Post on 17-Jan-2018
transcript
Implementation Workgroup MeetingDecember 6, 2006
Attribution of Haze Workgroup’sMonitoring Metrics Document Status:
1) 2018 Visibility Projections – Alternative Procedures using Relative Response Factors
2) Ambient Haze Monitoring Data Substitution
Monitoring Metrics Document Status• Issue identified at late July AoH Workgroup meeting• Document to provide consensus technical recommendations to
support haze planning• 4 major topics
Adopt revised IMPROVE equation – done Adopt alternate natural conditions values (by species) – done Adopt 2000-04 IMPROVE dataset for sites with complete data – done
Sites with insufficient data identified and data substitution underway Analysis of default and alternate visibility projections methods
2 calls completed, next call 12/13, maybe 1 additional call Potential methods explained later
• Final draft of Monitoring Metrics document out for review early January
Introduction to Visibility Projections• Difficult to meet 2018 Uniform Rate of Progress
(URP) goal for western Class I areas using EPA default or alternative modeled 2018 visibility projections methods due to large contributions of:– Fires (High EC and OC)– Dust (High Soil and CM)– International Transport
• Most of these emissions are natural, unpredictable and uncontrollable
• Unable to realistically forecast these sources in 2018– many source categories held constant from 2002 to 2018 – Two examples follow: Crater Lake (CRLA) OR and Salt
Creek (SACR) NM
13.7813.29
12.08
10.87
9.65
8.44
7.23
6.50
13.31
5
7
9
11
13
15
2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
Year
Haz
ines
s In
dex
(Dec
ivie
ws)
Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction
Uniform Rate of Reasonable Progress Glide PathSawtooth Wilderness - 20% Worst Days
5
7
9
11
13
15
2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
18.0317.29
15.43
13.57
11.71
9.85
8.00
6.88
17.14
5
8
11
14
17
20
2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
Year
Haz
ines
s In
dex
(Dec
ivie
ws)
Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction
Uniform Rate of Reasonable Progress Glide PathSalt Creek - 20% Worst Days
5
8
11
14
17
20
2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
2018 Emission Projections• Sources Held Constant 2000-04 to 2018 base case
– Biogenics– Wind Blown Dust (WRAP model)– Ammonia from WRAP model– Mexico and Canada– Off-Shore Marine– Boundary Conditions from GEOS-Chem Global Model
• Sources with emission reductions 2000-04 to 2018 base case– Mobile source NOx, SOx, EC & OC– Point and Area Source NOx and SOx (amount varies by state)– Nonattainment areas (mainly VOC & NOx in CA)– Many other anthropogenic sources relatively unchanged or
increase• Road dust, oil & gas, some uncontrolled area sources
2018 Visibility Projection Issues• EPA guidance (September 2006) recommends using
average modeling results for the 2002 Worst 20% (W20%) days to project 2018 visibility for W20% days from the 2000-2004 Baseline (Relative Response Factors, RRFs)
• W20% days in 2002 may not be representative of W20% days from other years in Baseline– 2002 W20% days may occur in different times of the year
and therefore emphasize different PM components– Episodic events may dominate W20% days in some years
• Fires dominate 2002 W20% days at some western Class I areas that makes the 2002 year derived RRFs very stiff
• Fire impacts in other years at Class I areas with little fires in 2002
Agua Tibia, CA (AGTI1) Distribution of 20% Worst Days by Year (IMPROVE data)
0
0.1
0.2
0.3
0.4
1 2 3 4 5 6 7 8 9 10 11 12Month
2001
2002
2003
2004
Salt Creek, NM (SACR1) Distribution of 20% Worst Days by Year (IMPROVE data)
0
0.1
0.2
0.3
1 2 3 4 5 6 7 8 9 10 11 12Month
2001
2002
2003
2004
Badlands (BADL1) Distribution of 20% Worst Days by Year (IMPROVE data)
00.1
0.20.3
0.40.5
1 2 3 4 5 6 7 8 9 10 11 12Month
2000
2001
2002
2003
2004
Concern 2002 May Not Capture Seasonal Variations
2018 Visibility Projection Issues• Missing IMPROVE data at some Class I areas hinders
visibility projection calculations at 18 sites in western U.S.– 5 IMPROVE sites did not meet RHR criteria of at least 3
complete years in 2000-2004 Baseline– 13 IMPROVE sites did not satisfy data completeness criteria
for 2002 so RRFs could not be calculated– Data substitution underway to address this issue
• Model performance for Coarse Mass (CM) sufficient bad we do not believe the RRFs are reliable– Suspect a lot of measured CM are subgrid-scale to the model
so the model 36 km CM estimates are not representative– Set RRFs for CM = 1.0
2018 Visibility Projection Issues• 2018 URP goal is not a NAAQS, just one element of
the Reasonable Progress (RP) determination • Four Factor Analysis another important element of RP• EPA default 2018 visibility projections one approach
for using modeling results in RP determination– Can we use alternative projection techniques that take into
account seasonal differences in W20% days during Baseline– Are there other ways we can use the modeling results to
assist in the Reasonable Progress determination?
Approaches for RRFs (1)• Method 1: Average RRF Approach from
September 2006 EPA Guidance– For each Class I area and Observed Worst/Best 20%
days from 2002 take the ratio of the average modeled 2018 to 2002 PM species concentrations
– Applied to observed daily PM components for each Worst/Best 20% day from each year from the Baseline, calculate daily Bext/dv, annual dv and 2018 projected dv same as before
N
iij
N
iij
N
iij
N
iij
j
SO
SO
SON
SONSORRF
1
1
1
1
)2002(4
)2018(4
)2002(41
)2018(41
)4(
Approaches for RRFs (2)• Method 2A: Average Quarterly RRF
Approach– Similar to Average RRF Approach only calculate
separate RRFs for each Quarter of the year using the observed Worst/Best 20% days for each Quarter in 2002
– Allows for seasonal variations in RRFs, has similarities to 24-Hour PM2.5 projection approach specified by EPA guidance
Approaches for RRFs (3)• Method 2B: Average Monthly RRF
Approach– Calculate separate RRFs for each Month of the
year using the observed Worst/Best 20% days for each Month in 2002
– Allows for seasonal variations in RRFs • Results follow for:
– 2002 Plan02c & 2018 Base18b– CMAQ 2002 36 km annual simulations– New IMPROVE equation
Visibility Projection Comparisons• Use DotPlots that present 2018 visibility at
Class I areas as a percentage of meeting 2018 URP benchmark– Compare Method 1 (Annual W20%) with
Method 2A (Quarterly W20%) and Method 2B (Monthly W20%) New IMPROVE Algorithm
– New IMPROVE equation– RRF for CM = 1.0– No Western US Class I area achieves 2018
URP benchmark• In contrast to eastern US where many Class I areas
achieve 2018 URP goal due to sulfate domination
Visibility Predictions for Colorado Plateau and Desert Southwest sites
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%B
RC
A1
CA
NY
1
GR
CA
2
ME
VE
1
SA
PE
1
WE
MI1
ZIO
N1
BA
ND
1
BO
AP
1
CH
IR1
GIC
L1
GR
SA
1
IKB
A1
SA
CR
1
SA
GU
1
SIA
N1
WH
IT1
WH
PE
1
Perc
ent o
f tar
get r
educ
tion
achi
eved
from monthly RRF
from quarterly RRF
from annual RRF
Colorado Plateau Desert Southwest
Visibility Predictions for North, Great Basin and Rockies sites
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%BAD
L1
CABI1
GAM
O1
LOST1
MELA
1
MO
NT1
SU
LA1
THR
O1
ULB
E1
WIC
A1
CR
MO
1
JAR
B1
SAW
T1
BR
ID1
NO
AB1
RO
MO
1
WH
RI1
YELL
2
Perc
ent o
f tar
get r
educ
tion
achi
eved
from monthly RRF
from quarterly RRFfrom annual RRF
North Great Basin Rockies
Visibility Predictions for Pacific Northwest and California sites
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%HECA1
KALM
1
MO
HO
1
MO
RA1
NO
CA1
OLY
M1
PASA1
SNPA1
STA
R1
THSI1
WHPA1
AG
TI1
BLI
S1
DO
ME1
HO
OV1
JOSH1
LAVO
1
PIN
N1
REDW
1
SAG
A1
SAG
O1
YO
SE1
Perc
ent o
f tar
get r
educ
tion
achi
eved
from monthly RRF
from quarterly RRF
from annual RRF
Pacific Northwest California
Worst 20% Obs (left) vs plan02c (right) at AGTI1
0
20
40
60
80
100
120
140
160
59 80 89 92 134 137 212 224 227 230 239 248 284 287 293 296 299 302 305 329 _ _ _ _ _ Avg
Julian Day in Worst 20% group
bEXT
(1/M
m) bCM
bSOILbECbOCbNO3bSO4
Bext Response (base18b - plan02c) at AGTI1 on Worst 20% Days
-25
-20
-15
-10
-5
0
5
59 80 89 92 134 137 212 224 227 230 239 248 284 287 293 296 299 302 305 329 Avg
Julian Day
Del
ta B
ext (
1/M
m) bCM
bSOILbECbOCbNO3bSO4
Uniform Rate of Reasonable Progress Glide PathAgua Tibia Wilderness - 20% Worst Days
23.5022.45
19.81
17.18
14.54
11.91
9.287.70
21.72
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
Haz
ines
s In
dex
(Dec
ivie
ws)
Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction
Worst 20% Obs (left) vs plan02c (right) at SAWT1
0
10
20
30
40
50
60
70
116 137 170 191 194 197 200 203 206 209 212 215 218 224 230 233 254 257 269 296 299 317 _ _ _ Avg
Julian Day in Worst 20% group
bEXT
(1/M
m) bCM
bSOILbECbOCbNO3bSO4
Bext Response (base18b - plan02c) at SAWT1 on Worst 20% Days
-4
-3
-3
-2
-2
-1
-1
0
1
116 137 170 191 194 197 200 203 206 209 212 215 218 224 230 233 254 257 269 296 299 317 Avg
Julian Day
Del
ta B
ext (
1/M
m) bCM
bSOILbECbOCbNO3bSO4
Uniform Rate of Reasonable Progress Glide PathSawtooth Wilderness - 20% Worst Days
13.7813.29
12.08
10.87
9.65
8.44
7.23
6.50
13.27
5
7
9
11
13
15
2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
Year
Haz
ines
s In
dex
(Dec
ivie
ws)
Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction
Worst 20% Obs (left) vs plan02c (right) at SACR1
0
10
20
30
40
50
60
70
80
90
100
2 8 56 83 92 95 110 116 125 131 143 146 155 161 164 218 221 227 248 251 272 344 359 _ _ Avg
Julian Day in Worst 20% group
bEXT
(1/M
m) bCM
bSOILbECbOCbNO3bSO4
Bext Response (base18b - plan02c) at SACR1 on Worst 20% Days
-20
-15
-10
-5
0
5
10
2 8 56 83 92 95 110 116 125 131 143 146 155 161 164 218 221 227 248 251 272 344 359 Avg
Julian Day
Del
ta B
ext (
1/M
m) bCM
bSOILbECbOCbNO3bSO4
Uniform Rate of Reasonable Progress Glide PathSalt Creek - 20% Worst Days
18.0317.29
15.43
13.57
11.71
9.85
8.00
6.88
17.10
5
8
11
14
17
20
2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
Year
Haz
ines
s In
dex
(Dec
ivie
ws)
Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction
Additional Visibility Projection Metrics
• Need to assess Glide Paths for each component of visibility impairment except CM– SO4, NO3, EC, OC and Fine Soil– Adding PM species Natural Conditions as end point– Presenting Species glide paths analysis on AoH call 12/13
• More likely [be closer] to meet 2018 URP benchmark when looking at controllable (SO4 and NO3) extinction
• Need to analyze results of alternative methods more closely
Conclusions• The EPA Default (Annual Average ) and
alternative (Quarterly/Monthly Average) 2018 projection can be used to estimate visibility levels in 2018 for comparisons with the URP benchmark
• Additional PM species-specific Glide Paths and 2018 projections will be made to assess progress in reducing the “controllable” portion of haze
• Data will be used in Reasonable Progress determinations