Causes of Haze Assessment (COHA) Update
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Transcript of Causes of Haze Assessment (COHA) Update
Causes of Haze Assessment (COHA) Update
Jin Xu
Update
• Visibility trends analysis (under revision)
• Assess meteorological representativeness of 2002 (modeling base year) (in progress)
• PMF Modeling and case study (in progress)
• Evaluate winds used in back-trajectory analysis (to be done)
Trends Analysis PagesAre there any statistically significant multi-year trends in the haze levels and causes of haze?
http://coha.dri.edu/web/general/tools_trendanaly.html
•National maps and tables
•Individual site analysis
Trends Analysis for Aerosol Light Extinction Coefficients (1/Mm) in 20% Worst Days
Note:: ncrease Trend : Decrease Trend The size of the arrow is related to the slope (1/Mm/Year). Red: P Value <= 0.05 Yellow: 0.05 < P Value <= 0.1 Light Blue: 0.1 < P Value <= 0.2 Dark Blue: P Value > 0.2
Trends Analysis for Nitrate Light Extinction Coefficients (1/Mm) in 20% Worst Days
20% Worst Days
0.00
1.00
2.00
3.00
4.00
5.00
1992 1994 1996 1998 2000 2002 2004
Year
So
il E
xtin
ctio
n (
1/M
m)
MEVE1
PEFO1
TONT1
20% Worst Days
0.00
1.00
2.00
3.00
4.00
5.00
1992 1994 1996 1998 2000 2002 2004
Year
Nit
rate
Ext
inct
ion
(1/
Mm
)
MEVE1
PEFO1
TONT1
Meteorological Representativeness of 2002- Backtrajectory Analysis
– Generate 8-day back-trajectories of all WRAP IMPROVE aerosol monitoring sites (every 3 hrs, from 3 starting heights) for 2003 and 2004 to give 5 years of trajectories
– Produce residence time maps for 2002 and the 5-year period (2000 – 2004), plus maps of ratios and of differences of 2002 and the 5-year period for each site
– Interpret the maps for each monitoring site and document on the COHA web site
Differences in residence times between July-October 2002 and the five-year (1998-2002) July-October average at Big Bend. Blue colors denote greater back trajectory residence times in 1998-2002, and red colors denote greater residence times in 1999.
Meteorological Representativeness of 2002- National Temperature and Precipitation Maps
Maps of average temperature and precipitation total averaged by state and whether it was normal, much above, much below etc. summarized by month, season, or year.
Positive Matrix Factorization for Groups of Sites
• PMF is a statistical method that identifies a user specified number of source profiles (i.e. relative composition particle species for each source) and source strengths for each sample period that reduce the difference between measured and PMF fitted PM2.5 mass concentration
• In matrix notation,X = GF + E
where X is the matrix of measured composition for each sample period, F is the source profile, G is the source strength or factor scores for each sample period, and E is the residual or error matrix.
Model Description
X = GF + EX (n * Sp) = a matrix of observed fine particulate species
concentrations with the dimensions of number of observations by the number of species
G (n * f) = a matrix of source contributions by observation day with the dimensions of number of observations by the number of factors
F (f * Sp) = a matrix of source profiles with the dimensions of number of factors by the number of species
E (n * Sp) = a matrix of random errors with the dimensions of number of observations by number of species
T h e F a n d G m a t r i c e s o f t h e f i n a l s o l u t i o n a r e t h e n n o r m a l i z e d a c c o r d i n g t o t h e f o l l o w i n g e q u a t i o n s t o d e t e r m i n e t h e q u a n t i t a t i v e s o u r c e c o n t r i b u t i o n s ( C i , g / m 3 ) ) a n d p r o f i l e s f o r e a c h s o u r c e ( S i , g / g ) .
i
iji FM
FS
w h e r e : S i = t h e r o w o f t h e s o u r c e p r o f i l e m a t r i x f o r s o u r c e i F i j = t h e s o u r c e p r o f i l e v a l u e f o r s p e c i e j o f s o u r c e i F M i = t h e c a l c u l a t e d a v e r a g e t o t a l f i n e m a s s c o n t r i b u t i o n f o r s o u r c e i
ikii FMGC *
w h e r e : C i = t h e c o l u m n o f t h e s o u r c e c o n t r i b u t i o n m a t r i x f o r s o u r c e i G k i = t h e s o u r c e c o n t r i b u t i o n o n d a y k f o r s o u r c e i F M i = t h e c a l c u l a t e d a v e r a g e t o t a l f i n e m a s s c o n t r i b u t i o n f o r s o u r c e i T h e F M i i s d e t e r m i n e d b y r e g r e s s i o n t o t a l P M 2 . 5 m a s s c o n c e n t r a t i o n s i n t h e k t h s a m p l e a g a i n s t e s t i m a t e d s o u r c e c o n t r i b u t i o n v a l u e s .
f
iikik FMGm
1
*
Model Description – Cont.
• Robust Mode – the value of outlier threshold distance = 4.0 i.e. if the residue exceeds 4 times of the standard deviation, a measured value is considered outlier. The least squares formulation thus becomes:
• Error Mode (decides the standard deviation of the data Sij):
EM = -12 (based on observed value) Sij = Tij + C*Xij
EM = -14 (based on observed and fitted value) Sij = Tij + C*max(Xij,
• FPEAK and FKEY Matrix (controls the rotation) – default: 0 (central), try different numbers
PMF Running Parameters
)1
f
hfjif fg
PMF Inputs• PM2.5 chemical speciation data from VIEWS web site.• Data are screened to remove the days when either
PM10 or PM2.5 mass concentration is missing.• Data value and associated uncertainty (T)If data is missing Then
data value = geometric mean of the measured valuesuncertainty = 4 * geometric mean of the measured values
Else if data bellows detection limitdata value = 1/2 * detection limituncertainty = 5/6 * detection limit
Elsedata value = measured datauncertainty = analytical uncertainty + 1/3 * detection limit
PMF Outputs
• Source factor profiles (ug/ug)
• Contribution of each source factor to aerosol mass and light extinction for each sampling day at each monitoring site (ug/m3)
How Many Source Factors?
• Regression coefficients for PM2.5 > 0
• Scaled source profiles <1
• Experience (arbitrary)
PMF for Group 1 – Washington State Class I Areas:
MORA1, NOCA1, OLYM1, PASA1, SNPA1, SPOK1, and WHPA1
0.0001
0.001
0.01
0.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.0001
0.001
0.01
0.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.0001
0.001
0.01
0.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.0001
0.001
0.01
0.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.0001
0.001
0.01
0.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.0001
0.001
0.01
0.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.0001
0.001
0.01
0.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.0001
0.001
0.01
0.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.0001
0.001
0.01
0.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
Urban/Diesel
Aged sea salt
Sulfate-rich secondary
Smoke
Dust
Industrial/Incinerator
Nitrate-rich secondary
Smoke II ?
Dust II
Two smoke factors are not correlated
0
1
2
3
4
5
6
7
8
9
0 2 4 6 8 10 12 14 16 18
Factor 4
Fac
tor
9
Two dust factors (factor 5 and factor 8) are highly correlated – Maybe 8 factors is enough
y = 6.2415x - 0.0941
R2 = 0.9036
-5
0
5
10
15
20
25
30
35
40
0 1 2 3 4 5
Factor 5
Fac
tor
8
0.00010.0010.010.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.00010.0010.010.1
110
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.0001
0.001
0.01
0.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.0001
0.001
0.01
0.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.00010.0010.010.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.00010.0010.010.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.0001
0.001
0.01
0.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.0001
0.001
0.01
0.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
Smoke
Nitrate-rich secondary
Sulfate-rich secondary
Dust
Urban/Diesel
Aged sea salt
Industrial/Incinerator
Smoke II
11%
26%
321%
429%
52%
62%
74%
89%
926%
Urban/DieselAged sea salt
Sulfate-rich secondary
Smoke
Dust
Smoke II
Dust II
134%
23%
31%4
23%
511%
62%
76%
820%
Smoke
Nitrate-rich secondary
Sulfate-rich secondary
Dust
Urban/Diesel
Aged sea salt
Smoke II
Industrial/Incinerator
Industrial/Incinerator
Nitrate-rich secondary
9 Factors
8 Factors
Two smoke factors from the 8 factor modeling correlated well with the two factors from the 9 factor modeling
y = 0.7684x + 0.0745
R2 = 0.92
0
2
4
6
8
10
12
14
16
18
0 5 10 15 20 25
Factor 1
Fac
tor
4
y = 1.0459x + 0.2375
R2 = 0.7044
0
1
2
3
4
5
6
7
8
9
0 1 2 3 4 5 6 7
Factor 8
Fac
tor
9
0.0001
0.001
0.01
0.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.0001
0.001
0.01
0.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.0001
0.001
0.01
0.1
1
10
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.0001
0.001
0.01
0.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.0001
0.001
0.01
0.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.0001
0.001
0.01
0.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
0.0001
0.001
0.01
0.1
1
AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR
Sulfate-rich secondary
Mixture
Nitrate-rich secondary
Dust
Urban/Diesel
Aged sea salt
Smoke
How about 7 factors – only one smoke factor left, no industrial/incinerator, add a mixture factor (smoke, dust, and urban/power plant?)
124%
213%
32%4
11%
542%
66%
72%
Urban/Diesel
Dust
Nitrate-rich secondary
Sulfate-rich secondaryAged sea salt
Smoke Mixture
Contributions to PM2.5 Mass (7 Factors)
The single smoke factor from 7 factor modeling is correlated to the sum of two smoke factors in 8 factor modeling
The correlation between the single smoke factor from 7 factor modeling and any one of the two smoke factors in 8 factor modeling is not very high
y = 0.3232x + 0.2414
R2 = 0.4827
y = 0.7976x + 0.0322
R2 = 0.6706
0
5
10
15
20
25
0 2 4 6 8 10 12 14 16 18
Factor1
Factor8
Linear (Factor8)
Linear (Factor1)
y = 1.1208x + 0.2736
R2 = 0.9113
y = 1.1208x + 0.2736
R2 = 0.9113
0
5
10
15
20
25
0 2 4 6 8 10 12 14 16 18
Factor 5
Fa
cto
r 1
+ F
ac
tor
8
Have we identified different smoke factors?
Let’s try 6 factors – no mixture factor any more.
Percentage Contributions of PMF Factors to Major PM2.5 Components at Mt. Rainier
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
AmmoniumSulfate
AmmoniumNitrate
OC EC FS
Major PM2.5 Component
Per
cen
tag
e C
on
trib
uti
on
Urban/Diesel
Smoke
N-Rich
Dust
S-Rich
Sea Salt
Percentage Contributions of Major PM2.5 Components to PMF Factors at Mt. Rainier
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Sea S
alt
S-Rich Dus
t
N-Rich
Smok
e
Urban
/Dies
el
PMF Source Factor
Per
cen
tag
e C
on
trib
uti
on
FS
EC
OC
Ammonium Nitrate
Ammonium Sulfate
Factor Contributions to PM2.5 Mass at Mt. Rainier (3/1988-2/2004)– Compare With Keith Rose’s PMF Results
0
0.1
0.2
0.3
0.4
0.5
0.6
Sea Salt Sulfate Soil Nitrate Smoke Mobile
Sources
Fra
cti
on
of
PM
2.5
Ma
ss
Jin
Keith Rose (91-95)
Keith Rose (2000-2003)
PMF application to Hawaii IMPROVE Particle Speciation Data
• All available PM2.5 speciation data for both sites (>2 years each) are used together in the PMF to explain measured PM2.5 mass
• Six factors seemed to separate reasonably explained source factors
• Multiple linear regression was used to explain coarse mass using the six PMF factors
Haleakula and Hawaii Volcano National Park Monitoring Sites
Six Source Profiles from Hawaii PMF Analysis
0.00
0.10
0.20
0.30
0.40
0.50
Al As Br Ca EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP Cl Cr Cu H Fe Pb Mg Mn Ni NO3 P K Rb Se Si Na Sr S Ti V Zn Zr
0.00
0.05
0.10
0.15
0.20
Al As Br Ca EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP Cl Cr Cu H Fe Pb Mg Mn Ni NO3 P K Rb Se Si Na Sr S Ti V Zn Zr
0.00
0.05
0.10
0.15
0.20
Al As Br Ca EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP Cl Cr Cu H Fe Pb Mg Mn Ni NO3 P K Rb Se Si Na Sr S Ti V Zn Zr
0.00
0.05
0.10
0.15
0.20
0.25
Al As Br Ca EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP Cl Cr Cu H Fe Pb Mg Mn Ni NO3 P K Rb Se Si Na Sr S Ti V Zn Zr
0.000.010.010.020.020.030.030.04
Al As Br Ca EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP Cl Cr Cu H Fe Pb Mg Mn Ni NO3 P K Rb Se Si Na Sr S Ti V Zn Zr
0.00
0.05
0.10
0.15
0.20
0.25
Al As Br Ca EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP Cl Cr Cu H Fe Pb Mg Mn Ni NO3 P K Rb Se Si Na Sr S Ti V Zn Zr
#1, Sea salt
#3, Dust
#4, Smoke
#5, Secondary Nitrate
#6, Secondary Sulfate & Nitrate
#2, Volcano sulfate
Contributions to PM2.5 by Source Factors
17%
233%
38%
422%
514%
616%
17%
270%
33%
47%
56%
67%
12%
241%
37%
419%
511%
620%
Haleakula
288%
54%4
3%
31%
63%
11%
HawaiiVolcano
All Days Worst 20% Haze Days
Volcano
Sea salt
DustSmoke
Nitrate
Sulfate & Nitrate
Site
0
5
10
15
20
25
30
2003 Date
PM
2.5
(u
g/m
3)
Series6Series5Series4Series3Series2Series1
0
1
2
3
4
5
6
7
8
2003 Date
PM
2.5
(u
g/m
3)
Series6Series5Series4Series3Series2Series1
Contributions of Source Factors to PM2.5 in 20% Worst Days of 2003
Haleakula
Hawaii Volcano
At Haleakula, about half of worst haze days are associated with volcano emissions, while the others are associated with different factors (e.g. smoke, secondary sulfate and nitrate)
At Hawaii Volcano, all worst haze days are dominated by the volcano sulfate factor.
Note that October 24, 27, & 30 had trajectories from the volcano to Haleakula
PMF Work Plan
• PMF modeling for each group of sites (based on AOH report) using all the IMPROVE data available at the site.
• Case study for selected sites: PMF modeling for individual site using data from certain time period (e.g. 2000-2004).
• Compare PMF results for the selected sites based on group modeling and individual modeling.
• Combine PMF modeling results with the backtrajectories and emission inventories to investigate the major source regions of certain aerosol sources (e.g. smoke) for each site.
• Episode analysis based on PMF results
Backtrajectory analysis for PMF modeled factor 5 (BWS5) (Weighted – Unweighted). This serves to confirm that the factor 5 is in actual fact a “vegetative burn” factor from wildfires to the northwest of Boundary Waters Canoe Area IMPROVE site (Engelbrecht et al., 2004).
Backtrajectory Analysis for PMF Factor - Example
PMF Modeling for Group 19 (BRCA1, CAPI1, ZICA1 and ZION1)
Secondary Sulfate
Smoke
Secondary Nitrate
Dust
Mobile & Other Urban
Time Series of Factor 2 (Fire) Contributions
0
0.5
1
1.5
2
2.5
1/3/
03
2/2/
03
3/4/
03
4/3/
03
5/3/
03
6/2/
03
7/2/
03
8/1/
03
8/31
/03
9/30
/03
10/3
0/03
11/2
9/03
12/2
9/03
BRCA1
ZION1
ZICA1
CAPI110/30/2003
Time Series of Factor 4 (Dust) Contributions
0
2
4
6
8
10
12
14
16
1/3/
03
2/2/
03
3/4/
03
4/3/
03
5/3/
03
6/2/
03
7/2/
03
8/1/
03
8/31
/03
9/30
/03
10/3
0/03
11/2
9/03
12/2
9/03
BRCA1
ZION1
ZICA1
CAPI1
10/30/2003
10/30/2003 –The worst day at all three sites
0
10
20
30
40
50
60
70
BRCA1 CAPI1 ZION1
CM_calculated:VAL
SOILf:VAL
ECf:VAL
OCf:VAL
ammNO3f:VAL
ammSO4f:VAL
Intense wildfires burning around Los Angeles and San Diego, very windy
Cedar City, Utah hourly wind speed on 10/30/2003, max gust 53mph