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Exhibit 3-48. Summary of strengths and weaknesses--local H/C methodology.
Criteria Strengths Weaknesses
Representation of physical Dominant physical processes typically Structure of methodology is fluid; it must be
processes included ensured that adequate representation is included
Sensitivity to input parameters High overall sensitivity to parameters, which are
generally uncertain; some sensitivity mitigated by
ensuring consistency between interim results
(e.g., average age)
Flexibility Extremely flexible--fluid method structure
allows variation for available inputs and
surrogates
Ability to incorporate effects of Straightforward to include effects in
emission reduction strategies calculations if use and effectiveness is
known
Representation of future Future-year populations calculated may be
emissions projected if growth factors are known
Consideration of alternative Alternative technologies may be included by
vehicle/fuel technologies adjusting emission factors and populations
Data quality No specific model on which to rely; information
often comes from sources and surrogates of
varying quality
Spatial variability Tailored methodology allows application to
range of domains, down to small/project
scale
Temporal variability Study may be designed for annual, daily, or
seasonal inventories, depending on input
data
Review process Varies by application
Endorsements Varies by application
Engine Power. Engine power represents the total rated Emission Factors. All methods require the use of emission
power of each of the engine types installed on commercial H/C. factors, although their source and quality level may vary. They
Calculation of H/C emissions may require either disaggrega- may be defined for a given combination of engine power,
tion into bins of specific type, age, and horsepower range or density, size, and age, or vary only by equipment type and/
may just sum individual engines or even use overall averages, or age. As for other factors used to calculate emissions, the
depending on the level of detail of the study. Because emissions result is linearly proportional to this value, thus the impact
are linearly related to total power, this can have a large impact of uncertainty in this parameter on that for the final calcu-
on the uncertainty of total emissions. For additional discus- lations can be significant. For additional discussion, see Sec-
sion, see Sections 3.7.3 and 3.7.4. tions 3.7.3 and 3.7.4.
Activity. Commercial H/C engine activity determines
the average operating hours of a given engine and vessel type 3.7 Cargo Handling Equipment
in an annual period, and is typically described in hours per
year. It may be broken down into bins of total power, power Cargo handling equipment (CHE) is used to move or
density, engine size, or left aggregated only at the H/C type support movement of freight between modes at intermodal
level, depending on the methodology. Because emissions are facilities, such as ports. Particularly at ports, a wide range of
linearly related to activity, uncertainty in this parameter can CHE is in use due to the diversity of cargo. Examples of
have a large impact on the uncertainty of total emissions. types of CHE include
However, because activity also figures into the age distribu-
tion of the NONROAD model, impact of its uncertainty may · Cranes,
be somewhat mitigated if parameters are adjusted to ensure · Forklifts,
consistency in the age distribution. For additional discussion, · Manlifts,
see Sections 3.7.3 and 3.7.4. · Sweepers,
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Exhibit 3-49. Parameters.
Geographic Pedigree Qualitative Quantitative
Parameter Methods/Models
Scale Matrix Assessment Assessment
Main Engine EPA RIA method, CARB H/C method National,
Population Regional
Auxiliary Engine EPA RIA method, CARB H/C method National,
Population Regional
Harbor Craft Secondary: used to derive engine Local
Population populations in local H/C method
Number of Secondary: used to derive engine Local
Engines per populations in local H/C method
Vessel
Load Factors All All
Emission Factor All All
Engine Power All All
Activity All All
Deterioration Optional and secondary: used to derive in- All
Factor use emission factors.
Growth Factor Optional and secondary: needed for future- All
year projections
Engine Age Optional and secondary: needed to All
determine average emission factors
Median Life Optional and secondary: needed to determine All
age distribution
Scrappage Intermediary: derived from equipment age All
and median life
Duty Cycle Secondary: used to derive load and transient All
adjustment factors
Use of Retrofit Optional, secondary. Used to calculate All
Devices control factors on resulting emissions and/or
correct modeled emission factors.
Fuel Type Secondary: used to determine emission All
factors
Key: indicates that a parameter is analyzed in the way denoted by the column: indicates that the parameter is not
discussed in the way denoted by the column.
· Container handlers, Container terminals use CHE most extensively, while truck-
· Generators, to-rail equipment and dry bulk terminals also have high use of
· Specialized bulk handlers, CHE. As examples, in 2007, the Port of Long Beach found that
· Nonroad vehicles, 81% of the CHE portwide was employed by its container ter-
· Rail pushers, minals and that 8% of total NOx emissions were due to CHE
· Stackers, (100); the Port of Houston found that 15% of its 2007 total
· Skid steer loaders, NOx emissions came from CHE (96); New York/New Jersey
· Top handlers, found that 25% of their 2006 NOx emissions were due to CHE.
· Tractors, (101) Thus, determining emissions from container terminal
· Excavators, CHE is important in any landside emission inventory.
· Welders, and Generally, CHE emissions from freight activities at ports
· Yard tractors. are estimated using either the NONROAD or OFFROAD
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Exhibit 3-50. Pedigree matrix--harbor craft equipment parameters.
Technological Correlation
Geographic Correlation
Temporal Correlation
Representativeness
Acquisition Method
Range of Variation
Impact on Result
Independence
Parameter
Main 4 Varies Varies Varies N/A Varies Varies 5
Engine
Population
Auxiliary 3 Varies Varies Varies N/A Varies Varies 5
Engine
Population
Load Factor 4 2-3 1 2 N/A Varies 2 4
Emission 4 2-3 1 2 N/A Varies 3 4
Factor
Engine 4 1 Varies Varies N/A Varies 1 1
Power
Activity 4 Varies 1-2 3 N/A Varies 3 4
emission models--or methods similar to those employed in of Equation 15). Evaluation of process uncertainty is presented
these models--combined with parameters representing the in Sections 3.7.1 to 3.7.4. Evaluation of parameter uncertainty
CHE present, such as rated power, model year, type of fuel is presented in Section 3.7.5. In both cases, any known biases
used, annual hours of operation, load data, use of retrofit de- should be corrected. The effects of quantifiable uncertainty in
vices or other emission mitigation measures, and fuel type. input parameters on total calculated uncertainty may be made
Uncertainty in each of these input parameters can lead to sig- using standard error propagation methods, discussed in Sec-
nificant uncertainty in the final emissions estimated. Mod- tion 4.3.4. If no covariance is assumed for the parameters in
els/methods and parameters are discussed separately in the Equation 15, the net error in total emissions would be given by
following sections, however, the relationship between the two Equation 16, where 2 indicates the variance.
must be kept in mind.
2 emissions = ( NPLA ) 2 f + ( f PLA) 2 N + ( f NLA) 2 P
2 2 2
For example, the OFFROAD model generates emission
+ ( f NPA) 2 L ( f NPL ) 2 A
2 2
inventories for a given type of equipment using an equipment- (Equation 16)
total power methodology as shown in Equation 15.
Emissions = f N P L A (Equation 15) 3.7.1 Summary of Methods and Models
Where Two general categories of methods are used to estimate
CHE emissions. These are referred to as the best practice and
f is the emission factor,
streamlined methodologies. (10) Generally, these two differ
P is the maximum rated equipment horsepower,
only in the level of direct information collected and employed
L is the load factor,
in the calculations. The best practice methodology dictates
A is the annual activity,
surveys of all equipment to establish correct parameters and
N is the equipment population, and
then employs the NONROAD or OFFROAD models; the
In which f incorporates adjustments due to deterioration, streamlined methodology allows for a greater degree of free-
transient use, and age-related effects. dom in collecting direct information by substituting surro-
Uncertainty in the resulting CHE emissions can then be gate or otherwise derived information. It may then either use
attributed to either the process uncertainty (that is, the degree the models, or adjust the methodologies of the models them-
to which Equation 15--or other OFFROAD algorithms-- selves for the available information. A special case, third
represents the actual emissions process) and parameter un- methodology is used in CARB's CHE inventory, which
certainty (that is, the uncertainty in the individual elements is essentially the best practice methodology without directly
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Exhibit 3-51. List of cargo handling equipment methods and models.
Method/Model Type Geographic Scale Pollutants Freight/Passenger
NONROAD Emissions Model County or Larger* HC, NOX, CO, CO2, Freight
SOX, and PM; for
exhaust and non-
exhaust emissions
OFFROAD Emissions Model County, Air Basin, or CO2 and CH4,** HC, Freight
Statewide (CA only) CO, NOX, and PM; for
exhaust, evaporative,
and start.
Best Practices Method All All Freight
Methodology***
Streamlined Method All All Freight
Methodology
ARB Methodology Method County, Air Basin, or All Freight
Statewide (CA only)
Notes:
* Model use is restricted to countywide definitions, but emission factors and methods may be extracted at scales down to
equipment level.
** CO2 and CH4 emissions are produced by OFFROAD, however, these estimates are not currently used as the basis for CARB's
official GHG inventory which is based on fuel usage information. (135 )
*** As documented in EPA's draft best practice document. (10 ) This method includes locally specific information on fleets and use
of models.
As documented in EPA's draft best practice document. (10 ) This method includes use of surrogates for missing locally specific
information.
As documented by CARB. (136 ) The method used to derive the statewide CHE inventory is a slightly modified version of the
best practices methodology, but without directly relying on the OFFROAD model, allowing a modified calculation of deterioration.
using the OFFROAD model. Exhibit 3-51 lists these three port of CARB's Mobile Cargo Handling Equipment Regulation
methods and two models. (adopted December 2005, effective December 2006), (138)
CARB developed a statewide emission estimation methodol-
ogy and corresponding emission inventory for CHE. (139)
3.7.2 Evaluation of National Methods
The regulation is in support of a statewide emission control
and Models
strategy for CHE at ports and intermodal railyards.
There are currently no national scale inventories of CHE
emissions exclusively. The EPA prepares the NEI every three CARB CHE Methodology
years, which includes emissions from nonroad sources, gener-
ally broken out by SCC. Similarly, for the 2004 (Tier IV) Non- The CARB methodology, based on a survey conducted by
CARB in early 2004 and the ports of Los Angeles (110) and
road Diesel Rulemaking, EPA prepared a baseline national
Long Beach (84) emission inventories available at that time, es-
emission inventory for nonroad engines with populations
timated population and activity data for CHE statewide by
based on commercial inventories of equipment sales and
equipment type. The study developed emissions estimates at
calculations made via national-scale runs of the NONROAD
16 ports and 14 intermodal railyards in the state for 8 equip-
model. (137) However, no details are given specifically to CHE,
ment types. (CHE emissions also were estimated for the health
as results are reported only for "land-based nonroad engines."
risk studies for major rail yards in California.) Exhibit 3-52
Given the lack of a national-scale CHE emissions inventory, no
shows these eight equipment types, the corresponding SCCs,
uncertainties in such modeling are addressed here.
and the SCC type. (Note that for most equipment types, multi-
ple fuels are possible. The SCCs shown here are for off-highway
3.7.3 Evaluation of Regional Methods diesel.)
and Models CARB (139) summarizes the methodology as follows:
California has conducted the only regional analysis of CHE Briefly, the approach used to develop the cargo handling
emissions. To evaluate statewide emissions from CHE in sup- equipment emissions inventory estimates entailed determining
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Exhibit 3-52. NONROAD cargo handling equipment certainty are (1) the appropriateness and representativeness
types. of the OFFROAD model characterizations of CHE, (2) the
groupings used to categorize CHE, and (3) the potential for
Aggregated CHE Type Estimated SCC SCC Type bias in survey results.
Cranes 2270002045 Construction
The OFFROAD model itself is discussed in the following
Excavators 2270002036 Construction
subsection. The parameters used in this method are shown in
Forklifts 2270003020 Industrial
Container handling equipment 2270003050 Industrial Exhibit 3-53 and discussed in Section 3.7.5.
Other general industrial equipment 2270003040 Industrial
Sweepers/scrubbers 2270003030 Industrial Equipment Groupings
Tractors/loaders/backhoes 2270002063 Construction
Yard trucks 2270003070 Industrial The CARB CHE methodology states its choice to group
equipment into eight categories (listed above) to make the
analysis compatible with the OFFROAD model. (Note that
this is different from the discussion in the Summary in that
the average annual emissions per engine for each equipment type
and then multiplying that value by the total number of engines aerial lifts are grouped into general industrial equipment.)
in that grouping. The majority of the inputs that went into There is no particular bias or additional process uncertainty
developing the average annual emissions came from individual associated with groupings as long as the parameters within
engine profiles developed using the information from a cargo each group are appropriately weighted and applied, and re-
handling equipment survey conducted by the [C]ARB in 2004 sults are provided at the same resolution. That is, the result of
and cargo handling equipment population information pro-
vided by the ports of Los Angeles and Long Beach. These inputs
total emissions calculations from more highly resolved cate-
were then processed using a template based on the [C]ARB's gories than those here should be consistent with the results of
OFFROAD model to estimate annual emissions per engine for this study if values within each group are appropriately con-
each equipment type. This data was then expanded to include sidered. As in all similar cases, resolution must be balanced
the estimated statewide population of cargo handling equip- with accuracy; here the level of resolution was dictated by the
ment fitting a specific age and horsepower range. To estimate
use in the OFFROAD model.
port specific emissions, the populations of cargo handling
equipment were allocated based on the [C]ARB Survey and the Specific discussion of uncertainty with parameters is given
port-specific data. Emission estimates were developed for the below. However, process uncertainty is associated with the as-
eight types of equipment described. . . . Estimates for NOx, HC, signment of average parameters to bins. For example, in
and PM were made. preparing emissions for cranes, the load factor used should be
a number-weighted average of the load factors from each
This methodology only differs from the best practice crane in the sample set. However, this value is not well known.
methodology by not relying directly on the OFFROAD (140) The error in this parameter is the difference between the value
model and, instead, slightly modifying the calculation of used and the true average from all equipment in the bin. This
deterioration. (141) uncertainty can be due to choice and assignment of values to
Total uncertainty in this method is due to both process and equipment groupings.
parameter uncertainty. Although the process used here is
generally believed to rely on the best information available at Potential Survey Bias. There is potential for bias in survey
the time, three potentially significant sources of process un- methods due to misreporting of equipment. This could be due
Exhibit 3-53. Parameters from the CARB CHE inventory.
Input Factor Source of Data (Gas and Diesel)
2004 CARB Survey of Statewide Ports & Rail Yards; POLA & POLB data
Population (base year 2004) (2002)
Useful life 2004 CARB Survey of Statewide Ports & Rail Yards
Activity (h/yr) 2004 CARB Survey of Statewide Ports & Rail Yards
2004 CARB Survey of Statewide Ports & Rail Yards; POLA & POLB data
Average horsepower (2002); Power Systems Research (1996)
Load factor Power Systems Research (1996)
2004 CARB Survey of Statewide Ports & Rail Yards; POLA & POLB data
Allocation factor (2002)
Growth factor 2002 POLA Container TEUs data
Survival rate Power Systems Research (1996)
Source: CARB, Cargo Handling Equipment One Pager.
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to a desire to underreport equipment or overstate control tech- produce model-year specific population distributions for
nologies to underestimate emissions resulting from activities at years 1970 to 2040, allocated to geographic regions. Baseline
a facility, omission of specific facilities due to a size cutoff, for emission factors are corrected for in-use and ambient condi-
example, or many other reasons. As noted earlier, any known tions. Emission inventories are resolved to the county, air
bias should be removed from a sample set prior to analysis. basin, or air district by fuel type, engine type, equipment cat-
Sampling was made by CARB for over 120 owner/operators egory, and horsepower group. (140)
statewide and results incorporated with detailed inventories Uncertainty in emission estimates by the OFFROAD
from the 2001/2002 Port of Los Angeles (POLA) and Port of model is driven by several aspects of the model, both in its
Long Beach (POLB) inventories. CARB corrected Los Ange- structure and its input parameters.
les and Long Beach inventories to a common year assuming
a 3% annual growth factor. To adjust for limited information, Calculation Method. The basic emissions calculations in
CARB applied corrections to survey results for equipment the model are summarized by Equation 17.
populations where data were under-reported or not reported.
Thus, no residual bias is likely for this study. Emissions = f N P L A (Equation 17)
Summary of Strengths and Weaknesses. CARB CHE As noted above, this is essentially a total power approach
methodology strengths and weaknesses are described in to emissions calculations, rather than a TIM calculation or
Exhibit 3-54. a fuel consumption approach. On average, a total power
approach and a TIM approach should agree, if the more
detailed activity profile and load in a given power setting
OFFROAD Model
agree with the average load factor employed by the power
The OFFROAD2007 model is CARB's current emissions approach. (As noted by the lack of use of OFFROAD in creat-
and emission factor model designed to incorporate effects of ing the California GHG inventory (10), a fuel consumption
proposed regulations, technology types, and seasonal condi- approach is not generally expected to agree.) However, uncer-
tions on emissions of nonroad equipment except ocean-going tainty is inherent in this parameterization due to the physical
vessels, commercial harbor craft, locomotives, agricultural representation of annual activity.
irrigation engines, and gas cans. The model consists of three Additional uncertainty due to best estimate parameters for
main modules: population, activity, and emissions factor. average use conditions also exists in the model. This is dis-
Population is determined from a calendar year 2000 baseline cussed in Section 3.7.5. However, OFFROAD model-specific
equipment population, adjusted for growth and scrappage to discussion follows here.
Exhibit 3-54. Summary of strengths and weaknesses--CARB CHE methodology.
Criteria Strengths Weaknesses
Representation of physical Dominant physical processes included
processes
Sensitivity to input parameters Method relies on well studied model inputs, Uncharacterized overall uncertainty
and modifies when necessary
Flexibility Tailored methodology
Ability to incorporate effects of Information included from local authorities on
emission reduction strategies reduction strategies implemented
Representation of future Method relies on well studied model inputs
emissions
Consideration of alternative Information included from local authorities on
vehicle/fuel technologies reduction strategies implemented
Data quality Information included from local authorities;
known biases corrected
Spatial variability Applicable only to CA; emissions resolved
only to county level
Temporal variability Produces only annual inventories
Review process Unclear from documentation
Endorsements ARB
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Population Parameters. Population in OFFROAD2007 Use of on-road deterioration rates, application of esti-
is determined as the calendar year 2000 baseline equipment mated duty cycles, and assumed zero-hour emission factors
population adjusted for growth and scrappage. Growth factors all may add uncertainty into model results. Nonroad CHE
are based on socioeconomic indicators such as housing units active at ports is likely to have a different duty cycle than sim-
and manufacturing employment by category, by county, and ilar nonroad equipment used in other industrial applications.
with respect to year 1990 sales. Scrappage is fixed by equipment Further, the use of on-road deterioration factors from a 1990
age and/or use and depends on engine type and horsepower study (142) seems unlikely to represent a current fleet of non-
group. For all CHE types, useful life is represented in years and road engines. Sources of zero-hour emission factors also are
is driven by the engine's expected life (note that useful life for unclear. Each of these leads to an unquantified uncertainty in
lawn and garden equipment and recreational vehicles is deter- the model results. Note that the CARB CHE inventory did
mined by the equipment life). As for the NONROAD model, not rely on deterioration rates in the OFFROAD model.
the equipment useful life is defined by the sample median; total
Summary of Strengths and Weaknesses. Strengths
lifetime is twice the useful life.
and weaknesses of the OFFROAD model are described in
Since emissions estimates are linearly proportional to pop-
Exhibit 3-55.
ulation, significant uncertainties may result from uncertainties
in population, as discussed in Section 3.7.5. For OFFROAD,
particularly, many of these uncertainties are driven by the pop- 3.7.4 Evaluation of Local/Project-Level
ulation projections to specific calendar years. These uncertain- Methods and Models
ties may be mitigated by using observed counts of CHE instead, Several studies of CHE emissions have been conducted at
as in the CARB CHE methodology. Uncertainties also exist in the local/project level. Principally, these include studies at ports
the methods used to allocate populations to smaller domains, throughout the United States, as detailed by Exhibit 3-56.
such as counties or air basins. Similarly, the shape of the age-to- Other studies of note include CHE active at intermodal rail-
median age curve could be inappropriate for a given equipment yards throughout California (143) and NEPA and CEQA
type. Neither of these uncertainties is generally quantifiable, but studies that have characterized impacts from CHE. (144)
could lead to uncertainty in resulting inventories. Typically, these studies either rely on the best practices
methodology directly or a variation of it, where calculations are
Activity Parameters. Activity estimates in OFFROAD made externally, but in a similar method to that of NONROAD
2007 include annual average usage, load factors, brake-specific or OFFROAD models. In some cases, particularly for the less
fuel consumption (BSFC), and number of starts per year. detailed studies, a streamlined approach is used. These methods
Values are included for each equipment category by fuel and and models are discussed in the following subsections.
engine types and horsepower group. Activity profiles also
include seasonal and temporal variations by industrial category. Best Practice Methodology
Uncertainty exists in these parameters on the appropriate cate-
gory binning and application across categories. Particularly, this Best practices in developing an emissions inventory from
is true for equipment that could have uses in multiple indus- CHE activity dictate that one should gather detailed informa-
tries or placed in a more general category. There also are issues tion on all CHE present at the port in question (within the
with attributing usage fractions to freight activity only. For study boundaries) and make simulations using the NON-
example, an average (no peak) usage pattern is exhibited by ROAD (outside of California) or OFFROAD (in California)
airport ground service and TRUs while construction and in- model. This methodology is rooted in observations of all active
dustrial equipment is assigned primarily a weekday profile. CHE, including information on the following:
However, much CHE is likely to be considered industrial · Equipment type,
equipment, although having a profile more similar to air · Rated horsepower,
GSE. Similarly, a skid steer loader used in a mining applica- · Model year,
tion is not likely to represent the activity profile of one used · Type of fuel used, including fuel sulfur level for diesel,
at a bulk cargo terminal. · Annual hours of operation,
· Equipment load data, and
Emission Factors. Exhaust emission factors are engine- · Retrofit devices or other emission mitigation measures
specific and vary by fuel type, horsepower group, and model employed.
year. Equipment-specific emission rates are based on the
combination of engine emission factors and equipment duty Using the data collected on equipment numbers, types,
cycles. Deterioration rates are generally based on on-road horsepower, model year, hours of operation and load data, in-
emissions data. puts can be generated for the various NONROAD (OFFROAD)
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Exhibit 3-55. Summary of strengths and weaknesses--OFFROAD model.
Criteria Strengths Weaknesses
Representation of physical Dominant physical processes included
processes
Sensitivity to input Model relies on user-customizable inputs; Uncharacterized overall uncertainty
parameters sensitivity to these inputs varies
Flexibility Moderately flexible; customization requires
familiarity with model, or replication of
calculations
Ability to incorporate effects May be included after model runs, using CARB-
of emission reduction certified reductions; unclear how to include in
strategies simulations
Representation of future Projections available in the model
emissions
Consideration of alternative Unclear
vehicle/fuel technologies
Data quality Generally structured from best available
information
Spatial variability Applicable only to CA; emissions resolved only
to county level
Temporal variability Produces only annual inventories
Review process Unclear from documentation
Endorsements ARB
Exhibit 3-56. Recently conducted port inventories containing CHE.
Year Data
Port Pollutants* Contractor*
Published Year
Charleston (94) 2008 2005 NOx, TOG, CO, PM10, PM2.5, SO2 Moffatt & Nichol
Houston/Galveston
2003 2001 NOx, VOC, CO Starcrest
(145)
Houston (96) 2009 2007 NOx, VOC, CO, PM10, PM2.5, SO2, CO2 Starcrest
Los Angeles (110) 2005 2001 NOx, TOG, CO, PM10, PM2.5, SO2, DPM Starcrest
Los Angeles (83) 2007 2005 NOx, TOG, CO, PM10, PM2.5, SO2, DPM Starcrest
NOx, TOG, CO, PM10, PM2.5, SO2, DPM, CO2,
Los Angeles (99) 2008 2007 Starcrest
CH4, N2O
Long Beach (146) 2004 2002 NOx, TOG, CO, PM10, PM2.5, SO2, DPM Starcrest
Long Beach (130) 2007 2005 NOx, TOG, CO, PM10, PM2.5, SO2, DPM Starcrest
NOx, TOG, CO, PM10, PM2.5, SO2, DPM, CO2,
Long Beach (100) 2009 2007 Starcrest
CH4, N2O
New York/New Jersey
2003 2002 NOx, VOC, CO, PM10, PM2.5, SO2 Starcrest
(147)
New York/New Jersey
2005 2004 NOx, VOC, CO, PM10, PM2.5, SO2 Starcrest
(148)
New York/New Jersey NOx, VOC, CO, PM10, PM2.5, SO2, CO2, N2O,
2008 2006 Starcrest
(101) CH4
Oakland** (102) 2008 2005 NOx, ROG, CO, PM, SOx Environ
NOx, HC, CO, SOx , PM 10, PM2.5, CO2, 9 Air Bridgewater
Portland (103) 2005 2004
Toxics Consulting
NOx, TOG, CO, PM10, PM2.5, SO2, DPM, CO2,
Puget Sound*** (104) 2007 2005 Starcrest
CH4, N2O
San Diego (105) 2008 2006 NOx, TOG, CO, PM10, PM2.5, SO2, DPM Starcrest
Notes:
* Starcrest = Starcrest Consulting Group LLC, Environ = Environ International Corp.
** Definitive results are not included for cargo handling equipment in this inventory.
*** Includes the ports of Anacortes, Everett, Olympia, Port Angeles, Seattle, and Tacoma.
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equipment types to determine emissions for CHE at the port. ditions, accidental omission of facilities or equipment, a desire
Use of retrofit or emission control devices must be treated to misrepresent activity, or incorrect survey methodology or
outside the model. In these cases, emission factors may be de- results processing, for example. As noted earlier, any known
termined using the NONROAD (OFFROAD) models for diesel bias should be removed from a sample set prior to analysis. If
equipment and then appropriate emission reduction per- an appropriate survey is conducted following the best practice
centages applied. For retrofit devices such as diesel oxidation guidelines, these uncertainties should be small.
catalysts, diesel particulate filters, or other technologies, re-
ductions specified in the following sources should be applied: Summary of Strengths and Weaknesses. Best practice
EPA's Verified Retrofit Technology website; (149) EPA's Diesel methodology strengths and weaknesses are provided in
Emission Quantifier; (150) or CARB's list of currently veri- Exhibit 3-57.
fied technologies. (151) Other sources may be relied upon, but
may be considered more uncertain.
Streamlined Methodology
Specific discussion of these models and their associated un-
certainties is given in Section 3.7.3. Total uncertainty in this In cases where all necessary information is not available,
method is due to both process and parameter uncertainty. The resulting emissions from CHE activity may be approximated
process described here is generally structured to rely on the best using a more streamlined approach than that of the best prac-
information available for a given project. However, at the time, tice approach, allowing emission estimations without directly
three potentially significant sources of process uncertainty observed equipment inventories and other parameters.
are (1) the appropriateness and representativeness of the Recently, a variety of detailed, local/project-scale CHE
NONROAD (OFFROAD) model characterizations, (2) the emission inventories have become available (see Exhibit 3-56).
groupings used to categorize CHE in analysis, and (3) the po- Unlike vessel emissions, there is no standardized methodol-
tential for bias or error in equipment inventory counts. ogy for developing estimates of port CHE emissions. Devel-
The appropriateness and uncertainty of the models is dis- oping a detailed CHE inventory may require extensive time
cussed in their respective sections. and resources to survey tenants within the study boundaries re-
garding their equipment. As an alternative to this level of effort,
Equipment Groupings. The best practice methodology
CHE emissions are sometimes estimated based on inputs
should minimize uncertainty associated with grouping CHE
developed for CHE inventories prepared by other sources. The
into categories by following the categories already provided
essence of a streamlined CHE evaluation is to estimate any
by each model to make the analysis compatible with the model
missing values in a local survey of equipment types, counts,
being employed. As in all similar cases, resolution must be bal-
and/or parameters from other published studies--commonly
anced with accuracy; here the level of resolution will be dic-
by applying ratios of known parameters, such as cargo tonnage
tated by the emissions model.
throughput--to other detailed ports, followed by calculations
There is no particular bias or additional process uncertainty
using the NONROAD (OFFROAD) model or methodology.
associated with groupings as long as the parameters within
Uncertainty in this method can be significant, although
each group are appropriately weighted and applied, and results
general quantification of this uncertainty is difficult. Uncer-
are provided at the same level of resolution. That is, the result
of total emissions calculations from more highly resolved cat- tainty is propagated into the analysis via the parameters input
egories should be consistent with the total emissions from a to the model, such as in the number inventory and properties
coarser study if values within each group are appropriately of CHE. For example, one might use tonnage throughput ra-
considered. Specific discussion of uncertainty with input pa- tios between two projects to determine the number of cranes
rameters for the OFFROAD model is given above; discussion at a second project from that at the first, but translate all other
of the NONROAD model is below. However, process uncer- parameters for those cranes (e.g., power, load, activity) di-
tainty is associated with the assignment of average parameters rectly from the values at the known port. The net uncertainty
to bins. This uncertainty can be due to choice and assignment on resulting emissions could be tracked from the uncertainty
of values to equipment groupings. Because the bins are deter- resulting from the scaled input parameters, but the source of
mined by model designations and the CHE sample size is this uncertainty is the process used to translate the parameters.
expected to be moderate to small for local/project-scale analy- Specifically, it is due to the assumptions used and choices
ses, fleet characterization should not cause much uncertainty made. Bias can be minimized by selecting projects that are sim-
in the emissions analysis. ilar, both in scope (by using methodologies such as the princi-
pal port-like analysis of the ARCADIS guidance, (116117) for
Survey Error. There is potential for bias and error in sur- example) and in equipment age, activity, and other parame-
vey methods due to miscounting of equipment at the facility ters. Regardless, uncertainty in this methodology is likely to
for a variety of reasons such as inappropriate boundary con- be significant.
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Exhibit 3-57. Summary of strengths and weaknesses--best practice methodology.
Criteria Strengths Weaknesses
Representation of physical Dominant physical processes included
processes
Sensitivity to input parameters Method relies on detailed user inputs that may General, overall uncertainty unknown
not be readily available, but should produce best
results
Flexibility Low Flexibility; requires detailed data
collection
Ability to incorporate effects of Best information available; effects may be
emission reduction strategies included after model runs
Representation of future Projections available in the model and
emissions customizable to local information
Consideration of alternative May be achieved in methodology with suitable
vehicle/fuel technologies model runs
Data quality Structured from best available information
Spatial variability Applicable to any location, but data requirements
likely limit to smaller spatial scales
Temporal variability Most likely limited to annual inventories
Review process Documented in EPA Methodology Guidance
Endorsements EPA
Summary of Strengths and Weaknesses. Strengths and NONROAD2008. The NONROAD model (152) predicts
weaknesses of the streamlined methodology are shown in emissions for recreational land and marine vehicles as well
Exhibit 3-58. as logging, agricultural, construction, industrial, and lawn
and garden equipment. It includes over 80 basic and 260 spe-
cific types of nonroad equipment stratified by horsepower
NONROAD Model
rating, and considers equipment fueled by gasoline, diesel,
In April 2009, EPA released the current version of its compressed natural gas (CNG), and liquefied petroleum gas
nonroad, mobile emissions and emission factor model, (LPG). NONROAD2008 also includes emission reductions
Exhibit 3-58. Summary of strengths and weaknesses--streamlined methodology.
Criteria Strengths Weaknesses
Representation of physical Dominant physical processes included
processes
Sensitivity to input Method relies on surrogates for missing inputs;
parameters results highly sensitive to quality of inputs
Flexibility Highly flexible; customizable to data limitations
Ability to incorporate effects Highly customizable.
of emission reduction
strategies
Representation of future Projections available in the model and customizable
emissions to local information
Consideration of alternative May be achieved in methodology with suitable
vehicle/fuel technologies model runs
Data quality Structured from available information
Spatial variability Applicable to any location. Data flexibility allows
multiple spatial scales
Temporal variability Designed for annual inventories, but scalable with
appropriate information
Review process Documented in EPA Methodology Guidance
Endorsements
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associated with the 2008 diesel recreational marine standards sions for a given calendar year, growth can be set to zero so
from the locomotive/marine and small spark-ignition (SI) that the emissions will not increase over time and the results
and SI recreational marine final rules. The model is capable will be accurate for the analysis year.
of estimating subcounty emissions with specific inputs. How- For future forecasts, updated inputs for population and
ever, the practical geographic domains vary between county activity are required. This is due to the NONROAD method-
and national extents. NONROAD is intended to eventually ology, which calculates both population and age distribution
be replaced by a version of the MOVES model that will incor- in which the model uses a population growth rate to project
porate nonroad modeling capability. EPA has indicated that equipment populations from a base year to an evaluation
it intends to include this capability in the release of the final year. (155) For all base years (projected or current) the model
version of MOVES2010 (focused on on-road vehicles and fits the population numbers to a predetermined form as a func-
scheduled to be released by the end of 2009), however, that tion of growth and scrappage. The number of units of each
version would not be expected to yield substantially different model year (or, equivalently, age) is determined for each age
results compared to NONROAD2008. (153) for 50 years back. Populations with ages greater than twice the
Exhibit 3-59 provides an example of equipment types and the median life are assumed scrapped. (156)
corresponding SCC used in the NONROAD model to estimate Significant uncertainty in emissions may arise from this for-
emissions from CHE. The majority of CHE can be classified mulation of age distribution, due to the assignment of engine
into one of these equipment types. (Note that for most equip- tiers to specific ages (and power bins). The driving parameters
ment types, multiple fuels are possible. The SCCs shown here here are the growth rates, shape of the population distribution
are for off-highway diesel.) curve, and median lifetime of equipment. Any event that
Uncertainty in emission estimates by the NONROAD model leads to a difference in real world age distribution from that
is driven by several aspects of the model, both in its structure
assumed by the model will lead to different average emission
and its input parameters.
factors, and thus different emissions. This bias could result
Population Parameters. NONROAD maintains 1996, from a mischaracterization of equipment median life or
1998, and 1999 baseline populations and determines future growth rates, both of which shift the overall curve of popula-
year populations by assigning an average growth rate to esti- tion versus age. The resulting uncertainty could bias the results
mate emissions in subsequent years. (154) To produce emis- in either direction, as an under- (over-) estimated median life
Exhibit 3-59. NONROAD cargo handling equipment types.
Aggregated CHE Type Estimated SCC SCC Type
Compressor 2270006015 Commercial
Crane 2270002045 Construction
Forklift 2270003020 Industrial
Manlift 2270003010 Industrial
Sweeper 2270003030 Industrial
Car loader 2270003050 Industrial
Chassis rotator 2270003040 Industrial
Empty container handler 2270003050 Industrial
Generator 2270006005 Commercial
Light tower 2270002027 Construction
Specialized bulk handler 2270003050 Industrial
Nonroad vehicle 2270002051 Construction
Gantry Crane 2270002060 Industrial
Rail pusher 2270003040 Industrial
Reach stacker 2270003050 Industrial
Roller 2270002015 Construction
Side handler 2270003050 Industrial
Skid steer loader 2270002072 Construction
Top handler 2270003050 Industrial
Tractor 2270002063 Construction
Excavator 2270002036 Construction
Welder 2270006025 Commercial
Yard Tractor 2270003070 Industrial
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would lead to relatively more (fewer) newer engines and lower wear out would lead to overestimation of the fleet, thus the net
(higher) overall emissions. Similarly, a difference in the general bias due to in-use lifetime is expected to be small. (157) Uncer-
shape of the real world age distribution from that parameter- tainty due to the representativeness of on-road engine lifetimes
ized in the model could lead to bias in either direction. This for off-road applications is unknown, although EPA did con-
bias is more difficult to quantify without explicitly knowing the sider the data underlying these estimates in NONROAD devel-
full age distribution of the sample population, but one exam- opment. (158)
ple could be in cases where a type of equipment, engine, or Load factors in NONROAD are based on seven operational
technology is newly introduced and the older tail of the age duty cycles for agricultural tractors, backhoe-loaders, crawler
distribution is not yet populated. In that case, the bias would tractors, skid-steer loaders, arc welders, wheel loaders, and
be to higher emissions estimates by over-predicting the num- excavators. Extrapolation of the seven duty cycles to every
ber of older, more polluting engines. type of equipment was done by grouping the seven cycles into
Uncertainty in the results may also be attributed to correct three categories--transient cycles with high loads (average of
estimation of growth factors by equipment type. This param- 0.59 with range 0.480.78), transient cycles with low loads
eter may be set in model inputs, however, and is directly con- (average of 0.21 with range 0.190.23), and steady-state cycles
trollable by the user. Mischaracterization, however, could (average of 0.43)--and assigning all equipment to one of these
lead to significant bias in resulting populations. A similar sce- three categories. (157) Uncertainty in the measured range of
nario exists with equipment population. Default NONROAD high- and low-cycle values is about 10% to 30%, although that
equipment populations by geographic areas are determined for the steady cycles is uncharacterized, but could be as high
from national-level estimates using economic factors, such as about 80%. Uncertainty due to assignment of measured
as construction expenditures, farm acreage, and building emission factors to equipment groups is unknown. However,
square footage. (157) Reliance on these, rather than directly the assumed load factor is likely to be a significant source of
observed current year population counts, may lead to bias in uncertainty in NONROAD modeling, both in directly calcu-
resulting emissions. lating equipment emissions and in determining population
As emissions estimates are linearly proportional to popu- age distribution. EPA claims that the effects of load and life-
lation, significant uncertainties may result from uncertainties time in determining emissions and population are offsetting
in population, as discussed in Section 3.7.5. In all cases, these when computing total emissions, such that uncertainties in
uncertainties may be mitigated by using observed counts of these parameters should have little effect on total emission
CHE of each age for the given project. uncertainty. (157)
Activity values in NONROAD are based on surveys of
Usage Parameters. Engine median lifetime shapes the equipment users by a private company using proprietary
population distribution, as discussed previously. Annual methods that estimate annual activity by equipment type but
activity and load determine the engine usage. All are dis- not by engine size, age, or model year. The uncertainty in
cussed together by EPA. (158) The parameters are related average values and the actual sensitivity of activity to equip-
because NONROAD uses annual activity and load factor ment size and age are all unknown. (157) Thus, the effects of
values to calculate emissions by engine type and uses activ- these on overall emissions estimates is unknown.
ity, load factor, and median life together to calculate fleet NONROAD estimates brake-specific fuel consumption
age distributions. See Equation 18. (BSFC) as 0.408 lb/bhp-hr for engines smaller than (or equal
to) 100 hp and 0.367 lb/bhp-hr for engines larger than 100 hp,
Median Life At Full Load ( hours )
Median Life ( years ) = based on measured fuel consumption values during engine
Activity ( hours year ) Load Factor certification. (157) Uncertainty in these estimates is unknown.
(Equation 18)
Emission Factors. Emission factors in NONROAD (159)
NONROAD assumes equipment lifetime equals engine life; consist of zero-hour, steady-state emission factors, transient
engine life is determined based on the expected lifetime of adjustment factors, and deterioration factors; fuel sulfur im-
highway diesel engines operated continuously at full load and pacts on emission rates are included. Zero-hour, steady-state
adjusted to in-use values by dividing by the average load factor emission factors (EFs) are a function of model year and power,
and annual activity. The NONROAD methodology assumes which defines the technology type. Transient adjustment fac-
that nonroad engines are not rebuilt and that equipment never tors (TAFs) vary by equipment type. Deterioration factors
fails before the engine is worn out. These underlying assump- (DFs) are functions of the technology type and engine age. See
tions in the model may lead to significant resulting uncertainty Equation 19.
in calculated emissions. However, engine rebuilds would lead
the model to underestimate the equipment fleet, while engine EFInUse = EFSteadyState TAF DF (Equation 19)
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In addition to exhaust emissions, crankcase HC emissions As noted, this is essentially a total power approach to
are computed as a simple 2% fraction of exhaust HC emis- emissions calculations, rather than a TIM calculation or a
sions for Tier 0 to III engines and are zero for Tier IV engines. fuel consumption approach. On average, a total power ap-
Zero-hour, steady-state emission factors are drawn from a proach and a TIM approach should agree, if the more de-
variety of sources, including NEVES (baseline engines > 50 hp), tailed activity profile and load in a given power setting
CARB's OFFROAD values (Tier 0 engines less than 50 hp), agree with the average load factor employed by the power
emission rate tests (Tier 0 engines greater than 50 hp), EPA en- approach. However, uncertainty is inherent in the model
gine certification data (all Tier I engines and Tier II engines due to the physical representation of annual activity. Addi-
300600 hp), methods for the remaining Tier II and all Tier III tional uncertainty due to best estimate parameters for aver-
engines (including compliance margins on emission standards, age use conditions also exists in the model. This is discussed
certification results, CARB engine test data, and engineering in Section 3.7.5.
judgment). All Tier IV emission factors are based on compli-
ance margins from emission standards. Since each element is Summary of Strengths and Weaknesses. NONROAD
chosen based on the best available information, all bias is as- model strengths and weaknesses are shown in Exhibit 3-60.
sumed to be minimized. However, significant but unquantified
uncertainty persists in most factors. Factors based on standards
OFFROAD Model
are likely to be less uncertain, since engines must be designed to
meet specific thresholds, but a range of values is still likely. The OFFROAD model was discussed in Section 3.7.3. Since
TAFs (159) are applied to the emission factors of all engines the model is appropriate at the project/local scale as well as
except Tier IV, where transient control is expected to be part the regional scale, the discussion is not repeated here.
of all engine design. TAFs in NONROAD were calculated by
averaging tests for each engine, pollutant, and test cycle, and
3.7.5 Evaluation of Parameters
comparing these measured emission factors for off-road equip-
ment duty cycles to the zero-hour steady state emission factors. Exhibit 3-61 summarizes all parameters relevant for calcu-
Thus, in-use emission factors should have reduced uncer- lating emissions from CHE. Each of these has been detailed
tainty relative to using zero-hour steady state emission rates as under the discussion of the appropriate model or method
emission factors. in Section 3.7.3 and 3.7.4. Only the primary parameters are
Deterioration factors (159) in NONROAD increase with en- discussed in detail here. That is, many of the parameters are
gine age up to its median life, at which point it is held constant, used to derive the parameters in Equations 15 and 20, but
under the assumption that increased deterioration is offset by not discussed here. The use of each is detailed above. Also
maintenance. For compression ignition engines, deterioration as discussed previously, no quantitative assessments are
is linear. In all cases, due to a lack of data for nonroad engines, provided, because the range of parameters is essentially
the factors are based on data derived from highway engines. unknown.
Uncertainty in these factors is unknown, particularly any addi-
tional effects due to deterioration, mal maintenance, tamper- Pedigree Matrix. Exhibit 3-62 shows the pedigree matrix
ing, or the effects from use of fuel with various sulfur levels. for the five primary parameters determining emissions from
CHE. Criteria to assign scores in the pedigree matrix are
Calculation Method. The basic emissions calculations in included in Appendix A. Note that population is ranked as a
the model are summarized by Equation 20. "5" for the range of values. This is actually because the varia-
tion in the variation of values between methods is wide, which
Emissions = i j Popi , j Powerj LFi Ai EFi , j is also considered a "5" in Appendix A.
(Equ uation 20)
Population. Emissions are linearly related to the equip-
ment population, as shown by the previously provided equa-
Where
tions. Populations should be determined for each type of
Popi,j is the population of engines of equipment type i equipment, for each horsepower and age bin employed. Thus,
within power bin j, although accurate assessment of the equipment inventory is
Powerj is the average power (hp) of bin j, critical, in many cases this parameter is uncertain, particularly
LFi is the load factor (fraction of available power) of equip- for projected years or streamlined methods. More discussion
ment type i, has been presented under Sections 3.7.3 and 3.7.4. Note that
Ai is the annual activity (hours/year) of equipment type i, population is shown as "varies" in Exhibit 3-62 because the
and range of values varies too widely to be ranked, depending on
EF is the emission factor (g/hp-hr). the methodology employed.
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Exhibit 3-60. Summary of strengths and weaknesses--NONROAD model.
Criteria Strengths Weaknesses
Representation of physical Dominant physical processes included
processes
Sensitivity to input Sensitivity to some parameters mitigated by
parameters model structure (load, activity); overall
sensitivity depends on the parameters
Flexibility Moderately flexible; most inputs adjustable in
input files
Ability to incorporate effects Unclear
of emission reduction
strategies
Representation of future Future year populations calculated in the
emissions model
Consideration of alternative Unclear
vehicle/fuel technologies
Data quality Model relies on best available information at
time of development, with public review
Spatial variability Applicable to domains from countywide to
national
Temporal variability Designed for annual inventories
Review process Publicly reviewed
Endorsements EPA
Exhibit 3-61. Parameters.
Geographic Pedigree Qualitative Quantitative
Parameter Methods/Models
Scale Matrix Assessment Assessment
Population All All
Load Factor All All
Emission All All
Factor
Engine All All
Power
Activity All All
Deterioration Optional and secondary: used to derive All
Factor in-use emission factors
Growth Optional and secondary: needed for All
Factor future-year projections
Engine Age Optional and secondary: needed to All
determine average emission factors
Median Life Optional and secondary: needed to All
determine age distribution
Scrappage Intermediary: derived from equipment All
age and median life
Duty Cycle Secondary: used to derive load and All
transient adjustment factors.
Use of Optional, secondary: used to calculate All
Retrofit control factors on resulting emissions
Devices and/or correct modeled emission factors
Fuel Type Secondary: used to determine emission All
factors
Key: indicates that a parameter is analyzed in the way denoted by the column: indicates that the parameter is not
discussed in the way denoted by the column.
