Causes of Haze AssessmentUpdate for the

Haze Attribution Forum Meeting By Marc Pitchford

9/24/04

Evaluation of the Transport Regression Attribution Approach

• Sensitivity to back trajectory start heights

• Relative merits of EDAS and FNL wind fields for back trajectories

• Utility of a Pacific coastal source region

• Annual versus seasonal (cool season and warm season) regression assessments

• Appropriate air quality parameters

• Regressions forced through zero or not

Transport Regression Model

The model finds regression coefficients (Ai) for each source region (i) that produces a best fit to the following relationship between the measured air quality (e.g. sulfate concentration) and residence time in each source region (Ti) for several years of data

Air Quality = Σ (Ai x Ti) = A1T1 + A2T2... +AnTn

Residence time is the amount of time air is calculated to be in a source region using a back trajectory model.

Source Regions of Grand Canyon (GRCA2)

PAC

CAN

ATLGulfMEX

NWNE

SECA

NV UT CO

NM

SWAZ

SEAZ

NWAZ

NEAZ

EDAS Domain

Comparison of Regression Modeling Results at Grand Canyon Using Residence Time (Based on EDAS) at 10m, 500m, 1500m and All Three

Heights

Su

lfu

r C

on

ce

ntr

ati

on

(n

g/m

3)

-10

0

10

20

30

40

50

60

70

80

SWAZ

NWAZ

NEAZ

SEAZCA NV UT

CO NMNW NE SE

PACCAN

ATL

GULFM

EX

3 Heights10m500m1500m

Results are similar, so we will use a start height of 500 meters.

Comparison Between Measured and Calculated Sulfur Concentrations (ng/m3) at Grand Canyon Based on Trajectory Regression at 500m

Using EDAS

0

100

200

300

400

500

600

700

11/26/1999 5/24/2000 11/20/2000 5/19/2001 11/15/2001 5/14/2002 11/10/2002

Measured S

Calculated S Based on Regression

10 per. Mov. Avg. (Measured S)

10 per. Mov. Avg. (Calculated S Based onRegression)

The regression model did a relatively good job. But it missed some peak values.

Su

lfu

r C

on

ce

ntr

ati

on

(n

g/m

3 )

Normalized Total Residence Time Maps at Grand Canyon

-20

-10

GRCA - S

0

10

20

30

40

50

60

70

80

90

SWAZ NWAZ NEAZ SEAZ CA NV UT CO NM NW NE SE PA CAN ATL GULF MEX

FNLEDAS

Comparison of Trajectory Regression Results for Sulfur at GRCA2 Using FNL and

EDAS

Similar results are found in GRCA2 using FNL and EDAS as HYSPLIT trajectory modeling inputs.

Su

lfu

r C

on

ce

ntr

ati

on

(n

g/m

3 )

Comparison of Trajectory Regression Results at BADL1 Using FNL and EDAS

Similar regression results at BADL1 using FNL and EDASSmaller contribution from the Pacific Ocean based on FNL

BADL - S

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

SWSD

NWSD

NESD

SESD WY

MT ND

MN IA

NEBRASKASW NW NE SE

PAC

CANATL

GULFM

EX

Source Region

Co

ntr

ibu

tion

to S

Co

nce

ntr

atio

n

FNL

EDAS

Comparison of Trajectory Regression Results at MORA1 Using FNL and EDAS

MORA1 - S

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

SWW

A

NWW

A

NEWA

SEWA O

R ID SW NW NE SEPA

CCAN

ATLM

EX

FNL

EDAS

Similar regression results at MORA1 using FNL and EDAS

300Km

500Km

GRCA - Create a coastal area (300 Km or 500 Km off of the coast line)

in the Pacific Ocean

Trajectory regression modeling suggests that a large amount of particulate sulfate is from the Pacific Ocean. Is it from the coastal area or from longer distance?

Comparison of trajectory

regression results (Using EDAS data)

at GRCA with / without the dividing

up of the Pacific Ocean

GRCA - S

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

SW

AZ

NW

AZ

NE

AZ

SE

AZ

CA

NV

UT

CO

NM

NW NE

SE

CA

N

AT

L

GU

LF

ME

X

PA

CC

oa

st

PA

C

Source Regions

Co

ntr

ibu

tio

n t

o S

Co

nc

.

500Km

300Km

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7R

egr

essi

on C

oeff

icie

nt

300Km

500Km

NoDividing

1. Similar regression coefficients are found at GRCA2 with / without the dividing up of the Pacific Ocean.2. About half of the sulfur from the Pacific Ocean is from within 300Km off of the coast – Shipping emissions, long range transported aerosols, or trajectory uncertainties?

Seasonal Variation of the Regression Results for sulfur at GRCA (EDAS with 300Km Coastal Area)

Su

lfu

r C

on

ce

ntr

ati

on

(n

g/m

3 )

GRCA - S - EDAS - 300Km Coast Area

-10

0

10

20

30

40

50

60Cool Season

Warm Season

Overall

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

SWAZ

NWAZ

NEAZ

SEAZ CA NV UTCO NM

NW NE SECAN

ATL

GULFM

EX

PACCOAST

PAC

Re

gre

ss

ion

Co

eff

icie

nt

Cool Season

Warm Season

Overall There are some seasonal variations in the regression coefficients. But the source areas with big variations in regression coefficients are those with relatively less end points (i.e. with relatively lower contribution to total sulfur concentration)

Overall, trajectory regressions give similar results in GRCA2 with and without the consideration of the seasonal variations.

Comparison of Trajectory Regression Results for Nitrate With/Without Seasonal Division (EDAS with 300Km Coastal

Area)C

on

trib

uti

on

to

Nit

rate

Overall trajectory regression results for nitrate are similar in GRCA2 with and without the consideration of the seasonal variation. The regression is better in the warm season (multiple regression with no intercept R2 ~ 0.75) than the cool season (multiple regression with no intercept R2 ~ 0.4).

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

SWAZ

NWAZ

NEAZ

SEAZCA NV UT

CO NM NW NE SECAN

ATL

GULFM

EX

PACCoas

tPA

C

Cool Season

Warm Season

Overall With Season Division

Without Season Division

Contributions to Sulfur, Extinction Coefficient, OC and Nitrate at GRCA2 (EDAS with 300Km Coastal Area)

The contributions to sulfur and Bep are similar, while the contributions of southwestern Arizona to Nitrate and California to OC are higher.The regressions for sulfur and Bep (multiple regression with no intercept R2 ~ 0.8) are better than for OC and Nitrate (multiple regression with no intercept R2 ~ 0.5)

GRCA-EDAS-300Km Coastal Area

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

SWAZ

NWAZ

NEAZ

SEAZCA NV UT

CO NMNW NE SE

CANATL

GULFM

EX

PACCoast

PAC

Co

ntr

ibu

tio

n

S

Bep

NO3

OC

Comparison of Percentage Contributions to Bep, Sulfur and Nitrate Concentrations at Joshua Tree Wilderness

Area

-20

-10

0

10

20

30

40

50

SWCA

NWCA

NECA

SECA OR NV AZ

SW NW NE SEPA

CCAN

ATL

GULF

MEX

Bep

S

Nitrate

Regression for Sulfur (multiple regression with no intercept R2 ~ 0.8) and Bep (multiple regression with no intercept R2 ~ 0.7) are better than Nitrate (multiple regression with no intercept R2 ~ 0.4)

Comparison of Trajectory Regression Results at JOSH1 Using FNL and EDAS

Nitrate

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

SWCA

NWCA

NECA

SECA OR NV AZSW NW NE SE

PAC

CANATL

GULFM

EX

FNL

EDAS

Con

trib

utio

n to

Nita

rte

S

-0.1

0

0.1

0.2

0.3

0.4

0.5

SW

CA

NW

CA

NE

CA

SE

CA

OR

NV

AZ

SW

NW NE

SE

PA

C

CA

N

AT

L

GU

LF

ME

X

Co

ntr

ibu

tio

n t

o S

ulf

ur

FNL

EDAS

Similar regression results using FNL and EDAS

Comparison of Trajectory Regression Results at JOSH1 Using FNL and EDAS (300 Km Coastal

Area)JOSH1 - S - 300 Km Coast Area

-0.1

0

0.1

0.2

0.3

0.4

0.5

SWCA

NWCA

NECA

SECA OR NV AZSW NW NE SE

CANATL

GULFM

EX

PACCoas

tPA

C

FNL

EDAS

Most of the Pacific Ocean sulfur is from the coastal area.More of the Pacific Ocean sulfur is attributed to the coastal area based on EDAS than FNL.

Comparison Between Regression Results With / Without Intercept at GRCA

GRCA - S (Average [S] = 206 g/m3)

-20

-10

0

10

20

30

40

50

Su

lfu

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on

ce

ntr

ati

on

s (g

/m3)

With Intercept (=67 ug/m^3)

Without Intercept

• Similar results whether intercept is forced to zero or not.

• Intercept could be some combination of global background influence and statistical noise.

• We’ll do both since it doesn’t require much extra work and may be useful

Summary of Evaluations Results• Back trajectory regression is not sensitive to start

heights so we’ll use 500 meters• EDAS and FNL wind fields give similar results so

we’ll use EDAS except for Alaska & Hawaii sites• Having a Pacific coastal source region is useful

so we’ve defined one at 300km from the coast• Seasonal regressions don’t seem to provide

sufficient value to justify their use• Sulfate is most appropriate and light extinction is

useful, so will do them in the first effort. • We will calculate regressions both forced through

zero or not forced through zero to maintain options to choose on an individual site basis

Next Steps & Schedule

• Transport Regression– Source regions are being defined & residence

times determined ~End of November – Regressions will be calculated & displays

generated ~Mid-December– All will be uploaded onto the COHA web site

~End of Year

• Conceptual Models w/o transport inclusion of transport regression ~End of November