Source apportionments of PM2.5 organic carbon using molecular marker Positive Matrix Factorization and comparison of results from different receptor models
被引:99
作者:
Heo, Jongbae
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Univ Wisconsin, Environm Chem & Technol Program, Madison, WI 53706 USAUniv Wisconsin, Environm Chem & Technol Program, Madison, WI 53706 USA
Heo, Jongbae
[1
]
Dulger, Muaz
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Univ Wisconsin, Environm Chem & Technol Program, Madison, WI 53706 USAUniv Wisconsin, Environm Chem & Technol Program, Madison, WI 53706 USA
Dulger, Muaz
[1
]
Olson, Michael R.
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Univ Wisconsin, Environm Chem & Technol Program, Madison, WI 53706 USAUniv Wisconsin, Environm Chem & Technol Program, Madison, WI 53706 USA
Olson, Michael R.
[1
]
McGinnis, Jerome E.
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Univ Wisconsin, Environm Chem & Technol Program, Madison, WI 53706 USAUniv Wisconsin, Environm Chem & Technol Program, Madison, WI 53706 USA
McGinnis, Jerome E.
[1
]
Shelton, Brandon R.
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Univ Wisconsin, Wisconsin State Lab Hyg, Madison, WI 53706 USAUniv Wisconsin, Environm Chem & Technol Program, Madison, WI 53706 USA
Shelton, Brandon R.
[2
]
Matsunaga, Aiko
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Univ Calif Riverside, Air Pollut Res Ctr, Riverside, CA 92521 USAUniv Wisconsin, Environm Chem & Technol Program, Madison, WI 53706 USA
Matsunaga, Aiko
[3
]
Sioutas, Constantinos
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Univ So Calif, Dept Civil & Environm Engn, Los Angeles, CA USAUniv Wisconsin, Environm Chem & Technol Program, Madison, WI 53706 USA
Sioutas, Constantinos
[4
]
Schauer, James J.
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Univ Wisconsin, Environm Chem & Technol Program, Madison, WI 53706 USA
Univ Wisconsin, Wisconsin State Lab Hyg, Madison, WI 53706 USAUniv Wisconsin, Environm Chem & Technol Program, Madison, WI 53706 USA
Schauer, James J.
[1
,2
]
机构:
[1] Univ Wisconsin, Environm Chem & Technol Program, Madison, WI 53706 USA
[2] Univ Wisconsin, Wisconsin State Lab Hyg, Madison, WI 53706 USA
[3] Univ Calif Riverside, Air Pollut Res Ctr, Riverside, CA 92521 USA
[4] Univ So Calif, Dept Civil & Environm Engn, Los Angeles, CA USA
CMB;
LA basin;
Organic molecular markers;
PMF;
UNMIX;
IMPROVING SOURCE IDENTIFICATION;
SYSTEMIC INFLAMMATION;
SEASONAL TRENDS;
SOURCE PROFILES;
FINE PARTICLES;
BLACK CARBON;
MEXICO-CITY;
GAS-PHASE;
AEROSOL;
SENSITIVITY;
D O I:
10.1016/j.atmosenv.2013.03.004
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
Four hundred fine particulate matter (PM2.5) samples collected over a 1-year period at two sites in the Los Angeles Basin were analyzed for organic carbon (OC), elemental carbon (EC), water soluble organic carbon (WSOC) and organic molecular markers. The results were used in a Positive Matrix Factorization (PMF) receptor model to obtain daily, monthly and annual average source contributions to PM2.5 OC. Results of the PMF model showed similar source categories with comparable year-long contributions to PM2.5 OC across the sites. Five source categories providing reasonably stable profiles were identified: mobile, wood smoke, primary biogenic, and two types of secondary organic carbon (SOC) (i.e., anthropogenic and biogenic emissions). Total primary emission factors and total SOC factors contributed approximately 60% and 40%, respectively, to the annual-average OC concentrations. Primary sources showed strong seasonal patterns with high winter peaks and low summer peaks, while SOC showed a reverse pattern with highs in the spring and summer in the region. Interestingly, smoke from forest fires which occurred episodically in California during the summer and fall of 2009 was identified and combined with the primary biogenic source as one distinct factor to the OC budget. The PMF resolved factors were further investigated and compared to a chemical mass balance (CMB) model and a second multi-variant receptor model (UNMIX) using molecular markers considered in the PMF. Good agreement between the source contribution from mobile sources and biomass burning for three models were obtained, providing additional weight of evidence that these source apportionment techniques are sufficiently accurate for policy development. However, the CMB model did not quantify primary biogenic emissions, which were included in other sources with the SOC. Both multivariate receptor models, the PMF and the UNMIX, were unable to separate source contributions from diesel and gasoline engines. (C) 2013 Elsevier Ltd. All rights reserved.