source apportionment of pm 2.5 in the southeastern us sangil lee 1, yongtao hu 1, michael chang 2,...
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Source Apportionment of PM2.5 in the Southeastern US
Sangil Lee1, Yongtao Hu1, Michael Chang2,Karsten Baumann2, Armistead (Ted) Russell1
1School of Civil and Environmental Engineering 2School of Earth and Atmospheric Sciences Georgia Institute of Technology, Atlanta, GA
Concerns
• Adverse Health Effects PM mass, chemical composition (sulfate, EC,
OC etc), size
• Identify PM sources • Understand a relationship between sources and
adverse health effects
• Develop control strategies of PM
EPA STN sites
• PM2.5 chemical composition (ionic species, OC & EC, trace elements)− covers from January 2002 to November 2003
Rome
Macon
Athens
Mobile
Douglas
AtlantaAugusta
Memphis
Decatur
SavannahColumbus
Columbia
Pensacola
Kingsport
Nashville
Greenville
CharlestonMontgomery
Birmingham
Tallahassee
Chattanooga
Chesterfield
Lawrenceburg
every 3 dayevery 6 day
y = 2.8x + 1.5
R2 = 0.97
y = 2.6x + 0.5
R2 = 0.95
y = 2.7x + 0.4
R2 = 0.98y = 2.9x
R2 = 0.970
2
4
6
8
10
12
14
0 1 2 3 4
EC, mg/m3
OC
, mg
/m3
All January July
Primary and Secondary OC
30%
5%
9%33%
6%
3%
14%
Sulfate
Nitrate
Ammonium
OC
EC
Trace Elements
UnIden
Atlanta, GA
SOC = OC - (OC/EC)primary x EC
SOC = OC - POCSignificant Uncertainty !
POC + SOC
Minimum OC/EC ratio approach (Castro et al., 1999)
SOC and ( Unidentified Mass+OC)/OC
0
20
40
60
80
100
Jan_
Feb
Mar
_May
June
_Aug
Sept_
Nov
Dec_F
eb
Mar
_May
June
_Aug
Sept_
Nov
SO
C /
OC
x10
0, %
1.0
1.2
1.4
1.6
1.8
2.0
2.2
(Un
i M
ass
+ O
C)/
OC
Atlanta, GA44 % SOC (20 % ~ 75 %) at Atlanta, August, 1999 (Lim and Turpin, 2002)
• seasonal variability of SOC• positive relationship between SOC and (Uni Mass + OC)/OC
max & minaveragestd
Primary OC/EC Ratios
Rome
Macon
Athens
Mobile
Douglas
AtlantaAugusta
Memphis
Decatur
Savannah
Columbus
Columbia
Pensacola
Kingsport
Nashville
Greenville
Charleston
Montgomery
Birmingham
Tallahassee
Chattanooga
Chesterfield
Lawrenceburg
6.3
4.1
5.5
3.9
4.1
6.0
2.9
5.5
6.4
3.34.7
4.1
6.2
4.8
3.5
2.9 4.4
4.2
3.6
4.25
3.5
4.4
3.6
2.5
Lower: major cities (more diesel vehicles) Higher: others (more biomass burning)
Source Apportionment- CMB Receptor Model -
m
jjjii SfC
1,
Ci : ambient concentration of species ifi,j : fraction of species i in source jSj : source contribution of source j
Wood burning: Fine et al. (2002)Motor vehicles: Schauer et al. (1999, 2002)Coal power plant: Chow et al. (2004)Dust, Pulp & Paper, Oil combustion,Metal, Mineral production: EPA SPECIATE 3.2
Source Apportionments
Rome
Macon
Athens
Mobile
Douglas
AtlantaAugusta
Memphis
Decatur
SavannahColumbus
Columbia
Pensacola
KingsportNashville
Greenville
Charleston
Montgomery
Birmingham
Tallahassee
Chattanooga
Chesterfield
Lawrenceburg
9.6
NH4HSO4
(NH4)2SO4
NH4NO3
SOC
Wood Burning
Motor Vehicles
Dust
Pulp, Paper
Coal
Mineral
Oil combustion
Metal
UnIden
Interpolation- Inverse Distance Weighted -
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20
12
mg/m3
PM2.5 mass
Interpolation- Inverse Distance Weighted -
NH4HSO4 (NH4)2SO4
NH4NO3 SOC
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4.2
0.5
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mg/m3
Interpolation- Inverse Distance Weighted -
Wood Burning Motor Vehicles
Coal Power Plant Pulp & Paper
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4.5
0.5
mg/m3
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1.5
0
mg/m3
Interpolation- Inverse Distance Weighted -
Dust Mineral Production
Oil Combustion Metal Production
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mg/m3
Port Shipping (?)
Comparisons with Emission InventoriesSource apportionment Emission
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mg/m3
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4.5
0.5
mg/m3
PM2.5
Motor VehiclesMax: 628 tons/yr
Max: 12,465 tons/yr
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mg/m3
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1.5
0
mg/m3
Mineral production
Pulp & Paper production
Comparisons with Emission InventoriesSource apportionment Emission
Max: 1,843 tons/yr
Max: 1,431 tons/yr
Spatial Correlations of Sources
• Which sources are/are not correlated in the region?
• Source correlation calculations– Pearson numbers between two sites were calculated for
each source based on daily source apportionment results– how daily source correlations are changed with distance
Spatial Correlations
PM2.5 mass
1.0
0.8
0.6
0.4
0.2
0.0
Pe
ars
on
No
.
10008006004002000
Distance (km)
y = exp(-0.0017x)
2= 4.29
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
Pe
ars
on
No
.
10008006004002000
Distance (km)
y = exp(-0.0020x)
NH4HSO4 + (NH4)2SO4
2= 6.31
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
Pea
rso
n N
o.
10008006004002000Distance (km)
y = exp(-0.0025x)
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
Pea
rso
n N
o.
10008006004002000
Distance (km)
y = exp(-0.0027x)
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
Pea
rso
n N
o.
10008006004002000Distance (km)
y = exp(-0.0015x)
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
Pea
rso
n N
o.
10008006004002000Distance (km)
y = exp(-0.0025x)
Spatial Source Correlations
NH4HSO4 (NH4)2SO4
NH4NO3 SOC
2= 8.00 2= 7.72
2= 6.27
2= 7.83
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
Pea
rso
n N
o.
10008006004002000Distance (km)
y = exp(-0.0029x)
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
Pea
rso
n N
o.
10008006004002000
Distance (km)
y = exp(-0.0033x)
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
Pea
rso
n N
o.
10008006004002000
Distance (km)
y = exp(-0.0018x)
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
Pea
rso
n N
o.
10008006004002000
Distance (km)
y = exp(-0.0045x)
Spatial Source Correlations
Wood Burning Motor Vehicles
Dust Pulp & Paper production
2= 9.64 2= 11.47
2= 19.70
2= 9.72
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
Pea
rso
n N
o.
10008006004002000
Distance (km)
y = exp(-0.0056x)
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
Pea
rso
n N
o.
10008006004002000
Distance (km)
y = exp(-0.0099x)
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
Pea
rso
n N
o.
10008006004002000
Distance (km)
y = exp(-0.0103x)
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
Pea
rso
n N
o.
10008006004002000
Distance (km)
y = exp(-0.0053x)
Spatial Source Correlations
Coal Power Plant
Mineral Production
Oil Combustion
Metal Production
2= 9.32
2= 11.582= 7.95
2= 7.02
Summary• SOC : 40 ~ 60 % of OC, Seasonal difference
• Secondary PM : more than 50 % of PM
• Significant spatial variability of source contributions
• Agreement or disagreement with emission inventories
• Significant regional correlation; secondary PM, wood burning, motor vehicles, dust
• Little regional correlation; industrial sources
• Can identify port shipping impacts?
Acknowledgements
• Funding Agencies– U.S. EPA (RD82897602, RD83107601, and
RD83096001)– GA DNR– Georgia Power