### Memorandum

From: Harvey Michaels

Assessment and Standards Division, Office of Transportation and Air Quality

To: Docket EPA

- HQ

- OAR

- 2011

- 0135

Subject: S MOKE

- MOVES Post

- Processing Inconsistency

Date: 29 March 2013



## Explanation of the problem

In generating emission inventories for air quality modeling, we use the Motor Vehicle Emission

Simulator (MOVES) to estimate emission rates and the Sparse Matrix Operator Kernel

Emissions (SMOKE) model to calculate emissions by grid cell. The post

- process ing script that

converts MOVES rate tables to SMOKE

- ready rate tables for representing counties includes

code to guarantee that there are no missing emission factors for specific SCCs. An error in this

script led to the use of emission factors for higher temperatures than appropriate in some cases

. The correct script was run for the control case.

What is the post

- processing script designed to do?

The post

- processing script that converts MOVES rate tables to SMOKE

- ready rate tables for

representing countie s includes code to guarantee that there are emission factors for all SCCs.

Without th is code, m issing values would occur if the representing county had

roadTypeVMTFraction=0 for one or more of the four road types in the roadtypedistribution table

of the MOVES county database (CDB)

. Such zeroes occurred if the county was predominantly

urban or rural and therefore lacked one or more of the urban or rural road types. I n representing

county mo de, which was used for this proposal , it is possible that some of the represent ed

counties have VMT on road types for which emission factors are missing in the representing

county. In addition, because separately

- developed VMT was applied in SMOKE, even

representing counties might be missing emission

- rates needed by SMOKE.

The post

- processing script was designed to prevent these missing emission

- rates. It fill s in

missing emission factors by using the corresponding rural or urban value

, since, for a given

vehicle type, road type, pollutant, process, tem perature, and average speed bin, the emission

factors for the urban and rural road types are very close if not identical.

What was incorrect ?

The problem was that an earl y versio n of the script (dated 1May2011 and contained in the

postprocess jar file d ated 20110628 ), which was run for the Tier 3 base and reference cases,

incorrectly substituted the emission factor for the highest temperature for all temperatures. For

PM, the highest emission factors are at the lowest temperatures, so this version of the script

produced lower emissions than the correct version (dated 27Jul2011 and contained in the

### postprocess jar file dated 20110727). This explanation is illustrate d in the Example section

below.



## Extent of the problem

To examine the effect of this error, counties were divided between those whose representing

county had one or more roadTypeVMTFraction=0 , referred to as "Affected," and those whose

representing counties had all roadTypeVMTFraction >0, referred to as "Unaffected." Table 1

summarizes the difference between reference and control cases for these two groups for PM 2.5 

### , NO X 

### , and VOC. In all, 1,2 18 counties were in the "A ffected " group and 1,891 were in the

"Unaffected" group

. The two groups are not strictly comparable, since they do not include the

same counties, but we believe the comparison provides a reasonable measure of the effect of the

er ror. The error had the largest impact on PM 2.5

, 

### for which it cause d reference case PM 2.5 

### to

decrease, resulting in an average increase of 1.5% in the emissions increase attributable to the

rule , compared with the unaffected counties. Since PM2.5 emission rates increase

monotonically with decreasing temperature , the error had directionally the same effect in all

affected counties. The error had smaller impacts on NOX and VOC , and, due to U

- shaped

temperature

- response curves, the effec t of the error could either increase or decrease emission

rates

. Larger effects occurred in individual counties.

Table 1

. Summary of Effects of Error on All Counties in 2017

Pollutant Case Tons

Affected Tons

Unaffected Total

Tons

PM2.5 R ef 43,313 86,104 129,416

PM2.5 C tl 44,178 86,541 130,719

PM2.5 ctl

- ref 865 437 1,302

PM2.5 ctl

- ref % 2.0% 0.5% 1.0%

PM2.5 ctl

- ref min % 0.5% 0.1%

PM2.5 ctl

- ref max % 12.7% 1.3%

NOX R ef 1,096,891 2,107,979 3,204,871

NOX C tl 1,022,030 1,947,826 2,969,856

NOX ctl

- ref

- 74,861

- 160,154

- 235,015

NOX ctl

- ref %

- 6.8%

- 7.6%

- 7.3%

NOX ctl

- ref min %

- 9.1%

- 9.3%

NOX ctl

- ref max %

- 2.1%

- 1.0%

VOC R ef 484,078 913,590 1,397,668

VOC ctl 469,575 889,657 1,359,232

VOC ctl

- ref

- 14,503

- 23,932

- 38,436

VOC ctl

- ref %

- 3.0%

- 2.6%

- 2.7%

VOC ctl

- ref min %

- 4.1%

- 4.0%

VOC ctl

- ref max %

- 2.3%

- 0.1%



### 75 out of 103 representing counties have a PM increase < 1% as shown in Figure 1

.

### Figure 1 . Distribution of percent increase in primary onroad PM.25 between reference and

control in 2017.

For percent PM increases less than 1% between reference and control cases , fuel properties are

highly explanatory. Figure 2 plots the percent change in primary onroad PM emissions

, for

changes less than one percent

, against percent change in start fuel adjustment . The fuel

adjustment is a formula in MOVES that generates a multiplicative fuel adjustment. It is a

function of the fuel properties : ethanol volume, aromatic content, and volatility. For percent PM

increases over the whole range, fuel properties are not explanatory. Figure 3 shows that w hen

percent changes in PM are less than one percent, the R

- square is 0.6, when all PM changes are

included, the R

- square drops to 0.009.

Figure 2

### Figure 3



## Example : Juab, Utah, elemental carbon PM

To sort out what is going on, Juab , Utah

, was used as an example county since it has the highest

increase in onroad PM (12%) between the reference and control cases in 2017

. Elemental carbon

PM is a good species to investigate since it is the same in both MOVES and SMOKE.

There is perfect agreement between SMOKE and MOVES emission factors for rural roads (see

Table 2 below )

.

For urban roads, MOVES emission factors are null, but SMOKE emission factors have been

filled in by SMOKE code designed to not have any missing emission factors in the represen ting

county context (s ee Table 3 below )

. All t he reference emission factors come from the 90 deg F

rural road emission factor, which is incorrect and low for the colder temperatures . The control

emission factors come from the whole set of temperature adj usted emission factors from the rural

road s , which is correct. At cold temperatures, the ratio of control to reference emission factors

for urban roads is very high, which is why control case PM is high. T he reason SMOKE’s fill

in

- the

- blanks algorithm is different for reference and control is that the reference case was run

first with an earlier version of the moves2smk code, and the control case was run with a later,

correct version.

The MOVES emission factors for all urban road s are null for Juab Count y because the

roadtypedistribution table in the Juab county database has zeroes for the urban roadtypes. There

is, however, urban VMT, which gets multiplied by the emission factors that SMOKE substituted

for MOVES’s nulls. What we intended to do in the r oadtypedistribution table was substitute

10 − 15 

### for the zero distribution numbers, as we have done in the MOVES default database. This

procedure guarantees that there will be emission factors on all road types wi thout changing the

sum of the distribution or the inventories when MOVES is run in inventory mode.

### The same problem exist s in 2030 for reference and control, since the same post

- processing

scripts were used as for 2017. However, l ower levels of PM in 2030 make the effect much less

noticeable than in 2017.

This problem affects all pollutant s , but most are much less temperature

- sensitive than gasoline

PM. It is only a problem for representing counties that have a zero for one or more road types in

the roadtypedistribution table of the county datab ase.

Table 2 . Rural road types. SMOKE PEC and MOVES EC emission factors.

SMOKE PEC SMOKE PEC SMOKE MOVES EC MOVES EC MOVES

Roadtype temp ref ctl ctl/ref

- 1 ref C tl ctl/ref

- 1

R ural

- 10 0.011759709 0.011916205 1.33% 0.01175971 0.011916205 1.33%

R ural

- 5 0.010030981 0.010164506 1.33% 0.01003098 0.010164506 1.33%

R ural 0 0.00855639 0.008670254 1.33% 0.00855639 0.008670254 1.33%

R ural 5 0.007298569 0.007395686 1.33% 0.00729857 0.007395686 1.33%

R ural 10 0.006225636 0.006308479 1.33% 0.00622564 0.006308479 1.33%

R ural 15 0.005310472 0.005381129 1.33% 0.00531047 0.005381129 1.33%

R ural 20 0.004529802 0.004590078 1.33% 0.0045298 0.004590078 1.33%

R ural 25 0.003863904 0.003915324 1.33% 0.0038639 0.003915324 1.33%

R ural 30 0.003295896 0.003339753 1.33% 0.0032959 0.003339753 1.33%

R ural 35 0.002811393 0.002848794 1.33% 0.00281139 0.002848794 1.33%

R ural 40 0.0023981 0.00243001 1.33% 0.0023981 0.00243001 1.33%

R ural 45 0.002045571 0.0020728 1.33% 0.00204557 0.0020728 1.33%

R ural 50 0.001744866 0.001768092 1.33% 0.00174487 0.001768092 1.33%

R ural 55 0.00148836 0.001508168 1.33% 0.00148836 0.001508168 1.33%

R ural 60 0.001269567 0.00128646 1.33% 0.00126957 0.00128646 1.33%

R ural 65 0.001082936 0.001097351 1.33% 0.00108294 0.001097351 1.33%

R ural 70 0.000923741 0.000936032 1.33% 0.00092374 0.000936032 1.33%

R ural 75 0.000866821 0.000878355 1.33% 0.00086682 0.000878355 1.33%

Rural 80 0.000866822 0.000878356 1.33% 0.00086682 0.000878356 1.33%

Rural 85 0.000866822 0.000878357 1.33% 0.00086682 0.000878357 1.33%

Rural 90 0.000866821 0.000878356 1.33% 0.00086682 0.000878357 1.33%



### Table 3 . Urban road types

. SMOKE PEC and MOVES EC emission factors.

SMOKE PEC SMOKE PEC SMOKE MOVES EC MOVES EC

Roadtype temp ref ctl ctl/ref

- 1 ref C tl

U rban

- 10 0.000866821 0.011916205 1274.70% NULL NULL

U rban

- 5 0.000866821 0.010164506 1072.62% NULL NULL

U rban 0 0.000866821 0.008670254 900.24% NULL NULL

U rban 5 0.000866821 0.007395686 753.20% NULL NULL

U rban 10 0.000866821 0.006308479 627.77% NULL NULLU rban 15 0.000866821 0.005381129 520.79% NULL NULL

U rban 20 0.000866821 0.004590078 429.53% NULL NULL

U rban 25 0.000866821 0.003915324 351.69% NULL NULL

U rban 30 0.000866821 0.003339753 285.29% NULL NULL

U rban 35 0.000866821 0.002848794 228.65% NULL NULL

U rban 40 0.000866821 0.00243001 180.34% NULL NULL

U rban 45 0.000866821 0.0020728 139.13% NULL NULL

U rban 50 0.000866821 0.001768092 103.97% NULL NULL

U rban 55 0.000866821 0.001508168 73.99% NULL NULL

U rban 60 0.000866821 0.00128646 48.41% NULL NULL

U rban 65 0.000866821 0.001097351 26.59% NULL NULL

U rban 70 0.000866821 0.000936032 7.98% NULL NULL

U rban 75 0.000866821 0.000878355 1.33% NULL NULL

U rban 80 0.000866821 0.000878356 1.33% NULL NULL

U rban 85 0.000866821 0.000878357 1.33% NULL NULL

U rban 90 0.000866821 0.000878356 1.33% NULL NULL



## Additional Files Docketed in Support of This Memo

Tier3ZeroesInRoadTypeDistn3.xlsx

— the source of Table 1.

Tier3PM2.xlsx

— source of Figures 1

- 3.