilized to estimate daily contributions of primary sources (e.g., coal combustion, biomass burning, dust source, mobile source) to organic carbon (OC). The sensitivity analysis of specific primary source was carried out in order to obtain the accurate contribution from primary sources. Our study indicated that CMB with inorganic and organic source tracer method was efficient for apportioning primary sources of OC in Beijing during high air pollution episodes.

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Section: Chemical Compositionsmentioning

confidence: 99%

Section: Cmb Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of source contributions to organic carbon in ambient fine particle using receptor model with inorganic and organic source tracers at an urban site of Beijing

Liu

Gong

2020

SN Appl. Sci.

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Particulate Amines in the Background Atmosphere of the Yangtze River Delta, China: Concentration, Size Distribution, and Sources

Wang

Yang

et al. 2021

Adv. Atmos. Sci.

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Amines are important for new particle formation and subsequent growth in the atmosphere. Consequently, the processes involved are receiving more attention in recent years. Here, we conduct a field observation in order to investigate the atmospheric particulate amines at a background site in the Yangtze River Delta (YRD) during the summer of 2018. Four amines in PM 2.5 , i.e., methylamine (MA), dimethylamine (DMA), diethylamine (DEA), and trimethylamine (TMA), were collected, twice daily and analyzed. During the campaign, our measurements found the concentrations of MA, DMA, DEA, and TMA of 15.0 ± 15.0, 6.3 ± 6.9, 20.4 ± 30.1, and 4.0 ± 5.9 ng m -3 , respectively, and the four amines correlated well with each other. The concentration of amines appear to be independent of whether they were collected during the day or night. Both MA and DMA exhibited a bimodal size distribution that had peaks at 0.67 and 1.1 μm, suggesting amines preferably distribute on submicron particles. Boundary layer height (BLH), relative humidity, and pH of aerosols were found have a negative relationship with amines, while aerosol liquid water content (ALWC) was found to have a positive relationship with amines. The PMF (positive matrix factorization) source apportionment results showed that the main source of amines in Chongming Island was of anthropogenic origin such as industrial and biomass emission, followed by marine sources including sea salt and marine biogenic sources. Given that the YRD region is still suffering from complex atmospheric pollution and that the knowledge on aerosol amines is still limited, more field studies are in urgent need for a better understanding of the pollution characteristics of amines.

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Estimation of aerosol acidity at a suburban site of Nanjing using machine learning method

Tao

Gong

et al. 2022

J Atmos Chem

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A hybrid source apportionment strategy using positive matrix factorization (PMF) and molecular marker chemical mass balance (MM-CMB) models

Cited by 62 publications

References 47 publications

Assessment of source contributions to organic carbon in ambient fine particle using receptor model with inorganic and organic source tracers at an urban site of Beijing

Assessment of source contributions to organic carbon in ambient fine particle using receptor model with inorganic and organic source tracers at an urban site of Beijing

Particulate Amines in the Background Atmosphere of the Yangtze River Delta, China: Concentration, Size Distribution, and Sources

Estimation of aerosol acidity at a suburban site of Nanjing using machine learning method

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