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Source Apportionment of PM 2.5 Mass and Carbon in Seattle using Chemical Mass Balance and Positive Matrix Factorization. Naydene Maykut, Puget Sound Clean Air Agency Joellen Lewtas, U.S. EPA Tim Larson, University of Washington. Introduction.

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slide1
Source Apportionment of PM2.5 Mass and Carbon in Seattle using Chemical Mass Balance and Positive Matrix Factorization

Naydene Maykut, Puget Sound Clean Air Agency

Joellen Lewtas, U.S. EPA

Tim Larson, University of Washington

introduction
Introduction
  • Extensive PM2.5 speciation data available from an urban IMPROVE site in Seattle (284 days over three years)
  • Source Apportionment comparison between traditional CMB approach with newer PMF method
  • For PMF method: include temperature resolved carbon fractions rather than traditional OC/EC split
slide3
Seattle

Beacon Hill Site

measured species in seattle improve protocol
Measured Species in Seattle(IMPROVE protocol)
  • >45 species measured on Wednesdays and Saturdays 4/96 to 1/99 (289 samples)
  • XRF (Fe to Zr, Pb) , PIXE (Na to Mn, Mo) , IC
  • Carbon measurements: OC & EC temperature dependent volatilization (TOR)
pmf method

PMF Method

Used 7 carbon fractions from TOR

(O1, O2, 03, O4, E1, E2, E3)

as well as usual elements and ions

Input species and uncertainties

Robust Mode : FPEAK = +0.2

tor analysis
TOR Analysis

800

700

Temperature Profile

600

Laser Signal

Temperature (C)

500

He

He + O2

400

Pyrolized carbon

300

Elemental

Carbon

Organic

Carbon

200

CH4 Calibration

100

FID Baseline

OC2

OC1

OC3

OC4

EC1

EC2

EC3

200

400

600

800

1000

1200

2200

1400

1600

1800

2000

Time (sec)

slide9
SO4

SO4

SO4

SO4

NO3

NO3

NO3

NO3

Na

Na

Na

Na

Cl

Cl

Cl

Cl

O1

O1

O1

O1

O2

O2

O2

O2

O3

O3

O3

O3

O4

O4

O4

O4

E1

E1

E1

E1

E2

H

Si

Al

Fe

Ca

V

Ni

K

Pb

Source Profiles from PMF (Mass %)

Road Dust

40

8

0.4

30

6

0.3

20

4

0.2

10

2

0.1

0

0

0

E3

Zn

Mn

Ti

As

Cu

Cr

Br

Marine

0.4

40

8

0.3

30

6

0.2

20

4

0.1

10

2

0

0

0

E3

Zn

Mn

Ti

As

Cu

Cr

Br

E2

H

Si

Al

Fe

Ca

V

Ni

K

Pb

Marine/Secondary/Pulp Mill

8

0.4

40

6

0.3

30

4

0.2

20

2

0.1

10

0

0

0

E2

H

Si

Al

Fe

Ca

V

Ni

K

Pb

E3

Zn

Mn

Ti

As

Cu

Cr

Br

Secondary

0.4

8

40

0.3

6

30

0.2

4

20

0.1

2

10

0

0

0

E3

Zn

Mn

Ti

As

Cu

Cr

Br

E2

H

Si

Al

Fe

Ca

V

Ni

K

Pb

slide10
40

30

20

10

0

SO4

SO4

SO4

SO4

NO3

NO3

NO3

NO3

Na

Na

Na

Na

Cl

Cl

Cl

Cl

O1

O1

O1

O1

O2

O2

O2

O2

O3

O3

O3

O3

O4

O4

O4

O4

E1

E1

E1

E1

E2

E2

E2

E2

H

H

H

H

Si

Si

Si

Si

Al

Al

Al

Al

Fe

Fe

Fe

Fe

Ca

Ca

Ca

Ca

V

V

V

V

Ni

Ni

Ni

Ni

K

K

K

K

Pb

Pb

Pb

Pb

Source Profiles from PMF (Mass %)

Diesel

0.4

8

0.3

6

0.2

4

0.1

2

0

0

E3

Zn

Mn

Ti

As

Cu

Cr

Br

Gasoline

0.4

40

8

0.3

30

6

0.2

20

4

0.1

10

2

0

0

0

E3

Zn

Mn

Ti

As

Cu

Cr

Br

Vegetative

0.4

40

8

0.3

30

6

0.2

20

4

0.1

10

2

0

0

0

E3

Zn

Mn

Ti

As

Cu

Cr

Br

Fuel Oil

0.4

40

8

0.3

30

6

0.2

20

4

0.1

10

2

0

0

0

E3

Zn

Mn

Ti

As

Cu

Cr

Br

source apportionment of organic and elemental carbon using pmf
Source Apportionment of Organic and Elemental Carbon using PMF

Source OC(%)EC(%)

Vegetative Burning 57 47

Diesel Vehicles 19 36

Gasoline Vehicles 5 1

Secondary 12 9

Fuel Oil 3 4

Road Dust 2 2

Marine (Sea Salt) 2 0

conclusions
Conclusions
  • CMB source profiles invaluable in identifying PMF “factors”
  • PMF “factors” may approximate local source profiles
    • Next step - use PMF factors as combustion-derived profiles in CMB analysis
  • Using both models adds insight into the understanding of the composition of the aerosol in the urban airshed
    • PMF – urban-specific, combustion-derived profiles
    • CMB – minor impacts from known point sources
why this study was important
Why This Study was Important
  • Use of Carbon Fractions in PMF
    • contributed to a defensible split between burning, diesel and gasoline
    • identified that carbon fractions may prove useful in identifying sources
    • raised the question whether PMF factors could be improved by de-coupling carbon
diesel gasoline pm ratios
Diesel/Gasoline PM Ratios
  • Diesel tailpipe/gasoline tailpipe emission-factor ratio (PM10)
    • 3.0 (EPA, 1995)
  • Diesel/gasoline PM2.5 source-contribution derived ratio
    • 3.2 Pasadena and 3.0 West Los Angeles (Schauer et al., 1996
    • 2.7 (Seattle 8 Factor) and 3.1 (Seattle 9 Factor)
    • 2.1 Spokane (Kim et al., 2001)
source composition of oc and ec pmf vs source tests
Source Composition of OC and EC (PMF vs Source Tests)

* Watson, Chow and Houck, 1996 **Watson et al., 1994

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