All criteria emission rates are dependent on vehicle age, and thus are stored in the emissionRateByAge table and, and total energy consumption is age independent, and therefore stored in the "emissionRate" table. Some of the published studies did not report total energy consumption directly, so it was necessary to compute energy from a stoichiometric equation based on the carbon content in the emitted pollutants or from reported values of miles per gallon equivalent of diesel fuel. In the former case, we used 0.8037 as the carbon fraction coefficient for nonmethane hydrocarbons (NMHC) when the bus was equipped with an oxidation catalyst and 0.835 without due to high ethene levels, using speciation profiles from Ayala et al. (2003) discussed later in this section. All other conversion factors to energy were taken from Melendez et al. (2005).70
On a similar note, MOVES does not report particulate matter (PM) as a single rate; it reports one rate for PM from elemental carbon (EC) of 2.5 microns or less, and another rate for non-elemental carbon of 2.5 microns or less. These separate rates for PM (EC) and PM (NonEC) from the emissionRateByAge table are added together for a total PM rate used for comparison to the measurements.
The number of unique measurements is approximately equal to the number of vehicles tested, although not exactly. Some vehicles were tested more than once because a variable in the experiment changed, such as the after-treatment technology.  All of the vehicles were in service with a transit agency at the time of testing. In addition, these measurements were typically reported as cycle averages based on multiple runs with the same vehicle and configuration over a specific driving cycle. Many of the testing programs also included diesel transit buses, but these vehicles were excluded from this analysis. Table 4-1 shows a summary of the number of unique CNG bus measurements by driving cycle for each study. Navistar published a similar study of CNG and diesel buses in 2008, and this analysis shares many of the same sources.
Table 4-1. Summary of external emissions testing programs by driving cycle and number of unique measurements
                                 Paper/Article
                              Lead Research Unit
                               Driving Cycle(s)
                         Number of Unique Measurements
Melendez 200570
National Renewable Energy Laboratory (NREL)
                                     WMATA
                                       7
Ayala 2002
California Air Resources Board (CARB)
                                      CBD
                                       2
Ayala 200371
CARB

                                      CBD
                                       6
Lanni 2003
New York Department of Environmental Conservation
                                      CBD
                                       3
McCormick 1999
Colorado School of Mines

                                      CBD
                                       3
LaTavec 2002
ARCO (a BP Company)

                                      CBD
                                       2
McKain 2000
WVU

                                      CBD
                                       3
Clark 1997
WVU

                                      CBD
                                      10
TOTAL

                                      35

As seen above, the CBD driving cycle was applied in each study except for one. Since it (a) had the largest sample size and (b) appeared to be representative of the data from other cycles, we focused our analysis on the CBD cycle results.
We approximated the vehicle's age by subtracting the year the study was conducted from the model year of the vehicle. Most vehicles tested were less than three years old (ageGroupID "3"), whereas 9 vehicles fell into the four to five year-old age group (ageGroupID "405"). In the CBD cycle, 5 out of 28 vehicles were in ageGroupID "405", and their performance was generally similar to the 0-3 age vehicle results.  Consequently, we combined the vehicles from age group 405 with the vehicles from group 3. Vehicle model years ranged from MY 2001 to MY 2004 for the WMATA cycle and from MY 1994 to MY 2001 for the CBD cycle.
Plots of Simulated Aggregates and Published Measurements
Below are graphs of the CBD measurements by model year for each pollutant compared to simulated MOVES2010b CNG (MHD gasoline) rates.

Figure 4-6. NOx emission comparisons of CNG transit bus dynamometer measurements and MOVES2010b simulated aggregates on the CBD cycle.

Figure 4-7. CO emission comparisons of CNG transit bus dynamometer measurements and MOVES2010b simulated aggregates on the CBD cycle.

Figure 4-8. PM emission comparisons of CNG transit bus dynamometer measurements and MOVES2010b simulated aggregates on the CBD cycle.

Figure 4-9. THC emission comparisons of CNG transit bus dynamometer measurements and MOVES2010b simulated aggregates on the CBD cycle.

Figure 4-10. CH4 emission comparisons of CNG transit bus dynamometer measurements and MOVES2010b simulated aggregates on the CBD cycle.
Figure 4-11. Total energy consumption comparisons of CNG transit bus dynamometer measurements and MOVES2010b simulated aggregates on the CBD cycle.

In Figure 4-6, the MOVES2010b CNG rates slightly under-predict the bus NOx measurements. As shown in Figure 4-7, MOVES2010b predictions for CO emissions are similar to the CNG measurements, particularly after 1999. Figure 4-8 shows that the MOVES2010b CNG predictions are lower for PM. As seen in Figure 4-9, MOVES2010b CNG predictions for THC emissions are lower than the measurements by an order of magnitude.  As seen in Figure 4-10, this underestimate of THC is largely attributable to a significant underestimate of CNG related CH4 in MOVES2010b. These relatively high CH4 emissions from CNG buses compared to gasoline or diesel buses are likely from the exhaust of uncombusted natural gas, but further study is warranted. Figure 4-11 shows that MOVES2010b under-predicts the total energy consumption seen in the literature. We address these discrepancies by using the published measurements to develop new CNG exhaust emission rates MOVES2014 rates across all the pollutants and total energy discussed above.
These comparisons show that updating the MOVES2010b with these additional data from CNG buses have significant impact on the emissions inventories from CNG buses. As shown in this analysis, the existing CNG rates based on MHD gasoline trucks are not adequate surrogates.  As discussed in the next section, we developed new rates based on cycle averages from the dynamometer measurements. The remainder of this report is devoted to a discussion on the development of new time-based emission rates (g/hr) for CNG buses.
Development of New Running Exhaust Emission Rates
Determining Model Year Groups
Ideally, new MOVES emission rates are developed through analysis of second by second data of vehicles of the appropriate regulatory class, model year, and age.  Unfortunately, such modal data is not readily available in this case. However, we substantially improved the CNG bus emission rates in MOVES2014 relative to MOVES2010b by raising or lowering the MY emission rates wholesale (as opposed to individual adjustments by operating mode).
Fundamentally, the first necessary step is assigning a set of appropriate model year groups for the data.  Using too few model year groups may miss important differences in the emission rates, while using too many model year groups can give false prominence to outliers and introduce artificial "jumps" in the emission rates. We chose to group the model year groups according to similar emission rates in the criteria pollutants (THC, CO, NOx and PM).
We separated CNG buses - equipped with oxidation catalysts and those not equipped, to determine if this was a reasonable distinction, and to see if these vehicles' criteria emission rates varied by model year and by age.  For some model years, there are both after-treatment equipped and non-equipped vehicles.  In these years, there was not a visible difference in the criteria emission levels between the vehicles in the testing programs with after-treatment (AT) equipment and those with no after-treatment (see the figures in Section Heavy-duty CNG Bus Emissions Supplemental Figures in the appendix).  Given the small sample size, the lack of clear trends among the individual vehicle models, and our lack of data on the relative distribution of after-treatment equipped vehicles versus unequipped vehicles in each of these model years, we chose to group all the CBD measurements from the literature into one model year group, spanning from MY 1994 to MY 2001. No data on CNG buses equipped with three-way catalysts (TWC) was readily available at the time of this analysis; we will look to incorporate data from buses that have TWCs and spark ignited, stoichiometric-burn engine technology as it becomes available.
Of the surveyed data, only one study had any vehicles newer than MY 2001. This paper, a joint study between NREL and WMATA, had a small sample of vehicles from MY 2004.  These vehicles have a visibly different emissions profile than the other vehicles.70 While these buses were only tested on the WMATA driving cycle, they were all equipped with oxidation catalysts and had substantially lower emissions from the 1994-2001 buses, particularly for PM emissions. As a result, a second model year group runs from MY 2002 to MY 2006 based on a group of MY 2004 WMATA buses.  This MY group ends before MY 2007 when a new series of stringent emission standards went into effect. We did, however, apply a sales-weighted adjustment factor to the unusually low CO rate for these John Deere CNG buses according to certification data, as described below.
Scaling Model Years After 2007
Without published data on in-use vehicles past MY 2004, we use emission certification levels as a proxies to estimate emission rate changes since then. Certification levels are reported in grams per brake horsepower-hour and are not directly used in formulating MOVES emission rates because they do not include real-world effects such as vehicle mileage or deterioration.[,]   These effects were present in the testing programs, so we created scaling factors that we could apply to the measured data from the testing programs to estimate rates after MY 2004.
Natural gas transit bus emission certification data by model year is publicly available on the EPA's Office of Transportation and Air Quality website. Analysis of these data showed that from MY 2002 to MY 2012 there have been changes in certification levels for all the pollutants considered in this report. In particular, NOx and PM levels have dropped dramatically over the past decade.  This effect is largely attributable to increasingly strict emission standards, which have affected both diesel and CNG buses. Table 4-2 below indicates the number of CNG transit bus models certified for each model year.
Table 4-2. A summary of the number of certified CNG transit buses by model year (USEPA OTAQ).

                                  Model Year
                           Number of Vehicle Models
                                     2002
                                       4
                                     2003
                                       4
                                     2004
                                       4
                                     2005
                                       6
                                     2006
                                       4
                                     2007
                                       3
                                     2008
                                       1
                                     2009
                                       1
                                     2010
                                       2
                                     2011
                                       2
                                     2012
                                       2
                                     TOTAL
                                      33

To improve the accuracy of the scaling factor we weighted the emission levels with projected US sales figures for the certified CNG buses. These figures are confidential business information and cannot be shared publicly but have been incorporated as ratios to calculate the MY group 2007-2012 emission rates. The aggregated certification levels with these annual sales weighted averages for MY group 2002-2006 and MY group 2007-2012 are shown in Table 4-3 below.
Table 4-3.  Model year group 2002-2006 and 2007-2012 certification levels for CNG buses used for scaling of measured emission rate data

                               Model Year Group
                                     NOx
                                      CO
                                      PM
                                    NMHC[1]
Certification
(g/bhp-hr)
                                   2002-2006
                                     1.208
                                     1.355
                                    0.0078
                                     0.147
Certification
(g/bhp-hr)
                                   2007-2012
                                    0.2902
                                     3.032
                                    0.0033
                                     0.057

      1. Certification data has measurements of organic material non-methane hydrocarbon equivalent (OMNMHCE). For this analysis they were treated as NMHC values.
Methane levels are not reported in the certification data, so we estimate CH4 rates for MY group 2007-2012 through an analysis of the CH4 to THC ratio by model year from the dynamometer measurements from the WMATA study.  The CH4/THC ratio for every model year fell within one standard deviation of the average ratio across all model years. The CH4/THC ratios are calculated from averaged CH4 and THC measurements on the respective CBD and WMATA cycles, as displayed in Table 4-4. We left the CH4/THC ratio constant from MY group 2002-2006 to MY group 2007-2012 and estimated the new CH4 rate (given in Table 4-5) off that previous ratio.
               Table 4-4 Summary of CH4/THC ratios for MOVES2014
Model Year
Age Group
CH4/THC Ratio
                                   1994-2001
                                      0-3
                                     0.917
                                   2002-2006
                                      0-3
                                     0.950
                                   2007-2012
                                      0-3
                                     0.950
We scaled the newer model year rates rp based off the measurements in the MY group 2002-2006 in proportion to the ratio of certification levels CLp from MY group 2007-2012 to MY group 2002-2006. In this case,

rp,MY2007-2012=rp,MY2002-2006∙CLp,MY2007-2012CLp,MY2002-2006 .			Equation 4-2
As mentioned before, the measured CO rate for MY group 2002-2006 was not used. We adjusted the original rate by the ratio between the sales-weighted average for the MY 2004 certification level of all models and the certification level for that particular MY 2004 John Deere bus with the anomalous CO rate.
The estimated CO rate for MY group 2007-2012 is notably greater than the previous MY group, but this change was reflected in our certification level proxies, and has been observed in more recent testing with three-way catalyst, stoichiometric CNG buses.
Note that there was limited data on older vehicles in the literature, so the ratios that were developed using vehicles in the 0-3 age group have been applied to all other age groups. Therefore, we are assuming that CNG buses exhibit deterioration rates in control equipment proportional to medium heavy-duty gasoline trucks.
Since there is no certification data on carbon dioxide (CO2) or other greenhouse gases, until 2011, we maintained the same total energy consumption rate from MY group 2002-2006 to MY 2007-2012.
Creating CNG Running Rates for Future Model Years
Table 4-5 shows CNG transit bus emissions on each drive cycle calculated using MOVES2010b rates for each MY group.  These calculations are shown using a single model year within the group. The table also shows the emission rates estimated from our meta-analysis of the literature. The ratios between these rates were applied to the 1997, 2004 and 2009 MOVES2010b CNG bus rates in order to calculate the MOVES2014 rates.

Table 4-5 Summary of MOVES2010b distance-dependent emission rates for CNG transit buses and the ratios to be applied to the MOVES2010b STP-based rates to compute the MOVES2014 rates.

                         MOVES2010b CNG Rates (g/mile)
                                      MY
                                   Age Group
                                     Cycle
                                     NOx
                                      CO
                                   PM_NonEC
                                     PM_EC
                             TOTAL ENERGY (BTU/mi)
                                     THC
                                      CH4
                                     1997
                                      0-3
                                      CBD
                                     9.63
                                     62.4
                                    0.0024
                                    0.0002
                                     31137
                                     1.84
                                     0.049
                                 2004 and 2009
                                      0-3
                                     WMATA
                                     5.45
                                     18.9
                                    0.0035
                                    0.0003
                                     35489
                                     1.43
                                     0.032

        MOVES2014 CNG Rates (g/mile - measured/estimated from analysis)
                                      MY
                                   Age Group
                                     Cycle
                                     NOx
                                      CO
                                   PM_NonEC
                                     PM_EC
                             TOTAL ENERGY (BTU/mi)
                                     THC
                                      CH4
                                   1994-2001
                                      0-3
                                      CBD
                                     20.8
                                     9.97
                                     0.037
                                    0.0038
                                     42782
                                     13.2
                                     12.1
                                   2002-2006
                                      0-3
                                     WMATA
                                     9.08
                                    2.17[1]
                                    0.0039
                                    0.0005
                                     40900
                                     11.2
                                     10.6
                                2007 and later
                                      0-3
                                     WMATA
                                     2.18
                                    5.93[2]
                                    0.0016
                                    0.0002
                                     40900
                                     4.33
                                     4.12

                Ratios Applied to the STP-Based MOVES2014 Rates
                                      MY
                                   Age Group
                                 Cycle ratioed
                                     NOx
                                      CO
                                   PM_NonEC
                                     PM_EC
                                 TOTAL ENERGY
                                     THC
                                      CH4
                                   1994-2001
                                      all
                                      CBD
                                     2.16
                                     0.16
                                     15.5
                                     21.6
                                     1.37
                                     7.17
                                      250
                                   2002-2006
                                      all
                                     WMATA
                                     1.67
                                     0.11
                                     1.09
                                     1.87
                                     1.15
                                     7.79
                                      330
                                2007 and later
                                      all
                                     WMATA
                                     0.40
                                     0.31
                                     0.46
                                     0.78
                                     1.15
                                     3.02
                                      128
      1. This CO rate was uncharacteristically low (0.14 g/mi), determined to be an outlier, and has been adjusted using sales-weighted certification data, as described in more detail in the text above.
      2. Note that the increase in the CO rate from MY group 2002-2006 to MY 2007 and later seems to be an outcome of transitioning from lean-burn CNG engines to stoichiometric-burn engines. This upward CO trend is supported by certification data as well as testing results from some recent studies comparing these two technologies.

The model year basis for scaling was selected based on temporal similarity. For instance, our choice to use MY 1997 for MY group 1994-2001 was due to it being a median year in the group.  For MY group 2002-2006, we selected MY 2004 because that was the year all the vehicles in that group were manufactured. As for MY group 2007-2012, MY 2009 was also chosen simply for being one of the two model years near the median for the group.
For 2014 and later model year, the CNG energy consumption rates are reduced by the same reduction as diesel Urban Bus vehicles, in response to the 2014 Medium and Heavy Duty Greenhouse Gas Rule as documented in Table 2-18.
Start Exhaust Emission Rates for CNG Buses
In the absence of any measured start exhaust emissions from CNG transit buses, their start rates are copies of heavy-duty diesel start rates, for all pollutants including energy rates. We believe this is an environmentally conservative approach, rather than assuming zero CNG start emissions. MOVES still estimates that the majority of emissions from CNG buses are from running emissions, which are based on CNG test programs. We readily acknowledge that the diesel start rates may not accurately represent CNG start rates. This assumption will be revisited for future releases of MOVES as new data on CNG start rates becomes available.
Applications to Other Model Years and Age Groups
We applied the ratios in Table 4-5 to all ages of CNG bus emission running rates in MOVES2010b.  In this way, the deterioration assumptions for criteria pollutants in the MOVES2010b running rates are preserved in the MOVES2014 CNG bus rates.  For completeness, CNG buses prior to MY 1994 use the same rates as MY group 1994-2001. Rates for buses built after MY 2012 maintain the same rate as MY group 2007-2012. As new certification data arrives and more testing programs are run, these rates will be revisited in future MOVES releases.
PM and HC Speciation for CNG Buses
MOVES estimate methane and nonmethane hydrocarbons (NMHC) through the use of CH4 /THC ratios, as shown in Table 4-4. The MOVES2014 CH4/THC ratios are constant across all age groups. We set the start CH4/THC ratios equal to the running ratios for all model years and ages.
MOVES calculates emissions of total organic gases (TOG), nonmethane organic gases (NMOG) and volatile organic carbons (VOC) using information regarding the hydrocarbon speciation of emissions. Studies have shown that the speciation of hydrocarbon can be drastically different between uncontrolled CNG buses and CNG buses with oxidation catalysts. For example, formaldehyde emissions can be quite large from uncontrolled CNG buses72[,] 74[,][,], but are significantly reduced with oxidation catalysts71. Large formaldehyde emissions have a large impact on the NMOG and VOC emissions estimated from THC emissions from CNG buses because THC-FID measurements have a small response to formaldehyde concentrations41,.
We used hydrocarbon speciation measurements from the Ayala et al. (2003) using measurements from the 2000 MY Transit Bus, with a Detroit Diesel Series 50G engine with and without an oxidation catalyst collected on the CBD cycle. We used the speciated measurements made on the single transit bus to isolate the impact of the oxidation catalyst. We used the CBD test cycle to be consistent with our analysis of the criteria emission rates. The NMOG and VOC conversion factors are located in Table 4-6. The NMOG values are calculated following EPA's regulation requirements using Equation 9 from the MOVES2014 Speciation Report41.
The VOC emissions are calculated from subtracting the ethane from the NMOG values. MOVES definition of VOC emissions from mobile-sources is NMOG minus ethane and acetone . The emissions of hazardous air pollutants, including formaldehyde and acetaldehyde, are also estimated from this study as documented in the MOVES2014 Toxics Emissions Report.
Table 4-6 NMOG and VOC Conversion values for CNG transit emissions with no control and with oxidation catalyst from Ayala et al. (2003)71
Measured values (mg/mile)
                                  No Control
                              Oxidation Catalyst
THC
                                                                           8660
                                                                           6150
Methane
                                                                           7670
                                                                           5900
Ethane
                                                                            217
                                                                           72.2
Acetone
                                                                           4.67
                                                                           5.51
Formaldehyde
                                                                            860
                                                                           38.4
Acetaldehyde
                                                                           50.7
                                                                           32.6
Calculated values (mg/mile)

NMHC
                                                                            990
                                                                            250
NMOG
                                                                         1881.0
                                                                          309.0
VOC
                                                                         1664.0
                                                                          236.8
Ratios

NMOG/NMHC
                                                                           1.90
                                                                           1.24
VOC/NMHC
                                                                           1.68
                                                                           0.95

In MOVES2014, we applied the uncontrolled NMOG and VOC factors to the pre-2002 model year vehicles. This analysis demonstrated that majority of the 2001 and earlier model year CNG transit buses were not equipped with exhaust aftertreament. The study conducted on MY 2004 buses70, suggested that 2002-2006 model year vehicles are equipped with oxidation catalysts. Therefore we apply the oxidization catalyst profile to CNG emissions for the 2002 to 2006 MY group. At the time of the analysis, we did not have information on 2007 and later CNG buses, and also applied the oxidation catalyst from the lean-burn engine results to 2007 and later groups.
The composition of PM2.5 emissions are estimated from CARB's measurementson the 2000 MY Detroit Diesel Series 50G with and without the oxidation catalyst. The EC/PM2.5 fractions are reported in Table 4-7 and are used to estimate the base PM components in MOVES: elemental carbon (EC) and non-elemental carbon (nonECPM) rates. By using the single bus, we again isolate the impact of the control, without confounding differences in different engine technologies. Similar for the HC speciation, we apply the uncontrolled EC/PM fraction to the pre-2002 MY CNG buses, and the oxidation catalyst equipped EC/PM profile for the 2002 and later buses.
Table 4-7 MOVES2014 EC/PM Fraction for CNG transit bus emissions by model year groups
                                   Pre-2002
                                     2003+
                                     9.25%
                                    11.12%
The CARB measurements are also used to estimate the individual PM2.5 composition, including organic carbon, elements, and sulfate as discussed in the TOG and PM2.5 speciation report. stoichiometric-burn spark ignition CNG engines with three-way catalysts have been introduced in 2007 and later buses. Future work should be done to improve the emission rates and speciation profiles used in MOVES to represent emissions from recent technology CNG buses.
Ammonia and Nitrous Oxide emissions
No data were available on ammonia emissions rates. As such, we used the ammonia emissions for heavy-duty gasoline vehicles, which are documented in a separate report6.
We did not update the nitrous oxide emission rates for CNG in MOVES2014. They remain unchanged from MOVES2009 and later versions as documented in a separate MOVES report5

Heavy-Duty Crankcase Emissions
Crankcase emissions, also referred to as crankcase blowby, are combustion gases that pass the piston rings into the crankcase, and are subsequently vented to the atmosphere. Crankcase blowby includes oil-enriched air from the turbocharger shaft, air compressors, and valve stems that enters the crankcase. The crankcase blowby contains combustion generated pollutants, as well as oil droplets from the engine components and engine crankcase.
Background on Heavy-duty Diesel Crankcase Emissions
Federal regulations permit 2006 and earlier heavy-duty diesel-fueled engines equipped with "turbochargers, pumps, blowers, or superchargers" to vent crankcase emissions to the atmosphere. Crankcase emissions from pre-2007 diesel engines were typically vented to the atmosphere, using an open unfiltered crankcase system, referred to as a `road draft tube'.88 Researchers have found that crankcase emissions vented to the atmosphere can be the dominant source of diesel particulate matter concentrations measured within the vehicle cabin   .
Beginning with 2007 model year heavy-duty diesel vehicles, federal regulations no longer permit crankcase emissions to be vented to the atmosphere, unless they are included in the certification exhaust measurements. Most manufacturers have adopted open crankcase filtration systems.88 These systems vent the exhaust gases to the atmosphere after the gases have passed a coalescing filter which removes oil and a substantial fraction of the particles in the crankcase blowby.88 In the ACES Phase 1 program, four MY2007 diesel engines from major diesel engine manufactures (Caterpillar, Cummins, Detroit Diesel, and Volvo) all employed filtered crankcase ventilation systems.
A summary of published estimates of diesel crankcase emissions as percentages of the total emissions (exhaust + crankcase) are provided in Table 5-1. For the conventional diesel technologies, hydrocarbon and particulate matter emissions have the largest contributions from crankcase emissions. There is a substantial decrease in PM emissions beginning with the 2007 model year diesel engines. The 2007 diesel technology reduces the tailpipe emissions more than the crankcase emissions, resulting in an increase in the relative crankcase contribution for HC, CO, and PM emissions. NOx emissions for the 2007 and later are reported as a negative number. In reality, the crankcase emission contribution cannot be negative, and the negative number is attributed to sampling variability from the tests with and without the crankcase emissions.

Table 5-1 Literature review on the contribution of crankcase emissions to diesel exhaust.
Study
                                  Model Year
                                     Type
# Engines/ Vehicles
                                      HC
                                      CO
                                      NOx
                                      PM
Hare and Baines, 197797
                                   1966, 1973
                                 Conv. Diesel
                                       2
                                   0.2%-3.9%
                                   0.01-0.4%
                                  0.01%-0.1%
                                   0.9%-2.8%
Zielinska et al. 200890, Ireson et al. 201191
                                  2000, 2003
                                 Conv. Diesel
                                       2

                                 13.5% - 41.4%
Clark et al. 200696, Clark et al. 2006
                                     2006
                                 Conv. Diesel
                                       1
                                     3.6%
                                     1.3%
                                     0.1%
                                     5.9%
Khalek et al. 2009
                                     2007
                                 DPF-equipped
                                       4
                                     95.6%
                                     27.2%
                                     -0.2%
                                     38.2%
Modeling Crankcase Emissions in MOVES
MOVES2014 calculates THC, CO, NOx, and PM2.5 using a gaseous and a particulate crankcase emission calculator. Within the calculator, crankcase emissions are calculated as a fraction of tailpipe exhaust emissions, including start, running, and extended-idle. As discussed in the background, the 2007 heavy-duty diesel emission regulations impacted the technologies used to control exhaust and crankcase emissions. The regulations also expanded the types of emissions data included in certification tests, by including crankcase emissions in the regulatory standards, which previously included only tailpipe emissions. Because heavy-duty diesel engine manufacturers are using open-filtration crankcase systems, the crankcase emissions are included in the emission certification results. In MOVES2014, the base exhaust rates for 2007 and later diesel engines are based on certification levels.
In response to the changes in certification testing, we changed the data and the methodology with which crankcase emissions are modeled in MOVES. For 2007 and later diesel engines, the crankcase emissions are included in the base exhaust emission rates. A new crankcase calculator in MOVES2014 divides the base exhaust emission rates into components representing the contributions from exhaust and crankcase emissions. The exhaust emission ratio is equal to 1.0 for all pre-2007 diesel engines, and less than 1.0 for all 2007 and later diesel engines, to account for the inclusion of crankcase emissions in the base rates. Unfortunately, due to budget and time constraints, only the PM2.5 species are incorporated using the new crankcase calculator in MOVES2014. An overview of the crankcase calculator is provided in the MOVES2014 Speciation Report.41
MOVES2014 continues to use the same calculator as MOVES2010 for the gaseous crankcase pollutants, THC, CO, and NOx. The gaseous crankcase calculator chains the crankcase emission rates to the base exhaust emissions, but it does not allow the ability to reduce the exhaust emission contribution, which is desired for the 2007+ diesel technologies. The 2007+ diesel subsection discusses how MOVES2014  handles THC, CO, and NOx to avoid double-counting crankcase emissions. We anticipate that future versions of MOVES will include the updated crankcase calculator for all crankcase emission pollutants, including THC, CO, and NOx.
Conventional Heavy-Duty Diesel
Table 5-2 includes the crankcase/tail-pipe emission ratios used for conventional diesel exhaust. For HC, CO, and NOx, we selected the values reported on the MY2006 diesel engine reported by Clark et al. 2006. These values compare well with the previous HC, CO, NOx values reported much earlier by Hare and Baines (1977),  which represent much older diesel technology. The similarity of the crankcase emission ratios across several decades of diesel engines, suggests that for conventional diesel engines, crankcase emissions can be well represented as a fraction of the exhaust emissions.
For PM2.5 emissions, we selected a crankcase/tail-pipe ratio of 20%. Zielinska et al. 200890 and Ireson et al. 201191 reported crankcase contributions to total PM2.5 emissions as high as 40%. Jääskeläinen (2012)88 reported that crankcase can contribute as much as 20% of the total emissions from a review of six diesel crankcase studies. Similarly, an industry report estimated that crankcase emissions contributed 20% of total particulate emissions from 1994-2006 diesel engines.
Table 5-2 MOVES2014 Conventional Diesel Crankcase/Tail-pipe Ratios for HC, CO, NOx and PM2.5
Pollutant
Crankcase/Tailpipe ratio
Crankcase/(Crankcase + Tailpipe) ratio
HC
0.037
0.036
CO
0.013
0.013
NOx
0.001
0.001
PM2.5
0.200
0.167

As outlined in the MOVES 2014 TOG and PM Speciation Report, MOVES does not apply the crankcase/tailpipe emission ratio in Table 5-4 to the total exhaust PM2.5 emissions. MOVES applies the crankcase/tailpipe emission ratios to PM2.5 subspecies: elemental carbon PM2.5, sulfate PM2.5, aerosol water PM2.5, and the remaining PM (nonECnonSO4PM). This allows MOVES to account for important differences in the PM speciation between tailpipe and crankcase emissions.
The pre-2007 diesel ratios are derived such that the crankcase PM2.5/exhaust PM2.5 ratio is 20%, and the crankcase emissions EC/PM fraction reflects measurements from in-use crankcase emissions. Zielinska et al. 200890 reported that the EC/PM fraction of crankcase emissions from two conventional diesel buses is 1.57%. Tailpipe exhaust from conventional diesel engines is dominated by elemental carbon emissions from combustion of the diesel fuel, while crankcase emissions are dominated by organic carbon emissions largely contributed from the lubricating oil. 90[,]91. The crankcase emission factors shown in Table 5-3 are derived such that the crankcase PM2.5 emissions are 20% of the PM2.5 exhaust measurements, and have an EC/PM split of 1.57%.

The PM10 emission rates are subsequently estimated from the PM2.5 exhaust and crankcase emission rates using PM10/PM2.5 emission ratios as documented in the MOVES2014 TOG and PM Speciation Report.

Table 5-3.  MOVES2014 Exhaust and Crankcase Emission Factors for pre-2007 Diesel by Pollutant, Process, and Model Year Group for PM2.5 species.
                                   Pollutant
                                    Process
                                     Start
                                    Running
                                 Extended Idle
                                      EC
                                    Exhaust
                                       1
                                       1
                                       1
                                 nonECnonSO4PM

                                       1
                                       1
                                       1
                                      SO4

                                       1
                                       1
                                       1
                                      H2O

                                       1
                                       1
                                       1
                                      EC
                                  Crank-case
                                     0.009
                                     0.004
                                     0.012
                                 nonECnonSO4PM

                                     0.295
                                     0.954
                                     0.268
                                      SO4

                                     0.295
                                     0.954
                                     0.268
                                      H2O

                                     0.295
                                     0.954
                                     0.268
2007 + Heavy-Duty Diesel
The 2007+ heavy-duty diesel THC, CO, and NOx crankcase emissions are included in the exhaust emissions. However, with the current gaseous crankcaseemission calculator, the crankcase contribution of THC, CO, and NOx to the base exhaust emission rates cannot be properly accounted. For MOVES2014, the crankcase to tailpipe emission ratios for THC, CO, and NOx are set to 0 as shown in Table 5-4, and MOVES2014 produces no crankcase emissions for each of the pollutants. Table 5-4 also lists the crankcase to tailpipe emission ratios based on ACES Phase 1 tests. Based on the ACES Phase 1 program, the MOVES2014 estimate of no crankcase emissions is reasonable for NOx, but not for THC and CO emissions. MOVES2014 does not report crankcase emissions for THC and CO because they are included in the exhaust emission rates for 2007 and later model years from heavy-duty diesel vehiclesl. Users can use the ratios listed in Table 5-4 to post-process the exhaust emission rates if the crankcase contributions to THC and CO emissions are desired.
Table 5-4 MOVES2014 2007 and Later Diesel Crankcase/Tailpipe ratio for HC, CO, and NOx.
                                   Pollutant
                      MOVES2014 Crankcase/Tailpipe ratio
                    ACES Phase 1 Crankcase/Tail-pipe ratio
             ACES Phase 1 Crankcase/(Crankcase + Tail-pipe)l ratio
                                      HC
                                       0
                                     21.95
                                     95.6%
                                      CO
                                       0
                                     0.37
                                     27.2%
                                      NOx
                                       0
                                     0.00
                                     0.0%

For PM2.5 emissions, we used data from the ACES Phase 1 test program to inform the crankcase and exhaust ratios for the updated PM2.5 crankcase emissions calculator. The crankcase emissions measured in the ACES Phase 1 test program contributed 38% of the total PM2.5 emissions on the hot-FTP driving cycle. Emission results reported from industry, have reported that the crankcase emissions can contribute to over 50% of the particulate matter emissions from 2007 and later diesel technologies98.
For PM2.5 emissions, MOVES applies crankcase ratios to each of the intermediate PM2.5 species (EC, nonECnonSO4PM, SO4, and H2O). For 2007+ heavy-duty diesel engines, the same crankcase ratio is applied to each of the intermediate species. The MOVES PM2.5 speciation profile developed from the ACES Phase 1 study combined the crankcase and tailpipe emissions. As such, MOVES2014 uses the same speciation profile for both crankcase and tailpipe emissions. The resulting exhaust and crankcase emission ratios for 2007 and later heavy-duty diesel are provided in Table 5-5. As shown, the exhaust crankcase emission factor is less than one for 2007+ diesel vehicles, to account for the contribution of crankcase emissions in the base exhaust emission rates.
Table 5-5 MOVES2014 Exhaust and Crankcase Emission Factors for 2007 + Heavy-duty Diesel by Pollutant, Process, and Model Year Group for PM2.5 species.
                                   Pollutant
                                    Process
                                 All processes
                                      EC
                                    Exhaust
                                     0.62
                                 nonECnonSO4PM

                                     0.62
                                      SO4

                                     0.62
                                      H2O

                                     0.62
                                      EC
                                  Crank-case
                                     0.38
                                 nonECnonSO4PM

                                     0.38
                                      SO4

                                     0.38
                                      H2O

                                     0.38
Heavy-duty Gasoline and CNG Emissions
The data on heavy-duty gasoline and CNG crankcase emissions are limited. All 1969 and later otto-cycle (spark ignition) heavy-duty engines are required to control crankcase emissions. All gasoline engines are assumed to use positive crankcase ventilation (PCV) systems, which route the crankcase gases into the intake manifold. For heavy-duty gasoline engines we use the same values of crankcase emission ratios as light-duty gasoline, which are documented in the MOVES2014 light-duty emission rates report.8 We assume 4% of PCV systems fail, resulting in the small crankcase to exhaust emission ratios shown in Table 5-6 for 1969 and later gasoline engines. Due to limited information, we used the gasoline heavy-duty crankcase emission factors for heavy-duty CNG engines because they have low blow-by particle emissions.
Table 5-6 Crankcase to Tailpipe Exhaust Emission Ratio for Heavy-duty Gasoline and CNG Vehicles for HC, CO, NOx and PM2.5
                                   Pollutant
                                   pre-1969)
                                1969 and later
                                      HC
                                     0.33
                                     0.013
                                      CO
                                     0.013
                                    0.00052
                                      NOx
                                     0.001
                                    0.00004
                               PM (all species)
                                     0.20
                                     0.008

The crankcase and exhaust ratios used by the crankcase calculator for PM2.5 emissions from heavy-duty gasoline and compressed natural gas vehicles are provided in Table 5-7. No information is available to estimate separate speciation between exhaust and crankcase, so the factors are the same between the PM subspecies.

Table 5-7 MOVES2014 Exhaust and Crankcase Emission Factors by Pollutant, Process, Model Year Group, and Fuel Type, and Source Type for PM2.5 Species

                          1960-1968 Gasoline Vehicles
                            1969-2050 Gasoline/ CNG
                                   Pollutant
                                    Process
                                 All processes
                                 All processes
                                      EC
                                    Exhaust
                                       1
                                       1
                                 nonECnonSO4PM

                                       1
                                       1
                                      SO4

                                       1
                                       1
                                      H2O

                                       1
                                       1
                                      EC
                                   Crankcase
                                      0.2
                                     0.008
                                 nonECnonSO4PM

                                      0.2
                                     0.008
                                      SO4

                                      0.2
                                     0.008
                                      H2O

                                      0.2
                                     0.008

Nitrogen Oxide Composition

Nitrogen oxides (NOx) are defined as NO + NO2.[,] NOx is considered a subset of reactive nitrogen species (NOy) with an nitrogen oxidation state of +2 or greater which contain other nitrogen containing species (NOz), thus NOy = NOx + NOz.99 NOz compounds are formed in the atmosphere as oxidation products of NOx100.
Chemiluminescent analyzers used for exhaust NOx measurements, directly measure NO, as NO is oxidized by ozone to form NO2  and produces florescence light. Chemiluminescent analyzers measure NOx (NO + NO2) by using a catalyst that reduces the NO2  to NO in the sample air stream before measurement. NO2  is calculated as the difference between NOx and NO measurements. The NOx converter within chemiluminescent analyzers can also reduce other reactive nitrogen species (NOz), including HONO to NO. If the concentrations of NOz interfering species in the sample stream are significant relative to NO2  concentrations, than they can bias the NO2  measurements high.
MOVES produces estimates of NO and NO2 by applying an NO/NOx or NO2/NOx fraction to the NOx emission rates. The NO/NO2  and NO2/NOx fractions are stored in a MOVES table called nono2ratio. The nono2ratio enables the nitrogen oxide composition to vary according to source type, fuel type, model year, and pollutant process. For the heavy-duty vehicle source types, the NOx fractions only vary according to fuel type, model year, and emission process. The NOx fractions in MOVES were developed from a literature review reported by Sierra Research to the EPA, from emission test programs conducted in the laboratory with constant volume sampling dilution tunnels.6
MOVES also produces estimates of one important NOz species, nitrous acid (HONO), from the NOx values. HONO emissions are estimated as a fraction (0.8%) of NOx emissions from all vehicle types in MOVES, based on HONO and NOx measurements made at road tunnel in Europe. In MOVES, we assume HONO contributes to the NOx values, because either (1) the  chemiluminescent analyzers are biased slightly high by HONO in the exhaust stream, or (2) HONO is formed almost immediately upon dilution into the roadway environment from NO2  emissions. To avoid overcounting reactive nitrogen formation, we include HONO in the sum of NOx in MOVES. HONO emissions are also estimated using the non2ratio MOVES table. For each source type, fuel type, and emission process, the NO, NO2, and HONO values in the non2ratio sum to unity.
MOVES users should be aware that the definition of NOx in MOVES (NO+NO2+HONO) is different than the actual NOx definition of NOx (NO + NO2).This is because we are correcting the exhaust NOx emission in MOVES for potential interference with HONO measurements. MOVES users should consider which measure they would like to use depending on their use-case. For example, for comparing NOx results with a vehicle emission test program, MOVES may want to simply use NOx (pollutantID 3), whereas a MOVES users developing air quality inputs of NO, NO2, and HONO, should estimate NOx as the sum of NO + NO2  (pollutantIDs 32 and 33), rather than using the direct NOx output in MOVES (polluantID 3).
Future work is needed to (1) update the NOx and HONO fractions in MOVES based on more recent measurements, (2) reconcile the definition of NOx in MOVES, while also correctly accounting for the emissions of NOz species that  may impact NOx measurements and (3) reconcile measurement differences that may occur between NOy species measured at the tailpipe, with NOy species measured on road side measurements.
Heavy-duty Diesel
The conventional diesel (1960-2006 model year) NOx fractions were estimated as the average reported fraction from three studies of heavy-duty vehicles not equipped with diesel particulate filters. 6 The 2010+ NO2  fractions are based on the average of three diesel programs of diesel vehicles measured with diesel particulate filters. The 2007-2009 values are an average of the 1960-2006 and 2010-2050 values, which assumes that the NOx fractions change incrementally, as trucks equipped with catalyzed diesel particulate filters were phased-into the fleet. The NOx fractions are the same across all diesel source types (including light-duty) and across all emission processes (running, start, extended idle), except for auxiliary power units, which use the conventional NOx fractions (1960-2006) for all model years because it is assumed that they are not fitted with diesel particulate filters. The NO2  fractions originally developed from the Sierra report6, were reduced by 0.008, to account for the HONO emissions.
       Table 6-1. NOx and HONO fractions for Heavy-Duty Diesel Vehicles
                                  Model Year
                                      NO
                                      NO2
                                     HONO
                                  1960-2006*
                                                                          0.935
                                                                          0.057
                                                                          0.008
                                   2007-2009
                                                                          0.764
                                                                          0.228
                                                                          0.008
                                   2010-2050
                                                                          0.594
                                                                          0.398
                                                                          0.008
* All Model Year of Auxiliary Power Units (APUs) use the 1960-2006 NOx and HONO fractions.
 Heavy-duty Gasoline
The NOx fractions for heavy-duty gasoline are based on the MOVES values used for light-duty gasoline measurements. Separate values are used for running and start emission processes. As stated in the Sierra Report,6 the values are shifted to later model year groups to be consistent with emission standards and emission control technologies. These values are shown in Table 6-2for both light-duty and heavy-duty gasoline vehicles. The NO2 fractions originally developed from the Sierra report6, were reduced by 0.008, to account for the HONO emissions.
Table 6-2. NOx and HONO Fractions for Light-duty (sourceTypeID 21, 31, 32) and Heavy-duty Gasoline Vehicles (sourceTypeID 41, 42, 43, 51, 52, 53, 54, 61, and 62)
                     Light-Duty Gasoline Model Year Groups
                     Heavy-Duty Gasoline Model Year Groups
                                    Running
                                     Start

                                      NO
                                      NO2
                                     HONO
                                      NO
                                      NO2
                                     HONO
                                   1960-1980
                                   1960-1987
                                                                          0.975
                                                                          0.017
                                                                          0.008
                                                                          0.975
                                                                          0.017
                                                                          0.008
                                   1981-1990
                                   1988-2004
                                                                          0.932
                                                                           0.06
                                                                          0.008
                                                                          0.932
                                                                          0.031
                                                                          0.008
                                   1991-1995
                                   2005-2007
                                                                          0.954
                                                                          0.038
                                                                          0.008
                                                                          0.987
                                                                          0.005
                                                                          0.008
                                   1996-2050
                                   2008-2050
                                                                          0.836
                                                                          0.156
                                                                          0.008
                                                                          0.951
                                                                          0.041
                                                                          0.008

Compressed Natural Gas
We used the average of three NO2/NOx fraction reported on three CNG transit buses with DDC Series 50 G engines by Lanni et al. (2003)74, along with the 0.008 HONO fraction assumed for other source types, to estimate the NOx fractions of NO, NO2, and the HONO fraction. These assumptions yield the NOx and HONO fractions in Table 6-3, which are used for all model year CNG transit buses.
              Table 6-3 NOx and HONO fractions CNG transit buses
                                  Model Year
                                      NO
                                      NO2
                                     HONO
                                   1960-2050
                                                                          0.865
                                                                          0.127
                                                                          0.008

Calculation of Accessory Power Requirements

              Table A-1. Accessory load estimates for HHD trucks

              Table A-2. Accessory load estimates for MHD trucks

                 Table A-3. Accessory load estimates for buses

 Tampering and Mal-maintenance
Tampering and mal-maintenance (T&M) effects represent the fleet-wide average increase in emissions over the useful life of the engines.  In laboratory testing, properly maintained engines often yield very small rates of emissions deterioration through time.  However, we assume that in real-world use, tampering and mal-maintenance yield higher rates of emissions deterioration over time.  As a result, we feel it is important to model the amount of deterioration we expect from this tampering and mal-maintenance.  We estimated these fleet-wide emissions effects by multiplying the frequencies of engine component failures by the emissions impacts related to those failures for each pollutant.  Details of this analysis appear later in this section.
Modeling Tampering and Mal-maintenance
As T&M affects emissions through age, we developed a simple function of emission deterioration with age.  We applied the zero-age rates through the emissions warranty period (5 years/100,000 miles), then increased the rates linearly up to the useful life.  Then we assumed that all the rates level off beyond the useful life age. Figure B-1 shows this relationship.

    Figure B-1. Qualitative Depiction of the implementation of age effects.
Final emission rate due to T&M
Zero-mile emission rate
End of warranty period

End of useful life
Age
Emission rate
Final emission rate due to T&M
Zero-mile emission rate
End of warranty period

End of useful life
Age
Emission rate
\s

The useful life refers to the length of time that engines are required to meet emissions standards.  We incorporated this age relationship by averaging emissions rates across the ages in each age group.  Mileage was converted to age with VIUS (Vehicle Inventory and Use Survey) data, which contains data on how quickly trucks of different regulatory classes accumulate mileage.  Table B-1 shows the emissions warranty period and approximate useful life requirement period for each of the regulatory classes.

     Table B-1. Warranty and useful life requirements by regulatory class
Regulatory class
                          Warranty age (Requirement:
                           100,000 miles or 5 years)
                      Useful life mileage/age requirement
                                Useful life age
HHD
                                       1
                                  435,000/10
                                       4
MHD
                                       2
                                  185,000/10
                                       5
LHD45
                                       4
                                  110,000/10
                                       4
LHD2b3
                                       4
                                  110,000/10
                                       4
BUS
                                       2
                                  435,000/10
                                      10

While both age mileage metrics are given for these periods, whichever comes first determines the applicability of the warranty.  As a result, since MOVES deals with age and not mileage, we need to convert all the mileage values to age equivalents, as the mileage limit is usually reached before the age limit.  The data show that on average, heavy-heavy-duty trucks accumulate mileage much more quickly than other regulatory classes.  Therefore, any deterioration in heavy-heavy-duty truck emissions will presumably happen at younger ages than for other regulatory classes.  Buses, on average, do not accumulate mileage quickly.  Therefore, their useful life period is governed by the age requirement, not the mileage requirement.
Since MOVES deals with age groups and not individual ages, the increase in emissions by age must be calculated by age group.  We assumed that there is an even age distribution within each age group (e.g. ages 0, 1, 2, and 3 are equally represented in the 0-3 age group).  This is important since, for example, HHD trucks reach useful life at four years, which means they will increase emissions through the 0-3 age group.  As a result, the 0-3 age group emission rate will be higher than the zero-mile emission rate for HHD trucks.  Table B-2 shows the multiplicative T&M adjustment factor by age.  We determined this factor using the mileage-age data from Table B-1 and the emissions-age relationship that we described in Figure B-1.  We multiplied this factor by the emissions increase of each pollutant over the useful life of the engine, which we determined from the analysis in this section.

  Table B-2. T&M multiplicative adjustment factor by age (fTM,age group).
Age Group
                                      LHD
                                      MHD
                                      HHD
                                      Bus
0-3
                                       0
                                     0.083
                                     0.25
                                    0.03125
4-5
                                       1
                                     0.833
                                       1
                                    0.3125
6-7
                                       1
                                       1
                                       1
                                    0.5625
8-9
                                       1
                                       1
                                       1
                                    0.8125
10-14
                                       1
                                       1
                                       1
                                       1
15-19
                                       1
                                       1
                                       1
                                       1
20+
                                       1
                                       1
                                       1
                                       1
In this table, a value of 0 indicates no deterioration, or zero-mile emissions level (ZML), and a value of 1 indicates a fully deteriorated engine, or maximum emissions level, at or beyond useful life (UL).  The calculation of emission rate by age group is described in the equation below.  TMpol represents the estimated emissions rate increase through the useful life for a given pollutant.

                                 Equation B-1

 Data Sources
EPA used the following information to develop the tamper and mal-maintenance occurrence rates used to develop emission rates used in MOVES:
      * California's ARB EMFAC2007 Modeling Change Technical Memo (2006).  The basic EMFAC occurrence rates for tampering and mal-maintenance were developed from the Radian and EFEE reports and internal CARB engineering judgment.
      * Radian Study (1988).  The report estimated the malfunction rates based on survey and observation.  The data may be questionable for current heavy-duty trucks due to advancements such as electronic controls, injection systems, and exhaust aftertreatment.
      * EFEE report (1998) on PM emission deterioration rates for in-use vehicles.  Their work included heavy-duty diesel vehicle chassis dynamometer testing at Southwest Research Institute.
      * EMFAC2000 (2000) Tampering and Mal-maintenance Rates
      * EMA's comments on ARB's Tampering, Malfunction, and Mal-maintenance Assumptions for EMFAC 2007
      * University of California  - Riverside (UCR) "Incidence of Malfunctions and Tampering in Heavy-Duty Vehicles"
      * Air Improvement Resources, Inc.'s Comments on Heavy-Duty Tampering and Mal-maintenance Symposium
      * EPA internal engineering judgment
T &M Categories
EPA generally adopted the categories developed by CARB, with a few exceptions.  The high fuel pressure category was removed.  We added a category for misfueling to represent the use of nonroad diesel, not ULSD onroad diesel.  We combined the injector categories into a single group.  We reorganized the EGR categories into "Stuck Open" and "Disabled/Low Flow."  We included the PM regeneration system, including the igniter, injector, and combustion air system in the PM filter leak category.
EPA will group the LHDD, MHDD, HHDD, and Diesel bus groups together, except for 2010 and beyond.  We assumed that the LHDD group will primarily use Lean NOx Traps (LNT) for the NOx control in 2010 and beyond.  On the other hand, we also assumed that Selective Catalyst Reduction (SCR) systems will be the primary NOx aftertreatment system for HHDD.  Therefore, the occurrence rates and emission impacts will vary in 2010 and beyond depending on the regulatory class of the vehicles.
T&M Model Year Groups
   EPA developed the model year groups based on regulation and technology changes.
      * Pre-1994 represents non-electronic fuel control.
      * 1998-2002 represents the time period with consent decree issues.
      * 2003 represents early use of EGR.
      * 2007 and 2010 contain significant PM and NOx regulation changes.
      * EPA issued a rule to require OBD for heavy duty trucks, beginning in MY 2010 with complete phase-in by MY 2013.
T &M Occurrence Rates and Differences from EMFAC2007
EPA adopted the CARB EMFAC2007 occurrence rates, except as noted below.
Clogged Air Filter:  EPA reduced the frequency rate from EMFAC's 15 percent to 8 percent.  EPA reduced this value based on the UCR results, the Radian study, and EMA's comments that air filters are a maintenance item.  Many trucks contain indicators to notify the driver of dirty air filters and the drivers have incentive to replace the filters for other performance reasons.
Other Air Problems:  EPA reduced the frequency rate from EMFAC's 8 percent to 6 percent based on the UCR results.
Electronics Failed:  EPA will continue to use the 3 percent frequency rate for all model years beyond 2010.  CARB increased the rate to 30 percent in 2010 due to system complexity.  EPA does not agree with CARB's assertion that the complexity of electronic systems will increase enough to justify a ten-fold increase in malfunction occurrence rates.  We believe that the hardware will evolve through 2010, rather than be replaced with completely new systems that would justify a higher rate of failure.  EPA asserts that many of the 2010 changes will occur with the aftertreatment systems which are accounted for separately.
EGR Stuck Open:  EPA believes the failure frequency of this item is rare and therefore set the level at 0.2 percent.  This failure will lead to drivability issues that will be noticeable to the driver and serve as an incentive to repair.
EGR Disabled/Low Flow:  EPA believes the EMFAC 20 percent EGR failure rate is too high and reduced the rate to 10 percent.  All but one major engine manufacturer had EGR previous to the 2007 model year and all have it after 2007.  Therefore, EMFAC's frequency rate increase in 2010 due to the increase truck population using EGR does not seem valid.  However, the Illinois EPA stated that "EGR flow insufficient" is the top OBD issue found in their LDV I/M program so it cannot be ignored.
NOX Aftertreatment malfunction:  EPA developed a NOx aftertreatment malfunction rate that is dependent on the type of system used.  We assumed that HHDD will use primarily SCR systems and LHDD will primarily use LNT systems.  We estimated the failure rates of the various components within each system to develop a composite malfunction rate.
The individual failure rates were developed considering the experience in agriculture and stationary industries of NOx aftertreatment systems and similar component applications.  Details are included in the chart below.  We assumed that tank heaters had a 5 percent failure rate, but were only required in one third of the country and one fifth of the year.  The injector failure rate is lower than fuel injectors, even though they have similar technology, because there is only one required in each system and it is operating in less severe environment of pressure and temperature.  We believe the compressed air delivery system is very mature based on a similar use in air brakes.  We also believe that manufacturers will initiate engine power de-rate as incentive to keep the urea supply sufficient.

                  Table B-3. NOx Aftertreatment Failure Rates

NOx aftertreatment sensor:  EPA believes the 53 percent occurrence rate in EMFAC2007 is too high and will use 10 percent.  CARB assumed a mix of SCR, which uses one sensor per vehicle, and NOx adsorbers, which use two sensors per vehicle.  They justified the failure rate based on the increased number of sensors in the field beginning in 2010.
   We developed the occurrence rate based on the following assumptions:
   * Population:  HHDD: vast majority of heavy-duty applications will use SCR technology with a maximum of one NOx sensor.  NOx sensors are not required for SCR  -  manufacturers can use models or run open loop.   Several engine manufacturers representing 30 percent of the market plan to delay the use of NOx aftertreatment devices through the use of improved engine-out emissions and emission credits.
   * Durability expectations:  SwRI completed 6000 hours of ESC cycling with NOx sensor.  Internal testing supports longer life durability.  Discussions with OEMs in 2007 indicate longer life expected by 2010.
   * Forward looking assumptions:  Manufacturers have a strong incentive to improve the reliability and durability of the sensors because of the high cost associated with frequent replacements.
PM Filter Leak:  EPA will use 5 percent PM filter leak and system failure rate.  CARB used 14 percent failure rate.  They discounted high failure rates currently seen in the field.
PM Filter Disable:  EPA agrees with CARB's 2 percent tamper rate of the PM filter.  The filter causes a fuel economy penalty so the drivers have an incentive to remove it.
Oxidation Catalyst Malfunction/Remove:  EPA believes most manufacturers will install oxidation catalysts initially in the 2007 model year and agrees with CARB's assessment of 5 percent failure rate.  This rate consists of an approximate 2 percent tampering rate and 3 percent malfunction rate.  The catalysts are more robust than PM filters, but have the potential to experience degradation when exposed to high temperatures.
Misfuel:  EPA estimated that operators will use the wrong type of fuel, such as agricultural diesel fuel with higher sulfur levels, approximately 0.1 percent of the time.

Tampering & Mal-maintenance Occurrence Rate Summary

NOx Emission Effects
EPA developed the emission effect from each tampering and mal-maintenance incident from CARB's EMFAC, Radian's dynamometer testing with and without the malfunction present, EFEE results, and internal testing experience.
EPA estimated that the lean NOx traps (LNT) in LHDD are 80 percent efficient and the selective catalyst reduction (SCR) systems in HHDD are 90 percent efficient at reducing NOx.
EPA developed the NOx emission factors of the NOx sensors based on SCR systems' ability to run in open-loop mode and still achieve NOx reductions.  The Manufacturers of Emission Controls Association (MECA) has stated that 75-90 percent NOX reduction with open loop control and >95 percent reduction with closed loop control.  Visteon reports 60-80 percent NOX reduction with open loop control.
The failure of the NOx aftertreatment system had a different impact on the NOx emissions depending on the type of aftertreatment.  The HHDD vehicles with SCR systems would experience a 1000 percent increase in NOx during a complete failure, therefore we estimated a 500 percent increase as a midpoint between normal operation and a complete failure.  The LHDD vehicles with LNT systems would experience a 500 percent increase in NOx during a complete failure.  We estimated a 300 percent increase as a value between a complete failure and normal system operation.
The values with 0 percent effect in shaded cells represent areas which have no occurrence rate.

PM Emission Effects
EPA developed the PM emission effects from each tampering and mal-maintenance incident from CARB's EMFAC, Radian's dynamometer testing with and without the malfunction present, EFEE results, and internal testing experience.
EPA estimates that the PM filter has 95 percent effectiveness.  Many of the tampering and mal-maintenance items that impact PM also have a fuel efficiency and drivability impact.  Therefore, operators will have an incentive to fix these issues.
EPA estimated that excessive oil consumption will have the same level of impact on PM as engine mechanical failure.  The failure of the oxidation catalyst is expected to cause a PM increase of 30 percent; however, this value is reduced by 95 percent due to the PM filter effectiveness.  We also considered a DOC failure will cause a secondary failure of PM filter regeneration.  We accounted for this PM increase within the PM filter disabled and leak categories.
The values with 0 percent effect in shaded cells represent areas which have no occurrence rate.

Tamper & Malmaintenance

PM Emission Effect

                                   1994-1997
                                   1998-2002
                                   2003-2006
                                   2007-2009
                                     2010
Federal Emission Standard
                                      0.1
                                      0.1
                                      0.1
                                     0.01
                                     0.01

Timing Advanced
                                     -10%
                                     -10%
                                     -10%
                                      0%
                                      0%
Timing Retarded
                                      25%
                                      25%
                                      25%
                                      1%
                                      1%
Injector Problem
                                     100%
                                     100%
                                     100%
                                      5%
                                      5%
Puff Limiter Mis-set
                                      20%
                                      0%
                                      0%
                                      0%
                                      0%
 Puff Limiter Dsabled
                                      50%
                                      0%
                                      0%
                                      0%
                                      0%
Max Fuel High
                                      20%
                                      0%
                                      0%
                                      0%
                                      0%
Clogged Air Filter
                                      50%
                                      50%
                                      30%
                                      2%
                                      2%
Wrong/Worn Turbo
                                      50%
                                      50%
                                      50%
                                      3%
                                      3%
Intercooler Clogged
                                      50%
                                      50%
                                      30%
                                      2%
                                      2%
Other Air Problem
                                      40%
                                      40%
                                      30%
                                      2%
                                      2%
Engine Mechanical Failure
                                     500%
                                     500%
                                     500%
                                      25%
                                      25%
Excessive Oil Consumption
                                     500%
                                     500%
                                     500%
                                      25%
                                      25%
Electronics Failed
                                      60%
                                      60%
                                      60%
                                      3%
                                      3%
Electronics Tampered
                                      50%
                                      50%
                                      50%
                                      3%
                                      3%
EGR Stuck Open
                                      0%
                                      0%
                                     100%
                                      5%
                                      5%
EGR Disabled/Low Flow
                                      0%
                                      0%
                                     -30%
                                     -30%
                                     -30%
Nox Aftertreatment Sensor
                                      0%
                                      0%
                                      0%
                                      0%
                                      0%
Replacement Nox Aftertreatment Sensor
                                      0%
                                      0%
                                      0%
                                      0%
                                      0%
Nox Aftertreatment Malfunction
                                      0%
                                      0%
                                      0%
                                      0%
                                      0%
PM Filter Leak
                                      0%
                                      0%
                                      0%
                                     935%
                                     935%
PM Filter Disabled
                                      0%
                                      0%
                                      0%
                                     2670%
                                     2670%
Oxidation Catalyst Malfunction/Remove
                                      0%
                                      0%
                                      0%
                                      0%
                                      0%
Mis-fuel - EPA
                                      30%
                                      30%
                                      30%
                                     100%
                                     100%

HC Emission Effects
EPA estimated oxidation catalysts are 80 percent effective at reducing hydrocarbons.  All manufacturers will utilize oxidation catalysts in 2007, but only a negligible number were installed prior to the PM regulation reduction in 2007.
We reduced CARB's HC emission effect for timing advanced because earlier timing should reduce HC, not increase them.  The effect of injector problems was reduced to 1000 percent based on internal experience.  We increased the HC emission effect of high fuel pressure to 10 percent because the higher pressure will lead to extra fuel in early model years and therefore increased HC.  Lastly, we used the HC emission effect of advanced timing for the electronics tampering since this was the most significant type of tampering that occurred.
The values with 0 percent effect in shaded cells represent areas which have no occurrence rate.

A separate tampering analysis was not performed for CO; rather,  the HC effects were assumed to apply for CO.
Combining all of the emissions effects and failure frequencies discussed in this section, we summarized the aggregate emissions impacts over the useful life of the fleet due to in the main body of the document in Table 2-7 (NOx), Table 2-14 (PM), and Table 2-17 (HC and CO).

HD OBD impacts
With the finalization of the heavy-duty onboard diagnostics (HD OBD) rule, we made adjustments to our draft 2010 and later model year to reflect the rule's implementation.
Specifically, we reduced our emissions increases for all pollutants due to tampering and mal-maintenance by 33 percent.  As data are not yet available for heavy-duty trucks equipped with OBD, this number is probably a conservative estimate.  Still, PM and NOx reductions from 2010 and later model year vehicles will be substantial compared to prior model years regardless of the additional incremental benefit from OBD.  We assumed, since the rule phases in OBD implementation, that 33 percent of all engines will have OBD in 2010, 2011, and 2012 model years, and 100 percent will have OBD by 2013 model year and later. Equation B-2 describes the calculation of TMpol, the increase in emission rate through useful life, where fOBD represents the fraction of the fleet equipped with OBD (0 percent for model years 2009 and earlier, 33 percent for model years 2010-2012, and 100 percent for model years 2013 and later).  The result from this equation can be plugged into Equation B-1 to determine the emission rate for any age group.

                                                                   Equation B-2
These OBD impacts apply to any truck in Class 4 and above. Any lighter trucks are assumed to follow light-duty OBD impacts and will be fully phased in starting in model year 2010. As data for current and future model years become available, we may consider refining these estimates and methodology.
Extended Idle Data Summary

2007 Extended Idle Emissions calculation:
* Assumed 8 hour idle period where the emissions controls, such as EGR, oxidation catalyst, and NOx aftertreatment, are still active for the first hour.
* HC emissions standards:
      o Pre-2007: 0.50 g/bhp-hr
      o 2007:  0.14 g/bhp-hr
* NOx emissions standards:
      o Pre-2010: 5.0 g/bhp-hr
      o 2010:  0.2 g/bhp-hr

Idle HC Rate Reduction = 1 - [(1/8 * 0.14 g/bhp-hr + 7/8 * 0.5 g/bhp-hr) / 0.5 g/bhp-hr] = 9%
Idle NOx Rate Reduction = 1 - [(1/8 * 0.2 g/bhp-hr + 7/8 * 5.0 g/bhp-hr) / 5.0 g/bhp-hr] = 12%

Developing PM emission rates for missing operating modes
In cases where an estimated rate could not be directly calculated from data, we imputed the missing value using a log-linear least-squares regression procedure.  Regulatory class, model year group and speed class (0 - 25 mph, 25-50 mph and 50+ mph ) were represented by dummy variables in the regression.  The natural logarithm of emissions was regressed versus scaled tractive power (STP) to represent the operating mode bins.  The regression assumed a constant slope versus STP for each regulatory class.  Logarithmic transformation factors (mean square error of the regression squared / 2) were used to transform the regression results from a log based form to a linear form.  Due to the huge number of individual second-by-second data points, all of the regression relationships were statistically significant at a high level (99% confident level).  The table below shows the regression statistics, and the equation shows the form of the resulting regression equation.
Table D-1. Regression Coefficients for PM Emission Factor Model
Model-year group
Speed Class (mph)
Type
Medium Heavy-Duty
Heavy Heavy-Duty
1960-87
1-25
Intercept (β0)
-5.419
-5.143

25-50

-4.942
-4.564

50+

-4.765
-4.678
1988-90
1-25

-5.366
-5.847

25-50

-4.929
-5.287

50+

-4.785
-5.480
1991-93
1-25

-5.936
-5.494

25-50

-5.504
-5.269

50+

-5.574
-5.133
1994-97
1-25

-5.927
-6.242

25-50

-5.708
-5.923

50+

-5.933
-6.368
1998-2006
1-25

-6.608
-6.067

25-50

-6.369
-5.754

50+

-6.305
-6.154

STP
Slope  (β1)
0.02821
0.0968

Transformation Coefficient (0.5σ[2])

0.5864

0.84035

Where :
β0 = an intercept term for a speed class within a model year group, as shown in the table above,
β1 =  a slope term for STP, and
σ2 = the mean-square error or residual error for the model fit,
STP = the midpoint value for each operating mode (kW/metric ton, see Table 1-4).

Heavy-duty Diesel EC/PM Fraction Calculation
Introduction
This memo describes the development and application of a "rough cut" emission model for estimating elemental and organic carbonaceous material (EC and OM) emission rates (or EC/OM ratios) from MOVES.  The memo describes the following steps involved in predicting EC/OM ratios.  The memo also briefly describes comparisons with independent emission data collected using the  "Mobile Emission Laboratory," Operated by the University of California Riverside.
The subsequent sections of the memo describe the following topics:
      * the extension of Physical Emission Rate Simulator (PERE)  to estimate heavy-duty fleet-average emission factors for any specified driving cycle;
      * the acquisition of data used in estimating EC/OC rates as a function of engine operating mode and the fitting of simple empirical models to them;
      * the application of PERE to estimate EC and OC emission rates for different test cycles; and,
      * the comparison of PERE-based EC and OC emission rates to those measured by independent researchers in HD trucks.
 PERE for Heavy-duty Vehicles (PERE-HD) and Its Extensions
The Physical Emission Rate Estimator (PERE) is a model employed by EPA in early development of MOVES.33  In particular, the MOVES team employed it in development of MOVES2004 to impute greenhouse gas emission rates for combinations of SourceBin and Operating Mode for which data was unavailable or of insufficient quality.
The underlying theory behind PERE and its comparison with measured fuel consumption data is described by Nam and Giannelli (2005).33 Briefly, PERE estimates fuel consumption and emission rates on the basis of fundamental physical and mathematical relationships describing the road load that a vehicle meets when driving a particular speed trace.  Accessory loads are handled by addition of an accessory power term.  In the heavy-duty version of PERE (hereafter, "PERE-HD"), accessory loads were described by a single value.
For the current project, PERE was modified to incorporate several "extensions" that allowed it to estimate fleet-average emission rates, simulate a variety of accessory load conditions, and predict EC rates for any given driving cycle.
PERE-HD Fleet-wide Average Emission Rate Estimator
PERE-HD requires a number of user-specified inputs, including:
      * vehicle-level descriptors (model year, running weight, track road-load coefficients (A,B,C), transmission type, class [MDT/HDT/bus]);
      * engine parameters (fuel type, displacement); and
      * driving cycle (expressed through a speed trace).

The specification of these inputs allows PERE to model the engine operation, fuel consumption, and GHG emissions for a HDV on a specified driving cycle.
However, the baseline PERE-HD provides output for only one combination of these parameters at once.  To estimate fleet-wide average a large number of PERE-HD runs would be required.  Furthermore, the specification of only fleet-wide average coefficients is likely to substantially underestimate variability in fuel consumption and emissions.  Emissions data from a large number of laboratory and field studies suggest that a very large fraction of total emissions from all vehicles derives from a small fraction of the study fleet.  Therefore, it is desirable to develop an approach that comes closer to spanning the range of likely combinations of inputs than using a small selection of "average" or "typical" values.
For the current application, PERE-HD (built within Microsoft Excel) was expanded to allow for a representative sample of [running weight] x [engine displacement] x [model year] combinations.  A third-party add-on package to Excel, @Risk 4.5 (Palisade Corporation, 2004), allows users to supplement deterministic inputs within spreadsheet models with selected continuous probability distributions, sample input values from each input distribution, and re-run the spreadsheet model with sets of selected inputs over a specified number of iterations.  This type of procedure is commonly referred to as "Monte Carlo" simulation.
Monte Carlo Simulation in PERE-HD
To illustrate how @Risk performs this process, we illustrate the application of a simple model, employing both deterministic calculations and stochastic Monte Carlo simulation:

This equation defines the body mass index for humans, a simple surrogate indicating overweight and underweight conditions.  According to the Centers for Disease Control and Prevention (CDC), the average U.S. woman weighed 164.3 lb (74.5 kg) in 2002 and was 5'4" (1.6 m) tall.  This result  corresponds to a BMI of 28, suggesting that the average U.S. woman is overweight.  While this is useful information from a public health perspective, it does not provide any indication as to which individuals are likely to experience the adverse effects of being overweight and obese.  However, if we were to assume (arbitrarily) that the range of weight and height within the U.S. population was +/-50% of the mean, distributed uniformly, and perform a Monte Carlo simulation (5,000 iterations) using @Risk, we would predict a probability distribution of BMI in the population as follows:

In contrast, here is the BMI distribution in the entire U.S. population, according to the CDC's National Health and Nutrition Examination Survey (NHANES):

These graphs illustrate how Monte Carlo simulation can be used to provide meaningful information about the variability in a population.  Although the model example is very simple, it illustrates the point that a model with "typical" inputs provides much less information than Monte Carlo simulation does with variable inputs.
For emission modeling purposes using PERE-HD, several key inputs were modeled as probability distributions.
Model Year
Model year is an important factor in PERE, as the frictional losses in the model, expressed as "friction mean effective pressure" (FMEP), vary by model year, improving with later model years.  As such, model year was simulated as a probability distribution, based on data from the Census Bureau's 1997 Vehicle Inventory and Use Survey (VIUS), which reports "vehicle miles traveled" (VMT) by model year. Accordingly these data were normalized to total VMT to develop a probability distribution.  Model year distributions in 1997 were normalized to the current calendar year (2008).  For instance, the fraction of 1996 vehicles reported in the 1997 VIUS is treated as the fraction of 2002 vehicles in the 2003 calendar year.  Although a 2002 VIUS is available, previous analyses (unpublished) have shown the "relative" model year distribution of trucks to have changed little between 1997 and 2002, though this assumption is one limitation of this analysis.
The model year distribution for PERE-HD was represented as a discrete probability distribution, as shown below:

Vehicle Weight and Engine Displacement
Vehicle running weights and engine displacements were modeled as a two-way probability distribution with engine displacement depending on running weight.  These data were derived from VIUS micro data obtained from the Census Bureau.  A two-way table was constructed to estimate VMT classified by combinations of [weight class] x [displacement class].  Analyses were restricted to diesel-powered trucks only.
As a first step, @Risk selects a running weight from a probability distribution representing the fraction of truck VMT occurring at a given running weight:

Because VIUS reports classes defined as ranges in running weight, any value of weight within each VIUS-specified class was considered equally likely and modeled as a uniform probability distribution within the class.  For the upper and lower bounds of the distribution the minimum and maximum running weights were assumed to be 7,000 and 240,000 lb, respectively.
After @Risk selects a running weight, it selects an engine displacement based on a discrete distribution assigned to every weight class in VIUS, represented below:

Again, because VIUS describes ranges of values for displacement, all values within each range were given uniform weight and assigned a uniform distribution.  For the extreme classes, the minimum and maximum engine displacements were assumed to be 100 in[3] and 915 in[3], respectively.

This procedure reflects the range in running weights present among HDV in operation, and constrains the combinations of weight and displacement to plausible pairs of values based on surveyed truck operator responses.  These steps allow for plausible variability in weight-engine pairings, which translates into differences in engine parameters influencing EC and OC emissions.
	For use in PERE-HD, all units were converted to SI units (kg and L).
Accessory Load
The original PERE-HD treats accessory load as a fixed value, which may be varied by the user.  It is set at 0.75, and used in calculating fuel rate and total power demand at each second of driving.
Following the development of PERE-HD, a more detailed set of accessory load estimates was developed based on several accessories' power demand while in use and the fraction of time each accessory is in use (see Table 2-4).  High, medium, and low accessory use categories were estimated for three vehicle classes:  HDT, MDT, and buses.  For the current version of the model, only the HDT accessory load estimates were employed, though a sensitivity analysis indicated that mean EC/OM ratios were most sensitive to accessory load during idle and creep driving cycles.  In the "base case," a mean ratio of 0.54 was predicted, while in the sensitivity case, a mean ratio of 0.50 was predicted.  This issue may be revisited at some point, although the limited sensitivity of total results limits the importance of the accessory terms within the current exercise.
Within @Risk, the variable in PERE-HD, Pacc for accessory use was substituted with a variable representing the distribution (in time) of accessory loads as estimated as the sum of a number of discrete probability distributions.
Depending on the assumption of high, medium or low use, the power demand for these accessories is distributed in time as follows:

Driving Cycle
For purposes of this exercise, the four phases of the California Air Resources Board's Heavy Heavy-Duty Diesel Truck (HHDDT) chassis dynamometer testing cycle were used to reflect variability in vehicle operations for PERE-HD.
Other Factors
Some elements of variability were not examined as part of this study.  Hybrid-electric transmissions and fuel cell power plants were excluded from the analysis, due to their low prevalence within the current truck fleet.
One important source of variability that was not examined in this analysis is the variation in resistive forces among vehicles with identical running weights.  This exclusion is important, given the potential role for aerodynamic improvements, low rolling resistance tires, and other technologies in saving fuel for long-distance trucking firms and drivers.  Such considerations could be incorporated into PERE-HD in the future as a means of estimating the emission benefits of fuel-saving technologies.

Prediction of Elemental Carbon and Organic Mass based on PERE-HD
Definition of Elemental and Organic Carbon and Organic Mass
In motor vehicle exhaust, the terms "EC," "elemental carbon," and "black carbon" refer to the fraction of total carbonaceous mass within a particle sample that consists of light-absorbing carbon.  Alternatively, they refer to the portion of carbonaceous mass that has a graphitic crystalline structure.  Further, one can define EC as the portion of carbonaceous mass that has been altered by pyrolysis, that is, the chemical transformation that occurs in high temperature in the absence of oxygen.
EC forms in diesel engines as a result of the stratified combustion process within a cylinder.  Fuel injectors spray aerosolized fuel into the cylinder during the compression stroke.  The high-pressure and high temperature during the cylinder cause spontaneous ignition of the fuel vaporizing from the injected droplets.  Because temperature can rise more quickly than oxygen can diffuse to the fuel at the center of each droplets, pyrolysis can occur as hydrogen and other atoms are removed from the carbonaceous fuel, resulting in extensive C-C bond interlinking.  As a result, pyrolyzed carbon is produced in a crystalline form similar to graphite.
"Organic carbon" or "organic mass" (OC or OM) is used to denote the portion of carbonaceous material in exhaust that is not graphitic.  Chemical analysis of this non-graphitic carbon mass indicates that it is composed of an extensive mixture of different organic molecules, including C15 to C44 alkanes, polycyclic aromatic hydrocarbons, lubricating oil constituents (hopanes, steranes, and carpanes), and a sizeable fraction of uncharacterized material.  This component of exhaust can derive from numerous processes inside the engine involving both fuel and oil.  Because of the complex chemical mixture that comprises this mass, its measurement is highly dependent on sampling conditions.  The wide range of organics that compose it undergo evaporation and condensation at different temperatures, and the phase-partitioning behavior of each molecule is dependent on other factors, such as the sorption of vapor-phase organics to available surface area in a dilution tunnel or background aerosol.

EPA Carbon Analysis Techniques in Ambient Air
The definitions of EC and OM are critical, as different groups use different techniques for quantifying their concentrations within a given medium.  For purposes of this document, it is assumed that EC, OC, and OM are operationally defined quantities, meaning that they are defined by the measurement technique used to quantify their concentrations on a filter or in air.
The different types of commonly used approaches for carbon include:
      * Thermal/optical techniques, where the evaporation and oxidation of carbon are used in conjunction with a laser to measure optical properties of a particle sample.  The major methods used for this type of analysis include:
            o Thermal/optical reflectance (TOR).  EPA is adopting this technique for the PM2.5 speciation monitoring network nationwide.  It is also employed by the IMPROVE program (Interagency Monitoring of Protected Visual Environments) in national parks.  This technique heats a punch from a quartz fiber filter according to a certain schedule.  A Helium gas atmosphere is first employed within the oven, and the evolved carbon is measured with a FID as temperatures are increased in steps up to 580°C.  All carbon evolved in this way is assumed to be volatilized organic material.  Next, 2% oxygen gas is added to the atmosphere, and temperatures are stepped up a number of times to a maximum of 840°C.  All carbon evolved after the introduction of oxygen is assumed to be elemental carbon.  The reflection of light from a laser by the filter is employed to account for the pyrolysis of organic carbon that occurs during the warm-up process.
            o Thermal/optical transmission (TOT). The National Institute of Occupational Safety and Health (NIOSH) uses this technique for measuring EC concentrations in occupational environments.  It is based on similar principles to TOR, but employs a different heating schedule and transmission of light as opposed to reflectance.
      * Radiation absorption techniques
            o Aethalometer(R)  -  This instrument reports "black carbon" (BC) concentrations based the extent of light absorption by a "filter tape," that allows for a time series of BC concentrations to be estimated.  It has a time resolution of several minutes.
            o Photoacoustic Spectrometer (PAS)  -  This instrument irradiates an air sample with a laser.  The resulting heat that occurs from the absorption of the laser light by light-absorbing carbon in the air sample produces a pressure wave that is measured by the device.  The signal from this pressure wave is proportional to the light-absorbing carbon content in exhaust.
      * Thermogravimetric techniques, where the "volatile organic fraction" (VOF) is separated by heat from the non-volatile refractory component of a particle sample.
      * Chemical extraction, where solvents are used to separate the soluble and insoluble components of exhaust.
A number of additional techniques are also described in the published literature, but the above techniques have been most commonly applied in emissions and routine ambient PM measurement.
Among the available techniques, it has been a point of controversy among academics as to which  method provides the "correct" carbon signal.  Rather than addressing these arguments in detail, this analysis adopts the technique employed by the EPA ambient speciation monitoring network, TOR.  Needless to say, different researchers employ different sampling, measurement and analysis techniques.  Desert Research Institute (DRI) employed TOR in analyzing the Kansas City gasoline PM emission study samples , while other prominent academics employ TOT, notably the University of California Riverside College of Engineering Center for Environmental Research and Technology (CE-CERT) and the University of Wisconsin-Madison (UWM) State Hygiene Laboratory.  As research results from these groups is employed throughout this analysis, an inter-comparison of the methods of TOT/TOR is necessary to "recalibrate" various datasets with respect to each other.
EPA defines measurement techniques for dynamometer-based sampling and analysis of particulate matter, in addition to techniques for sampling and analyzing particles in ambient air.  Inventories estimated for EC and OM can be considered to reflect both broad categories of measurement techniques, depending on context.
The user community for MOVES is predominantly concerned with emissions that occur into ambient air.  EPA regulations for demonstration of attainment of state implementation plans (SIPs) are based on monitored ambient particulate matter using Federal Reference Methods (FRM) for ambient air.  FRM monitors for particle speciation in ambient air undergo analysis for EC and OC according to a defined standard operating procedure.  That standard operating procedure defines thermal/optical reflectance (TOR) as the desired method for analysis of ambient carbon PM.
TOR  -  TOR Calibration Curve
In the course of the Gasoline/Diesel PM Split Study funded by the Department of Energy (DOE), researchers from DRI analyzed filter samples using both TOR and TOT methods[cite].  These data were obtained and analyzed in the SPSS 9.0 statistical package.
Briefly, the DOE study included emissions characterizations of 57 light-duty gasoline vehicles (LDGV) and 34 HD diesel vehicles (HDDV).  The vehicles were operated on a number of different test cycles including cold-start and warm-start cycles.  The data set employed in this study was generated by DRI and obtained from the DOE study web site. Both EC and OC were analyzed using the same approach.  All data from all vehicles were compiled.
First, EC and OC measured by TOR (denoted EC-TOR and OC-TOR) were regressed on EC-TOT and OC-TOT.  Studentized residuals from these regressions were noted, and those with Studentized residuals >3 were excluded from further analysis.
Second, each test in the reduced data set was assigned a random number (RAND) on the range [0,1].  Those cases with RAND >= 0.95 were set aside as a cross-validation data set, and excluded from additional regression analyses.
Third, those cases with RAND < 0.95 were regressed again, this time using an inverse uncertainty weighting procedure for each data point.  When DRI analyzes a filter sample, it reports an analytical uncertainty associated with the primary estimate of EC and OC.  Accordingly, the quality of each datum depends on the level of analytical uncertainty reported.  The inverse of the DRI-reported uncertainty (1/) associated with the TOR-based measurement was used to weight each point in the weighted regression.
It should be noted that for each regression, the intercept term was set to zero.  Models including intercepts did not have intercept terms that reached statistical significance.  As such, R[2] values are not considered valid.

      Coefficients from the weighted regression for EC and OC are reported below:
Slope
Beta
Std. Error
t-value
Sig.
EC-TOR
1.047
0.011
91.331
<0.0001
OC-TOR
1.014
0.007
153.923
<0.0001

To evaluate the quality of predictions resulting from these statistically-based adjustment factors, they were used to predict EC-TOR and OC-TOR values for the subset of data with RAND >= 0.95.  Scatter plots of the statistical fits are illustrated below (note logarithmic scaling).

	When measured values are regressed against predicted values, the following statistical estimates of fit are obtained:
Prediction
Slope
Std. Error
Intercept
Std. Error
EC
1.080
0.009
3.737
3.173
OC
1.092
0.069
-4.417
16.188
As shown, the prediction vs. observed comparison yields a slope near unity for both EC-TOR and OC-TOR, with nonsignificant intercepts.  On this basis, the "calibration" factors for converting EC-TOT and OC-TOT into their respective TOR-based metrics appear reasonable.
It remains an unverified assumption that the "calibration" factors derived from the emissions data derived from DRI as part of the DOE Gasoline / Diesel PM Split Study are general enough to apply to EC-TOT measurements obtained by other research groups.
EC and OC Emission Rates
 Selection of Engine Parameters for Predictive Modeling
PERE-HD produces estimates of engine operating conditions and fuel consumption for a given driving cycle.  Prediction of EC and OM emissions requires information on the composition of particulate matter as a function of some factor that may be related back to MOVES' activity basis, the time spent in a particular operating mode (opModeID).

It should be noted that continuous ("second-by-second, or  "real time") measurement of EC and OM is an exceptionally complicated endeavor.  While measurement techniques for EC have been developed that produce apparently good correlation with traditional filter-based methods,
While numerous publications report the EC and OM (or OC) exhaust emission rates across an entire driving cycle, it is not clear which parameter of a particular driving cycle, such as average speed (or power), might be applicable to the extrapolation of the observed rates to other vehicles or driving conditions.  As a result, identifying one or more engine parameters that explain the observed variation in driving cycle-based emission rates for EC and OM is desirable.  Such  parameter(s) will assist in estimating emission associated with short-term variations in driving.
One good candidate for establishing an engine-based emission model is mean effective pressure (MEP).  MEP is defined as:

Here, P is the power (in kW or hp), nR is the number of crank revolutions per power stroke per cylinder (2 for four-stroke engines, 1 for two-strokes), Vd is the engine displacement, and N is the engine speed.  In other words, MEP is the engine torque normalized by volume.
MEP can be broken into various components.  "Indicated MEP" or IMEP refers to the sum of BMEP (brake MEP) and FMEP (friction MEP).  Heywood (1988) writes that maximum BMEP is an indicator of good engine design and "essentially constant over a wide range of engine sizes.[cite]"  Nam and Giannelli (2004) note that it can be related to fuel MEP multiplied by the indicated or thermal efficiency of an engine, and have developed trend lines in FMEP by model year.  As such, since maximum BMEP is comparable across well-designed engines and FMEP can be well-predicted by Nam and Giannelli's trends within PERE, IMEP should be an appropriate metric for building an engine emission model that can be applied across vehicles with different loads and engine displacements.
 Emission Data
Kweon et al. (2004) measured particle composition and mass emission rates from a single-cylinder research engine based on an in-line 2.333 liter turbo-charged direct-injection six cylinder Cummins N14-series engine, with a quiescent, shallow dish piston chamber and a quiescent combustion chamber.  Emission data were obtained from all eight modes of the CARB 8-mode engine test cycle:

Mode 1
Mode 2
Mode 3
Mode 4
Mode 5
Mode 6
Mode 7
Mode 8
Speed
1800
1800
1800
1200
1200
1200
1200
700
Load%
100
75
50
25
100
75
50
10 (idle)
Equiv. Ratio (φ)
0.69
0.50
0.34
0.21
0.82
0.69
0.41
0.09
IMEP (MPa)
1.083
0.922
0.671
0.524
1.491
1.225
0.878
0.150
The study reports exhaust mass composition, including PM2.5, EC, and organic mass (OM, estimated as 1.2 x OC) measured with TOT (denoted here as EC-TOT and OC-TOT).  In the main study, the authors report that EC and OC are highly sensitive to the equivalence ratio.  However, IMEP is highly correlated with the measured equivalence ratio (R[2] = 0.96).  As such, it is reasonable to report the data as a function of IMEP, expecting it to have approximately equal explanatory power as has the equivalence ratio variable.  The figure below plots the emission data from Kweon et al. (2002) as a function of IMEP.

As shown in the figure, the EC-TOT work-specific emission rate is relatively insensitive to IMEP except between IMEP of approximately 0.85 and 1.1, where it undergoes a rapid increase.  Overall, the EC-TOR/IMEP curve is S-shaped, similar to a logistic curve or growth curve.  OC-TOT work-specific emissions are highest at low IMEP (i.e. idle) and are monotonically lower with higher IMEP.  Total work-specific PM2.5 is not monotonic, but appears to be described by a single global minimum around IMEP ~ 0.9 and two local maxima around IMEP of 0.2 and 1.2, respectively.
The oppositely signed slopes of the emission-IMEP curves for EC-TOT and OC-TOT suggest that there are different underlying physical processes.  It is not the intent of this document to explicitly describe the particle-formation mechanisms in a diesel engine.  However, the use of two separate functions to predict EC-TOT and OC-TOT separately is warranted.  This implies that the EC/OC ratio will vary by engine operating mode.  The following figure depicts the EC/OC ratio as a function of IMEP.

 Estimation of IMEP-based Emissions of EC and OC
To produce a relationship that generalizes the implied relationship between EC-TOT and OC-TOT work-specific emissions and IMEP in the data presented by Kweon et al. (2004), it is necessary to specify some functional form of a relationship between the two.
A priori, on the basis of visual inspection of the data, a flexible logistic-type curve was fit to the data by a least-squares minimization procedure using the Microsoft Excel "Solver" tool, which employs the GRG2 optimization approach.
The functional form of the logistic-type curves fit to both the EC-TOT and OC-TOT data from Kweon et al. (2004) is as follows:

A least-squared error approach was implemented within Microsoft Excel to derive the coefficients for the logistic curves for EC-TOT and OC-TOT.  The solutions to the fits are as follows:
                                       Y
                                       A
                                       B
                                       C
                                    EC-TOT
                                2.12 x 10[-5]
                                     -9.79
                                 4.67x 10[-5]
                                    OC-TOT
                                     0.155
                                    -2.275
                                    -0.859
Graphically, in comparison to observed values of EC-TOT and OC-TOT, the fitted curves result in predictions reasonably close to the observed values.  Furthermore, when compared to the observed PM2.5 values, the sum of predicted EC-TOT and OC-TOT values predict the lack of monotonicity and patterns of maxima and minimum seen in the PM2.5 data.
However, as a result of the values predicted by these sigmoid-type curves at high and low IMEP values, extreme patterns in the EC-TOT/OC-TOT ratios predicted occur.  These extreme values are artifacts that result solely from the behavior of simplistic logistic curves at the bounds of IMEP in the observed data sets.  As a result, for predictive purposes, the maximum and minimum observed EC-TOT and OC-TOT values observed in the data set were set as the artificial limits of predicted EC-TOT and OC-TOT, respectively.  While this approach is arbitrary, it does ensure that extreme predictions resulting from the selection of the logistic functional form do not occur.
The following graph (log-scale) depicts the behavior of the TOT-based EC/OC ratio as a function of IMEP.  As demonstrated on the graph, without the max/min constraints on predicted EC-TOT and OC-TOT, the predicted ratio assumes values with a much broader range than found in the data.

The approach of constraining predictions to the maximum and minimum values observed in the measured data set is not grounded in any theoretical basis, but is a "brute force" approach.  Future revisions to this analysis may consider alternative approaches more grounded in accepted theoretical or statistical methodology.
The logistic curves described above receive IMEP predictions from PERE to predict EC-TOT and OC-TOT emission rates (g/bhp-hr) for every second of a driving cycle.  Combined with real-time work estimates from PERE, emissions are expressed in g/s, the same units required for MOVES.
EC-TOT and OC-TOT emission rates are converted to TOR-equivalent rates for use in MOVES, using the TOT-TOR "calibration" relationships described above.  Alternatively, TOT-equivalent rates can be used to compare with data from studies employing TOT for carbon analysis.
It should be noted that these emission estimates are based on a single engine.  Therefore, predictions of EC and OC emission rates based on these relationships are insensitive to model year, although PERE-HD does vary frictional MEP as a function of model year.
 Organic Carbon to Organic Mass Conversion
Carbon is only one component of the organic material found in PM emission samples.  Hydrogen, oxygen, and nitrogen are also components of organic molecules found in exhaust PM.  For this study, a simple set of OC/OM conversion ratios were employed.
Heywood (1988) presents data on the chemical composition of diesel exhaust PM, presenting characterization of both the "extractable composition" and "dry soot" components of PM measured at idle and at 48 km/h.  The composition data is as follows:

Idle
48 km/h
Atomic formula
C23H29O4.7N0.21
C24H30O2.6N0.18
OM/OC Ratio
1.39
1.26

The data for the "extractable composition" is assumed to represent  the organic mass of particles.  The total molar weight to carbon molar weight ratio was used to convert OC to OM.  The idle data from Heywood were used when engine IMEP was 0.15 or under, corresponding to the idle mode of the cycle employed by Kweon et al. (2004).  All other engine conditions employed the ratio based on the 48 km/h sample in Heywood.

 Comparison of Predicted Emissions with Independent Measurements
To ensure that predicted EC and OC emission rates from this approach are reasonable prior to any application for MOVES, PERE-HD based EC and OC emission factors were compared with measured emission factors from an independent study.  Shah et al. (2004) report EC and OC emission factor and rates for a series of heavy heavy-duty diesel trucks (HHDT) in California.  Shah et al. report the results of emission testing using the CE-CERT Mobile Emissions Laboratory (MEL), a 53-foot combination truck trailer containing a full-scale dilution tunnel designed to meet Code of Federal Register (CFR) requirements.  The primary dilution tunnel is a full-flow constant volume sampler, with a double-wall insulated stainless steel snorkel that connects the MEL directly to the exhaust system of a diesel truck.  PM collection systems were designed to meet 2007 CFR specification, including a secondary dilution system (SDS).
The 11 trucks sampled in this study were all large HHDDTs with engine model years 1996-2000, odometers between approximately 9,000 and 547,000 miles, and rated powers from 360-475 hp.  It should be noted that these trucks, on average, have larger engines and higher rated power than "typical" trucks on the road.  Furthermore, they were loaded with only the MEL, which weighs 20,400 kg.  As a result, the emissions from these trucks do not reflect the expected variability in truck running weight described above and used in the PERE-HD runs for this study.
Shah et al. (2004) report emission data for each of the four modes of the CARB HHDDT cycle, including cold start/idle, creep, transient, and cruise.  The test cycle represents a wide range of driving patterns, as suggested in the table below.  Note that these test cycles are trip-based, so each begins and ends with the vehicle at stop.

Cycle
Distance (mi)
Duration (s)
Average Speed (mph)
Maximum Speed (mph)
Maximum Acceleration (mph/s)
Cold start/idle
0
600
0
0
0
Creep
0.124
253
1.77
8.24
2.3
Transient
2.85
668
15.4
47.5
3.0
Cruise
23.1
2083
39.9
59.3
2.3

The following table presents the EC-TOT and OC-TOT emission rates reported in Table 6 of the study:
                                     Rate
                                     Idle
                                     Creep
                                   Transient
                                    Cruise
                                  EC (mg/mi)

                                   340+-140
                                   446+-115
                                   175+-172
                                  OC (mg/mi)

                                   607+-329
                                  182.9+-51.2
                                  74.7+-56.3
                                  EC (mg/min)
                                  4.10+-2.38
                                   10.4+-4.8
                                  110.7+-27.0
                                  93.0+-68.3
                                  OC (mg/min)
                                  20.9+-11.6
                                   17.0+-6.4
                                  45.5+-13.2
                                  42.3+-26.8

The following graph illustrates the comparison between predicted EC-TOT and OC-TOT emission factors predicted by PERE-HD and those reported by Shah et al. (2004).  The letters "H," "M," and "L" refer to high, medium, and low accessory loads employed in the PERE-HD runs with IMEP-based emission rates.  As shown in the graph, it appears that for transient and cruise conditions, PERE-HD predicts the general between-cycle trends in EC-TOT and OC-TOT emission factors.  It appears that for the low-speed "creep cycle," PERE-HD or the IMEP-based emission rates underpredict total carbon (EC+OC) emission factors, but that the general trend in the EC/OC ratio is directionally correct.

Variability in Predicted EC and OC Emission Rates
Through the modeling approach used here the influence of variability in vehicle weight and engine displacement on heavy-duty EC and OC emission rates can be assessed.  It should be noted that these relationships are contingent on the particular algorithms employed in PERE-HD for estimating power and IMEP, as well as on the functional form of the IMEP-based emission relationship described above.  As such, the analysis of variability in EC and OC emission rates is constrained within the functional forms of all models employed.
The graph below depicts the TOR-specific ratios of the total amount of EC and OM emitted across the transient driving cycle.  As is apparent, increasing running weight per unit of engine displacement is associated with an increased EC/OC ratio.  The highest EC/OM ratios, located in the upper right-hand-quadrant of the graph, correspond to vehicles loaded with extreme weight relative to the total available engine displacement.

In general, these results reflect the role that running weight has on IMEP in a truck.  Since IMEP correlates highly with the air/fuel ratio (or equivalence ratio φ), the data suggest that EC/OC partitioning is driven by the pyrolysis that occurs in engines under load.
Very few weight/displacement pairings are greater than 3,300 kg/L.  The following graph depicts the cumulative frequency distribution (CFD) of simulated weight/displacement ratios in PERE-HD.