MEMORANDUM

SUBJECT: Impact of an Error in the Locomotive Particulate Matter (PM2.5) Inventory on RFS2 

Modeling Results 

FROM: Rich Cook 

 Environmental Scientist 

 Office of Transportation and Air Quality 

TO: Docket ID No. EPA-HQ-OAR-2005-0161 

The purpose of this memorandum is to document an analysis of the impact of an error in 

allocation of locomotive PM2.5 emissions on population-weighted locomotive emissions. This 

allocation error occurred in the process of developing calendar year 2022 county-level 

inventories of locomotive emissions to be used in air quality modeling. Inventories were 

developed for reference cases referred to in the Regulatory Impact Analysis as RFS1 Mandate 

(volumes mandated by the 2007 renewable fuels standard) and AEO 2007 (projected volumes 

from the Annual Energy Outlook, 2007 version), as well as the air quality control case (RFS2), 

described in Chapter 3 of the Regulatory Impact Analysis. These cases reflected differences in 

activity for this sector due to transported volumes of ethanol. 

In developing these inventories, adjustments were to be applied to a common base inventory 

developed for a 2022 projection of the 2005 air quality modeling platform 

(http://www.epa.gov/ttn/chief/emch/index.html). The result would be inventories which 

reflected county emissions given the RFS1, AEO 2007 and RFS2 fuel volumes. However, in 

processing the data, errors were introduced which led to inconsistencies in the common base 

inventory used to develop the PM2.5 inventories for the three modeling cases. These errors were 

random, resulting in PM2.5 emission changes that were too high in some counties and too low in 

other counties. 

Overall, the error had little impact on national-level locomotive PM2.5 emissions. Table 1 shows 

the overall impact on nationwide locomotive PM2.5 inventories for the three cases. 
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Table 1. Overall Impact of Locomotive Error on Locomotive PM2.5 Inventories (tons/year in 

2022) 

RFS1 Mandate AEO RFS2 

Locomotive PM2.5 

Inventory with Error 

13,061 11,861 13,342 

Corrected 

Locomotive PM2.5 

Inventory 

13,061 13,126 13,231 

Difference 0 1,265 -111 

Table 2 compares the error in locomotive PM2.5 inventory impacts for RFS2 minus AEO and 

RFS2 minus RFS1, relative to total PM2.5 inventory impacts of the rule. It is important to note 

that the total nationwide PM2.5 inventory is projected to be over 3.3 million tons in 2022. 

Table 2. Impacts of Inventory Error versus Overall PM2.5 Impacts of Rule (tons/year in 2022). 

RFS2 – RFS1 RFS2 – AEO 

Locomotive Inventory Error 111 1,377 

PM2.5 Inventory Change 17,918 15,795 

This comparison shows that on a nationwide level, the error results in a small overestimate of 

PM2.5 emission increases attributable to the rule. This error also likely results in an overall 

overestimate of the nationwide average ambient PM2.5 increase due to the rule, although at the 

local level, there will be both increases and decreases. However, it is important to note that other 

limitations resulting from air quality modeling assumptions may lead to underestimates of PM 

increases. For instance, the modeling relied upon interim inventories that assumed that ethanol 

will make up 34 of the 36 billion gallon renewable fuel mandate, that approximately 20 billion 

gallons of this ethanol will be in the form of E85, and that the use of E85 results in fewer 

emissions of direct PM2.5 from vehicles. In reality, E85 will likely play a much smaller role in 

meeting the renewable fuels mandate, and due to very little data on PM emission impacts of E85, 

the final rule emission inventories do not include vehicle-related PM reductions associated with 

E85 use. 

Since population-weighted air quality impacts are used to estimate air quality benefits and 

disbenefits of the rule, we also analyzed the nationwide impact of the error using populationweighted county-specific impacts as a metric. We did this by: 

1. For each U.S. county (excluding Alaska and Hawaii), calculating the ratio of the 

locomotive PM2.5 error impact versus the total PM2.5 impact of RFS2 with respect to each 

reference case (RFS1 mandate and AEO 2007). 

2. Weighting these ratios by fraction of the population. 

3. Summing the population-weighted differences. 

With respect to the RFS1 reference case, the analysis shows the population-weighted impacts of 
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the error were only about 6% of the total PM2.5 impacts. However, with respect to the AEO 

reference case, the population-weighted impacts of the error were larger, about 50% of total 

PM2.5 impacts. Calculations of these impacts can be found in the attached spreadsheet 

“Locomotive Error Impacts.xls.” It should be noted that analyses of population-weighted 

emission impacts cannot capture impacts of PM2.5 transport on ambient exposure. While PM2.5 

increases associated with ethanol production facilities may occur in less densely populated rural 

areas, they are also transported to more urban areas where larger numbers of people can be 

adversely impacted. Only air quality modeling can capture these impacts. 

Although the overall impact of the error on total nationwide PM2.5 was not large, the contribution 

of the error to estimates of total PM2.5 impacts of the rule was quite significant in some locations. 

Figure 1 presents the distribution of the absolute values of county ratios of the locomotive PM2.5

error impact to the total PM2.5 impact for the RFS2 case, with respect to the RFS1 mandate case. 

This figure shows that at the 70th percentile, the impact of the error is more than half of the total 

PM2.5 impact of the rule. Above the 90th percentile, it is greater than the total rule impact. 

Figure 1. Distribution of the absolute values of ratios of the locomotive PM2.5 error impact to 

the total PM2.5 impact for RFS2 control case (with respect to RFS1 mandate case). 

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

10 20 30 40 50 60 70 80 90 99

Percentile

ABS(Loco Delta/ Total PM2.5 Delta)
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However, the county-level impacts of the error on the PM2.5 inventory (as opposed to impacts of 

the rule on the PM2.5 inventory) are quite small (Figure 2). In no county are the impacts more 

than 4.5% of the total inventory, and in the vast majority, they are less than 1%. 

Figure 2. Distribution of the absolute values of ratios of the locomotive PM2.5 error impact to 

total PM2.5 for RFS2 control case (with respect to RFS1 mandate case). 

These results suggest that the error has little impact on the total PM2.5 inventories for U. S. 

counties or on national-level inventory impacts of the rule; however, since impacts of the rule are 

small, it can produce very misleading estimates of county-level PM2.5 impacts. 

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

0.045

0.050

10 20 30 40 50 60 70 80 90 99

Percentile

ABS(Loco Delta/RFS1 Total PM2.5 Inventory)