Summary of and Response to Peer Review Comments on the Draft NONROAD2002 Emissions Inventory Model Pertaining to the Nonroad Diesel Engine Final Rulemaking 1.0 Review of Nonroad Engine Growth Estimates 1.1 Comments from Andrew Bollman, E. H. Pechan 1.1.1 Comment: The data and approach used to develop growth factor estimates is reasonable and has advantages over other potential approaches. In addition to the advantages noted in the report ( e. g., ability to reflect fuel specific growth trends), two major advantages are that the approach is non data intensive and easy to explain.

EPA Response: No response necessary.

1.1.2 Comment: When the PSR based fuel specific growth rates ( from columns 4 7 in Table 1) are applied to the 1996 population data, the resulting total sector engine population growth rates will not equal the total growth rates calculated from the Power Systems Research ( PSR) historical data ( from column 3 in Table 1). It is my understanding that EPA adjusts the fuel specific engine population projection so that the resulting sector level population growth rates equal the PSR sector level population growth rates ( from column 3 in Table 1). If this understanding is correct, the report should explain how these adjustments are made. I suggest that an example of how these adjustments are made would help clarify the procedure ( perhaps at the bottom of page 4). As an alternative, I suggest that EPA consider developing values for each year that represent the percentage of total equipment in each sector that uses each of the four fuel types. For example, the percentage of Construction sector equipment in 1996 that is diesel is 70.92, while the percentage in 1996 that is gasoline is 29.08. Based on applying the growth rates for equipment in each fuel type from Table 1 to the 1996 equipment populations in Appendix A, these percentages would be 71.53 and 28.47 respectively in 1997. This approach should simplify the computations and be more straightforward to explain in the nonroad engine growth estimate report.

EPA Response: It is correct that the growth rates were calculated with an attempt to account for the market shifting somewhat from gasoline to diesel fueled engines. The total growth rates in column 3 of Table 1 do indeed match the weighted average of growth rates from columns 4 7, using the 1996 PSR fuel specific populations from Appendix A Table 1 to do the weighting. The following text can be added to the report to clarify how this weighting was done. " The total growth rates in column 3 of Table 1 were calculated using a weighted average of the fuel specific growth rates from columns 4 7 of that table. The weighting was based on the 1996 PSR fuelspecific populations from Appendix A Table 1. For example, the overall farm growth rate of 2.6% equals ( 3% x 0.70341) + ( 1.8% x 0.29442) + ( 10.2% x 0.002141), where 0.70341 = 3,302,604 / 4,695,124, which is the 1996 PSR diesel population divided by the total 1996 PSR farm equipment population, and similarly for each of the fuel types."

1.1.3 Comment: For aircraft ground support equipment, there is no discussion of how EPA plans to develop growth data beyond the period available from the FAA source ( i. e., beyond 1From BEA web site located at http:// www. bea. doc. gov/ bea/ dn/ gdplev. htm.

2 2011). I first note that there is a more up to date version of the FAA forecast data source that now goes through 2012. Secondly, to extrapolate beyond 2012, I suggest that EPA review the possibility of using projections data from a separate FAA report that provides forecasts for 2015, 2020, and 2025. This June 2000 report is entitled, " FAA Long Range Aerospace Forecasts Fiscal Years 2015, 2020, and 2025" ( see http:// api. hq. faa. gov/ apo_ pubs. htm# ANCHOR00_ 10).

EPA Response: EPA will take the suggested FAA report into consideration as it continues to analyze its options for updating and improving the NONROAD model's growth rates.

1.1.4 Comment: I suggest clarifying the discussion comparing PSR growth with BEA growth by identifying the specific BEA data that are being used in the comparison. It is not clear if you are referring to BEA earnings data or BEA gross state product data ( note that I would expect the BEA earnings data would show significantly lower growth than the BEA gross state product data since the gross state product data reflect increases in output produced per employee).

EPA Response: EPA used the gross state product data for the comparisons with PSR equipment population historic growth values. This is being addressed in the next update of the technical report on growth in the NONROAD model, NR 008c.

1.1.5 Comment: Comments on Current Methodology I agree with the comment in the report that there is a significant concern with the assumption that the 1989 1996 engine population growth rate will equal the growth rate over the 1997 2045 period. This approach implicitly assumes that the underlying factors that affected engine populations during this short time frame will continue to affect future engine populations throughout the long forecast period. More importantly, this approach assumes that the trends in these underlying factors will remain the same throughout the entire forecast period. One way of reviewing the reasonableness of these assumptions is to compare total economic growth over the 1989 1996 period with long run average growth. Based on BEA data, constant dollar gross domestic product ( GDP) grew at an annual average rate of 2.1 percent over the 1989 1996 period. 1 In the post war era ( 1946 2000), constant dollar GDP grew at an annual rate of 3.4 percent, and constant dollar GDP grew 3.6 percent per year between 1996 2000. These GDP growth rates indicate that economic growth over the 1989 1996 period is unrepresentative of the long run average. Assuming that total economic growth is related to nonroad engine population growth, then the1989 1996 growth in nonroad engine populations may not be representative of long term growth rates.

EPA Response: EPA has recently evaluated alternative approaches to revising the growth rates in the NONROAD Model, as well as the method used to generate them, as part of the current nonroad diesel engine rulemaking process. This analysis in discussed in the RIA and the summary and analysis of comments for the Nonroad Diesel Engine Tier 4 Rule. 3 <IP_ADDRESS> Comment: Publically Available Regional Forecast Data Sources The growth factors included in the draft NONROAD model are National and assume that 1989 1996 engine population growth rates will continue over the next 40 plus years. The EPA report requests recommendations on how to develop regional or State level growth factors. The three sources of publically available sub National level forecast data that I am aware of are:

1. The BEA forecast data described in the EPA nonroad engine growth estimates report. 2. Economic Growth Analysis System ( EGAS) Version 4.0 and underlying data sources. 3. DOE forecasts of energy consumption by sector and fuel type.

The BEA data have two main advantages over the other two sources   they are more geographic specific and they cover the entire forecast period of the NONROAD model. However, the BEA data are more outdated ( prepared in 1995) and less source specific ( at the 2 digit SIC code level) than the other two sources.

The Regional Economic Models, Inc. ( REMI) data that underlie EGAS 4.0 are generally specified at the State level, or for states with ozone nonattainment areas, at the ozone nonattainment area/" rest of state" level. Version 4.0 of EGAS has projections capability through the year 2020, but the REMI models that underlie EGAS project through 2035. The National growth factor data from EPA's draft NONROAD model are incorporated into EGAS. For nonroad sectors not included in the NONROAD model ( e. g., commercial marine vessels), EGAS uses State or nonattainment area/ rest of state constant dollar output data by Bureau of Labor Statistics sector ( 2 or 3 digit SIC code), or in some cases, a regression equation based approach. The regression based approach uses an equation that was statistically identified from the historical relationship between the emissions activity for the category ( e. g., amount of diesel fuel consumed by vessels) and a socioeconomic indicator available from the EGAS REMI models ( e. g., constant dollar output in the Water Transportation sector). This regression based approach is also similar to that used in EPA's Nonroad Engine and Vehicle Emission Study ( NEVES) to identify surrogate nonroad geographic allocation indicators. However, the NEVES analyses were flawed in that they used PSR state level data as dependent variables in the regression analyses. The PSR state level data are not actual equipment population data, but instead are summations of PSR county data. The PSR county data were estimated by applying surrogate county level allocation indicators to National equipment populations. Therefore, the indicators that were identified in NEVES as statistically correlated with the PSR data actually correlate with the geographic allocation indicators used by PSR, and not with equipment population data.

For its " Annual Energy Outlook" publication, the DOE develops energy production and consumption projections on a regional basis ( Census division) through 2020. Some of the DOE data have applicability for nonroad sectors ( e. g., oil production forecasts), but many nonroad sectors do not have an acceptable growth indicator available from the DOE data. 4 Alternative Methodology Recommendation Any of the above forecast data sources could assist in developing regional level growth factors. My recommendation concerning an alternative growth factor development methodology is to implement the more exploratory regression based approach used for some source categories in EGAS 4.0 ( and similar to the approach used to identify geographic allocation indicators in the NEVES). This approach first entails compiling National time series nonroad engine/ equipment data for the complete historical period available from PSR ( i. e., 1989 1998?). These data would then be used to identify equations that relate surrogate indicators with the engine/ equipment data. These equations would be identified based on regression analysis, and the surrogate indicators that would be tested for correlations with the PSR data would come from sources that have historical and regional forecast data available. One potential source of indicators for this effort is EGAS 4.0 and its associated underlying data sources ( e. g., REMI socioeconomic data). After statistically identifying relationships between the historical PSR and REMI data, the EPA could use the REMI forecast data to develop National or sub national engine population growth factors. This approach is similar to the method that is used for some EGAS nonroad categories that are not included in EPA's NONROAD model. This method could easily be incorporated into future versions of EGAS.

For Agricultural sector equipment populations, for example, the EPA could develop an equation relating equipment populations to surrogate indicators. To identify this equation, EPA would regress the PSR National time series data against potential explanatory variables ( e. g., Farm sector and Agricultural Services sector employment and output) that are available from the REMI models that underlie EGAS. Recent work conducted to estimate aircraft ground support equipment provides a similar example. Page 16 of the latest NONROAD geographic allocation report identifies the use of regression equations that predict growth in aircraft ground support equipment populations as a function of the number of landings and take offs ( LTOs) by narrow and wide body aircraft ( the distinction being that more pieces of ground support equipment are needed to service a wide body aircraft than a narrow body one). These regressions are from a May 1999 report entitled, " Technical Support for the Development of Airport Ground Support Equipment Emission Reductions." Because LTO forecast data are available from the FAA, it may be possible to use these data in conjunction with the regression equations to forecast ground support equipment ( note that it will be necessary to determine whether the LTO forecasts differentiate between narrow versus wide body aircraft).

This approach does not estimate fuel specific trends in equipment populations. To estimate these trends, EPA could conduct time series regression analyses based on the historical percentage of equipment in each sector using each type of fuel. In their simplest form, these equations would simply assume that historical trends will continue throughout the forecast period. This is similar to the current EPA approach. Unless there is evidence that each sector's fuelspecific trends will continue indefinitely into the future, EPA should consider employing upper/ lower limits on the percentage of each sector's equipment powered by each fuel type. A more exploratory/ robust approach would attempt to project fuel type percentages based on 5 potential correlations with explanatory variables such as fuel prices. This second approach, however, is likely to be hindered by the availability of long term forecast data for the major variables that affect the proportion of engines that use each type of fuel.

EPA Response: EPA has recently evaluated alternative approaches to revising the growth rates in the NONROAD Model, as well as the method used to generate them, as part of the current nonroad diesel engine rulemaking process. This analysis in discussed in the RIA and the summary and analysis of comments for the Nonroad Diesel Engine Tier 4 Rule.

At this time, EPA is not actively considering the implementation of state or regional growth rates in the NONROAD model. However, EPA will take this information under consideration for the next generation of mobile source emissions model, the Motor Vehicle Emission System ( MOVES). MOVES will most likely include the capability to model state or regional growth rates.

1.1.6 Comment: Forecast Data Alternatives The purpose of this comment is to make EPA aware of alternative forecast data sources; it was beyond the scope of this peer review to analyze/ compare these sources against EPA's current PSR based approach.

Freedonia Group, Inc.

The Freedonia Group, Inc. ( Freedonia) is a market research firm that develops forecasts for many sectors of the economy including engine/ equipment demand projections for certain nonroad applications. The Freedonia forecasts are limited in geographic and temporal scope in that they are National forecasts that are available for 5 year intervals over a ten year forecast period. For an Emissions Inventory Improvement Program ( EIIP) review of projections data, EPA purchased the following nonroad related Freedonia forecast data, which are reported in constant dollars:

° Recreational vehicle shipments; ° Lawn mowers sold; ° Lawn and garden tractor shipments; ° Marine equipment diesel engine demand; ° Construction equipment diesel engine demand; ° Agricultural equipment diesel engine demand; and ° Other markets equipment diesel engine demand ( Includes railroad equipment, nonmarine military equipment, recreational vehicles, and residential generators.)

It is important to note these data are sales projections, not projections of total engine/ equipment populations. For more information on the Freedonia data, please refer to the EIIP report entitled 6 " Evaluation of Emission Project Tools and Emission Growth Surrogate Data, Final Draft," November 17, 2000 ( see http:// www. epa. gov/ ttn/ chief/ eiip/ committee/ projections/ projects. html).

Department of Energy As an alternative to the use of BEA data for the oil field equipment sector, EPA may want to consider the use of domestic oil production forecasts available from Department of Energy ( DOE)' s " Annual Energy Outlook." The advantages of these forecasts are: ( 1) they are a direct measure of oil production activity ( barrels produced), rather than a measure of oil sector economic activity; ( 2) they are much more recent than the BEA forecasts, which were released in 1995; and ( 3) they explicitly model factors that affect domestic oil production such as oil price, international oil supply, and domestic oil demand. The two major disadvantages compared with the BEA data are: ( 1) the last forecast year is 2020; and ( 2) the forecasts are less geographicspecific ( i. e., are regional, while the BEA data are State specific).

EPA Response: EPA has recently evaluated alternative approaches to revising the growth rates in the NONROAD Model, as well as the method used to generate them, as part of the current nonroad diesel engine rulemaking process. This analysis in discussed in the RIA and the summary and analysis of comments for the Nonroad Diesel Engine Tier 4 Rule.

1.2 Comments from Rick Baker, Eastern Research Group 1.2.1 Comment: In general the report is very clearly written and well organized. The Background section is particularly effective in communicating the complexity of alternative approaches.

EPA Response: No response necessary.

1.2.2 Comment: In the third full paragraph on page 3, EPA discusses how oil field equipment growth rates were handled differently from other equipment categories due to their declining populations. Is this the only equipment type negative growth rates? If so, please state as such. If not, what other types of equipment have declining populations, and how were these handled?

EPA Response: Other equipment categories that have negative growth rates include CNG agricultural equipment, gasoline industrial equipment, and diesel logging equipment. These growth rates were derived using the PSR data. EPA included a separate discussion of oil field equipment because it used BEA estimates of gross state product of domestic oil production rather than PSR data. However, EPA will clarify this in the next revision of the growth rate technical report. 7 1.2.3 Comment: In the first paragraph on the second page, it is acknowledged that economic indicators and models in recent years have under predicted growth in the national economy. In light of the recent economic downturn, it may be that econometric models do in fact provide reasonable long term growth predictions, although their ability to predict short and medium term fluctuations is admittedly limited.

EPA Response: This is a relevant point that EPA has considered and should emphasize in the growth technical report.

1.2.4 Comment: In the first paragraph on the second page, it is noted that " Economic indicators may not be able to adequately predict the effects of substitution of equipment for labor in the market." While this is true, it is not clear that such substitution is a significant factor when it comes to many nonroad equipment applications. In general, one might anticipate significant substitution in the farm sector as smaller farms continue to undergo consolidation by large agribusiness interests. However, such a trend is not self evident for most of the other equipment sectors. For example, substantially new " labor saving technologies" have not been developed for many years in most construction and industrial applications, and most engines and machines continue to be used in much the same way they have for years ( e. g., welders, pavers, backhoes). And in the case of recreational equipment, the " substitution factor" is not even applicable.

EPA Response: This is a good point. The NONROAD model does not have the ability to account for subtle economic influences, even if it could be proven that they exist or have a significant impact on the equipment population of a given equipment category. EPA will clarify this in the next revision of the growth technical report.

1.2.5 Comment: Although I do have reservations concerning the points made in the first paragraph of page 2 regarding the limitations of economic indicators, I am in substantial agreement with the second paragraph, concerning the ability to identify trends within sectors. For this reason I concur with EPA's decision to use historical equipment populations as the basis for determining growth rates.

EPA Response: No response necessary.

1.2.6 Comment: In the third paragraph on page 2, EPA notes that equipment population is a reasonable surrogate for activity, given the high capital cost of equipment relative to operating costs. I would also add that future increases in equipment costs resulting from the adoption of increasingly stringent emission standards will tend to emphasize this effect.

EPA Response: This is a good point. EPA will consider incorporating this point in the next revision of this technical report.

1.2.7 Comment: In the last sentence of the third paragraph on page 2, it is not reasonable to assume that equipment will be " fully" utilized. This implies 100% utilization. I suggest saying 8 " efficiently utilized" instead.

EPA Response: This is a good point. EPA will consider incorporating this point in the next revision of this technical report.

1.2.8 Comment: EPA proposes to use simple linear regressions of historical engine populations to predict future populations. I believe this is a reasonable approach, as long as annual average growth factors are not substantially higher than overall population and economic growth. ( Of course exceptions are to be expected, such as with the recent upsurge in ATV populations, but these should be handled on a case by case basis.) In this light, most of the annual growth rates presented in Table 1 appear appropriate for linear projections, with the possible exceptions of light commercial ( 4.0%), and logging ( 4.5%). Unless evidence can be provided to justify the relatively rapid growth in these sectors indefinitely, I believe an adjustment should be made to either the annual growth rates, or the functional form of the predictive relationship, in order to slow the growth in the medium/ long term.

EPA Response: Significant uncertainty exists concerning the best growth rates to use for the NONROAD model. The uncertainty only increases when trying to determine what the rate of growth will be for each nonroad category in the long term. EPA will continue to study this issue and refine its approach, especially in the development of the MOVES model.

It should be noted that EPA does not use the total growth rates from Table 1 in the model. Rather, the NONROAD model applies the separate growth rates for diesel, gasoline, CNG, and LPG engines to each equipment category by fuel type. This will be clarified in the next revision of the growth rate technical support document.

1.2.9 Comment: Why is the Airport Service equipment category excluded from Table 1?

EPA Response: Table 1 compares growth rates used in the NONROAD model to BEA growth rates. BEA most likely did not have a category for aircraft ground support equipment. However, not including a summary table of all of the growth rates used in NONROAD was an oversight. In the next revision of the growth rate technical report, EPA will add another table including all the growth rates used in the NONROAD model.

1.2.10 Comment: On page 5 EPA acknowledges that there is some " concern about basing longterm growth estimates for nonroad equipment/ emissions on the seven years of data from PSR." In order to help assess the significance of this concern, EPA should provide some indication of the year to year variation in the data by sector. For example, from Appendix A we see that the yearto year population increases in the construction and airport service sectors are relatively constant, varying by less than 2.0% over the seven year period. While not definitive, this may imply that projections based on seven years of historical data may be relatively accurate for these sectors. On the other hand, annual changes in recreational equipment varied by 4.5%, and rail equipment by 6.1% over this same period, perhaps implying more volatility in sales and less confidence in 9 future year projections. Therefore I recommend at least providing a short discussion of year toyear variance in the existing data, and possibly additional analysis of variance, standard deviations, etc.

EPA Response: A comment about this issue is being added to the technical report on growth ( NR 008c).

1.2.11 Comment: The annual growth rates for commercial and logging equipment seem inordinately high. Although admittedly uninformed on the logging industry, I was under the impression that logging activity had been substantially curtailed in certain areas of the country such as the Pacific Northwest. If activity in the Southeast has increased dramatically to more than offset this downturn elsewhere, please note.

EPA Response: Various sources of information yield widely varying estimates of nonroad engine/ equipment growth. In regard to logging equipment, EPA uses a negative one percent growth rate for diesel logging equipment. Positive growth in this sector comes from sparkignition logging equipment, specifically industrial chainsaws over six horsepower.

1.2.12 Comment: An annual recreational equipment growth rate of 0.9% appears low, much lower than the demographic growth rate ( which one would expect recreational equipment to follow.) And given that recreational equipment purchases are likely tied to increased levels of disposable income, I would expect such purchases to have increased substantially during the mid 1990s.

EPA Response: The growth rate of 0.9 percent from Table 1 is a BEA estimate. EPA did not use the BEA estimates in the NONROAD2002 model. In addition, EPA revised the recreational growth rates for modeling done to support the Recreational and Large Spark Ignition Engine Rule. EPA developed separate growth rates for ATVs, snowmobiles, and off highway motorcycles based on data provided by the Motorcycle Industry Council and the International Snowmobile Manufacturers Association. This is discussed in the August 7, 2002 memorandum, " Updated Population Growth Projections for Snowmobiles, ATVs, and Off Highway Motorcycles. This memorandum was submitted to the peer reviewer. Recreational marine equipment, including boats using diesel engines, continued to use the PSR based growth estimate of 3.3 percent. EPA is incorporating the changes made to the recreational growth rates in the next revision to the technical report addressing growth estimates used in the model ( NR 008c) and will attempt to present the information in a clearer manner.

1.2.13 Comment: The current projection methodology based on national level population data can be adjusted to reflect locality specific factors at the county level. In order to account for growth at the county level, readily available surrogates must first be determined for each sector. For example, county level population projections can be obtained based on US Census data, which can be used to adjust national level projections for the construction, industrial, lawn and garden, recreational, and possibly light commercial sectors. ( Logging, railway, and farm 10 equipment populations may or may not lend themselves to this sort of adjustment, depending on the availability of other appropriate surrogates   e. g., acreage in crop production by county/ state and year for the farm sector.)

Under this proposal, demographic growth rates at the county level could be normalized relative to national demographic growth rates, with the sum of future county equipment population totals constrained to equal the national equipment total projection. This approach admittedly cannot account for regional differences in per capita equipment ownership, but should still provide an improvement over the current scheme.

EPA Response: EPA will be considering regional, state, and county specific growth rates for the MOVES model, which will be the next generation model for mobile source emissions inventory modeling.

2.0 Review of CI Emission, Deterioration, and Transient Adjustment Factors [ Penny]

2.1 Comments from Dr. Nigel Clark, University of West Virginia 2.1.1 Comment: Consider the language concerning " Percentage Phase in Allowance," on page 3, as well as Table 1 and Appendix A Table A1. The " cumulative total of eighty percent" language does not clearly convey the 0.2   0.1   0.1 ( for 2 years each) sequence in Table A1. If it is the intent to inform the reader fully, some extra words may be needed on page 3. If it is the intent just to refer the user to Table A1 as the primary source, the page 3 language is fine as it stands.

EPA Response: EPA will seek to clarify this in the next version of the diesel emission factor technical report ( NR 009b).

2.1.2 Comment: The issue of crankcase emissions ( p. 5) is very interesting. Presumably the report uses the 2% number across the board because so little is known about these emissions, but it would be good to cite the source on p. 5 ( the source is referenced only toward the end of the report). Crankcase emissions are equivalent in volume to the ring and valve stem blowby, and are probably around one or two percent of exhaust flow for most engines. Since combustion is incomplete during some of the blowby, the hydrocarbon concentration in the crankcase gases is likely to be higher than in the exhaust gases. The " 2%" value may be reasonable in some cases, but more likely underestimates the crankcase emissions. However, crankcase emissions are clearly small from an overall inventory perspective, so that no recommendation to change the present document is made, other than to add the reference on p. 5. In the longer term, crankcase emissions should receive some experimental attention. Even though these emissions will be controlled by requiring positive crankcase regulation in the future, off road engines enjoy a long life, and open crankcase emissions will remain in the inventory for decades to come.

EPA Response: EPA will add a citation of the source on page 5 during the next revision of the 11 technical report ( NR 009b).

2.1.3 Comment: On p. 5, perhaps state why the tier 2 emission factors have the 0.2% sulfur default level.

EPA Response: This will be added during the next revision of the emission and deterioration factor technical report.

2.1.4 Comment: The correction of NMHC to THC using 1.02 in Table 2 appears without reference or comment, though I doubt it is of much concern!

EPA Response: The references for the conversion from NMHC to THC and vice versa can be found in NR 002a, " Conversion for Hydrocarbon Emission Factor Components". It should be noted that the conversion table in NR 002a contains different values because the conversion is from THC to NMHC ( 0.984 for diesel), as well as other hydrocarbon types, while the value in the footnote of Table 2 in NR 009b ( 1.02) is to convert from diesel NMHC to THC ( 1/ 0.984). EPA will be clarify this in the next revision of NR 009b.

2.1.5 Comment: Consider Table D5. There is an entry above the Tier 1 standard in the 50 100 hp category for oxides of nitrogen ( 80hp, line 4, p. D23, 7.61 grams). How can this be? Page 3 language suggests that it cannot be a " Percentage Phase in Allowance," since it would be allowed only for engines 25 to 50hp in 1999.

Consider Table D5, 1999 standards for 25 to 50hp engines ( pp. D21 22). I examined oxides of nitrogen values. Is this table meant to contain values for engines that should have been Tier 1, but that had higher emissions due to the " Percentage Phase in Allowance?" If so, none appears, and this does not jibe with the p. 3 statement, that manufacturers took full advantage of the " Percentage Phase in Allowance." I assume that " Phase in" engines do not appear on this list, because they were Tier 0 and hence uncertified, and no data were provided.

EPA Response: Some engine families are certified above the standard if they are part of the Average Banking and Trading ( ABT) program. In other words, the manufacturer can use ABT credits to certify an engine family. The entry referred to by the reviewer is part of the ABT program. The NOx Family Emission Limit ( FEL) for that engine family is 8.3 g/ bhp hr. " Phasein engines are not included in the tables in Appendix D of the report.

2.1.6 Comment: On p. 11, the text states that NONROAD's Tier 1 emission factors are based on EPA certification data, and refers the reader to Appendix D. However, Appendix D Table D1 states that only boldface numbers were used. Why do small differences between cert. and actual NONROAD values differ in some cases? ( Example: Tier 1 NOx Tables 4 and A2 are 4.7279 for 25 to 50hp, but cert. average in Table D1 is 4.640553. If explanation appears in the text, I apologize for overlooking it. 12 EPA Response: For categories less than 50 hp, the Tier 1 standard for HC and NOx is expressed as a combination of HC and NOx. As a result, most of the certification data for these engines is provided as the sum of HC and NOx, although there are some data reported for HC and NOx separately. Using the example for 25 to 50 hp engines above, 72 engines reported NOx separately, while 270 engines reported results as a combination of HC and NOx. The cert average in Table D1 of 4.640553 is based on the data reporting NOx separately; however, due to the smaller sample size, this value was not used. Instead, HC fractions of HC+ NOx emissions were calculated for those tests which reported both HC and HC+ NOx emission factors. The average HC fraction was then calculated and multiplied by the sales weighted average HC+ NOx emission factor to obtain an HC emission factor ( referred to as HC calc in the tables). The remaining fraction of the HC+ NOx emission factor was assigned as the NOx emission factor ( referred to as NOx calc in the tables). For 25 to 50 hp engines, the NOx calc value of 4.7279 in Table A2 was used. This approach is discussed in Appendix D of the report.

2.1.7 Comment: On p. 11, Option ( 1) seems to assume implicitly that timing changes ( and maybe increased internal EGR due to valve timing changes) are the only tools to be used, hence yielding a NOx   PM tradeoff. This is probably reasonable, although continuous full load operation at about 4.5 g/ bhp hr NOx may thermally challenge an engine manufactured with conventional materials. In the case of highway engines with conventional materials, thermal relief was provided by " off cycle" timing operation that persisted through the 90' s. None of the options is flawed in its arguments. However, why have the seemingly arbitrary value of 75% in Option ( 4) when the highest value in appendix E is 27%? Also, Option ( 4) is worded generically, whereas data are presented only for NOx and PM in Appendix E. I do understand that these margins may be large for CO or HC.

EPA Response: Appendix E only presents results for NOx and PM because Option ( 4) was only applied for these pollutants. For CO and HC, the margins were greater than 80%, which is the basis for the upper limit of 75%.

2.1.8 Comment: On p. 14, is it fair to say that high HC compliance margins are " unrealistically large?" The issue is that in the case of highway engines, the HC standards have not driven technology development at all, and hence the standards for HC are unrelated to cert. values. These compliance margins will map from one application to another only if the standards are technology drivers. Upon re reading the report language, I do not think that I disagree with the report's intent, but a wording change might be good.

EPA Response: This clarification will be added to the next version of the CI emission factor technical report.

2.1.9 Comment: The choices of options on p. 14 seem reasonable. I am curious as to whether an overall comparison was made ( in a working table) to see how much the options differed. For NOx and PM ( where all options were credible), it would be comforting to know that the choice did not matter substantially. For HC and CO, clearly some options were unsuited and comparison 13 would not be useful.

EPA Response: Such an overall comparison was not made, but will be considered.

2.1.10 Comment: On p. 16, the deterioration factor approach is reasonable, but could be improved in future models. The use of a linear deterioration rate is acceptable over the engine life, but clearly engines operate beyond expected life and continue to decline in emissions control ( and ultimately in fuel efficiency), particularly as they are sold down the food chain. Unfortunately, it is hard to extrapolate this deterioration. Also, the Age Factor, being based on cumulative work performed by the engine over its life, is quite simple. In real use, I would expect that an engine running at high speed and low load ( e. g. An oversized constant speed generator set) may suffer substantial wear relative to the useful cumulative work done. The safety net is that deterioration is averaged in the inventory over many applications, but this might not apply if a specific engine application prevailed in a region. The report does observe that there is a lack of deterioration data for nonroad engines, and perhaps this could be an area for future study.

EPA Response: Agreed. No response necessary.

2.1.11 Comment: On p. 19, why is HC subtracted in arriving at carbon dioxide emissions, while CO is not? They are both consuming fuel carbon sources. However, they are also both small, so predictions will not be affected much.

EPA Response: HC is used as the correction for unburned fuel in this equation. We agree that this correction is insubstantial for diesel emissions.

2.1.12 Comment: On p. 19, 0.87 is " hard coded" as the carbon mass fraction in diesel. Allowing this to be a variable would adjust for runs with less aromatic fuels, but it will not enhance the model's utility appreciably. In fact, it is probably lost in the relative inaccuracy of BSFC data.

EPA Response: Agreed. No response necessary.

2.2 Comments from Michael Hutcheson, URS Corp.

2.2.1 Comment: The reviewer would like to state at the outset that the description of brake specific fuel consumption ( BSFC) as a pollutant or emission is an incorrect characterization. BSFC should be referred to and described in a purer context as a measure of the amount or rate of fuel consumed per horsepower of work output. BSFC is a function of engine speed and therefore varies with engine load conditions. Charts of BSFC versus engine speed vary from engine to engine. Correspondingly, BSFC is dependent on the equipment design and equipment use but should not be confused with an emission.

EPA Response: This is a good point. EPA will revise this in the next version of NR 009b. 14 2.2.2 Comment: On page 6, when discussing the use of Tier 0 BSFC for all engine model years ( presumably by hp/ category), it should be clarified that this has an affect on both CO 2 and SO 2 emission factors. The presumption being that since BSFC is not assumed to change then the related emissions of CO 2 and SO 2 do not change ( for a given fuel) even though new emission reduction technologies are being implemented. This correlation should be identified in order to clarify the affect of this assumption.

EPA Response: The correlation between BSFC and CO 2 and SO 2 will be stated more clearly in a future version of the CI emission factor technical report. CO 2 and SO 2 will change slightly as HC emissions are changed, since the carbon and sulfur that goes to exhaust HC ( unburned fuel) is subtracted in the CO 2 and SO 2 equations. For diesel emissions, this is insubstantial.

2.2.3 Comment: In the description of the sources of data used for generating emission factors for certain engine horsepower classifications the report states that no certification data is available for tier 2 engines < 300 hp and > 600 hp. Since Tier 2 for the 600 hp to 750 hp category started in 2002, data for Tier 2 for this horsepower class should be available. In addition, Tier 2 data for engines from 100 hp to 300 hp should be available now ( and at the time of writing the report) since the standard applies in 2003. It is understandable that the 100 hp to 300 hp data would not have been ready before the Nonroad2002 revision but the 600 hp to 750 hp Tier 2 certification data should have been ready and used.

EPA Response: This is a good point. Unfortunately, EPA was not able to incorporate the Tier 2 certification data for 600 to 750 horsepower engines before the model needed to be locked down for use in supporting the proposed Nonroad Diesel Engine Rule. EPA will address this during the next revision of NONROAD's diesel emission factors.

2.2.4 Comment: When describing the methods used to determine emission factors for Tier 2 and Tier 3 engines in each horsepower category, the report states that a different method of determining the emission factor was used for HC and for NOx for the 600 hp and above hp categories. For HC, the certification values for Tier 2 standards in the 300 to 600 hp category were used. For NOx, a default compliance margin of 10 % was used. This does not make intuitive sense. It is recognized that the Tier 2 certification values for NOx in the 300 hp to 600 hp engine category did not meet the 10 % compliance margin EPA assumes manufacturers will try to meet. The available certification evidence indicates that manufacturers are not beholden to a 10 % compliance margin. Since the standard for all these hp categories are the same and the report states that " the use of certification data is preferable to applying a compliance margin" the reviewer believes the NOx certification data for 300 hp to 600 hp category should be used for the horsepower categories above 600 hp.

EPA Response: The approach described by the reviewer could also have been used. Since the two approaches yield emission factors that only differ by 5 percent, the current approach was retained. 15 3.0 Review of Geographic and Temporal Allocation Factors 3.1 Comments from Kirsten Thesing, E. H. Pechan 3.1.1 Seasonal/ Monthly Allocation Factors <IP_ADDRESS> Comment: The discussion of the calculation of national average seasonal and monthly activity allocations was somewhat unclear ( as described in the March 1999 report).

° For Step 1, it states that emission estimates are generated for each of 50 States. Are the emissions then summed to the national level by accounting for each State?

° In looking briefly at the season. dat file, the number of different equipment types for each region is 65, and the number of different equipment types for the total US is 111. Additional national allocations are provided for commercial vs. residential lawn and garden, golf carts, oil field equipment, and other underground mining equipment. It is not clear how national average allocations are calculated for these additional equipment categories.

° Also, in doing quality assurance of a 1996 county level inventory developed using NONROAD ( in conjunction with EPA's Emission Factor and Inventory Group), I found that the results from a specific single State run did not equal results for the same State when doing a 50 State run. This was discovered during May 1999, and Craig Harvey documented this glitch in a May 11, 1999 email. Do the average national seasonal and monthly activity allocations reflect any corrections to fix this known error ( since the latest version of the " season. dat" file is dated 3/ 22/ 99).

EPA Response:

In order to calculate national average seasonal allocations, EPA did 12 runs of the model to generate outputs for all 50 states in one run for each month. The emissions for each of the 50 states were summed to the national level to account for each state.

The monthly allocations at the state/ regional level are given by aggregate SCC's wherever possible, such as using the entire Rec Equipment or Lawn & Garden segment, or even an overall average ( 2270000000 all diesel equipment not otherwise specified). But to properly weight together the allocations for all the states, taking into account the different populations and horsepower distributions within each state, it needs to be done at the level of individual SCC's. Reaggregating the individual SCC monthly allocations to the higher levels would actually decrease the precision of the results.

The discrepancy between the results of a single state run and the same state in a 50 state run has been fixed. 16 3.1.2 Weekday/ Weekend Day Allocation <IP_ADDRESS> Comment: I found this report to clearly explain the methods and data behind the weekday and weekend day fractions.

EPA Response: No Response needed.

<IP_ADDRESS> Comment: There was no discussion of how the weekday and weekend day fractions were developed for railway maintenance category. This category showed the highest total weekday use ( 90%), and the lowest weekend day use ( 10%). Were these values based on a survey, or expert judgement?

EPA Response: Lacking any definitive data for railway maintenance equipment weekly usage patterns, the week day and weekend day allocations were based on expert judgement.

<IP_ADDRESS> Comment: You assigned the same weekday/ weekend temporal profile for the agricultural category that is assigned to other commercial related categories which tend to operate equipment more during the week than the weekend. Depending on the season, though, and especially during harvest, it would seem that agricultural equipment may need to be operated throughout the week, including weekends. In the absence of actual survey data, I understand the assignment you made, but I would think that farm equipment has the potential to be used as much on the weekends as the weekdays.

EPA Response: EPA is aware that agricultural equipment could be operated on the weekends more than the current allocation allows. EPA would like to find documented information which would indicate whether a change to the allocation is warranted.

3.1.3 Geographic Allocation <IP_ADDRESS> Comment: Commercial and Industrial Equipment: As a result of my company's research and investigations into economic output data for other projects ( which may concern developing emissions projections of data, as well as geographical allocations), we have not found that any SIC specific output data are available at the county level for the whole U. S.

The surrogate used for industrial equipment, manufacturing employees, would appear to be a reasonable choice. The surrogate for light commercial, ( establishment for SIC 50: wholesale trade  durable goods) is consistent with what was used in the 1991 NEVES, as determined by Energy and Environmental Analysis. However, there is a limitation in how the surrogates were selected, as explained in the following write up from Pechan's September 1997 report, Evaluation of PSR Nonroad Population Data Base.

" As part of the NEVES, Energy and Environmental Analysis ( EEA) estimated county populations, and ultimately, nonattainment area populations, from national data ( EEA, 1991). In 17 developing its methodology, EEA estimated regression equations based on PSR State level data. With some notable exceptions, these regression equations were typically developed using number of employees in related industries ( e. g., State level logging equipment was regressed versus the number of employees in SIC 241   Logging). This approach uses indicators that are statistically correlated with PSR State distributions. The major concern with the EEA approach is that it is based on relationships with PSR's State estimates, which PSR has indicated are simply summations of its county estimates."

In addition to this limitation, SIC 50 may not be capturing nonroad engine activity related to all commercial activities. In developing forecasts for Economic Growth Analysis System, version 4.0 ( E GAS 4.0), for commercial sector categories, we have in some cases chosen to use the number of establishments for SICs 50 through 89, which encompasses the SICs for wholesale trade, retail trade, finance, insurance and real estate, and services. The finance, insurance and real estate SICs may not be appropriate to include for nonroad equipment, but the other three would intuitively seem to correlate better than wholesale trade alone. Ideally, one would want to perform regressions to test how this group of SICs correlates to light commercial activity/ populations. Based on the types of equipment that are classified as light commercial ( compressors, generators, welders), including establishments for retail trade and services in the allocation factor, as well as wholesale trade, may better reflect activity.

EPA Response: To the best of EPA's knowledge, the geographic allocation factors in the NONROAD 2002 model used for the proposed Nonroad Diesel Rule were the best available at the time. EPA appreciates receiving comments indicating better methods or data. EPA will consider improvements to the NONROAD 2002 model for the final Nonroad Diesel Rule analysis.

<IP_ADDRESS> Comment: Logging Equipment: You requested comments on the availability of data on the number of forest acres harvested per county. In trying to develop estimates for slash burning for EPA's Emission Factor and Inventory Group, we looked into obtaining these data at a county or State level, since the number of acres logged would provide an initial indicator of the amount of acres that are ultimately slash burned. The U. S. Forest Service does track the number of acres harvested, but they acknowledge that the uncertainty associated with these data at a county level is very high. The reliability increases when counties are grouped into multi county regions, or at the State level.

The U. S. Forest Service does maintain an on line timber product output ( TPO) data base retrieval system which provides county level estimates of the total timber removal, with a subset for roundwood product removal ( available at http:// srsfia. usfs. msstate. edu/ rpa/ tpo/. Logging equipment activity would be expected to correlate with the volume of timber removed. The data are reported for all land ownerships, including National Forest, Other Public, Forest Industry and Other Private. Through the current on line system, you can select all counties to obtaining data at the county level for the whole U. S., but this may be a bit cumbersome. In speaking to contacts at the Forest Service that work with the TPO data base, data could also be compiled by them for 18 little to no fee.

The reliability of the timber volume data at the county level is higher than the acres harvested data at the county level. However, as explained in the documentation for the TPO database, there are limitations associated with the data ( e. g., some counties have been combined due to data paucity or sensitivity, and the data contained is subject to being updated or revised based on reviewer and user comments). The employment data from County Business Patterns ( CBP) is likely to be a more consistent database, but the volume of timber removed may more accurately reflect logging equipment use. As this database is reviewed and refined further, this may be a potential allocation source for logging activity in the future.

EPA Response: This appears to be a promising source of data. EPA will take this information into consideration during development of the nonroad portion of the MOVES model.

<IP_ADDRESS> Comment: Railroad Maintenance and AC/ Refrigeration Units: You requested comment on the availability of railroad track mileage by county. In doing work for specific States ( e. g., Pennsylvania) railroad track mileage by county has been obtained and used to apportion State level rail activity and emissions to counties. I am not aware of one national source that would provide these data for each State by county. It is likely that each State's Department of Transportation or equivalent would need to be contacted to obtain these data.

For AC/ Refrigeration Units, since this activity is tied to vehicular truck travel, you may want to consider allocating to States based on vehicle miles traveled ( VMT) for light duty gas trucks ( LDGTs), light duty diesel trucks ( LDDTs), heavy duty gasoline vehicles ( HDGVs), and heavy duty diesel vehicles ( HDDVs). Consistent with how we allocate VMT to the counties for Trends, though, county level VMT would need to be estimated based on population. So, this method may improve upon the National to State allocation, but the county allocation is still based on population. To the extent that AC/ Refrigeration units may also be located on railroad boxcars, an approach based solely on VMT would be insufficient.

EPA Response: These might be useful sources of allocation data. The reviewer highlighted a challenge that EPA faces with allocation factors, namely finding county level data with nationwide coverage.

3.2 Comments from Sam Wells, Consultant 3.2.1 Comment: The three reports regarding geographic and temporal allocation for NONROAD2002 are well written. The discussion of activity versus equipment population seems a trifle awkward at times and could use some editing   the main concept should be that we are using secondary surrogates that would best describe activity and population, taken together, given that accurate ground count data is not available. In fact, use of surrogates is preferable to using ground data in some cases because of the mobile nature of the sources; the exception would be large construction projects that would be measured in terms of fractions of billions of dollars. 19 EPA Response: EPA will try to clarify the discussion of activity versus equipment population.

3.2.2 Geographic Allocation by Equipment Category <IP_ADDRESS> Comment: Construction Discussion of the development of adjustment factors from previous surveys done in Houston, Texas is a little confusing because there have been at least five major efforts dating back to the before the 1991 NEVES. The proposed factors ( for residential, highway, and other economic sectors) are not well documented, although it is obvious that construction activity is more intensive in highway and utility applications rather than others such as residential. One recommendation would be to use the existing factors and then further study the issue with special attention to regional construction practices. For example, the cost of labor and fuel can substantially affect how the " dollars of construction value" actually translates into construction activity. As an example, it was found that the cost of labor in Dallas was approximately 6 percent higher than in Houston. Therefore, one could make a further adjustment by dividing construction revenue statistics by 1.06. This approach has not been verified by comparing the method to hard data but could be something to consider.

Also, one might consider the difference between construction machinery used in relatively soft material ( e. g., sandy clay) versus hard material ( e. g., limestone and granite). Cranes and excavators are more likely to be used in soft media; rock drills, rock saws, boring machines, and trenchers are more likely to be used in harsher media. I am not exactly sure how to adjust the kinds of equipment from region to region, although when the Austin construction inventory was compared to one done for Houston, we noted that Houston had zero trenching equipment   and 46 were found just in the Austin area ( some were up to 1,000 HP, and had a significant contribution to the emission inventory). Perhaps use of USGS GIS maps to determine alluvial sands versus rocky soils would be something to explore.

As a word of caution, the equipment rental sector is very difficult to ascertain, and is a " wild card" in any scheme that uses secondary surrogates. For example, in most cases cranes are obtained from rigging companies that specialize in bridges and elevated highways, commercial high rise buildings, or even industrial uses ( e. g., refinery turnaround construction). To my recollection, none of the studies conducted in Houston were able to get a handle on the crane market sector, so extrapolations to other areas in the U. S. would probably have to be done in a top down manner. The ENVIRON memorandum notes that port and industrial activity in Houston might not be characteristic of other urban cities, but an unintended result is that perhaps other areas of application would suffer from potential misprediction of specific types of equipment such as heavy duty cranes ( 150 750 HP, excluding the larger ship cranes up to 2,500 HP). Again, these kinds of refinements can be incorporated over the next few years but are worthy of further attention.

With this said, I applaud the EPA for considering several metrics instead of one " total dollar of construction revenue" figure per county for the purposes of spatial allocation. 20 EPA Response: EPA is aware of differences in the cost of construction by region and the impact this has on under or over predicting construction equipment emissions. EPA plans to address this in future revisions to the model. The rental sector is another area in which EPA will need to do further investigation in the future.

EPA believes that the adjustments made to the dollar amount of construction allocation factors are generally representative of construction equipment usage on infrastructure projects versus usage on less intense building related construction projects. Since it is a national model, EPA's NONROAD model does not have the capability to account for all local variations in construction equipment usage.

<IP_ADDRESS> Comment: Agricultural Use of crop land acreage is the best method to allocate agricultural activity. However, the USA Counties data set is very crude, at best, and one could attempt to utilize other more robust data sources such as from the U. S. Natural Resources Conservation Service ( NRCS):

http:// www. nrcs. usda. gov/ technical/ NRI/ 1997/ summary_ report/ index. html Note that total crop land includes cultivated and non cultivated crop land, so variations in noncultivated crop land statistics would skew the allocation of agricultural activity. On average one sees approximately 10 percent of the total acreage is not cultivated ( meaning little tractor and no combine activity), but in non cultivated crop land in California is approximately 30 percent and in Connecticut it is larger than 50 percent of the total. Presumably, non cultivated crop land would be land that would be cultivated but is left fallow due to federal incentives and other factors. I strongly recommend the use of this publicly available NRCS database because it also includes irrigated versus non irrigated crop land, crop type, and historical trends. I am not sure if the data includes orchards as being " crop land," but it would be obvious that orchard spraying might need to be considered for the purposes of allocating sprayers ( especially SCC 2270005035).

EPA Response: It should be clarified that EPA used the number of harvested acres of crop land in the NONROAD model, so that fallow crop land is not counted. The EPA will examine the U. S. Natural Resources Conservation Service ( NRCS) data during development of the nonroad portion of the MOVES model.

<IP_ADDRESS> Comment: Airport Ground Support Equipment ( GSE) Suggested is that passenger jetliner landing and take off operations ( LTO) data from the Federal Aviation Administration ( U. S. DOT) be used instead of employment to allocate GSE. Only certified commercial carriers are included. The amount of GSE activity ought to be directly proportional to the number of jet airliner LTO. I have not found an internet site for the data, since we always used paper copies in the past.

It should be noted that while GSE is oriented towards passenger operations, there is a significant commercial cargo component ( e. g., Airborne, Emory, FEDEX, UPS) involving a unique piece of 21 equipment I have not seen in the NONROAD model: is it a curious cross between a forklift and an aerial lift that perhaps the EPA should consider them in the future. Again, allocation should be commercial cargo LTO for this category.

Finally, many airports have special GSE agreements to reduce emissions, which should be reflected in the improved model, and many GSE surveys now include support vehicles as being part of the mix ( security, food & beverage, refueling trucks, etc.). For this reason it might be prudent to " hard code" some of the larger international airports because each has a unique blend of LTO by airline and equipment usage.

EPA Response: EPA is aware of the FAA's landing and take off operations data, but gaps and suspected errors in the data set prevented EPA from utilizing it in the model.

<IP_ADDRESS> Comment: Light Commercial and Industrial The proposed indicators appear to be rational although I have concerns about ( 1) the rental market, especially for aerial lifts, and ( 2) industrial forklift activity. Aerial lifts are, at least from my experience, mostly rented and therefore the using number of equipment rental companies might be beneficial. The situation with industrial forklifts is far more complicated because many are used in wholesale and retail distribution centers, not just at manufacturing establishments.

EPA Response: This is good point. EPA will take this into consideration during the development of the nonroad portion of the MOVES model.

<IP_ADDRESS> Comment: Logging Equipment The USDA Forestry Service has its Forest Inventory and Analysis ( FIA) unit that maintains the Timber Product Output ( TPO) database. Annual round wood harvest statistics can be obtained at state and county levels of detail. Please see: http:// fia. fs. fed. us/

Use of employment data ( SIC 2410), while easy to obtain, can lead to misleading results because the logging industry consists of felling ( the main purpose for using non road equipment), transportation, and processing ( e. g., de limbering, de barking, etc.) for delivery to a mill. The TPO output for round wood is also useful because it contains data fields for firewood ( an area source, when burned). Units are usually expressed in terms of millions of cubic feet ( MCF).

EPA Response: EPA will take this under consideration during the development of the nonroad portion of the MOVES model. However, another peer reviewer stated that the TPO database may have significant limitations. 22 <IP_ADDRESS> Comment: Oil Field Equipment I have found that using SIC codes 1381, 1382, and 1389 ( what you call SIC 1300) is highly misleading, as it usually identified the company headquarters, which in Texas is predominantly based in Houston. However, little oil and gas extraction actually occurs in Houston or Harris County. Baker Hughes, a leading information center for the industry, has a database of oil and gas drilling sites that would be much more useful in allocating this kind of source. Please see: http:// www. bakerhughes. com/ investor/ rig/ index. htm Note that these statistics are available only for entire states or districts within states ( as is the case with Texas). Therefore, the EPA would probably have to purchase special county based data from Baker Hughes, similar to working with the Dodge Reports for allocating construction equipment. One benefit of using " real" rig count data is that it includes large rotary rigs and smaller work over rigs, which might be helpful in allocating drill rig HP categories.

Equipment other than drill rigs are also used at oil and gas extraction sites, and this should be addressed because there could be substantial non road emissions other than just the platform, tower crane, and winch engine combination. Mud pumping, well fracturing, and service pumps seem to be completely ignored.

EPA Response: EPA will take this under consideration during the development of the nonroad portion of the MOVES model.

<IP_ADDRESS> Comment: Railway Maintenance I have not been able to find a source of data preferable to use of human census population, but I am sure that the Federal DOT and the American Association of Railways ( AAR) know track mileage and gross ton miles by state and county. The purpose of such a refinement would be to not allocate railway maintenance activity to counties that do not have any significant track mileage or locomotive traffic. For example, approximately a third of the 254 Texas counties do not have any active track mileage. My suspicion is that railway maintenance would be proportional to the amount of gross ton miles on a given route and county ( in Texas, it is though that only about 40% of the track mileage is actually used on a daily basis). Since all Class I railways must report their GTM data, it should not be hard to allocate railway maintenance in a more logical manner.

EPA Response: EPA will take this under consideration for the development of the nonroad portion of the MOVES model. 23 <IP_ADDRESS> Comment: A/ C Reefers Like railway maintenance, this category was proposed to be allocated by human census population. However, there is significant truck traffic ( trucks having refrigeration units) even in rural counties, such as along major U. S. highways. Therefore, the Highway Performance Monitoring System ( HPMS) would be a preferable candidate for allocating this source type, using the HDDV vehicle miles of travel category as the main surrogate.

While use of the HPMS would be a definite improvement, there still could be some refinements such as by using employment in certain food services ( cold warehouses, slaughterhouses, grocery distribution centers, import export clearing houses, etc.); this would probably require a hybrid method of some kind.

EPA response: EPA will take this under consideration for the development of the nonroad portion of the MOVES model.

3.2.3 Seasonal Allocation <IP_ADDRESS> Comment: The method to develop monthly temporal factors appears to be logical, although perhaps it would help to explain why the SEASON. DAT file for use in nationwide runs requires annual and monthly NONROAD runs for each of the 65 types of equipment, as the rationale for doing so is not obvious in the text.

EPA Response: This approach is necessary due to the different seasonal activity allocations and different geographic allocations of the various types of equipment. Any set of equipment that has the same temporal and geographic allocations can be handled together, which is why there are only 65 different types of equipment for this purpose. EPA is clarifying this in the next revision of the seasonal allocation technical report.

3.2.4 Weekday/ Weekend Day Allocation <IP_ADDRESS> Comment: This paper is well researched, and the effort to include local data from the ARB and NCTCOG and others is truly appreciated ( recreational marine, residential lawn and garden). One thing that was obvious in the California studies was the indication of a potential " Friday Effect" that could be explored in the future. In many of the travel demand models for highway transportation, Monday through Thursday is treated as an average weekday, while Friday has the highest peaks in vehicle miles of travel. Therefore, for certain types of equipment, especially recreational vehicles and vessels, Friday could conceivably be treated as a " ramp up" day that could have a higher allocation than the weekdays.

A final recommendation that could affect both seasonal and weekday documentation packages would be special events such as Memorial Day, the 4th of July, and Labor Day vacation periods, which probably have higher residential and recreational activities than the average weekday would indicate in the model. This could be an important consideration, even though it 24 would be difficult to implement, because many of the ozone and particulate matter episodes being modeled occur on these kinds of vacation periods. For example, the Dallas attainment SIP was based on the 4th of July; the Houston attainment SIP has a September episode that follows Labor Day.

EPA Response: EPA is aware of the " Friday Effect" and that holidays falling on weekdays other than Friday could be more similar to weekend days. EPA is currently considering how to handle these issues.

4.0 Review of Activity, Load Factors, and Median Life Values 4.1 Comment from Sam Wells, Consultant 4.1.1 Comment: Several years ago I expressed some displeasure with the logistic curve associated with the median useful life calculations and desired that EPA would allow users to input stated by model year distributions   if local data were available. The problem is that if one modifies the median useful life values, one would corrupt several other parts of the model, thereby causing a " ripple effect" on engine useful life and other calculations in the model.

The issue is related to load factors because load factor is a key ingredient in estimating " in use" useful life   after the value has been entered into the model. Basically, what the model assumes is that median age is a function of 100 percent load, but load factor fractions are applied to extend the " in use" useful life in a very theoretical and largely undocumented manner. As a crude example, an engine might last 100 hours at full load but because it operated at 33 percent load factor, it would last three times longer ( 300 hours). Perhaps the reader could benefit from a discussion of how the following information sources were combined to obtain average load factor ratings:

Laboratory testing using fully loaded versus " average" loads ( SwRI method)

External data based upon maximum fuel consumption and average fuel consumption ( PSR method)

Manufacturer load factors that may have to do with de rating the engines, as opposed to true load factor statistics EPA Response: Currently, for diesel engines, EPA only uses laboratory testing data to calculate load factors. PSR data is not longer used, and manufacturer supplied load factors have never been used.

4.2 Comments from Rick Baker, Eastern Research Group 4.2.1 Comment: What procedure did EPA use to determine which test cycle ( High, Low, 25 Steady State) " most closely represents" each NONROAD CI equipment category? Were any inuse data used? Was it based on engineering judgement? Were any third party experts consulted? On a related issue, it is not clear why using three highly aggregated load factor categories provides an improvement in accuracy and/ or precision over the previous data derived from PSR. Please justify.

EPA Response: First, equipment types were designated as tending more toward transient versus steady state operation. Then, those that were considered transient were further examined and assigned to one of the seven transient test cycles. These assignments were made based on engineering judgment and our understanding of the type of equipment involved. These assignments were checked by an independent non EPA peer reviewer, and minor adjustments were made. We believe using engine test results based on various operating cycles developed specifically for nonroad engines is preferable to PSR's survey based approach for estimating load factors.

4.2.2 Comment: If the Regulatory Useful Life estimates reflect the effect of in use loads, and the New NONROAD Median Life estimates represent hours at full load, then shouldn't the ratio of the two be determined by the model's diesel load factors? This does not appear to be the case. As shown in the footnote, load factors ranging from 0.21 to 0.59 would require either much lower Median Life estimates or much higher Regulatory Useful Life estimates.

EPA Response: These two measures of engine life serve different purposes and are not directly comparable. The NONROAD median life values are intended to represent an overall average expected life for the purpose of estimating engine replacement timing and thus in use populations from initial sales estimates. The regulatory useful life is the time during which the engine must meet the applicable standard, as determined in the rulemaking notice and comment process.

4.2.3 Comment: Appendix A  There are several SCCs listed with 0 hrs/ year, but non zero load factors. These include:

° Concrete pavers ( all fuel types) ° Logging equipment, Fellers/ Bunchers ( all fuel types) ° Diesel ATVs and MCs ( unused)

If these SCCs are no longer used, they should either be removed from the table or footnoted.

EPA Response: These equipment types are not presently used but have been left in the activity input file in case they are needed again in the future.

4.2.4 Comment: An extensive off road engine cycle development effort has just been completed for telescoping boom excavators, wheeled loaders, small utility engines, and single and tandem axle dump trucks for the Texas Department of Transportation. The analysis utilized in 26 use data loggers for engine speed and load estimates. I recommend reviewing this report as an independent assessment of the representativeness of the cycle categories and load factors used for CI engines. The final report will be available March 15, 2003. Contact Don Lewis, TXDOT, ( 512) 416 2085.

Similarly, for an independent source of activity data for CI engines, please refer to Eastern Research Group, " Development of a Revised Emissions Inventory for Construction Equipment in the Houston Galveston Ozone Non Attainment Area", April 20, 2000, prepared for The Houston Galveston Area Council and the TNRCC Area and Mobile Source Emissions Assessment Section. Estimates for annual hours are developed from high response rate surveys of diesel construction equipment owners/ operators in the Houston area.

EPA Response: EPA will try to obtain these reports. They may indeed be useful as a check on the load factors, activity, and cycles used in the model. EPA plans to incorporate local/ regional data into nonroad emissions modeling, most likely as part of the development of the MOVES model, EPA's next generation emissions modeling system for mobile sources.

4.3 Comments from Michael Hutcheson, URS Corp.

4.3.1 Comment: The determination of median life for diesel engines appears to be flawed. It does not appear that the EEA analysis of PSR data was used in a meaningful manner. Instead it appears that the existing PSR data was ignored in favor of a more intuitive approach which lacks hard data.

A review of the EEA analysis in Appendix B of the report clearly shows that PSR data indicates that the average life for all diesel engines ( from the 50 to 500 hp range) is around 10,000 to 11,000 hours at rated loads. The value for median life used for NONROAD is 7,000 hours for engines above 300 hp and 4,667 hours for engines from 50 to 300 hp. If the PSR data is to be ignored, a better reasoning needs to be applied than EEA's subjective assertion that the PSR average life values " appeared incorrect".

EEA also gives no real factual basis or reference for the reduction in the median life estimate for medium duty engines and this reduction has been carried through in NONROAD. This is particularly troubling since this hp range has a very large population profile.

Additionally, the EEA analysis appears to place a large value on the difference between PSR population estimates and other estimates of populations. The reviewer does not place great confidence in any estimate of population short of extensive regional polling. As a result, the reviewer believes EEA's assertion that the PSR data is potentially in serious error is presumptive and not necessarily based on fact.

EPA Response: PSR determines engine life at rated load using a proprietary formula that is undocumented. EEA attempted to validate the PSR life estimates by comparing the PSR 27 population estimates with other population estimates. We acknowledge the reviewer's concern regarding the confidence in existing population estimates, but the comparison with a variety of other sources does suggest that PSR's diesel engine life estimates generally appear to be too high, even if this cannot be quantified reliably.

Given the limitations with the PSR data, EEA developed independent estimates based on a combination of manufacturer comments and available data from on highway engines. As such, we disagree with the statement that the EEA approach lacks hard data.

With regard to the reduction in the median life estimate for medium duty engines, manufacturers generally agreed that EPA's original durability finding that medium heavy duty engines have twothirds the life of heavy heavy duty engines was a reasonable estimate. We continue to believe that the useful life estimates in NONROAD are based on the best available information.

4.3.1. a Comment: EEA asserts that the problems with the PSR data result from errors in engine life data particularly for smaller engines. It occurs to the reader that the flaw in both analyses of the PSR data results from an incorrect characterization of scrappage instead of an incorrect characterization of median life. The reviewer believes that scrappage occurs not only as a result of a given engine reaching the end of its useful life, but as a result of damage to equipment, owner movement to newer models, and outright abandonment. If median life for scrappage and median life for emissions degradation are to be equated as is done in NONROAD, then the other factors affecting scrappage must be taken into account.

Adding in the other factors affecting scrappage to useful life inputs increases scrappage rates resulting in a lower median life. This is proper when calculating growth estimates but is not proper when using median life to estimate emissions degradation. The PSR data may be a better estimate of median life as it relates to emissions than it does growth.

As a result, the reviewer believes that the median life estimates are low for diesel engines especially in the 50 to 300 hp range. Even though the regulatory useful life values represent inuse loads, these should be lower bounds for median life because regulatory useful life should guarantee that 100 % of the engines maintain the emissions standard. Therefore, median life is logically higher than regulatory useful life in emission degradation estimates.

EPA Response: We concur that part of the reason for PSR's relatively long useful life estimates for smaller engines is likely due to considering only the engine life without considering equipment scrappage due to non engine causes. Additionally, given the proprietary nature of PSR's life estimation method, there may be other issues regarding use of the PSR life estimates in the NONROAD model. Thus, as we acquire documented and reasonable alternatives to the PSR values we reconsider the values used in NONROAD. The concept of using different life estimates for purposes of scrappage versus deterioration may have some technical validity, but it is beyond the current capabilities of the model, and since it applies mainly to the smallest diesel engines, its effect on emissions inventory estimates is considered too small to justify a significant redesign of 28 the model.

4.3.2 Comment: The reviewer believes EPA has appropriately used available data for determination of load factor as well as the averaging method used. The use of load factor categories also appears to be a good use of resources, however, the application of those categories should be based on a few definitive rules. For instance, for SCC categories which include an all or general category, use of the average 7 cycle load factor should prevail. A thorough review of Table 10 of the report was made by the reviewer and the following changes to load factor assignments are suggested.

SCC Current Load Factor Proposed Load Factor Reason 2270001000 Lo lf Avg. 7 cycle All category 2270002027 Avg 7 cycle Lo lf Low power requirements similar to Arc Welder 2270002033 Avg 7 cycle Lo LF Drill rigs have substantial down time and are over powered for difficult applications 2270002054 Avg 7 cycle Hi LF High Power requirements at steady state 2270002081 Hi LF Avg. 7 cycle General category 2270004060 Avg 7 cycle Lo LF Substantial down time.

2270005035 hi lf avg. 7 cycle same as 2270005050, steady state use.

2270005055 hi LF Avg. 7 cycle General category Since the errors in Table 9 have been fixed, no other comments are included.

EPA Response: The EPA response for each of these equipment types is listed below. In considering these assignments it should be kept in mind that the load factor category assignment ( high, low, or steady state) is also used in application of the in use transient adjustment factors ( TAFs) to the g/ hp hr emission factors. 2270001000 Recreational Equipment: The only diesel equipment in this category is specialty vehicles/ carts. Thus, even though the SCC is general, the actual application of the load factor is more specific. 2270002027 Signal Boards: These tend to have much more steady state operation than welders, which have very transient load characteristics. 2270002054 Crushing/ Processing Equipment: Although these would certainly have high power requirements, it does not change the more steady state operating characteristics expected of this type of equipment. 2270002081 Other Construction Equipment: Although it is a general category, the types of equipment in this category tend to be used in transient cycle operations. And since most other 29 specific construction equipment types have a high LF assignment, it is considered appropriate to also apply the high LF to this category. 2270004060 Wood Splitters: No longer used as an individual equipment type in the model ( see Population input file). Regarding the other equipment types mentioned in the comment: 2270002033 Bore/ Drill Rigs: We concur with the recommendation. This could be considered similar to the transient load operating characteristics of welding equipment. 2270005035 Sprayers: We concur with the recommendation. 2270005055 Other Agricultural Equipment: We concur with the recommendation. EPA will address these modifications in future revisions of the technical reports dealing with load factors and diesel transient adjustment factors.

4.4 Comments from Dr. Nigel Clark, University of West Virginia 4.4.1 Comment: The emissions equation presented on p. 1 represents a well established approach. Below the equation " Average Power" should be changed to " Average Rated Power" to avoid confusion. The authors may wish to consider adding a single sentence to describe what is meant by " average rated power" to avoid confusion, because equation 1 may be mis applied if taken out of context.

EPA Response: We will make this change during the next revision of NR 005b.

4.4.2 Comment: On p. 2, the equation to determine median lifetime is reasonable provided that the equipment is used in the original intended fashion. The following comments are an observation, and should not be considered a recommended revision to this version of the modeling approach. One assumes that engines are not generally oversized for the equipment that they power, and that load factors will therefore be a reasonably large fraction. Difficulties arise when an engine is used at far below its rated capacity, because the load factor may be very low, and the median lifetime implied by the p. 2 equation may therefore be high. As an example, a generator set that sees high occasional peak loads ( and is therefore specified to be large in power rating) but usually sees very light loads, would have a very low load factor. However, the engine would suffer wear that is disproportionately high in terms of the load based median life equation, because wear still occurs at light load. I recognize that the median life equation is not applied to individual pieces of equipment, but it can be argued that some types of equipment that run for long hours at light loads may have a shorter actual life than the median lifetime equation implies. This is because wear is partly a product of " cumulative revolutions turned" ( which may be represented reasonably by cumulative hours of operation) and partly a product of " cumulative energy produced" ( which is the concept behind the p. 2 equation). The EEA report ( attached to my copy of NR005b) agrees with this concept when it reports "[ The manufacturers] agreed that it ( the load factor) would overestimate life at very light load factors and underestimate life as the load factor approached one." I have discussed possible future remedies for this issue towards the end of this report. 30 EPA Response: EPA will take this into consideration for the development of the nonroad portion of the MOVES model.

4.4.3 Comment: On p. 2, the report states that " larger engines last longer than smaller ones." The reader will not know what " larger" means in this case. The text should be more specific and use a tangible term, such as " rated power," " displacement," or " displacement per cylinder." Perhaps the strongest indicator of engine design longevity is the rated speed ( which is closely related to displacement per cylinder.) I do believe that engines of larger displacement per cylinder last longer, but this is not synonymous with the statement in the report. On a " grand scale" displacement per cylinder rises in sympathy with engine power rating, but these two measures are not uniquely related.

° Consider that a 6 cylinder DDC 6V92 may be rated at around 300hp, and a 12 cylinder 12V92 may be rated at twice that value. There is no reason to believe that their lifetimes would differ, although the statement would imply that the 12V92 might last longer.

° In the same vein, a " highly boosted" 1800 rpm two liter per cylinder engine may be rated the same as a conservatively turbocharged 1200 rpm four liter per cylinder engine. I believe the larger displacement engine would prevail in lifetime. There may even be some concern that a high power version of an engine with the same block and hardware may have a slightly shorter life ( an issue discussed in the EEA attachment to NR006b.)

° To some extent the concept that more powerful engines last longer flies in the face of the median lifetime equation. Consider two engines with identical hardware ( including pistons, liners, valves and bearings), but different fueling calibrations, so that one engine is rated at 300hp and the other is rated at 400hp: this is not uncommon. If they are both used in a 100 hp ( average load) application, the load factor for one will be 0.33 and for the other will be 0.25. The median life equation would suggest that the 400hp version would last longer, which clearly it would not.

There is no immediate modeling remedy for this dilemma, but in the report the " Engine life varies ." sentence ( p. 2) might be altered or softened. Perhaps the authors could add that it is recognized that " within a power class there can still be variation in lifetime, although this level of detail cannot be captured without substantial additional data." It is evident that the PSR lifetimepower estimates ( as reported by EEA) were philosophically directly opposed to the lifetimepower relationship expressed in NR 005b, and the confusing nature of the issues that I raise above simply highlight the disagreement. Of course, of the two, the NR005b approach ( that lifetime increases with power rating) is far more correct, although it is approximate.

EPA Response: EPA appreciates the peer reviewer raising this issue and will clarify the discussion of engine life in the technical report.

4.4.4 Comment: On p. 2, it seems reasonable to adjust the ARB horsepower ranges slightly to 31 suit NONROAD. Since the cutpoints in this case are being used for inventory rather than enforcement, no entity is immediately injured by this adjustment. Moreover, two of these changes for diesel produce no change in median lifetime, they are a moot point for two stroke, and they all cause no change at all for four stroke and CNG/ LPG lifetime.

EPA Response: No response is necessary.

4.4.5 Comment: An on highway tractor trailer engine might have a life of 750,000 miles before a rebuild, and could accumulate these miles at 60mph cruise, which would represent about 50% of rated power ( e. g. 200hp. at 60mph on flat land out of 400hp. rated.) This would be 12,500 hours at 50% load, implying 6,250 hours of life at full load. This quick estimation supports the 7,000 hour full load lifetime adopted in this EPA report.

EPA Response: No response is necessary.

4.4.6 Comment: On p. 4, it is mentioned that off highway engines are generally derated from their on road counterparts. They may be derated with respect to power for the same displacement in comparison to an on road engine. However, in many cases on road engines " lead" in the horsepower race due to the need for light engines at high power ratings in truck applications. The off highway version may have older technology and be reflective of the on road engines of a few model years earlier. In this way some engines may not actually be derated, but have less sophisticated hardware components and hence a lower rating. This observation does not warrant any change to the report.

An exception to the " derating" of off highway engines is in marine applications, where power ratings are very high. Assured cooling allows these ratings.

EPA Response: No response is necessary.

4.4.7 Comment: On p. 7 it should be made clear how engine idle is included in the load factor ( and in the activity). Since the p. 2 median life equation is based solely on engine energy, it does not really matter if ( a) activity means " key on", and load factors are low due to long idling times, or ( b) whether activity does not include long idling periods and load factors are higher. However, there is real concern that if a load factor and an activity number ( hours/ year) are acquired from two different sources, and are inconsistent in definition with regard to idle, that the results will be in error. This is more a concern for a user choosing to use other data than for the default values in NR005b, because both values in most cases came from the same source.

EPA Response: This is correct. The load factors are intended to take into account the virtually zero load operation when an engine is idling. And " activity" refers to all times that the engine is running at any load, as it would show up on an engine hour meter. Thus it would be important for any user substituted model inputs to be consistent with this approach. 32 4.4.8 Comment: On pp. 7 and 8 the use of fuel consumption data to measure load factors is mentioned. No change to the report is recommended, although the following observations are made. Although no detail is given in the report, the use of fuel consumption to determine the load factor may prove approximate, particularly for throttled engines. If an engine is rated at, say, 2,400rpm, but is used at full torque at 1,600 rpm, it will use less fuel than an engine at 2,400rpm at part load. The Pechan report ( ref .8) cites Duleep in observing that the ratio of fuel consumed may overestimate the load factor. This is true. There are other confounding factors, such as full load enrichment and enrichment during transients for gasoline engines, and the considerable disparity in fuel consumption at part load between gasoline and diesel engines. However, it may be more accurate to estimate engine life based on the total fuel consumed by an engine over its life, so that this concern may prove to be a blessing!

EPA Response: Agreed. No response is necessary.

4.4.9 Comment: The CI equipment load factors represent the most mature and reliable data in the report. Clearly there is still a concern that only seven representative cycles ( for seven equipment types) exist, and that other equipment must be assigned to a " similar" cycle, but this is still an advance.

EPA Response: Agreed. No response is necessary.

4.4.10 Comment: It is important that each equipment type assigned to " low" or " high" falls into the same " bin" that it would for NONROAD transient adjustment factors for purposes of internal consistency, and I assume that Table 10 has the same assignments for load factors as for transient adjustment factors. The present associated match of equipment to cycle for transient adjustment factors does show very reasonable assignments. Table 10 is a showpiece for demonstrating the need for more usage data for off road equipment: the division of CI equipment into three load factor assignments is far too approximate, but this cannot be improved without greater knowledge. It would have been possible to use alternative approaches, namely either ° to assign at least the seven frequently encountered pieces of equipment for which there were original SwRI transient cycles the exact load factor implied in the associated cycle, or ° to assign each piece of equipment the same load factor as for the cycle chosen to represent its transient behavior best.

However, Occam's razor (" one should not increase, beyond what is necessary, the number of entities required to explain anything") is probably the greatest friend of this 005b report and suggests that the meager information available should not be " over editorialized." The three bins should stand for now, but should be reconsidered as more data become available.

It interesting to note inconsistencies that arise due to changing methodologies. CI leafblowers ( infrequently encountered!) have a load factor of 0.43 ( from Table 10) whereas the SI version 33 would have a load factor of 0.94 ( Table 6). This is true for several classes in Table 10, but it cannot be helped until a more consistent field data set is available. Of course, some of these classes in Table 10 do not exist in practice anyway.

EPA Response: Agreed. No response is necessary.

4.4.11 Comment: There is one entry in Table 10 that may raise concern, but this paragraph presents an observation for future consideration rather than a recommended correction. Sailboat auxiliary CI engines ( a common class) have been assigned a load factor from the 7 cycle composite because there is no other provision in the methodology that is reasonable. However, the marine engine assignments for SI were 0.21. This inconsistency could be remedied only by relaxing the CI methodology. In reality, sailboat inboard engines are often modest in output and are probably run at fairly high load factors, but the same issue also arises for recreational pleasure craft. Larger recreational vessels may be diesel powered and are not likely to deviate much in load factor from SI counterparts.

EPA Response: Agreed. EPA will keep this in mind for the development of the nonroad portion of the MOVES model.

4.4.12 Comment: The load factors in Table 10 will be used to determine engine life. In this case they are not fuel consumption based, but are assignments to load factors derived from real equipment operation ( even if coarsely binned.) The concern that engine life is a product of both operating hours and cumulative energy output arises again. A detailed model of engine life would be a substantial undertaking, but some simple remedies are possible. One possibility is to propose a " life factor" for purposes of calculating engine life. This factor could, for example, have the simple linear form:

Life factor = a*( load factor) + b Where an assignment of about 0.1 for " b" and 0.8 for " a" could be used. In this way engines at full load would live a little longer than is now projected, and engines at very light loads would live a little shorter than now projected. In fact, " a" and " b" could be selected, with justification, such that over the population of engines the grand average of engine age remained the same. This approach is justifiable in that an idling engine ( or worse, a high speed idling engine, as in a generator application) is performing indicated work at the pistons and is suffering wear from inertial forces. Indicated power at idle is typically five to ten percent of indicated rated power, which leads to a suggestion that " b" might be in the region of 0.1.

When the load factor is calculated from fuel consumption, it is probably philosophically quite close to the " life factor" suggested above.

EPA Response: EPA will consider this approach for the development of the nonroad portion of the MOVES model. 34 4.4.13 Comment: In the future, it will be important to watch technology whereby generator sets need not be constant speed devices, but may be operated at variable speed and high torque, with the electrical output waveform crafted by high current semiconductor devices. Generators of this kind will differ in efficiency and operation from the generators in use today.

EPA Response: EPA appreciates the reviewer identifying this as an issue to monitor in the future.

5.0 Equipment Population 5.1 Comment from Sam Wells, Consultant 5.1.1 Comment: It is not clear why the 1998 PSR data was not used; instead, the 1996 data was used in conjunction with the scrappage/ median life function " in order to maintain consistency." This would indicate that the 1996 and 1998 PSR estimates for CI engines were not consistent, and that the EPA was attempting to smooth data that did not fit their mathematical curves ( e. g., the " dog leg" in MOBILE5 that did not fit a linear or nonlinear function). Conversely, one could also surmise that the 1998 PSR data was somehow faulty. In my opinion, new data should be used if the survey design is valid, since as a contractor I have had to revise many of the CI population estimates, in some cases being twice or half what we found ( that is, in a given urban area, not on a statewide basis). Secondly, the on road MOBILE models are usually revised every three years or so, resulting in emission rate changes of up to 50 percent, so I do not understand the " consistency" issue.

EPA Response: EPA has updated the population input data for CI equipment in NONROAD2004 using PSR's data for 2000, the most recent available.

5.2 Comment from Rick Baker, Eastern Research Group 5.2.1 Comment: The USDA Agricultural Census may be a better source of data for certain agricultural equipment, including agricultural tractors and combines. The data is available for 1997 at the county level, and is based on comprehensive bottom up survey methods. While not fully disaggregated, information is available on age and hp bins. At the least, this information should be used as a validation of the current PSR data. ( Note that a preliminary comparison of national totals from Appendix A and the USDA Census indicate large discrepancies ( e. g., NONROAD combines at 206,517 versus USDA census at 460,606   although admittedly the base year is off by one year.) If this information was intentionally not used, please indicate why.

EPA Response: In 1999, as part of an effort to reconcile NONROAD's diesel fuel consumption estimates to those provided by the Department of Energy's ( DOE) Energy Information Administration ( EIA), EPA compared PSR's 1997 agricultural equipment populations to those found in the 1997 USDA Census of Agriculture.

As the reviewer mentions, the USDA National Agricultural Statistics Service ( NASS) conducts 35 periodic surveys of farm equipment and agricultural fuel consumption. The Census of Agriculture is conducted every five years. Results from the 1997 Census are available, and the 2002 Census is underway. The Census requests selected respondents to report information on " Equipment on Place" and expenditures for diesel fuel " purchased for the farm business." The equipment population data is of good quality, but limited in that results do not distinguish between gasoline and diesel equipment. Equipment is distinguished by size, but the size classes are very coarse relative to those used by EPA to define diesel emission standards. Equipment age is only addressed by dividing it into two ranges: 0 4 years and greater than 4 years old. These three limitations preclude the construction of age distributions and apportionment of diesel equipment by regulatory tier, which is necessary in the estimation and projection of equipment inventories at the level of detail required in the rulemaking analyses.

Differences between equipment populations as reported in the Census of Agriculture and as estimated using data provided by PSR are difficult to resolve directly, and are further complicated by questions of scope. The Census covers farm equipment used in within the agricultural sector only; it does not cover " farm" equipment ( such as tractors), purchased by organizations in other sectors, nor does it cover equipment of other " non farm" types, purchased and used in agriculture. In contrast, the populations used in NONROAD represent " entire" populations, across all economic sectors.

Thus, the fact that Census populations exceed NONROAD's is difficult to explain. One possibility is that high proportions of equipment in the populations reported by the Census represent equipment that has very low usage rates, whereas the activity rates used in NONROAD might be more representative of more " active" proportions of equipment populations. In any case, the tenor of most comments concerning NONROAD's fuel consumption and emissions estimates is that they appear to be overestimates relative to corresponding estimates derived from data published by the EIA for all sectors, including agriculture. In addition, an estimate of diesel fuel consumption in agriculture derivable from NASS's estimates of fuel expenditures and prices is in agreement with a corresponding estimate derived from DOE sources. Thus, resolving the differences between the Census of Agriculture and NONROAD appears to require improved understanding of the appropriate relationships between equipment populations and corresponding usage rates in the agricultural sector. At the present time however, combining equipment populations from the Census of Agriculture with usage rates estimated by PSR ( NASS does not report usage rates), would increase the difference between NONROAD's fuel consumption and emissions estimates and the information from the EIA that we used as a benchmark in our analysis.

5.3 Comments from Dr. Nigel Clark, University of West Virginia 5.3.1 Comment: The palette mounted engine is discussed as an example in the " stationary vs. mobile" debate. Conversely it would also be wise to comment illustratively on a piece of equipment, such as a front end loader or forklift, which moves, but is confined for many years to a very small radius of operation in a yard or building: it is still mobile. 36 The stationary/ mobile split is not critical, so long as the stationary and mobile inventories do not double count or miss equipment. Is there any additional guarantee that this will not happen?

EPA Response: EPA is aware of the potential for double counting mobile source emissions. A risk does exist that nonroad equipment may be double counted if a stationary source/ facility submits an emissions inventory to their state that includes nonroad equipment used there. However, EPA assigns all emission sources a unique source category code ( SCC). Given that a stationary source/ facility inventory would detail all of the SCCs included in its total inventory, a state or other downstream user of the data would most likely be able to detect and remove any nonroad equipment emissions that were included in a stationary source total if the potential to double count existed. There may also be purposes, such as for an environmental impact statement, where it might be necessary to include emissions for nonroad equipment ( as well as for on highway vehicles) used on the sight with the stationary source's emissions.

5.3.2 Comment: On p. 8 the forklift population is discussed, and this raises a specific issue. Equipment such as forklifts and backhoes is assigned, elsewhere in the model ( see NR005b), an annual activity ( as hours/ year of operation), and this activity does not vary with equipment age. Scrappage models usually employ a retirement curve that suggests that most equipment is retired close to its average engine life ( based on total life hours and load factor), and that all of the equipment is scrapped by the time twice the average life is reached. In reality, many pieces of equipment will linger on in a reduced activity mode as they become older, so that they last many more years ( but not more total life hours of operation) than a scrappage model might suggest [ Ref. 2 of NR006b , the Pechan 1998 report, supports this argument]. Even though the scrappage model, in using a fixed number of hours for annual activity, will deny the existence of very old equipment, that equipment is usually not very active in reality, and the ultimate emissions inventory may not be in great error. Alternatively, it would be reasonable to consider an actual audited population of ( say) forklifts, but then older forklifts in this population would need to be associated with reduced activity in computing emissions production. A danger arises if the actual population is used, and is then combined with annual activity ( hours per year) that is not indicative of the operation of older equipment. In this way, the inventory could be overestimated. Nonroad models would be most effective if the scrappage model considered declining annual activity. In this way the scrappage model would more closely reflect the true population curve ( with a " long tail" to the curve for old age equipment) which could then be combined with activity that reduces with age in reaching the inventory. The purpose of this discussion is twofold. First, I suggest that in future the use of activity that declines with age ( to feed scrappage models and compute the inventory) might be employed. Second, I observe that one needs to be cautious about taking the product of activity and population if the two factors are found in different ways. It would be valuable, in this NR 006b report, to illustrate to the reader how much the population of forklifts would change if a scrappage model were used. The difference between populations determined using different ( but credible) methods illustrates to some extent the uncertainty in the model.

EPA Response: EPA would like to incorporate the relationship between equipment age and activity. EPA has looked for data reflecting this relationship. Anecdotal information suggests 37 that activity does decline as equipment ages. However, EPA has only been able to find data on a handful of equipment types, which may not extrapolate well to other types of equipment ( e. g. construction versus recreational equipment). Although this comment was brought up in reference to the population technical report, EPA will consider incorporating a discussion of the effect of equipment age on activity in the NR 005b ( activity, median life, and load factor) technical report, where it is more appropriate.

5.3.3 Comment: On p. 8, there are 491,321 forklifts. The report explains that approximately 80% of these are propane, so that approximately 10% are diesel and approximately 10% are gasoline. So the LNG percentage of SI engines should reflect the fraction 80/( 80 + 10), which is 89%. Table 4 shows 95%. I assume that this disagreement is not due to any CNG forklift assignment.

EPA Response: Actually, as shown in the population input files, some of the SI forklifts are assigned to CNG. According to the PSR estimates, roughly ten percent of all SI forklifts were CNG fueled, and they were all in the 40 50 hp range. EPA is clarifying this in the next revision of the equipment population technical report.

5.3.4 Comment: I follow the logic of preparing Table 4, but it is clear that more field or sales data are really needed to be equitable. One example is the assignment of 100% LPG/ CNG to generators but only 50% to compressors, welders and crushing/ processing equipment. ( If generators are fueled by pipeline natural gas, then they must surely be stationary, so I assume that most of these LPG/ CNG generators are intended to be LPG.) One assumes that there are some SI gasoline portable generators in use, so that the 100% assignment to LPG/ CNG is probably high. Also, there is some similarity between generators and compressors ( as power sources) in site use, so that I am concerned about the strong contrast between the 100% and 50% LPG/ CNG assignments for those two equipment types. However, this cannot be argued further without new data in the future, and the 006b report should not be altered at present.

EPA Response: We agree with these observations and the desirability of more data. Although it was not explicitly stated in the population technical report or in the rulemaking document from which this information was taken ( Final Regulatory Support Document for the Final Rule published November 8, 2002, EPA420 R 02 022), the use of 100% LPG/ CNG for large SI generators was based on recent sales and other market data. Due to the lack of such data for compressors and other large SI equipment, the 50% default value was used for these equipment types.

5.3.5 Comment: On p. 11 the language suggests that there is population dating back to 1948. Surely the scrappage model would exclude such equipment, unless the load factors or annual activity was extremely low. If there is no population that is 55 years old using this scrappage method, then the report should give an indication that these older years have no population. I realize that quirks of marine engine use did cause old population overestimation in the marine case, but I do not believe that this was the case in construction. 38 EPA Response: As suggested in the comment, there are certain equipment types for which the combination of load factor and annual activity are very low, which results in the calculation of a small number of them still in use up to 50 years, which is the maximum age that the model can handle. The report language will be updated to reflect the use of year 2000 instead of 1998 as the point from which 50 years are subtracted.

5.3.6 Comment: Assignments for sailboat engines are reasonable. I would reword the sentence on p. 12 to emphasize that the sailboat engines resemble outboard engines only in horsepower ratings: the present wording could be misinterpreted.

EPA Response: EPA will clarify this.

5.3.7 Comment: This report does not consider the rebuilding of engines as a mechanism for preserving equipment life. In real use, some engines ( e. g., lawnmower engines) are abandoned when the equipment is scrapped, while some engines enjoy a longevity similar to the longevity of the technology that they power, and some are outlasted by the equipment. In on road use the truck population is swelled by vehicles with rebuilt engines, and in off road use the expense of replacing large pieces of equipment will cause engines to be rebuilt. Therefore, in the higher horsepower categories, population estimation from scrappage might consider rebuilds in the future. This could be achieved simply by estimating a rebuild fraction, and assigning a second median life ( somewhat less than for a new engine) to the rebuilds. I do realize that little information is available to account for rebuilds and that the term " rebuild" is poorly defined. Engines may be partly rebuilt ( just bearings, or just heads) to extend their lives and the terms " rebuild", " repair" and " maintenance" may merge seamlessly. Ultimately, the rebuild and repair activity may just serve to cause an extended effective average engine life, applicable mainly to large bore, expensive engines. No improvement to the NR 006b approach is possible without acquiring more real world data.

EPA Response: This is a good suggestion. EPA will address this during the development of the nonroad portion of the next generation MOVES model.

5.4 Comments from Michael Hutcheson, URS Corp.

5.4.1 Comment: It is not clear why NONROAD needs to break down the power levels above 750 hp into 5 categories for populations. Emission factors and median life estimates only require one category above 750 and the need for extra categories for populations should be explained.

EPA Response: EPA included five power level categories above 750 horsepower for flexibility in case more detailed data became available in the future or future regulatory efforts needed that level of detail.

5.4.2 Comment: The reviewer agrees with the changes in SCC codes to accommodate fuel types and new equipment classes and power ranges. As in comments on other NONROAD 39 reports, the reviewer would like to see better descriptions of the SCC classifications for nonroad equipment.

EPA Response: A complete description of the equipment and corresponding source category codes ( SCCs) modeled by NONROAD2002 is included in Appendix B of the user's guide. EPA will include a reference to this in the next revision of the Nonroad Engine Population Estimates report, NR 006b.

5.4.3 Comment: The reviewer agrees in principle with the use of the B. A. H. data on mobile vs. stationary engine breakdowns for appropriate source categories. The report should clarify the exact SCC categories to which these data apply and how the data is applied to the population estimates. For instance, if the percent mobile equipment fractions are applied to populations internally in NONROAD then the user should have this information. If the fractions are applied prior to inputting the estimates into NONROAD, then users who make their own estimates need to know to apply mobile equipment percentages to their data if appropriate.

EPA Response: Table 2 in the Nonroad Engine Population Estimates report, NR 006b, provides the source category codes ( SCCs) of the equipment types that include both mobile and stationary applications in the heading. EPA is clarifying how the mobile/ stationary fractions are applied to the engine populations in the next revision of this report ( NR 006c).

5.4.4 Comment: The reviewer disagrees with the use of the median life values developed for NONROAD for use in population estimates. As detailed in comments submitted previously on NR 005b, median life values and scrappage estimates do not take into account all factors affecting scrappage and therefore are inherently inaccurate when estimating growth.

EPA Response: Please see the EPA responses to comments 4.3.1 and 4.3.1a.