MEMORANDUM

SUBJECT: Impact of an Error in th e Locomotive Particulate Matter (PM 2.5 ) 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 impa ct of an error in allocation of locomotive PM 2.5 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 us ed in air quality mode ling. Inventories were developed for reference cases referred to in th e 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 qualit y 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 we re to be applied to a common base inventory developed for a 2022 projection of th e 2005 air quality modeling platform (http://www.epa.gov/ttn/chief/emch/index.html ). The result would be inventories which reflected county emissions give n 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 PM 2.5 inventories for the three modeli ng cases. These errors were random, resulting in PM 2.5 emission changes that were too hi gh in some counties and too low in other counties.

Overall, the error had little im pact on national-level locomotive PM 2.5 emissions. Table 1 shows the overall impact on nationwide locomotive PM 2.5 inventories for the three cases.2 Table 1. Overall Impact of Locomotive Error on Locomotive PM 2.5 Inventories (tons/year in 2022)

RFS1 Mandate AEO RFS2 Locomotive PM 2.5 Inventory with Error 13,061 11,861 13,342

Corrected Locomotive PM 2.5 Inventory 13,061 13,126 13,231

Difference 0 1,265 -111

Table 2 compares the error in locomotive PM 2.5 inventory impacts fo r RFS2 minus AEO and RFS2 minus RFS1, relative to total PM 2.5 inventory impacts of the rule. It is important to note that the total nationwide PM 2.5 inventory is projected to be over 3.3 million tons in 2022.

Table 2. Impacts of Inventory Error versus Overall PM 2.5 Impacts of Rule (tons/year in 2022).

RFS2 – RFS1 RFS2 – AEO Locomotive Inventory Error 111 1,377 PM 2.5 Inventory Change 17,918 15,795

This comparison shows that on a nationwide level, the error results in a small overestimate of PM 2.5 emission increases attributable to the rule. This error also likely results in an overall overestimate of the nationwide average ambient PM 2.5 increase due to the rule, although at the local level, there will be both incr eases and decreases. However, it is important to note that other limitations resulting from air qua lity modeling assumptions may l ead to underestimates of PM increases. For instance, the modeling relied upo n 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 PM 2.5 from vehicles. In reality, E85 will li kely 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 reduc tions 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 th e error using populationweighted county-specific impacts as a metric. We did this by:

1. For each U.S. county (excluding Alaska a nd Hawaii), calculating the ratio of the locomotive PM 2.5 error impact versus the total PM 2.5 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 populat ion-weighted impacts of3 the error were only about 6% of the total PM 2.5 impacts. However, with respect to the AEO reference case, the population-weighted impacts of the error were larger, about 50% of total PM 2.5 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 PM 2.5 transport on ambient exposure. While PM 2.5

increases associated with ethanol pr oduction facilities may occur in less densely populated rural areas, they are also transported to more urba n areas where larger numbers of people can be adversely impacted. Only air quality modeling can capture these impacts.

Although the overall impact of th e error on total nationwide PM 2.5 was not large, the contribution of the error to estimates of total PM 2.5 impacts of the rule was quite significant in some locations. Figure 1 presents the distribution of the absolu te values of county ratios of the locomotive PM 2.5 error impact to the total PM 2.5 impact for the RFS2 case, with respect to the RFS1 mandate case. This figure shows that at the 70 th percentile, the impact of the error is more than half of the total PM 2.5 impact of the rule. Above the 90 th percentile, it is greater than the total rule impact.

Figure 1. Distribution of the absolute values of ratios of the locomotive PM 2.5 error impact to the total PM 2.5 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

PercentileABS(Loco Delta/ Total PM2.5 Delta)4 However, the county-level impacts of the error on the PM 2.5 inventory (as opposed to impacts of the rule on the PM 2.5 inventory) are quite small (Figure 2) . In no county are the impacts more than 4.5% of the total inventory, and in th e vast majority, they are less than 1%.

Figure 2. Distribution of the absolute values of ratios of the locomotive PM 2.5 error impact to total PM 2.5 for RFS2 control case (with re spect to RFS1 mandate case).

These results suggest that the erro r has little impact on the total PM 2.5 inventories for U. S. counties or on national-level inventory impacts of th e rule; however, since impacts of the rule are small, it can produce very mislead ing estimates of county-level PM 2.5 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

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