Technical Support Document for the Proposed
Small Spark Ignition (SI) and Marine SI Emissions
Standards:
Ozone Air Quality Modeling
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EPA 454/R-07-006
April 2007
Technical Support Document for the Proposed Small
Spark Ignition (SI) and Marine SI Emissions
Standards:
Ozone Air Quality Modeling
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, NC 27711
April 2007
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Table of Contents
I. Introduction 3
II. Methodology 3
A. CAMx Inputs and Model Configuration 3
1. Model version 3
2. Model domain and grid resolution 3
3. Modeling period / Ozone episodes 5
4. Model emissions estimates 5
5. Model meteorological inputs 5
6. Model performance evaluation 6
B. Source Apportionment Modeling 7
C. Small/Marine SI Control Strategy Modeling 8
III. Summary of Ozone Modeling Results 9
A. Source Apportionment 9
B. Small/Marine SI Control Strategies 10
Appendix A: SCCs that comprise the Small SI and Marine SI sectors
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I. Introduction
This document was prepared to describe the ozone air quality modeling
performed by EPA in support of the proposed rule. Two basic types of modeling were
completed. First, source apportionment modeling was conducted with the
Comprehensive Air Quality Model with Extensions (CAMx) to gauge the potential
impacts of emissions from the entire sector subject to the proposed rule1 on future levels
of ozone. Second, CAMx was used to simulate the effects of the proposed emissions
reductions on ozone air quality in the future. The methodology and results of both
modeling approaches will be summarized below.
II. Methodology
A. CAMx Inputs and Model Configuration
The foundation for these ozone modeling analyses that estimate the impacts from
Small/Marine spark ignition (SI) engines, vessels and equipment was the CAMx
modeling that was done in support of the final Clean Air Interstate Rule (CAIR). The
CAIR modeling is fully described in the CAIR air quality modeling technical support
document (TSD)2, but a condensed description is provided below. The modeling
procedures used in this analysis (e.g., domain, episodes, meteorology) have been used for
several EPA rulemaking analyses over the past five years and are well-established.
1. Model version: The modeling simulations that comprised the Small/Marine SI
analyses were conducted using CAMx, version 3.103. CAMx is a non-proprietary
computer model that simulates the formation and fate of photochemical oxidants,
including ozone, for given input sets of meteorological conditions and emissions. CAMx
also contains a source apportionment tool which is designed to attribute ozone
concentrations predicted at a user-selected set of receptors to emissions from individual
source areas, as also specified by the user.
2. Model domain and grid resolution: The CAMx modeling analyses were
performed for a domain covering a large portion of the eastern United States, as shown in
Figure II-1. This domain has nested horizontal grids of 36 km and 12 km. The model
was configured such that only the lowest 4 km of the atmosphere was part of the
simulation. Table II-1 provides the remainder of the basic geographic information
regarding the simulations.
1 The proposed rule addresses emission standards for small land-based spark-ignition engines and
equipment as well as for marine spark-ignition engines and vessels.
2 U.S. Environmental Protection Agency; Technical Support Document for the Final Clean Air Interstate
Rule: Air Quality Modeling; Office of Air Quality Planning and Standards; Research Triangle Park, NC;
March 2005.
3 Environ International Corporation; User's Guide: Comprehensive Air Quality Model with Extensions
(CAMx), Version 3.10; Novato, CA; April 2002.
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Table II-1. Configuration of ozone modeling domain.
Map Projection
Grid Resolution
East/West extent
North/South extent
Dimensions
Vertical extent
Layer structure (m)
CAMx Modeling Configuration
Coarse Grid
latitude/longitude
1/2/longitude,
1/3/latitude (~ 36 km)
-99 W to -67 W
26Nto47N
64 x 63 x 9
Fine Grid
latitude/longitude
1/6/longitude,
l/9/latitude(~12km)
-92 W to -69. 5 W
32 N to 44 N
137x110x9
9 Layers: surface to 4 km
0-50, 50-100, 100-300, 300-600, 600-1000, 1000-1500, 1500-2000, 2000-2500,
2500-4000
Figure II-l. Map of the Eastern U.S. modeling domain. The outer box denotes the
entire modeling domain (36 km) and the shaded inner box is the fine grid (12 km).
189
19Z
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3. Modeling period / Ozone episodes: There are several considerations involved
in selecting episodes for an ozone modeling analysis4. In general, the goal is to model
several differing types of meteorological conditions leading to ambient ozone levels
similar to an area's design value. The same 30 episode days that were modeled for CAIR
were used in this analysis. For more detail on the synoptic meteorological patterns
during these episodes, please see the CAIR TSD. These ozone episodes are listed in
Table II-2. The first three days of each period are called the "ramp-up" days. These days
are used to minimize the effects of initial conditions and are not considered as part of the
output analyses.
Table II-2. Dates of CAMx ozone modeling episodes.
Episode 1
Episode 2
Episode 3
June 12-24, 1995
July 5-15, 1995
August 7-21, 1995
4. Model emissions estimates: The Small/Marine SI modeling analyses
considered a base year (2001) and three future years (2015, 2020, and 2030). The base
and future-year base emissions inventories used in this modeling analysis are the same as
2001 NEI-based (National Emission Inventory) estimates used in the CAIR analyses for
the CAIR-relevant years (i.e., 2001, 2015, & 2020). The CAIR control case emissions
were used in the Small/Marine SI future-year base modeling. Details on the development
of the CAIR base and control scenario emissions, the emissions processing needed to
create model-ready inputs, and summaries of the emissions data for each scenario can be
found in the CAIR Emissions Inventory Technical Support Document . The 2030
emissions were assumed to equal the 2020 CAIR control case emissions for all sectors
except for on-road and non-road sources, which were generated via an updated National
Mobile Inventory Model (NMIM) run. In the Small/Marine SI control case scenario, we
used percentage reductions estimated by NMIM for the proposed new standards to adjust
the future-year base levels to reflect the effects of controls. Details on the development
of the base and control scenario emissions for this proposal can be found in the docket.6
5. Model meteorological inputs: In order to solve for the change in pollutant
concentrations over time and space, the CAMx model requires certain meteorological
inputs that, in part, govern the formation, transport, and destruction of pollutant material.
In particular, the model requires seven meteorological input files: wind (u- and v-vector
components), temperature, water vapor mixing ratio, atmospheric air pressure, cloud
cover, rainfall, and vertical diffusion coefficient. The gridded meteorological data for the
three 1995 historical episodes were developed using the Regional Atmospheric Modeling
4 U.S. EPA, Guidance on the Use of Models and Other Analyses in Attainment Demonstrations for the 8
hour Ozone NAAQS; EPA-454/R-05-002; Research Triangle Park, NC; October 2005.
5 U.S. EPA, CAIR Emissions Inventory Technical Support Document; Office of Air Quality Planning and
Standards; Research Triangle Park, NC, March 2005.
6 Memo to the docket - Harvey Michaels, 2005: How NONROAD inventories were prepared for AQ
modeling for the Bond Rule NPRM.
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System (RAMS), version 3b7. RAMS is a numerical meteorological model that solves
the full set of physical and thermodynamic equations which govern atmospheric motions.
The output data from RAMS, which is run in a polar stereographic projection and a
sigma-p coordinate system, are mapped to the CAMx grid via an existing preprocessor.
RAMS was run in a nested-grid mode with three levels of resolution: 108 km, 36
km, and 12 km with 28-34 vertical layers. The top of the surface layer was
approximately 17 meters in the 36 and 12km grids. These two finer grids were at least as
large as their CAMx counterparts. In order to keep the model results in line with reality,
the simulated fields were nudged to an observation-based analysis field every six hours.
A model performance evaluation generally showed that the model accurately reproduced
the synoptic meteorological conditions of the episode days.8'9
In addition to the RAMS-based meteorological data, the photochemical grid
model requires several other types of data. In general, most of these miscellaneous model
files have been taken from existing regional modeling applications. Clean conditions
were used to initialize the model and were also used as lateral and top boundary
conditions as in previous regional modeling applications (e.g., CAIR). The model also
requires information regarding land use type and surface albedo for all layer 1 grid cells
in the domain. Existing regional data were used for these non-day-specific files.
Photolysis rates were developed using a preprocessor that comes with the CAMx code
(JCALC). Turbidity values were set equal to a constant thought to be representative of
regional conditions. Two separate meteorological CAMx inputs, cloud fractions and
rainfall rates, were developed based on observed data. Finally, a value of 1.0 m /sec was
chosen for the minimum-allowed vertical diffusivity (Kv).
6. Model performance evaluation: A performance evaluation of CAMx for the
1995 base year episodes was completed as part of the modeling analysis for the Nonroad
Land-based Diesel Engines Standards. The base year (evaluation year) modeling
configuration used in these analyses is identical to that simulation, thus that ozone
performance evaluation is still valid. For more detail, see the TSD for that rulemaking10.
As with most regional photochemical modeling studies, the degree that model
predictions replicate observed concentrations varies by day and by location over the large
7 Pielke, R.A., W.R. Cotton, R.L. Walko, C.J. Tremback, W.A. Lyons, L.D. Grasso, M.E.
Nicholls, M.D. Moran, D.A. Wesley, T.J. Lee, and J.H. Copeland, 1992: A Comprehensive
Meteorological Modeling System - RAMS, Meteor. Atmos. Phys., Vol. 49, pp. 69-91.
o
Lagouvardos, K., G. Kallos, V. Kotroni, and S.T. Rao, 2000: "An Analysis of the Meteorological
and Air Quality Conditions during an Extreme Ozone Episode over the Northeastern USA." Int.
J. Environment and Pollution, Vol. 14, Nos. 1-6, pp. 581-587.
9 Hogrefe, C. et. al. "Evaluating the performance of regional-scale photochemical modeling
systems: Part 1-meteorological predictions". Atmospherics Environment 35 (2001) 4159-4174.
10 U.S. EPA, Technical Support Document for the Nonroad Land-based Diesel Engines Standards
Air Quality Modeling Analyses; Office of Air Quality Planning and Standards; Research Triangle Park,
NC, April 2003.
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eastern U.S. modeling domain. From a qualitative standpoint, there appears to be
considerable similarity on most days between the observed and simulated ozone patterns.
Additionally, where possible to discern, the model appears to follow the day-to-day
variations in synoptic-scale ozone closely. More quantitative comparisons of the model
predictions and ambient data are provided below. When all hourly observed ozone
values (greater than 60 ppb) are compared to their model counterparts for the 30 episode
modeling days, the mean normalized bias is near-zero (-1.1 percent) and the mean
normalized gross error is a low 20.5 percent. As shown in Table II-3, the model
generally underestimates observed ozone values for the June and July episodes, but
predicts higher than observed amounts for the August episode.
Table II-3. Performance statistics for hourly ozone in the CAMx simulations.
Episode 1
Episode 2
Episode 3
Average Accuracy of the
Peak
-7.3
-3.3
9.6
Mean Normalized
Bias
-8.8
-5.0
8.6
Mean Normalized Gross
Error
19.6
19.1
23.3
Depending on the episode and region, however, the normalized biases can range
from an underestimation of 18 percent to an overestimation of 16 percent. Gross errors
tend to average between 17 and 25 percent. Most of the underestimations in the June and
July episodes are driven by the Northeast and Midwest quadrants (i.e., the two northern
ones). Conversely, most of the overestimated ozone in the August episode is due to the
Midwest, Southeast, and Southwest quadrants. In general, the model was determined to
be performing acceptably, with relatively-low levels of bias and error at most space/time
scales.
B. Source Apportionment Modeling
EPA used the CAMx source apportionment tool as part of the modeling analysis
to determine what contribution emissions from Small SI engines and equipment and
Marine SI engines and vessels would have toward residual ozone nonattainment in the
future. At its simplest level, the source apportionment technique in CAMx provides a
means to estimate the contributions of many individual source areas/categories to ozone
formation in one single model run. This is achieved by using multiple tracer species to
track the fate of ozone precursor emission (VOC and NOx) and the ozone formation
caused by these emissions within a CAMx simulation. Thus, for all receptor locations
and times, the ozone concentrations predicted by the CAMx are attributed to the source
categories selected for analysis. EPA used the Anthropogenic Precursor Culpability
Assessment (APCA) option for the Small SI and Marine SI source apportionment
modeling. The key feature of APCA is that it allocates all ozone production to manmade
precursor emissions, either through reactions among various manmade sources and/or
through reactions between manmade emissions and biogenic emissions. The source
apportionment modeling was completed for a single year (2020), using the Small/Marine
SI future-year base case. Emissions from Small SI engines and equipment and Marine SI
engines and vessels were analyzed separately, along with several other non-road sub-
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sectors. Appendix A shows the specific source categories that compose Small SI and
Marine SI categories.
There were 16 potential ozone nonattainment receptor areas chosen, as shown in
Figure II-2. There are many possible metrics to consider when investigating how a
sector's emissions impact future-year ozone levels. For this analysis we focused on one
of the outputs used in CAIR, the total average contribution metric. There are three parts
to the calculation of this metric. In step 1, the ozone values for each of the exceedance
periods in a particular downwind area are summed over the episode days. In step 2, the
total ozone from the previous step that is due to anthropogenic sources is calculated based
on the source apportionment results. In step 3, the contributions from a given source sub-
sector to this downwind area are summed over the exceedance periods. The total
contribution calculated in step 3 is then divided by the total ozone resulting from
manmade sources in step 2 to determine the fraction of ozone that is due to that particular
emissions category. This fraction is then multiplied by 100 to express the result as a
percentage.
Figure II-2. Receptor regions for 2020 CAMx source apportionment modeling.
CAM* SA rum
(1,255 grid* in 145 Counties, 16 Anatysis Area*)
C. Small/Marine SI Control Strategy Modeling
As discussed in Section II. A.4, the Small/Marine SI control scenario modeling
looked at multiple future years. The 1995 evaluation case and the 2001 base year
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simulations had already been completed as part of earlier rulemaking support modeling.
CAMx simulations completed as part of this analysis include:
2015 CAIR base case w/ Small SI / Marine SI controls
2015 CAIR control case w/ Small SI / Marine SI controls
2020 CAIR control case
2020 CAIR control case w/ Small SI / Marine SI controls
2030 CAIR control case
2030 CAIR control case w/ Small SI / Marine SI controls
Two basic types of modeling outputs were generated as part of the Small/Marine
SI CAMx modeling. The first set of outputs are model projections of future-year 8-hour
ozone design values. These projections are derived by combining base year ambient
design values with the relative response of the model between the model base year (2001)
and the model future year. For more detail on the calculation of future-year design
values, see EPA's attainment demonstration guidance (footnote #3). The second set of
output metrics quantify the absolute change in certain air quality metrics (e.g., average
change in exceedance counts/days, population-weighted average change in model outputs
>= 85 ppb, etc.). Both sets of modeling outputs are summarized in Section III.
III. Summary of Ozone Modeling Results
A. Source Apportionment Modeling
In the 16 potential residual ozone nonattainment areas, the source apportionment
modeling estimated that Small SI and Marine SI emissions were responsible for between
one and seven percent of all ozone concentrations above 85 ppb. Figure III-l shows the
percentage contributions for each sector to each of the 16 receptor areas. Not
surprisingly, gasoline-fueled pleasure craft contribute most in areas with access to
waterways (e.g., Cleveland and Providence). In these locations, Marine SI sources can be
responsible for as much as four percent of the overall ozone exceedance problem. The
average contribution of these sources is 2.0 percent. In general, the Small SI sources
(e.g., turf equipment, lawn tractors, and commercial generators) are a larger contributor;
the average is 2.3 percent. The impacts from the Small SI sources range from a low of
0.6 percent (Beaumont - Port Arthur) to a high of 3.2 percent (New York City).
Compared to other non-road sectors, these emissions were concluded to have larger
impacts than aircraft or large SI sources; comparable impacts to Category 3 marine
vessels; but smaller impacts than Category 1 and 2 marine vessels, locomotives, and
miscellaneous non-road diesel equipment. Overall, the non-road sector is projected,
according to the CAMx modeling, to be responsible for 25-40 percent of the 2020 ozone
problem in the residual nonattainment locations barring additional control of the sector.
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Figure III-l. Percent Contribution to 2020 8-Hour Ozone Exceedances from the
Small/Marine SI sectors, as determined by CAMx Source Apportionment
20.0 -i
1B.O -
1G.O -
_ 14.0 -
1 12.0 -
| 10.0 -
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a NR (small SI)
NR (marine SI)
B. Small/Marine SI Control Strategy Modeling
As indicated in the RIA document, the control scenario modeling indicates that
the reductions from this proposed rule will contribute to reducing ambient ozone
concentrations and potential exposures in future years. Table III-l shows the average
change in projected, future-year, eight-hour ozone design values (DV) in various years as
a result of the Small/Marine SI control scenario. Average DV changes are shown for:
1) all counties with ambient 8-hour ozone design values for the base year period,
2) those counties with baseline design values >= 85 ppb ("violating" counties), and
3) those counties with baseline design values within 10 percent of the 8-hour ozone
standard, i.e., between 76.5 and 85 ppb ("near-violating" counties).
Figures III-2 and III-3 display the projected county-level ozone air quality
changes expected from this rule. Not surprisingly, the largest impacts are in areas near
water, where Marine SI source contributions can be large.
As seen in Table III-l, the average effect of the proposed rule is a 0.5 ppb
reduction in 2020 and a 0.7 ppb reduction in 2030. The impact of the proposed
reductions has also been analyzed with respect to those areas that have the highest
projected design values. The impacts of the proposed rule tend to be greater for those
10
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counties with higher design values (e.g. violating counties). We project that there will be
13 counties in the eastern U.S. with design values at or above 85 ppb in 2030. After
implementation of this proposed action, we project that 7 of these 13 counties will be at
least 40% closer to a design value of less than 85 ppb, and on average the 13 counties
will be approximately 35% closer to a design value of less than 85 ppb.
Table III-l. Change in projected 2020 and 2030 8-hour ozone design values as a
result of the Small/Marine SI control strategy.
All counties
Violating counties
Near-violating
counties
County Count
525
270
185
2020 projected
-0.5 ppb
-0.6 ppb
-0.4 ppb
2030 projected
-0.7 ppb
-0.8 ppb
-0.5 ppb
Table III-2 is identical to Table III-l, except the modeled air quality changes have
been normalized by future-year population projections on a county-by-county basis. The
impacts of the rule tend to be largest in counties in which many people live. On a
population-weighted basis, the average change in future year design values for all 525
counties with monitors over the Eastern U.S. would be a decrease of 0.7 ppb in 2020 and
a decrease of 0.8 ppb in 2030. Considering only those counties with present-day design
values over the national standard, the population-weighted average county change is
projected to be -0.8 ppb in 2020 and -1.0 ppb in 2030.
Table III-2. Population-weighted change in projected 2020 and 2030 8-hour ozone
design values as a result of the Small/Marine SI control strategy.
All counties
Violating counties
Near-violating
counties
County Count
525
270
185
2020 projected
-0.7 ppb
-0.8 ppb
-0.5 ppb
2030 projected
-0.8 ppb
-1.0 ppb
-0.7 ppb
11
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Figure III-2. Change in projected 2020 8-hour ozone design values as a result of the
Small/Marine SI control strategy.
RSM 2020 compared against 2020 CAIR
Change in DV
Number of
Legend counties:
| | -0.3-0.0 215
| | -0.6--0.4 1"
| | -0.9--0.7 88
Figure III-3. Change in projected 2030 8-hour ozone design values as a result of the
Small/Marine SI control strategy
RSM 2030 compared against 2030 CAIR
Number of
Legend counties:
12
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Table III-3 summarizes some of the absolute model metrics. Again, the modeling
indicates that the reductions from this proposed rule will contribute to reducing ambient
ozone concentrations and potential exposures in future years.
Table III-3. Absolute model metrics of projected air quality change in 2020 and
2030 as a result of the Small/Marine SI control strategy.
Average change in total
nonattainment
Population-weighted change in
total nonattainment
Change in number of
exceedances
Change in number of
exceedance days
Change in average maximum 8-
hour ozone in nonattainment
areas
2020 projected
-8.7%
-10.3%
-8.1%
-5.7%
-0.7 ppb
2030 projected
-10.9%
-12.6%
-10.3%
-7.5%
-1.1 ppb
13
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Appendix A: Source Classification Codes (SCCs) that comprise the
Small SI and Marine SI sectors.
sec
2282005010
2282005015
2282010005
2260001060
2260002006
2260002009
2260002021
2260002027
2260002039
2260002054
2260003030
2260003040
2260004015
2260004016
2260004020
2260004021
2260004025
2260004026
2260004030
2260004031
2260004035
2260004036
2260004071
2260005035
2260005050
2260006005
2260006010
2260006015
2260007005
2265001050
2265001060
Subsector
Marine SI
Marine SI
Marine SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
SCC Descriptor
Mobile Sources: Pleasure Craft: Gasoline 2-Stroke: Outboard
Mobile Sources: Pleasure Craft: Gasoline 2-Stroke: Personal Water Craft
Mobile Sources: Pleasure Craft: Gasoline 4-Stroke: Inboard/Sterndrive
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Recreational Equipment:
Specialty Vehicles/Carts
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Construction and Mining
Equipment: Tampers/Rammers
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Construction and Mining
Equipment: Plate Compactors
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Construction and Mining
Equipment: Paving Equipment
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Construction and Mining
Equipment: Signal Boards/Light Plants
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Construction and Mining
Equipment: Concrete/Industrial Saws
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Construction and Mining
Equipment: Crushing/Processing Equipment
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Industrial Equipment:
Sweepers/Scrubbers
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Industrial Equipment: Other
General Industrial Equipment
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Lawn and Garden Equipment:
Rotary Tillers < 6 HP (Residential)
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Lawn and Garden Equipment:
Rotary Tillers < 6 HP (Commercial)
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Lawn and Garden Equipment:
Chain Saws < 6 HP (Residential)
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Lawn and Garden Equipment:
Chain Saws < 6 HP (Commercial)
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Lawn and Garden Equipment:
Trimmers/Edgers/Brush Cutters (Residential)
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Lawn and Garden Equipment:
Trimmers/Edgers/Brush Cutters (Commercial)
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Lawn and Garden Equipment:
Leafblowers/Vacuums (Residential)
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Lawn and Garden Equipment:
Leafblowers/Vacuums (Commercial)
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Lawn and Garden Equipment:
Snowblowers (Residential)
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Lawn and Garden Equipment:
Snowblowers (Commercial)
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Lawn and Garden Equipment:
Turf Equipment (Commercial)
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Agricultural Equipment:
Sprayers
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Agricultural Equipment: Hydro-
power Units
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Commercial Equipment:
Generator Sets
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Commercial Equipment: Pumps
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Commercial Equipment: Air
Compressors
Mobile Sources: Off-highway Vehicle Gasoline, 2-Stroke: Logging Equipment: Chain Saws
>6HP
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Recreational Equipment: Golf
Carts
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Recreational Equipment:
Specialty Vehicles/Carts
14
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2265002003
2265002006
2265002009
2265002015
2265002021
2265002024
2265002027
2265002030
2265002033
2265002039
2265002042
2265002054
2265002066
2265002072
2265002078
2265003030
2265003040
2265003050
2265003060
2265004010
2265004011
2265004015
2265004016
2265004025
2265004026
2265004030
2265004031
2265004035
2265004036
2265004040
2265004041
2265004046
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Construction and Mining
Equipment: Pavers
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Construction and Mining
Equipment: Tampers/Rammers
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Construction and Mining
Equipment: Plate Compactors
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Construction and Mining
Equipment: Rollers
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Construction and Mining
Equipment: Paving Equipment
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Construction and Mining
Equipment: Surfacing Equipment
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Construction and Mining
Equipment: Signal Boards/Light Plants
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Construction and Mining
Equipment: Trenchers
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Construction and Mining
Equipment: Bore/Drill Rigs
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Construction and Mining
Equipment: Concrete/Industrial Saws
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Construction and Mining
Equipment: Cement and Mortar Mixers
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Construction and Mining
Equipment: Crushing/Processing Equipment
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Construction and Mining
Equipment: Tractors/Loaders/Backhoes
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Construction and Mining
Equipment: Skid Steer Loaders
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Construction and Mining
Equipment: Dumpers/Tenders
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Industrial Equipment:
Sweepers/Scrubbers
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Industrial Equipment: Other
General Industrial Equipment
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Industrial Equipment: Other
Material Handling Equipment
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Industrial Equipment:
AC\Refrigeration
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Lawn and Garden Equipment:
Lawn Mowers (Residential)
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Lawn and Garden Equipment:
Lawn Mowers (Commercial)
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Lawn and Garden Equipment:
Rotary Tillers < 6 HP (Residential)
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Lawn and Garden Equipment:
Rotary Tillers < 6 HP (Commercial)
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Lawn and Garden Equipment:
Trimmers/Edgers/Brush Cutters (Residential)
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Lawn and Garden Equipment:
Trimmers/Edgers/Brush Cutters (Commercial)
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Lawn and Garden Equipment:
Leafblowers/Vacuums (Residential)
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Lawn and Garden Equipment:
Leafblowers/Vacuums (Commercial)
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Lawn and Garden Equipment:
Snowblowers (Residential)
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Lawn and Garden Equipment:
Snowblowers (Commercial)
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Lawn and Garden Equipment:
Rear Engine Riding Mowers (Residential)
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Lawn and Garden Equipment:
Rear Engine Riding Mowers (Commercial)
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke: Lawn and Garden Equipment:
Front Mowers (Commercial)
15
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2265004051
2265004055
2265004056
2265004066
2265004071
2265004075
2265004076
2265005010
2265005015
2265005030
2265005035
2265005040
2265005050
2265006005
2265006010
2265006015
2265006025
2265006030
2265007010
2265007015
2265008005
2265010010
2285004015
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Small SI
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
Shredders < 6 HP (Commercial)
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
Lawn and Garden Tractors (Residential)
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
Lawn and Garden Tractors (Commercial)
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
Chippers/Stump Grinders (Commercial)
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
Turf Equipment (Commercial)
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
Other Lawn and Garden Equipment (Residential)
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
Other Lawn and Garden Equipment (Commercial)
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
Wheel Tractors
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
Agricultural Tractors
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
Agricultural Mowers
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
Sprayers
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
6 HP
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
power Units
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
Generator Sets
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
Compressors
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
Pressure Washers
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
6 HP
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
- Feller/Bunch/Skidder
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
Equipment: Airport Ground Support Equipment
Mobile Sources: Off-highway Vehicle Gasoline, 4-Stroke
Field Equipment
Mobile Sources: Railroad Equipment: Gasoline, 4-Stroke:
Lawn and Garden Equipment:
Lawn and Garden Equipment:
Lawn and Garden Equipment:
Lawn and Garden Equipment:
Lawn and Garden Equipment:
Lawn and Garden Equipment:
Lawn and Garden Equipment:
Agricultural Equipment: 2-
Agricultural Equipment:
Agricultural Equipment:
Agricultural Equipment:
Agricultural Equipment: Tillers >
Agricultural Equipment: Hydro-
Commercial Equipment:
Commercial Equipment: Pumps
Commercial Equipment: Air
Commercial Equipment: Welders
Commercial Equipment:
Logging Equipment: Shredders >
Logging Equipment: Forest Eqp
Airport Ground Support
Industrial Equipment: Other Oil
Railway Maintenance
16
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United States Office of Air Quality Planning and Publication No. EPA
Environmental Standards 454/R-07-006
Protection Air Quality Assessment Division April 2007
Agency Research Triangle Park, NC
17
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