## Emission Factor Documentation for AP-42,

Section 13.2.1

Paved Roads

Measurement Policy Group Office of Air Quality Planning and Standards

U.S. Environmental Protection Agen c y

January 2011

## Emission Factor Documentation for AP-42,

Section 13.2.1

Paved Roads

Measurement Policy Group Office of Air Quality Planning and Standards

U.S. Environmental Protection Agen c y

January 2011

### NOTICE

The information in this document has been funded by the Office of Air Quality Planning and Standards, U.S. Environmental Pr otection Agency (EPA). This final report has been subjected to the Agency’s review, and it has been approved for publication as an EPA document. Mention of trade names or commercial products is not intended to constitute

endorsement or recommendation for use.iv 

### CONTENTS

List of Figures ......................................................................................................................v

List of Tables ................................................................................................................ ..... vi

1 Introduction .......................................................................................................... 1-1

2. Source Description ............................................................................................... 2-1

2.1 Public and industrial roads ................................................................ 2-1

2.2 Review of current paved road emission factors ................................ 2-1

2.2.1 September 1985 through January 1995. ................................. 2-1

2.2.2 January 1995 through October 2002 ....................................... 2-4

2.2.3 October 2002 through December 2003 ................................... 2-5

2.2.4 December 2003 through November 2006 ............................... 2-5

2.2.5 November 2006 through May 2010 ........................................ 2-6

2.2.6 May 2010 ................................................................................ 2-6

3. General Data Review and Analysis ..................................................................... 3-1

3.1 Literature search and screening ........................................................ 3-1

3.2 Emission data quality rating system ................................................. 3-1

3.3 Emission factor quality rating system ............................................... 3-3

3.4 Methods of emission factor determination ....................................... 3-3

3.4.1 Mass Emission Measurements ................................................ 3-4

3.4.2 Emission Factor Derivation .................................................... 3-5

3.5 Emission factor quality rating sc heme used in this study ................. 3-6

4. AP-42 Section Development................................................................................ 4-1

4.1 Revisions to section narrative ........................................................... 4-1

4.2 Pollutant emission factor development ............................................. 4-1

4.2.1 Review of Specific Data Sets ................................................. 4-2

4.2.2 Emissions Factor Development. ........................................... 4-36

4.3 Development of other mate rial in AP-42 section ........................... 4-69

4.4 References for Section 4 ................................................................. 4-70

Appendix A Response to Comments ........................................................................... A-1v 

### LIST OF FIGURES

Number ........................................................................................................................ .. Page

4-1 PM 10 

### Emissions Factor Data Base by Silt Loading (93 test runs). .................... 4-45

4-2 PM 10 

### Emissions Factor Data Base by Averag e Vehicle Weight ( 93 test runs). 4-46

4-3 Silt Loading vs. Average Vehicle Weight (93 Test Runs). ................................ 4-47

4-4 PM 10 

### Emissions Factors by Vehicle Speed. ....................................................... 4-48

4-5 Vehicle Speed vs Silt Loading. .......................................................................... 4-49

4-6 Paved Road Dust Emissions Factors, All Data. ................................................. 4-50

4-7 All Paved Road Data, Silt Loading by Vehicle Weight with EF. ...................... 4-51

4-8 Paved Road Dust Emissions F actor Data Excluding Z-3. ................................. 4-52

4-9 Cumulative Distribution of Pr edicted/Actual Ratios. ........................................ 4-57

4-10 Cumulative Distribution – Predic ted/Actual by Silt Loading. ........................... 4-58

4-11 Cumulative Distribution – Predicted/Ac tual by Average Vehicle Weight. ....... 4-59

4-12 Predicted vs Actual PM10 Emi ssions Factor by Silt Loading. .......................... 4-62

4-13 Predicted vs Actual PM10 Emissions Factor by Average Vehicle Weight. ...... 4-63

4-14 Predictive Accuracy by Silt Loading (unrestricted range). ................................ 4-64

4-15 Predictive Accuracy by Silt Loading (restricted range). .................................... 4-65

4-16 Predictive Accuracy by Average Vehicl e Weight (unrestricted range). ............ 4-66

4-17 Predictive Accuracy by Average Vehi cle Weight (restricted range). ................ 4-67vi 

### LIST OF TABLES

Number ........................................................................................................................ .. Page

4-1 Summary Information for Reference 15 .............................................................. 4-3

4-2 Summary Information for Reference 17 .............................................................. 4-6

4-3 Summary information for Reference 31 ............................................................ 4-11

4-4 Detailed Information From Paved Road Tests for Reference 31 ....................... 4-11

4-5 Summary Information for Reference 8 .............................................................. 4-12

4-6 Detailed Information From Pave d Road Tests for Reference 8 ......................... 4-13

4-7 Summary of Paved Road Emissi on Factors for Reference 7 ............................. 4-15

4-8 Detailed Information From Paved Road Tests for Reference 7 ......................... 4-16

4-9 Summary of Paved Road Emissi on Factors for Reference 6 ............................. 4-18

4-10 Detailed Information From Paved Road Tests for Reference 6 ......................... 4-19

4-11 Detailed Information From Paved Road Tests for Reference 30 ....................... 4-22

4-12 Summary of Emissions Da ta from MCP’s Marshall,

Minnesota Facility (Reference 33) ..................................................................... 4-24

4-13 Summary of Emissions Da ta from MCP’s Columbus,

Nebraska Facility (Reference 34) ...................................................................... 4-26

4-14 Summary of Emissions Data from Cargill’s Blair,

Nebraska Facility (Reference 35) ...................................................................... 4-27

4-15 Summary of Emissions Data from ADM’s Marshall,

Minnesota Facility (Reference 36 ) .................................................................... 4-27

4-16 Vehicle Fleet Assumption Used in 2003 MOBILE6.2 M odel .......................... 4-28

4-17 Final Paved Roads Emissions Factor Data Set ................................................. 4-41

4-18 Correlation Matrix for log-transformed PM 10 

### data ............................................ 4-53

4-19 Regression Analysis using Silt Loading and Weight. ........................................ 4-55

4-20 Comparison of Previous and New Equations for Es timating Paved Road Dust

Emissions ........................................................................................................... 4-611-1 

### SECTION 1

INTRODUCTION

The document "Compilation of Air Pollutant Emissions Factors" (AP-42) has been published by the U.S. Environmental Protection Agency (EPA) since 1968. Supplements to AP42 have been routinely published to add new em ission source categories and to update existing emission factors. AP-42 is pe riodically updated by EPA to res pond to new emission factor needs of EPA, State, and loca l air pollution control programs and industry.

An emission factor relates the quantity (wei ght) of pollutants emitted to a unit of activity

of the source. The uses for the emi ssion factors reported in AP-42 include:

1. Estimates of area-wide emissions.

2. Estimates of emissions for a specific facility.

3. Evaluation of emissions relative to ambient air quality.

The purpose of this report is to compile the existing background report and supplements into a single report, provide an update of the ba ckground information from test reports and other information to support preparation of a revise d AP-42 section to replace existing Section 13.2.1, "Paved Roads," dated November 2006.

The principal pollutant of interest in this re port is “particulate matter” (PM), with special emphasis placed on “PM 10 

### ” - particulate matte r no greater than 10 μ mA (micrometers in aerodynamic diameter) and PM 2.5 

### . PM 10 

### and PM 2.5 

### form the basis for the current National Ambient Air Quality Standards (NAAQSs) for particulate matter. PM 10 

### and PM 2.5 

### thus represent the two size ranges of particulate matter that are of greatest regul atory interest. Nevertheless,

formal establishment of PM 10 

### and PM 2.5 

### as the standard basis is relatively recent, and many

emission tests have referenced ot her particle size ranges. Other size ranges employed in this
report are:

TSP Total Suspended Particulate, as measur ed by the standard high-volume (hi-vol) air sampler. TSP was the basis for the previous NAAQSs for particulate matter. TSP consists of a relatively coarse particle size fraction. While th e particle capture charact eristics of the hi-vol sampler are dependent upon approach wind velocit y, the effective D50 (i.e., 50% of the particles are captured and 50% are not) varies roughly from 25 to 50 μ mA.

SP Suspended Particulate, which is used as a surrogate for TSP. Defined as PM no greater than 30 μ mA. SP also may be denoted as “PM 30 

### .”

IP Inhalable Particulate, de fined as PM no greater than 15 μ mA. Throughout the late 1970s and the early 1980s, it was clear that EPA intended to revise th e NAAQSs to reflect a particle size range finer than TSP. What was not clear was the size fraction that would be eventually used, with values between 7 and 15 μ mA frequently mentioned. Thus, many field1-2 

### studies were conducted using IP emission measur ements because it was believed that IP would be the basis for the new NAAQS. IP may also be represented by “PM 15 

### .”

FP Fine Particulate, define d as PM no greater than 2.5 μ mA. FP also may be denoted

as “PM 2.5 

### .”

This background report consists of five sect ions. Section 1 provi des an introduction to the report. Section 2 presents descriptions of the paved road source types and emissions from those sources as well as a brief history of the current AP-42 emission factors. Section 3 is a

review of emissions data collecti on and analysis procedures; it desc ribes the literature search, the screening of emission test repo rts, and the quality rating system for both emission data and emission factors. Section 4 details the developm ent of paved road emission factors for the draft AP-42 section; it includes the revi ew of specific data sets and the results of data analysis. Section 5 presents the AP-42 section for paved roads.2-1 

### SECTION 2

SOURCE DESCRIPTION

Particulate emissions occur whenever vehicles travel over a pave d surface, such as public and industrial roads an d parking lots. These emission s may originate from material previously deposited on the travel surface, resu spension of material carried by the vehicle, deposits from undercarriages, engine exhaust ga ses or tire and brake wear. Depending on the road surface characteristics, ve hicle mix, the most significant emissions may arise from the surface material loading (measured as mass of material per unit area), or a combination of engine exhaust, brake and tire emissions. Su rface loading is in turn replenished by other sources (e.g., pavement wear, deposition of ma terial from vehicles, deposition from other nearby sources, carryout from surrounding unpa ved areas, and litter). Because of the importance of the surface loading, available co ntrol techniques either attempt to prevent material from being deposited on the surface or to remove (from the travel lanes) any material that has been deposited.

2.1 PUBLIC AND INDUSTRIAL ROADS

While the mechanisms of particle deposition and resuspension are largely the same for public and industrial roads, there can be major differences in surface loading characteristics, emission levels, traffic characte ristics, and viable control options. For th e purpose of estimating particulate emissions and determini ng control programs, the distinction between public and industrial roads is not a question of ownership but rather a question of surface loading and traffic characteristics.

Although public roads generally tend to ha ve lower surface loadings than industrial roads, the fact that these road s have far greater traffic volume s may result in a substantial contribution to the measured air qua lity in certain areas. In addition, public roads in industrial areas can be often heavily load ed and traveled by heavy vehicl es. In that instance, better emission estimates might be obtai ned by treating these roads as industrial roads through the use of a silt loading and average vehicle weight appropriate for the road segment. In extreme cases, public roads, industrial roa d, or parking lots may have such a high surface loadings that the paved surface is covered wi th loose material and in extreme cases is mistaken for an unpaved surface. In that event, use of a paved road emission factor may actually result in a higher estimate than that obtained from the unpav ed road emission factor, and the road is better characterized as unpaved in nature rather than paved.

2.2 REVIEW OF PAST AND CURRENT PAVED ROAD EMISSION FACTORS

2.2.1 September 1985 through January 1995.

From September 1985 through January 1995, AP- 42 currently contained two sections concerning paved road fugitive emissions. The first, Section 11.2.5, is entitled "Urban Paved

Roads" and was first drafted in 1984 using test results from p ublic paved roads. Emission factors are given in the form of the following equation:2-2 

### E = k (sL/0.5) p (2-1)

where: E = p articulate emission factor (g/VKT)

s = surface material content si lt, defined as particles < 75 μ m diameter (%) L = surface material loading, defined as mass of particles per

unit area of the travel su r face ( g / m 2 ) k = base emission factor (g/VKT)

p = exponent (dimensionless)

The factors k and p are given by

Particle size

fraction k (g/VKT) p

TSP 5.87 0.9

PM 15 

### 2.54 0.8

PM 10 

### 2.28 0.8

PM 2.5 

### 1.02 0.6

The form of the emission fact or model is reasonably consiste nt throughout a ll particle size

fractions of interest.

The urban paved road emission factors repres ented by Equation 2-1 did not change since their inclusion in the 4th Edition (September 1985 ) and the January 1995 re vision. It should be noted that these emission factors were not quality rated "A" through "E." (See Section 3 for an overview of the AP-42 quality rating scheme.)

Section 11.2.6, "I ndustrial Paved Roads," was first pub lished in 1983 and was slightly modified in Supplement B (1988 ) to the 4th Edition. Secti on 11.2.6 contained three distinct

sets of emission factor models as described below.

(2-2)

For TSP, the following equation is recommended:

where: E = emission factor (kg/VKT) I = industrial augmen tation factor (dimensionless) n = number of traffic lanes (dimensionless) s = surface material silt content (%)

L = surface material loadi ng across all traffic lanes (kg/km) W = average vehicle weight (Mg)

The basic form of Equation 2- 2 dates from a 1979 report a nd was originally included in Supplement 14 to AP-42 (May 198 3). The version used in AP-42 was slightly revised in that 0.7

7.228010 s

n 4 I 0.022 E ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

= WL2-3 

### the leading term (i.e., 0.022 in Eq. [2-2]) was reduced by 14%. The industrial road augmentation factor (I) was includ ed to take into account for hi gher emissions from industrial

roads than from urban road s; it varied from 1 to 7. The em ission factor equation was rated "B"

for cases with I =1 and "D" otherwise.

For smaller particle size ranges, models so mewhat similar to those in Eq. (2-1) were

recommended:

E = k (sL/12) 0.3 

### (2-3)

where: E = emission factor (kg/VKT) k = base emission factor (kg/VKT), see below sL = road surface silt loading (g/m2)

The base emission factor (k) above varied with aerodynamic si ze range as follows:

Particle size

fraction k (g/VKT)

PM 15 

### 0.28

PM 10 

### 2.22

PM 2.5 

### 0.081

These models represented by Equa tion 2-3 were first developed in 1984 from 15 emission tests of uncontrolled paved roads and they were rated "A."

During the development of Eq. (2-3), tests of light-duty traffic on heavily loaded road surfaces were identified as a separate subset, fo r which separate single-valued emission factors were developed. Sectio n 11.2.6 recommended the following for light-duty (less than 4 tons) vehicles traveling over roads where the surfac e material was dry and the road was heavily loaded (silt loading greater than 15 g/m 2 

### ):

E = k (2-4)

where: E = emission factor (kg/VKT) k = single-valued factor depending on particle size range of interest (see below)

Particle size

fraction k (g/VKT)

PM 15 

### 0.12

PM 10 

### 0.093

The single-valued emission fa ctors was quality rated "C."

During the time that AP-42 had four methods for estimating emissions from paved roads
(Sections 11.2.5 and 11.2.6, AP-42 Fourth Edition, 1993), users of AP-42 noted difficulty selecting the
appropriate emission factor model to use in their a pplications. For example, inventories of industrial2-4 facilities (particularly of iron and steel plants) conducted throughout the 198 0s yielded measured silt
loading values substantially lower th an those in the Section 11.2.6 data base. In extreme cases when
the models were used with silt loading values outside the range for which they were developed,
estimated PM10 emission factors were larger than the corresponding TSP emission factors.



### Furthermore, the distinction between "urban " and "industrial" paved roads was blurred. For the purpose of estimating emissions, it was gr adually realized that source emission levels are not a question of ownership but rather a question of surface loading and traffic characteristics. Confirmatory eviden ce was obtained in a 1989 field program 29 

### which found that paved roads at an iron and steel facility far more closely resembled "urban" roads rather than

"industrial" roads in terms of emission characteristics.

Finally, it was unknown how well the emission f actors of that time performed for cases of increased surface loading on public roads, such as after application of antiskid materials or within areas of trackout from unpaved areas. 14 

### These situations were of considerable interest to several state and local regulatory agencies , most notably in the western United States.

2.2.2 January 1995 through October 2002

The January 1995 update attempted to correct as many of the shortcomings of the previous versions as possible. To that end, th e update employed an approach slightly different than that used in the past. In addition to re viewing test data obtained since the September 1988 update 8

, 

### the test data used for both of the 1988 sections were also included for reexamination in the final data set. In assembling the data base, no distinction was made between public and

industrial roads or between cont rolled and uncontrolled tests, w ith the anticipation that the reformulated emission factor will be applicable over a far greater range of source conditions.

The inclusion of controlled tests represente d a break with EPA previous guidelines for preparing AP-42 sections 9 

### . Those guidelines presented a clear preference that only uncontrolled tests be used to develop an emi ssion factor. However, the principal control measures for paved roads seek to reduce the valu e of an independent variable in the emission factor equation, i.e., the silt loading.

The revised emissions factor equation published in the January 1995 update of the paved road section included silt loading, aver age vehicle weight and a particle size multiplier as independent variables. The resulting equation was:

)3/()2/( 5.1 65.0 

### WsL kE = (2-5)



### where: E = pa rticulate emission factor (having units matching th e units of k) k = particle size multiplier for particle size range and units of interest (see below), sL = road surface silt loading (grams per square meter) (g/m2), and W = average weight (tons) of the vehicles traveling the road.

The selection of the value for the independent variable for the particle size multiplier was based upon the units of the emi ssions factor desired and the size range for the emissions.2-5 

### Particle Size Multipliers for Paved Road Equation

Size Range Multiplier k g/VKT g/VMT lb/VMT

PM 2.5 

### 2.1 3.3 0.0073

PM 10 

### 4.6 7.3 0.016

PM 15 

### 5.5 9.0 0.020

PM 30 

### 24 38 0.082

2.2.3 October 2002 through December 2003

Prior to October 2002, the basis of the pa rticle sizing informa tion for paved roads emissions factors was high volume sampler impact ors data. While the initial particle sizing was performed by cyclones, subs equent particle sizing was perf ormed by slotted impactors. The impactor data had biases created by particle bounce and reintrainment. As such particle sizing below 10 μm was questioned. In Octobe r 2002, a three city pa ved and unpaved road emissions study was completed that evaluated particle sizing at 10 and 2.5μm and assessed the default values for silt loading. The results of the three city st udy formed the basis for revising

the PM 2.5 

### particle size multiplier k from 2.1 g/VK T (3.3 g/VMT or 0.0073 lb/VMT) to 1.1 g/VKT (1.8 g/VMT or 0.0040 lb/V MT). The form of the pr edictive equation and the exponents for silt loading and average vehicle weight were un changed. The changes in the October 2002 revision provided recommended default silt loading data for normal and worst case public paved roads based upon the updated s ilt loading values for public paved roads. The remaining numerical revisions that were ma de in the emissions factor for paved roads included an adjustment for the normal mitigation effects due to rain events. For long term average conditions, a 25% reduction in the partic ulate emissions was included for every day

that there was measureable rain for that day. A similar adju stment was included that used hourly time intervals rather that a daily time interval.

2.2.4 December 2003 through November 2006

The December 2003 revision of the AP-42 Secti on for paved roads incorporated a constant in the predictive e quation for particulate emissions factors. The AP-42 equations prior to December 2003 estimated PM emissions from re-entrained road dust, and vehicle exhaust, brake wear and tire wear emissions. In the December 2003 revision of the section, the component of emissions due to exhaust, brak e wear and tire wear we re separated from the composite fugitive dust emission factor equation. The first stated reason for the separation was to eliminate the possibility of double counting emissions. W ith the introduc tion of EPA’s Mobile6.2 model, estimates of PM emissions fr om exhaust, brake wear and tire wear were calculated based upon the vehicl e mix, vehicle speed and road class. The double counting of emissions was a possibility when both the fugitive dust emission factors from AP-42 and

Mobile6.2 were used to estimate emissions from vehicle traffi c on paved roads. The second stated reason was to incorporate decreases in particulate matter emissions from the exhaust of

newer vehicle models and fuel sources. Since the majority of data supporting the paved road emission factor equation was deve loped at the time prior to when the vehicles in the fleet incorporated significant reductions of particulat e matter emissions. A technical memorandum provided the basis for estimating PM emissions due to exhaust, br eak wear and tire wear. The2-6 

### technical memorandum used estimated emissions from a 1980’s model year vehicle fleet since the emissions tests supporting the emissions fa ctors equation were pe rformed in the early 1980’s to early 1990’s. It was believed that since 19 80, there have been and will continue to be improvements in vehicles and fuel that wi ll result in a decrease in PM emissions from engine exhaust. Depending on the emissions factors units de sired, the constant that was

included in the emissions f actor equation had va lues of 0.2119 g/VKT, 0.1317 g/VMT or

0.00047 lb/VMT for PM 30 

### , PM 15 

### and PM 10 

### emissions. For PM 2.5 

### emissions, depending on the required emissions factors units, the constant us ed in the equation had values of 0.1617 g/VKT, 0.1005 g/VMT or 0.00036 lb/VMT.

2.2.5 November 2006 through May 2010

In November 2006, the particle size multip lier k was lowered to 0.66 g/VKT, 1.1 g/VMT or 0.0024 depending on the needed units for the emissions fact or. The revision was based upon a broad based assessm ent of the biases associated with the cyclone/impactor method for particulate sizes less than 10 μ m in aerodynamic diameter. While the December 2003 update revised the particle size multiplier, the update was based upon limited test data. In addition, the impact of biased emissions factor ratios for PM 2.5 

### impacted fugitive sources other than paved roads. The impact was du e to particle bounce from the cascade impactor stages to the backup filter potentially inflating PM 2.5 

### concentrations. The impact was possible even though steps were taken to minimize particle bounce in the earlier studies. The assessment study was sponsored by the Western Regional Air Partne rship and conducted by the Midwest Research Institute (MRI). The testing was conducted at MRI’s Aerosol Test Facility (ATF) in Deramus Field Station in Grandview, Missouri using surface dust collected from seven locations in five

western states. The test s provided the basis for comparing the average PM 2.5 

### concentration and

the collocated PM 10 

### concentration. The stud y compared the fine frac tion ratios derived from FRM samplers to those derived from the cycl one/impactor method. The cyclone/impactor samplers and operating method used in the study were the same as those that generated the original AP-42 emission f actors and associated PM 2.5 

### / PM 10 

### ratios. The study consisted of 100 test runs covering PM 10 

### concentration from approximately 0.3 mg/m 3 

### to 7 mg/m 3



### .

2.2.6 May 2010

This update recommends an updated equation for paved roads that is based upon additional test

data that was conducted on roads with slow moving traffic and stop and go traffic. The emissions tests were performed for the Corn Refiners Association by Midwest Research Institute (MRI). The testing focused on PM 10 

### emissions at four corn processing facilities. Unlike the development of earlier paved road equations, the equation development for this version adjusts the individual test data measured emissions by excluding exhaust emissions,

tire wear emissions and brake wear emissions prior to the equation development. As a result,

different values are subtracted from the resu lts of each test based upon the average vehicle weights, average vehicle speed, ambient temperatur e, year of test and estimated mix of light duty and heavy duty vehicles.3-1 

### SECTION 3

GENERAL DATA REVIEW AND ANALYSIS

To reduce the amount of literature collected to a final group of references from which

emission factors could be de veloped, the following general criteria were used:

1. Emissions data must be from a primary reference: a. Source testing must be from a re ferenced study that does not reiterate information from previous studies. b. The document must constitute the original source of test data. For example, a technical paper was not included if th e original study was contained in the previous document. If the exact source of the data could not be determined,

the document was eliminated.

2. The referenced study must contain test results based on more than one test run.

3. The report must contain sufficient data to evaluate the testing procedures and source operating conditions.

A final set of reference materials was compiled after a thorough review of the pertinent reports, documents, and informa tion according to these criteria.

3.1 LITERATURE SEARCH AND SCREENING

Review of available literature identified three paved road testing programs (presented later as Table 4-1) since the ti me of the last Section 11.2 update. 8 

### The individual programs are

discussed in detail in the next se ction. In addition, as discussed at the end of Section 2, earlier

controlled industrial road test data were reexamined. The previous update 8 

### noted that Eq. (2-4) yielded quite good estimates for emissions from vacuum swept and water flushed roads. Furthermore, it became apparent that previous distinctions between "industrial" and "urban"

roads had become blurred as interest focu sed on heavily loaded urban roads (e.g., after snow/ice controls) and on cleaner industrial roads (as the result of plant-wide control programs).

3.2 EMISSION DATA QUALITY RATING SYSTEM

As part of the analysis of the emission da ta, the quantity and quali ty of the information

contained in the final set of re ference documents were evaluated. The following data are to be

excluded from consideration:

1. Test series averages reported in units can not be converted to th e selected reporting

units.

2. Test series representing incompatible test methods (i.e., comparison of EPA Method 5

front-half with EPA Method 5 front- and back-half).3-2 

### 3. Test series of controlled emissions for which the control device is not specified.

4. Test series in which the source process is not clearly identified and described.

5. Test series in which it is not clear whether the emissions were measured before or after

the control device.

Test data sets that were not excluded were assigned a quality ra ting. The rating system used was that specified by EPA for preparing AP-42 sections. 9 

### The data were rated as

follows:

A Multiple tests that were performe d on the same source using sound methodology and reported in enough detail for adequate valid ation. These tests do not necessarily conform to the methodology specified in EPA reference test methods, although these methods were used as a guide for the methodol ogy actually used.

B Tests that were performed by a gene rally sound methodolog y, but lack enough detail for adequate validation.

C Tests that were based on an untested or new methodology or that lacked a significant amount of background data.

D Tests that were based on a generall y unacceptable method but may provide an order-of-magnitude value for the source.

The following criteria were used to evaluate source test reports for sound methodology and adequate detail:

1. Source operation . The manner in which the source was operated is well documented in the report. The source was operating within typical parameters during the test.

2. Sampling procedures . The sampling procedures conformed to a generally acceptable methodology. If actual procedur es deviated from accepted methods,

the deviations are well documented. Wh en this occurred, an evaluation was made of the extent such alternative pro cedures could influence the test results.

3. Sampling and process data . Adequate sampling and process data are documented in the report, and any va riations in the sampling and process operation are noted. If a large spread be tween test results cannot be explained by information contained in the test repo rt, the data are susp ect and were given a lower rating.

4. Analysis and calculations . The test reports contain original raw data sheets. The nomenclature and equations used were compared to those (if any) specified by EPA to establish equivalency. The de pth of review of the calculations was3-3 

### dictated by the reviewer's confidence in the ability and cons cientiousness of the tester, which in turn was based on fact ors such as consiste ncy of results and completeness of other areas of the test report.

3.3 EMISSION FACTOR QUALITY RATING SYSTEM

The quality of the emission factors developed from analysis of the test data was rated utilizing the following general criteria:

A—Excellent : Developed only from A-rated test data taken from many randomly

chosen facilities in the indus try population. The source cate gory is specif ic enough so that variability within the source category population may be minimized.

B—Above average : Developed only from A-rated te st data from a reasonable number of facilities. Although no specific bias is evid ent, it is not clear if the facilities tested represent a random sample of the industries. The source category is specific enough so that variability within the source category population may be minimized.

C—Average : Developed only from A- and B-rated test data from a reasonable number of facilities. Although no specific bias is evid ent, it is not clear if the facilities tested represent a random sample of the industry. In addition, th e source category is specific enough so that variability within the s ource category populatio n may be minimized.

D—Below average : The emission factor was develope d only from A- and B-rated test data from a small number of facilities, and ther e is reason to suspect that these facilities do not represent a random sample of the industry. There also may be evidence of variability within the sour ce category populatio n. Limitations on the use of the

emission factor are noted in the emission factor table.

E—Poor : The emission factor was developed fr om C- and D-rated test data, and there is reason to suspect that the facilities test ed do not represent a random sample of the industry. There also may be evidence of va riability within the source category population. Limitations on the use of these factors are always noted.

The use of these crit eria is somewhat subjective a nd depends to an extent on the

individual reviewer.

3.4 METHODS OF EMISSION FACTOR DETERMINATIO N

Fugitive dust emission rates and particle size distributions are difficult to quantify

because of the diffuse and variable nature of su ch sources and the wide range of particle size involved including particles which deposit immedi ately adjacent to the source. Standard source testing methods, which are designed for application to confin ed flows under steady state, forced-flow conditions, are not suitable fo r measurement of fugitive emissions unless the plume can be draw into a forced-flow system. The following presents a brief overview of applicable measurement techniques. More detail can be found in earlier AP-42 updates. 8,103-4 

### 3.4.1 Mass Emission Measurements

Because it is usually impractical to encl ose open dust sources or to capture the entire emissions plume, only the upwind-downwind and exposure profiling methods are suitable for measurement of particulate emissi ons from most open dust sources. 10 

### These two methods are discussed separately below.

The basic procedure of th e upwind-downwind method invo lves the measurement of particulate concentrations both upwind and downwind of the polluta nt source. The number of upwind sampling instruments depends on the de gree of isolation of the source operation of concern (i.e., the absence of in terference from other sources upwind). Increasing the number of downwind instruments improv es the reliability in determining the emission rate by providing better plume definition. In order to reasonably define the plume emanating from a point source, instruments need to be located at tw o downwind distances and three crosswind distances, at a minimum. The same sampling requ irements pertain to line sources except that

measurement need not be made at multiple crosswind distances.

Net downwind (i.e., downwind minus upwind ) concentrations are used as input to dispersion equations (normally of the Gaussian t ype) to back calculate the particulate emission rate (i.e., source strength) required to generate the pollutant concentratio n measured. Emission factors are obtained by dividing the calculated emission rate by a source activity rate (e.g., number of vehicles, or weight of material transferred per unit tim e). A number of meteorological parameters must be concurrently recorded for input to this disper sion equation.

At a minimum the wind direction and speed must be recorded on-site.

While the upwind-downwind method is applicab le to virtually all types of sources, it has significant limitations with re gard to development of source- specific emission factors. The major limitations are as follows:

1. In attempting to quantify a large ar ea source, ove rlapping of plumes from upwind (background) sources may preclude the determination of the specific

contribution of the area source.

2. Because of the impracticality of adju sting the locations of the sampling array

for shifts in wind direct ion during sampling, it cannot be assu med that plume position is fixed in the application of the dispersion model.

3. The usual assumption that an area sour ce is uniformly emitting does not allow for realistic representation of spa tial variation in source activity.

4. The typical use of uncalibrated atmo spheric dispersion models introduces the possibility of substantial error (a factor of three according to Reference 11) in the calculated emission rate, even if th e stringent requirem ent of unobstructed dispersion from a simplified (e.g., constant emission rate from a single point) source configuration is met.

The other measurement technique, exposure profiling, offers dis tinct advantages for3-5 

### source-specific quantification of fugitive emissions from open dust sources. The method uses the isokinetic profiling concept that is the basi s for conventional (ducte d) source testing. The passage of airborne pollutant immediately downwind of the s ource is measured directly by means of simultaneous multipoint sampling over th e effective cross section of the fugitive emissions plume. This technique uses a ma ss-balance calculation scheme similar to EPA Method 5 stack testing ra ther than requiring indi rect calculation throug h the application of a generalized atmospheric dispersion model.

For measurement of nonbuoyant fugitive emissions, profiling sampling heads are distributed over a vertical network positioned just downwind (usually about 5 m) from the source. If total particulate em issions are to be measured, samp ling intakes are pointed into the wind and sampling velocity is adjusted to ma tch the local mean wind speed, as monitored by

anemometers distributed over he ight above ground level.

The size of the sampling grid needed for e xposure profiling of a particular source may be estimated by observation of the visible si ze of the plume or by calculation of plume dispersion. Grid size adjustments may be required based on the re sults of preliminary testing. Particulate sampling heads should be symmetrica lly distributed over the concentrated portion of the plume containing about 90 % of the total mass flux (expos ure). For example, assuming that the exposure from a point source is normal ly distributed, the exposu re values measured by the samplers at the edge of the grid should be about 25% of the centerline exposure.

To calculate emission rates using the expo sure profiling technique, a conservation of mass approach is used. The passage of airborne particulate (i.e., the quantity of emissions per unit of source activity) is obt ained by spatial integration of distributed measurements of exposure (mass/area) over the effective cross sect ion of the plume. The exposure is the point value of the flux (mass/area/time) of airbor ne particulate integrated over the time of

measurement.

3.4.2 Emission Factor Derivation

Emissions factors are typically derived from the ratio of the emissions to an activity

level. It is assumed that the emissions are lin early proportional to the selected activity level. Usually the final emission factor for a given source operation, is the arithmetic average of the

individual emission factors calcula ted from each test of that sour ce type. In rare instances, the range of individual emission factor values is also presented.

As an improvement over the presentation of a final emission factor as a single-valued arithmetic mean, an emission factor may be pr esented in the form of a predictive equation derived by regression analysis of test data. The use of a pred ictive equation with a relatively good correlation coefficient (R 2 

### ) provides a means for improving the accuracy of the emissions factor in estimating the actual emissions when the independent variables are known. Such an equation mathematically relates emissions to pa rameters when characterize source conditions. These parameters may be grouped into three categories:

1. Measures of source activity or energy expended (e.g., the sp eed and weight of a vehicle traveling on an unpaved road).

2. Properties of the material being distur bed (e.g., the content of suspendable fines3-6 

### in the surface material on an unpaved road).

3. Climatic parameters (e.g., number of precipitation-free days per year on which emissions tend to be at a maximum).

An emission factor equation is us eful if it is successful in "e xplaining" much of the observed

variance in emission factor valu es on the basis of corresponding variance sin specific source parameters. This enables more reliable estimate s of source emissions on a site-specific basis.

A generic emission factor equation is one that is developed for a source operation defined on the basis of a single dust generation mechanism which crosses industry lines. An example would be vehicular traffi c on unpaved roads. To estab lish its applicability, a generic equation should be developed from test data obtained in different industries.

3.5 EMISSION FACTOR QUALITY RATING SCHEME USED IN THIS STUDY

The uncontrolled emission factor quality rating scheme used in this study is somewhat different than was used in earlier updates 8,11 

### of this section and repr esents a refinement of the rating system developed by EPA for AP-42 emissi on factors, as described in Section 3.3. The scheme entails the use of the same rating assessme nt of source test data quality followed by an initial rating assessment of the emission fact or(s) based on the number and quality of the underlying source test data.

Test data that were developed from well documented, sound methodologies were assigned an A rating. Data gene rated by a methodology that was generally sound but either did not meet a minimum test system requirements or lacked enough detail for adequate validation received a B rating.

In evaluating whether an upwind-downwin d sampling strategy qualified as a sound methodology, the following minimu m test system requirements were used. At least five particulate measuring devices mu st be operated during a test, with one device located upwind

and the other located at two downwind and three crosswind di stances. The requirement of

measurements at crosswind distances is waived for the case of line sources. Also wind direction and speed must be mo nitored concurrently on-site.

The minimum requirements for a sound expo sure profiling program were the following. A one-dimensional, vertical grid of at least three samplers is sufficient for measurement of emissions from line or moving poi nt sources while a two-dimensio nal array of at least five samplers is required for quantif ication of fixed virtual point source missions. At least one upwind sampler must be operated to measure ba ckground concentration, and wind speed must

be measured on-site.

Neither the upwind-downwind nor the expo sure profiling method can be expected to produce A-rated emissions data when applied to large, poorly defined area sources, or under very light and variable wind flow conditions. In these situations, data ratings based on degree of compliance with minimum test system requirements were reduced one letter.

Following the assignment of th e individual source test qualit y ratings, the factor quality3-7 

### rating of the single-valued emi ssion factor will be evaluated. Recently approximately 20 “A”

and “B” rated source test reports have been required to justif y a factor quality rating of “A”. Each halving of the number of source test report s results in a one letter grade reduction in the final factor quality rating. Several of the source test reports used as th e basis for the emissions factor development include measurements conducted at different locations. To the extent that

there are more than two tests at the different lo cations and that the different locations within a given reference represent differences in s ource conditions, each of the different source conditions will be counted as an independent test. The development of the paved road emissions factor differs from typical in that it includes the use of stepwise multiple non linear regression. Following the initial factor quality rating, the adjusted correlation coefficient will be used to increase the emissions factor quality rating. Only correlation coefficients above 0.4

will be used to increase the emissions factor quality rating.4-1 

### SECTION 4

AP-42 SECTION DEVELOPMENT

4.1 REVISIONS TO SECTION NARRATIVE

The AP-42 presented later in this bac kground document is intended to replace the

current version of Section 13.2.1 "Paved Roads" in AP-42. The last update of this section is dated November 2006. The general form of the emissions factor equation presented in the paved road section has been consistent sinc e the January 1995 major revision. Since this date revisions have been made addressing the in fluence of rain events, estimating default silt loading levels for various classes of roads, se parating particulate emi ssions associated with the roads verses those associated with the vehicles and addressing biases in the measurement

of PM 2.5 

### with devices that use impactor s to perform particulate sizing.

4.2 POLLUTANT EMISSION FACTOR DEVELOPMENT

This update to Sections 13.2.1 is planned to address the application of the emissions factor equation addressing only the component associated with paved road surface materials and at speeds lower than 10 miles per hour. In order to achieve this goal, the following general approach was taken

1. Assemble the available test data for pa ved roads in a single data base, making no distinction between pub lic and industrial roads or between controlled and

uncontrolled roads. 2. Develop PM 10 

### and PM 2.5 

### engine, tire wear and brake ware emissions

estimates for each of the available data sets. For each of the available data sets, estimate the emissions associated with the road surface material by subtracting the engine, tire wear a nd brake wear from the measured PM 10 

### emissions. 2. Conduct a series of step wise linear regression anal yses of the revised and adjusted data base to assess the most critical parameters and to develop an

emission factor model with: • silt loading, • mean vehicle weight, and, • mean travel speeds as potential correction parameters. 3. Conduct an appropriate validation study of the reformulated model.4-2 

### 4.2.1 Review of Specific Data Sets

4.2.1.1 Street Sanding Emissions And Control Study, PEI Associates, Inc., Cincinnati,
OH, October 1989. (Reference 15)

This test program was undertaken to characterize PM 10 

### emissions from six streets that were periodically sanded for anti- skid control within the Denver area. The primary objective was given as development of a predictive algor ithm for clean and sanded streets, with a secondary objective stated as defining the effectiveness of control measures. Summary information is given in Table 4-1.

Sampling employed six to eight 8 PM 10 

### samplers equipped with volumetric flow control. Samplers were arranged in two upw ind/downwind configurations. The "basic" configuration consisted of si x samplers arranged in identic al patterns upwind and downwind of the test road, with one samp ler and one pair of samplers at nominal distances of 20 and 5 m, respectively, from the road.

The second configuration was used for test s of control measure effectiveness. The road segment was divided into two halves, corresponding to the treated and experimental control (untreated) portions. Identical sampling arrays were again used upwind and downwind on both halves, at nominal distances of 20 and 5 m. Because this array employed all eight samplers available, no collocati on was possible for the second configuration.

In addition to the PM 10 

### concentration measurements, se veral other types of samples

were collected:

• Wind speed/direction and incoming solar radiation were collected on-site, and

the results were combined to estimate atmospheric stability class needed to

calculate emission factors.

• Colorado Air Pollution Control Divisi on (APCD) representatives collected traffic data, including tra ffic counts, travel speeds, and percentage of heavyduty vehicles. • Vacuums with disposable paper bags we re used to collect the loose material from the road surface. In addition to samples taken from the travel lanes, the field crew took daily samples of mate rial adjacent to curbs and periodic duplicate samples.

The study collected PM 10 

### concentration data on 24 diffe rent days and calculated a

total of 69 different emission rates for baseline , sanded and controlled paved road surfaces. Emission factors were obtained by back-cal culation from the CALINE3 dispersion model 12


### together with a series of assumptions involvi ng mixing widths and heights and an effective release height. Although da ta collected at the 20 m distance were used to evaluate results, the test report did not describe any sensitivity an alysis to determine how dependent the emission rates were on the underlying assumptions.TABLE 4-1. SUMMARY INFORMATION FOR REFERENCE 15PM10emission factor (g/VKT)OperationLocation State Test datesNo. of tests Geom. mean RangeVehicle trafficColfax Colorado 3-4/8917 1.33 0.53-9.01Vehicle trafficYork St. Colorado 4/891 1.07 1.07Vehicle trafficBelleview Colorado 4/894 1.62 1.10-4.77Vehicle trafficI-225 Colorado 4/899 0.31 0.17-0.51Vehicle trafficEvans Colorado 5-6/8929 1.06 0.21-7.83Vehicle trafficLouisiana Colorado 6/897 0.96 0.42-1.73

4-34-4 

### The testing program found difficulty in de fining "upwind" concen trations for several of the runs, including cases with wind reversals or winds nearly para llel to the roadway orientation. A total of eight of th e 69 tests required that either an average concentration from other test days or a downwind concentration be used to defi ne "upwind" conditions. In addition, the test report described another seven runs as invalid for reasons such as wet road surfaces, nearby dust sources or concentra tions increasing with downwind distance.

A series of stepwise regression analyses were conducted, with different predictive equations presented for (a) baseline conditions , (b) sanded roads, and (c) roads swept to remove the sand applied, and (d) all conditions combined. In each case, only one independent variable was included in the predictive equation: silt loading, for cases (a) and (d); and time since treatment, for (b) and (c).

In general, Reference 15 is reasonably well documented in terms of describing test conditions, sampling methodology, data reduction and analysis. A chief limitation lies in the fact that neither sampling configuration fu lly met minimum requirements for the upwinddownwind method presented in Section 3.4. Speci fically, only two or three samplers were

used downwind rather than the minimum of four.

Furthermore, a later report 6 

### drawing upon the results from Reference 15 and 17 effectively eliminated 24% of the combined ba seline tests because of wind directions. In addition, the later report 6 

### noted that the baseline data should be considered as "conservatively high" because roughly 70% of the data were calculated assuming the most unstable atmospheric class (which results in the highest back calculated emission factor). Because of these limitations, the emission data have been given an overall rating of "D."

4.2.1.2 RTP Environmental Associates 1990. Street Sanding Emissions and Control Study, prepared for the Colorado Departme nt of Health. July 1990. (Reference 17)

This test program was quite similar to that described in Reference 15 cited in paragraph 4.2.1.1 and used an essentially identi cal methodology. In fact, the two test reports are very similar in outline, a nd many passages in the two reports are identical. The primary objective was given as expanding the data base inReference 15 to further develop predictive algorithms for clean and sanded streets. Su mmary information is given in Table 4-2.

The test program employed the same two basic PM 10 

### sampling arrays as did Reference 15. A third configuration was used for "profile " tests, in which additional samplers were placed at 10 and 20 ft heights. (Analysis of resu lts from elevated samplers is not presented in Reference 17.)

As was the case in Reference 15, additio nal samples were collected including:

• Wind speed/direction were collected on-site , and the results used in estimating atmospheric stability class needed to calculate emissions factors. (Unlike Reference 15, solar radiation meas urements were not collected.)

• Traffic data, including traffic counts, tr avel speeds, and percentages of heavyduty vehicles were collected.

• Vacuums with disposable paper bags were used to collect the loose material4-5 

### from the road surface. The program developed an extensive set of collocated samples of material along the edges of the roadway.

The study collected PM 10 

### concentration data on 33 days and calculated a total of 131
different emission rates for ba seline, sanded and controlled pa ved road surfaces. Emission factors were obtained by back-calcula tion from the CALINE3 dispersion model 12 

### together with essentially the same assumptions as those in Reference 15. This report also noted the same difficulty as Reference 15 in defining " upwind" concentrations in cases with wind reversals or winds nearly para llel to the roadway orientati on. Unlike Reference 15, however, this report does not provide readily available information on how many tests used either an average concentration from other test days or a downwind concentration to define "upwind" conditions. Reference 6 does, however, describe seven tests as invalid because of filter problems or because upwind concentrati ons were higher than downwind values.

As with the Reference 15 program, a seri es of stepwise regression analyses were conducted. This test program combined data from Reference 15 and 17 and considered predictive equations for (a) ba seline conditions, (b) sanded ro ads, and (c) roads swept to remove the sand applied, and (d ) all conditions combined.

Unlike Reference 15, however, Reference 17 app ears to present silt loading values that are based on wet sieving (see page 8 of the test report) ra ther than the dry sieving technique (as described in Appendix E to AP-42) routinely used in fugitive dust tests. (MRI could not obtain any clarifying informati on during telephone calls to th e testing organization and the laboratory that analyzed the samples.) Wet sieving disaggregates composite particles and results from the two types of si eving are not comparable.

There is additional confusion over the silt loading values given in Reference 17 for cleaning tests. Specifically, the same silt loadi ng value is associated with both the treatment and the experimental control. This point co uld not be clarified during telephone conversation with the testing organization. Attempts to clarify using test report appendices were unsuccessful. Two appendices appear to interchang e silt loading with si lt percentage. More importantly, it could not be determined whet her the surface sample results reported in Appendix D to Reference 17 pertain to treated or the experimental cont rol segment, and with

which emission rate a silt lo ading should be associated.TABLE 4-2. SUMMARY INFORMATION FOR REFERENCE 17PM10emission factor (g/VKT)Operation Location State Test datesNo. of test Geom. mean RangeVehicle traffic Mexico Colorado 2/90 32.75 1.08-6.45Vehicle traffic State Hwy 36Colorado 1-3/90 13 1.31 0.14-4.18Vehicle traffic Colfax Colorado 2-4/90 41 1.32 0.27-5.04Vehicle traffic Park Rd. Colorado 4/90 11 1.26 0.69-3.33Vehicle traffic Evans Colorado 2-3/90 11 2.10 0.87-7.27Vehicle traffic Louisiana Colorado 1,3/90 9 3.24 1.40-5.66Vehicle traffic Jewell Colorado 1/90 1 6.36 6.36Vehicle traffic Bryon Colorado 4/90 3 8.38 5.53-14.72

4-64-7 

### Reference 17 contains substantial amounts of information, but is not particularly well documented in terms of describing test c onditions, sampling methodology, data reduction and analysis. In addition, the same limitati ons mentioned in connection with Reference 15 are equally applicable to Reference 17, as follows:

• not meeting the minimum number of samplers.

• numerous tests conducted under variable wind conditions.

• frequent use (70% to 80% of the tests) of the most unstable atmospheric stability
class in the CALINE 3 mode l which will result in the highest calculated emission

rate.

Because of these limitations, emission rate data have been given an overall rating of "D." Furthermore, the silt loading data in this report are considered suspect for reasons noted

above.

4.2.1.3 T. Cuscino, Jr., et al., Iron And Steel Plant Open Source Fugitive Emission Control Evaluation, EPA 600/2 83 110, U. S. Environmental Protection Agency, Cincinnati, OH, October 1983. (Reference 6, ref_06c13s0201_2011.pdf)

This study evaluated paved road control t echniques at two diffe rent iron and steel plants. (See Tables 9 and 10 in Reference 8.) Data were quality rated as "A," and

uncontrolled test results were in corporated into the data ba se for Section 11.2.6 published in 1983. The only use of the cont rolled test results, however, was the following addition to

Section 11.2.6.4 in 1988:

"Although there are relatively few quantitativ e data on emissions from controlled paved roads, those that are available indicate that ade quate estimates generally may be obtained by substituting controlled loadi ng values into .. [Equa tions (2-2) and (2- 3)].... The major exception to this is wate r flushing combined with broom sweeping. In that case, the equations tend to over estimate emissions substantially (by an average factor of 4 or more)."

In the current update, the controlled emission factors have been used as part of the overall data base to develop predictive models. Although PM 10 

### emission data are not specifically presented in the re port, appropriate values were previously developed by lognormal interpolation of the PM 15 

### and PM 2.5 

### factors. 8

4.2.1.4. G. E. Muleski, Measurement of Fugitive Dust Emissions from Prilled Sulfur Handling, Final Report, MRI Project No. 7995-L, Prepared for Gardinier, Inc., June 1984 (Reference 45)



### This was first report identified to suggest that heavily loaded paved roads may be better considered as unpaved in terms of em ission estimates. The program produced three

tests of emissions from end-loader travel ove r paved surfaces. Two of the three tests were conducted on very heavily loaded surface, while the third was on a cleaned paved surface. (See Tables 20 and 21 of the 1987 update.) 8



### No PM 10 

### emission factors were reported; result s were presented for total particulate (TP) and suspended particulate (SP, or PM 30 

### ). Data were quality rate d "A" in the 1987 report.4-8 

### Because no PM 10 

### data were given, Test Report 5 data were most directly useful as independent data against which the TSP emissi on factor model (Eq. (2-2)) could be assessed. This comparison showed generally good agr eement between predicte d and observed with agreement becoming better as source conditions approached those in the underlying data

base.

The 1987 update 8 

### developed PM 10 

### emission factors based on information contained in the test report. When compared to the si ngle valued factors (Equation [2-4]), agreement

for the first two tests was within a factor of a pproximately two. The third test — that of the

cleaned surface — could not be used to assess th e performance of either Eq. (2-1) or Eq. (2- 3) because the surface loading value could not be converted to the necessary units with information presented in the report.

4.2.1.5 T. F. Eckle and D. L. Trozzo, Verification of the Efficiency of a Road-Dust Emission-Reduction Program by Exposure Profile Measurement, Presented at EPA/AISI Symposium on Iron and Steel Pollution Ab atement, Cleveland, Ohio, October 1984. (Reference 46)

This paper discussed the development of an exposure profiling system as well as an evaluation of the effectiveness of a paved road vacuum sweeping program. Because no reference is made to an earlier test report, this paper is considered to be the original source of the test data. Although ten uncontrolled and five controlled tests are me ntioned, test data are reported only in terms of averages. (See Tabl es 24 and 25 in Reference 8.) Only TSP emission factors are presented. Although da ta were obtained using a sound methodology,

data were rated "C" because of inad equate detail in the paper.

Averaged data from Test Report 8 were used in an independent assessment of Eq. (2-2). Although only average emission leve ls could be compared, the data suggested
that TSP emissions could be esti mated within very acceptable limits.

4.2.1.6 Roadway Emissions Field Te sts at U.S. Steel’s Fair less Works, U.S. Steel Corporation, Fairless Hills, PA, US X Purchase Order No. 146-0001191-0068, May 1990. (Reference 31, ref_31c13s0201_2011.pdf).

This 1989 field program used exposure pr ofiling to characterize emissions from paved roads at an integrated iron and steel plant near Ph iladelphia, Pennsylvania, in November 1989. In many respects, this program arose because of un certainties with paved

road emission factor models used outside thei r range of applicability. During the preparation of an alternative emission reduction ("bubble") plan for the plant, questions arose about the use of AP-42 equations and other EPA guidance 13 

### in estimating roadway emissions involved in the emissions trade. This program provided site-specific data to support the bubble plan. This testing program also represented the fi rst exposure profiling da ta to supplement the AP-42 paved road data base since the 1984 re vision. Site “C” was located along the main access route and had a mix of light- and medium- duty vehicles. Site “E” was located near the southwest corner of the plant and the traffi c consisted mostly of plant equipment. Table 4-3 provides summary information and Tabl e 4-4 provides detailed information.

The program involved two paved road test sites. The first (site "C") was along the

four-lane main access route to th e plant. Average daily traffic (ADT) had been estimated as more than 4,000 vehicle passes per day, with most vehicles repres entative of "foreign"4-9 

### equipment (i.e., cars, pickups, and semi-trailers rather than plant haul trucks and other equipment). Site "E," on the other hand, was located near the iron- and steel-making

facilities and had both lower ADT and heavier vehicles than site "C." The plant regularly vacuum swept paved roads, and two cleaning frequencies (two times and five times per week) were considered during the test program.

Eight tests were conducted at Site C-1 and four tests were conducted at Site E-2. The paved road test sites were considered unc ontrolled. The road wi dth, moisture content, and mean number of wheels were not reported. The test data are assigned an “A” rating. Table 4-3 presents summary information and Table 4-4 presents detailed test information. Warm wire anemometers at two heights measured wind speed.

Depending on traffic characteristics of the road being tested, a 6 to 7.5 m high profiling array was used to measure downwind ma ss flux. This array consisted of four or five total particulate sampling heads spaced at 1.5 m heights and was positioned at a

nominal 5 m distance downwind from the road. A high-volume sampler with a parallel-slot cascade impactor and a cyclone preseparator (cutpoint of 15 μ mA) was employed to measure the downwind particle size distribut ion, and a standard high-volume sampler was

utilized to determine the downwind mass frac tion of total suspended particulate matter (TSP). The height for downwind sizing devices (2.2 m) was selected after review of prior test results. It approximated the height in a ro adway dust plume at which half the mass emissions pass above and half below. The upwind (background) part icle size distribution was determined with a high-volume cyclone/ impactor combination. Warm wire anemometers at two heights measured wind speed.

Additional samples included:

• Average wind speeds at two heights and wind direction at one hei ght were recorded during testing to maintain isokinetic sampling.

• Traffic data, including traffi c counts, travel speeds, and vehicle class were recorded manually.

• Vacuums with disposable paper bags were used to collect the loose material from the

road surface.

The sampling equipment met the requirements of a sound exposure profiling methodology specified in Section 3.4 so that the emission test data are rated "A." The test report presents emission factors for total part iculate (TP), total suspended particulate (TSP)

and PM 10 

### , for the ten paved road emission tests conducted.

Reference 31 found that the emission factor s and silt loadings more closely resembled

those in the "urban" rather than the "industrial" data base. Th at is to say, emissions agreed more closely with factors estimated by th e methods of September 1985 AP-42 Section 11.2.5 than by methods in Section 11.2.6. Given the tra ffic rate of 4000 vehicles per day at Site "C," this finding was not terribly surprisi ng. What was far more surprising was that

emissions at Site "E" were also more "urb an" than "industrial." Although the TSP and PM 10 

### models in Section 11.2.5 showed a slight tendency to underpredict, the Section 11.2.6 PM 10 

### model overestimated measured emissions by at least an order of magnitude. The performance of the industrial TSP model, on the other hand, was only slightly poorer than

that for the urban TSP model.4-10 

### 4.2.1.7 Midwest Research Institute, Pave d Road Particulate Emissions - Source Category Report, for U.S. EPA, Ju ly 1984. (Reference 8, ref_08c13s0201_2011.pdf)

This document reports the results of test ing of paved roads conducted in 1980 at sites in Kansas City, MO, St. Louis, MO, Tonganoxie, KS, and Granite City, IL. Paved road test sites included commercial/industrial roads, comm ercial/residential roads, expressways, and a street in a rural town. The expanded measur ement program reported in this document was used to develop emission factors for paved ro ads and focused on the following particle sizes:

PM 15 

### (inhalable particulate matter [IP]), PM 10 

### , and PM 2.5



### .

Total airborne PM emissions were ch aracterized using an exposure profiler containing four sampling heads. High-volume samplers with size selective inlets (SSI) having a cutpoint of 15 μ mA were used to characterize upwind and downwind PM 15 

### concentrations. A high- volume sampler with a SSI and a cascade impactor was also located downwind to characterize particle size distribution within the PM 15 

### component. Upwind and downwind standard high- volume samplers measured TSP concentrations. Warm wire anemometers at two heights measured wind speed.

A total of 19 paved road emission tests were conducted in four cities. These included four tests of commercial/industrial paved roads, ten tests of commercial/residential paved roads, four expressway te sts, and one test of a street in a rural town. Additionally, as part of this study, 81 dust samples were collected in 12 cities. The m ean number of vehicle wheels was not reported. The test data are assigned an A rating. Table 4-5 presents summary test data and Ta ble 4-6 presents detail ed test information.TABLE 4-3. SUMMARY INFORMATION FOR REFERENCE 31OperationLocationStateTestdatesNo. oftestsTSP emission factor, lb/VMTPM10emission factor, lb/VMTGeom. mean Range Geom. mean RangeVehicle trafficAU-X(Unpaved road)PA 11/89 2 0.61 0.39-0.960.160.14-0.18Vehicle trafficPaved roadPA 11/89 6 0.033 0.012-0.12 0.0095 0.0009-0.036Vehicle trafficPaved roadPA 11/89 4 0.078 0.033-0.30 0.022 0.0071-0.0361 lb/VMT = 281.9g/VKT.TABLE 4-4. DETAILED INFORMATION FROM PAVED ROAD TESTS FOR REFERENCE 31Test runsPM10emissionfactor, lb/VMTDuration,minMeteorologyVehicle characteristicsSiltloading,g/m2Silt, %Temperature,°FMean windspeed, mphNo. of vehiclepassesMean vehicleweight, tonMeanvehiclespeedaAU-C-30.00497103 50 12 836 5.5(27)0.42 10AU-C-40.0355147 63 11 1057 6.0 25 0.52 12AU-C-50.0337120 62 14 963 3.9 29 0.23 9.7AU-C-6c0.00816c187 3914685 6.2(27)0.23b8.6AU-C-70.00088796 42 12 703 3.0(27)0.26b7.7AU-C-80.0174218 40 15 779 2.0(27)0.15b9.9AU-E-10.00709154 43 12 210 12 15 4.0 17AU-E-20.023489 4413373 5.1 16 4.0 17AU-E-30.0355118 41 9.3 330 2.6(15)2.2 18AU-E-40.0199130 41 9.3 364 2.6(15)1.3 15a Value in parentheses is the average speed measured for test road during the field exercise.b Test conducted on a paved road surface vacuum-swept five times per week.c Mean TSP/TP or PM10/TP ratio applied.1 lb/VMT = 281.9 g/VKT.1 g/m2= 1.434 gr/ft2

4-11TABLE 4-5. SUMMARY INFORMATION FOR REFERENCE 8OperationStateTestdatesNo. oftestsPM15emission factor, lb/VMTPM10emission factor, lb/VMTPM2.5emission factor, lb/VMTGeom. mean Range Geom. mean Range Geom. mean RangeCommercial/IndustrialMO2/804 0.0078 0.0036 - 0.013 0.0068 0.0034 - 0.011 0.0045 0.0030 - 0.0063Commercial/ResidentialMO, IL2/8010 0.0021 0.0006 - 0.012 0.0017 0.0004 - 0.0093 0.0011 0.0002 - 0.0037ExpresswayMO5/804 0.0004 0.0002 - 0.0008 0.0004 0.0002 - 0.0007 0.0002 0.0001 - 0.0003Rural TownKS3/801 0.031 0.031 0.025 0.025 0.005 0.0051 lb/VMT = 281.9 g/VKT.

4-12TABLE 4-6. DETAILED INFORMATION FROM PAVED ROAD TESTS FOR REFERENCE8CategoryRun testNo.PM10emissionfactor,lb/VMTDuration,min.Temp.,°FMeanwindspeed,mphRoadwidth,ftNo. ofvehiclepassesMeanvehiclespeed,mphMeanvehicleweight,tonsSiltloading,g/m2Silt (%)Commercial/IndustrialM-10.0110 120 28 7.4 442,627 30 5.6 0.46 10.7Commercial/IndustrialM-20.00340 86 27 6.5 442,166 30 3.8 0.26 6.2Commercial/IndustrialM-30.00781 120 28 7.8 442,144 30 4.5 0.15 3.5Commercial/IndustrialM-90.00712 136 50 7.4 443,248 30 4.1 0.29 12.2Commercial/ResidentialM-40.000400 240 38 7.8 362,763 35 2.1 0.43 18.8Commercial/ResidentialM-50.00153 226 53 2.2 362,473 35 2.2 1.00 21.4Commercial/ResidentialM-60.00304 281 35 5.6 363,204 30 2.1 0.68 21.7Commercial/ResidentialM-130.000680 194 60 2.7 225,190 35 2.7 0.11 13.7Commercial/ResidentialM-140.00301 178 55 9.2 223,940 35 2.7 0.079 -Commercial/ResidentialM-150.00323 135 77 11.4 224,040 35 2.7 0.047 8.1Commercial/ResidentialM-170.00582 150 75 4.0 403,390 30 2.0 0.83 5.7Commercial/ResidentialM-180.000800 172 75 5.1 403,670 30 2.0 0.73 7.1Commercial/ResidentialM-190.000390 488 70 2.7 205,800 30 2.4 0.93 8.6ExpresswayM-100.000390 182 60 2.9 9611,148 55 4.5 0.022 -ExpresswayM-110.000700 181 56 8.7 9611,099 55 4.8 0.022 -ExpresswayM-120.000190 150 65 4.7 969,812 55 3.8 0.022 -ExpresswayM-160.000530 254 70 4.0 9615,430 55 4.3 0.022 -Rural TownM-80.0247 345 50 4.7 301,975 20 2.2 2.50 14.51 lb/VMT = 281.9 g/VKT.1 g/m2 = 1.434 gr/ft2

4-134-14 

### 4.2.1.8 Midwest Research Institute, Size Specific Particulate Emission Factors for Uncontrolled Industrial and Rural Roads, for U. S. EPA, January 1983. (Reference 7, ref_07c13s0201_2011.pdf).

This document reports the results of test ing conducted in 1981 a nd 1982 at industrial unpaved and paved roads and at ru ral unpaved roads. Unpaved indu strial roads were tested at a sand and gravel processing facility in Kans as, a copper smelting facility in Arizona, and

both a concrete batch and as phalt batch plant in Missouri . The study was conducted to increase the existing data base for size-speci fic PM emissions. The following particle sizes were of specific interest for the study: PM 15 

### , PM 10 

### , and PM 2.5



### .

Exposure profiling was utilized to characte rize total PM emissions. Five sampling heads, located at heights of up to 5 m, were deployed on the pr ofiler. A standard high-volume sampler and a high-volume sampler with an SSI (cutpoint of 15 μ mA) were also deployed downwind. In addition, two high-volume cyclone/impactor s were operated to measure particle size distribution. A st andard high-volume sampler, a high-volume sampler with an SSI, and a high-volume cy clone/impactor were utilized to characterize the upwind TSP and

PM 15 

### concentrations and the particle size distribution within the PM 15 

### fraction. Wind speed

was monitored with warm wire anemometers.

A total of 18 paved road te sts and 21 unpaved road tests ar e completed. The test data are assigned an A rating. Indu strial paved road tests were conducted as follows: three unpaved road tests at the sand a nd gravel processing plan t, three paved road tests at the copper smelting plant, four paved road te sts at the asphalt batch facilit y, and three paved road tests at

the concrete batch facili ty. The industrial road tests were considered uncontrolled and were conducted with heavy dut y vehicles at the sand and gravel processing plant and with medium duty vehicles at the asphalt batch, concrete batch, and copper smelting plants. Table 4-7 presents summary test data and Table 4-8 presents detailed test information.4-15TABLE 4-7. SUMMARY OF PAVED ROAD EMISSION FACTORS FOR REFERENCE 7IndustrialcategoryTypeTP, lb/VMTPM15, lb/VMTPM10, lb/VMTPM2.5, lb/VMTGeo.meanRange Geo. mean RangeGeo.meanRangeGeo.meanRangeAsphalt Batching Mediumduty1.83 0.750-3.65 0.437 0.124-0.7410.295 0.0801-0.4410.130 0.0427-0.214ConcreteBatchingMediumduty4.74 2.25-7.23 1.66 0.976-2.34 1.17 0.699-1.63 0.381 0.200-0.562Copper Smelting Mediumduty11.2 7.07-15.7 4.01 2.02-5.56 2.78 1.35-3.86 0.607 0.260-0.846Sand and GravelProcessingMediumDuty5.50 4.35-6.64 1.02 0.783-1.26 0.633 0.513-0.753 0.203 0.194-0.2111 lb/VMT = 281.9 g/VKT.

4-154-16TABLE 4-8. DETAILED INFORMATION FROM PAVED ROAD TESTS FOR REFERENCE 7RunNo.IndustrialcategoryTrafficPM10emissionfactor,lb/VMTDuration,min.Meanwindspeed,mphRoadwidth,ftNo. ofvehiclepassesVehicle characteristicsMoisturecontent, %Siltloading,g/m2Silt, %Meanvehicleweight,tonsNo. ofwheelsMeanvehiclespeed,mphY-1 Asphalt Batching MediumDuty0.257 274 5.37 13.8 47 3.6 6 10 0.22 91 2.6Y-2 Asphalt Batching MediumDuty0.401 344 4.70 14.1 76 3.7 7 10 0.51 76 2.7Y-3 Asphalt Batching MediumDuty0.0801 95 6.04 14.1 100 3.8 6.5 10 0.32 193 4.6Y-4 Asphalt Batching MediumDuty0.441 102 5.59 14.1 150 3.7 6 10 0.32 193 4.6Z-1 ConcreteBatchingMediumDuty0.699 170 6.71 24.3 149 8.0 10 10 a 11.3 6.0Z-2 ConcreteBatchingMediumDuty1.63 143 9.84 24.9 161 8.0 10 15 a 12.4 5.2Z-3 ConcreteBatchingMediumDuty4.01 109 9.62 24.9 62 8.0 10 15 a 12.4 5.2AC-4 Copper Smelting MediumDuty3.86 38 8.72 34.8 45 5.7 7.4 10 0.43 287 19.8AC-5 Copper Smelting MediumDuty3.13 36 9.62 34.8 36 7.0 6.2 15 0.43 188 15.4AC-6 Copper Smelting MediumDuty1.35 33 4.92 34.8 42 3.1 4.2 20 0.53 400 21.7AD-1 Sand and Gravel Heavy Duty 3.27 110 7.61 12.1 11 42 11 23 a 94.8 6.4AD-2 Sand and Gravel Heavy Duty 0.753 69 5.15 12.1 16 39 17 23 a 63.6 7.9AD-3 Sand and Gravel Heavy Duty 0.513 76 3.13 12.1 20 40 15 23 a 52.6 7.01 lb/VMT = 281.9 g/VKT.1 g/m2= 1.434 gr/ft2aNot measured

4-164-17 

### 4.2.1.9. Midwest Research Institute, Iron and Steel Plant Open Source Fugitive Emission

Control Evaluation , for U. S. EPA, August 1983, (Reference 6, ref_06c13s0201_2011.pdf).

This test report centered on the measuremen t of the effectiveness of different control techniques for PM emissions from fugitive dust sources in the iron and steel industry. The test program was performed at two integrated iron and steel plants, one located in Houston, Texas, and the other in Middlet own, Ohio. Control techniqu es to reduce emissions from paved roads, unpaved roads, and coal storage pile s were evaluated. For paved roads, control techniques included vacuum sw eeping, water flushing, and flus hing with broom sweeping.

Particle emission sizes of interest in this study were total PM, PM 15 

### , and PM 2.5



### .

The exposure profiling method was used to measure paved road particulate emissions at the Iron and Steel plants. Fo r this study, a profiler with four or five samplin g heads located at heights of 1 to 5 m was deployed. Two high-volume cascade impactors with cyclone preseparators (cutpoint of 15 μ mA), one at 1 m and the other at 3 m, measured the downwind particle size distribution. A standard high-volume sampler and an additional high-volume sampler fitted with a SSI (cutpoint of 15 μ mA) were located downwind at a height 2 m. One standard high-volume sampler a nd two high-volume samplers w ith SSIs were located upwind for measurement of background concentrations of TSP and PM 15



### .

Twenty-three paved road tests of controlled and un controlled emissions were performed. These included 11 uncontrolled tests, 4 vacuum sweeping te sts, 4 water flushing tests, and 4 flushing and broom sweeping tests. For paved road s, this test report does not present vehicle speeds, mean number of wheel s, or moisture contents. Because vehicle speeds above 15 MPH and moisture content are not expected to influence the emissions equation, the test data are assi gned an A rating. Table 4-9 pr esents summary test data and Table 4-10 presents detailed test information. The PM 10 

### emission factors presented in Table

4-10 were calculated from the PM 15 

### and PM 2.5 

### data using logarithmic interpolation.

After vacuum sweeping, emission s were reduced slightly more than 50 percent for two test runs and less than 16 percent for two te st runs. Water flushi ng applied at 0.48 gal/yd 2 

### achieved emission reductions ranging from 30 pe rcent to 70 percent. Flushing at 0.48 gal/yd 2 

### combined with broom sweeping resulted in em ission reductions ranging from 35 percent to 90 percent.TABLE 4-9. SUMMARY OF PAVED ROAD EMISSION FACTORS FOR REFERENCE 6ControlmethodLocation State Test dateNo. oftestsTP, lb/VMT PM15, lb/VMTPM2.5, lb/VMTGeo mean Range Geo mean Range Geo mean RangeNone A,D,F,J OH 7/80,10/80, &11/807 1.22 0.29-5.50 0.38 0.13-2.14 0.10 0.04-0.52VacuumSweepingA OH 10/80 &11/804 0.87 0.53-1.46 0.45 0.27-0.87 0.14 0.08-0.26WaterFlushingD,L TX 6/81 4 1.43 1.30-1.74 0.47 0.32-0.65 0.08 0.08-0.09Flushing &BroomSweepK,L,M TX 6/81 4 0.96 0.54-2.03 0.20 0.10-0.49 0.07 0.04-0.13None L,M TX 6/81 4 3.12 0.83-5.46 0.92 0.31-1.83 0.26 0.06-0.621 lb/VMT = 281.9 g/VKT.

4-18TABLE 4-10. DETAILED INFORMATION FROM PAVED ROAD TESTS FOR REFERENCE6SiteTestRun No.ControlmethodPM10emissionfactor, (lb/VMT)Duration(min.)Temp.,(°F)Mean windspeed, (mph)No. ofvehicle passesMeanvehicle weight,(tons)Silt loading,(g/m2)Silt, %AF-34None0.536 62 90 4.2 79 28 2.79 16AF-35None0.849 127 90 7.5 130 25 2.03 10.4AF-36VS0.147 335 50 5.9 263 8.3 0.202 18.3AF-37VS0.209 241 50 4.8 199 17 0.043 26.4AF-38VS0.430 127 50 4.5 141 18 0.217 27.9AF-39VS0.686 215 50 6.4 190 18 0.441 19.6DF-61None1.35 108 40 11.0 93 40 17.9 21.0DF-62None0.929 77 45 12.1 94 36 14.4 20.3DF-74WF1.32 205 50 9.0 67 29 5.59 9.45aFF-27None0.357 91 100 9.5 158 14 17.7 35.7FF-45None0.608 135 50 4.0 172 16 5.11 28.4JF-32none0.144 259 90 5.8 301 14 0.117 13.4KB-52FBS0.0946 60 90 2.9 119 12 7.19 34.3LB-50FBS0.230 104 90 5.6 123 9.4 13.6 28.2bLB-51FBS0.435 93 90 4.2 127 11 13.6 28.2bLB-54WF0.268 101 90 5.4 118 10 3.77 22.6LB-55WF0.575 82 90 8.5 98 11 6.29 19.6aLB-56WF0.398 61 90 6.3 118 9.2 2.40 11.2LB-58None1.08 96 90 6.7 67 18 10.4 17.9MB-53FBS0.161 81 90 5.3 72 20 -- 9.94MB-570.554None 101 90 3.6 68 12 2.32 6.45aMB-590.993None 114 90 6.1 67 11 2.06 14.0aMB-601.18None 112 90 5.0 50 12 3.19 13.5aAverage of 2+ valuesbSample used for more than 1 run.cPM10emission factors were calculated from the PM15andPM2.5data using logarithmic interpolation.VS = Vacuum sweeping; WF = Water flushing; FBS = Waterflushing and broom sweeping; 1 lb/VMT = 281.9 g/VKT; 1 g/m2=1.434 gr/ft2

4-194-20 

### 4.2.1.10. Midwest Research Institute, Fugitive Particulate Matter Emissions for U.S. Environmental Protection Agency, Emissi on Factor and Inventory Group, April 15, 1997. (Reference 30, ref_30c13s0201_2011.pdf).

This reference documents the performanc e of six field studies characterizing the vehicle emissions from three unpaved roads and three paved roads. Te sting of unpaved roads was performed in Kansas Cit y, MO; Raleigh, NC; and Reno, NV. Testing of paved roads was performed in Denver, CO; Raleigh, NC; a nd Reno, NV. Midwest Research Institute

measured the emission rates for PM 10 

### and PM 2.5 

### at all six locations based upon a plume profiling methodology. The test da ta are assigned an A rating.

Plume profiling calculates emission rates us ing a conservation of mass approach. The passage of airborne particulate (i.e., the quant ity of emissions per unit of source activity) is obtained by spatial integration of distributed measurements of exposure (mass/area) over the effective cross section of the pl ume. Exposure is the point valu e of the flux (mass/area time) of airborne particulate integr ated over the time of measurement or, equivalently, the net particulate mass passing through a unit area normal to the mean wi nd direction during the test. The steps in the calculation procedure ar e as follows. The concentration of PM 10 

### measured by a sampler is compared to the wind speed and corrected to standard conditions. The concentration for each sampler is multiplied by the wind velocity and sampling duration to obtain the exposure for each sa mpling height. The exposure is integrated over the plumeeffective cross section. The quantity obtained repr esents the total passage of airborne particulate matter (i.e., mass flux ) due to the source. The expo sure is set to zero at the maximum effective height of the plume wher e the net concentration equals zero). The maximum effective height of th e plume is found by linear extra polation of the uppermost net concentrations to a value of zero. Although at ground level the wind velocity is zero, for calculation, the exposure value at ground level is set equal to the value at a height of 1 m. The integration is then performed from 1 m to the plume height, H, using Simpson's approximation.

Testing in Denver CO was c onducted to characterize emi ssions from a high speed (55 mph speed limit) limited access interstate road and a medium speed ( 40 mph speed limit) one lane road (two lanes with a wide median). For this part of the study, a profiler with four or five sampling heads located at heights of 1, 3, 5 and 7 m were deployed. One high-volume cascade impactor with cyclone preseparators (cutpoint of 10 μ mA) and two dichotomous samplers were used to measured the downwind pa rticle size distribution. All of the particle sizing samplers were located at 2 m above ground level. A si ngle set of the same sampling equipment was located at 2 m above ground le vel and upwind for meas urement of background concentrations of TSP, PM 10 

### and PM 2.5 

### . To the extent possible, each of the emission tests was performed during periods following snowfall, after the test road surface had dried. In most cases, sand application was ordered, becau se the relatively li ght snow conditions

characteristic of the 19 96 winter did not trigger routine sand application.

This test program also assessed the potential bias associated with particle sizing using the historical impactors that followed the cyclone pre-separator. The use of the dichotomous samplers consistently yielded a lower ratio of PM 2.5 

### to PM 10 

### ratio than were measured by the cyclone/impactor samplers. The PM 2.5 

### / PM 10 

### ratios measured by th e dichotomous samplers are presented to the right of the PM 10 

### emissions factors column in Table 4-11. Where two4-21 

### values are presented in the colu mn, these are the ratios measured at two different heights. The ratios range from 0.26 to 0 .37. As a result of this study, the constant in the PM 2.5 

### emissions factor equation was revised to 25% of the PM 10 

### constant.

4.2.1.11. Paved Road Modifi cations to AP-42, Background Documentation For Corn Refiners Association, Inc. Washingto n, DC 20006 MRI Project No. 310842, May 20, 2008. (Reference 32, ref_32c13s0201_2011.pdf).

The Corn Refiners Association (CRA) funded four paved road PM 10 

### test programs because site conditions did not match source conditions underlying the AP-42 emission factor equation. The sites enforce speed limits of 5 or 15 mph and employ road sweeping programs to manage the build up of silt on the roadways. In addition, plants expe rience traffic queues (i.e., stop-and-go traffic) duri ng periods with high corn receipts. The combination of heavy trucks (delivering corn to the facilities) and fa irly low silt loading (sL) values on the plant roads was not typical of the AP -42 data base. Given these di fferences, the member companies undertook testing to develop more representative emission fa ctors. Midwest Research Institute designed and conducted the test programs at all four facilities.

Reference 32 compiles test data and inform ation from references 33, 34, 35 & 36. In addition, reference 32 proposes an expansion of the allowable sp eed parameters supported in the paved road equation. Lastly, reference 32 proposes a revised equation for paved roads to reflect the expanded test information. The data upon which the proposed equation was based

included emissions associated with the trucks (engine exhaust, tire wear and brake wear) and with material deposited on the roadway. Since testing document ed in references 7 through 10

were conducted at facilities with very similar operati ng conditions using te st procedures that were nearly identical , the following description provide s background for all four test

programs.

All four testing programs employed the same exposure profiling method used to develop the test data underlying the emission f actor predictive equations for both paved and unpaved roads. In each program, a test plan was submitted to the st ate agency for comment and review prior to the start of testing. The fi nal test reports and supp orting information were also submitted to state agencies . Because low emission levels we re expected (due to low sL and slow speeds), several precauti ons were taken to assure relia ble quantification. First, long sampling durations were employed. Samplers were operated up to 5 hours to collect adequate sample mass. Second, to ensure adequate traffic during test periods, th e facilities provided “drone” passes by corn semi-trailers. Drone traffic mimicked the actual traffic except those trucks returned to staging areas without emptyi ng corn. In addition, testing applied “lessons learned” throughout the programs. For example, when it became apparent how difficult it could be to separate net PM 10 

### concentrations (i.e ., due to traffic on th e road) from background (upwind) concentrations, change s were made in equipment deployment. The use of identical upwind and downwind vertical sampling arrays permitted better definition of the net contribution of roadway emissions.TABLE 4-11. DETAILED INFORMATION FROMPAVED ROAD TESTS FOR REFERENCE 30SiteTestRunNo.RoadSpeed1PM10emissionfactor,(g/VKT)PM2.5/PM10RatioDuration,min.Temp.,°FMean windspeed, mphNo. ofvehicle passesMeanvehicleweight, tonsSilt loading,g/m2Silt, %CO BH-1 55 1.08 0.20 163 18 2.7 6,561 2.2 0.184 9.4CO BH-2 55 0.102 0.34 360 37 17.0 17,568 2.2 0.0127 41.0CO BH-3 55 - 0.16 360 46 17.2 14,616 - 0.0127 41.0CO BH-4 55 -Blank - -- -- -CO BH-5 40 -Blank - -- -- -CO BH-6 40 4.68 0.03 240 48 3.1 3,112 2.2 1.47 1.2NC BJ-6 45 0.301 0.27/0.34 450 71 8.2 14,670 2.2 0.060 52NC BJ-7 45 1.94 0.44/0.44 143 68 9.4 3,748 2.2 0.060 52NC BJ-9 45 0.6/0.14 178 71 5.3 4,616 2.2 0.060 52NC BJ-10 45 0.44/0.33 288 68 3.7 10,218 2.2 0.060 52NV BJ-11 45 0.68/0.47 387 75 5.1 13,216 2.2 0.060 52NV BK-7 45 0.57 0.29/0.33 420 89 7.3 7,394 2.2 0.082 3.4NV BK-8 45 0.44 0.26/0.34 270 87 6.1 5,747 2.2 0.082 3.4NV BK-9 45 - 0.13/0.38 240 90 2.6 4,622 - 0.082 3.41Road Speed is the posted speed limit for the roadsegment.

4-224-23 

### In addition to PM 10 

### concentrations, each sampling program samples included:

• Measurement of average wind speeds at two heights and wind direction at one height

for 5-minute intervals th roughout the test period.

• Manual recording of traffic counts by vehicle type. The host facilities provided information on vehicle weights and corn receipts.

• Collection of road surface material by v acuums with disposable paper bags. The material collected within the bag was siev ed to determine the surface silt loading.

Reference 32 states that the four test programs conduc ted by CRA produced 14 and 8

PM 10 

### emission factor values for slowly moving and stop-and-go tr affic, respectively. Other observations in this report includes: that in all but one of the 22 cases, the AP-42 emission

factor overestimated the measured value; that for some tests, “stop-and-go” emission factors were substantially greater than the “slowly movi ng” factor (presumably because of the diesel exhaust as trucks moved from a dead stop) but that there was no significant difference between “slowly moving” and “sto p-and-go” results on average.

Furthermore, Tables 4-12, 4- 13, and 4-15 use bold font to indicate those tests that used identical upwind and downwind vertical sampling arrays. Thos e tests provided better

definition of net PM 10 

### mass thus producing more accurate emission factors. Although these

test results tended to be lower than the other emission factors, the two sets on average did not

differ significantly.

4.2.1.12 Midwest Research Institute, Emission Tests of Paved Road Traffic at Minnesota Corn Processors Marshall, Minnesota Facility, McVehil-Monnett Associates, July 6, 2001. (Reference 33, ref_33c13s0201_2011.pdf).

Truck traffic flow at the Minnesota Corn Processor’s (MCP’s) Marshall, Minnesota facility was characterized as either slowly moving (5 mph en forced speed limit) or stop-andgo in nature. In this testing program, data was collected over 5 days during April of 2001. During this period , three stop-and-go traffic situatio ns and six slowly moving traffic

instances were examined. Truck traffic progressing through the test site was held to two lanes for queued traffic. Silt content (sL, measured by MCP), truck weight, and number of passes, along with other pertin ent data was recorded for each run. For all r uns, a vertical network of samplers was operated downwind. The last test period used a vertical array of samplers upwind to better characterize upwind co ncentrations and to provide a more accurate

calculation of the net PM 10 

### emission factor.

The results of this testing program are su mmarized in Table 4-12. The test data are assigned an A rating. The test report remarked that the emission factors obtained were far below the value (0.453 lb/V MT) used in the plant emission i nventory. Use of test-specific silt loading and vehicle weight did not signif icantly improve the pred ictive accuracy of the

AP-42 factor. The tests found no di scernable relationship between emission levels and either silt loading or vehicle weight. Finally, it was noted that the shape of the exposure profile was more likely due to diesel exhaust than re-entrained road dust.4-24 

### Table 4-12. Summary of Emissions Data from MCP’s Marshall, Minnesota Facility (Reference 33 )

Run Test condition Traffic

rate (veh/hr) Traffic speed (mph) a Mean vehicle weight, W (tons) Surface silt loading, sL (g/m 2 ) Measured PM 10 emission factor (lb/VMT) CE-1 Stop-and-go 38 NA 36 1.16 0.059 CE-2 Stop-and-go 32 NA 36 0.86 0.14

CE-11 Slowly moving 35 5 12 1.34 0.34

CE-3 Stop-and-go 47 NA 39 0.86 0.10

CE-13 Slowly moving 48 5 13 1.34 0.051

CE-15 Slowly moving 30 5 40 1.91 0.14

CE-16 Slowly moving 28 5 40 1.41 0.17

CE-17 Slowly moving 29 5 40 2.93 0.091

CE-19 Slowly moving 61 5 38 0.76 0.041

a 

### Vehicle speed was maintained at the pl ant limit of 5 mph. NA = Not applicable.

Bold entries indicate that identical vertical sampling arrays were used to better isolate the

source contribution.

4.2.1.12. Midwest Research Institute, Emission Tests of Paved Road Traffic at Minnesota

Corn Processors Columb us, Nebraska Facility , McVehil-Monnett Associates, July 13, 2001. (Reference 34, ref_34c13s0201_2011.pdf).

Truck traffic flow at MCP’s Columbus, Nebr aska facility was characterized as either slowly moving (5 mph enforced speed limit) or stop-and-go in nature. Between June 12 and 15, 2001, four tests each of stop-and-go and sl owly moving traffic were performed. Trucks entered by the north gate and traveled past a vertical sampling array en route to a staggered queue at which a second vertical sampling array was positioned. In this way, testing evaluated both source conditions (stop-and-go and slowing moving) at once. Building on experience from testing at the MCP Marshall fac ility, the last two runs , CF-4 and CF-5, used identical upwind and downwind ve rtical sampling arrays to be tter characterize background concentrations. In that case, on ly one condition could be evalua ted during a test . The results of the MCP Columbus test program are summa rized in Table 4-13. The test data are assigned an “A” rating.

4.2.1.13. Midwest Research Institute, Emission Tests of Paved Road Traffic at Cargill

Sweeteners North America Blair, Nebraska Facility , McVehil-Monnett Associates, November 27, 2002. (Reference 35, ref_35c13s0201_2011.pdf).

This report describes a testi ng program conducted at Cargill’ s Blair, Nebraska facility during August 2002. The plant used a regular sweeping program to reduce surf ace loadings on paved roads. Testing relied on regular corn truck traffic at the site, a lthough the plant4-25 

### provided a limited amount of “d rone” traffic. The test data are assigned an “A” rating.

Eight PM 10 

### emission tests were attempted. The test report describes difficulty encountered in isolating net PM 10 

### mass due to traffic on the te st road. During test plan review, the Nebraska Department of Environmental Quality requested a change in test site to allow two trucks to pass by at the same time. The original site would have permitted upwind monitoring in the immediate vicinity of the test s road, but this was not possible at the second

location. Furthermore, steeply sloping ground on the upwind side of the te st road prevented

use of a vertical background sampling array (as used at the two MCP plants) to better isolate

the source contribution.

The results are summarized in Table 4-14. Only two tests (CI-7 and CI-8) had net

mass attributed to the source. In the re maining instances, the measured downwind PM 10 

### concentrations were lower than upw ind values. It was stated that this was believed to be an undesired result from moving th e test source. Runs CI-7 an d CI-8 showed the measured

emission factor to be much lower than that predicted by the AP-42 equation. Comments in the report indicated that exposure profiles showed a maximum more likely due to diesel

exhaust than from re-entra ined surface road dust.

4.2.1.14. Midwest Research Institute, Emission Tests of Paved Road Traffic at ADM’s Marshall, Minnesota Facility , McVehil-Monnett Associ ates, December 5, 2003. (Reference 36, ref_ 36c13s0201_2011.pdf).

The test program at ADM’s Marshall MN facility represented the last test by the Corn Refiners Association. By September 2 003, the Marshall facil ity had implemented a road sweeping program. Three tests of PM 10 

### emissions were conduc ted, one from stop-andgo traffic and two from slowly moving traffi c. Because of experience gained from the earlier tests, identical vertical networks of samplers were operated downwind and upwind during each test.

The results of this testing program are su mmarized in Table 4-15. The test data are assigned an A rating. Measured emission factors were all significantly lower than that predicted by the AP-42 equation. The test report also remarked that the measured emission rates were independent of traffic rate, while the AP-42 factor implies a linear dependency

between the emission and traffic rates.

The results are summarized in Table 4-14. Only two tests (CI-7 and CI-8) had net

mass attributed to the source. In the re maining instances, the measured downwind PM 10 

### concentrations were lower than upw ind values. It was stated that this was believed to be an undesired result from moving th e test source. Runs CI-7 an d CI-8 showed the measured

emission factor to be much lower than that predicted by the AP-42 equation. Comments in the report indicated that exposure profiles showed a maximum more likely due to diesel

exhaust than from re-entra ined surface road dust.Table 4-13. Summary of Emissions Data from MCP’s Columbus, Nebraska Facility (Reference 34)RunaTest conditionTraffic rate(veh/hr)Trafficspeed(mph)bMean vehicleweight, W (tons)Surface silt loading,sL (g/m2)Measured PM10emissionfactor (lb/VMT)CF-1/N Low Speed 47 5.0 400.970.011CF-1/S Stop-and-go 47 NA 400.970.043CF-2/N Slowly moving 66 5.3 410.810.036CF-2/S Stop-and-go 66 NA 410.810.14CF-3/N Slowly moving 54 5.1 410.630.0024CF-3/S Stop-and-go 54 NA 410.630.051CF-4/NSlowlymoving86 4.7 41 1.1 0.0068CF-5/N Stop-and-go 52 NA 411.40.036aSuffix indicates whether tests was conducted on the North or South portion of thecorn haul road. Trucks were held ina queue toward the south; trucks entering the north gatetraveled passed the north sampling array to reach the queue.bSpeed of moving trucks determined by accumulating time required to travel a measured distance. NA = notapplicable.Boldentries indicate that identical vertical sampling arrays were used tobetter isolate the source contribution.

4-26Table 4-14. Summary of Emissions Data from Cargill’s Blair, Nebraska Facility (Reference 35)Run Test conditionTraffic rate(veh/hr)Traffic speed(mph)aMean vehicle weight,W (tons)Surface silt loading,sL (g/m2)bMeasuredPM10emissionfactor (lb/VMT)cCI-1 Low Speed 4513.4 / 16.812.8 / 16.926 0.06 -CI-2 Low Speed 45260.06-CI-3Slowlymoving60d13.6 / 12.713.5 / 15.527 0.06 -CI-4 Low Speed 60d27 0.06 -CI-7Slowlymoving4715.2 / 16.213.6 / 16.127 0.05 0.0036CI-8 Low Speed 47270.050.0066CI-11 Low Speed 5613.5 / 12.727 0.025 -CI-12 Low Speed 56270.25-aVehicle speed for inbound (loaded) /outbound (empty) trucks determined by accumulating time required to travel ameasured distance.bSurface silt loading sample information provided by Cargill.c“-“ indicates that no net mass was attributed to the test road traffic.dTwenty of 238 total passeswere by “drone” trucks.Table 4-15. Summary of Emissions Data from ADM’s Marshall, Minnesota Facility (Reference 36)Run Test ConditionTraffic rate(veh/hr)Trafficspeed(mph)aMean vehicleweight, W(tons)Surface siltloading, sL(g/m2)MeasuredPM10emission factor(lb/VMT)CM-1Slowlymoving154 NA 40 0.72 0.014CM-2 Stop-and-go 42 NA 40 0.720.14CM-4Slowlymoving156 5 40 0.70 0.016aVehicles speeds maintained at plant limit of 5 mph. NA = not applicable.Boldentries indicate that identical vertical sampling arrays were used tobetter isolate the sourcecontribution.

4-274-28 

### 4.2.1.15. E.H. Pechan & Associates, Inc., Recommendations for Emission Factor Equations in AP-42 Paved Roads Section: TECHNICAL MEMORANDUM August 21, 2003. (Reference 28, ref_28c13s0201_2011.pdf).

This technical memorandum documents the pr ocedure that was used to separate the various components of paved road particulate matter emissions into two components. One component includes the emissi ons from exhaust, brake wear and tire wear. The other component includes the particulate matter reentrai ned from the road surf ace. The combined paved road particulate matter emissions we re estimated with the empirical equation published in the October 2002 AP-42 Section for Paved Roads. The vehicle exhaust,

brakewear and tirewear emissi on factors were obtained fr om the MOBILE6.2 model. A typical vehicle fleet and fuel source from 1980 was utilized for the model runs. The assumption included a vehicle fleet for July 1980, a gasoline sulfur content of 300 ppm, a

diesel sulfur content of 5 00 ppm and no use of reformulat ed gas. The vehicle fleet assumptions used in the analysis are presente d in Table 4-16. The model was run to estimate

PM 10 

### and PM 2.5 

### emission factors in g/VMT for each ve hicle class at spee ds of 25, 30, 35, 40, 45, 50, 55, and 60 mph. W ithin vehicle classes, the greate st standard deviation was lower
than 0.04% of the emi ssions factor. Based on the low rela tive standard de viation, it was assumed that the vehicle speed was not a fact or in exhaust, brak ewear and tirewear PM emissions. Table 4-16 presents the vehicle fleet characteristics used in the model and the calculated average PM 10 

### and PM 2.5 

### emission factors for exhaust, brakewear and tirewear for

each class of vehicle.

Table 4-16: Vehicle Fleet Assu mptions Used in 2003 MOBILE6.2 Model

VehicleType LDGV LDGT12 LDGT34 LDGT HDGV LDDV LDDT HDDV MC

GVWR 3,075 4,105 7,000 35,000 3,705 6,000 70,000 550

VMT Distribution 0.6748 0.1477 0.0758 0.0365 0.0088 0.0118 0.0352 0.0094



### PM 10 Emissions

Factor 0.1053 0.1061 0.2746 0.1632 0.3825 0.7206 0.7206 2.1227 0.0922



### PM 2.5 Emissions

Factor 0.0686 0.0690 0.1851 0.1084 0.2576 0.6519 0.6521 1.9272 0.0590



### The contractor developed “AP-42 Composite” PM 10 

### and PM 2.5 

### emission factors using the October 2002 AP-42 paved roads emission fact or equation with the mean vehicle weight set at 3.74 tons (a value they indicated was typi cal of the 1980 paved road vehicle fleet. The

contractor used silt loadi ngs ranging from 0.02 to 400 g/m 2 

### for calculating the emissions

factors. The contractor also calculated the fleet average PM 10 

### and PM 2.5 

### emission factors for exhaust, brakewear and tirewear by summing th e products of the VMT Distribution ratio and

the PM 10 

### and PM 2.5 

### emission factors for each vehicle cl ass. The calculated fleet average

values were 0.2119 for PM 10 

### and 0.1617 for PM 2.5 

### . The contractor then subtracted the fleet average emissions factors for exhaust, brakew ear and tirewear from the “AP-42 Composite” emissions factors to produce an emission f actor for only the re-entrained road dust component. The contractor noted that the whil e the stated applicable silt loadings for the October 2002 AP-42 paved road e quation ranged from 0.02 to 400 g/m 2 

### the PM 2.5 

### emissions factor became negative at si lt loadings less than 0.029 g/m 2



### . They stated that since negative emissions were not physically possible, the equation they recomme nded was only valid for4-29 

### silt loading ranging from 0.03 to 400 g/m 2 

### . While no test data are as sociated with this report, the report does provide estimates of engine exha ust, tire wear and br ake wear derived from an EPA emissions model which is based upon em issions testing by a va lidated test method on multiple vehicles for each type of vehicle. As a result, emissions estimates by vehicle class are assigned an A rating. Because the use of a national average vehicle fleet emissions estimate does not provide emissions that are representative of the mi x of vehicle classes measured during the above test reports, the composite emissions estimates are assigned a C rating.

4.2.1.16. E-mail communicati on between Ron Myers of EPA/OAQPS/SPPD/MPG, RTP, NC and Prashanth Gururaja and Ed Glover of EPA/OTAQ/ASD/HDOC re. Diesel exhaust, tire and brake wear for low speed stop and go traffic; January 2009 through May 2009. (Ref erence 37, ref_37c13s0201_2011.pdf).

This e-mail communication and spreadsh eet file concerns estimates of PM 10 

### emissions associated with slow moving and stop and go diesel engine se mi-trailer trucks. The purpose of the request was to provide a means to disaggregate the consolidated PM emissions measured of trucks during deliver y of product at corn storage and transfer facilities. The request stated that the trucks were 18 wheel semitrailers of about ten years of age, were queued for the delivery of their load to a transfer or processi ng facility and that the estimated vehicle speed averaged about 1 mph but that they we re stopped most of the time.

PM 2.5 

### emissions were estimated using the MOVE S mobile source emissions model. The trucks modeled were approximately ten years old, traveling at an average of 1.5 mph on level pavement. Emissions were estimated at 11.06035 g/hour or 8.789778 g/VMT. PM 10 

### emissions were estimated to be approximately 3% greater than PM 2.5 

### emissions. While no test data are associated with this report, the report does provide estimates of engine exhaust,

tire wear and brake wear derived from an EPA emissions model which is based upon emissions testing by a validated test method on multiple vehicles for the specific type of vehicle measured during the Corn Refiners A ssociation Studies. As a result, emissions estimates for slow moving trucks are assigned an A rating.

4.2.1.17. E-mail communication between Ro n Myers of EPA/OAQPS/SPPD/MPG, RTP, NC and Gary Dolce, David Br zezinski and Rudolph Kapichak of EPA/OTAQ/ASD/HDOC re. vehicle exhaust, tire and brake wear for urban unrestricted road-types; October 2010 through December 2010. (Reference 39, ref_39c13s0201_2011.pdf).

This e-mail communication and spreadsheet files concern improved estimates of

PM 10 

### emissions associated engine exhaust, tire wear and brake wear for free flowing traffic. The purpose of the estimates was to update the emissions estimates produced by E. H. Pechan using the 2003 version of MOBILE6.2. Th e emissions model used for this updated

emissions estimates was the 2010 version of the MOVES model. Like the MOBILE6.2 model, the emissions predicted with the M OVES model provide a means for disaggregating the emissions measured during the paved road fi eld studies that measured emissions due to road surface dust, vehicle exha ust, break wear and tire wear.

It is explained in the docume ntation that in order to deve lop an equation for road dust alone, estimates of the particulate emissions from vehicle exhaust, brak e wear and tire wear4-30 

### were required. The e-mail documentation states that the MOVES model includes significant

new data about PM emissions from both light duty and heavy duty on

‐ road vehicles which allow MOVES to account for the influences of ambient temperature, vehicle speed, and

vehicle deterioration on emissions. The docum entation further states that none of those

factors are accounted for in MOBILE6.2.

Documentation includes information provide d to OTAQ on the test date (sometimes month and year, sometimes just year), vehi cle speed, ambient temperature, and average vehicle weight for each of the paved road field studies. The documentation states that OTAQ created a MOVES2010a model input file that approximated the information for the paved road field studies as closely as possible. The documentation also states that since MOVES2010a provides output for calendar years 1990 and 1999

‐ 2050 alternative scenarios were developed to estimate emissions for y ears which MOVES2010a is not programmed to provide.

The documentation states that the speed and ambient temperature measured during the field study provided additi onal independent variables us ed in the MOBILE2010a model

to estimate emissions. The documentation indica tes that an emissions estimate was produced for each of the individual tests by allocati ng all of the vehicle activity to a single MOBILE2010a speed bin which included the vehicl e speed observed in the test. To reduce the number of number of total runs needed, temperatures for the individual tests were rounded to the nearest multiple of 5 degrees. In a small number of cases, vehicle speed or temperature data were not available for particular tests. In those cases , a vehicle speed of 25 mph or an ambient temperature of 75 degrees was used. All other inputs to MOVES were

national defaults.

All vehicle and fuel type combinations (except for electric vehicles) were included. Emissions were generated only for the urban unrestricted road

‐ type. Emissions were generated for all PM10 pollutants (primary exhaust PM 10 

### total, primary PM 10 

### brake wear, and primary PM 10 

### tire wear. Only running exhaust and crankcase running exhaust processes

were included in the exhaust emissions calculations as the te st sites did not include any starting or idling activity. Inve ntory results generated by MOVE S source type (vehicle type) were divided by VMT to get emission factors by source type for each speed and temperature bin in the original test data.

Emissions estimates for free flowing light du ty vehicles and trucks are assigned an B rating since most of the test data were fo r model years which an alternative emissions scenario (year, vehicle mix and assumed degr adion level) was used as the independent

variables used in the M OVES model input file. While it is likely that vehicle emissions prior to 1990 had tailpipe emissions very similar to the 1990 model year, this can not be verified. Also, while the emissions for each test are comp rised of a large number of vehicles and the emissions factor produced by the MOVES m odel are based upon a large number of supporting tests, it is unclear that the MOVES model is an accurate and precise indication of the vehicle exhaust, tire wear and brake wear emissions during each test series.4-31 

### 4.2.1.18. Midwest Research Institute; Analysis of the Fine Fraction of Particulate Matter in Fugitive Dust ; Western Governors’ Association - Western Regional Air Partnership (WRAP); October 12, 2005. (R eference 43, ref_43c13s0201_2011.pdf).

This project was conducted by Midwest Rese arch Institute for the Western Regional Air Partnership to provide more accurate PM 2.5 

### and PM 10 

### fugitive dust emissions inventories for regional haze regulatory purposes to addre ss the significant contri bution of fugitive dust to visibility impairment. The results of this project were expected to affect the quantity of dust apportioned to the fine versus coarse size mode s. It was stated that the results would be helpful in developing accurate emission invent ories for PM nonattainment, maintenance, and action plan areas in the WRAP re gion. Finally, it was stated that the results may be used to

seek modifications to the EPA’s AP-42 emission f actors to ensure widespread availability of the information developed in the study.

During the first testing phase of the project, PM 2.5 

### measurements using the highvolume cascade impactors were compared to simultaneous measurements obtained using EPA reference- method samplers for PM 2.5 

### . The tests were conduc ted in a flow-through wind tunnel and exposure chamber, where c oncentration level and uniformity were controlled. With the same test setup, a second phase of testing was performed with reference method samplers, for the purpose of measuring PM 2.5 

### to PM 10 

### ratios for fugitive dust from different geologic sources in the West. The testing provided information on the magnitude and variability of PM 2.5 

### to PM 10 

### ratios for source materials that were recognized as problematic with regard to application of mitigative dust control measures.

Three dust source materials were tested unde r the first Phase of the study. The three dust source materials included an Owens Dry Lake surface soil, and two Arizona road dust reference standards (one coarse and on e fine fraction material). Fixed PM 10 

### concentration levels in the range of 1, 2.5, and 5 milligrams per cubic meter (each with its naturally occurring PM 2.5 

### level) were tested. It was stated that those PM 10 

### concentration levels were selected as representative of dust plume concentrations under which major particle mass contributions to plume samples occur in emissi on factor development. The ratios of PM 2.5 

### to

PM 10 

### for fugitive dust from different geologic soil types were measured. A total of seven

source materials were tested. The materials included Alaska river bed sediment, Arizona alluvial channel, Arizona agricultural soil, New Mexico unpaved la ndfill road dust, New Mexico grazing soil, California Salton S ea shoreline soil, and Wyoming unpaved road

surface material. Test results includ ed the calculation of the average PM 2.5 

### concentration and

the collocated PM 10 

### concentration. It was inte nded that any variation in PM 2.5 

### / PM 10 

### ratio be evaluated as a function of the test soil pr operties (for example, position in soil texture triangle).

A total of 100 individual tests were performed, including 17 blank runs (for quality assurance purposes). The results of the te sting are well documented and the documentation is sufficient to assess that the study was well designed and implemented. This was a laboratory study designed to assess those emissions sources that were considered to have the greatest influence in PM 10 

### and PM 2.5 

### non attainment areas. As a result, the study is assigned a quality rating of B when applied within the bou nds of the type of surface material that was available and for dust generation characteristics comparable to those used in the study. The4-32 

### study included no paved road surface material and was weighted toward higher particulate

matter concentrations. Since the study was a laboratory study, di d not include any paved road surface materials, and was weighted toward highe r particulate concentrations, it is assigned a quality rating of “D” when used for paved roads.

The results of the Phase I testing indicated that the PM 2.5 

### concentrations measured by the cyclone/impactor system were consistently biased by a factor of about 2 relative the

PM 2.5 

### concentrations measured by the Partisol samplers. While there was some data separation of different test materials, the second phase testing showed a tendency of the

measured PM 2.5 

### / PM 10 

### ratio to decrease with increasing PM 10 

### concentration. At PM 10 

### concentrations above 1.0 mg/m3 the PM 2.5 

### / PM 10 

### ratio was between 0.1 and 0.15. The

PM 2.5 

### / PM 10 

### ratio increased to about 0.35 as the PM 10 

### concentration approached about 0.5 mg/m3.

4.2.1.19. Midwest Research Institute; Background Document for Revisions to Fine Fraction Ratios Used for AP-42 Fu gitive Dust Emission Factors ; Western Governors’ Association - Western Regional Air Partnership (WRAP); November 1, 2006. (Reference 44, http://www.epa.gov/ttn/chi ef/ap42/ch13/bgdocs/b13s02.pdf).

This report summarizes the results of the October 2005 WRAP study which evaluated

the PM 2.5 

### / PM 10 

### ratio measured by the cyclone/impactor system and measured by the Partisol samplers. While no additional analyses of the laboratory study were performed, suggested

PM 2.5 

### / PM 10 

### ratios were made for use in revising existing AP-42 emissions factor parameters

for PM 2.5 

### dust emissions factor equations in Sections 13.2.1 (paved roads), 13.2.2 (unpaved roads), 13.2.3 (material transfer and st orage piles), 13.2.4 (windblown dust) and 13.2.5 (industrial wind erosi on). A revised PM 2.5 

### /PM 10 

### ratio of 0.15 was recommended for the paved roads emissions factor.

4.2.1.20. Technical Memorandum from William B. Kuykendal to File, Subject:

Decisions on Final AP-42 S ection 13.2.1 “Paved Roads” , October 10, 2002. (Reference 38, ref_38c13s0201_2011.pdf).

This technical memorandum to the files su mmarizes and responds to comments on an October 2001, EPA proposed revision of S ection 13.2.1 “Paved Ro ads” for AP-42 and
request for comments. The memorandum also pr esents EPA’s decisions and rational supporting these decisions for th e final changes leading to th e final section. The proposed

revisions to the section included an adjustment for rain events (comparable to the adjustment in the unpaved road section) which in esse nce “zeroed” the emissions on days that more than 0.01 inch of rain was recorded. In addition, th e proposed revisions included the separation of vehicle engine exhaust, breakwear and tirew ear as recommended in the E. H. Pechan

Technical Memorandum of August 21, 2003. The memora ndum includes attachments with

the detailed comments that lead to the final revision of the em issions factor equation. The final changes to the emissions factor equation included:

• the subtraction of 0.2119 g/VMT for engine exhaust, brakewear and tirewear,

• an adjustment of (1- (P/4N)) for rain events (P = number of rain days and N = number of days in period), and

• an adjustment of (1- (1.2P/N)) for rain events (P = number of rain hours and N =4-33 

### number of hours in period).

4.2.1.21. Clark County (Nevada) Paved Road Dust E mission Studies in Support of Mobile Monitoring Technologies ; R. Langston, R.S. Merle Jr, V. Etyemezian, H. Kuhns, J. Gillies, D. Zhu, D. Fitz, K. Bumiller , D.E. James and H. Teng; Clark County Department of Air Quality and Environmenta l Management, Desert Research Institute, University of California, Riverside, Univ ersity of Nevada, Las Vegas; December 22,

2008. (Reference 42, http://www.epa.gov/ttn/chief/ap 42/ch13/related/Final_ Test_Report.pdf).

This report documents the fourth phase of a study by Clark C ounty to investigate alternative ways of estimating PM 10 

### emissions of surface dust entrained from paved roads. A new vehicle-mounted mobile sampling technol ogy was tested in comparison with the

traditional AP-42 method and its associated road surface sampling. In addition, the plume flux profiling method, was used to calib rate the mobile monitoring technology.

Two versions of the mobile monitori ng technology were tested—TRAKER and SCAMPER. Both technologies involve on-board sampling of the dust plume generated by a

test vehicle. Both use continuous optical based PM 10 

### particle monitors in conjunction with GPS systems, so that dust plume concentra tions can be mapped on to the road system traveled by the test vehicle. The SCAMPER samples the plume in the wake of the test

vehicle. The TRAKER I and II test vehicles sample the plumes from the front wheel wells of the respective vehicles. TRAK ER II has a dilution system to provide for use on unpaved roads. All three units have samplers that monitor the PM10 concentration in front of the vehicle so that “background” PM 10 

### can be subtracted.

The referenced study evaluated mobile monitoring technologies in comparison with the traditional AP-42 methodology, but in a controlled measurement environment that included restricted vehicle movement, controll ed vehicle speeds and controlled road surface material loadings. This was accomplished by dedi cating half of a divided roadway as the test course for the 5-day field study. The stated specific study objectives were as follows:

• Comparison of SCAMPER and TRAKER sy stem measurements with emission measurements using a downwind flux tower.

• Determination of the relationship between roadway silt loading and SCAMPER and

TRAKER measurements at several standard vehicle speeds (25, 35 and 45 mph).

• Comparison of SCAMPER and TRAKER measurements to AP-42 emission

estimates.

• Characterization of road surface silt deplet ion rate as a functio n of the number of vehicle passes.

• Characterization of quantified emissions vs. quantified silt loading mass.

• Data assessment and review for recomme ndations on performance specifications for vehicle-mounted mobile sampling systems.

Particle concentration measurements formed the basis for the mobile monitoring technologies as well as the ro adside emission flux measuremen ts. A continuously recording optical light scattering particle monitor (D ustTrak Model 8520, TSI Inc., Shoreview MN)

was the basic instrument used for PM 10 

### readings. A collocated mass-based reference monitor4-34 

### was used to correct the DustTr ak readings to equivalent PM 10 

### mass-based concentrations, using a plume profiling tower with various refe rence, reference equivalent and DustTrak monitors at different heights. Canister vacuum cleaners with ha rd-floor inlets were used to recover applied soil from the ro adway sites into pre-tared vacuum bags. Three soil recovery techniques were used during the study. Road dust emission factors were then calculated for the silt loadings using the 2006 AP-42 emissi on factor equation. A weight of 2.88 tons, based on the arithmetic average of the reported weights of the three mobile source vehicles

was used to calculate the AP-42 emi ssion factors from the silt loadings.

Thirteen different experiment al test conditions were perf ormed. Most consisted of approximately 30 vehicle passes, with each pass identified by the mobile samplingtechnology. Each run consisted of three passes by each mobile sampling technology. Crosscomparisons were performed to determine the ratio between the DustTrak reading and the

PM 10 

### mass-based concentration measured by a collo cated reference sampler. The correlation

between the DustTrak and TEOM showed that Du stTrak values would have to be multiplied by a factor of 2.8 ± 0.6 to obtain mass-equivalent PM 10 

### . A controlled laboratory tests was

also used to obtain a relati onship between the DustTrak measurements and mass-based measurements. These tests generated a Dust Trak correction multiplier of 2.4, which was chosen for use in this program.

Two conclusions were made from the te st results obtained in the study, when comparing mobile monitoring techno logies with the AP-42 methodology:

• The calibrated mobile methods measured em ission factors that were about 1.5 times higher than found with the AP-42 met hodology when higher silt loadings were applied to the test road.

• The mobile methods tracked each other quite well under most conditions.

It was concluded that a different silt mob ilization process occurred as a result of silt being distributed on top the embedded road surf ace aggregates and hence being more easily entrained by vehicle mechanical and aerodynamic shear. It was also st ated that aged silt found on most roads is more likely to be em bedded between the road surface aggregates.

Another conclusion identified in the field study was that implementation of mobile monitoring technologies provide for much easie r representation of sp atially distributed roadway emission characteristics, while e liminating the need to divert traffic.

4.2.1.22. Technical Support Document for Mobile Monitoring Technologies ; Prepared For Clark County Department of Air Qu ality and Environmental Management; Chatten Cowherd; Midwest Research Institute; January 9, 2009. (Reference 41, http://www.epa.gov/ttn/chief/ap42/ch13/related/Mobile_Monitoring_TSD_010909.pdf).

This report states that it documents a peer review process conducted to determine whether the mobile monitoring method is a su itable alternative to the traditional AP-42 method for developing road dust emission factors. The report identifies seven individuals which were requested to review the series of Clark County test reports and to judge the value of mobile monitoring technologies in relation to th e traditional approa ch for determining paved road dust emission factors.

The items addressed in this document include:4-35 

### • A summary of road dust entrainment dynamics,

• A brief discussion of the basis of the curre nt road dust emissions estimating method.
Also described were the methods used to characterize the road surface silt loadings, the statistical methods used in developing the AP-42 emission factor equations and the use of roadside plume exposure prof iling to quantify mass emissions rates.

• A brief discussion of the methods used to estimate independent variables required for the AP-42 emissions factor equations, asso ciated restrictions and the resulting limitations and a subjective a ssessment of the uncertainties.

• A more in depth discussion of the two m obile monitoring technologies (the Desert Research Institute (DRI) and the CE-CERT version) is provided. The report identifies the presence of high background dust concentration and high wind speeds as two restrictions for the use of mobile monitoring. The report discusses the subjectively established calibration require ments for mobile monitoring. Calibration requirements identified include determini ng the relationship between concentrations measured by the instrument used for mob ile monitoring and the Federal Register Measurement Method, the relationship between the concentrations measured at different vehicle speeds, different road dust characteristics a nd different vehicle weight during mobile monitoring and mass emissions measured by plume profiling.

• The report provides a discussion comparing of the implementation of the traditional application of the emissions factor and the use of m obile monitoring to develop

emissions inventories.

• Lastly, the report provides the charge provided to the reviewers, an overview of comments by the reviewers and an indication of what changes will be made to address the reviewers concerns in a Speci fication for Mobile Monitoring document.

While this document states that the purpos e is to demonstrate that mobile monitoring is equivalent or superior to the traditi onal AP-42 methodology, it provides only subjective opinions of the author and the selected revi ewers. While there were no quantitative indicators to compare the precision or accur acy of the mobile monitoring technologies over the normal range of road conditions (silt loadings , mix of vehicle weights, vehicle speed) and resultant emissions produced, the author and the majority of th e reviewers concluded that the method was more accurate and precise than th e traditions measurement and monitoring

methods. The review does reveal that there is an understanding that there is a lack of precision and understanding of independent vari ables other than silt loading, weight and speed which influence road dust emissions. Se veral reviewers highlig ht the potential of mobile monitoring methods to replace or s upplement the resource in tensive and dangerous collection of representative silt loading information. Severa l reviewers also highlight the need for further development and standardizatio n of mobile monitoring such that the method could be used for managing the ro ad dust emissions where required.

4.2.1.23. Mobile Monitoring Method Specifications; Prepared For Clark County Department of Air Quality and Environmental Management; Chatten Cowherd; Midwest Research Institute; February 6, 2009. (Reference 40, http://www.epa.gov/ttn/chief/ap42/ch13/related/MM_Method_Specifications_020609.pdf).



### This document provides inst ructions for performing a standardized methodology for the construction of a mobile sampling platform, specifications for instrumentation used with4-36 

### Federal Register Methods for PM10 or PM 2.5, calibrations required to correlate the combined sampling platform and instrumentati on with standardized plume profiling testing used to quantify mass emissions from roads and procedures for collecting information for use

in road surface characteristics or emissions.

4.2.2

. EMISSIONS FACTOR DEVELOPMENT.

A total of 103 individua l tests are available. All tests quantified PM 10 

### emissions. Lastly, plume profiling was the test method. Of these, 81 emissions tests included mean vehicle weight, road silt loadin g, and vehicle speed. The remaining tests included all of these parameters except vehicle speed. These emissions tests measured PM 10 

### emissions associated with engine exhaust, tire wear, brake wear and material deposited on the road surface. Policy decisions within EPA make it necessary to se parate particulate matter emissions associated with the operation of the vehicles (engine exhaust, tire wear and brake wear) and those

associated with the road surface characteristics. These policy decision s are based in part on the recent and future efforts to control engi ne exhaust emissions. Many of the emissions tests performed to quantify part iculate matter emissions from paved roads were conducted in

the mid 1980’s to middle 1990’s . Several of the emission s studies have experienced comparable upwind and downwind concentrations with downwind particulate that appears to consist of a large percentage of organic or carbonaceous materi al. The first separation of vehicle associated emissions and pavement associated emissions wa s in the 2003 update. This update used the national VMT weighted fleet average PM 10 

### emissions factor of 0.2119 g/VMT to subtract from the existing emissions factor equation as a means of separating the emissions from engine exhaust, tire wear a nd brake wear from the composite paved road

emissions factor. A fleet aver age vehicle weight of 3.75 tons is associated with this emissions factor. Since the average vehicle we ight used in the deve lopment of the paved road emissions factor equati on was about 10 tons, the PM 10 

### emissions factor for engine exhaust, tire wear and brake wear probably u nderestimated these emi ssions. In addition, because of the range and vari ation in mean vehicle weight , the use of an average for adjustment value introduces excessive error in the estimated road dust emissions estimates. Improved test specific adjustments for vehicle ex haust, tire wear and brake wear can be made since (1) average vehicle weights are av ailable for each test series, (2) PM 10 

### emissions

factors estimates for each vehicle class ar e available using the MOVES model and (3) PM 10 

### emissions estimates for slowly moving and st op and go truck traffic are available. By subtracting the estimated test specific vehicle emissions from the measured emissions prior to performing the stepwise multiple regression, emissions associated wi th the road surface

material will be isolated.

4.2.2.1. Compilation and Adjustment of Final Data Base.

In keeping with the results from the data set review, a final data base was compiled by combining the following sets:

1. The January 1983 EPA data base,

2. the August 1983 EPA data base,

3. the July 1984 EPA data base,

4. the May 1990 USX data base,4-37 

### 5. the April 1997 EPA data base, and

6. the May 2008 CRA data base.

While several of the test reports include detailed information on the number of light duty vehicles, moderate wei ght trucks and heavy weight trucks, none provide detailed

information on vehicle class as used to estimate emissions of vehicle exhaust, tire wear and

break wear. For this assessment the vehicle classes will be separated into two vehicle classes. One group of vehicle class will include the six classes of light duty vehicles/trucks and motorcycles. The other group of vehicle class includes gas an d diesel heavy duty trucks. Other assumptions used to estimate ve hicle associated emissions include:

• The test fleet includes a mixture of light duty vehicles, hea vy duty gas trucks and heavy duty diesel trucks when the averag e vehicle weight is less than 23 tons. • The test fleet includes a mixture of light duty vehicles and heavy duty diesel trucks when the average vehicle weig ht is between 23 tons and 35 tons. • The test fleet includes only heavy duty diesel trucks when the average vehicle weight is more than 35 tons.

First, the average vehicle weight and emi ssions are determined for the two classes of vehicles used to estimate the adjustment for the measured emissions. The vehicle weights and VMT distribution presented in Table 4-16 are used to calculate the average vehicle weight. The VMT adjusted gross vehicle weight is calculated for each class of vehicle by multiplying the VMT distribution by the average gross vehicle weight for the class. The individual vehicle class VMT adjusted gross vehi cle weights are summed to arrive at the two VMT adjusted gross vehicle wei ghts used in this assessment. For light duty vehicles, the VMT adjusted gross vehicle weight is 3320 pounds. For heavy du ty trucks, the VMT adjusted gross vehicle weight is 3742 pounds. The su ms of the VMT distributions for these

two classes of vehicles are obtained by summing the individua l VMT distributions for the

two classes of vehicles used in this a ssessment. For light duty vehicles, the VMT

distribution is 0.928. Fo r heavy duty trucks, the VMT distribution is 0. 0717. Dividing the VMT adjusted gross vehicle weights by the VMT distributions and conver ting to tons yields the average vehicle weights for the two classe s of vehicles. For lig ht duty vehicles, the average gross vehicle weight is 1.79 tons. For the combination of heavy duty gas and diesel trucks, the average gross vehi cle weight is 26.09 tons.

Next, an algorithm is developed to provi de test run specific ratios of light duty vehicles and heavy duty trucks. The algorithm is developed by solving the following two equations.

W t = (R LD x W LD ) + (R HD x R HD )

1.00 = R LD + R HD

where: W t = Test report average vehicle weight

W LD = Average Light Duty Vehicle Weight (1.78848 tons)

R HD = Average Heavy Duty Truck Weight (26.09135 tons)

R LD = Light duty vehicle ratio

R HD = Heavy duty truck ratio4-38 

### For test runs where the average vehicle weight is less than 23 tons, the resulting algorithm to estimate the ratio of heavy duty gas/diesel trucks in each test series is:

R HD = (W t - 1.78848) / (26.09135 - 1.78848)



### For tests where the average vehicle weight is more than 23 tons, the resulting algorithm to estimate the ratio of heavy dut y diesel trucks in each test series is:

R HD = (W t - 1.78848) / (35 - 1.78848)



### Run specific emissions estimates for vehicl e exhaust, brake wear and tire wear are estimated using the EPA Office of Tr ansportation and Air Quality MOVES (MOtor Vehicle Emission Simulator) 2010 model 29 

### . For all tests with vehicle speed greater than 10 mph only emissions for freely moving traffic is calculated. Emissions for a representative mix of light duty vehicles and for a representative mix of heavy duty trucks are calculated. For each test series, information on the date of the test, the location of the test program, ambient temperature during the test, average vehicle sp eed, and other general information required to generate a valid PM 10 

### emissions calculation with the MOVES model. While the MOVES model has the ability to generate start up emissions, all test co nditions are assumed to include only vehicles which have achieved normal operati ng temperatures. For all test series with average vehicle speeds greater than 10 mph, the MOVES model ca lculated only running exhaust, tire wear and brake wear emissions. For heavy duty vehicles , the running emissions ranged from 0.645 g/VMT to 4.896 g/VMT. For light duty vehicles, the running emissions ranged from 0.0196 g/VMT to 0.1324 g/VMT. For te st series with average vehicle speeds below 9.9 mph, in addition to running exhaust, tire wear and brake w ear emissions; exhaust emissions during acceleration and idling ar e included. A separate MOVES model run estimated the average emissions for the non steady state emissions at 11.06 g/hour. The

emissions factor for this dr iving condition was calculated by dividing the hourly emissions by the average vehicle speed. Summing the pr oduct of emissions factors from heavy duty trucks and light duty vehicles and the ratio of heavy duty vehi cles and light duty vehicles provides an estimate of the to tal engine exhaust; tire wear an d brake wear emissions for the

test run.

The test run specific emissions factor estimat e for engine exhaust, tire wear and brake

wear is subtracted from the test run measur ed emissions factor to produce the test run specific emissions factor due to road surface ma terial. To allow log transformation of the data, values of zero or less we re set to 0.01 g/VMT. Table 4- 17 presents the final dependent and independent variables for all of the useab le test series that were assembled for developing the paved road emissions factor equation. There were 10 test runs of the 103

available data where downwind emissions were not measureable. Six of the data were associated with low speed traffic at corn refini ng facilities and f our of the data were high or moderate speed urban traffic. None of these ten data were in cluded in the data analyzed to estimate the predictive emissions factor equation. There were 3 out of the 103 available data sets where the estimated emissions from engine exhaust, tire wear and break wear were equal to or comparable to the measured emissions. Two of the three test runs were on roads where the average vehicle speed was 55 mph. Emissions of two additi onal test runs with vehicle speeds of 55 mph had engine exha ust, tire wear and break wear emission s greater than 160%

of the road emissions. The silt level for one of the 55 mph test runs was greater than all4-39 

### other 55 mph data sets and was performed to characterize emissions from a road that had

been sanded for traction control. For slightly slower moving tr affic (40 – 45 mph), three of the five test runs had significant percentage of engine exhaust; tire wear and brake wear emissions. One of the remaining two runs had si lt levels greater than 60% of the entire data set and the test was performed to characterize emissions from a road that had been sanded

for traction control.

Graphical presentati ons of the final PM 10 

### data base are shown in Figures 4-1 through 4-5. Because of the large range of silt loadings and estimated emissions factors, the data are plotted on a logarithmic scale for the first three figures. Figure 4-1 pres ents the data base by silt loading with five ranges of average vehicle weight depi cted with different shape and color data points. The figure shows that with in creasing silt loading there is an increase in

the PM 10 

### emissions factor. Fi gure 4-2 presents the data base by average vehicle weight with seven ranges of silt loading depicted with di fferent shape and color data points. Although there is a significant overlap of the different vehicle weight da ta, there appears to be some relationship between average vehicle weight and the PM 10 

### emissions factor. As with silt loading, it appears that the PM 10 

### emissions factor increases with increasing vehicle weight. The wider spread of the data around the center line of the data makes the relationship more difficult to discern. Figure 4-3 presents th e relationship between si lt loading and average vehicle weight with eight ranges of emissions f actors depicted with different shape and color data points. Although very poor, there appe ars to be a weak relationship between silt loading and vehicle weight. The cause of this relationship is probably due to the selection of the test location and parameters than any physical force that would cause this relationship. Figure 4-4 presents the relationship be tween average vehicle speed and the PM 10 

### emissions factor. It appears that be tween 10 and 55 mph, the emissions factor decreases with increasing speed. Below 10 mph there does not a ppear to be a speed relationship. Figure 4-5 presents the relationship between silt loadi ng and vehicle speed with five ranges of PM 10 

### emissions factors. The silt loading appears to decrease with increasing speed above 10 mph. In addition, there seems to be a clear increase in PM 10 

### emissions factor as silt loading increases and speed decreases. Figure 4-6 pr esents a three dimensional view of the silt loading, vehicle weight and PM 10 

### emissions factors. One data point seems to be very uncharacteristic of the general trend of the data. Figure 4-7 provides a two dimensional view

of the data with the data identifier in the label. For three data points, the PM 10 

### emissions

factor is also included in the label. The point which has the uncharacteristic emissions is point Z-3 with a PM 10 

### emissions factor of 1819 g/VMT. While this value is the highest emissions factor of all of the 92 test data, both the vehicle weight and silt loading for this run are near other data which are under 100 g/VMT. As a result, this data was flagged as a potential outlier. This data was reassessed following log transformation and the variation was determined to be comparable with other da ta and was included in th e final data set used to estimate the predictive equation. Figure 4-8 presents the three dimensional view of the test data with silt loading, vehicle weight and PM 10 

### emissions factor with test run Z-3 removed. With point Z-3 removed, there appears to be two regimes of the data. Most of the data had silt loadings below 20 g/m 2 

### with few gaps down to 0.013 g/m 2 

### . There are ten data with silt loadings spread out from 50 g/m 2 

### to almost 400 g/m 2 

### with no data between these two regimes. There appears to be one incline associated with the lower silt loading data and a significantly greater incline for the higher silt loading data. This greater incline is the result

of a small number of data coll ected prior to 1983. These data have higher silt loadings that the default silt loading for the peak additive contribution value for roads with average daily4-40 

### traffic volume counts of less than 500. While there may be a very small number of streets that reach this silt loading le vel, these are believed to be unrepresentative of typical well managed urban or rural roads during any season. As a result, these data are flagged as extreme values and were not included in the final data set used to estimate the predictive equation.Table 4-17. Final Paved Roads Emissions Factor Data SetReference Run IDSilt loading(g/m2)Speed(mph)Weight(tons)DownwindConcentrationmg/m3Measured PM10Emission factor(g/VMT)EstimatedFractionHeavy DutyVehiclesEstimatedEngine, brake,tire emissionfactor(g/VMT)EstimatedPM10RoadDust Emissionfactor(g/VMT)AUC3 0.42 27 5.5 0.0112.25 0.153 0.3298 1.920AUC4 0.52 25 6 0.0416.1 0.173 0.3537 15.746AUC5 0.23 29 3.9 0.0715.3 0.087 0.1941 15.106AUC6 0.23 27 6.2 0.033.7 0.182 0.3961 3.304AUC7 0.26 27 3 0.010.402 0.050 0.1653 0.237USX 5/1990 AUC8 0.15 27 2 0.037.88 0.009 0.0936 7.786AUE1 4 15 12 0.013.22 0.420 0.9337 2.286AUE2 4 16 5.10.610.6 0.136 0.3709 10.229AUE3 2.2 15 2.6 0.0816.1 0.033 0.1804 15.920AUE4 1.3 15 2.6 0.069.01 0.033 0.1804 8.830M-1 0.46 30 5.6 0.1244.99 0.157 0.3610 4.629M-2 0.26 30 3.8 0.0331.55 0.083 0.2486 1.301M-30.147 30 4.5 0.0703.54 0.112 0.2845 3.256M-40.432 35 2.1 0.0300.177 0.013 0.0927 0.084M-5 1.01 35 2.2 0.0900.692 0.017 0.0749 0.617M-60.716 30 2.1 0.0631.38 0.013 0.1043 1.276M-7 0.59 35 2.3 0.1304.22 0.021 0.1146 4.105M-8 2.48 20 2.2 0.12011.2 0.017 0.1063 11.094M-90.293 30 4.1 0.1303.24 0.095 0.2190 3.021EPA 7/1984 M-10 0.022 55 4.5 0.1040.177 0.112 0.1798 0.010M-11 0.022 55 4.8 0.0800.322 0.124 0.2009 0.121M-12 0.022 55 3.8 0.0800.084 0.083 0.1403 0.010M-13 0.11 35 2.7 0.0650.306 0.038 0.0988 0.207M-14 0.079 35 2.7 0.0301.37 0.038 0.1044 1.266M-15 0.049 35 2.7 0.0901.47 0.038 0.0886 1.381M-16 0.022 55 4.3 0.0600.241 0.103 0.1581 0.083M-17 0.809 30 2 0.0562.64 0.009 0.0501 2.590M-18 0.731 30 2 0.0800.37 0.009 0.0501 0.320M-19 0.929 30 2.4 0.0500.177 0.025 0.0791 0.098

4-41Table 4-17. (Continued)Reference Run IDSilt loading(g/m2)Speed(mph)Weight(tons)DownwindConcentrationmg/m3Measured PM10Emission factor(g/VMT)EstimatedFractionHeavy DutyVehiclesEstimatedEngine, brake,tire emissionfactor(g/VMT)EstimatedPM10RoadDust Emissionfactor(g/VMT)Y190.7 10 3.6117 0.075 0.2274 116.773Y276.1 10 3.7182 0.079 0.2359 181.764Y3193 10 3.836.3 0.083 0.2443 36.056Y4193 10 3.7200 0.079 0.2359 199.764Z111.3 10 8317 0.256 0.6096 316.390EPA 1/1983 Z2 12.4 15 8740 0.256 0.5697 739.430Z312.4 15 81820 0.256 0.5697 1819.430AC4 287 10 5.71750 0.161 0.4090 1749.591AC5 188 15 71420 0.214 0.4852 1419.515AC6 399 20 3.1613 0.054 0.1466 612.853AD1 94.8 23 421480 1.000 1.8114 1478.189AD2 63.6 23 39342 1.000 1.8114 340.189AD3 52.9 23 40233 1.000 1.8114 231.189F34 2.78 NR 28 0.552188 0.789 1.4388 186.561F35 2.03 NR 25 0.057298 0.699 1.2790 296.721F360.201 NR 8.3 0.13454.7 0.268 0.5320 54.168F370.417 NR 17 0.16377.2 0.626 1.1617 76.038F380.218 NR 18 0.301167 0.667 1.2339 165.766F390.441 NR 18 0.177253 0.667 1.2339 251.766EPA 8/1983 F27 14.8 NR 14 0.531130 0.502 0.9292 129.071F320.117 NR 14 0.13853.1 0.502 0.9292 52.171F61 17.9 NR 40 0.327463 1.000 1.8261 461.174F45 5.11 NR 16 0.744212 0.585 1.0896 210.910F62 14.4 NR 36 0.294317 1.000 1.8226 315.177F74 5.59 NR 29 0.114545 0.819 1.5012 543.499

4-42Table 4-17. (Continued)Reference Run IDSilt loading(g/m2)Speed(mph)Weight(tons)DownwindConcentrationmg/m3Measured PM10Emission factor(g/VMT)EstimatedFractionHeavy DutyVehiclesEstimatedEngine, brake,tire emissionfactor(g/VMT)EstimatedPM10RoadDust Emissionfactor(g/VMT)B50 13.6 NR 9.4 0.22582.1 0.313 0.5936 81.506B51 13.6 NR 11 0.410140 0.379 0.7108 139.289B52 7.19 NR 12 0.10235.4 0.420 0.7836 34.616B54 3.77 NR 10 0.18793.3 0.338 0.6379 92.662EPA 8/1983 B55 6.3 NR 11 0.295183 0.379 0.7108 182.289B56 2.4 NR 9.2 0.229126 0.305 0.5794 125.421B58 10.4 NR 18 0.190368 0.667 1.2221 366.778B57 2.32 NR12 0.358195 0.420 0.7836 194.216B59 2.06 NR 11 0.149348 0.379 0.7108 347.289B60 3.19 NR 12 0.339439 0.420 0.7836 438.216BH10.184 55 2.2 0.2331.08 0.017 0.0306 1.049BH20.0127 55 2.2 0.0300.102 0.017 0.0306 0.071BH30.0127 55 2.20 0.017 0.0305BH6 1.47 40 2.2 0.3004.68 0.017 0.0343 4.646EPA 4/1997 BJ6 0.06 45 2.2 0.0450.301 0.017 0.0336 0.267BJ7 0.06 45 2.2 0.1301.94 0.017 0.0336 1.906BJ9 0.06 45 2.20 0.017 0.0305BJ10 0.06 45 2.20 0.017 0.0305BJ11 0.06 45 2.20 0.017 0.0305BK70.082 45 2.2 0.0330.57 0.017 0.0336 0.536BK80.082 45 2.2 0.0330.44 0.017 0.0336 0.406CE-1 1.16 1 36 0.05027 1.00011.06 15.940CE-2 0.86 1 36 0.07564 1.00011.06 52.940CE-11 1.34 5 12 0.200154 0.4202.212 151.788CRA 5/2008 CE-3 0.86 1 39 0.07045 1.00011.06 33.940CE-15 1.91 5 40 0.06563.5 1.0002.212 61.288CE-16 1.41 5 40 0.05077.1 1.0002.212 74.888CE-17 2.93 5 40 0.04041.3 1.0002.212 39.088CE-19 0.76 5 38 0.04018.6 1.0002.212 16.388

4-43Table 4-17. (Continued)Reference Run IDSilt loading(g/m2)Speed(mph)Weight(tons)DownwindConcentrationmg/m3Measured PM10Emission factor(g/VMT)EstimatedFractionHeavy DutyVehiclesEstimatedEngine, brake,tire emissionfactor(g/VMT)EstimatedPM10RoadDust Emissionfactor(g/VMT)CE-12 1.34 5 13 0.08523.1 0.4612.212 20.888CF-1N 0.97 5 40 0.0354.99 1.0002.212 2.778CF-1/South 0.97 1 40 0.04019.5 1.00011.06 8.440CF-2N 0.81 5.3 41 0.04416.3 1.000 2.0868 14.213CF-2/South 0.81 1 41 0.08063.5 1.00011.06 52.440CF-3N 0.63 5.1 41 0.0151.09 1.000 2.1686 0.010CF-3/South 0.63 1 41 0.02523.1 1.00011.06 12.040CF-4N 1.1 4.7 41 0.0193.08 1.000 2.3532 0.727CRA 5/2008 CF-5 1.4 1 41 0.03016.3 1.00011.06 5.240CI-1 0.06 15.1 260 0.729 1.0008CI-2 0.06 14.85 260 0.729 1.0008CI-3 0.06 13.15 270 0.759 1.0410CI-4 0.06 14.5 270 0.759 1.0410CI-7 0.05 15.3 27 0.0301.63 0.759 1.0409 0.589CI-8 0.05 15.3 27 0.0302.99 0.759 1.0409 1.949CI-11 0.025 13.1 270 0.759 1.0410CI-12 0.25 13.1 270 0.759 1.0410CM-1 0.72 5 39.8 0.0356.35 1.0002.212 4.138CM-2 0.72 1 39.6 0.05063.5 1.00011.06 52.440CM-4 0.7 5 39.5 0.0357.26 1.0002.212 5.048

4-444-45Figure 4-1. PM10Emissions Factor Data Base by Silt Loading (93 test runs).Figure 4-2. PM10Emissions Factor Data Base by Average Vehicle Weight (93 test runs).

4-464-47Figure 4-3. Silt Loading vs. AverageVehicle Weight (93 Test Runs).4-48Figure 4-4. PM10Emissions Factors by Vehicle Speed.4-49Figure 4-5. Vehicle Speed vs Silt Loading.4-50 

### Figure 4-6. Paved Road Dust Emissions Factors, All Data.4-51 

### Figure 4-7. All Paved Road Data, Silt Loading by Vehicle Weight with EF.4-52 

### Figure 4-8. Paved Road Dust Emissi ons Factor Data Excluding Z-3.4-53 

### 4.2.2.2. Emission Fact or Development.

Stepwise multiple linear regression was used to develop a predictive model with the final data set. The potential correction factors included:

- silt loading, sL

- mean vehicle weight, W

- mean vehicle speed, S

All variables were log-transformed in orde r to obtain a multiplicative model as in the past. Table 4-18 presents th e correlation matrix of the lo g-transformed independent and dependent variables. The most notable feature of the correlation matrix is the high degree of correlation between silt loading and emissions f actors. The correlation between emissions factor, weight and speed is much lower than with silt loading. Th e high correlation between weight and speed is beli eved to be the result of the large data collected by the corn refiners

association to characterize emissi ons at terminals. This suggests that obtaining accurate silt loading information is the most important independent variable to obtain for accurately estimating emissions factors.

Table 4-18 Correlation Matrix for log-transformed PM 10 

### data.

PM 10 

### Emission
factor (g/VMT) Silt loading
(g/m 2 

### ) Weight

(tons) Speed
(mph)

PM 10 

### Emission factor (g/VMT) 1

Silt loading (g/m 2 

### ) 0.8010 1

Weight (tons) 0.3280 -0.1841 1

Speed (mph) -0.4066 -0.2785 -0.7784 1

Initially several regression analysis were performed using the Data Analysis tools in MS Excel to evaluate a range of independent variab les. The independent variables included silt loading, average vehicle we ight, the product of silt loading and vehicle weight, the square of silt loading (after log transformation) and the square of the vehi cle weight (after log transformation). In addition, th e influence of including and excluding flagged test runs were explored. The primary criteria for selecting th e most appropriate form and supporting data set was the predictive performance of the equati on using the combinati on of the correlation coefficient, the P-value and the relative percent difference from the actual emissions factor for the test series with silt loadings and vehicle weig hts in the range of default values used in the national inventory. The stepwise regression was first performed using the “Regression” function in the “Analysis Tool” of Excel. It was determined that the use of the speed term either produced equations with P-values greater than 0.1 or produced equations with independent parameter relations hips that were illogical (i .e. increased emissions with decreased weight). It was also determined that the inclusion of data with silt loadings greater than 20 g/m 2 

### produced equations which uniformly overestimated te st data with lower silt loadings without a signif icant improvement in estimating the high silt loading data. Also, the

exclusion of the ten data with high silt loadings di d not significantly ch ange the predictive accuracy of the equation for the ten high silt loading test runs. The 93 test data with positive measured emissions were provid ed to a statistician for subs equent analysis with SAS.4-54 

### Several additional assessments we re performed to determine an equation that provided a high correlation coefficient, a low average percent er ror for test series with targeted independent variables and which provided a re asonable level of predictive ac curacy for test series where the independent variables were outside the targ eted range. The equa tion which produced the highest correlation coefficient was one which forced the inter cept to zero. This equation performed well and was consiste nt with engineering assessment s of the physical influences

on emissions. This e quation used only silt loading and average vehicle weight as the independent variables. It was decided that th e traditional scaling factors of 2 for silt loading and 3 for average vehicle weight were no longer required and resulted in simpler calculation of paved roads emissi ons factors. The re sulting equation for PM 10 

### is:



## ( ) ( ) 021.1912.0 

### WsL0.1EF =

Table 4-19 shows the statisti cal output. The predicted expo nents for silt and weight are 0.912 and 1.021 respectively and have a coefficient of determination (R 2 

### ) of 0.72. The standard

error associated with the silt and weight terms are 0.12 and 0.08 respec tively. As a result, it is expected that 95% of future data would fall with in equations with exponent s of 0.677 and 1.14 for

the silt term and 0. 852 and 1.19 for the weight term.

The range of conditions which exis ted at the test sites used in developing the equation was as follows:

Silt loading: 0.03 - 400 g/m2 0.01 - 570 grains/square foot (ft2) Mean vehicle weight: 1.8 - 38 megagrams (Mg) 2.0 - 42 tons Mean vehicle speed: 1 - 88 kilometers per hour (kph) 1 - 55 miles per hour (mph)Table 4-19. Regression Analysis using Silt Loading andWeight.SUMMARY OUTPUTAll positive testdata, sL < 20 force 0, sL WRegression StatisticsMultiple R 0.848347765R Square0.71969393Adjusted R Square 0.703887682Standard Error 1.921751464Observations 83ANOVAdf SSMSFSignificanceFRegression2 768.0593789 384.0296894 103.9849195 5.61978E-23Residual 81299.14342383.693128689Total83 1067.202803Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept 0#N/A#N/A#N/A#N/A#N/AWeight (tons) 1.0212836 0.084774552 12.04705393 9.58964E-20 0.852608836 1.189958364Silt loading (g/m2) 0.911843675 0.117787966 7.741399277 2.42283E-11 0.677482574 1.146204776

4-554-56 

### An assessment of the performance of th e predictive equation is difficult since the range of silt loadings and the associated emissions factors span s five orders of magnitude. This is further complicated by the focus of many of the field test s. Approximately half of the

field test locations were selected either due to concerns that these sources were major contributors to air quality impact s, or were selected because of elevated road silt levels to

allow the measurement of a difference from bac kground concentrations of particulate matter. Another complication is that PM emissions of the vehicle exha ust were not measured during the tests and a modeled average emission factor or rate was subtracted to arrive at the road

dust emissions.

One can assess the performance of the pred ictive equation by calculating the average predicted to actual ratio and pr oducing the cumulative distributi on of these ratios. For the two parameter equation, the aver age predicted to actual ratio is 49. This is significantly lower than the average predicted to actual ratio of 315 for the previous equation when applied to the existing data. When limited to silt loading levels of 20 g/m2, the new equation produces average predicted vs actu al ration of 38 compar ed to the previous equations ration of 221. It should be noted that the previous equation subtracted 0.2119 g/VMT (the estimated national average engine ex haust, brake wear and tire wear emissions factor) from the previous equation which was based upon measured emissions. The new equation subtracts the estimated engine; brake wear and tire wear emissions estimated for each test run. These emissions average 1. 565 g/VMT and range from 0.031 to 11.06 g/VMT depending on meteorologi cal conditions, vehicle speed and vehicle weight determined during the test. Figure 4-9 depicts th e cumulative distribu tion of the predicted to actual ratios for both the previous equation and the new equation. Figure 4-10 presents this same information but with ranges of silt loading depicted through th e use of different shapes and colors for the markers of the data. Figure 4-11 is this same information but with ra nges of vehicle weights depicted with different markers. It is diffic ult to discern any differences below the ratio of

1.0. Above the ratio of 1.0 the increased range of the predicted vs actual ratio of the older equation is evident. The ne w equation appears to demons trate an improved performance compared to the previous equation.

Another means of assessing the performa nce of the regression equations is to compare the calculated results of the equations to the actual value measured. With a large range of measured emissions factors, compar ing the relative percent difference between the results of the equation and the measured valu e places the differences in the smallest measured value and the largest measured valu e on comparable terms. Two comparisons were made to assess the relative predictive performance of the existing equation to the previous equation. As shown with the average percent error for the entire population in Table 4-23, the new equation pr ovides an order of magnitude improvement in estimating the

actual measured emissions over th e previous equation. Associated with the reduction in the percent difference from actual emissions is a 47 percent reduction in th e emissions factor. When the performance of the equation is evaluated within classes of the independent variables of silt loading, average vehicl e weight and speed; the new equation shows comparable or improved performance in al l groups of the variables except two.4-57Figure 4-9. Cumulative Distribution of Predicted/Actual Ratios.4-58Figure 4-10. Cumulative Distribution –Predicted/Actual by Silt Loading.4-59Figure 4-11. Cumulative Distribution – Predicted/Actual by Average Vehicle Weight.4-60 

### Figure 4-12 through Figure 4-9 provide gra phical indications of the performance of the updated equation to estimate the actual emissions. The first figure shows the relationship

of emissions to the road surf ace silt loading. Included in th is figure is information on the average vehicle weight through th e use of a different shape and color for different ranges of vehicle weight. While not shown, the previous equation had a greater spread than the new equations estimates. Figure 4- 13 shows the influence of vehi cle weight on the emissions factors. For all weight ranges, the spread of th e data is much greater th an is demonstrated in the figures with silt as the ordinate. Included in this figure is information on the silt loading

associated with the test. One can see a general increase in emissi ons with silt lo ading. This is probably due to the greater co rrelation between silt loading and PM 10 

### emissions factors
than between average vehicle weight and PM 10 

### emissions factors. Figure 4-9 shows the influence of speed on the emissions factors. As with vehicle weight, there is a greater spread

of the emissions factor than when silt is th e primary dependent variab le graphed. One can

also see a weak relationsh ip between silt loading and average vehicle speed.Table 4-20. Comparison of Previous and New Equations for Estimating Paved Road Dust Emissions.Predictive Performance of Paved Road DustEmissions EquationsAverage Relative Percent Difference1Relative Standard DeviationOld Equation vsActualNew Equationvs ActualOld Equation vsNew EquationOld Equationvs ActualNew Equationvs ActualOld Equation vsNew EquationPopulation Average31,378 3,142-47 5.775.84-1.2By Classes of Silt Loading (g/m2)≤0.233,601 3,858-71 2.121.38-0.620.2 – 0.75102,647 17,049-62 3.71 11.54-0.350.75 – 1.53,236669-61 2.480.41-0.461.5 – 5022147-45 3.570.11-0.46≥5024825373 1.810.271.20By Classes of Average Weight (ton)2 - 346733340 2.090.43-1.623 – 5718289350 1.710.729.915 – 10-2-4153 -394.30.37-0.7410 – 4038,248 4,906 74,840 3.06 24.73-0.18≥40128,217 21,549 68,550 4.27 112.84-0.22By Classes of Average Speed (mph)< 1090,216 15,112-79 4.30 20.67-0.0710 – 2554,063 7,034-6 2.174.94-15.1125 – 45293170-41 2.41 0.139-0.45451,041662-34 1.28 0.198-0.05551,404467-114 1.57 0.114-0.60

4-614-62Figure 4-12. Predicted vs Actual PM10Emissions Factor by Silt Loading.4-63Figure 4-13. Predicted vs Actual PM10Emissions Factor byAverage Vehicle Weight.4-64Figure 4-14. Predictive Accuracy bySilt Loading (unrestricted range).4-65Figure 4-15. Predictive Accuracy bySilt Loading (restricted range).4-66Figure 4-16. Predictive Accuracy by Average Vehicle Weight (unrestricted range).4-67Figure 4-17. Predictive Accuracy by Average Vehicle Weight (restricted range).4-68 

### 4.2.2.3 Emissions Factor Qu ality Rating Assessment.



### All of the source test data used to devel op the emissions factor equation were rated A since the test procedures used were profiling te sts and were all well documented. While only six reports are available that provide documen tation of emissions factors for paved roads, these test reports contain the results of 17 di fferent road conditions. The reports and the

number of test co nditions documented in the report are:

• USX 5/1990 - 2 tests (sL ~.3 & sL > 2),

• EPA 7/1984 - 2 tests (30 mph & 55 mph),

• EPA 1/1983 - 4 tests (<15 mph, >20 mph, W < 3 tons, W 5-8 tons, W > 30 tons),

• EPA 8/1983 - 2 tests for two parameter equation,

• EPA 4/97 - 3 tests (s peed 55, 45), 3 locations, and

• CRA 5/2008 - 4 tests (4 locations, 2 speeds, )

However, since the EPA 8/1983 report doe s not contain inform ation on the average speed of the vehicles in the study, none of the tests documente d in that report is usable for further data set groupings . The remaining five re ports contain the results of 15 different road conditions. While all of the tests were performed on paved roads, the ranges of conditions (silt loading, vehicle speed and vehi cle weight) were dive rse. An assessment of the variation associated with the data and the impact of th at variation on a single va lue emissions factor. The average of all the adjusted emissions fact ors is 140 g/VMT and the standard deviation is

387. A relative standard deviati on of 3 is greater than many ot her factors. As a result, the number of tests needed to achieve the predic tive accuracy of the mean is greater. The availability of 15 A or B rate d test reports would normally justify an initial assignment of a factor rating of B. However, the greater variability of the underlying data justifies a single value factor rating of C.

The stepwise regression of the available data indicated that a large portion of the

variation of the emissions factor was due to the large range of th e road silt loading that existed at the test locations. The preliminary regressions produced equations with varying constants and exponents with correlation coefficient below 60%. By excluding the high silt loading data and forcing a zero intercept, th e correlation coefficient (R2) fo r the final equa tion is 72%. This indicates that approximately 72% of the vari ations in the emissions factors are due to the silt level and average vehicle weight. As a resu lt of the improved ability of the equation to estimate the measured values over the single va lue emissions factor, a quality rating of B is assigned to the equation.

4.2.2.4 Assignment of equation parameters for PM 30 

### and PM 2.5 

### .



### While several of the reports include measurements of PM 2.5 

### , the WRAP studies suggest that many of these measurements are in error due to particle bounce issues with the impactor stages. The results of th e WRAP study indicated that the PM 2.5 

### concentrations measured by the cyclone/impact or system were consistently biased by a factor of about 2

relative the PM 2.5 

### concentrations measured by the Partis ol samplers. The second phase of the WRAP showed a tendency of the measured PM 2.5 

### / PM 10 

### ratio to decrease with increasing4-69 

### PM 10 

### concentration. At PM 10 

### concentrations above 1.0 mg/m3 the PM 2.5 

### / PM 10 

### ratio was

between 0.1 and 0.15. The PM 2.5 

### / PM 10 

### ratio increased to about 0.35 as the PM 10 

### concentration approached about 0.5 mg/m3. While some of the paved road test data

encountered concentrations above 1.0 mg/m3 much of the test data consisted of measured concentrations below 0.5 mg/m3. Th e paved road emissions factor for PM 2.5 

### was revised to
15% of the calculated PM 10 

### emissions factor in 2008. It is not clear whether the WRAP study

assessed the PM 10 

### concentrations measured during the paved roads testing prior to their recommendations for revising the PM 2.5 

### emissions factors. As shown in Table 4-17 the PM 10 

### concentrations associated with 58 of the 71 te st runs used to develop the three parameter emissions factor equation. Many of these te st runs involve traffi c volumes that would produce fairly constant particulate concentrations . Also, of these 58 test runs, only three runs were the highest PM 10 

### concentrations greater than 0.5 mg/m 3. An earlier report (Reference 5)

measured PM 2.5 

### / PM 10 

### ratios during field test s. The range of PM 2.5 

### / PM 10 

### ratios was from

0.25 to 0.37. Since essentia lly all of the measured PM 10 

### concentrations used for the stepwise regression were below 0.5 mg/m 3 

### and the ratios measured during field sampling of paved road emissions were between 0.25 and 0.37, the recommended PM 2.5 

### emissions factor is 25% of

the PM 10 

### emissions factor. Since th ere is little measured PM 2.5 

### data, an emissions factor quality rating of “D” is assigned.

While a stepwise regression could be performed to estimate the PM 30 

### emissions factor equation, it is believed that the number of available data w ould be significantly less and a comparable confidence in the resulting equati on could not be achieve d. The ratio of PM 30 

### to
PM 10 

### presented in the present AP-42 section is 5. 2 and is proposed for the revised equation.

4.2.2.5. Assignment of a preci pitation correction factor.



### As is presented in Reference 38, a corr ection parameter for precipitation events was

included in the revision of the AP-42 section in October 2002. As recommended in the Technical Memorandum to the files, the correction parame ters are retained in this version of

the AP-42 section.

4.3 DEVELOPMENT OF OTHER MATERIAL IN AP-42 SECTION



### Concurrent with the development of the revised AP-42 section for paved roads, a separate effort was conducted to assemble a silt loading data base for nonindustrial roads. Over the past 10 years, numero us organizations have collecte d silt loading samples from public paved roads. Unfortun ately, uniformity—in sampling and analysis methodology as well as roadway classification schemes—has been sorely lacking in these studies.

Silt loading data were compiled in the following manner. Persons knowledgeable

about PM 10 

### at each EPA regional office were asked to identify sL data for public roads. In many instances, the EPA representatives identifi ed state/local air regu latory personnel who were then asked to supply the data. Gi ven that the relative importance of PM 10 

### emissions from public sources is greater in the western United States, it is not surprising that most of the

data are from that area of the country. What is surprising, perhaps, is that Montana has collected roughly two- thirds of all data. Furthermore, only M ontana had data collected from the same road over extended periods of ti me, thus permitting examination of temporal4-70 

### variation.

The assembled data set did not yield any readily identifiable, coherent relationship between silt loading and ro ad class, average daily traffic (ADT), etc. Much of the difficulty is probably due to the fact that not all variable s were reported by each organization. Further complicating the analysis is the fact that, in many parts of the country, paved road silt loading varies greatly over the course of the year. Recall that repeated sampling at Montana municipalities indicated a very noticeable annual cycle. Ne vertheless, it is questionable

whether the seasonal variation noted in the Montana data base coul d successfully predict variations for many other sites. While one could possibly expect similar variations for, say, Idaho or Wyoming roads, there is far less reason to suspect a si milar cycle in, say, Maine or Michigan, in the absence of additional information.

Because no meaningful relationship coul d be established between sL and an independent variable, the decision was made to directly employ th e nonindustrial data base in

the AP-42 section. The draft AP -42 section presents the cumula tive frequency distribution for the sL data base, with subdivisions into (a ) low-ADT (< 5000 vehi cles/day) and high-ADT roads and (b) first and second halves of the year. Suggested default values are based on the 50th and 90th percentile values.

The second use of the assemb led data set recognizes that the end users of AP-42 are the most capable in identifying which roads in the data base are similar to roads of interest to them. The draft AP-42 section presents the pave d road surface loading values together with the city, state, road name, coll ection date (samples collected from the same road during the same month are averaged), road ADT if reported, classification of the roadway, etc. Readers

of AP-42 are invited to review th e data base and to select values that they deem appropriate

for the roads and seasons of interest.

4.4 References for Section 4



### 1. D. R. Dunbar, Resuspension Of Particulate Matter, EPA- 450/2-76-031, U. S. Environmental Protection Agency, Res earch Triangle Park, NC, March 1976.

2. R. Bohn, et al., Fugitive Emissions From Integrated Iron And Steel Plants, EPA600/2-78-050, U. S. Environmental Protec tion Agency, Cincinnati, OH, March 1978.

3. C. Cowherd, Jr., et al., Iron And Steel Pl ant Open Dust Source Fugitive Emission Evaluation, EPA-600/2-79-103, U. S. Environmental Prot ection Agency, Cincinnati, OH, May 1979.

4. C. Cowherd, Jr., et al., Quantification Of Dust Entrainment From Paved Roadways, EPA-450/3-77-027, U. S. Envi ronmental Protection Agency, Research Triangle Park, NC, July 1977.

5. Size Specific Particulate Emission Factor s For Uncontrolled I ndustrial And Rural Roads, EPA Contract No. 68-02-3158, Midwest Research Institute, Kansas City, MO, September 1983.4-71 

### 6. T. Cuscino, Jr., et al., Iron And Steel Plant Open Sour ce Fugitive Emission Control Evaluation, EPA-600/2-83-110, U. S. Environm ental Protection Agency, Cincinnati, OH, October 1983.

7. J. P. Reider, Size-specific Particulate Emission Factor s For Uncontro lled Industrial And Rural Roads, EPA Contract 68-02-3158, Midwest Research Institute, Kansas City, MO, September 1983.

8. C. Cowherd, Jr., and P. J. Englehart, Paved Road Particulate Emissions, EPA-600/7- 84-077, U. S. Environmental Protecti on Agency, Cincinnati, OH, July 1984.

9. C. Cowherd, Jr., and P. J. Englehart, Si ze Specific Particulate Emission Factors For Industrial And Rural Roads, EPA-600/7- 85-051, U. S. Environmental Protection Agency, Cincinnati, OH, September 1985.

10. Emission Factor Documentation For AP-42, Sections 11.2.5 and 11.2.6 — Paved Roads, EPA Contract No. 68-D0-0123, Midwes t Research Institute, Kansas City, MO,

March 1993.

11. Evaluation Of Open Dust Sources In The Vicinity Of Buffalo, New York, EPA Contract No. 68-02-2545, Midwest Research Institute, Kansas City, MO, March 1979.

12. PM-10 Emission Inventory Of Landfills In The Lake Calumet Area, EPA Contract No. 68-02-3891, Midwest Research Institut e, Kansas City, MO, September 1987.

13. Chicago Area Particulate Matter Emi ssion Inventory — Sampling And Analysis, Contract No. 68-02-4395, Midwest Research Institute, Kansas City, MO, May 1988.

14. Montana Street Sampling Data, Montana De partment Of Health And Environmental Sciences, Helena, MT, July 1992.

15. Street Sanding Emissions And Control St udy, PEI Associates, Inc., Cincinnati, OH,

October 1989.

16. Evaluation Of PM-10 Emission Factor s For Paved Streets, Harding Lawson Associates, Denver, CO, October 1991.

17. Street Sanding Emissions And Control Study, RTP Environmental Associates, Inc., Denver, CO, July 1990.

18. Post-storm Measurement Results — Salt La ke County Road Dust Silt Loading Winter 1991/92 Measurement Program, Aerovironment, Inc., Monrovia, CA, June 1992.

19. Written communication from Harold Glasse r, Department of Health, Clark County (NV).

20. PM-10 Emissions Inventory Data For Th e Maricopa And Pima Planning Areas, EPA Contract No. 68-02-3888, Engineering-Sc ience, Pasadena, CA, January 1987.4-72 

### 21. Characterization Of PM-10 Emissions From Antiskid Mate rials Applied To Ice- And Snow-Covered Roadways, EPA Contract No. 68-D0-0137, Midwest Research Institute, Kansas City, MO, October 1992.

22. C. Cowherd, Background Document for Re visions to Fine Fraction Ratios Used for AP-42 Fugitive Dust Emission Factors. Prep ared by Midwest Research Institute for Western Governors Association, Western Regional Air Partne rship, Denver, CO, February 1, 2006.

23. Climatic Atlas Of The United States, U. S. Department of Co mmerce, Washington, D.C., June 1968.

24. C. Cowherd, Jr., et al., Improved Activity Levels for National Emission Inventories of Fugitive Dust from Paved and Unpaved Roads, Presented at the 11th International Emission Inventory Conference, At lanta, Georgia, April 2002.

25. C. Cowherd, Jr., et al., Control Of Open Fugitive Dust Sources, EPA-450/3-88-008, U. S. Environmental Protection Agency, Res earch Triangle Park, NC, September 1988.

26. Written communication (Technical Memorandum) from G. Muleski, Midwest Research Institute, Kansas City, MO, to B. Kuykendal, U. S. Environmental Protection Agency, Research Triangle Park, NC, September 27, 2001.

27. EPA, 2002b. MOBILE6 User Guide, United States Envir onmental Protection Agency, Office of Transportation and Air Qual ity. EPA420-R-02-028, October 2002.

28. Written communication (Technical Memorandum) from P. Hemmer, E.H. Pechan & Associates, Inc., Durham, NC to B. K uykendal, U. S. Environmental Protection Agency, Research Triangle Park, NC, August, 21, 2003.

29. EPA, 2009, MOVES2010 User Guide, United States Environmental Protection Agency, Office of Transportation and Ai r Quality. EPA420B-09-041, December 2009.

30. Fugitive Particulate Matter Emissions, U.S. Environmental Protection Agency, Research Triangle Park, NC, Midwest Res earch Institute Project No. 4604-06, April 15, 1997.

31. Midwest Research Institut e, Roadway Emissions Field Te sts at U.S. Steel’s Fairless Works, U.S. Steel Corporation, Fairle ss Hills, PA, USX Purchase Order No. 146- 0001191-0068, May 1990.

32. Paved Road Modifications to AP-42, Background Documentation For Corn Refiners Association, Inc. Washington, DC 20006, Midwest Research In stitute Project No. 310842, May 20, 2008.

33. Emission Tests of Paved Road Traffic at Minnesota Corn Processors Marshall, Minnesota Facility, McVehil-Monnett Associ ates, Midwest Research Institute Project4-73 

### No. 310212.1.001, July 6, 2001.

34. Emission Tests of Paved Road Traffic at Minnesota Corn Processors Columbus, Nebraska Facility, McVehil- Monnett Associates, Midwest Research Institute Project No. 310212.1.002. July 13, 2001.

35. Emission Tests of Paved Road Traffic at Cargill Sweeteners North America Blair, Nebraska Facility, McVehil- Monnett Associates, Midwest Research Institute Project No. 310395.1.001. November 27, 2002.

36. Emission Tests of Paved Road Traffic at ADM’s Marshall, Minnesota Facility, McVehil-Monnett Associates, Midwest Res earch Institute Project No. 310479.1.001. December 5, 2003.

37. E-mail communication between Ron My ers of EPA/OAQPS/ SPPD/MPG, RTP, NC and Prashanth Gururaja and Ed Glover of EPA/OTAQ/ASD/HDOC re. Diesel exhaust, tire and brake wear for low speed stop a nd go traffic; January 2009 through May 2009.

38. Technical Memorandum from William B. Kuykendal to File, S ubject: Decisions on Final AP-42 Section 13.2.1 “Paved Roads”, October 10, 2002.

39. E-mail communication between Ron My ers of EPA/OAQPS/ SPPD/MPG, RTP, NC and Gary Dolce and Rudolph Kapichak of EPA/OTAQ/ASD/HDOC re. Paved Road Test Data; October 12, 2010 through December 16, 2010.

40. C. Cowherd, Mobile Monitoring Me thod Specifications, Prepared by Midwest Research Institute for Clark County Depart ment of Air Quality and Environmental Management, Las Vegas, NV, February 6, 2009.

41. C. Cowherd, Technical Support Docume nt for Mobile Monitoring Technologies, Prepared by Midwest Research Institute for Clark County Department of Air Quality and Environmental Management, La s Vegas, NV, January 9, 2009.

42. R. Langston, R. S. Merle Jr., et al., Cl ark County (Nevada) Paved Road Dust Emission Studies in Support of Mobile Monitoring T echnologies, Clark County Department of Air Quality and Environmental Manageme nt, Las Vegas, NV, December 22, 2008.

43. Midwest Research Institute; Analysis of the Fine Fracti on of Particulate Matter in Fugitive Dust; Western Governors’ Associ ation - Western Regional Air Partnership (WRAP); October 12, 2005.

44. Midwest Research Instit ute; Background Document for Revisions to Fine Fraction Ratios Used for AP-42 Fugitive Dust Emission Factors; Western Governors’ Association - Western Regional Air Pa rtnership (WRAP); November 1, 2006.

45. G. E. Muleski, Measurement of Fugitive Dust Emissions from Prilled Sulfur Handling, Final Report, MRI Project No. 7995-L, Pr epared for Gardinier, Inc., June 19844-74 

### 46. T. F. Eckle and D. L. Trozzo, Verificati on of the Efficiency of a Road-Dust EmissionReduction Program by Exposure Profile M easurement, Presented at EPA/AISI Symposium on Iron and Steel Pollution Abatement, Cleveland, Ohio, October 1984.A-1 

### Appendix A

Response to Comments

## Comments and responses on 2010 proposed revision of Section 13.2.1 Paved

Roads.

Commenter Page

Chatten Cowherd of Midwest Research Institute on be half of the Center for the Study of Open Source
Emissions (CSOSE)

...................................................................................................................................... 1

Rebecca Kies and Courtney Bokenkroger Senior Sta tistician of Midwest Research Institute, Kansas City,

MO.

............................................................................................................................................................... 5

Greg Muleski of Midwest Research Institute

............................................................................................... 7

Camille Sears for the Sierra Club

................................................................................................................. 9

David E. James, PhD PE; Associate Vice Provos t for Academic Programs; UNLV, Las Vegas, NV.

...... 14

Steve Zemba of Cambridge Environmental Inc for the National Asphalt Pavement Association.

............. 16

Catharine Fitzsimmons, Chief, Air Quality Bureau and Lori Hanson Iowa Department of Natural

Resources.

................................................................................................................................................... 16

Pat Davis of MARAMA for the States of New Jersey, Delaware, Maryland and Massachusetts.

............. 17

Julie McDill (MARAMA), David Fees (Delaware), Julie Rand (New Jersey).

......................................... 18

Gary Garman of McVehil-Monnett.

........................................................................................................... 19

## Comments and Responses on June 2010 Draft Revision
of AP-42 Section 13.2.1 for Paved Roads.

January 2011 Page 1 

### Chatten Cowherd of Midwest Research Institut e on behalf of the Center for the Study of
Open Source Emissions (CSOSE)

Comment: The general consensus among the Cent er for the Study of Open Source
Emissions (CSOSE) participants who have wo rked in this field is that the proposed
equation does not offer improved predictive capability but introduces additional data
requirements to the paved road emission inventory process.

Response: We disagree that the proposed e quation does not offer improved predictive
capability. The predictive equation publis hed in November 2006 produced negative

PM 10 

### emissions at very low silt loadings and negative PM 2.5 

### emissions estimates
whenever a silt loading of less than 0.06 and average vehicle weight of 3.75 tons (or silt loading of 0.1 and vehicle weight of 3 tons). As presented in Table 4-23 of the draft
background report, the 2006 equation had an average relative per cent error of over
27,000 compared to the proposed equation with a relative percent erro r of 1,200. Part of
the error imbedded in the 2006 equation is due to the use of the estimated 1980's fleet
average vehicle emissions (average vehicle weight of 3.75 tons) for adjustment of the
equation presented in the 2003 revision of the AP-42 section. This average

underestimated the vehicle emissions of the fl eets measured in almost 2/3 of the paved
road emissions test (58 of the 93 tests had average vehicle weights over 5 tons). Since
the proposed revision provided a correction to each test series based upon the average vehicle weight presented in th e test report and the correcti on used in the final revision
includes variations in speed, ambient temperatur e, year of vehicle fleet; this error has been reduced. Combining the reduction in erro r of the test data with the use of a more
traditional revised stepwise regression of the paved road emissions data, we believe the
revised equation will provide a supe rior basis than the 2006 equation.

Comment: There is also the broader issue of ad opting mobile monitoring as the basis for

more realistic emission inve ntorying of paved roads.

Response: EPA agrees that the adoption of mobile monitoring to estimate either the silt loading of the road system or the emissi ons factor provides a significant advance in
characterizing the system wide emissions and the variation that exists with different roads. The use of mobile monitoring offers the ability to characterize road classes which
have been problematic in the past due to resource constraints and safety issues. The ability of mobile monitoring to provide a te mporally and spatially resolved emissions
estimates and to characterize significantly more miles of roadways than were possible by the traditional vacuuming, screen ing and weighing techniques is a distinct advantage. In
addition, the mobile monitori ng method provides an excellent means for tracking system wide management controls instituted to provide emissions reduc tions from roadway

emissions.

## Comments and Responses on June 2010 Draft Revision
of AP-42 Section 13.2.1 for Paved Roads.

January 2011 Page 2 

### In the final version of the AP -42 section we describe the mobile monitoring technique along with a brief assessment that mobile m onitoring provides significant improvements

in the estimation of road dust emi ssions caused by vehicle traffic.

Comment: The proposed equation has a significant new data input requirement (vehicle
speed) that increases the difficulty of ge nerating paved road emission inventories.

Response: We disagree; access to the average vehi cle speed of road segments is an
existing requirement for the accurate estima tion of vehicle exhaust emissions in the
MOVES model. While the incorporation of the vehicle speed for every road segment
may increase the complexity of emissions inve ntory development, for most road systems emissions estimates can be assembled by grouping of road segments into a limited
number of groups.

The assessment of the influence of the sp eed term on the predictive accuracy of the
resulting equation is a better criterion to dete rmine whether this term should be used in the equation. Limited improvement (or degr adation) in the pred ictive accuracy of the
equation provides a more compelling rationale to exclude the speed term in the final equation than the alleged difficulty of generating the emissions inventory. The

reassessment of the form of the emissions f actor equation included the assessment of the influence of speed on the predictive accuracy of the equation, the improvement of the
equation to address the variance which may be due to the independent parameters, and the statistical significance of each variable in predicting the dependent variable.

Comment: Based on our discussions of the proposed equation a nd the technical analyses
presented by EPA, we find the scientific foundation for the revision unconvincing.

Response: The foundation upon which EPA proposed a revision of the paved road
equation was a proposal by the Corn Refiners Association (CRA) to perform emissions
tests to support the extension of the appli cable source conditions. The Corn Refiners

retained the services of Midw est Research Institute (MRI) in Kansas City to perform the emissions testing at lower average vehicl e speeds to support th e extension of the
applicable source conditions. Twenty two usable profiling tests were performed. In addition to designing and conduc ting the emissions tests, MR I provided EPA with three
options for incorporating the new data into the paved roads section. The Agency decided that returning to multiple estimation methods would recreate the problems that existed
prior to 1995 when there was two AP-42 sections for paved roads and multiple methods

within these two sections.

When MRI drafted the AP-42 section that in cluded the CRA data, it was highlighted by the Office of Transportation and Air Quality (OTAQ) that the proposal and adoption of a
revised equation had conformity implications th at needed to be addressed. Several issues associated with conformity were raised. These included the situation that areas

## Comments and Responses on June 2010 Draft Revision
of AP-42 Section 13.2.1 for Paved Roads.

January 2011 Page 3 

### containing low volume rural roads were pred icted to have greater emissions of PM 10 

### than
the previous equation predicted. Another s ituation was that the revised equation may
result in greater predicted emissions of PM 2.5 

### under some conditions. The greater
predicted emissions were the result of the existing equation generating negative
emissions for high volume roads. In an assessment to understand the extent and significance of these issues, it wa s revealed that the vehicle e xhaust, tire wear and break
wear emissions components were not addr essed properly. The estimates of vehicle
exhaust, tire wear and break wear used in the 2003 revision did not account for the
significant differences in these emissions during the available te sts and in addition significantly mis characterized for the additiona l data provided by the corn refiners. For
the historical data, the proposed revision incor porated test specific emissions estimates as calculated by MOBIL 6.2 and based upon the av erage vehicle weight reported for each
test. For the CRA data, the proposed revisi on incorporated test specific emissions estimates as calculated by the MOVES model and based upon the average vehicle weight,
vehicle speed and estimated acceleration ra tes. For consistency and for improved accuracy in predicting vehicle exhaust emissions, MOVES model estimates were

calculated for the historical data. While th e incorporation of the data provided by the
Corn Refiners Association extended the capab ilities of the equati on to 1 mph, the Corn
Refiners Association data highlighted the va riable significance of exhaust emissions and the need to address these emission on a test by test basis. An additional advantage of determining road emissions prior to developi ng the road emissions equation is that the
equation never predicted negative PM 10 

### or PM 2.5 

### emissions.

Comment: Besides the problems stated above, we find difficulty in understanding the
scientific basis for replacing the existing PM 2.5 

### /PM 10 

### ratio published in 2006 with the ratio that was previously used by EPA. Th e ratio in the existing equation was accepted by
EPA as an outcome of an experimental pr ogram supported by the Western Regional Air
Partnership (WRAP).

Response: In evaluating the data underlying the e quation proposed in this revision, all of

the data were assessed to understand the basi s and representativene ss of the data. The
WRAP laboratory study was eval uated and was found to focus primarily on categories of emissions that would generate very large c oncentrations of dust emissions and focused
primarily on western sources of these emissi ons. These types of emissions sources have a high probability of overloading air samp ling devices that depend on impaction to
collect particles of differing sizes. These sources are also predom inately dominated by sources where the emissions may have la rge variations over time depending on the
repetition rate of the activity which genera tes the emissions. Pave d roads, especially those with high traffic volumes and those that have neared their nor mal aged equilibrium
state generate dust emissions of greater cons istency in concentration and particle size characteristics. Not only are these emissions more consistent, the emissions

## Comments and Responses on June 2010 Draft Revision
of AP-42 Section 13.2.1 for Paved Roads.

January 2011 Page 4 

### concentrations are much lower except when th e roads silt loading is very high. These high silt loadings are not typi cal of public roads except for periods where sand is applied
as anti skid material, natural forces exacerba te the normal soil loading on the road or in areas where there is a large track out of dirt from an adjacent unpaved area.

The WRAP study included the collection of seven soil samples. The samples included
sediment from Alaska, Alluvial Channel from Phoenix AZ, Agricultural Soil from
Phoenix AZ, Road Dust from the Las Cruces Landfill in New Mexico, Grazing Soil from Radium Springs in New Mexico, Shoreline So ils from the Salton S ea in California and a
Barrow Pit from Thunder Basin Mine in Wyoming. In addition, three additional samples

which were used in the first Phase of the st udy were also used in the second Phase of the
study. These three samples included a Standard fi ne Arizona Test dust, a Standard coarse

Arizona Test dust and Lakebed Soil from Owen s Dry Lake in California. For each of
these samples, the WRAP study states that two five gallon containers of soil were

collected. To collect this vol ume of sample from paved ro ads which are in equilibrium
would require sweeping or vacuuming of multiple miles of roadway. Additionally, none of these samples are representative of aged material deposited on paved roads except for
paved roads which have had anti-skid abrasive s (such as sand) applied during winter or where significant windblown dust or track out dirt is deposited on paved roads.

Most of the laboratory tests perf ormed to assess the revised PM 10 

### /PM 2.5 

### ratio to assign to

historical data was conducted at PM 10 

### concentrations above 2.5 mg/m 3 

### . The greatest

downwind concentration measured in tests used to support the paved road equation
development was 0.74 mg/m 3 

### in run ID F45. Of the test s conducted in the wind tunnel laboratory, only 15 percent of the samples we re performed at concentrations below 0.74
mg/m 3 

### . The lowest PM 10 

### concentration measured dur ing the laboratory study was 0.381
mg/m 3 

### . Of the 80 profiling tests used to support the paved road emissions factor equation

and where the downwind concentrations were available, only five had concentrations greater than 0.358 mg/m 3 

### . In addition, over 80% of th e profiling tests had downwind
concentrations less than 0.2 mg/m 3 

### and 60% had downwind con centrations less than 0.1
mg/m 3 

### . In the wind tunnel laboratory studies, the only particulate us ed to challenge the
sampling devices was the material collected for the studies. The emissions measured during the paved road profiling tests was a combination of emissions from the road
surface, engine exhaust, break wear and tire wear emissions. As presented in Table 4-17 of the draft background document, vehicle emi ssions can be a significant component of
the emissions measured by the profiling samplers . In three cases, the estimated exhaust,

break wear and tire wear emi ssions exceed the measured emissions and were assigned an
emissions factor of 0.01 g/VMT (see test runs M10, M12 and CF-3N). In an additional 10% of the profiling tests, about half of th e measured emissions were estimated to be

exhaust break wear and tire wear emissions. And in approximately 35% of the profiling

## Comments and Responses on June 2010 Draft Revision
of AP-42 Section 13.2.1 for Paved Roads.

January 2011 Page 5 

### tests, the measured emissions were more th an 10% exhaust; break wear and tire wear

emissions.

Based upon a more careful and thorough examin ation of the experimental design of the WRAP study and the profile measurements conducted to characterize paved road

emissions it is concluded that EPA mistak enly accepted the conclusion that the PM 2.5 

### to

PM 10 

### ratio for paved roads should be estimated at 15%. While the WRAP study provides
a reasonable indicator that past measurements of the particle size distributions below 10 μm are unreliable due to particle bounce and re -entrainment associated with impactors, it

does not discredit PM 2.5 

### to PM 10 

### ratios established by field studies which used FRM or equivalent monitors for measuring PM 2.5 

### to PM 10 

### concentrations. While there were only
twelve test runs conducted during the prof iling tests documented in the April 15, 1997 report by MRI for EPA, the PM 2.5 

### to PM 10 

### ratios determined at these three locations
provide a superior estimate of a national ratio for estimating PM 2.5 

### emissions than an
extrapolation from the WRAP laboratory study.

Rebecca Kies and Courtney Bokenkroger Se nior Statistician of Midwest Research
Institute, Kansas City, MO.

Comment: The approach used by EPA to calcula te the proposed paved road equation differs from standard least-squares regr ession procedures. MRI recommends that
ordinary least squares regr ession procedures be used.

Response: EPA used the non standard approach in an attempt to provide an improved
predictor of emissions than the exponential form traditionally us ed for this section. In the
traditional form of regressing the equation, th e log transformed data would be regressed
and include an intercept. Then when retu rned to normal space, the inverse log of the
intercept constant would be the multiplier for the silt and weight terms. The regression terms for silt and weight would then be the exponents for those terms in the final
equation. More sophisticated statistical software and i ndividuals with more thorough knowledge in the application of stepwise non lin ear regression were not available at the
time but were used in the equation devel opment for the published final section. EPA
used SAS which is more robust statistical software than Excel for developing the
equation used in the final AP-42 section. W ith guidance from the statistician, EPA used
Excel to explore limited alternative forms of the equation that coul d potentially provide an equation with better predictive accuracy. EP A assessed the influence of test data that
potentially would adversely influence the re sulting equation and a ssessed the use of
composite factors in an attempt to allevi ate the additional problems identified by MRI’s

statisticians. These assessments led EPA to ex clude ten test data wh ere the silt loadings were greater than 20 g/m 2 

### and to exclude test data wher e field measurements could not
quantify emissions due to traffic on the road. Additional regression methods available in

## Comments and Responses on June 2010 Draft Revision
of AP-42 Section 13.2.1 for Paved Roads.

January 2011 Page 6 

### SAS were evaluated following the explorator y assessment within Excel. The equation which had the best predictive accuracy wa s based upon the traditional least squares
regression of the log transformed data with the intercept forced to zero.

Comment: Additional concerns about gaps in th e range of data surfaced during our
statistical analysis. Notice the major holes highlighted by the circle s in the speed-silt loading and speed-weight boxes. The dataset is missing low silt loading, low speed; low silt loading, high speed data; and low weight, low speed data. Ideally, the boxes relating
silt loading, weight, and speed should be complete ly filled with data points in order to cover all ranges of possible o ccurrences and consider them to be independent factors in

the model.

Response: It is recognized that there are gaps in the data. In most cases, the contractor
performing the study (MRI in all cases) and the studies sponsor (EPA, industry) was interested only in un-managed road systems at the test location. In some of these instances, the condition highlight ed would not be expected due to the physical forces
influencing the independent variables. Fo r example, low silt loading would not be a normal condition when the average vehicle speeds are low since the aerodynamic energy
imparted on the road surface would not be great enough to move the silt to the road

shoulder. This situation of low silt loadi ng and low average speed may be a possibility
should there be active management of the si lt loading on the road. Either the active management of the road silt loading lacks the frequency to achieve lower silt loadings or

there was not a need to achieve these lower silt loadings. In other cases, the data may be missing due to safety concerns associated with the collection of one or more pieces of
information. For example, the collection of data at roads with high speed and low silt loading requires extensive time to collect su fficient material to quantify the low silt
loading. Should resources become available in the future improving the emissions factor
for paved roads, the collection of test data to fill in these data gaps will be suggested. In
addition, mobile monitoring methods may be a viable alternative to the vacuuming of roads to estimate the silt loading of roads where there are safety concerns.

Comment: It is recommended that different modeling options be explored to find the
best fit and set of predictors for the data provided. Two such options are:

• Look at low speed and high speed mode ls separately, potentially excluding
vehicle speeds under 5 mph from equation development.

• Use a composite factor of weight and speed together with either weight or speed
as independent variables in the regressi on. This helps alleviate the problems due to multicollinearity between weight and speed seen in these data.

## Comments and Responses on June 2010 Draft Revision
of AP-42 Section 13.2.1 for Paved Roads.

January 2011 Page 7 

### Response: EPA assessed different m odeling options to find a best fit. A return to
multiple sets of equations or values as predictors which introduce multiple results for
similar independent variables has been shown to create confusion, "results in shopping

for a fortuitous estimate" and adversarial de bates. Any set of predictors should have
nearly identical results for comparable independent parameters where there multiple
predictors could be used.

EPA evaluated the exclusion of atypical inde pendent parameter conditions such as the
very low speed conditions. Other conditions that were evaluated were very high silt loading conditions. It was decided to exclude emissions tests with silt loading levels over
20 g/m 2 

### due to the potential comple xity of an equation needed to incorporate the different
characteristics that these few data present. While these high silt loadings may have been
representative of conditions which would be tolerated by the s ources (or regulatory authorities) in the mid to early 1980's, they are unusual conditions and may not be
reasonable to use in developing or assessing the best predictor for the more representative

and dominant situations. It is believed that management practices would be implemented
by sources and regulatory authorities to address extended durations of high silt loading conditions. Additionally, an assessment of the final equations ability to estimate the
emissions of the ten tests with high silt loadi ng. While there were changes in the percent

difference from actual emissions for individual test runs, the average percent difference
from actual emissions was almost the same as the 2006 equation.

Greg Muleski of Midwest Research Institute

Comment: The measured emission factor for CM-2 should be "63.5" rather than "6.35"
so the independent variable in Table 4-17 s hould have been about 52 g/vmt (rather than
the default value of 0.02 g/vmt).

Response: The measured PM 10 

### emissions factor in Table 4-15 was checked against the
value reported in the test report. The valu e of 0.14 lb/VMT in the table was consistent
with the value in the submitted test report. As indicated in the comment, there was an error in transcribing or units conversion to tr ansfer the value from Table 4-15 to Table 4-

17. The emissions factor for the Corn Refi ners Association test numbered CM-2 was revised from 6.35 grams/VMT to 63.5 grams/VMT in Table 4-17. As a result, the
subtraction of the estimated vehicle exhaust, tire wear and break wear resulted in Road Dust Emissions of 52.44 grams/VMT rather than 0.02 grams/VMT.

Comment: The two-step regression process de scribed in Section 4.2.2.2 differs from
standard stepwise multiple regression used in the past AP-42 updates. It is not clear how R-squared values at each step can be combined to obtain a meaningful value.

## Comments and Responses on June 2010 Draft Revision
of AP-42 Section 13.2.1 for Paved Roads.

January 2011 Page 8 

### Response: As indicated by several comments from individuals at MRI, the multi-step regression used by EPA does not conform to traditional stepwise multiple regression
techniques. More traditional techniques were used in the development of the equation

used in the final section and SAS (which is more robust soft ware for statistical analysis)
was employed to assess the predictive accuracy of the final equation.

Comment: The high degree of correlation betwee n speed and weight precludes both being included as independent term s in the emission factor equation.

Response: It was believed that the large numbe r of tests where the road surface silt loading was artificially changed through eith er the addition of sand or through removal

with mechanical means altered the normal correlation between the vehicle speed and the road silt loading. With the use of more robus t statistical software, the presence of inter
correlation between speed and silt loading wa s re assessed. In addition, the more robust

software allows a better determination of th e potential improvement of an equation which
includes speed to predict road dust emissions. This assessment revealed that the use of
the speed term was contraindicated and the fi nal equation contains only silt loading and average vehicle weight as independent variables.

Comment: The goal should be to develop a pred ictive tool for situations without measured emissions rather than trying to get the best fit for the set of measured

emissions.

Response: The use of Excel to generate the pr edictive equation made an evaluation of the capability for the equation to predict data that was not part of the existing data set

difficult and labor intensive. The use of SAS allows for a more reliable assessment of the
equations predictive capabilities.

Comment: The geometric mean is the better choice than the arithmetic average when
working with the pred icted/observed ratios.

Response: It is assumed that the use of the geom etric mean is a metric to evaluate the predictive accuracy of the e quation through the use of the av erage predicted to observed
values. With the use of SAS, several indi cators of the predictive capabilities of the resulting equation were evaluated.

Comment: The document would have benefitted from a thorough review/edit prior to
being posted on the CHIEF web site.

Response: Prior to posting the final bac kground report, the AP-42 Section and
background report was reviewed and edited mo re thoroughly and the Table of contents
was updated to provide an accurate indicat ion of the contents of the chapters.

## Comments and Responses on June 2010 Draft Revision
of AP-42 Section 13.2.1 for Paved Roads.

January 2011 Page 9 

### Camille Sears for the Sierra Club

Comment: I have a few concerns regarding USEPA’s proposed revision to AP-42

Section 13.2.1:

• USEPA’s multiple regression analysis incorporating vehicle speed excludes a

valuable data set for assessing paved road PM emissions from industrial facilities.

• USEPA’s proposed revision to AP-42 Sec tion 13.2.1 results in a very significant

reduction in PM 10 

### and PM 2.5 

### emission factors from paved roads in industrial
settings.

• It is unclear whether USEPA’s propos ed revision to AP-42 Section 13.2.1

improves upon predictive performance of the existing 2006 emission factor.

Response: The performance of the multiple stepwi se regression of the data recognized
that incorporation of the speed term invol ved the exclusion of 22 test runs. EPA recognized that the exclusion of these data could affect the resulting equation and decided
to include the speed term since the correlati on coefficient showed a modest improvement.

Another commenter indicated that there are better software and pr ocess available than
were used by EPA to develop the equation. EPA employed software more suited for stepwise multiple nonlinear regression than Microsoft Excel in the final equation
development. EPA used this improved softwa re for a more rigorous assessment of the influence of incorporating the speed term in the equation in this reassessment. (In EPA's
reassessment, it was revealed that the sp eed term provided no improvement in the predictive accuracy of the resu lting equation. As a result, the equation published in the
final AP-42 section includes only silt loading and average vehicle weight).

While EPA is cognizant of poten tial impact of any changes that may result in revising the emissions factors in AP-42, the primary goal of emissions factors development is to
provide factors that provide as accurate of a prediction of the target population as possible. The underlying data has consider able variation even when several of the
independent parameters are ne arly identical. With the in creased number of independent
parameters, it is possible that some situati ons where emissions will be greater than the
previous equation and some where emissions will be less.

While there may be some situations where the predictive performance of the proposed equation performed poorer at predicting the unde rlying data, there were others where the
predictive performance was improved. Seve ral measures were used to assess the
predictive performance of the revised equati on and the final equation performs better than
the previous equation.

## Comments and Responses on June 2010 Draft Revision
of AP-42 Section 13.2.1 for Paved Roads.

January 2011 Page 10 

### Comment: USEPA excluded 22 tests performed at two integrated iron and steel plants
due to lack of vehicular speed data. These ir on and steel plant source tests are crucial for
calculating fugitive dust emissions from industria l facilities, and excl uding these data has a very significant impact on pr edicted paved road emission rates. As discussed in the
following section, USEPA’s proposed revision to the paved road emission factor will reduce particulate emission calculations at typi cal industrial sites by roughly an order of
magnitude. This large, and perhaps unrealis tic, reduction in calcul ated industrial paved road emissions is an artifact of trying to develop an emission fact or based on tests that
must include vehicle speed data.

Response: The exclusion of the 22 tests perfor med at iron and stee l facilities did not significantly bias the equation. An evaluation of the predictive precision of the equation

in the November 2006 version of the AP-42 Section for Paved Roads reveals that on average the equation over predicted the 92 individual data by over 11,000%. While
approximately 50% of the predicted estimates underestimated the measured emissions
and 50% overestimated emissions, overestimat es were significantly greater than the
underestimates. The 25 percen tile value underestimated actu al emissions by 54% while the 75 percentile value overestimated actual emissions by 713%. The equation using
only silt loading and average vehicle weight which was reje cted for the equation that
included speed overestimates actual emi ssions by 1,429%. The equation that was
proposed and includes the speed term overest imates actual emissions by only 890%. For both the previously published equation and the proposed equation, the majority of the
overestimation appears to be associated with the lowest speeds, silt loading in the middle third of the data and in the highest averag e vehicle weights. In these categories, it
appears that the previously published equati on overestimates emissions more than the
proposed equation. With respect to roads with greater average vehi cle weights such as
may be present at industrial facilities, th e equation in the November 2006 AP-42 section

tended to overestimate emissions more th an the proposed equation. Table 1 below
presents the independent parameter variables, estimated measured emissions, predicted
emissions by the 2006 AP-42 equation, the equa tion considered in the proposal that includes only silt loading and average vehicl e weight and the equation proposed that
includes silt loading, average ve hicle weight and speed (with an average speed of 35 mph assigned for unrecorded speeds). For those test conditions wh ere average vehicle weight
was greater than 8 tons, the 2006 AP-42 equati on tended to overestimate actual emissions factors by about 350%. The equation that co nsidered only silt loading and average
vehicle weight tended to overestimate act ual emissions factors by about 3%. The equation that considered silt loading, aver age vehicle weight and speed tended to
underestimate actual emissions factors by about 12%. A comparison between the
equation proposed for use and the equation th at was considered but did not include the
speed term shows that the ex clusion of the 22 test data that were missing the average

## Comments and Responses on June 2010 Draft Revision
of AP-42 Section 13.2.1 for Paved Roads.

January 2011 Page 11 

### speed did not adversely affect the average pr edictive capabilities of the equation. As stated elsewhere, a more rigorous and capable statistical software package was used to
develop the final equation used in the AP-42 section.

For the equation published in the 2011 final AP-42 section, the predictive accuracy is
slightly improved over the equation proposed in the draft AP-42 section. As presented in
Table 2, the equation published in the final section provides a moderately better or worse
predictor of actual emissions for a few tests, but does not provide a significantly different
accuracy that the equation in the draft AP-42 section. While the equation presented in the
AP-42 section published in 2006 overestimat es actual emissions factors by 350%, the
equation presented in the final 2011 section ov erestimates actual emissions by an average

of 77%.Comments and Responses onJune 2010 Draft Revisionof AP-42 Section 13.2.1 for Paved Roads.January 2011Page 12Table 1. Performance of 2006 AP-42 equation andequations considered for 2010 draft section revision.Predicted Emissions (g/VMT)Percent difference from MeasuredID #SiltLoadingAverageSpeedAverageWeightMeasured EF(g/VMT)Old AP-42(sL, W)Rejected Proposal(sL, W)Proposed(sL, W, s)Old AP-42(sL, W)Rejected Proposal(sL, W)Proposed(sL, W, s)AD194.823421478.1894696.251575.711156.43218%7%-22%F6117.9NR40461.1741476.91325.20235.28220%-29%-49%AD352.92340231.1892987.29881.27636.211192%281%175%AD263.62339340.8073241.811020.25751.93853%200%121%F6214.4NR36315.1771094.65241.87179.78247%-23%-43%F745.59NR29543.498427.7383.1963.42-21%-85%-88%F342.78NR28186.561257.6242.3731.3938%-77%-83%CI-70.0515.3270.58917.711.020.532907%73%-11%CI-80.0515.3271.94917.711.020.53809%-48%-73%F352.03NR25296.721177.1128.6221.73-40%-90%-93%F380.218NR18165.76625.192.732.05-85%-98%-99%F390.441NR18251.76639.955.214.09-84%-98%-98%B5810.4NR18366.778313.0895.4290.51-15%-74%-75%F370.417NR1776.03835.334.703.76-54%-94%-95%F455.11NR16210.911165.2244.5842.38-22%-79%-80%F320.117NR1452.17011.421.220.98-78%-98%-98%F2714.8NR14129.070270.08105.02111.94109%-19%-13%B572.32NR12194.21664.1016.5916.78-67%-91%-91%B603.19NR12438.21678.8922.2422.93-82%-95%-95%B527.19NR1234.616133.9546.9850.85287%36%47%AUE1415122.28691.4327.3924.993900%1098%993%B592.06NR11347.28952.0413.7414.26-85%-96%-96%B556.3NR11182.289107.8438.4342.66-41%-79%-77%B5113.6NR11139.289177.9778.0290.6828%-44%-35%B543.77NR1092.66266.8721.9724.52-28%-76%-74%B5013.6NR9.481.506140.5467.6283.4372%-17%2%B562.4NR9.2125.42143.9213.4415.07-65%-89%-88%F360.201NR8.354.1687.331.251.26-86%-98%-98%Average358%3%-12%Comments and Responses onJune 2010 Draft Revisionof AP-42 Section 13.2.1 for Paved Roads.January 2011Page 13Table 2. Performance of 2006 AP-42 equation, equation proposed in 2010 draft section and Final 2010 section.PredictedEmissions(g/VMT)PercentdifferencefromMeasuredID#SiltLoadingAverageSpeedAverageWeightMeasuredEF(g/VMT)OldAP‐42(sL,W)Proposed(sL,W,s)Final(sL,W)OldAP‐42(sL,W)Proposed(sL,W.s)Final(sL,W)AD194.823421478.1894696.251156.432886.277218%-22%95%F6117.9NR40461.1741476.91235.28600.570220%-49%30%AD352.92340231.1892987.29636.211613.1691192%175%598%AD263.62339340.8073241.81751.931859.513853%121%447%F6214.4NR36315.1771094.65179.78442.254247%-43%40%F745.59NR29543.498427.7363.42149.639-21%-88%-72%F342.78NR28186.561257.6231.3976.35938%-83%-59%CI-70.0515.3270.58917.710.531.8862907%-11%220%CI-80.0515.3271.94917.710.531.886809%-73%-3%F352.03NR25296.721177.1121.7351.060-40%-93%-83%F380.218NR18165.76625.192.054.773-85%-99%-97%F390.441NR18251.76639.954.099.073-84%-98%-96%B5810.4NR18366.778313.0890.51161.994-15%-75%-56%F370.417NR1776.03835.333.768.133-54%-95%-89%F455.11NR16210.911165.2242.3875.113-22%-80%-64%F320.117NR1452.17011.420.982.093-78%-98%-96%F2714.8NR14129.070270.08111.94172.829109%-13%34%B572.32NR12194.21664.1016.7827.253-67%-91%-86%B603.19NR12438.21678.8922.9336.436-82%-95%-92%B527.19NR1234.616133.9550.8576.446287%47%121%AUE1415122.28691.4324.9944.7853900%993%1859%B592.06NR11347.28952.0414.2622.375-85%-96%-94%B556.3NR11182.289107.8442.6662.006-41%-77%-66%B5113.6NR11139.289177.9790.68125.07428%-35%-10%B543.77NR1092.66266.8724.5235.222-28%-74%-62%B5013.6NR9.481.506140.5483.43106.52572%2%31%B562.4NR9.2125.42143.9215.0721.429-65%-88%-83%F360.201NR8.354.1687.331.262.010-86%-98%-96%Average358%-12%77%

## Comments and Responses on June 2010 Draft Revision
of AP-42 Section 13.2.1 for Paved Roads.

January 2011 Page 14 

### Comment: USEPA prepared a consequence analys is of the National Emission Inventory (“NEI”) resulting from their proposed revisi on.8 USEPA found that their revised paved
road emission factor will significantly reduce PM 10 

### emissions in the NEI (up to 200%
reduction), while PM 2.5 

### emissions are only slightly a ffected (some NEI calculations
increase, some decrease). USEPA, however, did not examine the a ffect of their draft revised paved road equation on fugitive dust emissions from industrial sources.

Response: The estimated impact on State Em issions Inventories and the NEI was
performed as a tool for decisions which may need to be made to address conformity requirements. The Agency may provide Stat es with extensions of times for adopting

revised emissions estimates in their SIP and Transportation plans. These estimates were also produced to assist State and local agenci es understand the potenti al impact that the
revised emissions factors may have on their PM 10 

### and PM 2.5 

### inventories which are being
prepared to address non attainment conditions and required SIP plan development. The
emissions inventory impact estimates were not produced as a decision criteria for revision of the emissions factor equation. The only criteria used in asse ssing the proper equation
to publish are the representativeness of the unde rlying test data and the comparison of the
equation to the actual measured emissions. Although not presented in the background
report, the performance of the equation was ma de by ordering the available test data by silt loading, average vehicle weight and by speed to evaluate whether there was any
systematic bias which was driven by one or more outlying data. Table 4-23 of the background report for the proposed revision did include the average percent error for the
2006 equation and the proposed equation. When arranged by weight, the 2006 equation
produced errors of about 70,000 percent for ve hicle weights of over 10 tons while the
proposed equation produced errors of about 2,500 percent. The equation published in the
final section produces errors of 5,000 percent for vehicle weights between 10 and 40 tons
and errors of about 20,00 percent for vehi cle weights over 40 t ons. Although when limited to these high weight classes the perf ormance appears to be worse, for lower weight classes the new equation demonstrates superior performance to both the previous
published equation and the proposed equation.

David E. James, PhD PE; Associate Vice Pr ovost for Academic Programs; UNLV, Las
Vegas, NV.

Comment: In many parts of the country where ther e is significant rain or a rainy season,
rain days may considerably effect estimated PM 10 

### emissions in the inventory. However, for Las Vegas and other places lik e it in arid places, I tend to use a 'pessimistic' approach
that doesn't include the rain days, since rain occurs sporadically, and what rain does fall is often very light. For the desert southwest, I th ink that it is best to look at the data without
rain adjustments.

## Comments and Responses on June 2010 Draft Revision
of AP-42 Section 13.2.1 for Paved Roads.

January 2011 Page 15 

### Response: It is recognized that the mitigation ad justment for rain events in AP-42 is imperfect. It is recognized that with very li ght rain events, the silt loading on paved roads
may increase due to the removal of soil on the under carriage of vehicles. For most areas of the US, these very light rain events are o ffset with heavier rain events. Over a month
to a year, these enrichment and mitigation events balance out. It should also be noted that the mitigation level is not based upon any measur ed data and is an "engineering or expert

elicitation" estimate.

The emissions factors and the adjustment fact ors in AP-42 are educated estimates of the national average value and do not include variations that may occur due to local and
regional influences. While some variation in the emissions factors for paved road has been reduced through the incorporation of th e independent variables silt loading, vehicle
weight and number of rain events, the remain ing variation is still substantial. EPA does
not prohibit the use of altern ative emissions factors or adjustments when accompanied by
a scientifically credible rationale and supporting data.

Comment: With locally derived data, we obtain resu lts that are different from those that might be predicted using default silt loadi ng data. The actual impact on total estimated

PM 10 

### emissions in an inventory or SIP woul d depend on how much VMT was assigned to each roadway category.

Response: It is recognized that the default silt loading information presented in AP-42 does not provide the precision and accuracy th at may be needed to properly represent the
influence of emissions from paved or unpaved roads. It is also recognized that the
resources required collecting representative s ilt loadings for large numbers of roads is
substantial. However, where roads are believ ed to be significant contributors to the
levels of ambient air particulate matter, obt aining this information is valuable to accurately estimate emissions. To address the needs to obtain this information in a cost
effective manner, we have included a discus sion of the potential advantages of mobile monitoring to develop temporally and spatia lly resolved silt loading (or emissions)

information.

Comment: I also ran a hypothetical sensitiv ity analysis comparing arbitrary combinations of vehicle weight and silt load ing, to see what the impacts of the new PM 10 

### equation might be.

Response: It is recognized that different road classes may have different silt loadings
and the vehicles using these roads may have different average vehi cle weights. These variables will have differing influences on the predicted emissions from these roads. As
a result, the use of locally derived silt loading information is strongly encouraged.

## Comments and Responses on June 2010 Draft Revision
of AP-42 Section 13.2.1 for Paved Roads.

January 2011 Page 16 

### Steve Zemba of Cambridge Environmental Inc for the National Asphalt Pavement

Association.

Comment: The recommended default values for silt loading in draft Table 13.2.1-3, and
particularly that for asphalt batching, may be too high for t ypical current applications. The recommended value is 120 g/m2, but, as you know, in EPA’s 2000 Emission Assessment Report
for Hot Mix Asphalt Plants, a silt-loading value 3 g/m2 is suggested for paved roads at typical
hot-mix asphalt production facilities. Also, site -specific measurements at a hot mix asphalt
facility in Alexandria, Virginia in 2005 (using the sampling and analytical methods described in AP42 Appendix C) found a silt loading level of 0 .5 g/m2. This facility, which we analyzed in
detail for the City of Alexandria, empl oys aggressive dust su ppression techniques.

Response: Values presented in Table 13.2.1-3 are base d upon road dust samples collected in the mid to late 1970's through the mid to late 1980' s. It is unclear wh ether any management
practices were used at these facilities to control the silt loading of the roads where these samples
were collected. It is possible th at current normal main tenance practices woul d achieve lower silt
loadings than are presented in the table. Statem ents in the documentati on included in the reports by the Corn Refiners Associa tion and several other test programs used in the equation
development indicate that there was active manage ment of the road surface dust levels. As a result, the silt loading data coll ected during those test programs are lower than they would be

otherwise. While there is no re quirement to use the silt loading va lues provided in the tables of AP-42 updated silt loading data can be collected by any individual as long as they follow the
procedures presented in the AP-42 appendices . It is recommended that in addition to documenting the sampling and analyses, the documentation include normal housekeeping
practices and special monitoring and maintenance practices at the collection sites. While we cannot guarantee rapid incorporation of new silt loading data into the table, any reports
submitted will be posted for use by subsequent users.

Catharine Fitzsimmons, Chief, Air Quality Bu reau and Lori Hanson Iowa Department of

Natural Resources.

Comment: The DNR supports the revision of this section to incorpor ate new data from
corn wet mills and to account for mean vehicle speeds below 10 miles per hour.

Response: Thanks for your support.

Comment: The proposed form of the equation requi res that a mobile source emissions
model be run in order to determine a pa ved road emission factor. Obtaining the

emissions factor for vehicle emissions in th is manner will be problematic as the DNR does not have the resources to generate speci fic emissions factors for vehicle emissions

## Comments and Responses on June 2010 Draft Revision
of AP-42 Section 13.2.1 for Paved Roads.

January 2011 Page 17 

### by running MOVES20I0 for every construction permitting project that includes a paved haul road. The DNR suggests that either th e empirical equation be developed to include
vehicle emissions from engine exhaust, tire and brake wear, or that a table of default

values be included in the sect ion to account for vehicle emi ssions as an alternative to
running a mobile source emission model.

Response: While vehicle exhaust emissions may have been relatively stable for the last twenty or thirty years, se veral regulatory programs which cover mobile source emissions
are expected to produce decreasi ng exhaust emissions over the next five to ten years. In addition, engine exhaust like road dust em issions is highly dependent on the road
characteristics, meteorological conditions , vehicle speed, vehicle class and other
environmental conditions. As a result, a defa ult engine exhaust equation will result in
unknown errors and may lead to incorrect decisions on different programs. While decisions for many programs may not requ ire the accuracy that would occur with
individual selection of the requisite parame ters needed for the most accurate emissions
estimates, this would be a decision that shoul d be made for each application. While State
agencies (Department of Transportation or Ai r Quality) may not have the resources or time to generate a project specific emissions estimate for every project, individual States
are in a better position to develop default pa rameters (engine exhaus t, silt and average vehicle weight) which is appropriate for use for projects with different sensitivities.

Pat Davis of MARAMA for the States of New Jersey, Delaware, Maryland and

Massachusetts.

Comment: We have been examining the ERTAC/ PECHAN emission factors for Road Dust and Maryland noticed that the PM 2.5 

### emission factors were zeroed out for the following road

types:

• Urban Collector

• Urban Minor Arterial

• Urban Other Principal Arterial

• Urban Other Freeways and Expressways

• Urban Interstate

Emission factors for PM 10 

### were found and there was no me ntion in the documentation of
why the PM 2.5 

### emission factors were zeroed ou t, so we are bit confused.

Response: As a result of a revision of the ratio of the PM 2.5 

### to PM 10 

### recommended by the
Western States Air Resources Council (WESTAR) from 25% to 15%, the multiplier k in the
predictive equation for PM 2.5 

### was revised from 1.8 (for gram s/VMT) to 1.1 (for grams/VMT)
in the 2006 revision of the paved roads AP-42 Section. With a constant emissions factor of

## Comments and Responses on June 2010 Draft Revision
of AP-42 Section 13.2.1 for Paved Roads.

January 2011 Page 18 

### 0.1617 subtracted for the vehicle exhaust br eak wear and tire wear emissions, these emissions result in a negative calculated road dust emission when one enters an average
vehicle weight of 4 tons or less and a silt load ing of 0.2 grams/square meter or less. While
the k value used in the previ ous version of the equation resulted in negative emissions
whenever the silt loading was less than 0.03 grams/ square meter, this affected only Freeways, Expressways and Interstates and was believed to be rational since roadways with average
speeds of 55 mph (and the normal level of silt fo r that speed) had a high number of tests with

low measured emissions and were considered to be composed primarily of exhaust

emissions.

In the equation presented in the final version of this update, the estimated exhaust component
was subtracted from each source test prior to th e stepwise regressions of the test data to
develop the predictive equa tion. As a result of the absence of vehicle exhaust, tire wear and
break wear in the predictive equation, there are no conditions that will result in negative

emissions for the road dust emissions.

Julie McDill (MARAMA), David Fees (Del aware), Julie Rand (New Jersey).

Comment: Here is Delaware's paved road dust spreadsheet for 2007, using the new equation. We got very detailed with this category; estimating emissions by month.
Regarding the new equation, PM 10 

### was reduced by 58% from the emissions submitted to
MACTEC; while PM 2.5 

### increased by 48%. I believe the PM 2.5 

### increase is caused by two
factors-first, the PM 2.5 

### / PM 10 

### ratio was increased to 25% (p reviously 15%). The second
reason is that under the old equa tion, one had to apply a correcti on factor, C, to remove the
exhaust, brake, and tire wear from the front pa rt of the equation. By subtracting C at the end of the equation, the resulting PM 2.5 

### value went negative for several roadway types. Of course
we zeroed these out, but with the new method there is never a situation where the emission factor value can go negative. Ha ving negative emission factors re sult from the use of the old
equation was obviously a flaw in the method, so I expect the new equation is more accurate. I look forward to NJ's results when they appl y the new equation, to s ee if they get changes

similar to mine.

New Jersey has similar results, but even more drastic for PM 2.5 

### . An increase in PM 2.5 

### of

350% and a decrease in PM 10 

### of 46% I think one big cause is the difference in k factor,
among other changes. The k factor for PM 2.5 

### went down from the 2003 AP-42 to the 2006 AP-42, and back up again in this new draf t. We guessed at the new vehicle speed
requirement, but a slight varia tion in speeds will not make that much of a difference.

Response: It is correct that the k value and the C value both influence the predictive value
for the emissions factor. In addition, the e xponents associated with the silt loading and the
average vehicle weight also influence the emissi ons estimates. It is also correct that the

## Comments and Responses on June 2010 Draft Revision
of AP-42 Section 13.2.1 for Paved Roads.

January 2011 Page 19 

### updated equation will not generate a negative em issions factor since the vehicle emissions,

tire wear and break wear will not be included in the equation development. Based upon an
assessment of the predicted to actual emissions factor for each of the available emissions
tests, the updated equation provi des an improved estimate of the emissions compared to the
previous equation. It is also be lieved that the return to the PM 2.5 

### to PM 10 

### ratio of 25% is a

better indicator of the PM 2.5 

### than the 15% ratio that was based upon laboratory assessment

conducted for WESTAR.

Gary Garman of McVehil-Monnett.

Comment: It's good to see the paved road section is being revised. Thanks. It has been a challenge in the past explaining to industrial clients that pavi ng a road would actually result
in higher predicted emissions than if the road is left unpaved. I think we'll see more paving

and actual emission reductions as a result of the new equation. A few editorial comments on
the draft paved road section:

Page 13.2.1-1, third paragraph, first sentence..cha nge to "The particulate emission factors
presented in a previous version.."

Page 13.2.1-5, third paragraph, last sentence..c hange "Table 13.2.1-3" to "Table 13.2.1-2"

Page 13.2.1-8, fifth paragraph, first sentence. .change "Table 13.2.1-3" to "Table 13.2.1-

2"

Page 13.2.1-9, second paragraph, second sent ence..remove hyphen between "not" and
"suggest"

Table 13.2.1-3...the page number this tabl e is on should be changed to 13.2.1.10. Also, total loading range for iron and steel should be 0.006-4.77, not 43.0-64.0.

Page 13.2.1-11, first paragraph, fourth senten ce..remove hyphen between "any" and "of"

Thanks again. I look forward to this draft being finalized.

Response: An assessment of the paved verses unpaved road equation performance will be
conducted. A statement will be added to the pa ved road section explaining that under some
high silt loading conditions the equation may predict higher emissions than for an unpaved

road and that for these c onditions the unpaved road equa tion should be used. The
typographical errors will be co rrected in the final version.Comments on Proposed Paved Road Equation Cowherd, Chatten to: Ron Myers 08/31/2010 03:00 PM

Cc: "Kies, Rebecca", "Muleski, Greg"

History: This message has been forwarded.

Hello Ron,

Thank you for the opportunity to comment on the proposed revision to the paved road dust equation in

AP

‐ 42 section 13.2.1. The attached letter presents comments developed on behalf of the Center for the Study of Open Source Emissions (CSOSE).

As you know, the revised equation (proposed by EPA as a replacement for the existing equation ) and its

technical foundation were topics of discussion during the August 18 teleconference hosted by the

CSOSE. During this teleconference and in related information exchanges, the general consensus among
CSOSE participants who have worked in this field is that the proposed equation does not offer improved predictive capability but introduces additional data requirements to the paved road emission inventory

process

.

There is also the broader issue of adopting mobile monitoring as the basis for more realistic emission
inventorying of paved roads. In previous conversations, I believe that you have acknowledged the clear advantages of mobile monitoring over the traditional AP

‐ 42 method for determining paved road dust

emissions with its reliance on limited and difficult measurements of silt loading

.

We believe that the CSOSE constitutes a substantial resource in resolving these issues and in assisting

EPA with the goal of developing improved emission factors such as those applicable to paved road dust

emissions

.

Please contact me with any questions or comments.

Sincerely,

Chat Cowherd

Chatten Cowherd, Jr., Ph.D.

Midwest Research Institute

425 Volker Boulevard

Kansas City, MO 64110 (816) 753

‐ 7600 ext. 1586 (816) 360

‐ 5346 direct dial

This message is intended exclusively for the individual or entity to which it is addressed. This communication may contain information that is confidential, proprietary, privileged or otherwise legally exempt from disclosure. If you have received this message in error, please notify the sender immediately by facsimile, e-mail or phone and delete all copies of the message.

### Chatten Cowherd, Jr., Ph.D.

Director ccowherd@mriresearch.org (816) 360-5346

August 31, 2010

Mr. Ron Myers U.S. Environmental Protection Agency Research Triangle Park NC 27711

RE: Proposed Revision to AP-42 Emission Factor Equation for Paved Road Dust

Dear Mr. Myers:

The Center for Study of Open Source Emissions (CSOSE) is pleased with the opportunity to submit comments in response to EPA’s proposed revision of the emission factor equation in

AP -42 Section 13.2.1. It should be noted that these comments were prepared by the undersigned as Director of CSOSE, taking into account verbal and written communications from interested members of the Center, including those provided during a presentation and discussion of this topic in the August 18 teleconference hosted by the Center. However, this letter was not circulated to CSOSE participants for review prior to submission.

One of the goals of CSOSE is to promote transparency and collaboration in the

documentation of test results and in the use of those results to derive effective tools for compliance with air quality standards. We believe that this goal is consistent with EPA’s stated goal to develop a self-sustaining emissions factors program that produces high quality, timely emissions factors, better indicates the precision and accuracy of emissions factors, encourages the appropriate use of emissions factors, and ultimately improves emissions quantification (see
EPA’s Advance Notice of Proposed Rulemaking on “Emissi on Factors Program Improvements, ” Oct. 14, 2009).

We acknowledge the concerns of various parties related to the scientific foundation for the proposed equation as well as the increased effort required in developing vehicle speed data to include in paved road emission inventories. CSOSE participants have presented analyses demonstrating that the proposed equation does not provide an improved predictive capability above that provided by the current equation. In addition the proposed equation has a significant new data input requirement (vehicle speed) that increases the difficulty of generating paved roademission inventories and that has possible implications on projected effectiveness of current SIPmandated control strategies.

Based on our discussions of the proposed equation and the technical analyses presented by EPA, w e find the scientific foundation for the revision unconvincing. This leads us to question the process used in advancing this proposed equation. Our understanding of the rationale for revision of the existing equation might be clarified if there were evidence of an internal review process within EPA that raised issues and resolved them appropriately.

### Besides the problems stated above, we find difficulty in understanding the scientific basis

for replacing the existing PM-2.5/PM-10 ratio published in 2006 with the ratio that was previously used by EPA. The ratio in the existing equation was accepted by EPA as an outcome of an experimental program supported by the Western Regional Air Partnership (WRAP). That experimental program included regular progress updates in WRAP teleconferences with participation from EPA representatives. To our knowledge, WRAP was never directly informed in advance that the stated conclusions of their study are now being discounted.

We hav e encouraged others to present comments on the proposed equation that are supportive of the goal of providing improved emission factors. At the time of this writing, we are aware that separate comments are being submitted by Midwest Research Institute (Ms. Courtney Bokenkroger and Dr. Greg Muleski), by the Clark County Department of Air Quality and Environmental Management (Mr. Rodney Langston) and by the University of Nevada at Las Vegas (Dr. David James).

We trust that EPA will publish all comments as well as the responses to each comment. This will be of great assistance to all in moving toward the best possible use of the test data in supporting a meaningful and appropriate emission factor equation for entrained dust from paved

roads.

In summary, we conclude that there is no compelling scientific justification for adopting the proposed emission factor equation as a replacement for the existing equation. This problem is compounded by the requirement for additional input data and the potential impact on current and future emission inventories as tools for compliance determination. We conclude that an internal EPA review may not have been performed prior to posting the proposed equation for public comment. Finally we emphasize the importance of publishing all comments submitted to EPA along with EPA’s responses to each comment.

If you have questions about these comments submitted on behalf of CSOSE, please contact the undersigned by email ( ccowherd@mriresearch.org ) or by telephone (816) 360-5346. We look forward to your responses to these comments. We believe that CSOSE constitutes a substantial resource in resolving the above issues and in assisting EPA with the goal of developing improved emission factors for open sources. Thank you again for the opportunity to submit comments on the proposed revision to the current AP-42 equation for paved road dust

emissions.

Sincerely yours,

C ENTER FOR 

### S TUDY OF 

### O PEN 

### S OURCE 

### E MISSIONS



### Chatten Cowherd, Jr., Ph.D.

DirectorFrom: "Kies

To: Ron Myers/RTP/USEPA/US@EPA

Date: Tuesday, August 31, 2010 11:17AM Subject: Statistical Comments on Draft AP-42 Section 13.2.1

History: This message has been forwarded.

Ron,

Thank you for the opportunity to comment on the proposed AP-42 paved roads section 13.2.1. Attached to this email are MRI’s comments resulting from statistical analysis of the proposed changes to the paved road equation by MRI Seni or Statistician, Courtney Bokenkroger. These comments have been reviewed by myself , Chat Cowherd, and Greg Muleski.

Please feel free to respond with any questions or comments.

Sincerely,

Becky Kies



### Rebecca Kies

Assistant Scientist

Midwest Research Institute

425 Volker Blvd. KCMO 64110

(816) 360-3825 (direct)

(816) 753-7600 x1818

rkies@mriresearch.org

This message is intended exclusively for the individual or entity to which it is addressed. This communication may contain information that is confidential, proprietary, privileged or otherwise legally exempt from discl osure. If you have received this message in error, please notify the sender immediately by facsimile, e-mail or phone and delete all c opies of the message.

Attachments:

Comments in Response to EPA Proposed Section 13.2.1 Paved Road Equation.pdf 

### Pa g e 1 of 1

10/22/2010 htt p s://rtairmail1.rt p .e p a. g ov/mail/rm y ers.nsf/9ff539a1e24f 5aaf852577890046a8f6/C48E

...1 Courtney Bokenkroger

Senior Statistician

816

- 360 -5303

August 31, 2010

Mr. Ron Myers U.S. Environmental Protection Agency Research Triangle Park NC 27711

RE: Draft AP-42 Section 13.2.1 Paved Roads

Dear Mr. Myers:

Midwest Research Institute (MRI) is pleased with the opportunity to submit comments in response to

EPA’s proposed draft revisions to AP -42 Section 13.2.1 Paved Roads and corresponding background

documents. We applaud EPA’s effort to improve the quality of the emission factor model for paved roads and appreciate your consideration of external comments.

MRI has a productive history of work in air pollutant source testing, process characterization, and development of emission factors for EPA ’s Emission Factor Handbook (AP -42). Besides serving for more than 25 years as an EPA contractor in the testing of ducted sources and in associated methods development, we have made unique contributions to the development and application of test methods for open (non-ducted) sources. The open sources that we have tested over the past 35 years include agricultural operations, paved and unpaved roads, construction activities, surface mining activities, military training operations, and open area wind erosion. Because of the large natural variability of these sources, MRI pioneered the concept of predictive emission factor equations rather than relying on simple averaging of test results for fugitive dust sources. This approach reduced the uncertainty of emission factor estimates for unpaved roads

-- as the largest contributor to the national PM-10 emission total

-- by up to two orders of magnitude.

Our comments on the draft AP-42 Section 13.2.1 Paved Roads focus on a statistical analysis of the data set and procedure used to calculate the proposed new paved road emission factor equation and can be

summarized as follows:

The approach used by EPA to calculate the proposed paved road equation differs fromstandard least-squares regression procedures. MRI recommends that ordinary leastsquares regression procedures be used. In using ordinary least squares regression to compare models for only the field measurements that included vehicle speed, we find that inclusion of speed in the model takes away from the explanation of variance of the model (R 2 ) and that vehicle speed does not have a statistically significant relation to emission factor. It is recommended that different modeling options be explored to find the best predictive equation from the data provided. Two such options are:

o Look at low speed and high speed models separately, potentially excluding vehicle speeds under 5 mph from equation development.2 o Use a composite factor of weight and speed together with either weight or speed as independent variables in the regression. This helps alleviate the problem of the multicollinearity of weight and speed seen in these data.

Model Comparison

The data set used by EPA to develop the proposed paved road equation included emission factor, silt loading, weight, and speed. Out of 93 total observations, 71 included speed data. The 71 observations that included speed data were the ones used by MRI for model comparison.

It is not reasonable to compare the proposed model with other possible models for the data using the approach taken by EPA to calculate the proposed model. The double-regression approach used renders two different R-square values (one for each regression), neither of which accurately represent the proportion of variability explained by the final resulting model.

The resulting equations obtained from running least-squares regression on the log transformed, normalized values with and without inclusion of speed on the set of 71 data points appear below.

Regression without speed: EF = 6.51 *(silt loading/2) 0.97 * (weight/3) 0.36
Regression including speed : EF = 6.41 *(silt loading/2) 0.97 * (weight/3) 0.27 * (speed/30) -0.12

Variance Explained by M odel Variable p

- value “Proportion of Variance Explained”
Regression without speed R 2 = 0.6335 Silt loading < 0.0001 0.62673 Weight 0.0739 0.04621
Regression including speed R 2 = 0.6288 Silt loading < 0.0001 0.62673 Weight 0.3892 0.04621 Speed 0.7140 0.00202

The R-square value from a standard least-squares regression represents the proportion of variability explained by the model. When speed is included in the regression, the R-square is slightly lower than when speed is not included. This means that the model explains less of the variance seen in emission factor when speed is included than when it is not.

The column labeled p-value represents the statistical significance of the factor in the prediction of the dependent variable (the lower the p-value, the greater the significance). In order to be considered statistically significant for inclusion in the model, generally p-values are less than or equal to 0.15. Note that the p-values for the equation that includes speed indicate that speed and weight are both statistically insignificant (this is because there is likely a relationship between weight and speed). When speed is not included, weight is statistically significant.

The column labeled “proportion of variance explained” is the proportion of R-square that is explained by each individual variable. Speed contributes almost no additional “explanation of variance” to the model (i.e. speed doesn’t add much to the predictive power of the model).3 Gaps in the Data

Additional concerns about gaps in the range of data surfaced during our statistical analysis. Notice the major holes highlighted by the circles in the speed-silt loading and speed-weight boxes. The dataset is missing low silt loading, low speed; low silt loading, high speed data; and low weight, low speed data. Ideally, the boxes relating silt loading, weight, and speed should be completely filled with data points in order to cover all ranges of possible occurrences and consider them to be independent factors in the

model.

Conclusions and Recommendations

The proposed approach used by EPA to calculate the proposed paved road equation differs from standard regression procedures. The two-regression approach used results in two different R-square values, neither of which accurately represent the proportion of variability explained by the final resulting model. Additionally, different data sets were used to develop the two models.

In using ordinary least-squares regression to compare data models for the same data, inclusion of speed in the model does not significantly add to the explanation of variance in emission factor. Also, speed does not have a statistically significant relationship with emission factor.

The low-speed data ( ≤ 5 mph) have an un-proportionally large effect on the fit of the model. This is of concern because there are not enough low speed data to represent all ranges of weight and silt loading.4 Because t he correlation between the log-transformed, normalized weight and speed in the model is approximately -0.78, inclusion of both factors introduces issues related to multicollinearity. The problem with having highly correlated variables in a model is that the coefficients are easily influenced by the dataset used in estimation and may not be meaningful ly interpreted because they are not independent.

It is recommended that different modeling options be explored to find the best fit and set of predictors for the data provided. Two such options are: Look at low speed and high speed models separately, potentially excluding vehicle speeds under 5 mph from equation development. Use a composite factor of weight and speed together with either weight or speed as independent variables in the regression. This helps alleviate the problems due to multicollinearity between weight and speed seen in these data.

If you have questions or comments about this evaluation of the proposed paved road equation in EPA

AP -42 Section 13.2.1, please contact the undersigned by email (cbokenkroger@mriresearch.org) or telephone (816- 360-5303). We look forward to your response on this matter.

Sincerely yours,

MIDWEST RESEARCH INSTITUTE

Courtney Bokenkroger

Senior StatisticianComments on Section 13.2.1 draft Muleski, Greg to: Ron Myers 08/30/2010 02:58 PM

History: This message has been forwarded.

Ron

Thank you for the opportunity to comment on the draft paved road emission factor. Based on my analysis for the Corn Refiner Association member companies, I know that the revision moves the power on the "mean vehicle weight term" in the right direction.

My specific comments are as follows:

1. The measured emission factor for CM-2 should be "63.5" rather than "6.35" so the independent variable in Table 4-17 should have been about 52 g/vmt (rather than the default value of 0.02 g/vmt).

2. The two-step regression process described in Section 4.2.2.2 differs from standard stepwise multiple regression used in the past AP-42 updates. It is not clear how R-squared values at each step can be combined to obtain a meaningful value.

3. The high degree of correlation between speed and weight precludes both being included as independent terms in the emission factor equation. Furthermore, it is not clear what inclusion of speed does for the model. The goal should be to develop a predictive tool for situations without measured emissions rather than trying to get the best fit for the set of measured
emissions.

4. The geometric mean is the better choice than the arithmetic average when working with the predicted/observed ratios.

5. The draft background document is in rough shape. It would have been better to have posted only Section 4 to avoid confusion arising from the table of contents, references, etc. The document would have benefitted from a thorough review/edit prior to being posted on the CHIEF web site.

Please feel free to contact me with any questions or comments.

Greg Muleski

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is addressed. This communication may contain information that is confidential, proprietary, privileged or otherwise legally exempt from disclosure. If you have received this message in error, please notify the sender immediately by facsimile, e-mail or phone and delete all copies of the

message.FW: Message from KMBT

_ 421 Muleski, Greg to: Ron Myers 08/26/2010 09:52 AM

History: This message has been replied to and forwarded.

Ron

Sorry I missed your phone call. I've attached 2 annotated pages from your draft background document that show the problem.

-----Original Message----- From: copier211h@mriresearch.org [mailto:copier211h@mriresearch.org] Sent: Thursday, August 26, 2010 8:50 AM To: Muleski, Greg Subject: Message from KMBT_421

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is addressed. This communication may contain information that is confidential, proprietary, privileged or otherwise legally exempt from disclosure. If you have received this message in error, please notify the sender immediately by facsimile, e-mail or phone and delete all copies of the

message.From: "Camille Sears" <camille .marie@sbcglobal.net>

To: Ron Myers/RTP/USEPA/US@EPA

Date: Monday, August 30, 2010 11:53PM Subject: Re: AP-42 13.2.1

History: This message has been forwarded.

Hi Ron,

Attached are our comments. Please let me know if you have questions.

Your help is greatly appreciated!

Best wishes,

Camille

Attachments:

SC-13.2.1.comments.pdf 

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...

## Camille Sears 502 W. Lomita Ave., Ojai, CA 93023

Tel: (805) 646

- 2588 e

- mail: camille.marie@sbcglobal.net



### August 30, 2010

Mr. Ron Myers U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Research Triangle Park, NC 27711

Subject: Proposed Revision to AP

- 42 Section 13.2.1

– Paved Roads

Dear Mr. M yers,

Thank you for the opportunity to provide comments on the proposed revision to AP

- 42 Section

13.2.1. I have reviewed the proposed AP

- 42 revisions and associated reference documents and on behalf of Sierra Club offer the following brief comments.

1. Introduction

The existing USEPA air pollution emission factor for fugitive dust from vehicle traffic on paved

roads is as follows : 1



### E = k (S L 

### /2) 0.65 

### * (W/3) 1.5



### – C

Where: E = annual or other long

- term average emission factor in the same units as k

k = particle size multiplier (from Table 13.2.1

- 1, k = 0.0024 lb /vehicl e mile traveled (VMT) for

PM 2.5 

### and 0.016 lb /VMT for PM 10 

### )

S L 

### = road surface silt loading (g/m 2 

### )

W = average weight of vehicles (tons)

C = emission factor for 1980s vehicle fleet exhaust, brake wear, and tire wear (from Table 13.2.1

- 2,

C = 0. 00036 lb/VMT for PM 2.5 

### and 0.00047 lb/VM T for PM 10 

### )

The existing version of AP

- 42 Section 13.2.1 appears to be based on 64 source tests p erformed prior
to 1995, the date when the paved road emission fa ctor first takes its current form.

In July 2008, the Corn Refiners Association (“CRA”) proposed a revision to AP

- 42 Section 13.2.1.

CRA’ s proposed revision is based on 22 additional source tests performed at ethanol plants in 2001

1 USEPA, Offic e of Air Quality Planning and Standards, AP

- 42, Section 13.2.1, Paved Roads, November 2006 , p. 13.2.1

- 4.Revisions to AP

- 42, 13.2.1 August 30, 2010 Page

- 2



### through 2003. 2 

### CRA rec alculated a paved road emission factor including the 64 source tests used by
USEPA as the bases for the existing emission factor, plus the 22 additional CRA source tests (for a
total of 86 tests). Based on their r egression analyses, CRA proposed a rev ise d paved road emission

factor with the following form: 3



### E = k (S L 

### /2) 0. 8 

### * (W/3) 0.8



### – C

CRA also proposed a revised particle size multiplier ( k), where k = 0.0034 lb / VMT for PM 2.5 

### and

0.023 lb /VMT for PM 10 

### )

The additional 22 tests performed by the CRA incl ude :

**•** Nine tests performed on roads at the Minnesota Corn Processors facility, Marshall, Minnesota, during April 2001;

**•** Eight tests performed on roads at the Minnesota Corn Processors facility, Columbus, Nebraska, during June 2001;

**•** Two tests performed on ro ads at the Cargill Sweeteners North America facility, Blair, Nebraska, during August 2002;

**•** Three tests performed on roads at ADM’s facility, Marshall, Minnesota, during September 2003 (this is the same facility as the April 2001 tests).

In May 2010, USEPA developed and proposed a revision to AP

- 42 Section 13.2.1, “Paved Roads.”

From USEPA:

This update recommends an updated equation for paved roads that is based upon additional test data that was conducted on roads with slow moving traffic and stop and g o traffic. The emissions tests were performed for the Corn Refiners Association by Midwest Research Institute (MRI). The testing focused on PM10 emissions at four corn processing facilities. 4



### USEPA’s update to AP

- 42 Section 13.2.1, however, incorporates other data than that collected by

the Corn Refiners Association, and, more importantly, USEPA’s update excludes important data that have been used in developing the existing paved road emission factor . In summary, USEPA’s 2010 update to AP

- 42 Section 13. 2.1 incorp o rates the following data base changes:

**•** Including the 22 CRA tests performed in 2001 through 2003;

**•** Including three tests performed on public roads in Denver, Colorado, during March 1996;

2 Corn Refiners Association, Paved Road Modifications at AP

- 42, Background Documentation, MRI Project No. 310842, July 18, 2008, p. 4.

3 Id., p. 20.

4 USEPA, Emission factor Documentation for AP

- 42, Section 13.2.1, Paved Roads, Draft, June 2010, p. 2

- 9.Revisions to AP

- 42, 13.2.1 August 30, 2010 Page

- 3



### • Including two tests performed on public roads in Raleigh, North Carolina

, during April 1996;

**•** Including two tests performed on public roads in Reno, Nevada

, during June 1996;

**•** Excluding 22 tests performed at two i ntegrated iron and steel plants

– one located in Houston, Texas, and the other in Middletown, Ohio (dur ing 1980 and 1981)

. 5



### USEPA developed a proposed multiple regression equation based on paved road silt loading, mean vehicle weight, and vehicle speed

. The existing version of AP

- 42 Section 13.2.1 is based on regression analyses of silt loading and mean v ehicle weight. Since vehicle speed was not measured

at the 22 tests from the two i ntegrated iron and steel plants ( Houston, Texas and Middletown, Ohio during 1980 and 1981) , these tests were excluded from the data set.

USEPA’s proposed revision to AP

- 42 Section 13.2.1 , which is based on 71 individual source tests,

takes the form: 6



### E = k (S L 

### /2) 0. 98 

### * (W/3) 0.53 

### * (S /3 0 ) 0.16



### Where: E = annual or other long

- term average emission factor in the same units as k

k = particle size multiplier; k = 0 .0037 lb / VMT for PM 2.5 

### and 0 .015 lb /VMT for PM 10



### S L 

### = road surface silt loading (g/m 2 

### )

W = average weight of vehicles (tons)

S = average vehicle speed (miles per hour)

This equation does not incorporate emissions from engine exhaust and brake and tire wear , which

will ne ed to be estimated and added using USEPA’s MOBILE6.2 or MOVES2010 models.

I have a few concerns regarding USEPA’s proposed revision to AP

- 42 Section 13.2.1:

**•** USEPA’s multiple regression analysis incorporating vehicle speed excludes a valuable data

set for assessing paved road PM emissions from industrial facilities

.

**•** USEPA’s proposed revision to AP

- 42 Section 13.2.1 results in a very significant reduction in

PM 10 

### and PM 2.5 

### emission factors from paved roads in industrial settings.

**•** It is unclear whether USEPA ’s proposed revision to AP

- 42 Section 13.2.1 improves upon predictive performance of the existing 2006 emission factor.

5 Id., p. 4

- 18.

6 USEPA, Office of Air Quality Planning and Standards, AP

- 42, Draft Section 13.2.1, Paved Roads , p. 13.2.1

- 4.Revisions to AP

- 42, 13.2.1 August 30, 2010 Page

- 4



### 2

. Key Industrial Source Tests are Excluded from USEPA’s Revised Factor

USEPA’s proposed revision to the paved road emission factor includes a third variable, mean vehicle speed

. Vehicle speed, however, does not appear to be an important predictive aid to the overall emission factor equation . This is evidenced by vehicle speed having a small (0.16) exponential te rm in USEPA’s propose d paved road emission factor. Furthermore, t he CRA, in their analyses of the source test data, state :

Taken together, these observations indicate that (a) silt loading and vehicle weight may be used as independent variables and that (b) inclusion of spee d would add very

little to the predictive cap ability of the model. 7



### I understand that USEPA has been asked to include vehicle speed in the revised paved road emission

factor. Doing so , however, excludes valuable source tests that were performed without measuring vehicle speed. In particular, USEPA is e xcluding 22 tests performed at two i ntegrated iron and steel plants due to lack of vehicular speed data

. These iron and steel plant source tests are crucial for calculating fugitive dust emissions from in dustrial facilities, and excluding these data has a very significant impact on predicted paved road emission rates. As discussed in the following section,

USEPA’s proposed revision to the paved road emission factor will reduce particulate emission

calcula tions at typical industrial sites by roughly an order of magnitude. This large

, and perhaps

unrealistic

, reduction in calculated industrial paved road emissions is an artifact of trying to develop

an emission factor based on tests that must include vehicl e speed data.

3

. USEPA’s Proposed Update will Result in a Roughly Order of Magnitude Emission

Reduction at Industrial Sites

In addition to developing an updated paved road emission factor, USEPA prepared a consequence analysis of the National Emission In ventory (“NEI”) resulting from their proposed revision

. 8



### USEPA found that their revised paved road emission factor will significantly reduce PM 10 

### emissions in the NEI (up to 200% reduction), while PM 2.5 

### emissions are only slightly affected (some NEI

calcu lation s increase, some decrease). USEPA, however, did not examine the affect of their draft revised paved road equation on fugitive dust emissions from industrial sources.

I prepared two tables that compare the existing paved road emission factor with US EPA’s proposed

revision

– one for PM 10 

### (Table 1A), and one for PM 2.5 

### (Table 1B). These tables include a range of silt loading, vehicle weight, and vehicle speed conditions. For each set of silt loading, weight, and speed, I calculated the emission factor using both the existing and proposed paved road emission factor. As can be seen, the reduction in calculated emissions for industrial sites using the revised

factor is very large

– about an order of magnitude lower for PM 10 

### and somewhat less for PM 2.5



### .

7 Cor n Refiners Association , Paved Road Modifications at AP

- 42, Background Documentation, MRI Project No. 310842, July 18, 2008, p. 15

. 8 See Excel spreadsheet: Impact_of_revised_paved_roads_pm_emission_factors_on_NEI.xls.Revisions to AP

- 42, 13.2.1 August 30, 2010 Page

- 5



### USEPA’s choice to go ahead with their proposed paved road emission factor would have serious ramifications for NAAQS and PSD increment compliance . This is particularly true for proposed major sources of PM 10 

### and PM 2.5 

### which have paved haul road emission sources. Using USEPA’s proposed revision, s ources that are currently being scrutinized for PM 10 

### PSD increment and PM 2.5 

### NAAQS compliance would most likely be well below any regulatory design concentrations, even with significantly relaxed control measures

. Again, USEP A’s proposed revision is largely due to excluding a significant portion of the existing industrial source test data base, and is not due to any

tests that contradict the excluded data. In effect , USEPA ’s revision would be “sweeping under the rug” what is perhaps the greatest impact caused by many industrial sources.

In terms of the modeling analyses for NAAQS and PSD increment compliance, the 24

- hour PM 10 

### PSD increment, which is 30 micrograms per cubic meter “μg/m 3 

### ,” is almost always the m ost problematic regulatory design concentration. Proposed industrial sources, such as coal

- fired power plants, pig iron facilities, coal

- to

- liquid operations, coal

- to

- synthetic gas plants, and lime production facilities, often cause air impacts that are q uite close to exceeding the 24

- hour PM 10 

### PSD increment. It is common to see proposed PSD permit application modeled impacts co n suming some 80 to 99%

of the allowable 24

- hour PM 10 

### PSD increment. The majority of this modeled impact is caused by

low

- level o pen source fugitive emissions, including paved haul roads.

There is no basis to assume that the existing paved road emission factor overpredicts fugitive dust emissions from these major sources. And as we discussed earlier, it is common for major sources of emissions to be permitted without any PSD pre

- construction or post

- construction ambient air monitoring, even when such requirements are triggered by PM 10 

### significant monitoring concentrations identified in 40 CFR 52.21(i). Thus, there is no current wa y to verify whether source

PM 10 

### impacts at the fenceline are realistically handled by the applied fugitive dust emission factor and subsequent air dispersion modeling.

I have also prepared two tables that compare the existing paved road emission factor wi th the CRA’s proposed revision

– one for PM 10 

### (Table 2A), and one for PM 2.5 

### (Table 2B). While CRA’s proposed

revision results in lower industrial source PM 10 

### and PM 2.5 

### emission factors, they are not nearly as severe as the changes proposed by USEPA.

The CRA source tests , however, include an apparent contradiction. CRA’s source tests were designed for low vehicular speeds and stop

- and

- go conditions. 9 

### But CRA also acknowledges that “inclusion of speed would add very little to the predictive capability of the model.” 10 

### So, the basis for including CRA’s source tests in AP

- 42 Section 13.2.1 seems unnecessary.

Revising AP

- 42 Section 13.2.1, using either CRA’s or USEPA’s proposed revisions, will greatly reduce calculated fugitive dust emissions at most industr ial facilities. This would make it easier for applicants to meet regulatory design concentration compliance, and to do so with fewer emission controls. These revisions, however, are based on data that are not representative of the majority of

9 Corn Refiners Association, Paved Road Modifications at AP

- 42, Background Documentation, MRI Project No. 310842, July 18, 2008, p. 4

. 10 Id., p. 15.Revisions to AP

- 42, 13.2.1 August 30, 2010 Page

- 6



### major emissi on sources. For example, the CRA source tests are for ethanol plants with low to very low silt loading levels. These conditions are not representative of the scores of proposed coal

- fired

power projects that have recently submitted permit applications to State agencies . And USEPA’s modification of the source test data base, to add public road source tests and to eliminate the integrated steel plant tests, probably makes things even worse. The silt loading levels (and associated emission factors) measure d at the integrated steel plant sites are representative of many industrial facilities. 11 

### Excluding these data will weigh the equation in a manner that reduces predictive performa n ce for most industrial plants.

4

. USEPA’s Proposed Update may not Improve P redictive Performance

As part of the proposed revision to AP

- 42 Section 13.2.1, it would be helpful if USEPA present ed performance analyses of both the existing and proposed paved road emission factors. Furthermore, it would be helpful if USEPA presente d performance criteria for sub

- categories of emission sources, such as public roads, industrial roads wi th low silt loading levels, and perhaps industrial roads with higher silt loading levels. From this analysis, USEPA and the reviewing public could get a better idea of whether the proposed changes will provide better predictive capability than does the existing

method. And just as important

, would be information on predictive performance for each sub

category of emission sources. In other words, we cou ld tell whether improving performance for one source category, ethanol plants for example, would have a detrimental effect on emission prediction for other industrial sources with higher silt loadings.

Likewise, focusing on performance of public roads, wi th vehicle speed included, greatly affects

industrial source emission rates. But what effect does it have on the predictive performance of

industrial sources? As we discussed earlier, the coefficient of determination (r 2 

### ) is not particularly great for th e proposed revision (all data sets included)

. It would be useful to examine the predictive performance on various subsets of the existing data base, with both the existing and proposed

emission factors.

5. Other Factors Affecting USEPA’s Paved Road Emiss ion Factor

Following are a few observations that will affect the predictive emission factor equation when used

on industrial s ources

. I believe USEPA should address these concerns prior to revising their existing paved road emission factor.

**•** The paved ro ad emission factor should consider whether the road shoulder is paved and

whether there is a source of dust fallout present

. For example, facilities with dust

- generating storage piles, and truck traffic moving between these piles, are likely to have high particulate

emission rates. This is particularly true for f acilities with unpaved road shoulders.

11 USEPA, Emission factor Documentation for AP

- 42, Section 13.2.1, Paved Roads, Draft, June 2010, pp. 4

- 42 to 4

- 45.Revisions to AP

- 42, 13.2.1 August 30, 2010 Page

- 7



### • Some vehicles have exhaust pipes pointing skyward, others are parallel to the ground, and still others pointing down to the ground. Downward

- pointing exhau st can exacerbate resuspension of dust, as I have often observed with forklifts and delivery vehicles with such an exhaust configuration. It is unclear whether industrial vehicles with downward

- pointing

exhaust are accounted for in the paved road emission factor.

**•** In developing the revised emission factor, USEPA subtracted a “C” term from the CRA

source tests. This results in very small or even negative emission rates for certain tests. 12


### Given the plume rise of exhaust from the slow

- moving CRA test vehicl es, it is possible that most, if not all, of the exhaust plume passed above the downwind air samplers. In other words, the “ C ” term used by USEPA may be too large for the CRA (and other ) source tests.
USEPA should reevaluate to what extent, if any, exhaust, and brake and tire wear impact the downwind profile measurements.

6

. Concluding Remarks

USEPA’s proposed revision to AP

- 42 Section 13.2.1 excludes a valuable industrial source paved road data base simply because vehicle speed was not included in the study. USEPA’s revised emission factor will result in a roughly order of magnitude emission reduction in industrial source paved road emissions

. This very significant change may not be appropriate given that a key data set was excluded from the regression analyses.

USEPA may be trying to fit too many source categories into a one

- size

- fits

- all emission factor. Under the umbrella of “paved roads” fits urban freeways, local street traffic, industrial sites with a

wide

- range of truck sizes and weights, parking lots, and all shapes and sizes of vehicles using these paved surfaces

. I understand tha t USEPA has a very difficult task in develop ing a paved road

emission factor that meets the n eeds of all affected sources. It is likely that “clean” roads are

downward

- biasing the emission factor for high

- emitting facilities. And the opposite is also tru e

– industrial roads with high silt loading are likely upward

- biasing the emission factor for cleaner roads with lighter vehicles.

I offer the suggestion that US EPA should consider develop ing separate paved road emission factors for public and industrial roads

. It may not be too far

- fetched to examine separate emission factors

for sub

- categories of industrial source paved road emissions as well. Also , USEPA may want to focus on silt loading and vehicle weight, as variability in vehicle speed seems to hav e a less significant impact on predicted emission performance.

Until USEPA has addressed whether the severe reduction in industrial source paved road emission

calculations is warranted , I believe that the existing AP

- 42 paved road emission factor should

c ontinue to be used

.

12 Id.Revisions to AP

- 42, 13.2.1 August 30, 2010 Page

- 8



### I greatly appreciate your help in reviewing and commenting on t he proposed revisions to AP

- 42

Section 13.2.1

. Please contact me if you have any questions or require additional information.

Sincerely

,

Camille Sears

### Table 1A
AP-42 Section 13.2.1: Paved Roads Comparison of Existing and Draft Paved Road PM10 Emission Factors (Silt loading resuspension only)

Setting sL (g/m 2 ) W (tons) S (mph) Draft E (lb/VMT) Existing E (lb/VMT) Draft E / Existing E

Public 0.2 3.75 25 0.0017 0.0045 0.38

Public 0.2 3.75 35 0.0018 0.0045 0.40

Public 0.2 3.75 45 0.0019 0.0045 0.42

Public 0.6 3.75 25 0.0050 0.0098 0.52

Public 0.6 3.75 35 0.0053 0.0098 0.55

Public 0.6 3.75 45 0.0055 0.0098 0.57

Industrial 0.6 10 5 0.0066 0.0441 0.15

Industrial 0.6 10 15 0.0078 0.0441 0.18

Industrial 0.6 10 25 0.0085 0.0441 0.19

Industrial 0.6 20 5 0.0095 0.1255 0.08

Industrial 0.6 20 15 0.0113 0.1255 0.09

Industrial 0.6 20 25 0.0122 0.1255 0.10

Industrial 0.6 30 5 0.0117 0.2309 0.05

Industrial 0.6 30 15 0.0140 0.2309 0.06

Industrial 0.6 30 25 0.0152 0.2309 0.07

Industrial 2.0 10 5 0.0213 0.0969 0.22

Industrial 2.0 10 15 0.0254 0.0969 0.26

Industrial 2.0 10 25 0.0276 0.0969 0.28

Industrial 2.0 20 5 0.0308 0.2749 0.11

Industrial 2.0 20 15 0.0367 0.2749 0.13

Industrial 2.0 20 25 0.0398 0.2749 0.14

Industrial 2.0 30 5 0.0382 0.5055 0.08

Industrial 2.0 30 15 0.0455 0.5055 0.09

Industrial 2.0 30 25 0.0494 0.5055 0.10

Industrial 5.0 10 5 0.0523 0.1762 0.30

Industrial 5.0 10 15 0.0624 0.1762 0.35

Industrial 5.0 10 25 0.0677 0.1762 0.38

Industrial 5.0 20 5 0.0756 0.4992 0.15

Industrial 5.0 20 15 0.0901 0.4992 0.18

Industrial 5.0 20 25 0.0977 0.4992 0.20

Industrial 5.0 30 5 0.0937 0.9174 0.10

Industrial 5.0 30 15 0.1117 0.9174 0.12

Industrial 5.0 30 25 0.1212 0.9174 0.13

Industrial 10.0 10 5 0.1032 0.2767 0.37

Industrial 10.0 10 15 0.1230 0.2767 0.44

Industrial 10.0 10 25 0.1335 0.2767 0.48

Industrial 10.0 20 5 0.1490 0.7835 0.19

Industrial 10.0 20 15 0.1777 0.7835 0.23

Industrial 10.0 20 25 0.1928 0.7835 0.25

Industrial 10.0 30 5 0.1847 1.4398 0.13

Industrial 10.0 30 15 0.2203 1.4398 0.15

Industrial 10.0 30 25 0.2390 1.4398 0.17

Notes:

E = resuspension emission factor; calculations exclude vehicle exhaust, brake wear, and tire wear emissions
sL = silt loading; W = mean vehicle weight; S = mean vehicle speed

For comparison purposes, no rain adjustments or control efficiencies applied

### Table 1B
AP-42 Section 13.2.1: Paved Roads Comparison of Existing and Draft Paved Road PM2.5 Emission Factors (Silt loading resuspension only)

Setting sL (g/m 2 ) W (tons) S (mph) Draft E (lb/VMT) Existing E (lb/VMT) Draft E / Existing E

Public 0.2 3.75 25 0.0004 0.0004 1.08

Public 0.2 3.75 35 0.0004 0.0004 1.14

Public 0.2 3.75 45 0.0005 0.0004 1.19

Public 0.6 3.75 25 0.0012 0.0012 1.06

Public 0.6 3.75 35 0.0013 0.0012 1.12

Public 0.6 3.75 45 0.0014 0.0012 1.16

Industrial 0.6 10 5 0.0016 0.0063 0.26

Industrial 0.6 10 15 0.0019 0.0063 0.30

Industrial 0.6 10 25 0.0021 0.0063 0.33

Industrial 0.6 20 5 0.0023 0.0185 0.13

Industrial 0.6 20 15 0.0028 0.0185 0.15

Industrial 0.6 20 25 0.0030 0.0185 0.16

Industrial 0.6 30 5 0.0029 0.0343 0.08

Industrial 0.6 30 15 0.0034 0.0343 0.10

Industrial 0.6 30 25 0.0037 0.0343 0.11

Industrial 2.0 10 5 0.0053 0.0142 0.37

Industrial 2.0 10 15 0.0063 0.0142 0.44

Industrial 2.0 10 25 0.0068 0.0142 0.48

Industrial 2.0 20 5 0.0076 0.0410 0.19

Industrial 2.0 20 15 0.0091 0.0410 0.22

Industrial 2.0 20 25 0.0098 0.0410 0.24

Industrial 2.0 30 5 0.0094 0.0755 0.12

Industrial 2.0 30 15 0.0112 0.0755 0.15

Industrial 2.0 30 25 0.0122 0.0755 0.16

Industrial 5.0 10 5 0.0129 0.0261 0.49

Industrial 5.0 10 15 0.0154 0.0261 0.59

Industrial 5.0 10 25 0.0167 0.0261 0.64

Industrial 5.0 20 5 0.0186 0.0746 0.25

Industrial 5.0 20 15 0.0222 0.0746 0.30

Industrial 5.0 20 25 0.0241 0.0746 0.32

Industrial 5.0 30 5 0.0231 0.1373 0.17

Industrial 5.0 30 15 0.0275 0.1373 0.20

Industrial 5.0 30 25 0.0299 0.1373 0.22

Industrial 10.0 10 5 0.0255 0.0412 0.62

Industrial 10.0 10 15 0.0303 0.0412 0.74

Industrial 10.0 10 25 0.0329 0.0412 0.80

Industrial 10.0 20 5 0.0368 0.1172 0.31

Industrial 10.0 20 15 0.0438 0.1172 0.37

Industrial 10.0 20 25 0.0476 0.1172 0.41

Industrial 10.0 30 5 0.0456 0.2157 0.21

Industrial 10.0 30 15 0.0543 0.2157 0.25

Industrial 10.0 30 25 0.0590 0.2157 0.27

Notes:

E = resuspension emission factor; calculations exclude vehicle exhaust, brake wear, and tire wear emissions
sL = silt loading; W = mean vehicle weight; S = mean vehicle speed

For comparison purposes, no rain adjustments or control efficiencies applied

### Table 2A
AP-42 Section 13.2.1: Paved Roads Comparison of Existing and CRA-Proposed Paved Road PM10 Emission Factors (Silt loading resuspension only)

Setting sL (g/m 2 ) W (tons) S (mph) CRA E (lb/VMT) Existing E (lb/VMT) CRA E / Existing E

Public 0.2 3.75 25 0.0039 0.0045 0.86

Public 0.2 3.75 35 0.0039 0.0045 0.86

Public 0.2 3.75 45 0.0039 0.0045 0.86

Public 0.6 3.75 25 0.0100 0.0098 1.03

Public 0.6 3.75 35 0.0100 0.0098 1.03

Public 0.6 3.75 45 0.0100 0.0098 1.03

Industrial 0.6 10 5 0.0225 0.0441 0.51

Industrial 0.6 10 15 0.0225 0.0441 0.51

Industrial 0.6 10 25 0.0225 0.0441 0.51

Industrial 0.6 20 5 0.0396 0.1255 0.32

Industrial 0.6 20 15 0.0396 0.1255 0.32

Industrial 0.6 20 25 0.0396 0.1255 0.32

Industrial 0.6 30 5 0.0549 0.2309 0.24

Industrial 0.6 30 15 0.0549 0.2309 0.24

Industrial 0.6 30 25 0.0549 0.2309 0.24

Industrial 2.0 10 5 0.0598 0.0969 0.62

Industrial 2.0 10 15 0.0598 0.0969 0.62

Industrial 2.0 10 25 0.0598 0.0969 0.62

Industrial 2.0 20 5 0.1044 0.2749 0.38

Industrial 2.0 20 15 0.1044 0.2749 0.38

Industrial 2.0 20 25 0.1044 0.2749 0.38

Industrial 2.0 30 5 0.1447 0.5055 0.29

Industrial 2.0 30 15 0.1447 0.5055 0.29

Industrial 2.0 30 25 0.1447 0.5055 0.29

Industrial 5.0 10 5 0.1250 0.1762 0.71

Industrial 5.0 10 15 0.1250 0.1762 0.71

Industrial 5.0 10 25 0.1250 0.1762 0.71

Industrial 5.0 20 5 0.2179 0.4992 0.44

Industrial 5.0 20 15 0.2179 0.4992 0.44

Industrial 5.0 20 25 0.2179 0.4992 0.44

Industrial 5.0 30 5 0.3016 0.9174 0.33

Industrial 5.0 30 15 0.3016 0.9174 0.33

Industrial 5.0 30 25 0.3016 0.9174 0.33

Industrial 10.0 10 5 0.2179 0.2767 0.79

Industrial 10.0 10 15 0.2179 0.2767 0.79

Industrial 10.0 10 25 0.2179 0.2767 0.79

Industrial 10.0 20 5 0.3797 0.7835 0.48

Industrial 10.0 20 15 0.3797 0.7835 0.48

Industrial 10.0 20 25 0.3797 0.7835 0.48

Industrial 10.0 30 5 0.5254 1.4398 0.36

Industrial 10.0 30 15 0.5254 1.4398 0.36

Industrial 10.0 30 25 0.5254 1.4398 0.36

Notes:

E = resuspension emission factor; calculations exclude vehicle exhaust, brake wear, and tire wear emissions
sL = silt loading; W = mean vehicle weight; S = mean vehicle speed

CRA = Corn Refiners Association

For comparison purposes, no rain adjustments or control efficiencies applied

### Table 2B
AP-42 Section 13.2.1: Paved Roads Comparison of Existing and CRA-Proposed Paved Road PM2.5 Emission Factors (Silt loading resuspension only)

Setting sL (g/m 2 ) W (tons) S (mph) CRA E (lb/VMT) Existing E (lb/VMT) CRA E / Existing E

Public 0.2 3.75 25 0.0003 0.0004 0.73

Public 0.2 3.75 35 0.0003 0.0004 0.73

Public 0.2 3.75 45 0.0003 0.0004 0.73

Public 0.6 3.75 25 0.0012 0.0012 1.02

Public 0.6 3.75 35 0.0012 0.0012 1.02

Public 0.6 3.75 45 0.0012 0.0012 1.02

Industrial 0.6 10 5 0.0030 0.0063 0.48

Industrial 0.6 10 15 0.0030 0.0063 0.48

Industrial 0.6 10 25 0.0030 0.0063 0.48

Industrial 0.6 20 5 0.0056 0.0185 0.30

Industrial 0.6 20 15 0.0056 0.0185 0.30

Industrial 0.6 20 25 0.0056 0.0185 0.30

Industrial 0.6 30 5 0.0078 0.0343 0.23

Industrial 0.6 30 15 0.0078 0.0343 0.23

Industrial 0.6 30 25 0.0078 0.0343 0.23

Industrial 2.0 10 5 0.0085 0.0142 0.60

Industrial 2.0 10 15 0.0085 0.0142 0.60

Industrial 2.0 10 25 0.0085 0.0142 0.60

Industrial 2.0 20 5 0.0151 0.0410 0.37

Industrial 2.0 20 15 0.0151 0.0410 0.37

Industrial 2.0 20 25 0.0151 0.0410 0.37

Industrial 2.0 30 5 0.0211 0.0755 0.28

Industrial 2.0 30 15 0.0211 0.0755 0.28

Industrial 2.0 30 25 0.0211 0.0755 0.28

Industrial 5.0 10 5 0.0182 0.0261 0.70

Industrial 5.0 10 15 0.0182 0.0261 0.70

Industrial 5.0 10 25 0.0182 0.0261 0.70

Industrial 5.0 20 5 0.0319 0.0746 0.43

Industrial 5.0 20 15 0.0319 0.0746 0.43

Industrial 5.0 20 25 0.0319 0.0746 0.43

Industrial 5.0 30 5 0.0443 0.1373 0.32

Industrial 5.0 30 15 0.0443 0.1373 0.32

Industrial 5.0 30 25 0.0443 0.1373 0.32

Industrial 10.0 10 5 0.0319 0.0412 0.77

Industrial 10.0 10 15 0.0319 0.0412 0.77

Industrial 10.0 10 25 0.0319 0.0412 0.77

Industrial 10.0 20 5 0.0558 0.1172 0.48

Industrial 10.0 20 15 0.0558 0.1172 0.48

Industrial 10.0 20 25 0.0558 0.1172 0.48

Industrial 10.0 30 5 0.0774 0.2157 0.36

Industrial 10.0 30 15 0.0774 0.2157 0.36

Industrial 10.0 30 25 0.0774 0.2157 0.36

Notes:

E = resuspension emission factor; calculations exclude vehicle exhaust, brake wear, and tire wear emissions
sL = silt loading; W = mean vehicle weight; S = mean vehicle speed

CRA = Corn Refiners Association

For comparison purposes, no rain adjustments or control efficiencies appliedFw: Dave's comments on the Excel workbook - do not need to be mentioned
on the call

dave. j ames to: Ron Myers 08/18/2010 03:07 PM

Dear Ron,

Please find attached some comments on the proposed new AP42 paved road equation

A) I think that, on tab PM10 Paved Roads EF's, column Z, the column labeled "Percent Total Emissions Factor Increase" uses the formula (column x - column s) /

column x
to calculate percent changes. I think this should be, instead (column x - column s) / column s, so that
the percent change is calculated relative to the 2006 emissions factor equation instead
of the proposed new 2010 emissions factor equation Column AD is the recalculated percent reduction for the rain corrected EF's based on this suggested
equation revision

B) For the desert southwest, I think that it is best to look at the data without rain adjustments

C) In my edited tab "PM10 Paved Roads EF's " I have added several columns, AB, AC, and AD (1) Column AB is the calculated raw reduction of 2010 dry EF's compared to 2006 dry EF's. (column u - column o)
(2) Column AC is the calculated percent reduction of 2010 dry EF's compared to 2006 dry EF's using
the equation (column u - column o)/column o

D) based on the 7,632 row data set in the tab PM10 Paved Roads EF's

(1) The new 2010 dry EF's are much lower overal l than the 2006 dry EF's. see the chart in the new tab labeled "compareNewOLDPM10EF's"

(2) The reductions of dry 2010 EF's compared to dry 2006 EF's linearly increase in magnitude with the magnitude of the original 2006 emissions factor (see the chart in the new tab labeled "reductions" - calculated in column AB)

(3) When I plot the percentage changes of the dry 2010 PM 10 EF's calculated ) above against 2006 emissions factors, they are all around 70-80% (see the chart new tab labeled "percent reductions")

E) Athough national data might show reductions, since the new equation 1) raises the influence of silt loading (new exponent 0.98, old exponent 0.65)
2) lowers the influence of vehicle weight (new exponent 0.53, old exponent 1.5)
3) adds in an influence of vehicle speed,
4) eliminates the influence of the correction factor for exhaust brake and tire wear,
I would recommend that any assessment of the impact of the proposed new equation be
based on locally sampled data and not use the national data.

Thank you for the opportunity to comment.Sincerely,

Dave

David E. James, PhD PE Associate Vice Provost for Academic Programs
Office of the Vice Provost for Academic Affairs
Box 451099 4505 South Maryland Parkway Las Vegas, NV 89154-1099 Direct Line (702) 895-5804 Main Office (702) 895-1267 FAX (702) 895-3670 FDH 704 Mail Stop 1099

email: dave. j ames@unlv.edu
http :// provost

. unlv

. edu / acadaffairs

. htmlRe: Fw: Dave's comments on the EPA Excel workbook - some additional follow up thoughts

dave. j ames to: Ron Myers 09/15/2010 02:44 PM

Cc: Rodney Langston, Russell Merle

Hi Ron,
Thank you for your good email below and for the additional information.
I apologize for taking so long to get back to you with my thoughts and responses. Here they are:

A) Understood about the zeros being problematic

B) In many parts of the country where there is significant rain or a rainy season, rain days may considerably effect estimated PM10 emissions in the inventory. However, for Las Vegas and other places
like it in arid places, I tend to use a 'pessimistic' approach that doesn't include the rain days, since rain occurs sporadically, and what rain does fall is often very light.

C) I'm glad that my extra columns in your Excel workbook are helpful

D) Yes, from the default data it looks like many of the estimated EF's would go down with the new
proposed equation

E) Since we last corresponded, 1) I ran some calculations for Clark County's AP42 measured 2003-2006 silt loading data set using their locally derived fleet weights. Please see the attached file "comparison20062010

_ AP42 road dust

EFs2003

_ 2006.pdf" If you examine the bottom-most table on the page, where percentage EF changes are computed, that the net impact of the new proposed equation on Clark County's estimated paved road dust PM-10 emissions
would be to a) increase estimated PM10 emissions as grams/VMT 23% on local roads, b) decrease them by 3% on collector roads (probably not significant), and c) increase them by 1% on minor arterials (also probably not significant).

With locally derived data, we obtain results that are different from those that might be predicted using default silt loading data. The actual impact on total estimated PM10 emissions in an inventory or SIP would depend on how much VMT was assigned to each roadway category.

2) I also ran a hypothetical sensitivity analysis comparing arbitrary combinations of vehicle weight and silt loading, to see what the impacts of the new PM10 equation might be. Please see the attached file "new2010EFsensitivityanalysis.pdf" Table 1 shows the 2006 equation predicted PM10 emissions Table 2 shows the proposed 2010 equation predicted PM10 emissions
Table 3 shows the changes in predicted emissions (2010 EF - 2006 EF)

and
Table 4 shows the Percentage changes, (2010 EF - 2006 EF)/2006 EF

Table 4 shows that the net effect of using the new proposed 2010 equation is that predictedPM10 emissions a) increase for lower silt loadings at all fleet average vehicle weights, and b) decrease for higher silt loadings, espeically at lower fleet average vehicle weights

I hope that these preliminary calculations are helpful. I have also sent them as PDF and as the original

Excel files to
my research sponsors, Clark County Dept of Air Quality and Environmental Management.

Sincerely,

Dave

David E. James, PhD PE Associate Vice Provost for Academic Programs
Office of the Vice Provost for Academic Affairs
Box 451099 4505 South Maryland Parkway Las Vegas, NV 89154-1099 Direct Line (702) 895-5804 Main Office (702) 895-1267 FAX (702) 895-3670 FDH 704 Mail Stop 1099

email: dave. j ames@unlv.edu
http :// provost

. unlv

. edu / acadaffairs

. html

From: Myers.Ron@epamail.epa.gov

To: dave. j ames@unlv.edu

Date: 08/18/2010 06:34 PM

Subject: Re: Fw: Dave's comments on the Excel workbook - do not need to be mentioned on the call

Dave: Thanks for looking at the proposed revisions of the paved road equation.

First, I was trying to replicate the emissions estimates that are being made for the 2008 NEI, any rain adjustments or other mitigation that I included in the spreadsheet are the same as I estimated were used in the NEI emissions estimates. As with you I would not have included as much mitigation for rain and "Street Sweeping" and other silt management as there is is used in the NEI
estimates.

A. You are correct. I should have divided by the estimated 2008 emissions as calculated with the existing AP-42. I think this was a hold over from when I was just looking at the road dust emissions estimates. When looking only at road dust emissions, all the zero emissions estimates is problematic since dividing by zero only generates errors in Excel. I added in the vehicle emissions when I saw how many 2008 NEI estimates were zero.B. I would tend to agree with you as there are not many rain days. As I stated above, I don't know what mitigation is included in the "adjusted" emissions data in the NEI. Frankly to documentation of the NEI emissions estimates doesn't help me much to recreate their emissions estimates (see paved_roads_2294000000_documentation.doc which is attached).

C. Thanks for the calculations. I did these calculation only because a few internal EPA people suggested that I provide State/local agencies with some information to provide an indication of how this change might affect their
inventories.

D. My original assessment also showed that the revised equation generates much lower PM10 estimates than the previous equation. From a combined emisions inventory perspective and use in the modeling for SIP development this should get support from inventory developers, modelers and Air Quality Assessors as it has always been difficult to explain how fugitive dust emissions are the majority of the emissions in the inventory but comprise less than 10% of the emissions on PM monitors. This will not get the inventory there but it goes in the right direction. I agree that for best emissions estimates, locally derived silt loadings are needed. However, no one wants to develop these and would rather complain that EPA's default values aren't good enough and they
want better defaults. There is so much variation in silt levels on roads no single number is good enough for every road.

_________________________________ Ron Myers U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Sector Policy and Programs Division Monitoring Policy Group, D243-05

RTP NC 27711

Tel. 919.541.5407

Fax 919.541.1039 E-mail myers.ron@epa.gov

dave.james---08/18/2010 03:10:06 PM---Dear Ron, This is a resend, using a compressed version of the Excel file to reduce

From:

dave.james@unlv.edu

To:

Ron Myers/RTP/USEPA/US@EPA

Date:

08/18/2010 03:10 PM

Subject:

Fw: Dave's comments on the Excel workbook - do not need to be mentioned on the

callDear Ron,

This is a resend, using a compressed version of the Excel file to reduce the file size, in case my earlier send was rejected.

Please find attached some comments on the proposed new AP42 paved road equation

A) I think that, on tab PM10 Paved Roads EF's, column Z, the
column labeled "Percent Total Emissions Factor Increase" uses the formula
(column x - column s) / column x to calculate percent changes. I think this should be, instead (column x -
column s) / column s, so that the percent change is calculated relative to the 2006 emissions factor equation instead of the proposed new 2010 emissions factor equation Column AD is the recalculated percent reduction for the rain corrected EF's based on this suggested equation revision

B) For the desert southwest, I think that it is best to look at the data without rain adjustments

C) In my edited tab "PM10 Paved Roads EF's" I have added several columns, AB,
AC, and AD (1) Column AB is the calculated raw reduction of 2010 dry EF's compared to 2006 dry EF's. (column u - column o) (2) Column AC is the calculated percent reduction of 2010 dry EF's compared to 2006 dry EF's using the equation (column u - column o)/column o

D) based on the 7,632 row data set in the tab PM10 Paved Roads EF's

(1) The new 2010 dry EF's are much lower overal l than the 2006 dry EF's. see the chart in the new tab labeled "compareNewOLDPM10EF's"

(2) The reductions of dry 2010 EF's compared to dry 2006 EF's linearly increase in magnitude with the magnitude of the original 2006 emissions factor (see the chart in the new tab labeled "reductions" - calculated in column AB)

(3) When I plot the percentage changes of the dry 2010 PM 10 EF's calculated ) above against 2006 emissions factors, they are all around 70-80% (see the chart new tab labeled "percent reductions")

E) Athough national data might show reductions, since the new equation 1) raises the influence of silt loading (new exponent 0.98, old exponent 0.65)

2) lowers the influence of vehicle weight (new exponent 0.53, old exponent
1.5) 3) adds in an influence of vehicle speed, 4) eliminates the influence of the correction factor for exhaust brake and tire wear, I would recommend that any assessment of the impact of the proposed new equation be based on locally sampled data and not use the national data.Thank you for the opportunity to comment. Sincerely,

Dave

David E. James, PhD PE Associate Vice Provost for Academic Programs
Office of the Vice Provost for Academic Affairs

Box 451099 4505 South Maryland Parkway Las Vegas, NV 89154-1099 Direct Line (702) 895-5804 Main Office (702) 895-1267 FAX (702) 895-3670 FDH 704 Mail Stop 1099

email: dave.james@unlv.edu

http :// provost

. unlv

. edu / acadaffairs

. html

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*********************** ATTACHMENT NOT DELIVERED ***********************[attachment "paved_roads_2294000000_documentation.doc"
deleted by Dave James/UNLV]From: Steve Zemba <zemba@cambridgeenvironmental.com>

To: Ron Myers/RTP/USEPA/US@EPA

cc: gfore@hotmix.org, Mike <ames@cambridgeenvironmental.com>, Laura Green <green@cambridgeenvironmental .com>, HMarks@hotmix.org

Date: Tuesday, August 31, 2010 02:32PM Subject: Comment on AP42 Paved Roads Draft Section 13.2.1

History: This message has been replied to and forwarded.

Dear Ron,

I write to provide the attached comment on the draft AP42 section on Paved Road dust emissions. As described in the comment, NAPA (who sponsored the review) is potentially interested in collecting data to provide more representative parameters for applications to the asphalt pavement industry. We would appreciate your advice on how best to gather these data so that they could be submitted for consideration in

the AP42 section.

Thanks for your help and consideration,

Steve

-- Stephen G. Zemba, Ph.D., P.E. Senior Engineer

*Cambridge Environmental Inc*

58 Charles Street Cambridge, MA 02141

Office: 617-225-0810 x34 M-W 518-306-4603 Th-F

Cell: 339-223-9328

Fax: 617-225-0813 http://www.CambridgeEnvironmental.com



### AP42PavedRoadsSectionComment083110.pdf Type: application/pdf

Name:

AP42PavedRoadsSectionComment 0 Pa g e 1 of 1

10/22/2010 htt p s://rtairmail1.rt p .e p a. g ov/mail/rm y ers.nsf/9ff539a1e24f 5aaf852577890046a8f6/8F3D

...Cambridge Environmental Inc

58 Charles Street Cambridge, Massachusetts 02141 617-225-0810 www.CambridgeEnvironmental.com



### August 31, 2010

Ronald Myers U.S. Environmental Protection Agency

109 T.W. Alexander Drive

Mail Code: D243-05 Research Triangle Park, NC 27709

Dear Ron,

It was a pleasure speaking with you again recently – thank you for the background information on the draft update to the AP42 section on Pave d Road emissions (Section 13.2.1).

I have reviewed the draft update on behalf of th e National Asphalt Pavement Association (NAPA), andwrite to comment on a specific aspect of interest. I believe that the recommended default values for siltloading in draft Table 13.2.1-3, and particularly th at for asphalt batching, may be too high for typical current applications. The recommended value is 120 g/m 2 , but, as you know, in EPA’s 2000 Emission Assessment Report for Hot Mix Asphalt Plants, a silt-loading value 3 g/m 2 is suggested for paved roads at typical hot-mix asphalt production facilities. Also , site-specific measurements at a hot mix asphalt facility in Alexandria, Virginia in 2005 (using the sampling and analytical methods described in AP42 Appendix C) found a silt loading level of 0.5 g/m 2 . This facility, which we analyzed in detail for the City of Alexandria, employs aggressive dust suppression techniques.

More generally, as you know, best management pr actices (BMPs) such as wa ter spraying and road sweeping can effectively control dust emissions; by the same token, the absence of these practices can indeed result in dusty roads. Perhaps the value of 120 g/m 2 , which appears to be based on older data, derives from testing at one or more facilities that failed to employ BMPs. If so, then perhaps 120 g/m 2

could be considered to be a default value in the absence of BMPs, whereas the value of 3 g/m 2 , as used in EPA’s Emission Assessment Report, could be a default value in the presence of typical BMPs.

Of course, more data are always better. In that regard, we have spoken with representatives from NAPA , and they have expressed potential willingness to coor dinate a study to provide updated values for silt loading and other emission factor parameters that refl ect current practices in the hot-mix asphalt industry. At your convenience, might we schedule a call to discuss whether this would be of interest to you and your colleagues at the Agency?

Thank you for your consideration, and best regards.

Sincerely,

Stephen G. Zemba, Ph.D., P.E. Senior EngineerIDNR comment on proposed AP 42 Section 13.2.1 Paved Roads Hanson, Lori [DNR] to: Ron Myers 08/20/2010 10:45 AM

Cc: "McGraw, Jim [DNR]"

History: This message has been forwarded.

Mr. Myers,

I have attached the Iowa Department of Natural Resources comments on the proposed revision toAP42

section 13.2.1 on paved roads. Thank you for the opportunity to provide comments, Lori HansonRE: PECHAN/ERTAC Road Dust Emissions

Pat Davis t

o

: Roy Huntley, Ron Myers 07/26/2010 01:22 PM

History: This message has been replied to.

Hi Ron,

Have you had a chance to look into this issue?

To refresh your memory we noticed that a number of the PM2.5 emission factors were zeroed out for a number of road types. Can you please tell us why the road types listed below were zeroed out?

Urban Collector
Urban Minor Arterial Urban Other Principal Arterial Urban Other Freeways and Expressways

Urban Interstate

Thanks,
Pat Davis

-----Original Message----- From: Huntley.Roy@epamail.epa.gov [ mailto:Huntley.Roy@epamail.epa.gov] Sent: Tuesday, July 13, 2010 1:38 PM To: Myers.Ron@epamail.epa.gov
Cc: Pat Davis Subject: Fw: PECHAN/ERTAC Road Dust Emissions

Ron, could you answer Pat question?

Roy Huntley Environmental Engineer Emission Inventory and Analysis Group Mail Drop (C339-02) Environmental Protection Agency
RTP, NC 27711
Voice - 919 541-1060

Fax - 919 541-0684
Office C341H ----- Forwarded by Roy Huntley/RTP/USEPA/US on 07/13/2010 01:24 PM ----- | > | From: Pat Davis <pdavis@marama.org> | | > | To: Roy Huntley /RTP/USEPA/US@EPA |

> |> | Cc: Judy Rand <Judy.Rand@dep.state.nj.us>, Julie McDill <jmcdill@marama.org>, Pat Davis <pdavis@marama.org>, "Fees David F. (DNREC)" <David.Fees@state.de.us>, |

" WRBARNARD@mactec.com" <WRBARNARD@mactec.com>, Walter Simms <wsimms@mde.state.md.us>,

"kenneth.santlal@state.ma.us"<kenneth.santlal@state.ma.us ||------------> | Date: 07/13/2010 12:02 PM | | | Subject: PECHAN/ERTAC Road Dust Emissions |

>Hi Roy,

We have been examining the ERTAC/PECHAN emission factors for Road Dust and Maryland noticed that the PM2.5 emission factors were zeroed out for the following road types:

Urban Collector
Urban Minor Arterial Urban Other Principal Arterial Urban Other Freeways and Expressways

Urban Interstate

Emission factors for PM10 were found and there was no mention in the documentation of why the PM2.5 emission factors were zeroed out, so we
are bit confused.

We were hoping that you might have answer for us, or be able to point us in the direction of someone who might know why the PM2.5 emission

factors are zeroed out.

Thanks, and I hope you are well!
Pat DavisFW: [chief] Proposed revisions to AP 42 section 13.2.1 Paved Roads

Julie McDill to: Ron Myers 06/23/2010 03:29 PM

History: This message has been replied to.

Hello Ron,

I called and left a message about possibly getting on a call with the MARAMA states in the next couple of weeks to discuss proposed changes to the Paved
Road PM emissions estimation method.

Please respond to let me know if and when that might be possible. I can set up a conference call and distribute a slide set. It would be best sometime between July 7 and 16th. What follows (and the attachment) are some emails that give you a flavor of the changes that states are finding as a result of the new calculations. As you probably know, the PM emission from paved roads has always posed problems in modeling. In general, modelers take our inventories and reduced the paved road emissions by about 90% before running

the model.

Thanks for your help.

Julie McDill

MARAMA

________________________________________ From: Judy Rand [Judy.Rand@dep.state.nj.us] Sent: Wednesday, June 16, 2010 4:43 PM
To: Julie McDill; Pat Davis; rthunell@mde.state.md.us; David.Fees@state.de.us
Cc: Nicholle Worland; WRBARNARD@mactec.com; kenneth.santlal@state.ma.us Subject: RE: [chief] Proposed revisions to AP42 section 13.2.1 Paved Roads

Thanks Dave. We have come up with similar results, but even more drastic for
PM2.5. An increase in PM2.5 of 350% and a decrease in PM10 of 46% I think one big cause is the difference in k factor, among other changes. The k

factor for PM2.5 went down from the 2003 AP-42 to the 2006 AP-42, and back up again in this new draft. We guessed at the new vehicle speed requirement, but a slight variation in speeds will not make that much of a difference.

See NJ's attached calcs and compare spreadsheet. I won't be in til Monday. If you want to have a call either Nicholle can cover it tomorrow, or we are in on Monday.

Judy

>>> "Fees David F. (DNREC)" <David.Fees @state.de.us> 6/16/2010 2:02 PM >>>
Roger, Here is Delaware's paved road dust spreadsheet for 2007, using the new equation. We got very detailed with this category; estimating emissions by

month. Regarding the new equation, PM10 was reduced by 58% from the emissions submitted to MACTEC; while PM2.5 increased by 48%. I believe the PM2.5 increase is caused by two factors-first, the PM2.5/PM10 ratio was increased to 25% (previously 15%). The second reason is that under the old equation, one had to apply a correction factor, C, to remove the exhaust, brake, and tire wear from the front part of the equation. By subtracting C at the end of the equation, the resulting PM2.5 value went negative for several roadway types. Of course we zeroed these out, but with the new method there is never asituation where the emission factor value can go negative

. H aving negative emission factors result from the use of the old equation was obviously a flaw in the method, so I expect the new equation is more accurate. I look forward to NJ's results when they apply the new equation, to see if they get changes similar to mine. If you have any questions about the calculations within the spreadsheet, just give a call. Regards,

Dave David F. Fees, P.E. Managing Engineer Emission Inventory Development Program Air Quality Management Section, DNREC
tel. (302) 739-9402, fax (302) 739-3106
e-mail: david.fees@state.de.us<mailto:david.fees@state.de.us>

Blue Skies Delaware; Clean Air for Life

From: Roger Thunell [mailto:rthunell@mde.state.md.us] Sent: Monday, June 14, 2010 3:00 PM To: Judy Rand; Julie McDill; Pat Davis Cc: WRBARNARD@mactec.com; Fees David F. (DNREC); kenneth.santlal@state.ma.us Subject: RE: [chief] Proposed revisions to AP42 section 13.2.1 Paved Roads

Judy/Dave/Kenneth: Could any of you send me a spreadsheet calculating emissions in this manner? I am not sure if we are using the latest methods or not.

Thanks
Roger

>>> Pat Davis <pdavis@marama.org> 6/14/2010 12:54 PM >>> Thanks a lot for sending this along, Judy. Please let us know what you find when you look at the changes in emissions.

Pat

-----Original Message----- From: Judy Rand [mailto:Judy.Rand@dep.state.nj.us] Sent: Monday, June 14, 2010 9:16 AM
To: Julie McDill; Pat Davis
Cc: WRBARNARD@mactec.com; rthunell@mde.state.md.us; David.Fees@state.de.us;

kenneth.santlal@state.ma.us Subject: Fwd: [chief] Proposed revisions to AP42 section 13.2.1 Paved Roads

Pat and Julie, We are going to look at this to see how it affects emissions. In the past, each change to this category has changed emission calculations.
Thanks, Judy

Judy Rand, PE Environmental Engineer NJDEP Air Quality Planning
(609) 984-1950 jrand@dep.state.nj.usFW: Proposed revisions to AP 42 section 13.2.1 Paved Roads

Julie McDill to: Ron Myers 06/30/2010 04:25 PM

Hi Ron, Here is the announcement for our call next week. Can you send me a slide set by noon next Tuesday and I will distribute it to the group and post it on our ftp. Thanks,
Julie

________________________________________ From: Julie McDill Sent: Tuesday, June 29, 2010 3:21 PM To: Paul.Bodner@ct.gov; mark.prettyman@state.de.us; David.Fees@state.de.us; jessica.daniels@dc.gov; melanie.loyzim@maine.gov; rthunell@mde.state.md.us; kenneth.santlal@state.ma.us; david.healy@des.nh.gov; judy.rand@dep.state.nj.us; Nicholle.Worland@dep.state.nj.us; jdbarnes@gw.dec.state.ny.us; rwstanna@gw.dec.state.ny.us; sbogart@state.pa.us; karen.slattery@dem.ri.gov; jeff.merrell@state.vt.us; Thomas.Foster@deq.virginia.gov; laura.boothe@ncdenr.gov; Robert.J.Betterton@wv.gov; mcconnell.robert@epamail.epa.gov; Forde.Raymond@epamail.epa.gov; kremer.janet@epamail.epa.gov; huntley.roy@epa.gov; Susan Wierman Cc: cooke.donald@epamail.epa.gov ; burkhart.richard@epamail.epa.gov; Garcia.Ariel@epamail.epa.gov; Kelly.Bob@epamail.epa.gov; Salomone.Jenna@epamail.epa.gov; Wieber.Kirk@epamail.epa.gov; Moltzen.Michael@epamail.epa.gov; Laurita.Matthew@epamail.epa.gov; Feingersh.Henry@epamail.epa.gov; Kremer.Janet@epamail.epa.gov; Ellsworth.Todd@epamail.epa.gov; Leon -Guerrero.Tim@epamail.epa.gov; Cripps.Christopher@epamail.epa.gov; Rehn.Brian@epamail.epa.gov; Kotsch.Martin@epamail.epa.gov; Dolce.Gary@epamail.epa.gov; Kapichak.Rudolph@epamail.epa.gov; Houyoux.Marc@epamail.epa.gov; Timin.Brian@epamail.epa.gov; Stackhouse.Butch@epamail.epa.gov; Broadwell.Valerie@epamail.epa.gov; Ling.Michael@epamail.epa.gov; Fox.Tyler@epamail.epa.gov; Cook.Leila@epamail.epa.gov; Spink.Marcia@epamail.epa.gov; Wayland.Richard@epamail.epa.gov; Hemby.James@epamail.epa.gov; Wilkie.Walter@epamail.epa.gov; Fernandez.Cristina@epamail.epa.gov; Ruvo.Richard@epamail.epa.gov; Werner.Raymond@epamail.epa.gov; arnold.anne@epamail.epa.gov; Baker.William@epamail.epa.gov; Arnold.David@epamail.epa.gov; Conroy.Dave@epamail.epa.gov Subject: FW: Proposed revisions to AP42 section 13.2.1 Paved Roads

Hello all,

This email is to announce a teleconference on July 7 at 2:30 PM Eastern concerning the proposed change to the equation used to estimate PM 10 and 2.5 emissions from paved roads. Ron Myers of OAQPS will provide a presentation on the development of the new equation and will answer your questions. Modellers and planners from MANE-VU state agencies along with some USEPA regional staff
have been invited. Call in information is as follows:

Number: 866-202-1783 Code: *5743656* - Make sure you press * before and after the number. Date: July 7
Time: 2:30 - 4:00 P.M. Eastern

BACKGROUND FOR THE CALL This equation is used to calculate emissions for the area source modeling inventory. Delaware and New Jersey have already done some preliminary calculations and find the new equation results in very different values than the old equation. I attach their spreadsheets for your review. Toward the bottom of this email are texts of emails discussing the differences. In addition is the text distributed by NACAA which provides links to materialsfor your formal comment to USEP A

.

As you are no doubt aware, modellers have applied a transport fraction reduction to fugitive road dust emissions in the past to bring the calculated impact on ambient PM in line with measured concentrations. The new equation may require a revision to the transport fraction calculation. I have invited our NY modellers to join the call to hear the discussion so that they can consider any impact on the transport fraction calculation.

The new equation is proposed, so we can decide to use the old calculation method for our modeling inventory. That is what is in our current draft area source inventory files. However, States will then face a disconnect with the model for future emission calculations. At any rate, it seems to me that all states should use the same methodology so that the inventory is consistant accross our region.

Julie McDill

MARAMA

Relevant Email texts

________________________________________ From: Judy Rand [Judy.Rand@dep.state.nj.us] Sent: Wednesday, June 16, 2010 4:43 PM

Thanks Dave. We have come up with similar results, but even more
drastic for PM2.5. An increase in PM2.5 of 350% and a decrease in PM10 of 46% I think one big cause is the difference in k factor, among other changes. The k factor for PM2.5 went down from the 2003 AP-42 to the 2006 AP-42, and back up again in this new draft. We guessed at the new vehicle speed requirement, but a slight variation in speeds will not
make that much of a difference.

See NJ's attached calcs and compare spreadsheet. I won't be in til Monday. If you want to have a call either Nicholle can cover it tomorrow, or we are in on Monday.

Judy

--------------- From: "Fees David F. (DNREC)" <David.Fees@state.de.us> 6/16/2010 2:02 PM

Roger, Regarding the new equation, PM10 was reduced by 58% from the emissions submitted to MACTEC; while PM2.5 increased by 48%. I believe the PM2.5 increase is caused by two factors-first, the PM2.5/PM10 ratio was increased to 25% (previously 15%). The second reason is that under the old equation, one had to apply a correction factor, C, to remove the exhaust, brake, and tire wear from the front part of the equation. By subtracting C at the end of the equation, the resulting PM2.5 value went negative for several roadway types. Of course we zeroed these out, but
with the new method there is never a situation where the emission factor value can go negative. Having negative emission factors result from the use of the old equation was obviously a flaw in the method, so I expect the new equation is more accurate. I look forward to NJ's results when they apply the new equation, to see if they get changes similar to mine. If you have any questions about the calculations within the spreadsheet, just give a call. Regards,

Dave

------------------------------ TO: NACAA EMISSIONS & MODELING COMMITTEE Please information below regarding a proposed revision of the AP-42 paved roads section. The proposed draft can be found here - http://www.epa.gov/ttn/chief/ap4 2/ch13/index.html; scroll down to section 13.2.1, paved roads. EPA will take comments on the draft until July 30, 2010. For more information, please contact Ron Myers at myers.ron@epa.gov.

__________________AP-42 k factors (g/mile)2002 2007 (Existing) 2007(new)% Change2003 2006 2010Annual pm-10 tpy37,606.28 38,210.45 20,532.18-46%PM-107.3000 7.3000 6.79pm-2.5 tpy3,788.42 1,142.03 5,110.37347%PM-2.51.8000 1.1000 1.69Summerpm-10 tpd115.11 105.70 56.75-46%pm-2.5 tpd11.56 3.13 14.12351%Winter pm-10 tpd95.87 101.69 54.74-46%pm-2.5 tpd9.69 3.13 13.63336%Spring pm-10 tpd99.94 105.08 56.41-46%pm-2.5 tpd10.07 3.11 14.04351%Fall pm-10 tpd101.03 106.23 57.08-46%pm-2.5 tpd10.18 3.15 14.21352%Emissions Comparison2007 CAP Emissions CalculationsKentNew Castle Sussex KentNew Castle SussexRural Oth. Princ. Art.January3.0368 3.8504 5.6417 0.7558 0.9584 1.4042February2.8324 3.6256 5.2929 0.7050 0.9024 1.3174March3.3463 4.3680 6.3490 0.8329 1.0872 1.5802April3.4705 4.4744 6.6084 0.8638 1.1137 1.6448May4.3379 5.4584 8.1201 1.0797 1.3586 2.0210June4.4049 5.1675 8.5339 1.0964 1.2862 2.1240July5.1486 5.3205 10.4483 1.2815 1.3243 2.6005August4.8552 5.4994 10.3512 1.2084 1.3688 2.5764September4.2558 5.1069 7.7932 1.0592 1.2711 1.9397October3.6182 4.6311 6.9531 0.9006 1.1527 1.7306November3.2676 4.3388 6.2977 0.8133 1.0799 1.5675December2.9585 4.0132 5.7959 0.7364 0.9989 1.442645.5327 55.8543 88.1854 11.3329 13.9019 21.9489Rural Minor ArterialJanuary19.7917 0.9224 2.9397 4.9261 0.2296 0.7317February19.4746 0.9004 2.7698 4.8472 0.2241 0.6894March7.3003 1.0547 1.7424 1.8170 0.2625 0.4337April6.7427 1.0176 1.8092 1.6782 0.2533 0.4503May7.5361 1.1867 2.1881 1.8757 0.2954 0.5446June6.8577 1.0829 2.5017 1.7069 0.2695 0.6226July7.5020 1.1643 1.5249 1.8672 0.2898 0.3795August7.2545 1.1817 2.8365 1.8056 0.2941 0.7060September7.2050 1.1851 2.0514 1.7933 0.2950 0.5106October7.0923 1.1383 1.7639 1.7652 0.2833 0.4390November6.6536 1.0271 1.5527 1.6561 0.2556 0.3865December6.7191 0.9796 1.4285 1.6723 0.2438 0.3556110.1298 12.8408 25.1086 27.4108 3.1960 6.2494Rural Major CollectorJanuary17.4130 10.8453 81.4407 4.3340 2.6993 20.2702February15.2798 9.2386 74.5901 3.8031 2.2994 18.5651March6.6067 4.1449 31.0497 1.6444 1.0317 7.7281April6.3955 4.2942 30.7597 1.5918 1.0688 7.6560May8.4396 5.6208 36.4193 2.1006 1.3990 9.0646June8.9101 5.3072 11.1730 2.2177 1.3209 2.7809July8.7305 6.1122 13.2889 2.1730 1.5213 3.3075August7.9809 5.1746 12.3685 1.9864 1.2879 3.0785September9.0665 5.4730 33.1489 2.2566 1.3622 8.2506October8.0543 4.6414 30.6447 2.0047 1.1552 7.6273November6.8855 3.8170 28.0237 1.7138 0.9500 6.9750December5.8669 3.4370 27.2418 1.4602 0.8555 6.7804109.6292 68.1062 410.1489 27.2862 16.9513 102.0842Rural Minor CollectorPM10-FIL (TPY)PM2.5-FIL (TPY)2007 CAP Emissions CalculationsKentNew Castle Sussex KentNew Castle SussexPM10-FIL (TPY) PM2.5-FIL (TPY)January7.5825 3.1843 9.1770 1.8872 0.7926 2.2841February6.6536 2.7126 8.4051 1.6561 0.6751 2.0920March2.8769 1.2170 3.4988 0.7160 0.3029 0.8708April2.7849 1.2609 3.4661 0.6931 0.3138 0.8627May3.6750 1.6504 4.1039 0.9147 0.4108 1.0214June3.8799 1.5583 4.0969 0.9657 0.3878 1.0197July3.8017 1.7946 4.8727 0.9462 0.4467 1.2128August3.4753 1.5194 4.5352 0.8650 0.3782 1.1288September3.9480 1.6069 3.7353 0.9826 0.4000 0.9297October3.5072 1.3628 3.4532 0.8729 0.3392 0.8595November2.9983 1.1207 3.1578 0.7463 0.2789 0.7860December2.5547 1.0092 3.0697 0.6359 0.2512 0.764047.7380 19.9970 55.5718 11.8818 4.9772 13.8316Rural LocalJanuary72.2816 14.2450 182.5225 17.9906 3.5455 45.4290February63.4268 13.0827 167.1691 15.7866 3.2562 41.6076March20.6871 5.3708 17.8860 5.1489 1.3368 4.4517April20.0256 4.9021 17.7190 4.9843 1.2201 4.4102May26.4262 6.0689 20.9791 6.5774 1.5105 5.2216June9.5065 4.9294 20.9434 2.3661 1.2269 5.2127July27.3372 5.0483 24.9096 6.8041 1.2565 6.1999August24.9900 5.2372 23.1843 6.2199 1.3035 5.7705September9.6733 6.0687 19.0953 2.4076 1.5105 4.7527October25.2198 5.8458 17.6527 6.2771 1.4550 4.3937November21.5599 5.2538 47.3761 5.3662 1.3077 11.7917December18.3704 5.0929 46.0544 4.5723 1.2676 11.4627339.5044 81.1457 605.4913 84.5011 20.1968 150.7040Urban InterstateJanuary0.0000 11.7187 0.0000 0.0000 2.9167 0.0000February0.0000 12.5883 0.0000 0.0000 3.1332 0.0000March0.0000 13.3265 0.0000 0.0000 3.3169 0.0000April0.0000 14.0185 0.0000 0.0000 3.4891 0.0000May0.0000 15.5068 0.0000 0.0000 3.8596 0.0000June0.0000 14.5005 0.0000 0.0000 3.6091 0.0000July0.0000 15.7325 0.0000 0.0000 3.9158 0.0000August0.0000 16.9323 0.0000 0.0000 4.2144 0.0000September0.0000 14.9430 0.0000 0.0000 3.7192 0.0000October0.0000 13.8368 0.0000 0.0000 3.4439 0.0000November0.0000 13.6508 0.0000 0.0000 3.3976 0.0000December0.0000 12.6716 0.0000 0.0000 3.1539 0.00000.0000 169.4261 0.0000 0.0000 42.1694 0.0000Urban Oth. FreewayJanuary2.2724 2.2051 0.0000 0.5656 0.5488 0.00002007 CAP Emissions CalculationsKentNew Castle Sussex KentNew Castle SussexPM10-FIL (TPY) PM2.5-FIL (TPY)February2.1240 2.3687 0.0000 0.5287 0.5896 0.0000March2.6382 2.5076 0.0000 0.6566 0.6241 0.0000April2.6584 2.6378 0.0000 0.6617 0.6565 0.0000May3.3363 2.9179 0.0000 0.8304 0.7262 0.0000June3.5141 2.7285 0.0000 0.8747 0.6791 0.0000July4.2085 2.9604 0.0000 1.0475 0.7368 0.0000August4.1031 3.1861 0.0000 1.0213 0.7930 0.0000September3.2980 2.8118 0.0000 0.8209 0.6998 0.0000October2.6345 2.6036 0.0000 0.6557 0.6480 0.0000November2.3741 2.5686 0.0000 0.5909 0.6393 0.0000December2.1895 2.3844 0.0000 0.5450 0.5935 0.000035.3512 31.8807 0.0000 8.7988 7.9349 0.0000Urban Oth. Princ. Art.January1.3373 13.5266 3.0648 0.3328 3.3667 0.7628February1.2472 13.0598 2.8754 0.3104 3.2505 0.7157March1.4735 14.9285 3.4491 0.3668 3.7156 0.8585April1.5282 14.5413 3.5900 0.3804 3.6193 0.8935May1.9102 16.7660 4.4112 0.4754 4.1730 1.0979June1.9397 15.0529 4.6360 0.4828 3.7466 1.1539July2.2672 15.1950 5.6760 0.5643 3.7820 1.4127August2.1380 15.2299 5.6233 0.5321 3.7906 1.3996September1.8740 15.1373 4.2336 0.4664 3.7676 1.0537October1.5933 14.6302 3.7772 0.3966 3.6414 0.9401November1.4389 13.7450 3.4212 0.3581 3.4211 0.8515December1.3028 13.7174 3.1486 0.3243 3.4142 0.783720.0502 175.5298 47.9065 4.9904 43.6886 11.9237Urban Minor ArterialJanuary4.3310 4.8764 2.4339 1.0780 1.2137 0.6058February4.2617 4.8104 2.2835 1.0607 1.1973 0.5683March4.6884 5.7734 1.3887 1.1669 1.4370 0.3456April4.3304 5.8589 0.7328 1.0778 1.4582 0.1824May4.8399 7.1419 0.9004 1.2046 1.7776 0.2241June4.4042 6.2220 0.9463 1.0962 1.5486 0.2355July4.8180 6.2667 1.1586 1.1992 1.5597 0.2884August4.6591 6.1525 1.1478 1.1596 1.5313 0.2857September4.6273 6.2988 0.8642 1.1517 1.5677 0.2151October4.5549 5.6747 0.7710 1.1337 1.4124 0.1919November4.2731 4.7114 0.6983 1.0636 1.1727 0.1738December4.3152 4.3381 1.2677 1.0740 1.0797 0.315554.1031 68.1252 14.5932 13.4660 16.9561 3.6322Urban CollectorJanuary31.7118 16.1893 32.0745 7.8929 4.0294 7.9832February30.9520 0.0378 29.3765 7.7038 3.4325 7.31172007 CAP Emissions CalculationsKentNew Castle Sussex KentNew Castle SussexPM10-FIL (TPY) PM2.5-FIL (TPY)March3.7702 9.2060 3.7580 0.9384 2.2913 0.9353April3.4498 9.5377 3.7229 0.8586 2.3739 0.9266May3.8650 12.4840 4.4079 0.9620 3.1072 1.0971June3.5323 11.7876 4.4004 0.8792 2.9339 1.0952July3.6517 13.5754 5.2337 0.9089 3.3789 1.3026August11.2659 11.4931 4.8712 2.8040 2.8606 1.2124September3.5772 12.1557 4.0121 0.8903 3.0255 0.9986October3.5078 10.3087 3.7090 0.8731 2.5658 0.9231November10.2321 8.4777 11.0368 2.5467 2.1101 2.7470December10.0602 7.6337 10.7289 2.5039 1.9000 2.6704119.5761 122.8866 117.3317 29.7620 34.0090 29.2033Urban LocalJanuary43.1321 149.3299 23.6401 10.7354 37.1675 5.8839February42.0987 137.1463 21.6515 10.4782 34.1351 5.3890March16.6866 56.3019 9.0129 4.1532 14.0133 2.2433April15.2685 51.3884 8.9287 3.8003 12.7903 2.2223May17.1063 63.6201 10.5715 4.2577 15.8347 2.6312June15.6337 51.6748 10.5535 3.8912 12.8616 2.6267July16.1621 52.9218 12.5521 4.0227 13.1720 3.1242August15.3230 54.9018 11.6827 3.8138 13.6648 2.9078September15.8323 63.6183 9.6223 3.9406 15.8343 2.3949October15.5252 61.2816 8.8953 3.8641 15.2527 2.2140November13.9169 55.0759 8.1345 3.4639 13.7081 2.0246December13.6832 53.3890 7.9076 3.4057 13.2883 1.9682240.3684 850.6498 143.1529 59.8266 211.7228 35.6301All RoadwayTypesPM10-PRI PM2.5-PRIJanuary202.8901 230.8934 342.9350 50.4984 57.4683 85.3550776.7185 193.3217February188.3509 199.5712 314.4139 46.8797 53.0954 78.2562702.3359 178.2313March70.0744 118.1994 78.1346 17.4412 29.4193 19.4473266.4084 66.3078April66.6544 113.9317 77.3368 16.5900 28.3571 19.2488 257.9230 64.1958May81.4724 138.4216 92.1014 20.2781 34.4525 22.9236311.9955 77.6543June62.5833 120.0116 67.7849 15.5767 29.8703 16.8714250.3798 62.3184July83.6273 126.0918 79.6648 20.8145 31.3837 19.8282289.3839 72.0263August86.0449 126.5080 76.6007 21.4162 31.4873 19.0656289.1537 71.9690September63.3575 134.4055 84.5562 15.7694 33.4529 21.0457282.3192 70.2680October75.3075 125.9550 77.6201 18.7437 31.3496 19.3193278.8826 69.4126November73.6000 113.7869 109.6988 18.3187 28.3210 27.3035297.0857 73.9433December68.0204 108.6660 106.6430 16.9300 27.0465 26.5430283.3294 70.51941121.9831 1656.4422 1507.4902 279.2565 415.7040 375.2074 4285.9155 1070.1679STATEWIDEcomments to draft AP-42 paved road section

Gary Garman t

o

: Ron Myers 06/24/2010 12:58 PM

Please respond to ggarman

History: This message has been replied to and forwarded.



### Ron,

It's good to see the paved road section is being revised. Thanks. It has been a challenge in the

p ast explaining to industrial clients that paving a road would actually result in higher predicted emissions than if the road is left unpaved. I think we'll see more paving and actual emission reductions as a result of the new equation. A few editorial comments on the draft paved road

section:

Page 13.2.1-1, third paragraph, first sentence..change to "The particulate emission factors

p resented in a p revious version.." Page 13.2.1-5, third paragraph, last sentence..change " Table 13.2.1-3" to "Table 13.2.1-2" Page 13.2.1-8, fifth paragraph, first sentence..change " Table 13.2.1-3" to "Table 13.2.1-2" Page 13.2.1-9, second paragraph, second sentence..remove hyphen between "not" and "suggest" Table 13.2.1-3...the page number this table is on should be changed to 13.2.1.10. Also, total loading range for iron and steel should be 0.006-4.77, not 43.0-64.0. Page 13.2.1-11, first paragraph, fourth sentence..remove hyphen between "any" and "of"

Thanks again. I look forward to this draft being finalized.

Gary

-- Gary Garman
Environmental Scientist

McVehil-Monnett Associates, Inc. 44 Inverness Drive East, Bldg C Englewood, CO 80112

303-790-1332