Memorandum

From: Harvey Michaels

Assessment and Standards Division, Office of Transportation and Air Quality

To: Docket EPA-HQ-OAR-2011-0135

Subject: SMOKE-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-processing 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 counties includes code to guarantee that there are emission factors for all SCCs. 

Without this code, missing 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. In representing 

county mode, which was used for this proposal, it is possible that some of the represented

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 fills in 

missing emission factors by using the corresponding rural or urban value, since, for a given 

vehicle type, road type, pollutant, process, temperature, 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 early version of the script (dated 1May2011 and contained in the 

postprocess jar file dated 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 illustrated 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 PM2.5, 

NOX, and VOC. In all, 1,218 counties were in the "Affected" 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 

error. The error had the largest impact on PM2.5, for which it caused reference case PM2.5 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 effect 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 Ref 43,313 86,104 129,416

PM2.5 Ctl 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 Ref 1,096,891 2,107,979 3,204,871

NOX Ctl 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 Ref 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.
0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

16

111621263136414651566166717681869196

101

Distribution of % increases in PM2.5 

between ref & ctl in 2017

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 when 

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.

R2 = 0.6007

0.00%

0.10%

0.20%

0.30%

0.40%

0.50%

0.60%

0.70%

0.80%

0.90%

1.00%

5.00% 5.50% 6.00% 6.50% 7.00% 7.50% 8.00%

PM%change<1% v. start fuel adj change 

Figure 2
R2 = 0.0009

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

5.00% 6.00% 7.00% 8.00% 9.00%

PM%change v. start fuel adj change 

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 representing 

county context (see Table 3 below). All the 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 adjusted emission factors from the rural 

roads, 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. The reason SMOKE’s fillin-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 roads are null for Juab County 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 roadtypedistribution 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 without changing the 

sum of the distribution or the inventories when MOVES is run in inventory mode.
The same problem exists in 2030 for reference and control, since the same post-processing 

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

noticeable than in 2017.

This problem affects all pollutants, 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 database. 

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 Ctl ctl/ref-1

Rural -10 0.011759709 0.011916205 1.33% 0.01175971 0.011916205 1.33%

Rural -5 0.010030981 0.010164506 1.33% 0.01003098 0.010164506 1.33%

Rural 0 0.00855639 0.008670254 1.33% 0.00855639 0.008670254 1.33%

Rural 5 0.007298569 0.007395686 1.33% 0.00729857 0.007395686 1.33%

Rural 10 0.006225636 0.006308479 1.33% 0.00622564 0.006308479 1.33%

Rural 15 0.005310472 0.005381129 1.33% 0.00531047 0.005381129 1.33%

Rural 20 0.004529802 0.004590078 1.33% 0.0045298 0.004590078 1.33%

Rural 25 0.003863904 0.003915324 1.33% 0.0038639 0.003915324 1.33%

Rural 30 0.003295896 0.003339753 1.33% 0.0032959 0.003339753 1.33%

Rural 35 0.002811393 0.002848794 1.33% 0.00281139 0.002848794 1.33%

Rural 40 0.0023981 0.00243001 1.33% 0.0023981 0.00243001 1.33%

Rural 45 0.002045571 0.0020728 1.33% 0.00204557 0.0020728 1.33%

Rural 50 0.001744866 0.001768092 1.33% 0.00174487 0.001768092 1.33%

Rural 55 0.00148836 0.001508168 1.33% 0.00148836 0.001508168 1.33%

Rural 60 0.001269567 0.00128646 1.33% 0.00126957 0.00128646 1.33%

Rural 65 0.001082936 0.001097351 1.33% 0.00108294 0.001097351 1.33%

Rural 70 0.000923741 0.000936032 1.33% 0.00092374 0.000936032 1.33%

Rural 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 Ctl

Urban -10 0.000866821 0.011916205 1274.70% NULL NULL

Urban -5 0.000866821 0.010164506 1072.62% NULL NULL

Urban 0 0.000866821 0.008670254 900.24% NULL NULL

Urban 5 0.000866821 0.007395686 753.20% NULL NULL

Urban 10 0.000866821 0.006308479 627.77% NULL NULL
Urban 15 0.000866821 0.005381129 520.79% NULL NULL

Urban 20 0.000866821 0.004590078 429.53% NULL NULL

Urban 25 0.000866821 0.003915324 351.69% NULL NULL

Urban 30 0.000866821 0.003339753 285.29% NULL NULL

Urban 35 0.000866821 0.002848794 228.65% NULL NULL

Urban 40 0.000866821 0.00243001 180.34% NULL NULL

Urban 45 0.000866821 0.0020728 139.13% NULL NULL

Urban 50 0.000866821 0.001768092 103.97% NULL NULL

Urban 55 0.000866821 0.001508168 73.99% NULL NULL

Urban 60 0.000866821 0.00128646 48.41% NULL NULL

Urban 65 0.000866821 0.001097351 26.59% NULL NULL

Urban 70 0.000866821 0.000936032 7.98% NULL NULL

Urban 75 0.000866821 0.000878355 1.33% NULL NULL

Urban 80 0.000866821 0.000878356 1.33% NULL NULL

Urban 85 0.000866821 0.000878357 1.33% NULL NULL

Urban 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.