Chemical characterization and source apportionment of atmospheric submicron particles on the western coast of Taiwan Strait, China

Cao, Liming; Zhu, Qiao; Huang, Xiaofeng; Deng, Junjun; Chen, Jinsheng; Hong, Youwei; Xu, Lingling; He, Lingyan

doi:10.1016/j.jes.2016.09.018

Cited by 12 publications

(6 citation statements)

References 60 publications

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“…Oxygenated fractions from AMS, MOOOC (more oxidized oxygenated OC), and LOOOC (low oxidized oxygenated OC) also exhibited a good correlation with secondary inorganics (LOOOC vs. secondary inorganics (r 2 = 0.6, n = 27, p < 0.05, LOOOC = 2.9 µg m −3 , secondary inorganics = 1.6 µg m −3 ), MOOOC vs. secondary inorganics (r 2 = 0.7, n = 27, p < 0.05, MOOOC = 4.4 µ m −3 )). This was also confirmed by LOOOC and MOOOC showing a good correlation with NO − 3 and SO 2− 4 previously (Cao et al, 2017). The formation of both secondary inorganic aerosol and oxygenated organic aerosol is dependent upon largely the same set of oxidant species, notably but not solely the hydroxyl and nitrate radicals.…”

Section: With Online Ams-pmf Factors At Iap (Winter)supporting

confidence: 82%

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Insight into PM2.5 sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

Srivastava

et al. 2021

Atmos. Chem. Phys.

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Abstract. This study presents the source apportionment of PM2.5 performed by positive matrix factorization (PMF) on data presented here which were collected at urban (Institute of Atmospheric Physics – IAP) and rural (Pinggu – PG) sites in Beijing as part of the Atmospheric Pollution and Human Health in a Chinese megacity (APHH-Beijing) field campaigns. The campaigns were carried out from 9 November to 11 December 2016 and from 22 May to 24 June 2017. The PMF analysis included both organic and inorganic species, and a seven-factor output provided the most reasonable solution for the PM2.5 source apportionment. These factors are interpreted as traffic emissions, biomass burning, road dust, soil dust, coal combustion, oil combustion, and secondary inorganics. Major contributors to PM2.5 mass were secondary inorganics (IAP: 22 %; PG: 24 %), biomass burning (IAP: 36 %; PG: 30 %), and coal combustion (IAP: 20 %; PG: 21 %) sources during the winter period at both sites. Secondary inorganics (48 %), road dust (20 %), and coal combustion (17 %) showed the highest contribution during summer at PG, while PM2.5 particles were mainly composed of soil dust (35 %) and secondary inorganics (40 %) at IAP. Despite this, factors that were resolved based on metal signatures were not fully resolved and indicate a mixing of two or more sources. PMF results were also compared with sources resolved from another receptor model (i.e. chemical mass balance – CMB) and PMF performed on other measurements (i.e. online and offline aerosol mass spectrometry, AMS) and showed good agreement for some but not all sources. The biomass burning factor in PMF may contain aged aerosols as a good correlation was observed between biomass burning and oxygenated fractions (r2= 0.6–0.7) from AMS. The PMF failed to resolve some sources identified by the CMB and AMS and appears to overestimate the dust sources. A comparison with earlier PMF source apportionment studies from the Beijing area highlights the very divergent findings from application of this method.

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Section: With Online Ams-pmf Factors At Iap (Winter)supporting

confidence: 82%

“…In addition, the contribution to PM mass at IAP in this study was similar to that reported by Tian et al (2016), and the studied urban site in both cases was the same. Crustal dust mass was also estimated based on the concentrations of Al, Si, Fe, Ca, and Ti using the equation below (Chan et al, 1997).…”

Section: Road Dustmentioning

confidence: 99%

Insight into PM2.5 sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

Srivastava

et al. 2021

Atmos. Chem. Phys.

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“…and LOOOC (low oxidised oxygenated OC) also exhibited a good correlation with secondary inorganics (LOOOC vs secondary inorganics (r 2 =0.6, n=27, p<0.05, LOOOC=2.9 µg m -3 , secondary inorganics=1.6 µg m -3 ), MOOOC vs secondary inorganics (r 2 =0.7, n=27, p<0.05, MOOOC=4.4 µg m -3 )). This was also confirmed by LOOOC and MOOOC showing a good correlation with NO3and SO4 2previously (Cao et al, 2017). This suggests the oxygenated fractions from AMS and secondary inorganics are subject to similar controls in the atmosphere.…”

Section: (C) With Online Ams Pmf Factors At Iap (Winter)supporting

confidence: 76%

Insight into PM2.5 Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing

Srivastava¹,

Xu²,

Vu³

et al. 2021

Preprint

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Abstract. This study presents the source apportionment of PM2.5 performed by PMF on data collected at an urban (Institute of Atmospheric Physics – IAP) and a rural site (Pinggu-PG) in Beijing as part of the Atmospheric Pollution and Human Health in a Chinese megacity (APHH-Beijing) field campaigns. The campaigns were carried out from 9th November to 11th December 2016 and 22nd May to 24th June 2017. The PMF included both organic and inorganic species, and a seven-factor output provided the most reasonable solution for the PM2.5 source apportionment. These factors are interpreted to be traffic emissions, biomass burning, road dust, soil dust, coal combustion, oil combustion and secondary inorganics. Major contributors to PM2.5 mass were secondary inorganics (22–24 %), biomass burning (30–36 %), and coal combustion (20–21 %) sources during the winter period at both sites. Secondary inorganics (48 %), road dust (20 %) and coal combustion (17 %) showed the highest contribution during summer at PG, while PM2.5 particles were mainly composed of soil dust (35 %) and secondary inorganics (40 %) at IAP. Despite this, factors that were resolved based on metal signatures were not fully resolved and indicate a mixing of two or more sources. PMF results were also compared with sources resolved from another receptor model (i.e. CMB) and PMF performed on other measurements (i.e. online and offline aerosol mass spectrometry (AMS)) and showed a good agreement for some but not all sources. The biomass burning factor in PMF may contain aged aerosols as a good correlation was observed between biomass burning and oxygenated fractions (r2 = 0.6–0.7) from AMS. The PMF failed to resolve some sources identified by the CMB and AMS, and appears to overestimate the dust sources. A comparison with earlier PMF source apportionment studies from the Beijing area highlights the very divergent findings from application of this method.

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“…In recent years, most studies have generally focused on the Jing-Jin-Ji region and its coastal areas with severely degraded atmospheric environments in China [22][23][24]. Only a few researchers have investigated PM 2.5 pollution in Yunnan Province, which is a remote southwestern region.…”

Section: Introductionmentioning

confidence: 99%

Mass Concentration, Chemical Composition, and Source Characteristics of PM2.5 in a Plateau Slope City in Southwest China

et al. 2021

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In order to investigate the seasonal variations in the chemical characteristics of PM2.5 at the plateau slope of a mountain city in southwest China, 178 PM2.5 filters (89 quartz and 89 Teflon samples for PM2.5) were collected to sample the urban air of Wenshan in spring and autumn 2016 at three sites. The mass concentrations, water-soluble inorganic ions, organic and inorganic carbon concentrations, and inorganic elements constituting PM2.5 were determined, principal component analysis was used to identify potential sources of PM2.5, and the backward trajectory model was used to calculate the contribution of the long-distance transmission of air particles to the Wenshan area. The average concentration of PM2.5 in spring and autumn was 44.85 ± 10.99 μg/m3. Secondary inorganic aerosols contributed 21.82% and 16.50% of the total PM2.5 in spring and autumn, respectively. The daily mean value of OC/EC indicated that the measured SOC content was generated by the photochemical processes active during the sampling days. However, elements from anthropogenic sources (Ti, Si, Ca, Fe, Al, K, Mg, Na, Sb, Zn, P, Pb, Mn, As and Cu) accounted for 99.38% and 99.24% of the total inorganic elements in spring and autumn, respectively. Finally, source apportionment showed that SIA, dust, industry, biomass burning, motor vehicle emissions and copper smelting emissions constituted the major components in Wenshan. This study is the first to investigate the chemical characterizations and sources of PM2.5 in Wenshan, and it provides effective support for local governments formulating air pollution control policies.

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Chemical characterization and source apportionment of atmospheric submicron particles on the western coast of Taiwan Strait, China

Cited by 12 publications

References 60 publications

Insight into PM<sub>2.5</sub> sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

Insight into PM<sub>2.5</sub> sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

Insight into PM<sub>2.5</sub> Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing

Mass Concentration, Chemical Composition, and Source Characteristics of PM2.5 in a Plateau Slope City in Southwest China

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Chemical characterization and source apportionment of atmospheric submicron particles on the western coast of Taiwan Strait, China

Cited by 12 publications

References 60 publications

Insight into PM&lt;sub&gt;2.5&lt;/sub&gt; sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

Insight into PM&lt;sub&gt;2.5&lt;/sub&gt; sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

Insight into PM&lt;sub&gt;2.5&lt;/sub&gt; Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing

Mass Concentration, Chemical Composition, and Source Characteristics of PM2.5 in a Plateau Slope City in Southwest China

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Insight into PM<sub>2.5</sub> sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

Insight into PM<sub>2.5</sub> sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

Insight into PM<sub>2.5</sub> Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing