Improvements to the Spatial and Temporal Representativeness of Modeling Emission Estimates:
Phase 1 Findings and Recommendations
Presented by:Lyle R. Chinkin
Stephen B. ReidSonoma Technology, Inc.
Petaluma, CA
Presented to:The CCOS Technical Committee
Sacramento, CANovember 28, 2006
906036.04?-????
2
Project Overview – Phase 1
Objective:• Assess spatial and temporal allocations applied
to base-year and future-year anthropogenic emission inventories (EI). Identify potential improvements.
Key Benefits:• Identify strengths and areas for improvement in
the spatial and temporal allocations of the CCOS EIs.
• Rank the potential impacts of suggested improvements on the EIs. (Facilitate cost effective plan for Phase 2.)
3
Project Overview – Phase 2
Objective:• Implement improvements by developing
specific methods or data sets to spatially and temporally allocate anthropogenic emissions.
Key Benefits:• Improve photochemical modeling results by
characterizing more accurately the temporal and spatial variations in ozone precursor emissions.
• Increase confidence in the accuracy of the EIs’ spatial and temporal variations.
4
Today’s Agenda
Review and discuss the findings and recommendations produced during Phase 1.
• On-road mobile sources• Area, off-road mobile, and point sources.
Discuss potential plans for Phase 2.• On-road mobile sources
$215k• Area, off-road mobile, and point sources
$140k• Final report and meetings
$20k
5
On-Road Mobile Sources
Findings and recommendations will be presented by Tom Kear of Dowling Associates, Inc.
Temporal Representativeness of Non-road, Area, and Point
Sources
Presented by:Lyle R. Chinkin
Stephen B. ReidSonoma Technology, Inc.
Petaluma, CA
Presented to:The CCOS Technical Committee
Sacramento, CANovember 28, 2006
906036.04?-????
7
Background (1 of 5)
Temporal codes are used to assign applicable temporal allocation factors (TAFs) to emission sources.
TAFs allocate annualized emissions to:• Months of the year• Days of the week• Hours of the day
8
Background (2 of 5)
NOx
Profile 2737%
Profile 51%
Profile 283%
Profile 201%
Other1%Profile 24
5%
Profile 215%
Profile 747%
ROG
Other2%
Profile 2810%
Profile 757%
Profile 249%
Profile 219%
Profile 162%
Profile 53%
Profile 275%
Profile 223%
NOx
Profile 6046%
Profile 2446%
Profile 373%
Profile 332%
Other1%
Profile 81%
Profile 201%
ROG
Profile 6019%
Profile 563%
Profile 164%
Profile 332% Other
4%
Profile 85%
Profile 3711%
Profile 2452%
Statewide emissions associated with various day-of-week profiles
Statewide emissions associated with various diurnal profiles
9
Background (3 of 5)
Temporal variations in NOx and ROG emissions by major source type
0%
2%
4%
6%
8%
10%
12%
1 2 3 4 5 6 7 8 9 10 11 12
Month
Per
cent
of
NO
x E
mis
sion
s
Point
Area
Non-road
0%
2%
4%
6%
8%
10%
12%
1 2 3 4 5 6 7 8 9 10 11 12
Month
Per
cent
of
RO
G E
mis
sion
s
Point
Area
Non-road
0%
5%
10%
15%
20%
25%
Mon Tue Wed Thu Fri Sat Sun
Day of Week
Per
cent
of
NO
x E
mis
sion
s
Point
Area
Non-road
0%
5%
10%
15%
20%
25%
Mon Tue Wed Thu Fri Sat Sun
Day of Week
Per
cent
of
RO
G E
mis
sion
s
Point
Area
Non-road
10
Background (4 of 5)
Temporal variations in NOx and ROG emissions by major source type
0%
1%
2%
3%
4%
5%
6%
7%
8%
0 2 4 6 8 10 12 14 16 18 20 22
Hour
Per
cent
of
NO
x E
mis
sion
s
Point
Area
Non-road
0%
1%
2%
3%
4%
5%
6%
7%
8%
Hour
Per
cent
of
RO
G E
mis
sion
s
Point
Area
Non-road
11
Background (5 of 5)
Year-2002 annual-average emissions by major source type
Total NOx = 3,556 tons/day
Point7%
Area8%
Non-road36%
On-road49%
Total ROG = 2,828 tons/day
Point5%
Area37%
Non-road24%
On-road34%
12
Overview of Approach (1 of 3)
• Visually examined the temporal distribution of emissions
• Assessed existing temporal profiles and their general usage
• Identified and evaluated the temporal characteristics of key source categories
• Investigated alternatives (e.g., literature search).
13
Overview of Approach (2 of 3)
OF
F-R
OA
D E
QU
IPM
EN
T
SH
IPS
FA
RM
EQ
UIP
ME
NT
TR
AIN
S
Oth
er
So
urc
es
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Calif SF SJV SV
Geographic Area
Co
ntr
ibu
tion
of
So
urc
e t
o T
ota
l NO
x E
mis
sio
ns
Other Sources
PESTICIDES/FERTILIZERS
FUEL STORAGE AND HANDLING
CONSUMER PRODUCTS
ARCHITECTURAL COATINGS AND RELATED PROCESS SOLVENTS
PETROLEUM MARKETING
FARMING OPERATIONS
DEGREASING
COATINGS AND RELATED PROCESS SOLVENTS
OFF-ROAD RECREATIONAL VEHICLES
FOOD AND AGRICULTURE
WASTE BURNING AND DISPOSAL
OIL AND GAS PRODUCTION
SERVICE AND COMMERCIAL
RECREATIONAL BOATS
AIRCRAFT
FOOD AND AGRICULTURAL PROCESSING
EXTCOMB BOILER
INTERNLCOMBUSTION
RESIDENTIAL FUEL COMBUSTION
MANUFACTURING AND INDUSTRIAL
TRAINS
FARM EQUIPMENT
SHIPS AND COMMERCIAL BOATS
OFF-ROAD EQUIPMENT
Key NOx sources by region
14
Overview of Approach (3 of 3)
Key ROG sources by region
CO
NS
UM
ER
P
RO
DU
CT
S RE
C.
BO
AT
S
OF
F-R
D.
EQ
UIP
.
FA
RM
ING
O
PE
RA
TIO
NS
OF
F-R
D.
RE
C V
EH
. AR
CH
. C
OA
TIN
G
WA
ST
E
BU
RN
ING
Oth
er
So
urc
es
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Calif SF SJV SV
Geographic Area
Co
ntr
ibu
tion
of
So
urc
e t
o T
ota
l RO
G E
mis
sio
ns
Other Sources
MANUFACTURING AND INDUSTRIAL
SERVICE AND COMMERCIAL
EXTCOMB BOILER
FOOD AND AGRICULTURAL PROCESSING
TRAINS
SHIPS AND COMMERCIAL BOATS
INTERNLCOMBUSTION
FOOD AND AGRICULTURE
AIRCRAFT
FARM EQUIPMENT
DEGREASING
PESTICIDES/FERTILIZERS
RESIDENTIAL FUEL COMBUSTION
OIL AND GAS PRODUCTION
COATINGS AND RELATED PROCESS SOLVENTS
FUEL STORAGE AND HANDLING
PETROLEUM MARKETING
WASTE BURNING AND DISPOSAL
ARCHITECTURAL COATINGS AND RELATED PROCESS SOLVENTS
OFF-ROAD RECREATIONAL VEHICLES
FARMING OPERATIONS
OFF-ROAD EQUIPMENT
RECREATIONAL BOATS
CONSUMER PRODUCTS
15
Types of Potential Improvements
1. Corrections to temporal profile assignments for specific sources/regions
2. The incorporation of readily-available data that would increase the accuracy of temporal emission variations for specific sources/regions
3. The collection of new data that would increase the accuracy of temporal emission variations for specific sources/regions
16
Key Findings and Recommendations (1 of 7)
• Mis-assignments in the temporal cross-reference file need to be corrected.
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
Mon Tue Wed Thu Fri Sat Sun
Day of Week
Per
cent
of
NO
x E
mis
sion
s
Point
Area
Non-road
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
Mon Tue Wed Thu Fri Sat Sun
Day of Week
Per
cent
of
RO
G E
mis
sion
s
Point
Area
Non-road
Day-of-week variations in emissions for the SF air basin.
17
Key Findings and Recommendations (2 of 7)
• Update other temporal profile assignments in the temporal cross-reference file.
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
Per
cent
age
of D
aily
Em
issi
ons
ARB profile 24 (SF)
ARB profile 33 (other air bas ins)
EPA profile 26
Diurnal profiles assigned to residential natural gas combustion.
18
Key Findings and Recommendations (3 of 7)
• Double-check diurnal and day-of-week temporal profiles for trains in the San Francisco Bay Area.
Emissions from trains in the San Francisco Bay Area peak on the weekends.
0%
5%
10%
15%
20%
25%
30%
35%
40%
Mon Tue Wed Thu Fri Sat Sun
Day of Week
Per
cent
of N
Ox
Em
issi
ons
19
Key Findings and Recommendations (4 of 7)
• Apply consistent temporal profiles for fuel combustion.
Diurnal profiles for service and commercial fuel combustion (pictured) and for manufacturing fuel combustion vary widely between air basins and sometimes within air basins.
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Hour
Per
cent
of N
Ox
Em
issi
ons
Butte, Colusa, Placer,Shasta, Tehama, andYuba countiesSacramento County
Solano and Yolo counties
San Joaquin Valley
San Francisco
California
20
Key Findings and Recommendations (5 of 7)
• Apply temporal profiles recommended by STI (2001)—e.g., for architectural coatings.
0%
2%
4%
6%
8%
10%
12%
1 2 3 4 5 6 7 8 9 10 11 12
Month
Per
cent
of R
OG
Em
issi
ons
Top 6 architectural coatings - San Francisco
Top 6 architectural coatings - San Joaquin Valley
Top 6 architectural coatings - Sacramento Valley
Top 6 architectural coatings - California
Thinning and cleanup solvents - San Francisco
Thinning and cleanup solvents - San JoaquinValley
Thinning and cleanup solvents - SacramentoValley
Thinning and cleanup solvents - California
21
Key Findings and Recommendations (6 of 7)
• Develop and apply temporal profiles for petroleum marketing.
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Hour
Per
cent
of R
OG
Em
issi
ons
Spillage: San Francisco
Spillage: Sacramento Valley
Spillage: California
Vapor Displacement: SanFrancisco
Vapor Displacement:Sacramento Valley
Vapor Displacement: SanJoaquin ValleyVapor Displacement: California
Current diurnal profiles are unlikely to represent weekend conditions.
Flat monthly profiles (not pictured) can be updated based on statewide gasoline sales.
22
Key Findings and Recommendations (7 of 7)
• Verify the magnitude of snowmobile emissions
• Other (low-priority) recommendations- Develop diurnal profiles for commercial jets in the SFBA- Analyze CEM data for major point sources- Double-check seasonal patterns for planned burning
23
Phase 2 Priorities and Costs for Temporal Representativeness
Recommendation Level of Effort
Reconsider temporal profiles by Chinkin et al. (2001). $5k
Approximate temporal patterns for weekend light-duty vehicle activities.
$15k
Various and miscellaneous tasks (suggested for in-kind actions by ARB or districts). For example,
•Correct mis-assignments in the temporal cross-reference file.•Apply monthly profiles based on statewide fuel consumption for petroleum marketing.•Double-check local seasonal patterns for burning (agricultural and land management).
$40k
Spatial Representativeness of Non-road, Area, and Point
Sources
Presented by:Lyle R. Chinkin
Stephen B. ReidSonoma Technology, Inc.
Petaluma, CA
Presented to:The CCOS Technical Committee
Sacramento, CANovember 28, 2006
906036.04?-????
25
Background (1 of 3)
For area and non-road sources, spatial allocation factors (SAFs) are used to spatially distribute county-level emissions.
Current SAFs derived from spatial surrogates developed by STI in 2001 from:• Land use and land cover data• Demographic and socioeconomic data• Location-based information
65 base-year surrogates and 26 future-year surrogates (2005, 2010, 2020) are available
26
Background (2 of 3)
27
Background (3 of 3)
For point sources, location coordinates are available for individual facilities/stacks.
28
Overview of Approach
• Visually examined the spatial distribution of emissions
• Assessed existing spatial surrogate data and its general usage
• Identified and evaluated the spatial distribution of key source categories
• Investigated alternatives (e.g., literature search).
29
Key Findings and Recommendations (1 of 5)
• Point source locations have been reviewed by ARB and STI and no discrepancies were found.
• Update the spatial surrogate cross-reference file for area and non-road mobile sources. Issues include:- 49 unique EIC codes missing
- Over 1,600 county/EIC code combinations unaccounted for- Current scheme makes limited use of available surrogates (14 of 65 available surrogates not utilized)
30
Key Findings and Recommendations (2 of 5)
• Outdated spatial surrogate data need to be updated, especially those that affect the majority of the emissions (20 of 65 available surrogates). Reactivity-
weightedTOG
0%
20%
40%
60%
80%
100%
NOX ROG
Pe
rce
nta
ge
of a
nn
ua
l em
issi
on
s
Other
DMO3 (total housing)
RR2 (rail netw ork)
MAR4 (shipping lanes)
AG2 (agricultural land)
DMO9 (service/commercial employment)
CS3 (Non-residential)
LOC6 (location of oil w ells)
AG1 (cropland)
DMO13 (housing + commericalemployment)ELV1 (elevation>5000')
DMO5 (single dw elling units)
CS1 (housing + employment)
DMO8 (industrial employment)
WAT1 (lakes, reservoirs, coastlines)
DMO15 (Population)
31
Key Findings and Recommendations (3 of 5)
• Future-year spatial distributions need to be prepared so that they represent future land use patterns.
+ =
Future urbanization (red) overlaid on base-year agricultural lands (green) produces affected agricultural lands (blue) for future years.
32
Key Findings and Recommendations (4 of 5)
• The spatial distribution of recreational boats should account for popularity or restrictions on boating use at different bodies of water.
Survey results (right) produce a different spatial distribution than simple surface area of water (left) in the Midwest.
33
Key Findings and Recommendations (5 of 5)
• The spatial distribution of construction activities should be improved for the base year and future years, potentially on the basis of construction permits and proposed developments.
Residential completions in 2002 for Greater Phoenix.
34
Phase 2 Priorities and Costs for Spatial Representativeness
Recommendation Level of Effort
Use a land-use allocation model, such as UPLAN, to generate future-year spatial surrogates.
orFollow a low-cost approach. (Calculate differences in future-year housing or commercial building density projections.)
$150k-$200k
or
$15k-$20k
Update the SAFs by gathering the most recent versions of surrogate data.
30k
Further refine SAFs by using newly available, better data.
10k
Conduct a statewide survey to improve spatial distribution of recreational boating activities.
$80k
Various and miscellaneous tasks (suggested for in-kind actions by ARB or districts). For example,
•Correct emissions for snowmobiles and commercial jets.•Update spatial surrogate cross-references.
$15k
35
Recommended Tasks for Phase 2 Funding
Recommendation Level of Effort
Produce Final Report and attend final meeting. $20k
Produce 2010-2020 forecasts for on-road mobile sources: revise trip tables, run DTIM, and grid results.
$80k
Reconsider temporal profiles by Chinkin et al. (2001). $5k
Use a land-use allocation model, such as UPLAN, to generate future-year spatial surrogates.
orFollow a low-cost approach. (Calculate differences in future-year housing or commercial building density projections.)
$150k-$200k
or
$15k-$20k
Update the SAFs by gathering the most recent versions of surrogate data.
30k
Further refine SAFs by using newly available, better data.
10k
36
Recommended Tasks for Phase 2 Funding
Recommendation Level of Effort
Identify and implement best method for speed post- processing (on-road mobile sources).
$45k
Model truck activity on highways and arterials, integrate w/2010-2020 forecasts.
$75k ($115k with
counts)
Conduct a statewide survey to improve spatial distribution of recreational boating activities.
$80k
Approximate temporal patterns for weekend light-duty vehicle activities.
$15k
SUBTOTAL $375k
Build a weekend travel demand model. (Create weekend trip tables, validate/calibrate relative distributions.)
$75k
37
Potential In-Kind Actions ($50k-$60k)
Correct emissions for snowmobiles.
Correct emissions for commercial jets in the South Coast Air Basin.
Update spatial surrogate cross-references for un- or mis-matched emission sources.
Correct temporal profile mis-assignments in the cross-reference file.
Quality assure point source locations.
Double-check potentially incorrect point source locations identified by STI.
Apply monthly profiles based on statewide fuel consumption for petroleum marketing.
Develop weekend diurnal profile for gasoline refueling (from traffic volumes).
Apply diurnal profiles to gasoline refueling emissions in the SFBA air basin.
Double-check diurnal and weekly patterns for trains in the SFBA air basin.
Develop diurnal profiles for commercial jets in the SFBA air basin.
Double-check local seasonal patterns for burning (agricultural and land management).
Apply diurnal profiles for fuel combustion (manufacturing/industrial and service/commercial).
38
Additional Tasks for Consideration
Recommendation Level of Effort
Improve the spatial distribution of construction activities by analyzing residential and commercial building permits.
$100k
Collect data to improve the spatial distribution of selected individual source categories.
Varies widely
Model on-road mobile sources with link-level EFs. $50k-$75k
Analyze CEM data for external combustion boilers at major point sources.
$15k
39
Discussion
Questions or comments?