Comments on "Source apportionment of PM2.5 in Shanghai based on hourly molecular organic markers and other source tracers"

Abstract. Sources of PM2.5 at the Fresno Supersite during high PM2.5 episodes occurring from 15 December 2000–3 February 2001 were estimated with the Chemical Mass Balance (CMB) receptor model. The ability of source profiles with organic markers to distinguish motor vehicle, residential wood combustion (RWC), and cooking emissions was evaluated with simulated data. Organics improved the distinction between gasoline and diesel vehicle emissions and allowed a more precise estimate of the cooking source contribution. Sensitivity tests using average ambient concentrations showed that the gasoline vehicle contribution was not resolved without organics. Organics were not required to estimate hardwood contributions. The most important RWC marker was the water-soluble potassium ion. The estimated cooking contribution did not depend on cholesterol because its concentrations were below the detection limit in most samples. Winter time source contributions were estimated by applying the CMB model to individual and average sample concentrations. RWC was the largest source, contributing 29–31% of measured PM2.5. Hardwood and softwood combustion accounted for 16–17% and 12–15%, respectively. Secondary ammonium nitrate and motor vehicle emissions accounted for 31–33% and 9–15%, respectively. The gasoline vehicle contribution (3–10%) was comparable to the diesel vehicle contribution (5–6%). The cooking contribution was 5–19% of PM2.5. Fresno source apportionment results were consistent with those estimated in previous studies.

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Seasonal Variation and Source Apportionment of Inorganic and Organic Components in PM2.5: Influence of Organic Markers Application on PMF Source Apportionment

10.21203/rs.3.rs-1131595/v1 ◽

2022 ◽

Author(s):

Qianqian Xue ◽

Ying-Ze Tian ◽

Yang Wei ◽

Danlin Song ◽

Fengxia Huang ◽

...

Keyword(s):

Source Apportionment ◽

Coal Combustion ◽

Water Soluble ◽

Secondary Source ◽

Positive Correlation ◽

Diesel Vehicles ◽

Soluble Ions ◽

Organic Markers ◽

Polycyclic Aromatic ◽

Cu And Zn

Abstract PM2.5 samples collected over a 1-year period in a Chinese megacity were analyzed for organic carbon (OC), elemental carbon (EC), water soluble ions, elements and organic markers such as polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes, and n-alkanes. In order to study the applicability of organic markers in source apportionment, this study analyzes the relationship between organic and inorganic components, and four scenarios were implemented by incorporating different combinations of organic and inorganic tracers. A positive correlation of SO42− with 4 rings PAHs can prove that coal burning directly emits a portion of sulfate. A positive correlation of NO3− with 5-7 rings PAHs are found, implying collective impacts from the vehicle source. The concentrations of OC and EC positively correlate with the 5-7 rings PAHs and Cu and Zn, which proves that part of Cu and Zn comes from vehicle emissions. Five factors were identified by incorporating only conventional components, including secondary source (SS, 30%), urban fugitive dust (UFD, 14%), cement dust (CD, 4%), traffic source (TS, 19%) and coal combustion (CC, 14%). Six factors were identified by incorporating conventional components and PAHs, including SS (28%), UFD (15%), CD (4%), CC (13%), gasoline vehicles (GV, 12%) and diesel vehicles (DV, 10%). Eight factors were identified by incorporating conventional components, PAHs, hopanes, and n-alkanes, including SS (26%), UFD (17%), CD (3%), GV (14%), DV (8%), immature coal combustion (ICC, 5%), mature coal combustion (MCC, 10%) and biogenic source (BS, 1%).

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Application of partial least squares as a complementary and preliminary receptor model for source apportionment of ambient aerosol based on molecular organic markers

Journal of Chemometrics ◽

10.1002/cem.3136 ◽

2019 ◽

Vol 33 (6) ◽

Author(s):

Yasmany Mancilla ◽

Alberto Mendoza

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Source Apportionment ◽

Receptor Model ◽

Ambient Aerosol ◽

Organic Markers

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Source apportionment of PM<sub>2.5</sub> in Shanghai based on hourly molecular organic markers and other source tracers

10.5194/acp-2019-951 ◽

2020 ◽

Cited By ~ 1

Author(s):

Rui Li ◽

Qiongqiong Wang ◽

Xiao He ◽

Shuhui Zhu ◽

Kun Zhang ◽

...

Keyword(s):

Source Apportionment ◽

Yangtze River ◽

Yangtze River Delta ◽

Pollution Sources ◽

River Delta ◽

Water Soluble ◽

The Yangtze River ◽

Organic Markers ◽

The Yangtze River Delta ◽

Molecular Makers

Abstract. Identification of various sources and quantification of their contributions are a necessary step to formulating scientifically sound pollution control strategies. Receptor model is widely used in source apportionment of fine particles. However, most of the previous studies are based on traditional filter collection and lab analysis of aerosol chemical species (usually ions, elemental carbon (EC), organic carbon (OC) and elements) as inputs. In this study, we conducted robust online measurements of a range of organic molecular makers and trace elements, in addition to the major aerosol components (ions, OC and EC), in urban Shanghai in the Yangtze River Delta region, China. The large suite of molecular and elemental tracers, together with water-soluble ions, OC and EC, provide data for establishing measurement-based source apportionment methodology for PM2.5. We conducted source apportionment using positive matrix factorization (PMF) and compared PMF solutions with molecular makers added (i.e. MM-PMF) and those without organic markers. MM-PMF identified 11 types of pollution sources, with biomass burning, cooking and secondary organic aerosol (SOA) as the additional sources identified. The three sources accounted for 4.9 %, 2.6 % and 14.7 % of the total PM2.5 mass, respectively. During the whole campaign, the secondary source is an important source of atmospheric pollution, the average contribution of secondary pollution sources is as high as 63.8 % of the total PM2.5 mass. Grouping different sources to secondary and primary, we note that SOC and POC contributed 45.1 % and 54.9 %, respectively. It is worth noting that the contribution of cooking to PM2.5 mass only account for 2.6 %, but it contributed to 10.7 % of OC. Episodic analysis indicated that secondary nitrate was the always the main cause of PM2.5 pollution, while during non-episodic hours, vehicle exhaust made a significant contribution. Through the application of the above-mentioned techniques to the Yangtze River Delta, more insights are gained on the sources, formation mechanism and pollution characteristics of PM2.5 in this region.

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Evaluation of organic markers for chemical mass balance source apportionment at the Fresno Supersite

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-6-10341-2006 ◽

2006 ◽

Vol 6 (5) ◽

pp. 10341-10372 ◽

Cited By ~ 2

Author(s):

J. C. Chow ◽

J. G. Watson ◽

D. H. Lowenthal ◽

L.-W. A. Chen ◽

B. Zielinska ◽

...

Keyword(s):

Mass Balance ◽

Source Apportionment ◽

Vehicle Emissions ◽

Motor Vehicle ◽

Simulated Data ◽

Water Soluble ◽

Chemical Mass Balance ◽

Wood Combustion ◽

Organic Markers ◽

Diesel Vehicle

Abstract. Sources of PM2.5 at the Fresno Supersite during high PM2.5 episodes occurring from 15 December 2000–3 February 2001 were estimated with the Chemical Mass Balance (CMB) receptor model. The ability of source profiles with organic markers to distinguish motor vehicle, residential wood combustion (RWC), and cooking emissions was evaluated with simulated data. Organics improved the distinction between gasoline and diesel vehicle emissions and allowed a more precise estimate of the cooking source contribution. Sensitivity tests using average ambient concentrations showed that the gasoline vehicle contribution was not resolved without organics. Organics were not required to estimate hardwood combustion contributions. The most important RWC marker was the water-soluble potassium ion. The estimated cooking contribution did not depend on cholesterol because its concentrations were below the detection limit in most samples. Winter time source contributions were estimated by applying the CMB model to individual and average sample concentrations. RWC was the most significant source, contributing 29–31% of the measured PM2.5. Hardwood and softwood combustion accounted for 16–17% and 12–15%, respectively. Secondary ammonium nitrate and motor vehicle emissions accounted for 31–33% and 9–15%, respectively. The gasoline vehicle contribution (3–10%) was comparable to the diesel vehicle contribution (5–6%). The cooking contribution was 5–19% of PM2.5. Fresno source apportionment results were consistent with those estimated in previous studies.

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