corroborative and weight-of-evidence development and analyses 11-08ccos envair charles blanchard...
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Corroborative and Weight-of-Evidence
Development and Analyses11-08CCOS
EnvairCharles Blanchard
Shelley Tanenbaum
Alpine GeophysicsJames Wilkinson
May 29, 2012
2
Overview of Presentation
• Project objectives
• Weight-of-evidence framework
• Trends in emissions, O3 precursors, and O3
• Generalized additive model (GAM)
• Using the GAM to project future O3 response
• (Uncertainty analyses and design value variability)
• (Model demonstration after presentation)
3
Objectives
• Identify innovative new methods to reduce uncertainty in O3 attainment demonstrations
• Develop and demonstrate use of new methods for weight-of-evidence evaluation:
– Enhance confidence in future year projections
– Provide additional evidence for effectiveness of VOC and NOx emission reductions
– Further assess local and regional influences
• Provide data, software, and documentation suitable for ongoing Study Agency use
4
Weight-of-Evidence Framework
• Corroborate VOC and NOx emission reductions by comparison with trends in ambient NMOC & NOx concentrations
• Quantify O3 reductions
• Link O3 reductions to observed ambient VOC (NMOC) and NOx concentrations
• Project O3 response(s) to future emission reductions and precursor concentrations
• Reconcile weight-of-evidence analyses with modeling predictions
5
Our Study Domain -Central California ShowingO3 Monitoring Sites in 15 Subregions
Legend
C BA
C SJ
EBA
N BA
N C C
N SF
N SJ
SAC
SBA
SC C
SEQ
SSF
SSJ
SVN
W BA
6
0
50
100
150
200
250
300
350
400
1994 1996 1998 2000 2002 2004 2006 2008 2010 2012Year
NMOC Mean Daily Max
NMOC Mean 7-10 am
NMOC Mean Daily
NMOC Mean Daily Max = 27010.592 - 13.352 * Year; R̂ 2 = .773NMOC Mean 7-10 am = 14538.417 - 7.197 * Year; R̂ 2 = .699NMOC Mean Daily = 12532.614 - 6.207 * Year; R̂ 2 = .676
NM
OC
(pp
bC)
Ambient CO, NO, NO2, NMOC Trends Are Downward and Significant
0
200
400
600
800
1000
CO
-AM
(pp
bv)
1994 1996 1998 2000 2002 2004 2006 2008 2010 2012Year
CO-AM (ppbv) = 41980.112 - 20.76 * Year; R̂ 2 = .957
0
20
40
60
80
100
NO
(pp
bv)
1988 1993 1998 2003 2008Year
Y = 4257.307 - 2.103 * X; R̂ 2 = .917
0
5
10
15
20
25
30
35
40
45
NO
2-A
M (
ppbv
)
1994 1996 1998 2000 2002 2004 2006 2008 2010 2012YEAR
NO2-AM (ppbv) = 1057.888 - .521 * YEAR; R̂ 2 = .855
Central San Joaquin Valley (CSJ)
Mean of 7 – 10 a.m. CO
Fresno 1st
Mean of 7 – 10 a.m. NO2
Mean of daily max NO
All sites All sites
All sites
7
Ambient CO Trends Are Consistent With Emission Trends in Most Subregions
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
CBA SBA NBA EBA WBA NSJ CSJ SSJ NCC SCC NSF SSF SAC SVN
Bay area San Joaquin NorthCentralCoast
SouthCentralCoast
MountainCounties
Sacramento
Rat
e o
f C
han
ge
(per
cen
t/ye
ar)
CO Emissions 1995-2004 Ambient CO 1995-2004 Ambient CO 1995 - 2010
8
Ambient NO2 Trends Exceed NOx Emission Trends in Some Subregions
-7
-6
-5
-4
-3
-2
-1
0
CBA SBA NBA EBA WBA NSJ CSJ SSJ NCC SCC NSF SSF SAC SVN
Bay area San Joaquin NorthCentralCoast
SouthCentralCoast
MountainCounties
Sacramento
Rat
e o
f C
han
ge
(per
cen
t/ye
ar)
NOx Emissions 1995 - 2004 Ambient NO2 1995 - 2004 Ambient NO2 1995 - 2010
9
Ambient NMOC Trends Exceed VOC Emission Trends, But Limited Data
-35
-30
-25
-20
-15
-10
-5
0 CSJ SSJ SCC SAC
San Joaquin South Central Coast Sacramento
Rat
e o
f C
han
ge
(per
cen
t/ye
ar)
VOC Emissions 1995-2004 Ambient NMOC 1997-2010
10
CSJ Peak 8-Hour O3 Metrics Comparison
0
20
40
60
80
100
120
140
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Year
Mea
n P
eak
8-H
ou
r O
3 (
pp
bv)
0
5
10
15
20
25
30
35
40
Sit
e-d
ays
or
Exc
ess
> 7
5 p
pb
v
Subregion Max 4th Highest 8-Hour Mean Top 10% Daily Subregion Max 8-Hour
Mean Top 60 Daily Subregion Max 8-Hour Mean Daily Subregion Max 8-Hour
Mean Daily Subregion Mean 8-Hour Percent Site-days > 75 ppbv
Mean Subregion Excess > 75 ppbv
11
Downward Trends in Peak 8-Hour O3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1CBA SBA NBA EBA WBA NSJ CSJ SSJ NCC SCC NSF SSF SEQ SAC SVN
Bay area San Joaquin NorthCentralCoast
SouthCentralCoast
Mountain Counties Sacramento
Rat
e o
f C
han
ge
(pp
bv/
year
)
4th highest Top 10 percent of days Top 60 days
12
Conclusions from Trends Analyses
• Ambient precursor trends confirm emission reductions
• Peak 8-hour O3 is trending downward at rates of ~0.2 – 0.7 ppbv per year with exception of CBA (upward) and SCC (downward at 1.5 – 2 ppbv per year)
• The top 10% of days and the top 60 days per subregion per year provide good subsets for study – trends are relevant to 4th-highest and to subregion mean daily excess of 75 ppbv
• Season average subregion daily maximum peak 8-hour O3 is also useful metric
13
Generalized Additive Model (GAM)
• GAM developed by U.S. EPA to determine meteorologically-adjusted O3 trends
• We adapted the GAM to link peak 8-hour O3 to ambient NO, NO2, and other precursors, while accounting for the influence of weather
• Tested many meteorological and air-quality variables as predictors – focus on final model
• We developed estimates of uncertainty and an approach for projecting future O3 response
14
EPA published GAM in Atmospheric Environment, 2007
Area of application was eastern US
• EPA used GAM to determine meteorologically-adjusted O3 trends• GAM generates sensitivity of O3 to each predictor variable
15
The Basic GAM
• Model says that predicted log O3 on day “i” of year “k” is an additive function of:– Overall mean of data from all days of all years, – Mean effects Y=year, W=day of week, J=julian day
– Contributions due to nonlinear functions, f, of meteorological and air quality predictor variables
• Log transform of O3 is useful but optional
• Flexible choice of functions “fi”
– GAM is set up to use natural splines
– Natural splines are (special) cubic polynomials
log(O3)ik = + Yk + Wd + f1(Ji) + f2(xik) + …
16
Programming Aspects of GAM
• Original software written by EPA as R program (R is nonproprietary, available, runs under LINUX, Windows, MacOS )
• We modified program to generate output files– Graphs of annual average trends (various formats)
– Statistical summaries (text files)
– Daily data linking O3 to predictors (CSV files)
• Output files can be manipulated to select subsets of data and develop projections
17
GAM Application to Central California
• Predict subregion max daily peak 8-hour O3
– Find daily peak 8-hour O3 for each site
– Take maximum site for each day
• Meteorological variables– Daily max T, 10 a.m. – 4 p.m. RH, 7 – 10 a.m. & 1 –
4 p.m. WS & WD, HYSPLIT 24-hour back trajectory distance & direction, solar radiation, 850 mb T, delta 850 mb T – surface min T, pressure gradients
– Tested precipitation, 925 mb T, lagged met data
• Air quality variables
– Subregion mean daily max NO, 7 – 10 a.m. NO2
– Tested CO, NMOC, visibility, PM TC
18
Data Used for Application
• 1995 – 2010 O3 season (March – October): 3920 days (3424 – 3661 days data available)
• One surface meteorological site per subregion (Redding, Sacramento, etc.) – also ran HYSPLIT for each surface met site
• Nearest upper-air site (Medford, Oakland, San Diego)
• Means of CIMIS data in each subregion
• Means of NO, NO2 data in each subregion – (CO and NMOC data tested, not in final)
• IMPROVE data in each subregion (tested)
19
CIMIS Sites
20
NMOC Data Limitations
• Inconsistencies between measurement methods, changes in methods, incomplete canister sampling – longest, consistent record is for continuous NMOC coded as Method 164 (TEI 55 instrument)
• 14 NMOC sites in 8 subregions
• 5 Bay area sites with 5 – 6 years data plus 9 other sites with 11 – 13 years
• Variability of NMOC data greater than variability of CO, NO, and NO2 measurements
21
GAM Results
• Fit
• Sensitivity coefficients
• Factors contributing to high O3
• Projections
• Uncertainty
22
0
20
40
60
80
100
120
140
160
Mea
sure
d O
3 (p
pbv)
0 20 40 60 80 100 120 140 160Modeled O3 (ppbv)
SCC
NCC
Measured O3 (ppbv) = -.668 + 1.01 * Modeled O3 (ppbv); R^2 = .703 (SCC)Measured O3 (ppbv) = -.223 + 1.004 * Modeled O3 (ppbv); R^2 = .618 (NCC)
0
20
40
60
80
100
120
140
160
Mea
sure
d O
3 (p
pbv)
0 20 40 60 80 100 120 140 160Modeled O3 (ppbv)
WBA
SBA
NBA
EBA
CBA
Measured O3 (ppbv) = .279 + .991 * Modeled O3 (ppbv); R^2 = .543 (WBA)Measured O3 (ppbv) = -.082 + 1.002 * Modeled O3 (ppbv); R^2 = .707 (SBA)Measured O3 (ppbv) = -.189 + 1.004 * Modeled O3 (ppbv); R^2 = .722 (NBA)Measured O3 (ppbv) = -.495 + 1.012 * Modeled O3 (ppbv); R^2 = .69 (EBA)Measured O3 (ppbv) = .152 + .996 * Modeled O3 (ppbv); R^2 = .628 (CBA)
Coastal
Bay Area
0
20
40
60
80
100
120
140
160
Mea
sure
d O
3 (p
pbv)
0 20 40 60 80 100 120 140 160Modeled O3 (ppbv)
SSJ
SSF
SEQ
NSJ
CSJ
Measured O3 (ppbv) = -.347 + 1.005 * Modeled O3 (ppbv); R^2 = .846 (SSJ)Measured O3 (ppbv) = -.352 + 1.005 * Modeled O3 (ppbv); R^2 = .702 (SSF)Measured O3 (ppbv) = -.035 + 1.001 * Modeled O3 (ppbv); R^2 = .817 (SEQ)Measured O3 (ppbv) = -.194 + 1.003 * Modeled O3 (ppbv); R^2 = .821 (NSJ)Measured O3 (ppbv) = .006 + 1 * Modeled O3 (ppbv); R^2 = .85 (CSJ)
0
20
40
60
80
100
120
140
160
Mea
sure
d O
3 (p
pbv)
0 20 40 60 80 100 120 140 160Modeled O3 (ppbv)
SVN
SAC
NSF
Measured O3 (ppbv) = -.271 + 1.004 * Modeled O3 (ppbv); R^2 = .721 (SVN)Measured O3 (ppbv) = -.502 + 1.008 * Modeled O3 (ppbv); R^2 = .791 (SAC)Measured O3 (ppbv) = -.418 + 1.006 * Modeled O3 (ppbv); R^2 = .752 (NSF)
SJV and Sequoia
Sacramento Valley & Sierra
23
Which Variables Are Important?Variable CBA CSJ EBA NBA NCC NSF NSJ SAC SBA SCC SEQ SSF SSJ SVN WBA
T maxa 116 115 62 45 49 21 46 57 100 38 86 31 114 82 78
RHb 105 27 92 75 16 19 14 28 84 6 30 7 53 21 154
WS amc 6 8 1 6 0 5 5 7 6 1 1 4 5 5 5
WS pmd 21 33 20 16 11 3 26 26 14 13 14 4 2 0 28
WD amc 4 8 0 2 5 1 5 1 15 0 1 2 6 1 2
WD pmd 5 6 16 8 1 4 5 5 1 4 3 5 23 13 6
delta Te 7 31 54 37 24 57 28 41 5 34 36 23 9 27 12T 850 mb 10 6 11 19 25 7 7 12 11 14 12 24 4 2 16
Tran Disf 7 38 21 21 39 3 29 9 5 12 5 8 49 5 23
Tran Dirf 8 3 6 4 7 4 7 1 2 35 3 11 10 13 11
DoWg 42 17 44 25 3 4 28 12 39 10 8 4 10 4 17Year 9 41 21 20 20 18 58 11 23 24 21 13 14 22 25Julian 70 53 48 8 11 36 51 27 67 17 17 72 16 34 17Solar rad 25 60 12 31 84 27 75 50 25 106 10 14 96 19 6NO 92 51 51 114 15 67 69 96 67 63 15 21 69 51 64NO2 144 115 52 192 56 69 176 128 122 177 24 64 204 95 44
SF_Mfh 1 6 6 22 1 5 2 3 5 10 2 4 2 3 7
SF_Rnh 2 9 20 17 33 12 4 10 25 1 2 15 25 14 6
SF_Fah 5 7 41 21 4 6 19 8 6 7 8 7 12 9 5
SF_LVh 2 20 11 19 8 16 15 16 2 12 40 11 21 12 6
(Higher values of F-to-remove statistics indicate greater importance)
24
Sensitivity to Daily Max Temperature
-30
-10
10
30
50
70
90
Per
cent
_O3_
Cha
nge_
Dai
ly_M
ax_T
275 280 285 290 295 300 305 310 315 320Daily Max Temperature (Kelvins)
SVN
SSF
SAC
NSF
-30
-10
10
30
50
70
90
Per
cent
_O3_
Cha
nge_
Dai
ly_M
ax_T
280 285 290 295 300 305 310 315 320Daily Max Temperature (Kelvins)
WBA
SBA
NBA
EBA
CBA
-30
-10
10
30
50
70
90
Per
cent
_O3_
Cha
nge_
Dai
ly_M
ax_T
280 285 290 295 300 305 310 315 320Daily Max Temperature (Kelvins)
SCC
NCC
-30
-10
10
30
50
70
90
Per
cent
_O3_
Cha
nge_
Dai
ly_M
ax_T
280 285 290 295 300 305 310 315 320Daily Max Temperature (Kelvins)
SSJ
SEQ
NSJ
CSJ
Sacramento Valley and Sierra
Bay Area
SJV and Sequoia Coastal
25
Sensitivity to Mid-day RH
-40
-30
-20
-10
0
10
20
30
40
Per
cent
_O3_
Cha
nge_
Mid
_Day
_RH
0 20 40 60 80 100RH 10 am - 4 pm
SSJ
SEQ
NSJ
CSJ
-40
-30
-20
-10
0
10
20
30
40
Per
cent
_O3_
Cha
nge_
Mid
_Day
_RH
0 20 40 60 80 100RH 10 am - 4 pm
SCC
NCC
-40
-30
-20
-10
0
10
20
30
40
Per
cent
_O3_
Cha
nge_
Mid
_Day
_RH
0 20 40 60 80 100RH 10 am - 4 pm
SVN
SSF
SAC
NSF
-40
-30
-20
-10
0
10
20
30
40
Per
cent
_O3_
Cha
nge_
Mid
_Day
_RH
0 20 40 60 80 100RH 10 am - 4 pm
WBA
SBA
NBA
EBA
CBA
Sacramento Valley and Sierra
Bay Area
SJV and Sequoia Coastal
Bay Area
26
Sensitivity to 850 mb Temperature
-40
-30
-20
-10
0
10
20
30
Per
cent
_O3_
Cha
nge_
850M
b_T
_Del
ta
-20 -15 -10 -5 0 5 10 15 20Anomaly 850 mb T
SSJ
SEQ
NSJ
CSJ
-40
-30
-20
-10
0
10
20
30
Per
cent
_O3_
Cha
nge_
850M
b_T
_Del
ta
-20 -15 -10 -5 0 5 10 15 20Anomaly 850 mb T
SVN
SSF
SAC
NSF
-40
-30
-20
-10
0
10
20
30
Per
cent
_O3_
Cha
nge_
850M
b_T
_Del
ta
-20 -15 -10 -5 0 5 10 15 20Anomaly 850 mb T
SCC
NCC
-40
-30
-20
-10
0
10
20
30
Per
cent
_O3_
Cha
nge_
850M
b_T
_Del
ta
-20 -15 -10 -5 0 5 10 15 20Anomaly 850 mb T
WBA
SBA
NBA
EBA
CBA
Sacramento Valley and Sierra
Bay Area
SJV and Sequoia Coastal
27
Sensitivity to Daily Max NO
-40
-20
0
20
40
60
80
100
Per
cent
_O3_
Cha
nge_
NO
-1 -.5 0 .5 1 1.5 2 2.5 3Log NO
SVN
SSF
SAC
NSF
-40
-20
0
20
40
60
80
100
Per
cent
_O3_
Cha
nge_
NO
-1 -.5 0 .5 1 1.5 2 2.5 3Log NO
WBA
SBA
NBA
EBA
CBA
-40
-20
0
20
40
60
80
100
Per
cent
_O3_
Cha
nge_
NO
-1 -.5 0 .5 1 1.5 2 2.5 3Log NO
SSJ
SEQ
NSJ
CSJ
-40
-20
0
20
40
60
80
100
Per
cent
_O3_
Cha
nge_
NO
-1 -.5 0 .5 1 1.5 2 2.5 3Log NO
SCC
NCC
Sacramento Valley and Sierra
SJV and Sequoia Coastal
Bay Area
28
Sensitivity to 7 – 10 a.m. NO2
-60
-40
-20
0
20
40
60
80
Per
cent
_O3_
Cha
nge_
NO
2
-.5 0 .5 1 1.5 2Log NO2
WBA
SBA
NBA
EBA
CBA
-60
-40
-20
0
20
40
60
80
Per
cent
_O3_
Cha
nge_
NO
2
-.5 0 .5 1 1.5 2Log NO2
SVN
SSF
SAC
NSF
-60
-40
-20
0
20
40
60
80
Per
cent
_O3_
Cha
nge_
NO
2
-.5 0 .5 1 1.5 2Log NO2
SSJ
SEQ
NSJ
CSJ
-60
-40
-20
0
20
40
60
80
Per
cent
_O3_
Cha
nge_
NO
2
-.5 0 .5 1 1.5 2Log NO2
SCC
NCC
Sacramento Valley and Sierra
SJV and Sequoia Coastal
Bay Area
29
Sensitivity to Day of Week
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
14
Per
cent
_O3_
Cha
nge_
Day
_of_
Wee
k
1-Sun 2-Mon 3-Tue 4-Wed 5-Thu 6-Fri 7-SatDay of Week
WBA
SVN
SSJ
SSF
SEQ
SCC
SBA
SAC
NSJ
NSF
NCC
NBA
EBA
CSJ
CBA
30
Declining NO2 Has Reduced Peak O3
-15
-10
-5
0
5
10
15
Per
cent
_O3_
Cha
nge_
NO
2
1994 1996 1998 2000 2002 2004 2006 2008 2010 2012Year
WBA
SVN
SSJ
SSF
SEQ
SCC
SBA
SAC
NSJ
NSF
NCC
NBA
EBA
CSJ
CBA
31
Declining NO Has Increased Peak O3
-15
-10
-5
0
5
10
15
Per
cent
_O3_
Cha
nge_
NO
1994 1996 1998 2000 2002 2004 2006 2008 2010 2012Year
WBA
SVN
SSJ
SSF
SEQ
SCC
SBA
SAC
NSJ
NSF
NCC
NBA
EBA
CSJ
CBA
32
Net Effect of Declining NOx Has Been to Decrease Mean Peak 8-Hour O3
-15
-10
-5
0
5
10
15
Per
cent
_O3_
Cha
nge_
NO
x
1994 1996 1998 2000 2002 2004 2006 2008 2010 2012Year
WBA
SVN
SSJ
SSF
SEQ
SCC
SBA
SAC
NSJ
NSF
NCC
NBA
EBA
CSJ
CBA
33
Net NOx Effect is Robust to Change in Model Formulation
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12P
erce
nt_O
3_C
hang
e_N
Ox_
Mod
els_
9_10
-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12Percent_O3_Change_NOx_Model_6
WBA
SVN
SSJ
SSF
SEQ
SCC
SBA
SAC
NSJ
NSF
NCC
NBA
EBA
CSJ
CBA
34
Higher Peak O3 is Related to Stagnation (Shorter Transport Distances)
a. WBA
-15
-10
-5
0
5
10
15
O3
Cha
nge
(per
cent
)
0 50 100 150 200 250 300 350Direction (degrees)
> 900 km
700-900 km
500-700 km
300-500 km
200-300 km
100-200 km
0-100 km
b. CBA
-15
-10
-5
0
5
10
15
O3
Cha
nge
(per
cent
)
0 50 100 150 200 250 300 350Direction (degrees)
c. EBA
-15
-10
-5
0
5
10
15
O3
Cha
nge
(per
cent
)
0 50 100 150 200 250 300 350Direction (degrees)
35
Multiple Factors Enhance Peak O3 on High O3 Days (Top 60)
-5
0
5
10
15
20
25
30
35
CBA EBA NBA SBA WBA NCC SCC SAC SVN NSF NSJ SSF CSJ SEQ SSJ
Bay Bay Bay Bay Bay Coast Coast SV SVN Sierra SJV Sierra SJV Sierra SJV
O3
En
ha
nc
em
en
t (p
erc
en
t)
Surface Temperature &RH NO and NO2 Temperature Aloft Season & Solar
Airflow Local Wind Day of Week & Trend Pressure Gradients
36
Precursor Reductions Lowered O3 in CSJ
-10
0
10
20
30
40
50
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
O3
En
ha
nc
em
en
t (p
erc
en
t)
Surface Temperature & RH NO & NO2 Temperature Aloft Season & Solar
Airflow Local Wind Pressure Gradients Day-of-Week & Trend
CSJ Top 60 Days per Year
37
Precursor Reductions Lowered O3 in NSJ
-10
0
10
20
30
40
50
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
O3
En
ha
nc
em
en
t (p
erc
en
t)
Surface Temperature & RH NO & NO2 Temperature Aloft Season & Solar
Airflow Local Wind Pressure Gradients Day of Week & Trend
NSJ Top 60 Days per Year
38
Projecting Future Progress
• Method I: Combine annual O3 sensitivities to NOx with projections of NOx emissions
• Method II: Combine daily O3 sensitivities to NOx with projected ambient NOx concentrations generated from synthetic data
• Implicit assumption in both methods: ratio of VOC/NOx remains constant or follows trends similar to historical trends
39
Projection Method I
-25
-20
-15
-10
-5
0
5
10
15
Y V
aria
bles
1990 1995 2000 2005 2010 2015 2020 2025Year
Percent_O3_Change_NOx: NBA
Percent_O3_Change_NO2: NBA
Percent_O3_Change_NO: NBA
-25
-20
-15
-10
-5
0
5
10
15
Y V
aria
bles
250 300 350 400 450 500 550 600 650 700 750Basin NOx Emissions (tpd)
Percent_O3_Change_NOx: NBA
Percent_O3_Change_NO2: NBA
Percent_O3_Change_NO: NBA
Project historical trend lines to estimate effects of future basin NOx emissions
40
Projection Method II
• Use 2008 – 2010 as base period, utilizing daily monitoring data with daily R sensitivities
• For each month and day of week, remove date with highest NO2 – for ties, remove date with highest NO
• For each month and day of week, retain 5 dates using random selection
• Recode data as 2011
• Repeat steps to generate 2012 – 2020
• Aggregate daily sensitivities to NO, NO2, NOx
41
NO2 and NO Concentrations “Continue” Declining at Historical Rates
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
CBA CSJ EBA NBA NCC NSF NSJ SAC SBA SCC SEQ SSF SSJ SVN WBA
Subregion
Tre
nd
(p
pb
v p
er y
ear)
Historical NO2 1995 - 2010 Synthetic NO2 2011 - 2020
42
Decreasing NOx Concentrations Will Continue to Decrease Peak O3
-25
-20
-15
-10
-5
0
5
10
15
Y V
aria
bles
1990 1995 2000 2005 2010 2015 2020 2025Year
Percent O3 Change NOx: CSJ
Percent O3 Change NO2: CSJ
Percent O3 Change NO: CSJ
-25
-20
-15
-10
-5
0
5
10
15
Y V
aria
bles
1990 1995 2000 2005 2010 2015 2020 2025Year
Percent O3 Change NOx: NBA
Percent O3 Change NO2: NBA
Percent O3 Change NO: NBA
-25
-20
-15
-10
-5
0
5
10
15
Y V
aria
bles
1990 1995 2000 2005 2010 2015 2020 2025Year
Percent O3 Change NOx: SAC
Percent O3 Change NO2: SAC
Percent O3 Change NO: SAC
-25
-20
-15
-10
-5
0
5
10
15
Y V
aria
bles
1990 1995 2000 2005 2010 2015 2020 2025Year
Percent O3 Change NOx: SSJ
Percent O3 Change NO2: SSJ
Percent O3 Change NO: SSJ
43
Decreasing Peak O3 on High O3 Days
-20
-10
0
10
20
30
40
50
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019
O3
En
han
cem
ent
(per
cen
t)
Surface Temperature and RH NO & NO2 Temperature Aloft Season & Solar
Airflow Local Wind Pressure Gradients Day-of-Week and Trend
NSJ High O3 Days (Top 60)
44
Compare and Contrast Modeling and Weight-of-Evidence Analyses
• Need to consider prediction uncertainties for modeling and weight-of-evidence analyses
• GAM uncertainties quantified in two ways
– Parameter standard errors from R
– Bootstrap uncertainties
• Design value variability assessment used to characterize one type of modeling uncertainty
45
GAM Prediction Uncertainties
• Parameter standard errors are computed for each day by R – but are they realistic?
• Tested using bootstrap uncertainties– Leave out one year at a time (16 combinations)– Leave out one group of meteorological variables at
a time (10 combinations)– Add AQ variables one at a time (4 combinations)– Generate variances from each– Sum variances
46
Bootstrap and R Standard Errors are Comparable and ~10% of Coefficients
0
2
4
6
8
10
12
14
Y V
aria
bles
-50 -30 -10 10 30 50 70 90Percent O3 Change NO2
R Std Error of Coef. NO2
Bootstrap Std Error of Coef. NO2 NBA
0
2
4
6
8
10
12
14
Y V
aria
bles
-50 -30 -10 10 30 50 70 90Percent O3 Change NO2
R Std Error of Coef. NO2
Bootstrap Std Error of Coef. NO2 SAC
0
2
4
6
8
10
12
14
Y V
aria
bles
-50 -30 -10 10 30 50 70 90Percent O3 Change NO2
R Std Error of Coef. NO2
Bootstrap Std Error of Coef. NO2 NSJ
0
2
4
6
8
10
12
14Y
Var
iabl
es
-50 -30 -10 10 30 50 70 90Percent O3 Change NO2
R Std Error of Coef. NO2
Bootstrap Std Error of Coef. NO2 CSJ
47
Annual Effects of NO, NO2, and NOx on Peak O3 with Uncertainties, CSJ
-10
-8
-6
-4
-2
0
2
4
6
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Year
Eff
ect
on
Mea
n P
eak
O3
(Per
cen
t)
NO NO2 NOx
48
Design Value Variability Assessment – Baseline Design Values Vary 1 – 14 ppbv
40
50
60
70
80
90
100
110
Arvin
(ssj)
Beth
el Is. (nb
a)
Co
ol (n
sf)
Fo
lsom
(sac)
Fresn
o 1st (csj)
Liverm
ore (eb
a)
Merced
(nsj)
Pin
nacles (n
cc)
San
Martin
(sba)
San
Rafael (cb
a)
San
ta Ro
sa (wb
a)
Seq
uo
ia (seq)
Sim
i (scc)
Tu
scan (svn
)
Yo
semite (ssf)
Site
Alt. F
Alt. E
Alt. D
Alt. C
Alt. B
Alt. A (2008 DVr)
EPA DVb
DV
b (
pp
b)