AoH Work GroupWeight of Evidence Framework

WRAP Meeting – Tucson, AZJanuary 10/11, 2006

Joe Adlhoch - Air Resource Specialists, Inc.

Overview

Review of RHR visibility goals What do we mean by weight of evidence

(WOE) approach? Review of model approach to determine

reasonable progress Review of other data inputs

Review of RHR Visibility Goals

Define current conditions at each Class I area using the 2000-04 baseline period

Define “natural conditions” Improve visibility such that the average Haze Index

for the 20% worst days in the baseline period reach “natural conditions” by 2064

Ensure that visibility on the 20% best days does not degrade

Periodically assess the improvement in visibility between the baseline period and 2064 and show that “reasonable progress” is being achieved

Schematic of Glide Path

From: From: Guidance for Estimating Natural Visibility Conditions Under the Guidance for Estimating Natural Visibility Conditions Under the Regional Haze RuleRegional Haze Rule, EPA 2003, EPA 2003

WOE Definition

Set of analyses supplemental to primary measurement/modeling efforts

WRAP AoH working definition: Review of all available analyses that bear on Class I area visibility

Monitoring data Emissions data Model results Attribution results (combination of multiple methods) Review of trends (monitoring and emissions) Review of episodic (“natural” ?) events Back trajectory and other analyses

Assigning appropriate weight to each analysis (based on relevance and uncertainty)

Ultimately, this will take the form of a checklist of things to review and instructions on how to weigh each piece

Use of AQ Model to Estimate 2018 Visibility (simplified)Assumption: the AQ model is better at predicting relative changes in

concentration than absolute concentrationsSteps:1. Determine the 20% worst days from the 2002 IMPROVE data2. Model species concentrations for 20023. Model species concentrations for 2018 base and scenarios4. Determine a species-specific relative reduction factor (RRF) for

the average of the 20% worst days (based on step #1 above):

RRFsulfate = 2018sulfate / 2002sulfate

5. Project 2018 concentrations by applying the RRFs to the IMPROVE data for the 20% worst days in each baseline year:

Projected 2018concentration ~ Avg. [RRF x Baselineconcentration]

6. Calculate projected 2018 visibility for 20% worst days and compare to the Glide Path

2002 Model Performance: Agua Tibia, CAWorst 20% Obs (left) vs Plan02a (right) at AGTI1

0

20

40

60

80

100

120

140

160

59 89 92 134 137 191 212 224 227 230 239 248 284 287 293 296 299 302 305 329 - - - - - avg

Julian Day in Worst 20% group

bE

XT

(1/

Mm

) bCMbSOILbECbOCbNO3bSO4

2018 -2002 Model Change: Agua Tibia, CABext Response (base18a - plan02a) at AGTI1 on Worst 20% Days

-25

-20

-15

-10

-5

0

5

59 89 92 134 137 191 212 224 227 230 239 248 284 287 293 296 299 302 305 329 Avg

Julian Day

Del

ta B

ext

(1/M

m)

bCMbSOILbECbOCbNO3bSO4

2002 Model Performance: Zion, UT Worst 20% Obs (left) vs Plan02a (right) at NOAB1

0

5

10

15

20

25

30

35

59 83 92 95 113 116 119 131 137 140 158 164 179 206 212 230 233 248 269 293 320 - - - - avg

Julian Day in Worst 20% group

bE

XT

(1/

Mm

) bCMbSOILbECbOCbNO3bSO4

2018 -2002 Model Change: Zion, UTBext Response (base18a - plan02a) at ZION1 on Worst 20% Days

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

35 44 50 59 89 107 116 125 140 158 191 194 206 218 227 233 239 305 332 338 341 Avg

Julian Day

Del

ta B

ext

(1/M

m)

bCMbSOILbECbOCbNO3bSO4

Is Model Prediction of Reasonable Progress… Reasonable? Determine if the major species causing visibility impairment are

handled well by the model The variability in the 5-year baseline could be used as an “uncertainty

range” to bound the projected 2018 visibility: Which species most affect variability? Meteorological dependencies? Could this be tied to monitoring uncertainties?

Are there episodic events that could justifiably be removed from the data set (e.g., large fire episodes during baseline period)?

Review attribution source regions and their emissions: How well do attribution methods agree? If source regions can be identified with confidence, do the projected

emissions reductions for 2018 support the model’s visibility reductions?

Median Uncertainty of IMPROVE Data Across WRAP Uncertainty based only on lab reported uncertainties

for daily samples (2000 – 2004)

OC, EC, Soil, and CM uncertainty determined from standard propagation of error analysis on individual component terms

Uncertainty due to flow/size cut errors not included

Monitored Species

Median Uncertainty

(%)

Sulfate 5Nitrate 9

Organic C 18Elemental C 47

Soil 4Coarse Mass 12

Glide Path for Agua Tibia, CAUniform Rate of Reasonable Progress Glide Path

Agua Tibia Wilderness - 20% Worst Days

23.0421.98

19.33

16.69

14.05

11.40

8.76

7.17

21.76

0

5

10

15

20

25

30

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

Ha

zin

ess

In

de

x (D

eci

vie

ws)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Uniform Rate of Reasonable Progress Glide PathAgua Tibia Wilderness - 20% Worst Days

23.0421.98

19.33

16.69

14.05

11.40

8.76

7.17

21.76

0

5

10

15

20

25

30

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

Ha

zin

ess

In

de

x (D

eci

vie

ws)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Baseline Variability (dv)

Glide Path for Agua Tibia, CA

Baseline Variability by Species

Glide Path for San Gabriel, CAUniform Rate of Reasonable Progress Glide Path

San Gabriel Wilderness - 20% Worst Days

19.4318.61

16.57

14.53

12.48

10.44

8.407.17

17.65

0

5

10

15

20

25

30

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

Ha

zin

ess

In

de

x (D

eci

vie

ws)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Glide Path for San Gabriel, CAUniform Rate of Reasonable Progress Glide Path

San Gabriel Wilderness - 20% Worst Days

19.4318.61

16.57

14.53

12.48

10.44

8.407.17

17.65

0

5

10

15

20

25

30

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

Ha

zin

ess

In

de

x (D

eci

vie

ws)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Baseline Variability (dv)

Baseline Variability by Species

Uniform Rate of Reasonable Progress Glide PathGoat Rocks Wilderness - 20% Worst Days

12.54 12.2211.44

10.659.86

9.088.29 7.82

11.81

0

5

10

15

20

25

30

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

Ha

zin

ess

In

de

x (D

eci

vie

ws)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Glide Path for Goat Rocks, WA

Uniform Rate of Reasonable Progress Glide PathGoat Rocks Wilderness - 20% Worst Days

12.54 12.2211.44

10.659.86

9.088.29 7.82

11.81

0

5

10

15

20

25

30

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

Ha

zin

ess

In

de

x (D

eci

vie

ws)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Glide Path for Goat Rocks, WA

Baseline Variability (dv)

Baseline Variability by Species

Large Episodic Fire Impacts in 2002

Point and Area Source 2002 and 2018 SO2 Emissions

0

25,000

50,000

75,000

100,000

125,000

150,000

175,000

Em

issi

ons

(tp

y)

2002-Point 6,809 93,752 42,120 97,011 17,597 36,879 50,722 37,436 156,668 17,587 14,021 42,838 52,969 119,645 38,208

2018-Point 7,777 106,113 49,632 68,476 10,813 43,055 24,041 40,825 162,705 21,687 15,268 52,953 51,355 145,100 32,895

2002-Area 5,531 2,677 8,314 6,559 2,916 3,299 12,954 6,559 5,748 9,932 10,167 3,581 7,388 17,902 49

2018-Area 6,044 3,410 9,772 7,499 2,721 3,432 14,194 15,753 5,856 8,422 11,667 3,587 8,667 23,109 2

AK AZ CA CO ID MT NV NM ND OR SD UT WA WY Tribes

SO2 Point and Area Emissions Reductions

NOx Point and Area Emissions ReductionsPoint and Area Source 2002 and 2018 NOx Emissions

0

25,000

50,000

75,000

100,000

125,000

150,000

175,000

Em

issi

ons

(tpy

)

2002-Point 74,472 64,084 104,435 117,869 11,487 53,415 59,775 100,352 87,425 24,959 20,698 91,044 43,631 117,883 87,215

2018-Point 67,959 77,737 109,515 112,153 13,946 62,583 69,016 74,874 91,895 31,761 24,726 96,974 49,397 132,591 92,580

2002-Area 8,488 9,049 114,471 34,846 30,318 12,072 5,787 85,576 15,457 14,825 6,345 11,335 18,355 34,891 2,932

2018-Area 9,293 12,559 117,717 44,041 42,068 36,053 7,488 172,319 21,129 17,027 7,207 21,636 22,746 79,196 6,639

AK AZ CA CO ID MT NV NM ND OR SD UT WA WY Tribes

Expected Attribution Results

The modeled attribution results (CAMx and PSAT method) will tell us how much species mass is likely due to specific source regions (states, Canada, Mexico, Pacific, etc.)

The results can be displayed as: Amount or percent of species mass attributed by a

region Amount or percent of extinction attributed by a

region

Phase I Attribution Graphics

Phase 2 Attribution “Footprint”

The following maps show mock ups for how attribution results might be displayed in Phase 2 (data shown is from Phase I)

Helps to answer the questions: Which states need to consult on visibility issues What contributions to haze might be coming from

outside the WRAP or the U.S.

Phase I SO 4 + NO 3Attributed to Arizona

AZ (SO 4)

AZ (NO 3)

0 M m -1

4 M m -1

8 M m -1

Phase I Sulfate and Nitrate Extinction Attributed to Arizona (TSSA Analysis)

Phase I Sulfate and Nitrate Extinction Attributed to Oregon (TSSA Analysis)

Phase I SO 4 + N O 3Attributed to O regon

O R (SO 4)

O R (NO 3)

0 M m -1

3 M m -1

7 M m -1

P h a s e I S O 4 A t t r i b u t e db y 1 0 s t a t e s i n t h e W R A P

( U T , W A , W Y n o t i n c l u d e d )

A Z

C A

C O

I D

M T

N V

N M

N D

O R

S D

1 M m - 1

1 3 M m - 1

2 5 M m - 1

Phase I Sulfate Extinction Attributed to WRAP States

(excluding UT, WA, WY)

1

2

34

5

6

78

910

11

12

13

14

15

1617

18

1920

Phase I clustering based on SO4/NO3 attribution

Phase I SO 4 A ttributed tonon-W RAP Source Regions

EA US (SO 4)

Can (SO 4)

M ex (SO 4)

0 M m -1

7 M m -1

14 M m -1

Phase I Sulfate Extinction Attributed to non-WRAP Source Regions

Phase I N O 3 A ttributed tonon-W RAP Source Regions

EA US (N O 3)

Can (NO 3)

M ex (NO 3)

0 M m -1

3 M m -1

7 M m -1

Phase I Nitrate Extinction Attributed to non-WRAP Source Regions