Improvements to the Spatial and Temporal Representativeness of Modeling Emission Estimates: Phase 1 Findings and Recommendations Presented by: Lyle R

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



Petaluma, CA



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%


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

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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).

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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


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

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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%


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%


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


• 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


• 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%


Day of Week

Per

cent

of N

Ox

Em

issi

ons

19


• 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


• 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


• 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


• 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



Petaluma, CA



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


• 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


• 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


• 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


• 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


• 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


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


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


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


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?

Documents

Improvements to the Spatial and Temporal Representativeness of Modeling Emission Estimates: Phase 1 Findings and Recommendations Presented by: Lyle R