Post on 30-Dec-2015
description
Regional Haze Rule• Promulgated in 1999• Requires states to set RPGs based on 4 statutory factors
and consideration of a URP• URP = 20% reduction in manmade haze (dv) per planning
period (10 years)• URP heavily dependent on:
– Assumptions regarding future natural conditions– Contribution of non-WRAP sources to baseline– Representativeness of 2000-04 baseline
• 24 of the 77 Class I sites have no more than 3 years of data in baseline period
– These issues more accute in the West
Why A Species-Based Approach?
• Species differ significantly from one another in their:– Contribution to visibility impairment– Spatial and seasonal distributions– Source types– Contribution from natrual and international sources– Emissions data quality– Atmospheric science quality– Tools available for assessment and projection
SO2 NOx OC CM
Emission Sources
Almost entirely anthro.
Mostly point sources.
Mostly anthro.
Mix of combustion sources.
Diverse.
Mix of anthro, fire, and biogenic VOCs.
Diverse.
Very difficult to partition wb dust into nat/anthro.
Emissions Data Quality
Very good overall.
Activity data less good for area sources.
Good.Activity data less good, some coding concerns w/ smaller point, area, and O&G sources.
Fair.Good activity data & conf. in PM2.5 emissions, but uncertain spec. of PM2.5 & bio. VOCs.
Poor, except for some locales.Categorically complete but accuracy very uncertain.
Emission Projections
Very good.
Uncertain about area sources.
Good.
Uncertain about offshore and O&G.
Fair.
What to expect from fire?
Fair.
What to expect from wb dust?
Atmospheric Science Quality
Very good.
Meteorology probably largest uncertainty.
Fair.
Chemistry more complex, but meteorology too.
Fair.
Most complex, least understood, but model perf. OK.
Fair.
No major chemistry, but model resolution, met. insufficient.
WRAP Tools Emission Inv.
CMAQ Proj.
PSAT Apport.
Emission Inv.
CMAQ Proj.
PSAT Apport.
Emission Inv.
CMAQ Proj.
PMF, WEP.
Emission Inv.
Causes of Dust.
WEP.
What Is A Potential Process?
• For each site and species:• Estimate progress expected from Base Case + BART
in 2018• Determine any other LTSs which may be reasonable
for that pollutant and recalculate 2018 species concentration
• Add up improvements from all species into dv• This becomes the RPG for the 20% worst days• Explain why this is less than URP
– Large international and natural contributions, large uncertainties in dust inventory preclude action, etc.
Determining Non-BART LTSs
• Determine species glidepath and 2018 URP value• Estimate progress expected from Base Case +
BART in 2018• If progress is better than or equal to 2018 URP:
– Check inventory for “important sources” which may be uncontrolled
• If progress is worse than 2018 URP, but WRAP antho contribution declines by at least 20%:– Check inventory for important sources which may be
uncontrolled
Determining Non-BART LTSs
• If progress is worse than 2018 URP, and WRAP antho contribution declines by less than 20%:– Evaluate air quality & emission trends in more detail– Check inventory for important sources which may be
uncontrolled or undercontrolled– Identify LTSs for these sources considering the 4
RPG factors and 7 LTS factors, where applicable– Either adopt these strategies, commit to adopting
them post 2007, or commit to evaluating them further
“Important Sources”
• Identified and qualitatively ranked based on some or all of the following:– Size, proximity, current/potential degree of control,
feasibility of control, cost effectiveness, etc.• If point sources important, identify ~10 facilities• If area sources important, identify 3-5 categories
• Identification of important sources should not be limitted by state boundaries
Determine URP for a species
IsBase+BART
projection better than
URP?
IsWRAPAnthro
reduction> 20%?
Are thereany importantuncontrolled
sources?
Are thereany important
uncontrolled orundercontrolled
sources?
Repeat for other species.
Evaluate emission & airquality trends more closely
Identify LTSs forthese sources.
Adopt, commit to adopt, orcommit to further evaluation.
Determine reductions at C1A.
Add up all species reductionsto get a RPG. Explain whyit’s less than default URP
but still reasonable.
Y
Y Y
N
N N
N
Y
Note, if projection is better than URP and/or WRAP anthro reduction is >20%, the 4 RPG factors are inherently taken into account via BART.
Crater Lake Example
Do SO4, NO3, OC, and EC meet their glidepaths? No, Yes, No, Yes.Then do the WRAP anthro contributions for SO4 and OCdecline by 20%?
SO4 and NO3 Data
• The following 6 slides describe and show the results of the CAMx-PSAT source apportionment model results for SO4 and NO3.
• Results are used to determine the rank and significance of sources and to evaluate the percent change in their contribution to haze in the baseline and future years. They are not used to predict a specific amount / concentration of pollution.
SO4 and NO3 Apportionment
• CAMx air quality model with PM Source Apportionment Technology (PSAT)
• PSAT completed for 2 cases:– Plan 2002c– Base 2018b
• Tracks sources of sulfate and nitrate• Tracking organic carbon too computationally
intensive. However, data is available that delineates primary OC, from biogenic SOA, and anthropogenic SOA.
18 Source Regions on a 36 km Grid
- 2 7 3 6 - 2 4 1 2 - 2 0 8 8 - 1 7 6 4 - 1 4 4 0 - 1 1 1 6 - 7 9 2 - 4 6 8 - 1 4 4 1 8 0 5 0 4 8 2 8 1 1 5 2 1 4 7 6 1 8 0 0 2 1 2 4 2 4 4 8- 2 0 8 8
- 1 8 7 2
- 1 6 5 6
- 1 4 4 0
- 1 2 2 4
- 1 0 0 8
- 7 9 2
- 5 7 6
- 3 6 0
- 1 4 4
7 2
2 8 8
5 0 4
7 2 0
9 3 6
1 1 5 2
1 3 6 8
1 5 8 4
1 8 0 0
26
9
1 2
4
58
1 3
1 13
1 7
1 5
1 5
1 6
1 6
1 6
1 8 1 8
1 4
1 7
1 4
1 0
Eight Source Categories
• Examples of PSAT “sources”:– MV_CO = mobile sources in Colorado
– PT_CE = point sorces in CENRAP
– BCON = transport from modeling domain boundaries (derived from GEOS-CHEM)
ICON Initial conditions
BCON Boundary conditions
PT Point sources
MV Mobile sources
ANF WRAP anthropogenic fires
Natural WRAP natural fires and biogenics
NWF Elevated fires in other RPOs
AR All other sources (non-elevated fires in other RPOs, area sources, offshore, oil & gas area sources, etc.)
Status of Modeling
• Plan 2002c just completed• Base 2018b completed end of August• Results currently available for daily and monthly
averages• Results will be provided for 20% best and worst
visibility days at each Class I area• See “Apportionment” link on TSS for results
Do WRAP anthropogenic SO4 contributions decline by 20%? Not quite (18%). Note: WRAP reductions would be significantly larger if 2001 were used as a base year because the first Centralia cut occurred in 2002. Also, OR_PT contribution will likely decline with BART, especially at the Boardman power plant.
Do WRAP anthropogenic NO3 contributions decline by 20%? Yes (39%). Again, note potential reductions from Boardman.
Carbon and Dust Apportionment
• PSAT results for OC and EC not available due to computational resources.
• No air quality modeling results available whatsoever for CM, and FS due to poor model peformance.
• For these pollutants, an alternative technique developed by the WRAP could be used to evaluate sources and progress.– Weighted Emissions Potential (WEP)
Weighted Emissions Potential Method
• Combine gridded emissions data with gridded backtrajectory residence times to determine sources with the most potential to affect a site.
• Sources with the greatest potential will tend to be both upwind on the worst visibility days and have relatively large emissions.– 2002 and 2018 annual average emissions– 3-5 years of 20% worst days back trajectories– Discount sources based on distance from site– Ignore grid cells with very low residence times– Does not account for chemistry, dispersion, deposition– Method being finalized
Weighted Emissions Potential MethodPrototype example for Salt Creek, New Mexico
Emissions ResidenceTimes
Weighted EmissionsPotential
X =
CRLA1 Weighted Emissions Potential Primary OC Emissions (2002)
POA x Res Time
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
Arizon
a
Califo
rnia
Colora
doId
aho
Mon
tana
Nevad
a
New_M
exico
North
_Dak
ota
Orego
n
South
_Dak
ota
Utah
Was
hingt
on
Wyo
ming
Pacific
_Offs
hore
CENRAP
Easte
rn_U
S
Mex
ico
Canad
a
Region
Rel
ativ
e C
on
trib
uti
on
WB_DustFugitive_DustRoad_DustNatural_FireAnthro_FireOff-Road_MobileOn-Road_MobileOff-ShoreOil&GasAreaPointBiogenic
Do WRAP upwind weighted anthro OC emissions decline by 20%?
No. They hardly change.
CRLA1 Weighted Emissions Potential Primary OC Emissions (2018)
POA x Res Time
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
Arizon
a
Califo
rnia
Colora
doId
aho
Mon
tana
Nevad
a
New_M
exico
North
_Dak
ota
Orego
n
South
_Dak
ota
Utah
Was
hingt
on
Wyo
ming
Pacific
_Offs
hore
CENRAP
Easte
rn_U
S
Mex
ico
Canad
a
Region
Re
lati
ve
Co
ntr
ibu
tio
n
WB_Dust
Fugitive_DustRoad_Dust
Natural_Fire
Anthro_FireOff-Road_Mobile
On-Road_Mobile
Off-ShoreOil&Gas
Area
PointBiogenic
Do WRAP upwind weighted anthro VOC emissions decline by 20%?
No. They hardly change because growth in point and area sources offsets mobile source reductions.
CRLA1 Weighted Emissions Potential Primary VOC Emissions (2002)
VOC x Res Time
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
Arizon
a
Califo
rnia
Colora
doId
aho
Mon
tana
Nevad
a
New_M
exico
North
_Dak
ota
Orego
n
South
_Dak
ota
Utah
Was
hingt
on
Wyo
ming
Pacific
_Offs
hore
CENRAP
Easte
rn_U
S
Mex
ico
Canad
a
Region
Rel
ativ
e C
on
trib
uti
on
WB_DustFugitive_DustRoad_DustNatural_FireAnthro_FireOff-Road_MobileOn-Road_MobileOff-ShoreOil&GasAreaPointBiogenic
CRLA1 Weighted Emissions Potential Primary VOC Emissions (2018)
VOC x Res Time
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
Arizon
a
Califo
rnia
Colora
doId
aho
Mon
tana
Nevad
a
New_M
exico
North
_Dak
ota
Orego
n
South
_Dak
ota
Utah
Was
hingt
on
Wyo
ming
Pacific
_Offs
hore
CENRAP
Easte
rn_U
S
Mex
ico
Canad
a
Region
Rel
ativ
e C
on
trib
uti
on
WB_Dust
Fugitive_DustRoad_Dust
Natural_Fire
Anthro_FireOff-Road_Mobile
On-Road_Mobile
Off-ShoreOil&Gas
Area
PointBiogenic
Do WRAP upwind weighted anthro EC emissions decline by 20%?
Yes, 28% due to mobile source controls.
CRLA1 Weighted Emissions Potential Primary EC Emissions (2002)
PEC x Res Time
0
5,000
10,000
15,000
20,000
25,000
Arizon
a
Califo
rnia
Colora
doId
aho
Mon
tana
Nevad
a
New_M
exico
North
_Dak
ota
Orego
n
South
_Dak
ota
Utah
Was
hingt
on
Wyo
ming
Pacific
_Offs
hore
CENRAP
Easte
rn_U
S
Mex
ico
Canad
a
Region
Rel
ativ
e C
on
trib
uti
on
WB_Dust
Fugitive_Dust
Road_Dust
Natural_Fire
Anthro_Fire
Off-Road_Mobile
On-Road_Mobile
Off-Shore
Oil&Gas
Area
Point
Biogenic
CRLA1 Weighted Emissions Potential Primary EC Emissions (2018)
PEC x Res Time
0
5,000
10,000
15,000
20,000
25,000
Arizon
a
Califo
rnia
Colora
doId
aho
Mon
tana
Nevad
a
New_M
exico
North
_Dak
ota
Orego
n
South
_Dak
ota
Utah
Was
hingt
on
Wyo
ming
Pacific
_Offs
hore
CENRAP
Easte
rn_U
S
Mex
ico
Canad
a
Region
Re
lati
ve
Co
ntr
ibu
tio
n
WB_Dust
Fugitive_Dust
Road_Dust
Natural_Fire
Anthro_Fire
Off-Road_Mobile
On-Road_Mobile
Off-Shore
Oil&Gas
Area
Point
Biogenic
Do WRAP upwind weighted anthro CM emissions decline by 20%?
No, they increase 32%.
CRLA1 Weighted Emissions Potential Primary PMC Emissions (2002)
PMC x Res Time
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
Arizon
a
Califo
rnia
Colora
doId
aho
Mon
tana
Nevad
a
New_M
exico
North
_Dak
ota
Orego
n
South
_Dak
ota
Utah
Was
hingt
on
Wyo
ming
Pacific
_Offs
hore
CENRAP
Easte
rn_U
S
Mex
ico
Canad
a
Region
Re
lati
ve
Co
ntr
ibu
tio
n
WB_Dust
Fugitive_Dust
Road_Dust
Natural_Fire
Anthro_Fire
Off-Road_Mobile
On-Road_Mobile
Off-Shore
Oil&Gas
Area
Point
Biogenic
CRLA1 Weighted Emissions Potential Primary EC Emissions (2018)
PMC x Res Time
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
Arizon
a
Califo
rnia
Colora
doId
aho
Mon
tana
Nevad
a
New_M
exico
North
_Dak
ota
Orego
n
South
_Dak
ota
Utah
Was
hingt
on
Wyo
ming
Pacific
_Offs
hore
CENRAP
Easte
rn_U
S
Mex
ico
Canad
a
Region
Re
lati
ve
Co
ntr
ibu
tio
n
WB_Dust
Fugitive_Dust
Road_Dust
Natural_Fire
Anthro_Fire
Off-Road_Mobile
On-Road_Mobile
Off-Shore
Oil&Gas
Area
Point
Biogenic