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


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


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


0.00010.0010.010.1

110


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


0.00010.0010.010.1

1


0.00010.0010.010.1

1


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


Smoke



Dust

Urban/Diesel

Aged sea salt


Smoke II

11%

26%

321%

429%

52%

62%

74%

89%

926%

Urban/DieselAged sea salt


Smoke

Dust

Smoke II

Dust II

134%

23%

31%4

23%

511%

62%

76%

820%

Smoke



Dust

Urban/Diesel

Aged sea salt

Smoke II




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


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1

10


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1



Mixture


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


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


0.00

0.05

0.10

0.15

0.20


0.00

0.05

0.10

0.15

0.20

0.25


0.000.010.010.020.020.030.030.04


0.00

0.05

0.10

0.15

0.20

0.25


#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

Causes of Haze Assessment (COHA) Update

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