Appendix D

Pinal County PM10 Nonattainment Area 

2018 Attainment Demonstration and 

Controlled Emissions Inventories

Air Quality Division

December 21, 2015 Final
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Appendix D

December 21, 2015 Final Page i

Table of Contents

Table of Contents............................................................................................................................. i

Index of Tables.................................................................................................................................ii

Index of Figures...............................................................................................................................iii

Glossary of Agencies.......................................................................................................................iii

Glossary of Terms ...........................................................................................................................iii

D1 Introduction ........................................................................................................................ 1

D2 Base Scenario...................................................................................................................... 2

D3 Attainment Control Measures............................................................................................ 5

D3.1 Agricultural Best Management Practices ............................................................... 5

D3.1.1 Requirements for Commercial Crop Operations..................................... 6

D3.1.2 Requirements for Commercial Dairy Operations.................................. 24

D3.1.3 Requirements for Commercial Beef Cattle Feedlots............................. 32

D3.1.4 Requirements for Irrigation Districts..................................................... 36

D3.1.5 Other Provisions..................................................................................... 37

D3.2 Attainment Inventories......................................................................................... 37

D3.3 Modeled Attainment Results................................................................................ 39

D4 On-Road Motor Vehicle Emissions Budget....................................................................... 40

D4.1 Scope..................................................................................................................... 40

D4.1.1 Unpaved Roads...................................................................................... 40

D4.1.2 Paved Roads........................................................................................... 42

D4.1.3 Unpaved Parking Lots ............................................................................ 42

D4.1.4 Road Construction/Maintenance .......................................................... 43

D4.2 Allowances............................................................................................................ 44

D5 Reasonable Further Progress............................................................................................ 45

D5.1 Implementation Schedule..................................................................................... 45

D5.2 Contingency Control Measures ............................................................................ 45

D5.2.1 Ag BMP Rule Dairy Provision ................................................................. 46

D5.2.2 Pinal County Construction Rule ............................................................. 46

D5.2.3 Pinal County General Fugitive Dust Rule ............................................... 53

D6 References ........................................................................................................................ 60

Exhibit DI Agricultural Supplemental Information....................................................................DI-1

DI1 Agricultural Crop Surveys....................................................................................DI-2

DI2 Agricultural Crop Control Efficiencies...............................................................DI-13

DI3 Crop Season Distribution ..................................................................................DI-23

DI4 Dairy Operation Survey.....................................................................................DI-24

DI5 Dairy Operation Control Efficiencies.................................................................DI-25

Exhibit DII MAG Methodology for MOVES2014........................................................................DII-1

DII1 Pinal County PM-10 Emissions for Five Episode Days.......................................DII-2

DII2 Pinal County PM-10 Annual Emissions..............................................................DII-5
Appendix D

December 21, 2015 Final Page ii

Index of Tables

Table D2-1. Summary of 2018 Base Scenario Inventories.............................................................. 3

Table D2-2. Summary of Base Scenario Modeling Results............................................................. 4

Table D3-1. Summary of Attainment Measure Reductions............................................................ 5

Table D3-2. Survey Representativeness for Major Crops Cultivated in the Pinal County PM10 

Nonattainment Area ....................................................................................................................... 7

Table D3-3. Pinal County Farm Size Distribution .......................................................................... 16

Table D3-4. Estimated Crop Farm Size Distribution by Associated Acreage ................................ 16

Table D3-5. Commercial Farm Operation Control Effectiveness Estimated by Scenario............. 18

Table D3-6. Commercial Farm Operation Annual Activity Related Control Effectivenesses ....... 18

Table D3-7. Commercial Farm Operation Annual Windblown Control Effectivenesses.............. 19

Table D3-8. 2018 Crop Agriculture Emission Reductions for Stagnation Design Days................. 22

Table D3-9. 2018 Crop Agriculture Emission Reductions for High Wind Design Days................. 22

Table D3-10. 2018 Crop Agriculture Emission Reductions for the Annual Inventory .................. 24

Table D3-11. State of Arizona Dairy Farm Distribution ................................................................ 30

Table D3-12. Commercial Dairy Operation Control Effectivenesses by Operation / Sector........ 31

Table D3-13. Dairy Cattle Emission Factors by Livestock Activity ................................................ 31

Table D3-14. Average Annual Dairy Control Effectiveness........................................................... 31

Table D3-15. 2018 Emission Reductions Attributable to Dairy Operation Controls .................... 32

Table D3-16. Typical Cattle Pen Dust Reductions......................................................................... 34

Table D3-17. High Risk Day Cattle Pen Dust Reductions.............................................................. 34

Table D3-18. CAFO Activity-Based Emissions Distribution ........................................................... 36

Table D3-19. Summary of Attainment Inventories....................................................................... 38

Table D3-20. Summary of Attainment Modeling Results............................................................. 39

Table D3-21. Attainment Modeling Results Apportioned by Source Category............................ 39

Table D4-1. Attainment Scenario Unpaved Road Emissions........................................................ 41

Table D4-2. Attainment Scenario Paved Road Emissions............................................................. 42

Table D4-3. Distribution of Public Dirt Road Improvements Planned for the NA, 2016-2018..... 43

Table D4-4. MVEB Summary......................................................................................................... 44

Table D5-1. Summary of Contingency Measure Reductions........................................................ 46

Table D5-2. Resolved Construction Site Emissions....................................................................... 49

Table D5-3. ADEQ Assumed Controls and Efficiencies for Construction Site Operations............ 50

Table D5-4. Uncontrolled Construction Site Disturbance Ratios.................................................. 51

Table D5-5. Controlled Construction Site Disturbance Ratios...................................................... 52

Table D5-6. 2018 Annual Emission Reductions Estimated for Construction Controls................. 53

Table D5-7. Open Area and Vacant Lot Emissions Reductions..................................................... 54

Table D5-8. Summary of Uncontrolled Emissions from Unpaved Lots ........................................ 55

Table D5-9. Distribution of Public Dirt Road Improvements Planned for the NA, 2016-2018..... 56

Table D5-10. Windblown Dust Emission Factor Reductions ........................................................ 58

Table DI2-1. Crop Control Efficiencies......................................................................................DI-13

Table DI3-1. Crop Season Length (% of the year) .....................................................................DI-23

Table DI5-1. Dairy Operation Control Efficiencies....................................................................DI-25
Appendix D

December 21, 2015 Final Page iii

Index of Figures

Figure D5-1. West Pinal PM10 NA RFP Schedule ........................................................................... 45

Glossary of Agencies

ADEQ Arizona Department of Environmental Quality

EPA United States Environmental Protection Agency

FHWA United States Department of Transportation, Federal Highway Administration

MAG Maricopa Association of Governments

PCAQCD Pinal County Air Quality Control District

SCMPO Sun Corridor Metropolitan Planning Organization

SJVAPCD San Joaquin Valley Air Pollution Control District

Glossary of Terms

μg/m3 Micrograms per cubic meter

A.A.C. Arizona Administrative Code

ADT Average Daily Traffic (equivalent to average daily one-way vehicle trips)

BMPs Best Management Practices

C.F.R. Code of Federal Regulations

CWT Cowtown monitor

EI Emissions Inventory

MC Maricopa monitor

MVEB Motor Vehicle Emissions Budget

NA Nonattainment Area (herein, the West Pinal PM10 NA)

NAAQS National Ambient Air Quality Standards (herein, the 1987 primary and secondary 24-

hour PM10 NAAQS)

PCH Pinal County Housing or Eleven Mile Corner monitor

PM10 Particulate matter with diameters up to, but no more than, 10 microns

RFP Reasonable Further Progress

SIP State Implementation Plan (or, herein, a 2015 proposal to revise the parts of the 

Arizona SIP which apply to the West Pinal PM10 NA)

STF Stanfield monitor

TPY Tons Per Year (herein, “tons” are U.S. short tons)

VMT Vehicle Miles Traveled
Appendix D D1. Introduction

December 21, 2015 Final Page 1

D1 Introduction

This document is an appendix to a proposed revision to the Arizona State Implementation Plan 

(SIP). Unless otherwise noted, references to other appendices (e.g. “Appendix B”) should be 

understood to describe other appendices to the same proposed SIP revision, not appendices to 

this document (which are instead referred to as “Exhibits”).

The proposed SIP revision applies to the West Pinal PM10Nonattainment Area (NA), as designated 

by the U.S. Environmental Protection Agency (EPA) on July 2, 2012 (and as defined in 40 C.F.R.

§81.303). This document discusses the technical methodologies and rationales whose results 

support claims made in that proposed revision. Previous appendices discuss the principles used 

to quantify and account for the PM10 baseline in the NA. The essential results of those appendices 

are reviewed in Section D2. The foci of this appendix are:

 the technical analysis used as evidence that the controls in the proposed revision will 

bring the NA into attainment of the 1987 primary and secondary 24-hour PM10 National 

Ambient Air Quality Standards (NAAQS),

 the accounting choices underlying the motor vehicle PM10 emissions budget (MVEB) for 

on-road vehicles and road construction), and

 the technical analysis used as evidence that the controls in the proposed revision will 

satisfy the reasonable further progress (RFP) and contingency measure requirements for 

the NA.

These subjects are discussed in Sections D3, D4, and D5, respectively. For readability, some 

portions of this document omit routine calculation details or large datasets. Some details can be 

found in the other appendices, in the references listed in Section D6, or else in the exhibits. 

Additional details are available from the SIP Section of the Arizona Department of Environmental 

Quality (ADEQ)’s Air Quality Division, upon request.
Appendix D D2. Base Scenario

December 21, 2015 Final Page 2

D2 Base Scenario

The West Pinal PM10 NA strategy for attaining the NAAQS by 2018 was formulated based on an 

analysis of PM10 emissions in 2008, and projections of what those emissions would look like in 

2018 without intervention. Days representing the PM10 problem around each exceeding monitor 

(design days) were modeled to identify which sources of PM10 emissions contribute the most to 

the exceedances on each design day. Each design day is classified as either a stagnation day (in 

which the exceedance was related to poor mixing conditions, allowing dust to accumulate) or a 

high-wind day (in which the exceedance was related to the generation of large amounts of 

windblown dust). The design day’s meteorological classification determines which PM10 sources 

are considered and how their emissions are modeled. Three monitors were considered for 

stagnation days: the Cowtown monitor, abbreviated CWT; the Pinal County Housing monitor, 

sometimes called the Eleven Mile Corner monitor, abbreviated PCH; and the Stanfield monitor, 

abbreviated STF. For high-wind days, the same three monitors were considered, as well as the 

Maricopa monitor, abbreviated MC.

Appendix B describes the methodology by which the annual, NA-wide EI was developed for 2008 

and projected to 2018. It also details the method used to identify a spatial extent to model for 

each design day (the modeling domain) and how the annual, nonattainment-area-wide EI was 

temporally and spatially allocated to facilitate modeling for each design day. A summary of the 

2018 EIs, itemized by source type, is provided in Table D2-1.

Appendix C explains how each design day EI was modeled to determine the resulting 

concentrations at the relevant monitor. In the case of high-wind modeling, the hourly emission 

total from each source in the appropriate domain was weighted by the source’s proximity to the 

relevant monitor. The 24-hour average of these relative impacts, summed over all sources in the 

appropriate domains (i.e., local sources), is listed for each base scenario high wind design day in 

Table D2-2.

For stagnation modeling, each emissions source in the appropriate domain was run through the 

AerMOD dispersion model, whose raw output was the concentration of PM10 at the monitor 

attributable to each local source for a given hour. These values were adjusted to approximate 

the persistence of certain particles (those under 9 μm in diameter) in the stagnant air from one 

hour to the next. The 24-hour average of the adjusted impacts at the monitor, summed over local 

sources, is included in Table D2-2.

However, these models only address the impact of local emissions sources in the area 

immediately surrounding the monitor. The impact of other, regional sources was estimated for 

both high-wind and stagnation days. Using these background concentrations, B, ADEQ calibrated 

the models using the relationship

M = B + cL

where M is the concentration at the monitor, L is the modeled impact of local sources, and c is 

the scaling factor used to calibrate the model for each design day. In order to determine the
Appendix D D2. Base Scenario

December 21, 2015 Final Page 3

Table D2-1. Summary of 2018 Base Scenario Inventories

Source Category

Annual NA-Wide 

Emissions in Tons 

Per Year (TPY)

Emissions for Modeling Domains in Pounds per Day (lbs/day)

Stagnation Design Days High-Wind Design Days

CWT PCH STF CWT MC PCH STF

Windblown Dust

Developed Urban 248.1 - - - 31.1 896.9 155.1 -

Developed Rural 1,959.7 - - - 257.9 1,307.2 1,257.6 161.1

Unpaved Roads 4,653.0 - - - 1,018.9 917.3 4,027.0 521.3

Cleared Areas 457.0 - - - 141.8 2,317.1 1,590.5 19.7

Construction 1,831.1 - - - 905.5 8,175.8 - -

Dairies 449.6 - - - - - - 126.0

CAFOs 155.2 - - - - - - 463.6

Desert Shrubland 38,276.7 - - - 3,881.5 1,063.1 4,600.3 967.2

Agriculture 19,510.1 - - - 3,905.9 4,899.2 31,617.4 4,618.7

Other 4,243.9 - - - 0.5 185.6 - -

Activity-Based Emissions

Agriculture 2,853.2 366.0 379.2 879.6 1,110.8 292.8 954.0 1,058.4

CAFOs 1,620.6 3,233.5 - 3,347.8 3,233.5 - - 4,121.1

Paved Roads 1,408.0 501.3 112.5 125.5 510.0 591.8 71.3 125.2

Unpaved Roads 45,105.31 12,352.3 3,923.6 12,735.7 11,241.5 4,389.9 6,917.0 11,842.7

Fuel Combustion 34.9 - - - - - 2.2 0.4

Fires 22.2 6.7 2.0 7.4 7.2 3.0 3.5 6.8

Open Burning 16.8 1.7 0.1 0.2 2.2 13.7 0.4 0.2

Nonroad 144.4 16.8 1.2 2.4 20.4 130.8 3.6 2.4

Railroad 45.4 15.1 - - 15.5 15.6 - -

Construction 8,499.8 3,651.0 1,130.7 2,348.0 - 3,667.0 935.0 14.0

Dairies 184.0 - 25.5 141.0 - - - 187.7

Point Sources 781.3 560.8 24.3 7.1 560.2 - 24.3 7.1

Unpaved Parking 251.5 99.2 57.6 45.0 112.1 48.3 101.5 43.3

Total 132,751.8 20,804.5 5,656.7 19,639.7 26,956.4 28,915.1 52,260.6 24,287.0

 

1 Note that emissions from unpaved test tracks at permitted vehicle testing facilities are not included here, but instead in the annual total for Point Sources. 

However, test track emissions are modeled just like other unpaved road emissions. Therefore, in design day inventories, they are included in Unpaved Roads.
Appendix D D2. Base Scenario

December 21, 2015 Final Page 4

value of c for a given design day, ADEQ substituted the modeled value of L for that design day in 

2008, substituted the appropriate background value (either the high-wind or the stagnation 

value), and substituted the actual 24-hour average concentration observed at the relevant 

monitor on the appropriate day in 2008 for M. The approximate values of

the resulting scaling factors are shown in Table D2-2, and were reused for 2018 modeling. For 

example, the Cowtown stagnation design day in 2008 yields

244.5 μg/m3 = 11.5 μg/m3 + c ∙ 849.6 μg/m3

so that c ≈ 27.42%. When interpreting the model results for the 2018 base scenario in 

Cowtown, that value of c is used:

M μg/m3 = 11.5 μg/m3 + 27.42% ∙ 645.1 μg/m3

so that M ≈ 188.4. The same background concentrations and scaling factors are used again to 

interpret the attainment modeling results in Section D3.3.

Table D2-2. Summary of Base Scenario Modeling Results

Year

Design

day

Modeled 

impact of local 

sources2

Scaling 

factor2

Local impact 

only (μg/m3)

Background 

concentration 

(μg/m3)

Concentration at 

the monitor

(μg/m3)

2008

Stagnation

CWT 849.6 27.4% 233.0

11.5

244.5

Monitor observations

PCH 87.5 190.3% 166.5 178.0

STF 64.6 233.1% 150.5 162.0

High-Wind

CWT 289.5 52.1% 150.8

17.5

168.3

MC 456.5 31.1% 141.9 159.4

PCH 1,053.7 23.5% 247.4 264.9

STF 214.1 75.2% 161.1 178.6

2018

Stagnation

CWT 645.1 27.4% 176.9

11.5

188.4

Modeled projections

PCH 88.3 190.3% 168.1 179.6

STF 79.9 233.1% 186.2 197.7

High-Wind

CWT 280.7 52.1% 146.2

17.5

163.7

MC 394.0 31.1% 122.5 140.0

PCH 1,044.3 23.5% 245.2 262.7

STF 224.0 75.2% 168.5 186.0

 

2 Because of the different methods used to model the high-wind and stagnation scenarios, the units for these values 

vary. High-wind day local impacts, before scaling, are in terms of (lbs/hr)/mile, whereas stagnation impacts are in 

μg/m3. In either case, the resulting scaled impact is in μg/m3. Therefore, the empirically-derived scaling factor is 

unitless for stagnation modeling, but carries the unit (μg/m3)/([lbs/hr]/mile) for high-wind modeling.
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 5

D3 Attainment Control Measures

The controls discussed in Sections D3.1.1, D3.1.3, and D3.1.4 result in significant emissions 

reductions, which are summarized in Table D3-1. Incorporating these reductions into the 2018 

modeling scenarios results in lower modeled concentrations – as described in Section D3.3—

which indicate that the NA will attain the PM10 standard in 2018. Reductions due to controls 

implemented by commercial dairy operators, described in Section D3.1.2, are reserved for 

contingency measures (see Section D5.2).

D3.1 Agricultural Best Management Practices

The controls, or best management practices (BMPs) codified in R18-2-610, -611, and -612 are 

generally organized into menus of options for each PM10 emissions source, from which the 

operator selects which controls to implement. Whenever possible, ADEQ relied on surveys of 

operators to determine which of the available controls would most likely be implemented.

As a part of the overall control strategy, ADEQ and PCAQCD will alert the regulated community 

when forecasters identify a high risk of PM10 problems in the NA, giving the operators sufficient 

warning to implement more stringent controls in order to avert a PM10 exceedance. Certain

provisions require the high-risk-day controls to be implemented both on the high risk day and on 

the day preceding it, to reduce the surface dust available for the following day—others require 

the enhanced controls only on the high-risk day itself. The required BMPs for high risk days are 

organized as shortlists of the most effective BMPs from the menus that the operator had 

previously been choosing from. If that operator’s every-day BMP option happens to be on the 

short list, then no further action may be required. Otherwise, the operator will have to select a 

BMP from the short menu to complement the every-day BMP already in place.

In determining how frequently these high-risk-day BMPs would be implemented, PCAQCD 

evaluated the relevant data that would have been available to them each day in 2011-20133. 

Using the current forecasting methodology, 106 high risk days were identified for windblown 

dust generation, so about 35 high-wind days per year are expected to be forecast as high risk. 

For stagnation conditions, an additional 10 days are expected to be forecast as high risk each 

 

3 Email correspondence between ADEQ and Scott Dibiase of Pinal County on 2/4/2014 and 5/28/2015.

Table D3-1. Summary of Attainment Measure Reductions

A.A.C. Rule # Controlled Dust Sources Implementation 

date

NA-Wide 2018 

Reductions (TPY)

R18-2-610.03 Harvesting and Tilling January 1, 2016 13,376.2

R18-2-610.03 Unpaved Agricultural Roads January 1, 2016 8,046.5

R18-2-611.03 Beef Cattle Feedlots January 1, 2016 281.3

R18-2-612.01 Canal Roads January 1, 2016 722.3

Total 22,426.3
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 6

year, for a total of 45 expected high-risk forecast days per year. Recall that the design days 

collectively cover 2 calendar days exemplifying stagnation conditions in the NA and 4 calendar 

days exemplifying high winds in the NA. Because these are precisely the types of days that the 

high-risk forecast seeks to identify, ADEQ assumed that the high-risk-day BMPs would be in place 

for each design day in the attainment scenario.

D3.1.1 Requirements for Commercial Crop Operations

R18-2-610.03 establishes the rules targeted at controlling dust generation from crop operations 

within the Pinal County PM10 Nonattainment Area. Controls adopted under the general permit 

covered in this rule span the following operations on a commercial crop farm:

 Tillage (R18-2-610.03(B)(1)),

 Ground Operations and Harvest (R18-2-610.03(B)(2)),

 Noncropland (R18-2-610.03(B)(3)),

 Commercial Farm Roads (R18-2-610.03(B)(4)),

 Cropland (R18-2-610.03(B)(5)), and

 Earthmoving Activities (R18-2-610.03(B)(6))

For these operations, a commercial farmer is required to implement controls from a menu of 

ADEQ approved measures which will reduce activity related and/or windblown PM10 emissions 

on all days of the year. For days forecasted as high risk, commercial crop farmers must implement 

controls from a more restrictive menu of options to ensure increased PM10 emission reductions 

are realized.

This section describes the methods by which emission reductions were estimated in response to 

the implementation of R18-2-610.03. ADEQ utilized emission reductions from control of the 

above listed operations towards modeling attainment in the year 2018.

D3.1.1.1 Crop Operation Best Management Practices Surveys

In order to estimate commercial crop farm emission reductions associated with the 

implementation of this rule, ADEQ required an understanding of which BMPs a commercial crop 

farmer would be most likely to implement for each operation. With the help of the Agricultural 

Best Management Practices Committee (Ag BMP Committee), ADEQ surveyed Pinal County crop 

farmers to determine the likelihood of individual BMP implementation.

The distributed survey requested crop specific control measure implementation feedback from 

farmers in the format listed in Exhibit DI1. The survey requests feedback on which control 

measure/s each farmer expects to implement for each operation type. Individual surveys were 

requested from each farmer for every crop grown on their property. Surveys were distributed to 

farmers on the Pinal County Farm Bureau distribution list by the Ag BMP Committee. The crop 

surveys were initially distributed to farmers in the Pinal County PM10 nonattainment area via 

email on April 3, 2014. A follow-up reminder email was sent on May 2, 2014. Additional outreach 
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 7

was conducted through the Pinal County irrigation districts. Surveys were either made available 

directly to growers or an electronic copy was provided to irrigation district managers for 

additional disbursement to their respective members. Compiled survey results for all farmers 

were received electronically by ADEQ from the Ag BMP Committee on May 19th, 2014. ADEQ later 

verified individual survey results against the compiled survey results provided by the Ag BMP 

Committee to ensure quality.

Responses to the agricultural crop survey were received from 22 farmers in the Pinal County 

nonattainment area. Responses from these 22 farmers covered a total of 51,155 acres of 

cropland. Utilizing the PCAQCD and ADEQ developed nonattainment area land use map, ADEQ 

estimates that the total acreage of agricultural cropland in the nonattainment area is 249,039 

acres. Therefore, the survey responses represent agricultural land acreage that covers 21% of the 

total agricultural acreage in the nonattainment area. ADEQ then reviewed the responses of each 

farmer to determine if the information provided would comply with the requirements for at least 

one of the operations listed above for any crop. Those responses that met the requirements for 

any single operation were utilized in the calculation of crop specific control efficiency for that 

crop. This reduced the survey response acreage from 51,155 acres due to the removal of 

nonviable data (i.e., data that did not comply with a subsection of the rule). ADEQ presents an 

overview of viable data by major crop type in Table D3-2. The acreage reported in Table D3-2

represents the acreage used for each crop when calculating tillage and harvesting control 

efficiencies by crop.

Table D3-2. Survey Representativeness for Major Crops Cultivated in the Pinal County 

PM10 Nonattainment Area

Crop

Total 

Acreage 

Surveyed 

(Tillage)

Total 

Acreage 

Surveyed 

(Harvest)

NAA Crop 

Acreage**

Percentage 

of NAA 

Acreage 

Surveyed 

(Tillage)

Percentage 

of NAA 

Acreage 

Surveyed 

(Harvest)

Alfalfa 7,422 13,683 80,116 9% 17%

Cotton 9,286 8,223 92,554 10% 9%

Vegetables 89 80 12,067 1% 1%

Hay* 1,000 1,193 3,561 28% 33%

Corn & Grain 8,742 9,281 53,221 16% 17%

*Assumed to be equivalent to non-alfalfa forage

**Nonattainment crop acreage is estimated as the total acreage of ADEQ/PCAQCD land use map crop 

acreage multiplied by the percentage of each crop in the county as determined from the 2007 USDA 

Agricultural Census.

The 2007 USDA Agricultural Census reported a total of 480 cropland farms in Pinal County in 

20074. ADEQ estimates that 467 of those farms occur in the nonattainment area. This estimate 

was derived by multiplying the total number of cropland farms by the ratio of crop acreage 

reported for the county and dividing by the total crop acreage derived from ADEQ and PCAQCD 

 

4

See Arizona Table 1 of (USDA, 2012)
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 8

land use maps for the nonattainment area (e.g., 476 = 480 * [249,039 / 256,032]). 22 farmers 

reported results for the agricultural survey, indicating a 5% response rate by farm number (e.g., 

5% = 22 / 467). ADEQ then reviewed the responses of each farmer to determine if their responses 

would comply with the requirements of at least one of the operations listed above for any crop.

Based on this review, 19 of 22 farmers submitted viable data towards the calculation of control 

efficiencies for an operation, for at least one crop. Assuming survey respondents do not own 

more than one farm in the nonattainment area, this results in a calculated, viable response rate 

of 4% (e.g., 4% = 19 / 467).

Based on the survey responsiveness metrics presented above ADEQ concluded that the survey 

results provide adequate representation of the controls that are likely to occur in Pinal County.

While the survey responses appear to have originated primarily from large farmers in the region 

(responses originated from only 5% of the farms, but constitute 21% of the crop acreage in the 

region), the percentage of crop land covered from survey responses indicates a robust sample.

D3.1.1.2 Tillage Control Efficiency Calculation

Controls implemented on tillage operations (R18-2-610.03(B)(1)) were utilized for attainment 

demonstration of the NAAQS for the year 2018. Crop specific control efficiencies were estimated 

for tillage on non-high risk forecast days and high risk forecast days. Crop surveys were used as 

guidance to determine the percentage of farmers implementing a given BMP. Each survey which 

provided responses adequate to meet the requirements of R18-2-610.03(B)(1) for a given crop 

was utilized to estimate the distribution of tillage controls that could be expected for a given 

crop. Survey responses for tillage operation control were organized by crop type. For each crop 

type only survey results that met the rule requirements were utilized. Where farmers reported a 

number of controls to be implemented that exceeded the number of controls required by the 

rule, the farmer was given credit for that control which had the highest control efficiency based 

on efficiencies researched from previous PM10 plans or studies (a list of utilized control 

efficiencies and their references are presented in Exhibit DI2). In those instances where control 

efficiencies were not identified for a given crop, the lowest reported efficiency researched for 

that control from any crop was assumed adequate, in order to assure a conservative emission 

reduction estimate.

Average non-high risk day crop specific activity related tillage control efficiency was calculated 

by weighting individual farmer control efficiencies (assigned using the methods outlined in the 

preceding paragraph) by the acreage of cropland reported in cultivation by the farmer using the 

sample method below:

Simplified Example Calculation

 Farmer A responds that he/she will implement ‘Combining Tractor Operations’ (43% 

control efficiency for tillage activities) as a control for cotton tillage on his/her 150 acres 

of cultivated cotton.

 Farmer B responds that he/she will implement ‘Equipment Modification’ (50% control 

efficiency for tillage activities) as a control for cotton tillage on his/her 500 acres of 

cultivated cotton.
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 9

 Farmer C responds that he/she will implement ‘Planting Based on Soil Moisture’ (30% 

control efficiency for tillage activities) as a control for cotton tillage on his/her 293 acres 

of cultivated cotton.

Based on this information we can calculate an average non-high risk day cotton tillage control 

efficiency utilizing the following equation:

CEavg =

(CEa × Aa) + (CEb × Ab) + (CEc × Ac)

Aa + Ab + Ac

which becomes

CEavg =

(43% × 150 acres) + (50% × 500 acres) + (30% × 293 acres)

150 acres + 500 acres + 293 acres

= 43%

where

CEavg = average crop specific activity emission control efficiency (%),

CEx = the crop specific activity emission control efficiency assigned to farmer x given 

his/her survey response (where x = A, B, C...), and

Ax = the acreage of cultivated crop reported by farmer x (where x = A, B, C...).

Acceptable non-high risk day BMPs for tillage emission control are outlined in R18-2-

610.03(B)(1)(a). Some of these controls reduce not only activity related emissions, but also work 

to stabilize the soil surface or provide ground cover for the reduction of windblown dust 

emissions during tillage season. As such, average windblown dust emission control efficiencies 

were calculated using the method described above except where activity related emission 

control efficiencies were replaced by researched windblown dust reported efficiencies.

Additionally, R18-2-610.03(B)(1)(b) outlines those controls which are acceptable for 

implementation during high risk forecasted days. Utilizing this list of BMPs and the methods 

described above, ADEQ estimated crop specific average tillage control efficiencies for activity 

emissions and windblown dust emissions. High risk control efficiencies were applied to calculated 

design day tillage activity emissions and windblown emissions under the assumption that all 

design days would be forecasted as high risk5. For annual emission calculations, ADEQ relied on 

estimates of the number of annual days assumed to qualify as high risk for the period 2011-2013 

provided by PCAQCD and calculated a weighted crop specific annual control efficiency assuming 

45 days forecasted for high risk and the remainder of days forecasted as non-high risk. While 

R18-2-610.03(A) requires implementation of high risk BMPs on the day prior to a forecasted high 

risk day, ADEQ conservatively only took credit for the 45 days forecasted as high risk and not the 

preceding days (i.e., up to 90 days annually) when calculating the weighted annual tillage control 

efficiencies. Furthermore, as the tillage season for any given crop only occurs during a portion of 

the year, the crop specific annual average windblown control efficiency was only applied to the 

proportion of the year that a given crop was reported to experience tillage activities (presented 

in Exhibit DI3). This was determined from the crop calendar provided by PCAQCD (Appendix B).

 

5

See Section D3.1.
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 10

Additional Assumptions/Notes:

 Farmers who only chose multi-year crop as a control for a given crop were excluded from 

control efficiency calculations as the emissions reductions associated with this control are 

already inherently taken into account through emission rate calculations for those crops 

through the use of the vegetative factor.

 Through research, ADEQ could not identify an adequate set of control efficiencies for 

forage. In the absence of a set of control efficiencies for this crop, alfalfa control 

efficiencies were utilized since alfalfa is considered a forage crop and is also the second 

most abundantly harvested crop in the county as reported in (USDA, 2012). 

D3.1.1.3 Ground Operations and Harvest Control Efficiency Calculation

Controls implemented on ground operations and harvest (R18-2-610.03(B)(2), henceforth 

referred to as harvest) were credited towards attainment demonstration of the NAAQS for the 

year 2018. Crop specific control efficiencies were estimated for harvest on non-high risk forecast 

days and high risk forecast days. Crop surveys were used as guidance to determine the 

percentage of farmers implementing a given BMP. Each survey which provided responses 

adequate to meet the requirements of R18-2-610.03(B)(2) for a given crop was utilized in the 

estimation of harvest controls distribution that could be expected for a given crop. Survey 

responses for harvest operation control were organized by crop type. For each crop type only 

survey results that met the rule requirements were utilized. Where farmers reported a number 

of controls to be implemented that exceeded the number required by the rule, the farmer was 

given credit for that control which had the highest control efficiency based on efficiencies 

researched from previous PM10 plans or studies (a list of utilized control efficiencies and their 

references are presented in Exhibit DI2). In those instances where control efficiencies could not 

be identified for a given crop, the lowest reported efficiency researched for that control from any 

reported crop was assumed adequate in order to assure a conservative emission reduction 

estimate.

Average non-high risk day crop specific activity related harvest control efficiency was calculated 

by weighting individual farmer efficiencies (assigned using the methods outlined in the preceding 

paragraph) by the acreage of cropland reported in cultivation by the farmer using the sample 

method below:

Simplified Example Calculation

 Farmer A responds that he/she will implement ‘Combining Tractor Operations’ (43% 

control efficiency for harvest activities) as a control for cotton harvest on his/her 150 

acres of cultivated cotton.

 Farmer B responds that he/she will implement ‘Equipment Modification’ (50% control 

efficiency for harvest activities) as a control for cotton harvest on his/her 500 acres of 

cultivated cotton.
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 11

 Farmer C responds that he/she will implement ‘Chemical Irrigation’ (9% control efficiency 

for harvest activities) as a control for cotton harvest on his/her 293 acres of cultivated 

cotton.

Based on this information we can calculate an average non-high risk day cotton harvest control 

efficiency utilizing the following equation:

CEavg =

(CEa × Aa) + (CEb × Ab) + (CEc × Ac)

Aa + Ab + Ac

which becomes

CEavg =

(43% × 150 acres) + (50% × 500 acres) + (9% × 293 acres)

150 acres + 500 acres + 293 acres

= 36%

where

CEavg = average crop specific activity emission control efficiency (%),

CEx = the crop specific activity emission control efficiency assigned to farmer x given 

his/her survey response (where x = A, B, C...), and

Ax = the acreage of cultivated crop reported by farmer x (where x = A, B, C...).

Acceptable non-high risk day BMPs for harvest emission control are outlined in R18-2-

610.03(B)(2)(a). Some of these controls reduce not only activity emissions, but also work to 

stabilize the soil surface or provide ground cover to reduce windblown dust emissions during 

harvest season. As such, average windblown dust emission control efficiencies were calculated 

using the method described above except where activity emission control efficiencies were 

replaced by windblown efficiencies.

Additionally, R18-2-610.03(B)(2)(b) outlines those controls which are required for 

implementation during high risk forecast days. Utilizing this list of BMPs and the methods 

described above, ADEQ estimated crop specific average harvest control efficiencies for activity 

emissions and windblown emissions. High risk control efficiencies were applied to design day 

harvest activity emissions and windblown emissions under the assumption that all design days 

would be forecast as high risk.6 For annual emission calculations, ADEQ relied on estimates of the 

number of annual days assumed to qualify as high risk for the period 2011-2013 and calculated 

a weighted crop specific annual control efficiency assuming 45 high risk forecast days, with the 

remainder of days forecast as non-high risk. While R18-2-610.03(A) requires implementation of 

high risk BMPs on the day prior to a forecasted high risk day, ADEQ conservatively only took credit 

for 45 high risk days and not the preceding days (i.e., up to 90 days annually) when calculating 

the weighted annual harvest control efficiencies. Furthermore, as the harvest season for any 

given crop only occurs during a portion of the year, the crop specific annual average windblown 

control efficiency was only applied to the proportion of the year that a given crop was reported 

to experience harvest activities (presented in Exhibit DI3). This was determined from the crop 

calendar provided by PCAQCD.

 

6

See Section D3.1.
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 12

Additional Assumptions/Notes:

 Farmers who only chose multi-year crop as a control for a given crop were excluded from 

control efficiency calculations as the emissions reductions associated with this control are 

already inherently taken into account through emission rate calculations for those crops 

through the use of the vegetative factor.

 Through research, ADEQ could not identify an adequate set of control efficiencies for 

forage. In the absence of a set of control efficiencies for this crop, alfalfa control 

efficiencies were utilized since alfalfa is considered a forage crop and is also the second 

most abundantly harvested crop in the county as reported in (USDA, 2012).

 ‘Conservation Tillage’ and ‘Reduced Tillage System’ BMPs were assumed to qualify as 

viable BMPs when reviewing surveys for crop specific harvest control responses. These 

BMPs were assigned an activity related control efficiency of 0% since they do not reduce 

activity related harvest emissions, but were assigned non-zero control efficiencies for 

windblown dust since their use increases ground cover. The windblown dust control 

efficiencies for these BMPs were assumed to be equal to those experienced during the 

tillage season.

D3.1.1.4 Noncropland Control Efficiency Calculation

Noncropland was never explicitly defined during the creation of the Pinal County land use map.

As such, emission reductions associated with the adoption of R18-2-610.03(B)(3) could not be 

quantified.

D3.1.1.5 Commercial Farm Road Control Efficiency Calculation

Controls implemented on commercial farm roads (R18-2-610.03(B)(4)) were utilized in the 

demonstration of attainment towards the NAAQS for the year 2018. Control efficiencies were 

estimated for non-high risk forecasted days and high risk forecasted days. Agricultural BMP crop 

surveys were used as guidance to determine the percentage of farmers implementing any given 

BMP. Each survey which provided responses adequate to meet the requirements of R18-2-

610.03(B)(4) was utilized in the determination of commercial farm road control distribution that 

could be expected on a farm. Where farmers reported a number of controls to be implemented 

that exceeded the number of controls required by the rule, the farmer was given credit for that 

control which had the highest efficiency based on efficiencies researched from previous PM10

plans or studies (a list of utilized control efficiencies and their references are presented in Exhibit 

DI2).

Average non-high risk day commercial farm road activity related control efficiency was calculated 

by weighting individual farmer activity control efficiencies (assigned using the methods outlined 

in the preceding paragraph) by the total miles of unpaved road reported by the farmer using the 

sample method below:
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 13

Simplified Example Calculation

 Farmer A responds that he/she will implement ‘Access Restriction’ (2% control efficiency) 

as a control on his/her 10 miles of unpaved roads.

 Farmer B responds that he/she will implement ‘Reduced Vehicle Speed’ (26% control 

efficiency) as a control on his/her 12.5 miles of unpaved roads.

 Farmer C responds that he/she will implement ‘Aggregate Cover’ (46% control efficiency) 

as a control for his/her 58 miles on unpaved roads.

Based on this information we can calculate an average non-high risk day commercial farm road 

control efficiency utilizing the following equation:

CEavg =

(CEa × La) + (CEb × Lb) + (CEc × Lc)

La + Lb + Lc

which becomes

CEavg =

(2% × 10 miles) + (26% × 12.5 miles) + (46% × 58 miles)

10 miles + 12.5 miles + 58 miles = 37%

where

CEavg = average activity emission control efficiency (%),

CEx = the activity emission control efficiency assigned to farmer x given his/her survey

response (where x = A, B, C...), and

Lx = the mileage of unpaved roads reported by farmer x (where x = A, B, C...).

Acceptable non-high risk day BMPs for commercial farm road control are outlined in R18-2-

610.03(B)(4)(a) Some of these controls reduce not only activity emissions, but also work to 

stabilize the soil surface or provide ground cover and reduce windblown dust emissions. As such, 

average windblown dust emission control efficiencies were calculated using the method 

described above except where activity emission control efficiencies were replaced by windblown 

dust efficiencies.

Additionally, R18-2-610.03(B)(4)(b) outlines those controls required for implementation during 

high risk forecast days. Utilizing this list of BMPs and the methods described above, ADEQ 

estimated commercial farm road control efficiencies for activity emissions and windblown dust 

emissions. However, after calculation of average control efficiencies for non-high risk and high 

risk days, ADEQ decided to assume all design days and annual emissions would only experience 

non-high risk day controls. While this was a conservative approach, underestimating emission 

reductions, high risk day control efficiency is only slightly higher (0.5%) than the non-high risk day 

control efficiency for activity related emissions on commercial farm roads and equivalent for nonhigh risk days and high risk days for windblown dust emissions. Therefore, assuming design day 

and annual reductions will not benefit from high risk day controls does not significantly affect 

estimated emission reductions.
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 14

Additional Assumptions/Notes:

 For the survey initially created by ADEQ the ‘Organic Material Cover’ BMP was 

inadvertently excluded. As no data was received for this BMP, it was not included in the 

calculation of average control efficiencies.

 Farmers who chose ‘Track-out Control Systems’ as a BMP also chose other BMPs that 

were expected to have higher efficiency; therefore, ADEQ did not perform calculations to 

try to determine ‘Track-out Control System’ efficiency.

D3.1.1.6 Cropland Control Efficiency Calculation

Cropland refers to those lands on a commercial farm that are suitable for crop production but 

are either: 1) used as a turn-row, 2) fallow, or 3) between the harvesting and planting seasons.

As little activity occurs on these lands, controls target the reduction of windblown emissions 

through ground cover or stabilization.

Controls implemented on cropland (R18-2-610.03(B)(5)) were utilized to demonstrate 

attainment of the NAAQS for the year 2018. Crop and fallow land specific control efficiencies 

were estimated for cropland on non-high risk forecast days and high risk forecast days. Crop 

surveys were used as guidance to determine the percentage of farmers implementing a given 

BMP. Each survey which provided responses adequate to meet the requirements of R18-2-

610.03(B)(5) for a given crop or fallow land was utilized to determine the distribution of cropland 

controls that could be expected for that given crop or fallow land. Survey responses for cropland 

control were organized by crop type or fallow land. For each crop type, only survey results that 

met the rule requirements were utilized. Where farmers reported a number of controls to be 

implemented that exceeded the number of controls required by the rule (two), the farmer was 

given credit for those two controls which had the highest control efficiency and met the rule 

requirements (a list of utilized control efficiencies and their references are presented in Exhibit 

DI2). In those instances where control efficiencies could not be identified for a given crop, the 

lowest reported efficiency researched for that control from any reported crop was assumed 

adequate in order to assure conservative emission reduction estimates.

Average non-high risk day crop/fallow land specific windblown dust cropland control efficiency 

was calculated by weighting individual farm efficiencies (assigned using the methods outlined in 

the preceding paragraph) by the acreage of cropland reported in cultivation by the farmer using 

the sample method below:

Simplified Example Calculation

 Farmer A responds that he/she will implement ‘Cross-wind Ridges’ and ‘Surface 

Roughening’ (57% and 45% control efficiency respectively) as cropland controls on his/her 

109 acres of fallow land.

 Farmer B responds that he/she will implement ‘Chips/Mulches’ and ‘Wind Barriers’ (33% 

and 45% control efficiency respectively) as cropland controls on his/her 150 acres of 

fallow land.
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 15

Based on this information average non-high risk day cropland control efficiency for fallow fields 

can be calculated utilizing the following equation:

CEavg =

((1 − (1 − CEa1

) × (1 − CEa2)) × Aa) + ((1 − (1 − CEb1) × (1 − CEb2)) × Ab)

Aa + Ab

which becomes

CEavg =

((1− (1 −57%) × (1 − 45%)) × 109 acres) + ((1 −(1 − 33%) × (1 − 45%)) × 150 acres)

109 acres + 150 acres

= 69%

where

CEavg = average crop specific/fallow field windblown dust emission control efficiency (%),

CEx = crop specific/fallow field windblown dust emission control efficiency assigned to 

farmer x given his/her survey responses (where x = A, B, C...), and

Ax = the acreage of fallow land reported by farmer x (where x = A, B, C...).

R18-2-610.03(B)(1)(b) outlines those controls which are required for implementation during high 

risk forecast days. For all crops and for fallow land, calculated non-high risk and high risk day 

controls were equivalent for windblown dust.

Additional Assumptions/Notes:

 Farmers who only chose multi-year crop as a control for a given crop were excluded from 

control efficiency calculations as the emission reductions associated with this control are 

already inherently taken into account through emission rate calculations for those crops 

through the use of the vegetative factor.

 Due to insufficient survey response data, Cropland control efficiencies could not be 

calculated for corn. Therefore, the Cropland control efficiencies for vegetables (the lowest 

calculated Cropland control efficiencies) were used as a surrogate.

D3.1.1.7 Commercial Crop Operation Control Effectiveness

Control effectiveness is calculated as the product of control efficiency, rule effectiveness, and 

rule penetration. For each of the control efficiencies described above, control effectiveness was 

calculated in order to adequately reflect emission reductions. For all operations outlined above 

a consistent rule penetration and rule effectiveness was estimated for all crops. The methods by 

which these were estimated are presented below.

Rule Penetration Estimation

ADEQ utilized 2007 USDA Agricultural Census data for determining the rule penetration of R18-

2-610.03. R18-2-610 definitions describe a commercial crop farm as having a lower acreage limit 

of 10 contiguous acres. The 2007 Agricultural Census reported farm sizes by acreage in Pinal 

County (Table D3-3). Based on the farm number for each size category, ADEQ calculated the 

distribution of farms by number within each farm size bin. While this distribution includes animal 

and crop farms, ADEQ assumed the crop-only farm distribution would be equivalent.
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 16

Table D3-3. Pinal County Farm Size Distribution

Farm Size Farm Number in Pinal Percentage of Total Farms

1 to 9 acres 217 28%

10 to 49 acres 192 24%

50 to 179 acres 122 16%

180 to 499 acres 72 9%

500 to 999 acres 78 10%

1,000 acres + 104 13%

Total 785 100%

As emissions from croplands are estimated based on the crop field’s aerial extent, rule 

penetration is dependent on the area of land to which the rule is applicable, not the number of 

farms. Therefore, ADEQ estimated the area associated with each size category listed in Table 

D3-3.

ADEQ estimated the number of crop farms within the nonattainment area by multiplying the

reported number of crop farms in the County (480) by the ratio of ADEQ/PCAQCD identified crop 

acreage in the nonattainment area 249,039 to the acreage reported in (USDA, 2012), 256,042.

This resulted in an estimated 467 crop farms located in the nonattainment area (467 farms = 

[249,039 acres / 256,042 acres] * 480 farms). ADEQ then assumed these farms would have the 

same size distribution as listed in Table D3-3. ADEQ further assumed all farms within a size 

category would have acreage equivalent to the mid-point acreage of that size category. In the 

instance of the largest size category where an upper bound is not listed, ADEQ calculated the 

total acreage from all farm size categories <1,000 acres, subtracted this acreage from the total 

nonattainment area identified crop acreage, and divided this value by the number of 1,000+ acre 

farm to estimate an average size. ADEQ was then able to estimate the percentage of farmland, 

by acreage, associated with each farm size bin (Table D3-4). ADEQ interpreted rule penetration 

as the percentage of crop farm area associated with farms of 10+ acre size (i.e., 99.7%). This rule 

penetration was applied to all crop farm operation control efficiencies.

Table D3-4. Estimated Crop Farm Size Distribution by Associated Acreage

Farm Size Estimated 

Farms in NAA

Estimated Average 

Farm Size (acres)

Total 

Acreage

Total 

Cropland (%)

1 to 9 acres 129 5 645 0.26%

10 to 49 acres 114 30 3,369 1.35%

50 to 179 acres 73 115 8,308 3.34%

180 to 499 acres 43 340 14,538 5.84%

500 to 999 acres 46 750 34,769 13.96%

1,000 acres + 62 3,030 187,410 75.25%

Total 467 249,039 100.00%
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 17

Rule Effectiveness

Rule effectiveness was estimated using the criteria outlined in (EPA, 2005). This guidance outlines 

three ranges for which rule effectiveness are primarily anticipated to occur for non-point sources.

The guidance lists inspection compliance as the most important factor when making a 

determination of rule effectiveness, followed by other factors including, but not limited to:

 Source subjection to compliance certification;

 Detail of inspections;

 Frequency of unannounced inspections;

 Percent of facilities inspected within a given year;

 Frequency of enforcement action for violations; and

 Availability of compliance assistance programs.

As farmer compliance with the new Agricultural BMP Rule is not currently available since the rule 

had not been implemented at the time of this SIP preparation, ADEQ relied on compliance 

experience with the previous version of the Agricultural BMP Rule to estimate rule effectiveness.

Given the factors listed above, ADEQ determined a rule effectiveness of >75% was appropriate.

ADEQ compliance staff report 100% compliance of farmers with the current agricultural rule; 

however, certain limitations exist in response to other listed important factors for rule 

effectiveness determination:

 Sources are not subject to compliance certification;

 Inspections are generally announced;

 <15% of facilities are inspected per year; and 

 Past enforcement action has been limited.

While a few limitations occur, the reported compliance rate is currently 100% and a compliance 

assistance program for the crop farming community exists that is well staffed between ADEQ, 

the Arizona Department of Agriculture, and the Agricultural BMP Committee. Therefore, ADEQ 

determined it was appropriate to assume 80% rule effectiveness. This rule effectiveness was 

applied to all crop farm operation control efficiencies.

Control Effectiveness Calculation

Control Effectiveness is calculated as the product of control efficiency, rule effectiveness, and 

rule penetration. ADEQ calculated a suite of crop operation control effectivenesses listed below 

(Table D3-5). Additionally, farm road control effectiveness for windblown dust on non-high risk 

days and high risk days was calculated as 38.8%, and farm road control effectiveness for activity 

related emissions were calculated as 44.9% for non-high risk days and 45.4% for high risk days 

(only non-high risk day control effectiveness was utilized in emission reduction calculations).

Finally, fallow land has a calculated windblown dust control effectiveness of 77.2%.
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 18

Table D3-5. Commercial Farm Operation Control Effectiveness Estimated by Scenario

Scenario / 

Season Emissions Forecast Alfalfa Cotton Forage Vegetables Corn Grain

Tillage

Activity Non-High Risk 25.1% 35.0% 36.5% 19.1% 45.8% 42.0%

High Risk 79.8% 51.4% 79.8% 19.1% 75.1% 66.9%

Windblown Non-High Risk 14.4% 61.0% 64.7% 51.7% 64.7% 19.6%

High Risk 20.5% 61.0% 64.7% 51.7% 64.7% 24.5%

Harvest

Activity

Non-High Risk 17.6% 30.4% 25.5% 1.6% 37.5% 29.9%

High Risk 24.8% 58.6% 73.2% 79.8% 74.2% 62.0%

Windblown Non-High Risk 40.6% 66.9% 51.2% 50.3% 72.0% 69.0%

High Risk 40.6% 66.9% 51.2% 50.3% 72.0% 69.0%

Cropland Windblown

Non-High Risk 73.2% 76.2% 61.2% 50.3% 50.3% 76.7%

High Risk 73.2% 76.2% 61.2% 50.3% 50.3% 76.7%

Annual tillage and harvest activity control effectiveness were calculated by weighting the nonhigh risk day and high risk day crop specific control effectiveness by the number of days per year 

estimated to be forecasted as non-high risk (366 – 45 = 321) and those estimated to be forecasted 

as high risk days (45), respectively. The results are presented in Table D3-6.

Table D3-6. Commercial Farm Operation Annual Activity Related Control Effectivenesses

Scenario Alfalfa Cotton Forage Vegetables Corn Grain

Tillage 31.9% 37.0% 41.9% 19.1% 49.4% 45.1%

Harvest 18.5% 33.9% 31.3% 11.2% 42.0% 33.9%

Annual windblown control effectivenesses were calculated for each crop for two separate 

scenarios, disturbed seasons and stabilized seasons. Disturbed seasons were assumed to occur 

when soils were actively being disturbed during periods of little to no ground cover. This included 

tillage/planting seasons and harvesting seasons in the crop calendar for each crop. The stabilized 

season was assumed to occur during the cropland season (after harvest and prior to planting). As 

windblown emissions do not occur during the growing season (ground cover and regular watering 

are assumed to eliminate windblown emissions during this portion of the year), the growing 

season is excluded from control effectiveness calculations. The methods by which annual average 

disturbed and stabilized windblown control effectivenesses are calculated are outlined in the 

examples below:

Disturbed Windblown Control Effectiveness for Cotton

CET-NHR = Non-high risk day control effectiveness (tillage) = 61.0%

CET-HR = High risk day control effectiveness (tillage) = 61.0%

CEH-NHR = Non-high risk day control effectiveness (harvest) = 66.9%

CEH-HR = High risk day control effectiveness (harvest) = 66.9%

D = High risk days per year = 45

LT = Length of Tillage season (% of annual) = 19.44%
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 19

LH = Length of Harvest season (% of annual) = 38.89%

CE =

(

CET−NHR × [366 − D] +CET−HR × [D]

366 ) × LT + (

CEH−NHR × [366 −D] + CEH−HR × [D]

366 ) × LH

LT + LH

which becomes

64.9% =

(

61.0% × [366 − 45] +61.0% × [45]

366 ) × 19.4% + (

66.9% × [366 − 45] + 66.9% × [45]

366 ) × 38.9%

19.4% + 38.9%

Stabilized Windblown Control Effectiveness for Cotton

CEC-NHR = Non-high risk day control effectiveness (cropland) = 76.2%

CEC-HR = High risk day control effectiveness (cropland) = 76.2%

D = High risk days per year = 45

CE =

CEC−NHR ∗ (366 − D) + CEC−HR ∗ (D)

366

which becomes

76.2% =

76.2% ∗ (366 − 45) + 76.2% ∗ (45)

366

Utilizing these methodologies, ADEQ was able to calculate annually averaged disturbed and 

stabilized windblown emission control effectivenesses for each crop of interest. The results of 

these calculations are presented in Table D3-7.

Table D3-7. Commercial Farm Operation Annual Windblown Control Effectivenesses

Scenario Alfalfa* Cotton Forage* Vegetables Corn Grain

Disturbed 40.6% 64.9% 51.2% 50.7% 68.8% 46.2%

Stabilized - 76.2% - 50.3% 50.3% 76.7%

*Alfalfa and forage are assumed to not experience cropland periods and so stabilized control effectivenesses 

could not be calculated.

Control Effectiveness Application

Emission reduction estimates depend not only on the magnitude of control effectiveness, but 

also the manner in which control effectiveness is applied to emissions. Given the transient nature 

of crop seasons it is important to pay special attention to the method by which reductions are 

applied. This section outlines the methods by which calculated control effectivenesses were 

applied to crops and commercial farm roads on design days and annually for activity emissions 

and windblown emission calculations.

Crops

Table D3-5 presents the calculated tillage, harvest, and cropland control effectivenesses for each 

crop inventoried. Activity emission controls are applied to those emissions originating from 

human induced mechanical entrainment of dust particles into the atmosphere. Windblown 

control effectivenesses calculated and presented in Table D3-5 apply to those emissions from 
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 20

disturbed and stabilized soils which result from the mechanical lifting of particles by wind and 

thus only apply during periods of high wind (>12 mph).

Design days were assumed to be forecast as high risk. As such, crop specific control 

effectivenesses utilized for design days were all assumed to correspond to those calculated under 

the high risk scenarios presented in Table D3-5. Additionally, non-windblown estimated 

emissions on stagnation and high wind design days were assigned those crop specific control 

effectivenesses which are listed in the “Activity” rows in Table D3-5. Finally, for activity emissions, 

crop specific control effectivenesses were applied to tillage or harvesting emission factors 

dependent on the activity taking place. For tillage and harvesting emissions originating from 

unidentified agricultural land, the crop acreage weighted average control effectiveness for the 

modeling domain was calculated and applied to the emission factor for those agricultural lands.

Application of crop specific control effectivenesses for windblown dust presented in Table D3-5

was dependent on the crop season in which a design day took place. Consultation of the Pinal 

County crop calendar (Appendix B) determined whether a given crop was in the tillage/planting, 

growing, harvesting, or cropland season. Determination of the season dictated which control 

effectiveness from Table D3-5 was utilized. Once applicable control effectiveness was 

determined, it was applied to a given crop’s domain specific windblown emission rate. These 

controlled emission rates were then used to calculate revised emissions for each commercial 

crop field within the design day domain of interest. To maintain consistency with the 

methodology used for calculation of windblown dust originating from unidentified agricultural 

lands, the fallow field windblown dust control effectiveness was applied to these fields.

Annual activity control effectivenesses were applied to the tillage and harvest emission rate of 

each crop. Annual emissions were then recalculated utilizing the controlled emission rates.

As crop cultivation on individual agricultural fields in the nonattainment area were not resolved 

outside of the modeling domains, ADEQ relied on the crop distribution reported in the 2007 

Agricultural Census to determine an average controlled emission rate for all parcels. This was 

performed by first determining the percentage of the year each crop was either in a disturbed 

state (tillage and harvesting seasons) and a stabilized state (cropland season) (Exhibit DI3). These 

percentages were reduced by the control effectivenesses previously reported (by multiplying by 

[1 – CE] listed in Table D3-7). These values were then multiplied individually by their 

corresponding proportion of agricultural land within the nonattainment area and crop cover 

value assigned to each crop (detailed in Appendix B, Section 3.1.2). Disturbed and stable values 

were then summed across all crop types in order to calculate a weighted disturbance level and 

stabilized disturbance level for all agricultural fields within the nonattainment area. These 

controlled and weighted disturbance levels were then multiplied by wind speed dependent 

emission rates for each high wind hour of the year for each agricultural crop parcel in order to 

calculate total nonattainment area crop windblown dust.

Commercial Farm Roads

While ADEQ calculated a slightly increased control effectiveness for activity related emissions on 

commercial farm roads on high risk days as opposed to non-high risk days (44.9% vs. 45.4%), 

ADEQ did not have adequate information to identify individual commercial farm roads in the 
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 21

nonattainment area that experience >20 VDT. Therefore, a conservative approach was taken by 

ADEQ and all agricultural roads were assumed to be controlled using the non-high risk day control 

effectiveness for activity related emissions on all design days and for the annual inventory 

calculation. The emission reduction calculation due to these controls was realized by multiplying 

the agricultural unpaved road reentrained road dust emission factor by 1 minus the activity 

control effectiveness. This revised emission factor was then used to calculate controlled 

agricultural unpaved road dust.

Windblown dust control effectiveness for commercial farm roads was calculated as 38.8% on 

both non-high risk days and high risk days. The emission reduction calculation due to these 

controls was realized by multiplying the agricultural unpaved road reentrained road dust 

emissions calculated for each road segment by 1 minus the windblown control effectiveness.

D3.1.1.8 Crop Operation Control Emission Reductions

As previously noted, crop operation controlled emission reductions were credited towards 

attainment demonstration of the NAAQS for 2018. This section presents the calculated emission 

reductions for each modeling domain for the high wind and stagnation design days and within 

the nonattainment area for the year 2018.

Design Day Emission Inventory Reductions

Table D3-8 and Table D3-9 present the emission reductions attributable to crop operation 

controls for each design day modeled for attainment. It should be noted that the Cowtown and 

Maricopa modeling domains contain Tribal and non-Tribal agriculture. Controls were assumed to 

not impact Tribal agricultural operations as these operations are not under the jurisdiction of 

R18-2-610.03. As such, their emissions are excluded from Table D3-8 and Table D3-9.
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 22

Table D3-8. 2018 Crop Agriculture Emission Reductions for Stagnation Design Days

Modeling Domain EI Sector Uncontrolled 

Emissions (lbs/day)

Controlled 

Emissions (lbs/day)

Emission Reductions 

(lbs/day)

Cowtown

Non-Tribal Crop Agriculture* 232 96 136

Unpaved Agricultural Roads 3,798 2,093 1,705

Pinal County Housing Crop Agriculture 379 156 223

Unpaved Agricultural Roads 1,837 1,012 825

Stanfield Crop Agriculture 880 364 516

Unpaved Agricultural Roads 7,628 4,203 3,425

Table D3-9. 2018 Crop Agriculture Emission Reductions for High Wind Design Days

Emission Type Modeling Domain EI Sector

Uncontrolled 

Emissions 

(lbs/day)

Controlled 

Emissions 

(lbs/day)

Emission 

Reductions 

(lbs/day)

Activity

Cowtown

Non-Tribal Crop 

Agriculture*

645 238 407

Unpaved Agricultural Roads 3,253 1,792 1,461

Maricopa

Non-Tribal Crop 

Agriculture*

178 74 104

Unpaved Agricultural Roads 2,023 1,114 909

Pinal County 

Housing

Crop Agriculture 954 367 587

Unpaved Agricultural Roads 3,422 1,886 1,536

Stanfield Crop Agriculture 1,058 413 645

Unpaved Agricultural Roads 7,268 4,004 3,264

Windblown

Cowtown

Non-Tribal Crop 

Agriculture*

3,906 1,127 2,779

Unpaved Agricultural Roads 340 208 132

Maricopa

Non-Tribal Crop 

Agriculture*

4,899 1,281 3,618

Unpaved Agricultural Roads 539 330 209
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 23

Pinal County 

Housing

Crop Agriculture 31,617 10,112 21,505

Unpaved Agricultural Roads 2,303 1,410 893

Stanfield Crop Agriculture 4,619 1,084 3,535

Unpaved Agricultural Roads 280 171 109

*The Cowtown and Maricopa domains included Tribal crop agriculture which was assumed to not be affected by controls.
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 24

Annual Emission Inventory Reductions

Table D3-10 presents the annual emission reductions attributable to crop operation controls. 

Table D3-10. 2018 Crop Agriculture Emission Reductions for the Annual Inventory

Emission 

Type EI Sector

Uncontrolled 

Emissions 

(TPY)

Controlled 

Emissions 

(TPY)

Emission 

Reductions 

(TPY)

Activity Crop Agriculture 2,853 1,865 988

Unpaved Agricultural Roads 16,350 9,009 7,341

Windblown Crop Agriculture 19,510 7,122 12,388

Unpaved Agricultural Roads 1,756 1,051 705

D3.1.2 Requirements for Commercial Dairy Operations

R18-2-611.03 establishes the rules targeted at controlling dust generation from commercial dairy 

operations within the Pinal County PM10 Nonattainment Area. Controls adopted under the 

general permit established in this rule focus on the following commercial dairy 

sectors/operations:

 Arenas, Corrals, Pens (R18-2-611.03(D)(1)),

 Animal Waste (and Feed) Handling and Transporting (R18-2-611.03(D)(2)),

 Animal Access Connections (R18-2-611.03(D)(3)),

 Unpaved Roads or Feed Lanes (R18-2-611.03(D)(4)),

For these sectors/operations, a commercial farmer is required to implement controls from a 

menu of ADEQ approved measures which will reduce activity related and/or windblown PM10 

emissions on all days of the year. For high risk forecast days, commercial crop farmers must 

ensure implementation of controls on unpaved roadways of >20 vehicle daily trips (VDT) from a 

more restrictive menu of options.

This section describes the methods by which emission reductions were estimated in response to 

the implementation of R18-2-611.03(D). Emission reductions calculated from the control of the 

above listed sector/operations are credited toward meeting contingency goals for the SIP.

D3.1.2.1 Dairy Operation Best Management Practices Surveys

In order to estimate commercial dairy operation emission reductions associated with the 

implementation of this rule, ADEQ needed to understand which BMPs a commercial dairy 

operation would be most likely to implement for each subsection of the rule. With the help of 

the Agricultural Best Management Practices Committee, ADEQ surveyed Pinal County dairy 

farmers to determine the likelihood of individual BMP implementation.

Control measure implementation feedback from dairy farmers was requested using the survey 

presented in Exhibit DI4. The survey requests information on which control measure/s each 
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 25

farmer expects to implement for each sector/operation. Individual surveys were requested from 

each dairy farmer operating in the nonattainment area. Surveys were distributed by the Ag BMP 

Committee. Surveys were either made available directly to growers or an electronic copy was 

provided. Compiled and raw survey results for all farmers were received electronically by ADEQ 

from the Ag BMP Committee designated personnel on June 13th, 2014. 

Responses to the dairy operation surveys were received from 23 farmers in the Pinal County 

nonattainment area. Ag BMP committee designated personal reported that 25 operations 

operate within the Pinal PM10 nonattainment area. This represents a 92% responsiveness rate 

from surveyed dairies in the nonattainment area, indicating that the sample size utilized for dairy 

control efficiency calculation is extremely robust. Furthermore, analysis of survey results by 

ADEQ personnel revealed that all surveys received responded in a manner that was consistent 

with all requirements of R18-2-611.03(D) (i.e., each farmer responded with at least one BMP they 

plan to implement for each of the commercial dairy sectors/operations listed above). Therefore, 

all survey results received were included in the calculation of dairy control efficiencies.

D3.1.2.2 Arenas, Corrals, Pens Control Efficiency Calculation

Emission reductions from control of arenas, corrals, and pens (R18-2-611.03(D)(1), henceforth 

referred to as arenas) were credited toward meeting contingency measure goals. Dairy operation 

BMP surveys were used as guidance to determine the percentage of farmers implementing a 

given BMP. Each survey provided responses adequate to meet the requirements of R18-2-

611.03(D)(1). Where farmers reported multiple BMPs for implementation, the farmer was only 

given credit for that control with the highest efficiency (a list of control efficiencies and their 

references are presented in Exhibit DI5).

Average arena activity control efficiency was calculated by weighting individually assigned dairy 

facility control efficiencies (assigned using the methods outlined in the preceding paragraph) by 

the dairy facility’s head of cattle reported, using the sample method below:

Simplified Example Calculation

 Farmer A responds that he/she will ‘Feed Green Chop’ (10% control efficiency for arena 

activity emissions) as a control for his/her 11,000 head of cattle.

 Farmer B responds that he/she will ‘Cement Dairy Cattle Walkways to Milk Barn’ (25% 

control efficiency for arena activity emissions) as a control for his/her 2,000 head of cattle.

 Farmer C responds that he/she will use ‘Water Misting Systems’ (10% control efficiency 

for arena activity emissions) as a control for his/her 6,900 head of cattle.

Based on this information average activity emission control efficiency for arenas was calculated 

utilizing the following equation:

CEavg =

(CEa × Na) + (CEb × Nb) + (CEc × Nc)

Na + Nb + Nc

which becomes
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 26

CEavg =

(10% × 11,000 head) + (25% × 2,000 head) + (10% × 6,900 head)

11,000 head + 2,000 head + 6,900 head = 12%

where

CEavg = average dairy activity emission control efficiency (%),

CEx = the activity emission control efficiency assigned to facility x given the survey 

response (where x = A, B, C...), and

Nx = the head of cattle reported for facility x (where x = A, B, C...).

BMPs for arena emission control are outlined in R18-2-611.03(D)(1). Some of these controls 

reduce not only activity emissions but also stabilize the soil surface, reduce wind speed or alter 

its path across the soil surface, or provide ground cover to reduce windblown dust emissions. As 

such, average windblown dust emission control efficiencies were calculated using the method 

described above except where activity emission efficiencies were replaced by windblown 

emission efficiencies. ADEQ did not take any additional emission reduction credits for those days 

identified as high risk.

D3.1.2.3 Animal Waste (and Feed) Handling and Transporting Control 

Efficiency Calculation

Emission reductions from control of animal waste (and feed) handling and transporting 

(henceforth referred to as handling and transporting) (R18-2-611.03(D)(2)) were credited toward 

meeting contingency measure goals. Dairy operation BMP surveys were used as guidance to 

determine the percentage of farmers implementing a given BMP. Each survey provided 

responses adequate to meet the requirements of R18-2-611.03(D)(2). Where farmers reported 

multiple controls for implementation, the farmer was only given credit for that control with the 

highest control efficiency (a list of control efficiencies and their references are presented in 

Exhibit DI5).

Average activity emission handling and transporting control efficiency was calculated by 

weighting individually assigned dairy facility control efficiencies (assigned using the methods 

outlined in the preceding paragraph) by the dairy facility’s head of cattle reported, using the 

sample method below:

Simplified Example Calculation

 Farmer A responds that he/she will ‘Cover Manure Hauling Trucks’ (10% control efficiency 

for handling and transporting activity emissions) as a control for his/her 11,000 head of 

cattle.

 Farmer B responds that he/she will ‘Feed High Moisture Feed’ (10% control efficiency for 

handling and transporting activity emissions) as a control for his/her 2,000 head of cattle.

 Farmer C responds that he/she will ‘Store Silage in Bunkers’ (10% control efficiency for 

handling and transporting activity emissions) as a control for his/her 6,900 head of cattle.
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 27

Based on this information we can calculate average activity emission control efficiency for 

handling and transporting utilizing the following equation:

CEavg =

(CEa × Na) + (CEb × Nb) + (CEc × Nc)

Na + Nb + Nc

which becomes

CEavg =

(10% × 11,000 head) + (10% × 2,000 head) + (10% × 6,900 head)

11,000 head + 2,000 head + 6,900 head = 10%

where

CEavg = average dairy activity emission control efficiency (%),

CEx = the activity emission control efficiency assigned to facility x given the survey 

response (where x = A, B, C...), and

Nx = the head of cattle reported for facility x (where x = A, B, C...).

BMPs for handling and transport emission control are outlined in R18-2-611.03(D)(2). Some of 

these controls reduce not only activity emissions but also stabilize the soil surface, reduce wind 

speed or alter its path across the soil surface, or provide ground cover to reduce windblown dust 

emissions. As such, average windblown dust emission control efficiencies were calculated using 

the method described above except where activity efficiencies were replaced by windblown 

efficiencies. ADEQ did not take any additional emission reduction credits for those days identified 

as high risk.

D3.1.2.4 Unpaved Access Connection Control Efficiency Calculation

Emission reductions from controls implemented on unpaved access connections (R18-2-

611.03(D)(3)) were credited toward meeting contingency measure goals. Dairy operation BMP 

surveys were used as guidance to determine the percentage of farmers implementing a given 

BMP. Each survey provided responses adequate to meet the requirements of R18-2-611.03(D)(3).

Where farmers reported multiple controls for implementation, the farmer was only given credit 

for that control with the highest control efficiency (a list of control efficiencies and their 

references are presented in Exhibit DI5).

Average activity emission unpaved access connection control efficiency was calculated by 

weighting individually assigned dairy facility efficiencies (assigned using the methods outlined in 

the preceding paragraph) by the dairy facility’s head of cattle reported, using the sample method 

below:

Simplified Example Calculation

 Farmer A responds that he/she will ‘Apply and Maintain Aggregate Cover’ (46% control 

efficiency for unpaved access connection activity emissions) as a control for his/her 

11,000 head of cattle.
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 28

 Farmer B responds that he/she will ‘Install Signage to Limit Vehicle Speed to 15mph’ (23% 

control efficiency for unpaved access connection activity emissions) as a control for 

his/her 2,000 head of cattle.

 Farmer C responds that he/she will ‘Restrict Access to Through Traffic’ (2% control 

efficiency for unpaved access connection activity emissions) as a control for his/her 6,900 

head of cattle.

Based on this information we can calculate average activity emission control efficiency for 

unpaved access connections utilizing the following equation:

CEavg =

(CEa × Na) + (CEb × Nb) + (CEc × Nc)

Na + Nb + Nc

which becomes

CEavg =

(46% × 11,000 head) + (23% × 2,000 head) + (2% × 6,900 head)

11,000 head + 2,000 head + 6,900 head = 28%

where

CEavg = average dairy activity emission control efficiency (%),

CEx = activity emission control efficiency assigned to facility x given their survey 

response (where x = A, B, C...), and

Nx = the head of cattle reported for facility x (where x = A, B, C...) 

(Note: Please note that ADEQ assumed the length of unpaved access connections for a 

facility is proportional to the size of the facility, which is in turn proportional to 

the number of head of cattle within a facility. As such, cattle head is used as a 

surrogate for unpaved access connection length.)

BMPs for unpaved access connection emission control are outlined in R18-2-611.03(D)(3). Some 

of these controls reduce not only activity emissions but also stabilize the soil surface, reduce wind 

speed or alter its path across the soil surface, or provide ground cover to reduce windblown dust 

emissions. As such, average windblown dust emission control efficiencies were calculated using 

the method described above except where activity efficiencies were replaced by windblown 

efficiencies. ADEQ did not take any additional emission reduction credits for those days identified 

as high risk.

D3.1.2.5 Unpaved Roads or Feed Lanes Control Efficiency Calculation

Emission reductions from control of unpaved roads or feed lanes (henceforth referred to as 

unpaved roads) (R18-2-611.03(D)(4)) were credited toward meeting contingency measure goals.

Dairy operation BMP surveys were used as guidance to determine the percentage of farmers 

implementing a given BMP. Each survey provided responses adequate to meet the requirements 

of R18-2-611.03(D)(4). Where farmers reported multiple controls for implementation, the farmer 

was only given credit for that control with the highest efficiency (a list of control efficiencies and 
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 29

their references are presented in Exhibit DI5). ADEQ assumed these controls would be applied to 

all unpaved roads within the operation.

Average activity emission unpaved road control efficiency was calculated by weighting 

individually assigned dairy facility efficiencies (assigned using the methods outlined in the 

preceding paragraph) by the dairy facility’s head of cattle reported, using the sample method 

below:

Simplified Example Calculation

 Farmer A responds that he/she will ‘Apply and Maintain Water as a Dust Suppressant’ 

(67% control efficiency for unpaved road activity emissions) as a control for his/her 11,000 

head of cattle.

 Farmer B responds that he/she will ‘Install Engine Speed Governors on Feed Trucks to 15 

mph’ (23% control efficiency for unpaved road activity emissions) as a control for his/her 

2,000 head of cattle.

 Farmer C responds that he/she will ‘Restrict Access to Through Traffic’ (2% control 

efficiency for unpaved access connection activity emissions) as a control for his/her 6,900 

head of cattle.

Based on this information we can calculate average activity emission control efficiency for 

unpaved roads utilizing the following equation:

CEavg =

(CEa × Na) + (CEb × Nb) + (CEc × Nc)

Na + Nb + Nc

which becomes

CEavg =

(67% × 11,000 head) + (23% × 2,000 head) + (2% × 6,900 head)

11,000 head + 2,000 head + 6,900 head = 40%

where

CEavg = average dairy activity emission control efficiency (%),

CEx = activity emission control efficiency assigned to facility x given their survey 

response (where x = A, B, C...), and

Nx = the head of cattle reported for facility x (where x = A, B, C...) 

(Note: Please note that ADEQ assumed the length of unpaved roads for a facility is 

proportional to the size of the facility, which is in turn proportional to the number 

of head of cattle within a facility. As such, cattle head is used as a surrogate for 

unpaved road length.)

BMPs for unpaved road emission control are outlined in R18-2-611.03(D)(4). Some of these 

controls reduce not only activity related emissions but also stabilize the soil surface, reduce wind 

speed or alter its path across the soil surface, or provide ground cover to reduce windblown dust 

emissions. As such, average windblown dust emission control efficiencies were calculated using 

the method described above except where activity efficiencies were replaced by windblown 
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 30

emission efficiencies. ADEQ did not take any additional emission reduction credits for those days 

identified as high risk.

D3.1.2.6 Commercial Dairy Operations Control Effectiveness

Control effectiveness is calculated as the product of control efficiency, rule effectiveness, and 

rule penetration. For each of the control efficiencies described above, control effectiveness was 

calculated in order to adequately reflect emission reductions. For all operations/sectors outlined 

above a facility-wide rule penetration and rule effectiveness was estimated. The methods by 

which these were estimated are presented below.

Rule Penetration Estimation

ADEQ utilized 2007 USDA Agricultural Census data for determining the rule penetration of R18-

2-611.03(D). R18-2-611 defines a commercial dairy as having a minimum of 150 cattle head. The 

2007 Agricultural Census reported the number of statewide dairies by resident cattle head range 

and the total head within each range for the State of Arizona (Table D3-11). ADEQ was unable to 

locate county specific data for dairy cattle, and thus, State of Arizona data is used as a surrogate.

As emissions from dairies are estimated based on the head of cattle, rule penetration is 

dependent on the head count to which the rule is applicable. Therefore, ADEQ utilized Table 

D3-11 to estimate the percentage of cattle to which the rule would apply. This percentage was 

assumed to equate to rule penetration.

Table D3-11. State of Arizona Dairy Farm Distribution

Head / Farm Farms (#) Total head (#)

1 to 9 103 *

10 to 19 - -

20 to 49 1 *

50 to 99 1 *

100 to 199 4 630

200 to 499 6 2,340

500 or more 67 180,517

Total 182 183,744

*Below reported limit

R18-2-611 defines a commercial dairy operation as having >150 dairy cattle. As (USDA, 2012)

reports cattle head per farm for the size distribution of 100-199 (Table D3-11), ADEQ assumed 

that half of the 630 cattle within this range would reside on operations of >150 head.

Furthermore, while Table D3-11 does not specify the cattle head associated with the operation 

ranges below 100 head/farm, it can be calculated as the total head reported less those cattle 

reported on operations >100 head (i.e., 183,744 – [180,517 + 2,340 + 630] = 257 head). Therefore, 

ADEQ estimates that 572 cattle head occur on farms with <150 head. Finally, ADEQ estimates 

rule penetration as 99.7% (i.e., 99.7% = [183,744 – 572] / 183,744).
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 31

Rule Effectiveness

ADEQ assumed rule effectiveness to be equal to that of Commercial Beef Cattle Feedlots. Please 

refer to Section D3.1.3 for a description of how rule effectiveness was estimated for these 

operations.

Control Effectiveness Calculation

Control Effectiveness is calculated as the product of control efficiency, rule effectiveness, and 

rule penetration. ADEQ calculated an activity emission and windblown emission control 

effectiveness for each dairy operation/sector (Table D3-12).

Table D3-12. Commercial Dairy Operation Control Effectivenesses by Operation / Sector

Operation / Sector Activity Control 

Effectiveness (%)

Windblown Control 

Effectiveness (%)

Unpaved Access Connections and Equipment Areas 33.9% 33.1%

Unpaved Roads or Feed Lanes 33.4% 32.6%

Animal Feeding, Waste Handling and Transporting 6.9% 6.9%

Arenas, Corrals and Pens 18.2% 12.8%

As dairy emissions are estimated by one emission factor that takes into account all emission 

sources within a facility on a per head basis, ADEQ estimated facility-wide control effectivenesses 

by utilizing emission rate estimates from (Governor's Agricultural BMP Committee, 2010) (see 

Table D3-13). From these emission factors ADEQ was able to calculate the relative fraction each 

operation/sector produces. The sum product of the fraction of total emissions presented in Table 

D3-13 was multiplied by the activity control effectiveness and windblown control effectiveness 

for each operation/sector (Table D3-12) in order to calculate average annual dairy control 

effectiveness for activity emissions and windblown emissions respectively (Table D3-14).

Table D3-13. Dairy Cattle Emission Factors by Livestock Activity

Operation / Sector Emission Factor 

(lbs/hd/yr)

Fraction of Total 

Emissions (%)

Unpaved Access Connections and Equipment Areas 0.123 2.36%

Unpaved Roads or Feed Lanes 0.369 7.08%

Animal Feeding, Waste Handling and Transporting 0.123 2.36%

Arenas, Corrals and Pens 4.60 88.2%

Table D3-14. Average Annual Dairy Control Effectiveness

Emission Scenario Control Effectiveness (%)

Activity Emissions 19.4%

Windblown Emissions 14.5%
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 32

Control Effectiveness Application

As dairy emission reductions were only credited towards contingency requirements, emission 

reductions were only calculated at the annual, nonattainment area-wide scale. Activity emissions 

were reduced by the control effectiveness listed in Table D3-14 by multiplying the uncontrolled 

dairy emission factor by 0.806 (i.e., 0.806 = 1 - 0.194). This controlled emission factor (4.20 

lbs/head/yr) was then used to calculate controlled dairy emissions nonattainment area-wide.

Dairy windblown emission reductions were calculated on the annual scale for the nonattainment 

area by alteration of the disturbed : stable land ratio of dairies. Uncontrolled dairies are assumed 

to have a disturbed : stable ratio of 50% : 50% (Appendix B, Section 3.1.2). ADEQ assumed dairy 

controls would reduce disturbed and stable emissions by the control effectiveness listed in Table 

D3-14. As such, the disturbed : stable ratio was reduced to 42.7% : 42.7% (i.e., 42.7% = 50% -

[50% * 14.5%]). This ratio was then applied to all dairy operations and uncontrolled stable and 

disturbed windblown dust emission factors were applied to calculate windblown dust originating 

from controlled dairies within the nonattainment area for 2018.

D3.1.2.7 Dairy Operation Control Emission Reductions

As previously noted, dairy operation controlled emission reductions were credited towards 

contingency measure requirements. This section presents the calculated annual nonattainment 

area-wide emission reductions for the year 2018. As this control was utilized for contingency 

purposes, design day emissions reductions were not analyzed.

Annual Emission Inventory Reductions

Table D3-15 presents the annual emission reductions attributable to crop operation controls. 

Table D3-15. 2018 Emission Reductions Attributable to Dairy Operation Controls

Emission Type Uncontrolled 

Emissions (TPY)

Controlled 

Emissions (TPY)

Emission 

Reductions (TPY)

Activity 184 148 36

Windblown 450 384 66

D3.1.3 Requirements for Commercial Beef Cattle Feedlots

Unlike crop and dairy operations, the number of commercial beef cattle feedlots covered by the 

rule is quite limited. Only 3 beef feedlots with more than 500 head of cattle (the size threshold 

prescribed in R18-2-611) were identified in the 2018 inventory of the NA, as the result of the 

closure, consolidation, or repurposing of the other operations in the 2008 inventory. These 3 

feedlots account for all of the CAFO emissions in the 2018 inventory (there are no operations 

which expect to raise under 500 head in 2018; see Table D3-16), and so the overall rule 

penetration for this section is set at 100%.

Because there is no inspection and compliance history for these 3 operations, the rule 

effectiveness was conservatively assumed to be in the lowest interval specified for non-point 

sources in Attachment 2 of (EPA, 2005), Range 3 (less than 70%). To acknowledge the 
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 33

recordkeeping/compliance requirements in Subsections H, I, and J of R18-2-611.03, ADEQ 

selected a rule effectiveness of 69% (the highest whole number percentage in Range 3).

Subsections A and E of the rule require commercial beef cattle feedlot operators to select a BMP 

from each of 4 menus, covering emissions from various activities on the lot. Since only 3 

operations are included in the inventory, the accuracy of ADEQ’s estimates of controlled 

emissions relies heavily on the selection of BMPs at each feedlot. The operators of each feedlot 

were surveyed by an expert from the Arizona Cattle Feeders’ Association— (Aja, 2015)—who 

provided ADEQ with detailed information regarding activity levels and emission factor 

parameters prior to BMP implementation, as well as each operation’s expected BMP choices.

Unless otherwise noted, all activity levels and emission factor parameters below are estimated 

based on that source.

D3.1.3.1 Arena, Corral, and Pen Floors

The first menu, R18-2-611.03(E)(1), prescribes control options for the arenas, corrals, and pens 

in which the cattle are usually kept. Two operations are expected to remove manure from pen 

floors every six months (the baseline frequency was once per year), while the third is expected 

to apply at least 3 gallons of water per head to the pen floor each day (the baseline range was 1-

3 gal/head-day; for simplicity, ADEQ assumed an average of 2 gal/head-day). PM10 emissions 

from pen floors, both activity-driven and windblown, originate from manure on the pen floor. If 

the manure is dehydrated, the mechanical activity of the cattle’s hooves will cause it to crumble, 

making it vulnerable to suspension by high winds or by further hoof activity. If it is sufficiently 

hydrated, hoof action will instead compress the manure into a less emissive, compacted layer. 

Therefore, the control methods discussed in this section are preventative to the extent that they 

cause manure to remain hydrated. The average moisture content of the manure depends on how 

much manure is present and how much moisture is added. According to (Sakirkin, Maghirang, 

Amosson, & Auvermann, 2012), 650 gallons per acre per inch of manure is the conservative limit 

of added moisture required to reach 30% moisture content. ADEQ therefore used the equation 

M =

30%

650 gal/acre-in

∙

W∙H

A∙D

to determine the manure moisture content, where M is the percent 

moisture content, W is the daily water volume in gal/head, H is the number of head on the lot, A

is the area of the lot in acres, and D is the depth of the uncompacted manure in inches. The 

baseline manure depth (when removal occurs only once per year) maxed out at around 4 inches 

just before removal, meaning that the average depth was around half of that maximum, i.e., 2 

inches. If removed twice as frequently, that average depth is expected to be reduced to about 1 

inch. Table D3-16 shows the calculated moisture contents of each feedlot before and after BMP 

implementation, and the resulting reductions in relative potential to generate dust. The average 

reduction (weighted by head) is 24.74%.
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 34

Table D3-16. Typical Cattle Pen Dust Reductions

Head 

count

Pen 

Area 

(acres)

BMPs

Average 

Uncompacted 

Manure Depth 

(inches)

Daily 

Water 

Application 

(gal/hd)

Moisture 

Content

Dust 

Potential

7

Dust 

Potential 

Reduction8

35,000 155

None (baseline) 2 2 10.42% 81.61%

13.27%

Add water to pens 2 3 15.63% 70.79%

45,000 140

None (baseline) 2 2 14.84% 72.47%

Frequent manure 41.84%

removal 1 2 29.67% 42.15%

48,000 300

None (baseline) 2 2 7.38% 87.57%

Frequent manure 17.08%

removal 1 2 14.77% 72.61%

Subsection C requires all commercial beef feedlots, regardless of their election for ordinary days, 

to apply at least 3 gal/head on high-risk days. Thus the latter two feedlots in Table D3-16 would 

have to take additional action on such days. Table D3-17 shows the resulting impacts, which 

changes the weighted average reduction to 42.33%.

Table D3-17. High Risk Day Cattle Pen Dust Reductions

Head 

count

Pen 

Area 

(acres)

BMPs

Average 

Uncompacted 

Manure Depth 

(inches)

Daily 

Water 

Application 

(gal/hd)

Moisture 

Content

Dust 

Potential

Dust 

Potential 

Reduction

35,000 155

None (baseline) 2 2 10.42% 81.61%

13.27%

Add water to pens 2 3 15.63% 70.79%

45,000 140

None (baseline) 2 2 14.84% 72.47%

Frequent manure 72.86%

removal, add water 1 3 44.51% 19.67%

48,000 300

None (baseline) 2 2 7.38% 87.57%

Frequent manure 34.89%

removal, add water 1 3 22.15% 57.01%

On an annual basis, 45 days out of every 365.33 is expected to be high risk9. The resulting annual 

average control efficiency for activity-based emissions is calculated as

45 ∙ 42.33% + (365.33 − 45) ∙ 24.74%

365.33 ≈ 26.91%.

For windblown dust, the average reductions were weighted not by head count, but by pen 

acreage. The resulting averages are 21.91% for a typical day and 38.19% for a high-risk day. 

Because windblown dust is emitted only during high-wind hours, the annual average of these 

 

7 Moisture contents were converted to relative dust potentials using Figure 4 of (Sakirkin, Maghirang, Amosson, & 

Auvermann, 2012).

8 Relative dust potential reduction is calculated as a percentage of the old potential, e.g., 

81.61%−70.79%

81.61%

≈ 13.27%.

9 The 45-day estimate was based on a study of 3 years of data, including a leap day. See Section D3.1.
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 35

control efficiencies is calculated based on high-wind hours, rather than calendar days. The CWT 

and STF modeling domains contain all three feedlots in the NA. Since each the CWT and STF highwind design days have 7 high-wind hours, ADEQ assumed that each of the 35 high-wind, high risk 

forecast days would also include 7 high-wind hours. Thus, 245 high-wind hours per year are 

covered by the forecast. The STF monitor had 287 total high-wind hours in 2008, and the CWT 

monitor had 692, for an average of 489.5 high wind hours per year. Therefore, the annual average 

control efficiency for windblown dust is calculated as

245 ∙ 38.19% + (489.5 − 245) ∙ 21.91%

489.5

≈ 30.04%.

D3.1.3.2 Feed Handling

The second menu, R18-2-611.03(E)(2), prescribes control options for the handling and transport 

of animal waste and feed. In keeping with (Governor's Agricultural BMP Committee, 2010), ADEQ 

estimated control efficiency at a conservative 10% for every BMP on the menu.

D3.1.3.3 Unpaved Access Connections

The third menu, R18-2-611.03(E)(3), prescribes control options for unpaved feedlot roads which 

intersect the public paved road network. These BMPs are intended to reduce trackout from 

unpaved feedlot roads onto the paved roads. Trackout is incorporated into paved road emissions, 

and so these controls do not affect the CAFO emissions total. Moreover, the amount of trackout 

from feedlots as a subset of agricultural trackout is not quantified; because of the relatively small 

number of beef feedlots relative to the large number of crop operations, it is likely that any 

appreciable rule penetration would be an overestimate. Thus the reductions due to this provision 

are not quantified.

D3.1.3.4 Unpaved Roads

The fourth menu, R18-2-611.03(E)(4), prescribes control options for travel on unpaved roads or 

feed lanes. All 3 operations are expected to choose a BMP which reduces their vehicle speeds to 

15 mph on unpaved roads (they are also expected to install signs and fences restricting thru 

traffic, but since the rule only requires one BMP from this category, ADEQ is only considering the 

effects of the speed reduction). Using the unpaved road dust equation in Section 13.2.2 of (EPA, 

Updated 2006), ADEQ calculated emission factors for both the baseline speed of 25 mph and the 

reduced speed.10 The control efficiency for this BMP was calculated as the relative difference of 

these emission factors, 1.857 lbs/VMT−1.438 lbs/VMT

1.857 lbs/VMT ≈ 22.55%.

 

10 For both the baseline and controlled scenarios, silt and moisture contents of the road surface were assumed to 

match the agricultural road parameters in Table 5-11 of Appendix B.
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 36

D3.1.3.5 Summary of Reductions

Activity-based emissions from feedlots are apportioned across 4 subcategories according to 

Table 3-19 in Appendix B. The overall distribution is summarized in Table D3-18.

Table D3-18. CAFO Activity-Based Emissions Distribution

Source Group Proportion of Uncontrolled Emissions

Unpaved areas 10.25%

Unpaved roads 15.00%

Feed 6.00%

Pens 68.75%

We note that (Aja, 2015) indicated that all 3 operations apply water to stabilize unpaved areas 

(equipment staging and storage areas) on a voluntary basis. However, since unpaved area

controls are not required in the beef cattle feedlot provision, the resulting reductions are not

enforceable. ADEQ therefore estimated no reductions in that subset of emissions.

The average annual control efficiency for activity-based emissions from beef cattle feedlots 

(weighted by the above distribution) is 22.48%. Thus, the annual reductions for activity-based 

emissions are 100% ∙ 69% ∙ 22.48% ∙ 1,620.6 TPY ≈ 251.4 TPY. For windblown dust, ADEQ 

compared the total area of the pens (595 acres) to the total CAFO land use area in the NA (910 

acres), determined via GIS. Using this ratio as a sort of rule penetration, ADEQ calculated a control 

effectiveness of 595 acres

910 acres

∙ 69% ∙ 30.04% ≈ 13.57%. However, because the ratio was based on land 

area rather than emissions, ADEQ did not apply this control effectiveness directly to the annual 

windblown dust emissions total. Instead, ADEQ adjusted the disturbed vs. stable proportions of 

the CAFO land use type from 50%-50% to 63.57% stable and 36.43% disturbed, essentially 

stabilizing 13.57% of the total area of feedlots in the NA. The resulting annual emissions 

reductions totaled 29.9 TPY.

For the CWT and STF design day inventories (the only ones which included CAFO emissions), 

ADEQ followed the same procedure, but used high risk day control efficiencies instead of annual 

average control efficiencies. The resulting activity-based emissions reductions were 738.4 

lbs/day at CWT, 764.2 lbs/day for the STF stagnation design day, and 940.9 lbs/day for the STF 

high-wind design day11. An additional reduction of 55.0 lbs/day of windblown dust was estimated 

for the STF high-wind design day, for a total reduction of 995.9 lbs/day.

D3.1.4 Requirements for Irrigation Districts

R18-2-612.01 provides a menu of BMPs for unpaved operation and maintenance roads, canal 

maintenance, and unpaved utility access roads. Because emissions from canal maintenance were 

not separately quantified in the EI, no reductions were quantified for those BMPs (e.g., 

 

11 The difference in activity-based reductions at STF is due to the slightly different shapes and sizes of the activitybased modeling domains for the stagnation design day and the high-wind design day. The larger of the two CAFOs 

in Stanfield straddles the domains’ boundaries; over 100 of its acres are inside the high-wind day domain but outside 

of the stagnation day domain.
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 37

windblown fugitives from dredged material dumped onto land use category X were not singled 

out among the windblown emissions from X in general). Similarly, because access roads were not 

distinguished among unpaved roads, no credit was taken for those reductions.

In a GIS analysis of the unpaved road network, PCAQCD identified a subset of private unpaved 

roads as canal roads (i.e., irrigation operation and maintenance roads). For each such road, ADEQ 

applied the same control efficiencies as were used for agricultural unpaved roads in general (see 

Section D3.1.1.5). The resulting reductions were 577.6 TPY of activity-based unpaved road dust 

and 144.7 TPY of windblown unpaved road dust.

D3.1.5 Other Provisions

R18-2-611.03 also provides for emissions regulation in commercial swine and poultry operations, 

but the 2018 NA inventory does not include any such operations. Therefore ADEQ estimated no 

reductions from these provisions.

D3.2 Attainment Inventories

Table D3-19 summarizes the annual NA-wide inventory as well as the design day inventories, 

itemized by source category (compare to the base scenario emissions in Table D2-1).
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 38

Table D3-19. Summary of Attainment Inventories

Source Category Annual NA-Wide 

Emissions (TPY)

Emissions for Modeling Domains (lbs/day)

Stagnation Design Days High-Wind Design Days

CWT PCH STF CWT MC PCH STF

Windblown Dust

Developed Urban 248.1 - - - 31.1 896.9 155.1 -

Developed Rural 1,959.7 - - - 257.9 1,307.2 1,257.6 161.1

Unpaved Roads 3,803.1 - - - 840.3 705.2 2,766.0 356.6

Cleared Areas 457.0 - - - 141.8 2,317.1 1,590.5 19.7

Construction 1,831.1 - - - 905.5 8,175.8 - -

Dairies 449.6 - - - - - - 126.0

CAFOs 125.3 - - - - - - 408.6

Desert Shrubland 38,276.7 - - - 3,881.5 1,063.1 4,600.3 967.2

Agriculture 7,122.0 - - - 1,126.7 1,281.3 10,111.9 1,083.8

Other 4,243.9 - - - 0.5 185.6 - -

Activity-Based Emissions

Agriculture 1,865.1 230.0 156.4 363.9 703.2 188.9 366.9 412.6

CAFOs 1,369.2 2,495.1 - 2,583.6 2,495.1 - - 3,180.2

Paved Roads 1,408.0 501.3 112.5 125.5 510.0 591.8 71.3 125.2

Unpaved Roads 37,186.412 10,180.6 2,922.1 8,901.7 9,345.3 3,404.1 5,086.5 8,230.3

Fuel Combustion 34.9 - - - - - 2.2 0.4

Fires 22.2 6.7 2.0 7.4 7.2 3.0 3.5 6.8

Open Burning 16.8 1.7 0.1 0.2 2.2 13.7 0.4 0.2

Nonroad 144.4 16.8 1.2 2.4 20.4 130.8 3.6 2.4

Railroad 45.4 15.1 - - 15.5 15.6 - -

Construction 8,499.8 3,651.0 1,131.0 2,348.0 - 3,667.0 935.0 14.0

Dairies 184.0 - 25.5 141.0 - - - 187.7

Point Sources 781.3 560.8 24.3 7.1 560.2 - 24.3 7.1

Unpaved Parking 251.5 99.2 57.6 45.0 112.1 48.3 101.5 43.3

Total 110,325.5 17,758.5 4,432.7 14,525.8 20,956.4 23,995.4 27,076.4 15,333.4

 

12 Note that emissions from unpaved test tracks at permitted vehicle testing facilities are not included here, but instead in the annual total for Point Sources. 

However, test track emissions are modeled just like other unpaved road emissions. Therefore, in design day inventories, they are included in Unpaved Roads.
Appendix D D3. Attainment Control Measures

December 21, 2015 Final Page 39

D3.3 Modeled Attainment Results

The results of the controlled design day modeling are summarized below. All of the values in 

Table D3-21 have been post processed, as described in Section D2 and summarized in Table 

D3-20. When rounded to the nearest 10, no expected concentration at the monitor exceeds 150 

μg/m3(though this limit is approached as closely as possible, as discussed in Section D4.2). Thus, 

since these are estimates of the 24-hour average PM10 concentrations monitored on the highrisk days expected in 2018, the NA is expected to comply with the PM10 NAAQS in 2018.

Table D3-20. Summary of Attainment Modeling Results

Design

day

Modeled 

local impact13

Scaling 

factor13

Local impact 

only (μg/m3)

Background concentration (μg/m3)Concentration at the 

monitor (μg/m3)

Stag.

CWT 514.1 27.4% 141.0

11.5

152.5

PCH 75.3 190.3% 143.4 154.9

STF 61.5 233.1% 143.2 154.7

High-Wind

CWT 198.4 52.1% 103.3

17.5

120.8

MC 343.6 31.1% 106.8 124.3

PCH 501.8 23.5% 117.8 135.3

STF 125.8 75.2% 94.7 112.2

Table D3-21. Attainment Modeling Results Apportioned by Source Category

Source Category

24-Hour Average Concentrations for Modeling Domains (μg/m3)

Stagnation Design Days High-Wind Design Days

CWT PCH STF CWT MC PCH STF

Windblown Dust

Developed Urban - - - 0.2 7.4 0.9 -

Developed Rural - - - 3.5 6.3 9.9 3.7

Unpaved Roads - - - 11.6 3.2 15.9 6.9

Cleared Areas - - - 1.2 15.4 5.7 2.6

Construction - - - 7.0 45.6 - -

Dairies - - - - - - 1.4

CAFOs - - - - - - 5.2

Desert Shrubland - - - 42.0 8.5 20.6 18.3

Agriculture - - - 13.1 5.0 55.5 17.7

Other - - - 0.0 0.8 - -

Activity-Based Emiss.

Agriculture 0.1 2.6 2.6 0.4 0.1 0.6 1.5

CAFOs 105.2 - 37.1 5.5 - - 9.2

Paved Roads 9.2 5.5 7.0 0.5 1.1 0.1 0.7

Unpaved Roads 24.3 93.9 91.1 17.9 1.8 8.9 27.1

Construction 1.8 35.7 0.3 - 11.3 - 0.2

Dairy - 0.0 0.0 - - - 0.1

Point Sources 0.2 0.0 - 0.5 - - -

Unpaved Parking 0.1 5.6 5.2 0.1 0.3 0.1 0.2

Regional Background 11.5 11.5 11.5 17.5 17.5 17.5 17.5

Total 152.5 154.9 154.7 120.8 124.3 135.7 112.2

 

13 The units for these values vary. See Footnote 2 on page 3 for details.
Appendix D D4. On-Road Motor Vehicle Emissions Budget

December 21, 2015 Final Page 40

D4 On-Road Motor Vehicle Emissions Budget

D4.1 Scope

This Motor Vehicle Emissions Budget (MVEB) establishes emissions limits for PM10 in the NA. The 

budget considers the combined contributions of on-road motor vehicles as well as active 

construction projects on those roads. Emissions from non-road vehicles are not included in the 

budget, nor are windblown dust emissions from the roads and construction sites. Subcategories 

of sources whose emissions are expected to remain fairly constant from one year to the next are 

also omitted. The budget is established for annual emissions totals, and anticipates various 

changes in traffic, fleet characteristics, road work, and new rules and regulations up through 

calendar year 2018. In a separate analysis, ADEQ also took into account the control measures 

described in Section D5.2, and determined that this budget would provide adequate room for 

anticipated growth for decades beyond 2018.

The purpose of this budget is to safeguard the air quality of the NA with respect to the 24-hour 

PM10 NAAQS. As discussed in Section D3.3, modeling indicates that the NA should attain the 

NAAQS as long as this budget is not exceeded, as long as the emissions are distributed 

appropriately (using planning tools such as hotspot analyses to prevent high concentrations of 

PM10 building up in a given place and time), and as long as no unforeseen growth occurs in 

emissions from PM10 source sectors other than those included in this budget.

The following sections describe which source categories are included in the budget and reviews 

the emissions estimation methodologies for each, and how the emissions subtotals in the budget 

were determined.

D4.1.1 Unpaved Roads

Unpaved road emissions were estimated for each of a variety of road types, under a variety of 

circumstances (e.g., public roads were subdivided into classes based on their average traffic 

volumes). For each road type and circumstance, two top-level equations were used to estimate 

PM10 emissions. Vehicle exhaust was calculated as:

EExhaust

PM10 = VMT × EFExhaustPM10

where EExhaust

PM10 is the total PM10 (in TPY) emitted as vehicle exhaust on unpaved roads in the NA, 

VMT is the total number of Vehicle Miles Traveled per year, and EFExhaust

PM10 is the speeddependent emission factor (in tons/VMT) generated by the MOVES model, which accounts for

changes in the vehicle fleet over time, among other factors. The overwhelming majority of 

unpaved road dust comes from reentrainment of PM10 from the physical disturbance of the road 

surface by vehicles, not from exhaust. That portion of road dust was calculated as:

EReentrainment

PM10 = VMT × EFReentrainmentPM10

where EFReentrainment

PM10 is an emission factor (in tons/VMT) calculated according to Section 13.2.2 

of (EPA, Updated 2006), and which accounts for average vehicle speed and weight as well as road 
Appendix D D4. On-Road Motor Vehicle Emissions Budget

December 21, 2015 Final Page 41

surface moisture and silt content. The road dust emission factor is not expected to change as a 

result of the controls described in Chapter D3, except for agricultural roads. The emission factors 

for some public irrigation roads are expected to be reduced by the controls described in Section 

D5.2, but those changes are not accounted for explicitly in this budget.

Most of the traffic counts used to determine the VMT for road types, categories and subtypes, 

and even individual roads were undertaken in January 2013 and January 2014. For Public and 

Private unpaved roads, ADEQ scaled VMT to better represent 2018 traffic volumes using the 

equation

E2018 = E2013 ×

POP2018

County

POP2013

County

where E2018 is the estimated emissions total for 2018, E2013 is the estimated emissions total as 

calculated using the 2013 traffic count, POP2018

County is the ADOA projection of the population in 

Pinal county in 2018, and POP2013

County is the corresponding estimate for 201314

. ADEQ did not use 

this forecasting adjustment on agricultural unpaved roads or on unpaved trails, because their 

traffic volumes do not necessarily covary with public road traffic volumes. Access to these roads

is generally restricted, which is also true of canal roads and roads located on the grounds of 

industrial/commercial/institutional stationary sources such as vehicle testing facilities or beef 

cattle feedlots. Emissions from such roads are omitted from the MVEB.

Recall from Table D3-19 that the NA-wide, activity-based emissions from unpaved roads total 

37,186.4 TPY in the 2018 attainment scenario. That total breaks down as follows:

Table D4-1. Attainment Scenario Unpaved Road Emissions

Unpaved Road Type Road Dust (TPY) Vehicle Exhaust (TPY) Total (TPY)

Public 17,300.5 1.1 17,301.6

Agricultural (omitted) 9,009.1 0.9 9,009.9

Irrigation (omitted) 1,038.3 0.1 1,038.4

Other Private 9,133.0 0.5 9,133.5

Trails (omitted) 702.9 0.1 702.9

Total 37,183.7 2.7 37,186.4

Taking only the Public and Other Private subtotals from Table D4-1 yields an unpaved road dust 

budget of 26,433.5 TPY as well as 1.6 TPY of vehicle exhaust.

 

14 ADOA projects county populations using 3 different sets of assumptions. ADEQ used projections from the Low 

Series, because these have been most closely correlated with actual population growth in the county from 2012-

2014. ADEQ obtained the Pinal County Detail Table projections for the Low Series from (ADOA, 2015). The forecasting 

ratio is 441,890

394,066

≈ 112%.
Appendix D D4. On-Road Motor Vehicle Emissions Budget

December 21, 2015 Final Page 42

D4.1.2 Paved Roads

Paved road PM10 emissions were estimated for each of the following sources: exhaust, tire wear, 

brake wear, bow wake, and physical reentrainment by vehicle travel. The Maricopa Association 

of Governments (MAG) estimated exhaust, tire and brake wear using the latest onroad emissions 

model approved by EPA, MOVES2014. The methodology employed by MAG for preparing input 

files for the MOVES2014 design day and annual inventory modeling were supplied to ADEQ on 

10/20/2014, and are presented in Exhibit DII.

PM10 generated from the bow wake of large vehicles traveling on roads with unpaved shoulders 

was calculated using the following emission rate formula EBow wake

PM10 = 0.03 × (S⁄55)

2

, where 

EBow wake

PM10 is the PM10 emission rate (in lbs/VMT) and S is the average speed (in miles per hour) 

for a given roadway segment. The Arizona Department of Transportation (ADOT) generated a 

map labeling the major roadways in the county with shoulders capable of producing bow wake 

emissions. The MAG traffic assignment dataset and network were used to calculate bow wake 

emissions for each road segment specified by ADOT. Bow wake from paved road traffic 

contributed less than 25 tons of PM10 to the 2018 inventory, and so is not considered a significant 

enough source to include in the MVEB.

Vehicle reentrained road dust emissions were estimated for all MAG-defined road network 

segments. Annual average emission factors were developed for each road type according to 

§13.2.1 of (EPA, Updated 2006). Emission rates were scaled by a factor of 381.59% on roadways 

identified by PCAQCD as trackout hotspots, as described in Section 3.1.1.6 of Appendix B.

Recall from Table D3-19 that the NA-wide, activity-based emissions from paved roads total 

1,408.0 TPY in the 2018 attainment scenario. That total is itemized in Table D4-2. Augmenting 

the exhaust, tire-, and brake-wear subtotal with 1.6 TPY of vehicle exhaust from included 

unpaved roads yields a total of 173.7 TPY.

Table D4-2. Attainment Scenario Paved Road Emissions

Emissions Type Annual Emissions (TPY)

Exhaust, tire-, & brake-wear 172.1

Bow wake (omitted) 24.8

Reentrained road dust 1,211.1

Total 1,408.0

D4.1.3 Unpaved Parking Lots

The NA’s physical extent was subdivided to represent the primary land use in each area. On three 

of these land use categories (cleared areas, developed urban lands, and developed rural lands) 

ADEQ identified disturbed areas which might be used as parking lots. According to (MCAQD, 

2011), approximately 24% of such disturbed land is, in fact, parking lots. Using this estimate, 

ADEQ determined that there were 500 acres of unpaved parking lots in the NA. Following the 

methodology of (MCAQD, 2011), ADEQ estimated that 251.53 tons of PM10 were emitted from 

these parking lots each year. Because the annual rates in that methodology are not forecasted 

to change over time, these emissions are omitted from the MVEB.
Appendix D D4. On-Road Motor Vehicle Emissions Budget

December 21, 2015 Final Page 43

D4.1.4 Road Construction/Maintenance

The controls discussed in Section D5.2.3.3 include paving, chipsealing, spraying, and graveling 

unpaved roads. Although these controls are expected to reduce large amounts of unpaved road 

dust emissions, they will create some additional emissions from the construction required to 

implement the controls. 15 miles of controls are expected to be installed each year.

ADEQ obtained lists of expected road work projects from ADOT, two planning organizations 

(MAG and SCMPO), Pinal County, and a variety of municipalities which operate local roads in the 

NA. 84 of these projects were determined to be likely to occur within the NA between 2016 and 

2018. ADEQ estimated appropriate construction emissions for each project according to the 

methodology in Section 3.2.4 of (Countess Environmental, 2006). Table D4-3 summarizes those 

emissions for projects which implement the controls listed above.

The average emission factor for those projects is 0.29 tons/acre-month. The acre wise duration 

of each chipsealing or paving project is conservatively estimated to be 1.03 months15. For each 

of the 15 miles of construction associated with the rule discussed in Section D5.2.3.3, ADEQ 

assumed a 73’-wide construction zone16, so that construction is expected to produce

73 ft ∙ 5,280 ft/mi

43,560 ft2/acre

∙ 0.29 tons/acre-month ∙ 1.03 months ≈ 2.64 tons/mi

of uncontrolled emissions. However, a baseline 22.9% control effectiveness is estimated for the 

current Pinal County Construction rule (see Section 3.1.1.8 of Appendix B). Thus 2.04 tons of PM10

emissions are expected from each mile of additional controls.

 

15 Table 3-3 of (Jiang & Wu, 2004) provides a maximum estimate of 33 working days per lane-mile for non-bridge 

road construction. ADEQ assumed 22 working days per month, to account for weekends, and per Section 3.2.4 of 

(Countess Environmental, 2006), ADEQ assumed a lane width of 12’. Thus the maximum work duration estimate is 

33 working days/lane-mile∙ 12 ft/lane∙5280 ft/mi

43560 ft2/acre

22 working days

1 month

⁄ ≈ 1.03 months/acre. Although this rate should be 

regarded as conservative when applied to any non-bridge road construction project, it is especially conservative for 

chip sealing projects (the most extensively planned control for 2016-2018). Chip sealing generally requires fewer 

operations than hot mix asphalt paving, and can often be completed without interruption due to Pinal County’s hot, 

dry climate; see, e.g., the FHWA National Highway Institute’s Chip Seal Best Practices training course.

16 The typical high-traffic public unpaved road is assumed to be 48’ wide, including shoulders, and Section 3.2.4 of 

(Countess Environmental, 2006) recommends adding 25’ beyond the road area to the construction footprint.

Table D4-3. Distribution of Public Dirt Road Improvements Planned for the NA, 2016-2018

Control type

Uncontrolled 

construction 

emissions factor 

(tons/acre-month)

Disturbed acre-months planned for 2016-2018 

uncontrolled 

emissions 

(tons)

2016 2017 2018

2016-

2018 

Total

Gravel/aggregate cover - - - - - -

Synthetic dust palliative - - - - - -

Chip/double chip sealing 0.27 73.31 109.97 39.68 222.96 60.20

Paving 0.42 0.00 14.66 25.88 40.54 17.03

Total 73.31 124.63 65.55 263.49 77.22
Appendix D D4. On-Road Motor Vehicle Emissions Budget

December 21, 2015 Final Page 44

Across all 84 projects—not just those implementing the controls discussed above—a controlled 

total of 163.5 tons of PM10 are expected to be emitted, 59.6 tons of which are expected in 201817. 

7.6 miles of projects in that 2018 total implement controls discussed above. As discussed in 

Section D5.2.3.3, ADEQ estimates that 14.9% of those projects will overlap the 15 mile subtotal. 

Thus, to avoid double counting, only 15 mi − (14.9% ∙ 7.6 mi) ≈ 13.9 mi of additional projects 

are accounted for, totaling 2.04 tons/mi ∙ 13.9 mi ≈ 28.4 tons of PM10 in 2018. For modeling 

purposes, these emissions were spread uniformly over the roads which are candidates for the 

additional projects (unpaved public roads classified as Class D or Class E).

Together with the 59.6 tons already planned, 88.1 tons of emissions are expected altogether. 

However, as discussed below, the road construction budget allocation is higher than 88.1 TPY.

D4.2 Allowances

In order to leave as much room for growth in the MVEB as possible, ADEQ revised the 2018

inventories (both baseline and attainment) to include additional road work emissions. ADEQ 

scaled up the emissions rates from each road work project in the inventory until the model no 

longer showed attainment. At a growth factor of 91.4%, each design day still showed attainment. 

This was true even when substituting the projects expected for 2016 or 2017 into the modeling, 

rather than the 2018 projects. However, at 91.5%, the PCH stagnation design day model (using

the 2018 projects) showed an exceedance. Thus, ADEQ used the 2018 projects (rather than those 

planned for 2016 or 2017) in developing the budget, and added 91.4% more road work emissions 

to the 2018 inventories, to budget for potential unforeseen road work increases. Note that this 

analysis relied on assumptions about the placement and timing of hypothetical construction 

projects. Therefore, the success of this budget in protecting the NAAQS will require hotspot 

modeling to predict the impact of each new project based on its actual location and timing.

Table D4-4. MVEB Summary

Source Annual NA-Wide Emissions (TPY)

Exhaust, tire-, & brake-wear 173.7

Unpaved road dust 26,433.5

Reentrained paved road dust 1,211.1

Road construction/maintenance 168.8

Total 27,987.1

Even beyond road work inflation, the projected emissions from road work and road dust are 

intended to be overestimates, leaving significant room for additional growth in motor vehicle 

traffic. This is because they disregard the substantial reductions described in Section D5.2, which

are set aside as contingency measures to offset any PM10 emissions beyond those accounted for 

in the above methodology.

 

17 Section D4.2 explains why 2018 construction was used for the budget.
Appendix D D5. Reasonable Further Progress

December 21, 2015 Final Page 45

D5 Reasonable Further Progress

D5.1 Implementation Schedule

Recall from Section D3.2 that NA-wide reductions from the base 2018 annual inventory to the 

attainment inventory totaled 22,426.3 TPY. With a three-year horizon from present (2015) to 

modeled attainment (2018), a linear implementation schedule would require annual reductions 

of 22,426.3

2018−2015

= 7,475.4 TPY. Since the provisions of the AgBMP Rule are effective beginning 

January 1, 2016, the actual implementation schedule is heavily front-loaded, keeping the NA well 

below the linear glide path, as illustrated in Figure D5-1.

Figure D5-1. West Pinal PM10 NA RFP Schedule

D5.2 Contingency Control Measures

In addition to the attainment reductions, the NA must be able to implement an additional 

average year’s worth of controls—achieving an additional 7,475.4 of reductions—by 2018, in case

the attainment controls do not produce the expected reductions at the monitors. However, in an 

effort to reach attainment as expeditiously as practicable, all of these additional controls will be 

105,000

110,000

115,000

120,000

125,000

130,000

135,000

140,000

2015 2016 2017 2018

Annual NA-Wide Emissions (TPY)

Glide Path Actual Implementation Schedule
Appendix D D5. Reasonable Further Progress

December 21, 2015 Final Page 46

implemented proactively. The following sections describe the assumptions and methodology 

used to estimate the pursuant reductions. Those estimates are summarized in Table D5-1. Note 

that the total, 7,814.5 TPY, satisfies the 7,475.4 TPY requirement.

D5.2.1 Ag BMP Rule Dairy Provision

Recall that the reductions derived in Section D3.1.2 were not credited towards the attainment 

inventories. The 65.4 TPY of windblown dust and 35.8 TPY of activity-based emissions reduced 

by the controls on commercial dairy operations are instead counted as contingency measures.

D5.2.2 Pinal County Construction Rule

The new construction rule for Pinal County replaces an existing rule which was taken into account 

in the base scenario. Based on inspection and compliance history discussed in Section 3.1.1.8 of 

Appendix B, ADEQ assigned the baseline rule 80% rule effectiveness, near the middle of “Nonpoint source Rule Effectiveness Range 2” (70% to 85%) in (EPA, 2005). Because of the more 

stringent recordkeeping and reporting requirements imposed in §4-3-180.C, the new rule is 

assigned an effectiveness of 85%, at the top of Range 2. The Rule penetration, as calculated in 

Section 3.1.1.8 of Appendix B for the old rule, remains at 99.48% for the new rule.

The reductions from this rule are accounted for in two parts. Reductions due to the trackout 

provision are calculated in Section D5.2.2.1, together with reductions due to similar controls from 

the general fugitive dust rule. Reductions due to the remaining construction rule provisions are 

discussed in Section D5.2.2.2.

 

18 Up to 5 TPY of reductions attributed to the construction rule may actually be attributable to the general fugitive 

dust rule instead, but were consolidated in the construction rule subtotal as an expediency. See Sections D5.2.2.1

and D5.2.3.4 for details.

Table D5-1. Summary of Contingency Measure Reductions

Rule Controlled Dust Sources Implementation 

date

NA-Wide 2018 

Reductions (TPY)

Arizona Administrative 

Code, R18-2-611.03

Commercial dairy 

operations January 1, 2016 101.2

PCAQCD Code of 

Regulations, §4-3-160, 

-170, -180, and -190

Permitted construction 

projects January 1, 2016 4,691.718

PCAQCD Code of 

Regulations, §4-1-010, 

-015, -020, -030, -040, 

-045, -050, and -060

Several prescribed area 

sources of fugitive dust, 

including vacant lots and 

other open areas, roads, 

and other traffic areas.

Effective January 1, 

2016, with some 

controls being 

implemented 

progressively

3021.7

Total 7,814.5
Appendix D D5. Reasonable Further Progress

December 21, 2015 Final Page 47

D5.2.2.1 Trackout Controls

Since the rule penetration of 99.84% applies to dust emitted directly from construction sites, it 

cannot be applied to trackout emissions, which are a subset of reintrained paved road dust. Of 

the 84.8 TPY of reintrained road dust from roads identified as trackout hotspots, 75.0 TPY are 

emitted on roads adjacent to agricultural land. ADEQ therefore assumed that the trackout on 

those roads was attributable to agriculture (not construction). However, the other 9.8 TPY are 

not necessarily all attributable to trackout from construction sites. Therefore, this discussion will 

take into account both the controls required in §4-3-180.B.1 for construction sites and those 

discussed in Section D5.2.3.4 (which apply to the remainder of the 9.8 TPY subtotal). Combined, 

these rules have a penetration of 9.8 TPY

84.8 TPY

= 11.5% with respect to trackout emissions. To be 

conservative, ADEQ used the lower of the two rule effectivenesses, 69%.

When effectively implemented, trackout controls (in the case of the construction rule) and 

trackout removal (in some construction cases and all cases covered by the general fugitive dust 

rule) would reduce silt loading on paved roads to what they would have been in the absence of 

trackout. Section 3.1.1.6 of Appendix B provides the emission factors used to calculate 

reintrained road dust emissions at trackout hotspots (2.63 g/mi) and elsewhere (0.69 g/mi). The 

overall control efficiency is estimated as 2.63 g/mi−0.69 g/mi

2.63 g/mi = 73.8%, so that the collective 

reductions due to these two provisions are about 11.5% ∙ 69% ∙ 73.8% ∙ 84.8 TPY = 5.0 TPY. 

Because this is a small subtotal relative to the overall reductions for each rule, and because the 

portion that should be attributed to each rule is unknown, ADEQ has arbitrarily decided to group 

all 5 tons in with the construction rule total, for bookkeeping simplicity.

D5.2.2.2 Other Provisions

Additional controls within the construction rule target the stabilization of land to reduce 

windblown dust emissions and measures to reduce emissions from non-wind dust generating 

operations within a work site. Under section 4-3-180.A. the rule requires that no dust generating 

operations exceed 20% opacity on the work site and that all disturbed surface areas are stabilized 

to a level which passes the drop ball test. ADEQ assumes this section of the rule will require 

control methods equivalent to or greater than the control efficiency water application would 

achieve on lands that have previously been or are actively being disturbed. Additionally, ADEQ 

assumed vehicle speed reductions onsite to 15 mph and access restrictions to the site, in line 

with additional requirements of the rule. Relying upon these controls as the minimum controls 

necessary to meet the rule requirements, ADEQ used the following methodology to estimate 

control efficiency.

Activity Construction Control Efficiency Calculation

Uncontrolled construction emissions were calculated utilizing a construction site wide emission 

factor. As such, it is difficult to separate the impacts of individual controls required in §4-3-180.

In order to better represent the emission reductions associated with the implementation of §4-

3-180, ADEQ resolved construction site emissions to individual dust generating operations or 

emitting sources within a construction site. First, research was performed to determine the 
Appendix D D5. Reasonable Further Progress

December 21, 2015 Final Page 48

availability of literature resolving construction site emissions to individual emitting sectors. ADEQ 

was able to identify a California power plant expansion plan that resolved construction emissions 

by emitting activities19. The emission distribution from a project of this scale will differ from much 

smaller, local construction projects found within Pinal County; however, in the absence of more 

adequate information, ADEQ assumed the emission distribution reported in this analysis is 

scalable for construction work performed within the nonattainment area. Table D5-2 reports the 

emitting activities and the distribution of emissions for the California Power Plant expansion.

From this emission distribution, ADEQ determined that 2% of construction site emissions were 

uncontrollable by §4-3-180 as they originated from combustion emissions. ADEQ reviewed the 

remaining emission sources to determine which control measures defined in §4-3-180 would be 

applicable. These remaining sources were split into two general emission generation categories: 

1) emissions from travel on construction sites, and 2) emissions from construction or 

earthmoving activities.

Given knowledge of control implementation within the Maricopa County PM10 nonattainment 

area, ADEQ assumes that water is the most likely control for earthmoving activities and for 

ground stabilization following earthmoving activities. Additional ground stabilizers that are 

acceptable under the rule include chemical stabilizers; however, these stabilizers cost more to 

the site operator and are, thus, less likely to be implemented as a control. As such, ADEQ assumed 

that all dust generating operations and ground stabilization would rely on watering. The type of 

watering control assigned to each emitting activity was dependent on which of the two general 

emission generation categories, previously described, an operation belonged. Emissions from 

travel on construction sites were assumed to be controlled by unpaved road watering, reduced 

vehicle speed, and access restriction; while emissions from construction or earthmoving activities 

were assumed to be controlled by either pre-watering or operational watering. As reported in 

Table D5-3, pre-watering/operational watering was assumed to have a control efficiency of 

61%20. The control efficiency of 69% for construction site traffic related emissions was calculated 

by ADEQ utilizing the AP-42 unpaved road equation21 amongst other resources.

ADEQ calculated control efficiency for construction site traffic related emissions by first assuming 

that the emissions would be subject to three primary controls: 1) watering of traffic areas to meet 

stabilization and 20% opacity requirements, 2) reduced vehicle speeds, and 3) access restriction 

to unauthorized vehicles. ADEQ calculated the emission reductions associated

 

19 (L.S. Power South Bay, 2006)

20 Reported in (Countess Environmental, 2006) in Table 3-7 as the control efficiency for test series 701 from MRI 

(2001), assuming a 3.2-hour watering interval.
Appendix D D5. Reasonable Further Progress

December 21, 2015 Final Page 49

Table D5-2. Resolved Construction Site Emissions

Emission 

Type Equipment

No. 

of 

Units

Daily 

Process 

Rate 

Per 

Unit

Total 

Process 

Rate

Process 

Rate 

Units

PM10

Emission 

Factor 

(lbs/unit)

Uncontrolled 

PM10

Emissions 

(lbs/day)

Fraction 

of Site 

Emissions 

(%)

Combustion 

Emissions SUB-TOTAL 4.4 2.0%

Fugitive Dust

Excavator/Backhoe 2 504.0 1,008.0 tons 0.0015 1.5 0.7%

Truck, Flatbed, Ford - Unpaved Road Travel 3 2.2 6.6 vmt 1.4328 9.5 4.4%

Dozer 2 8.0 16.0 hr 0.4194 6.7 3.1%

Truck, Concrete Pump - Unpaved Road Travel 2 9.0 18.0 vmt 2.9806 53.7 24.9%

Excavator/Loader, Cat 936 F 1 588.0 588.0 tons 0.0001 0.1 0.0%

Excavator/Loader, Cat 938 F 1 588.0 588.0 tons 0.0001 0.1 0.0%

Truck, Water - Unpaved Road Travel 1 13.4 13.4 vmt 2.8400 38.1 17.6%

Truck, Fuel/Lube - Unpaved Road Travel 3 6.7 20.1 vmt 2.1349 42.9 19.9%

Truck, Articulating - Unpaved Road Travel 1 16.5 16.5 vmt 2.9806 49.2 22.8%

Truck, Articulating - Unloading 1 1,176.0 1,176.0 tons 0.0001 0.1 0.1%

Excavator/Mtr Grdr 1 13.4 13.4 vmt 0.2754 3.7 1.7%

Delivery Truck Unpaved Road Travel 13.1 0.2 2.6 vmt 2.3088 6.1 2.8%

SUB-TOTAL 211.5 98.0%

TOTAL 215.9 100.0%

Adapted from Table 8.1F-2, Daily and Annual Dust Emissions, from (L.S. Power South Bay, 2006)
Appendix D D5. Reasonable Further Progress

December 21, 2015 Final Page 50

Table D5-3. ADEQ Assumed Controls and Efficiencies for Construction Site Operations

Emission 

Type Equipment

Fraction 

of Site 

Emissions 

(%)

Assumed Control Measure

Control

Efficiency 

(%)

Combustion 

Emissions SUB-TOTAL 2.04% N/A 0%

Fugitive 

Dust

Excavator/Backhoe 0.70% Pre-Wetting / Operational Watering 61%

Truck, Flatbed, Ford - Unpaved Road Travel 4.38% Reduce Vehicle Speed / Watering / Access Restriction 69%

Dozer 3.11% Pre-Wetting / Operational Watering 61%

Truck, Concrete Pump - Unpaved Road Travel 24.85% Reduce Vehicle Speed / Watering / Access Restriction 69%

Excavator/Loader, Cat 936 F 0.03% Pre-Wetting / Operational Watering 61%

Excavator/Loader, Cat 938 F 0.03% Pre-Wetting / Operational Watering 61%

Truck, Water - Unpaved Road Travel 17.63% Reduce Vehicle Speed / Watering / Access Restriction 69%

Truck, Fuel/Lube - Unpaved Road Travel 19.88% Reduce Vehicle Speed / Watering / Access Restriction 69%

Truck, Articulating - Unpaved Road Travel 22.78% Reduce Vehicle Speed / Watering / Access Restriction 69%

Truck, Articulating - Unloading 0.05% Pre-Wetting / Operational Watering 61%

Excavator/Mtr Grdr 1.71% Pre-Wetting / Operational Watering 61%

Delivery Truck Unpaved Road Travel 2.80% Reduce Vehicle Speed / Watering / Access Restriction 69%

SUB-TOTAL 97.96% - -

TOTAL 100.00% Site Fraction Weighted CE (%) 68%
Appendix D D5. Reasonable Further Progress

December 21, 2015 Final Page 51

with the first two controls by utilizing the AP-42 unpaved reentrained road dust equation21. ADEQ 

assumed soil moisture content would increase from a native, uncontrolled 0.3% to an average 

soil moisture of 12%22. Utilizing the dry-down soil moisture curve reported by (Midwest Research 

Institute, 2001) for test series 701, this assumes watering of traffic areas every 1.5 hours of the 

work day. Additionally, ADEQ assumed onsite vehicle speed would reduce to 15 mph in 

accordance with R4-3-170.4. For each hour of the day, ADEQ calculated an emission rate using 

the AP-42 unpaved road equation. This emission factor was averaged for the 10 hour work day 

and compared to an uncontrolled emission factor to reveal 66% control efficiency. Additionally, 

ADEQ assumed 3% control efficiency for access restriction of unauthorized vehicles, in line with 

the 1-3% reduction reported for this control on unpaved agricultural roads in (ADEQ, 2001). 3% 

was utilized in this case as construction sites are assumed to occur with more regularity in urban 

areas than agricultural sites (which assumed 2% control efficiency), and thus are assumed to 

experience a higher amount of unauthorized traffic. These combined controls were calculated to 

result in estimated control efficiency for traffic related emissions of 69%.

ADEQ then calculated emission-weighted control efficiency for the site of 68% (see Table D5-3).

This was the overall control efficiency assumed for construction sites following the 

implementation of §4-3-180.

Windblown Construction Control Efficiency Calculation

ADEQ did not take credit for controlled windblown dust emission reductions from road 

construction. Therefore, this sector will not be discussed in this section or subsequent 

construction sections reviewing windblown dust emissions.

A suite of wind-speed-dependent emission factors for stabilized and disturbed lands were 

developed for calculation of emissions from these lands throughout the year (discussed in Section 

3.1.2 of Appendix B). Windblown emissions from a given construction site for any particular hour 

are thus dependent on: 1) the average wind speed for that hour, 2) the acreage of the 

construction site, and 3) the type of construction occurring on a given site. The assumed 

uncontrolled disturbed to stabilized ratios for each construction type are listed in Table D5-4.

Table D5-4. Uncontrolled Construction Site 

Disturbance Ratios

Construction 

Type

Percentage of Construction Site

Disturbed Stabilized

Residential 84% 16%

Site Development 91% 9%

Commercial 100% 0%

 

21 See Section 13.2.2 of (EPA, Updated 2006).

22 Series 701 data presented in (Midwest Research Institute, 2001) exhibited meteorological characteristics that most 

reasonably resemble Pinal County. This series also showed a drastic decrease in control efficiency 1.5 hours after 

watering. ADEQ anticipates this control efficiency reduction will not allow compliance with the 20% opacity standard, 

requiring water re-application every 1.5 hours for traffic areas during operating hours. The average soil moisture 

over this 1.5 hour dry-down is 12%.
Appendix D D5. Reasonable Further Progress

December 21, 2015 Final Page 52

§4-3-180.A.2 requires that all disturbed surface areas on a work site must be stabilized. As such, 

ADEQ altered the disturbance to stabilization ratios listed in Table D5-4 so that sites were fully 

stabilized (except where rule penetration or rule effectiveness reduced the percentage of land 

undergoing stabilization, discussed below).

Rule Penetration Calculation

GIS maps of 2008 permitted construction sites supplied by PCAQCD were analyzed to determine 

how many permitted construction sites would exceed the rule-defined lower limit of 0.1 acres 

for earthmoving activities requiring a construction permit, and thus subject to the new rule (§4-

3-170.28). 2,668 construction sites were identified from the GIS layer. Of these, only 14 were 

found to be smaller than 0.1 acres. Therefore, ADEQ assumed a rule penetration of 99.48% (i.e., 

99.48% = 1 – 14/2,668).

Rule Effectiveness Estimation

As described at the beginning of Section D5.2.2, estimated rule effectiveness for construction 

sites subject to §4-3-180 is 85%.

Control Effectiveness Calculation

Control Effectiveness is calculated as the product of control efficiency, rule penetration, and rule 

effectiveness. Therefore activity construction emissions were calculated to have a rule 

effectiveness of 57.2% (i.e., 57.2% = 67.6% * 99.5% * 85.0%). Windblown control effectiveness 

utilizes the same basic methodology; however, instead of the control effectiveness being directly 

applied to emissions, it was utilized to determine the percentage of disturbed lands that should 

be converted to stabilized lands. Using this approach, control efficiency is assumed to be 100% 

(i.e., all disturbed lands are converted to stable lands, in accordance with rule requirements).

Therefore, calculated control effectiveness for windblown emissions is 84.6% (i.e., 84.6% = 

100.0% * 99.5% * 85.0%). The percentage of disturbed land associated with each construction 

site type was reduced by 84.6% so that revised disturbed to stabilized land ratios were those 

reported in Table D5-5. Please note that this does not result in a flat 84.6% reduction in emissions.

Total emission reductions are dependent on the ratios listed in Table D5-5, the area of land 

associated with each construction type, and the meteorological activity in the area of interest 

(particularly, wind speed for the area and time of interest).

Table D5-5. Controlled Construction Site 

Disturbance Ratios

Construction 

Type

Percentage of Construction Site

Disturbed Stabilized

Residential 13% 87%

Site Development 14% 86%

Commercial 15% 85%

Control Effectiveness Application

As construction emission reductions were only credited towards contingency requirements, 

emission reductions were only calculated at the annual nonattainment area-wide scale. Activity 
Appendix D D5. Reasonable Further Progress

December 21, 2015 Final Page 53

emissions were reduced by the control effectiveness reported above by multiplying the 

uncontrolled construction site specific emissions by 0.428 (i.e., 0.428 = 1 - 0.572).

Construction windblown emission reductions were calculated on the annual scale for the 

nonattainment area by alteration of the disturbed to stable land ratio of uncontrolled 

construction projects. Uncontrolled construction projects are assumed to have the disturbed to 

stable ratios listed in Table D5-4. Please note that these disturbance ratios do not represent those 

used in the base 2018 emission inventory, as this emission inventory assumed control of 

construction sites by previous Pinal County construction rule. The ratios in Table D5-4 were 

adjusted by 84.2%, resulting in the ratios reported in Table D5-5. These ratios were then applied 

to individual construction sites. Uncontrolled stable and disturbed windblown dust emission 

factors were then utilized to calculate windblown dust originating from controlled construction 

sites within the nonattainment area for 2018.

Annual Emission Inventory Reductions

As previously noted, construction operation controlled emission reductions were credited 

towards contingency measure requirements. Table D5-6 presents the calculated annual 

nonattainment area-wide emission reductions for the year 2018. As this control was utilized for 

contingency purposes, design day emission reductions were not analyzed.

Table D5-6. 2018 Annual Emission Reductions Estimated for Construction Controls

Emissions Construction 

Type

Uncontrolled 

Emissions (TPY)

Controlled 

Emissions (TPY)

Emission 

Reductions (TPY)

Activity

Residential 411.9 228.7 183.2

Commercial 3,274.3 1,817.7 1,456.6

Site Development 4,644.7 2,578.4 2,066.3

Road 168.8 93.7 75.1

Sub-Total 8,499.8 4,718.5 3,781.3

Windblown

Residential 837.5 430.5 407.0

Commercial 441.4 216.3 225.1

Site Development 552.2 278.9 273.3

Sub-Total 1,831.1 925.7 905.4

Total 10,330.9 5,644.2 4,686.7

D5.2.3 Pinal County General Fugitive Dust Rule

Unlike the construction rule, the general fugitive dust rule for Pinal County does not replace a 

comparable rule. Because there was no predecessor, there is no compliance history on which to 

base the rule effectiveness. Therefore, the rule effectiveness was conservatively assumed to be 

in the lowest interval specified for non-point sources in Attachment 2 of (EPA, 2005), Range 3 

(less than 70%). To acknowledge the recordkeeping/compliance requirements in §4-1-040, -50, 

and -60 of the rule, ADEQ selected 69% (the highest whole number percentage in Range 3) as the 

rule effectiveness for this provision.
Appendix D D5. Reasonable Further Progress

December 21, 2015 Final Page 54

D5.2.3.1 Requirements for Owners of Open Areas or Vacant Lots

The open area and vacant lot provision of the Fugitive Dust Rule (§4-1-030.2) prescribes several control options for stabilizing the 

disturbed portions of undeveloped or partially developed areas 1 acre or larger which hold at least 0.5 disturbed acres.

Explicit exemptions to this provision include vacant or open portions of agricultural land (exempted from this rule by §4-1-015.3, but 

controlled by the rules described in Section D3.1) and construction sites (exempted in §4-1-015.4, and instead subject to the controls 

described in Section D5.2.2.2). ADEQ therefore restricted its analysis of open areas and vacant lotsto the Developed Urban, Developed 

Rural, Cleared Area, and Desert Shrubland land use types, excluding the various land use types associated with agriculture or 

construction, as well as roads and the Other category.

ADEQ examined maps of parcels throughout the NA23, and used GIS to identify the predominant land use type of each parcel. For each 

land use type, ADEQ then identified the minimum parcel size required to hold a lot with 0.5 disturbed acres, assuming that the 

disturbance level for the land use type (e.g., 5% for Developed Rural) was uniform across each parcel that it dominated. For example, 

for Developed Rural parcels, the minimum sufficient size was 0.5 acres

5%

= 10 acres. Table D5-7 shows the thresholds for each included 

land use categories, and compares the total acreage of parcels in each category with the subtotal for parcels of qualifying size24. For 

each category, the rule penetration was calculated as the proportion (by area) of qualifying parcels.

Table D5-7. Open Area and Vacant Lot Emissions Reductions

Land Use Category

Uncontrolled 

Disturbance 

Level

Min. Qualifying 

Parcel Size 

(acres)

Parcels (acres) Rule 

Penetration

Rule 

Effectiveness

Controlled 

Disturbance 

Level

2018 NA-wide 

Emissions 

Reductions (TPY) Total Qualifying

Developed Urban 2.50% 20.0 19,121.7 4,517.2 23.62% 69.00% 2.09% 33.8

Developed Rural 5.00% 10.0 20,025.1 1,916.5 9.57% 69.00% 4.67% 25.5

Cleared Area 15.00% 3.3 3,020.8 2,580.2 85.41% 69.00% 6.16% 114.5

Desert Shrubland 5.00% 10.0 469,304.4 141,236.324 30.09% 69.00% 3.96% 1,554.2

Total 511,472.0 150,250.1 1,727.9

 

23 Digital plat maps were provided by the Pinal County Assessor’s Office on May 14, 2015.

24 Because the primary enforcement targets are likely to be determined based on complaints, it is unlikely that controls will be applied to Desert Shrubland 

parcels in unincorporated areas (which tend to be sparsely populated, relative to municipalities). Although this last point speaks to rule effectiveness, it is more 

easily incorporated into GIS-based calculations; Desert Shrubland parcels which were not inside of municipal boundaries were excluded from the qualifying 

acreage subtotal. For Developed Urban/Rural and Cleared Areas, this criterion was not considered; only parcel size was used to determine qualification.
Appendix D D5. Reasonable Further Progress

December 21, 2015 Final Page 55

Taking into account the rule penetration and rule effectiveness, ADEQ adjusted the proportion 

of each land use category which was disturbed. For example, Developed Rural Land, with a rule 

penetration of 9.57%, had 9.57% ∙ 69% ∙ 5% = 0.33% of its total area stabilized, i.e., its 

disturbance level reduced to 5% − 0.33% = 4.67% and its stable level increased to 95% +

0.33% = 95.33%. The revised disturbance levels and the pursuant windblown dust reductions 

are summarized in Table D5-7.

D5.2.3.2 Requirements for Operators of Unpaved Lots

The unpaved lot provision of the Fugitive Dust Rule (§4-1-030.3) prescribes several control 

options for unpaved parking lots, unpaved vehicle storage areas, unpaved material transfer 

stations, or similar traffic areas. One of these controls must be implemented to stabilize the 

unpaved lot if its size exceeds 5,000 square feet regardless of location and ownership, except in 

the case of agricultural land (exempted from this rule by §4-1-015.3, but controlled by the rules 

described in Section D3.1) and construction sites (exempted in §4-1-015.4, and instead subject 

to the controls described in Section D5.2.2). ADEQ therefore restricted its analysis of unpaved 

lots to the Developed Urban, Developed Rural, and Cleared Area land use types, excluding the 

various land use types associated with agriculture or construction, as well as Desert Shrubland 

(which does not cover any large traffic areas) and roads (which do not include staging/parking 

areas). The Other category was also excluded.

ADEQ estimated the unpaved parking lot emissions from each of these land use categories 

separately, as summarized in Table D5-8. A separate rule penetration was calculated for each 

land use category, as follows. ADEQ examined maps of parcels throughout the NA23, and used 

GIS to identify the parcels whose land use type was, for example, predominantly Cleared Area. 

About 3,021 acres of such parcels were identified, 3,001 acres (or 99.35%) of which were at least 

5,000 square feet—large enough to contain a parking lot to which the rule applies. The rule 

penetrations for Developed Urban and Developed Rural Lands were calculated in the same way, 

and are summarized in Table D5-8. The average rule penetration, weighted by uncontrolled 

emissions, is about 99.04% for activity-based emissions and about 98.93% for windblown dust.

Table D5-8. Summary of Uncontrolled Emissions from Unpaved Lots

Land Use 

Category

Unpaved 

Lots (acres)

Uncontrolled Emissions (TPY) Rule 

Activity-Based Windblown Dust Penetration

Developed Urban 142.5 48.5 47.2 96.09%

Developed Rural 261.2 131.3 108.4 99.96%

Cleared Areas 96.5 71.7 54.0 99.35%

Total 500.2 251.5 209.5 -

To provide a conservative estimate, ADEQ assumed that lot operators would opt for the least 

effective among the prescribed controls. For both activity (46% control efficiency25) and 

windblown (45% control efficiency26) emissions, this control was gravel. Thus, this provision is 

estimated to reduce 99.04% ∙ 69% ∙ 46% ∙ 251.5 TPY ≈ 79.0 TPY of activity-based emissions 

and 98.93% ∙ 69% ∙ 45% ∙ 209.5 TPY ≈ 64.4 TPY of windblown dust, or 143.4 TPY altogether.
Appendix D D5. Reasonable Further Progress

December 21, 2015 Final Page 56

D5.2.3.3 Requirements for Operators of Unpaved Roads

The unpaved road provision of the Fugitive Dust Rule (§4-1-030.4) prescribes several control 

options for public unpaved roads, listed in Table D5-9. In its transportation conformity 

consultation process, ADEQ compiled a list of public road work projects planned for the NA in

2016-2018, as described in Section D4.1.4. Of those projects, ADEQ identified which ones

implemented controls on the prescribed list. ADEQ tabulated the number of miles on which each 

control would be implemented. Using the distribution of controls in that sample (weighted by 

2016-2018 total miles), ADEQ calculated the average control efficiencies. The weighted average 

control efficiencies are about 97.1% for activity based emissions and 53.7% for windblown dust, 

similar to the control efficiencies for chip/double chip sealing, the most popular control category 

among the currently planned 2016-2018 projects.

Table D5-9. Distribution of Public Dirt Road Improvements Planned for the NA, 2016-2018

Control type

Control efficiency Miles planned to be implemented in

ActivitybasedWindblown 2016 2017 2018 2016-2017 

Subtotal

2016-2018 

Total

Gravel/aggregate cover 46.00%25 45.00%26 0.00 0.00 0.00 0.00 0.00

Synthetic dust palliative 73.00%27 45.93%

28 3.50 0.00 0.00 3.50 3.50

Chip/double chip sealing 99.86%

29 54.55%28 6.00 11.25 6.60 17.25 23.85

Paving 99.86%30 54.55%28 2.50 3.20 1.00 5.70 6.70

Total - - 12.00 14.45 7.60 26.45 34.05

The provision covers unpaved, public roads which carry over 150 one-way vehicle trips per day 

(abbreviated as “ADT” for “average daily traffic”). ADEQ considered two functional classes of 

unpaved public roads which carried over 150 ADT, labeled “Class D” and “Class E” roads. With 

identical surface characteristics and comparable average vehicle speeds, the primary difference 

between these classes (for the purpose of calculating road dust emissions for a given mile of 

road) are that Class E roads carry a much higher traffic volume, and so emit more than triple the 

amount of dust emitted from a Class D road, on a per-mile basis31. To be conservative, therefore, 

ADEQ assumed that the controls would be installed on Class D roads.

 

25 (SJVAPCD, 2006)

26 (Li, Liu, & Gong, 2001)

27 (ADEQ, 2010)

28 For road work projects, ADEQ used lane- and shoulder-width assumptions for Groups 1-3 in Table 3-3 of (Countess 

Environmental, 2006), which collectively correspond to the Class C-E public road designations used in this document. 

Each travel lane was assumed to be 12 feet wide, with 10 foot wide shoulders. To be conservative, ADEQ assumed 

that each road would have just two lanes, so that road controls applied to the travel lanes only cover 54.55% of the 

road surface. Thus, both paving and chip or double chip sealing—which eliminate windblown dust from the travel 

lanes—have an average control efficiency of 54.55%. Meanwhile, the windblown dust control efficiency for 100% 

stabilization is calculated as 84.2% for the NA, on average (see Section D5.2.3.4). Since synthetic dust suppressants 

must be applied in sufficient quantity to stabilize the travel lanes, their average control efficiency is about 54.55% ∙

84.2% ≈ 45.93%.

29 See Appendix A to (Sierra Research, Inc., 2006).

30 Ibid.

31 See Section 3.1.1.4 of Appendix B
Appendix D D5. Reasonable Further Progress

December 21, 2015 Final Page 57

Assuming meteorology and road conditions in 2018 are similar to those in 2008, the AP-42 

unpaved road dust emission factor for a Class D road in Pinal County is about 1.35 lbs/vehicle 

mile traveled (VMT)32. ADEQ projects that the typical traffic volume on such a road would be 182 

ADT in 201833, resulting in an uncontrolled emission factor of nearly 44.8 TPY/mile. The 97.1% 

control rate, therefore, would reduce approximately 43.5 TPY/mi.

ADEQ did not calculate explicit rule penetration and rule effectiveness for this provision, since

Subsection B of the provision determines the amount of uncontrolled emissions to which controls 

will be applied. The provision as a whole is satisfied when 15 miles of unpaved public road (>150 

ADT) are controlled each year. Conservatively, ADEQ assumed that only the 2016 and 2017 

controls will be in place in 2018, i.e., that the 2018 controls would not necessarily be 

implemented until the end of 2018. Therefore, the reduction in 2018 annual emissions due to 

this provision is provided by 30 miles of controls (15 each from 2016 and 2017).

Among the 34.05 miles of controls sampled in Table D5-9, ADEQ was only able to estimate traffic 

volumes for 10.75 miles(because those roads pass through modeling domains, they are classified 

by functional type). Of these, one project spanned 1.6 miles of Peters Road, slightly east of the 

Stanfield modeling domains. The span of Peters Road within the domains is classified as Class D; 

the span controlled by this project was assumed to carry a similar traffic volume. The other 

projects in the 10.75 mile subset were all on Class B and C roads (< 150 ADT). ADEQ therefore 

assumed that 1.6 mi

10.75 mi

≈ 14.9% of planned controls from Table D5-9 would be on roads carrying 

over 150 ADT, and would therefore qualify towards the 15 mile per year total. There are 20 

projects planned for 2016-2017, which account for 26.45 of the miles in the table, so about

14.9% ∙ 26.45 mi ≈ 3.94 mi would be among the 30 miles implemented by 2018.

However, all of the pre-2018 projects in the table (in fact, all of the pre-2018 projects which 

implement any controls, not just those listed in the table) are already considered in the base 

scenario inventory for 201834. Therefore, they cannot also be credited towards contingency 

measures. Deducting these 3.94 miles from the 30 mile total leaves 26.06 miles of 2016-2017 

controls to be credited towards contingency measures. At a rate of 43.5 TPY/mi, therefore, this 

provision is expected to reduce 1,133.3 TPY of activity-based unpaved road dust by 2018. The 

corresponding reduction for windblown dust is 9.4 TPY, for a total of 1142.7 TPY reduced by this 

provision of the rule.

Note that this brings the contingency reduction total above the needed 7,475.4 TPY. In fact, the 

15-mile-per-year requirement could have been several miles lower and still resulted in sufficient 

expected reductions. However, due to the uncertainty of the 3.94 mile extrapolation, the rule 

used the conservative35 15-mile-per-year total.

 

32 Ibid.

33 Ibid.

34 See Section 5.1.4 of Appendix B

35 The 15-mile-per-year level overcomes the uncertainty associated with the 3.94 mile reconciliation figure at the 

99% confidence level, as follows. We assume that each of the projects summarized in Table D5-9 have an 

independent 14.9% chance of covering qualifying road (road carrying over 150 ADT). Then among the 26.45 miles to 

be completed in 2016-2017, the probability that more than 10.4 miles would cover qualifying road is less than 1%. 

Therefore, there is more than a 99% chance that at least 19.6 miles would be credited towards the contingency 
Appendix D D5. Reasonable Further Progress

December 21, 2015 Final Page 58

D5.2.3.4 Requirements for Operators of Paved Public Roads

The paved public road provision of the Fugitive Dust Rule (§4-1-030.5) calls for controls on two 

sources of paved road dust. Subsection A seeks to reduce reintrained road dust caused by 

deposition of mud or dirt onto roadways, primarily from trackout. However, agricultural 

operations are exempted from these trackout controls by §4-1-015.3 (they are instead subject to 

the trackout controls described in Section D3.1) as are construction projects by §4-1-015.4 (they 

are instead subject to the trackout controls described in Section D5.2.2.1). For applicable 

roadways, this subsection requires the prompt removal of trackout and other large deposits of 

reentrainable material. Although ADEQ has an estimate for the portion of trackout in the NA 

attributable to agriculture, no such estimate exists for construction. Therefore, ADEQ was not 

able to disentangle the reductions due to this provision from those attributable to the 

construction rule. Instead, the trackout reductions from this provision are rolled into the 5 TPY 

total discussed in Section D5.2.2.1. The reductions due to non-trackout cleanup (for example, the 

cleanup of material deposited by stormwater) are not quantified.

Subsection B of the rule requires that work which disturbs unpaved road shoulders (e.g., weed 

control activities) be followed by stabilization of the shoulder surface. For each wind speed bin 

used in the windblown dust inventory, disturbed and stable emission factors were compared.36

Table D5-10 shows the reductions associated with switching from the disturbed factor to the 

stable factor for each bin. The control efficiency for stabilization was calculated as the average 

reduction across all bins, 84.2%.

Table D5-10. Windblown Dust Emission Factor Reductions

Wind Speed 

Bin (mph)

Stabilization 

Reduction

Wind Speed 

Bin (mph)

Stabilization 

Reduction

Wind Speed 

Bin (mph)

Stabilization 

Reduction

0-11 - 18 82.7% 25 86.7%

12 79.8% 19 82.7% 26 86.7%

13 79.8% 20 85.1% 27 86.7%

14 79.8% 21 85.1% 28 86.7%

15 82.7% 22 85.1% 29 86.7%

16 82.7% 23 85.1% 30 87.9%

17 82.7% 24 85.1% 31+ -

ADEQ evaluated the distribution of land use types (and therefore the disturbed vs. stable 

proportions) throughout the NA. Using GIS to extract and process a 10-foot buffer37 surrounding 

the paved road network, ADEQ calculated that 5.56 TPY of windblown dust originated from the 

disturbed shoulders of roads through desert shrubland. Similarly, after taking into account 

previous controls, ADEQ calculated that 2.07 TPY of windblown dust came from disturbed

 

measure total. Even if this provision only contributed 19.6 miles worth of reductions, ceteris paribus, the total 

reductions would still be 7,531.2 TPY, well above the 7475.4 TPY requirement.

36 The proportions between disturbed and stable windblown dust emissions factors throughout this inventory are 

derived from Table A4-6 from Appendix A4 to (MCAQD, 2011).

37 Per Table 3-3 of (Countess Environmental, 2006), ADEQ assumed that shoulders were typically 10 feet wide on 

public roads which carry high-speed traffic (Classes 1-3 in that table).
Appendix D D5. Reasonable Further Progress

December 21, 2015 Final Page 59

shoulders in developed rural land, 0.81 TPY from those in developed urban land, 0.17 TPY from 

those in cleared areas, and 3.06 TPY from those passing through the “other” land use category. 

Altogether, 11.67 TPY of windblown dust emissions were attributed to disturbed road shoulders. 

These subtotals reflect the emissions from both the disturbed and stable portions of the road 

shoulders. The rule penetration should therefore single out those emissions which are 

attributable to disturbed land. However, due to time constraints and the small impact of the 

stable road shoulder subtotal, ADEQ did not make this adjustment (it will be included in a 

forthcoming revision of this document). Instead, a rule penetration of 100% is used as a 

placeholder. Therefore, the overall reductions due to the provision are approximately 100% ∙

69% ∙ 82.4% ∙ 11.67 TPY ≈ 6.8 TPY. These reductions are distributed proportionally among the 

above listed land use categories.

The activity-based emissions from unpaved shoulders are listed as a subtotal of paved road dust, 

namely Bow Wake. Because the mechanical lifting process in this case is through vehicle’s wake

in air (which is essentially wind, even if it is man-made), the windblown dust control efficiency is 

also used for bow wake. The total emissions from bow wake in 2018 are estimated as 24.8 TPY 

(see Table 5-10 in Appendix B). ADEQ assumed that the high-speed traffic carried on private roads 

is negligible relative to that carried on public ones, and so attributed 100% of these emissions to 

public roads. However, only the emissions from disturbed portions of public road shoulders are 

impacted by this provision, so ADEQ calculated the rule penetration as the proportion of road 

shoulder which would be disturbed. Assuming that the disturbed vs. stable proportions of the 

underlying land use category apply to the road shoulders, the disturbed portion was estimated 

as 5.56% (note that the majority of paved road in the NA passes through developed rural land 

and desert shrubland, both of which have just 5% disturbed area). Thus overall reductions to bow 

wake are about 5.56% ∙ 69% ∙ 84.2% ∙ 24.8 TPY ≈ 0.8 TPY, and the total reductions for the 

provision as a whole are 7.6 TPY.
Appendix D D6. References

December 21, 2015 Final Page 60

D6 References

ADEQ. (2001). Technical Support Document for Quanitification of Agricultural Best Management 

Practices. Appendix B.

ADEQ. (2010). Agricultural Best Management Practices Technical Support Document.

ADOA. (2015). Population Projections. Retrieved May 18, 2015, from Employment and Population 

Statistics: https://population.az.gov/population-projections

Aja, B. (2015, March 18-26 email exchange). Executive Vice President of the Arizona Cattle 

Feeders' Association. (E. C. Massey, & A. Ross, Interviewers)

Countess Environmental. (2006). WRAP Fugitive Dust Handbook.

EPA. (2005). Emissions Inventory Guidance for Implementation of Ozone and Particulate Matter 

National Ambient Air Quality Standards (NAAQS) and Regional Haze Regulations, 

Appendix B: Revised Rule Effectiveness Guidance.

EPA. (Updated 2006, additional updates after 2006 have been made, but were not considered in 

this report, except for the 2011 updates to Section 13.2.1). AP 42, Compilation of Air 

Pollutant Emission Factors, Volume I: Stationary Point and Area Sources. (Fifth Edition).

Governor's Agricultural BMP Committee. (2010). Agricultural Best Management Practices for 

Livestock Operations.

Jiang, Y., & Wu, H. (2004). Determination of INDOT Highway Construction Production Rates and 

Estimation of Contract Times.

L.S. Power South Bay. (2006). South Bay Replacement Project Application for Certification, 

Appendix 8.1F: Construction and Demolition Emissions and Impact Analysis. Retrieved 

March 24, 2015, from California Energy Comission: 

http://www.energy.ca.gov/sitingcases/southbay/documents/applicants/afc/SBRP%20AF

C%20Volume%202/Appendix%208.1/Appendix%208.1F%20Emissions%20and%20Impac

t%20Analysis.pdf

Li, X.-Y., Liu, L.-Y., & Gong, J.-D. (2001). Influence of pebble mulch on soil erosion by wind and 

trapping capacity for windblown sediment. Soil & Tillage Research, 59, 137-142.

MCAQD. (2011). 2008 PM10 Periodic Emissions Inventory for the Maricopa County, Arizona, 

Nonattainment Area.

Midwest Research Institute. (2001). Particulate Emission Measurements from Controlled 

Construction Activities, prepared for EPA. EPA/600/R-01/031.

Sakirkin, S. L., Maghirang, R., Amosson, S., & Auvermann, B. (2012). Dust Emissions from CattleFeeding Operations, Part 2 of 2: Abatement. eXtension.org.

Sierra Research, Inc. (2006). Cost-Effectiveness of Selected PM10 Control Measures, prepared for 

Maricopa County Department of Transportation.

SJVAPCD. (2006). Conservation Management Practices Program Report for 2005.

USDA. (2012). 2007 Census of Agriculture, Volume 1, Chapter 2: County Level Data. Retrieved 

August 18, 2015, from National Agricultural Statistics Service: 

http://www.agcensus.usda.gov/Publications/2007/Full_Report/Volume_1,_Chapter_2_

County_Level/Arizona/st04_2_001_001.pdf
Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-1

Exhibit DI Agricultural 

Supplemental Information

Contents

DI1 Agricultural Crop Surveys.......................................................................................DI-2

DI2 Agricultural Crop Control Efficiencies..................................................................DI-13

DI2.1 References..............................................................................................DI-22

DI3 Crop Season Distribution .....................................................................................DI-23

DI4 Dairy Operation Survey........................................................................................DI-24

DI5 Dairy Operation Control Efficiencies....................................................................DI-25

DI5.1 References..............................................................................................DI-28
Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-2

DI1 Agricultural Crop Surveys
Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-3
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Appendix D Exhibit DI. Agricultural Supplemental Information

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Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-12
Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-13

DI2 Agricultural Crop Control Efficiencies

All control efficiencies utilized in the calculation of agricultural crop control efficiencies are presented below with references. Except 

where specified in Section D3.1.1, if a crop/BMP combination is not included in this list, it was not reported as a BMP of interest for 

any farmer surveyed. For those crops that did not have reported control efficiencies by the references utilized, control efficiency is 

assumed transferrable from lowest crop control efficiency reported for that BMP from the reference cited. Wheat and barley control 

efficiencies were assumed adequate to use for grain crops. The control efficiencies listed for cropland only control windblown dust.

Tillage and harvest control efficiencies listed that do not specify “activity” or “windblown” in the comments section are specific to 

activity emissions and the corresponding BMP windblown dust control efficiency is assumed to be zero.

Table DI2-1. Crop Control Efficiencies

Category Practice Crop Control 

Efficiency Reference Comments

Cropland Chips / Mulches Barley 32.5% San Joaquin

Cropland Chips / Mulches Cotton 32.5% San Joaquin

Cropland Chips / Mulches Fallow 32.5% San Joaquin

Cropland Chips / Mulches Vegetables 32.5% San Joaquin

Cropland Chips / Mulches Wheat 32.5% San Joaquin

Cropland Conservation Tillage Barley 63% ADEQ 2001

Assumed 20% ground cover.

Cropland Conservation Tillage Cotton 63% ADEQ 2001

Cropland Conservation Tillage Fallow 63% ADEQ 2001

Cropland Conservation Tillage Wheat 63% ADEQ 2001

Cropland Cover Crop Alfalfa 33% San Joaquin

Cropland Cover Crop Barley 33% San Joaquin

Cropland Cover Crop Citrus 33% San Joaquin

Cropland Cover Crop Corn 33% San Joaquin

Cropland Cover Crop Cotton 33% San Joaquin

Cropland Cover Crop Fallow 33% San Joaquin

Cropland Cover Crop Vegetables 33% San Joaquin

Cropland Cover Crop Wheat 33% San Joaquin
Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-14

Table DI2-1. Crop Control Efficiencies

Category Practice Crop Control 

Efficiency Reference Comments

Cropland Cross-wind Ridges Alfalfa 57% ADEQ 2010

Cropland Cross-wind Ridges Barley 57% ADEQ 2010

Cropland Cross-wind Ridges Corn 57% ADEQ 2010

Cropland Cross-wind Ridges Cotton 57% ADEQ 2010

Cropland Cross-wind Ridges Fallow 57% ADEQ 2010

Cropland Cross-wind Ridges Wheat 57% ADEQ 2010

Cropland Permanent Cover Alfalfa 92% ADEQ 2001

Calculated 92% CE for residue mgt for 50% 

surface coverage, SLR method. Li 2007 

showed comparable results from field 

studies.

Cropland Permanent Cover Barley 92% ADEQ 2001

Cropland Permanent Cover Citrus 92% ADEQ 2001

Cropland Permanent Cover Fallow 92% ADEQ 2001

Cropland Permanent Cover Wheat 92% ADEQ 2001

Cropland Reduced Tillage System Alfalfa 63% Walters & Jasa / ADEQ 2001

Assumed RTS was equivalent to residue 

management with 20% ground cover.

Cropland Reduced Tillage System Barley 63% Walters & Jasa / ADEQ 2001

Cropland Reduced Tillage System Cotton 63% Walters & Jasa / ADEQ 2001

Cropland Reduced Tillage System Fallow 63% Walters & Jasa / ADEQ 2001

Cropland Reduced Tillage System Vegetables 63% Walters & Jasa / ADEQ 2001

Cropland Reduced Tillage System Wheat 63% Walters & Jasa / ADEQ 2001

Cropland Residue Management Alfalfa 92% ADEQ 2001

Calculated 92% CE for residue mgt for 50% 

surface coverage, SLR method. Li 2007 

showed comparable results from field 

studies.

Cropland Residue Management Barley 92% ADEQ 2001

Cropland Residue Management Corn 92% ADEQ 2001

Cropland Residue Management Cotton 92% ADEQ 2001

Cropland Residue Management Fallow 92% ADEQ 2001

Cropland Residue Management Wheat 92% ADEQ 2001

Cropland Sequential Cropping Alfalfa 50% ADEQ 2010

Cropland Sequential Cropping Barley 50% ADEQ 2010

Cropland Sequential Cropping Citrus 50% ADEQ 2010

Cropland Sequential Cropping Cotton 50% ADEQ 2010
Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-15

Table DI2-1. Crop Control Efficiencies

Category Practice Crop Control 

Efficiency Reference Comments

Cropland Sequential Cropping Fallow 50% ADEQ 2010

Cropland Sequential Cropping Wheat 50% ADEQ 2010

Cropland

Stabilization of Soil prior to 

plant emergence

Alfalfa 65% Calculated Annual emission CE given, assumes fallow 

lands --> fully stabilized under 25 mph 

winds conditions at PCH. This is a 

conservative approach as many of these 

lands will be >10% disturbed (assumed 

disturbance for fallow fields).

Cropland Barley 65% Calculated

Cropland Cotton 65% Calculated

Cropland Fallow 65% Calculated

Cropland Wheat 65% Calculated

Cropland Surface Roughening Alfalfa 45% ADEQ 2010

Cropland Surface Roughening Barley 45% ADEQ 2010

Cropland Surface Roughening Corn 45% ADEQ 2010

Cropland Surface Roughening Cotton 45% ADEQ 2010

Cropland Surface Roughening Fallow 45% ADEQ 2010

Cropland Surface Roughening Vegetables 45% ADEQ 2010

Cropland Surface Roughening Wheat 45% ADEQ 2010

Harvest Chemical Irrigation Alfalfa 2% San Joaquin

Harvest Chemical Irrigation Barley 7% San Joaquin

Harvest Chemical Irrigation Citrus 9% San Joaquin

Harvest Chemical Irrigation Corn 1% San Joaquin

Harvest Chemical Irrigation Cotton 9% San Joaquin

Harvest Chemical Irrigation Lettuce 2% San Joaquin

Harvest Chemical Irrigation Vegetables 2% San Joaquin

Harvest Chemical Irrigation Wheat 7% San Joaquin

Harvest Combining Tractor Operations Alfalfa 43% ADEQ 2001

Harvest Combining Tractor Operations Barley 43% ADEQ 2001

Harvest Combining Tractor Operations Citrus 43% ADEQ 2001

Harvest Combining Tractor Operations Corn 43% ADEQ 2001

Harvest Combining Tractor Operations Cotton 43% ADEQ 2001
Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-16

Table DI2-1. Crop Control Efficiencies

Category Practice Crop Control 

Efficiency Reference Comments

Harvest Combining Tractor Operations Wheat 43% ADEQ 2001

Harvest Conservation Tillage Barley 63% ADEQ 2001

Windblown Emissions - Assumed 20% 

ground cover.

Harvest Conservation Tillage Corn 63% ADEQ 2001

Harvest Conservation Tillage Cotton 63% ADEQ 2001

Harvest Conservation Tillage Wheat 63% ADEQ 2001

Harvest Equipment Modification Alfalfa 50% ADEQ 2010

Harvest Equipment Modification Barley 50% ADEQ 2010

Harvest Equipment Modification Corn 50% ADEQ 2010

Harvest Equipment Modification Cotton 50% ADEQ 2010

Harvest Equipment Modification Wheat 50% ADEQ 2010

Harvest Green Chop Alfalfa 10% San Joaquin

Activity Emissions

Harvest Green Chop Barley 20% San Joaquin

Harvest Green Chop Corn 17% San Joaquin

Harvest Green Chop Wheat 20% San Joaquin

Harvest Green Chop Alfalfa 10% Assumed

Windblown Emissions

Harvest Green Chop Barley 10% Assumed

Harvest Green Chop Corn 10% Assumed

Harvest Green Chop Wheat 10% Assumed

Harvest Integrated Pest Management Alfalfa 8% San Joaquin

Activity Emissions

Harvest Integrated Pest Management Barley 14% San Joaquin

Harvest Integrated Pest Management Corn 12% San Joaquin

Harvest Integrated Pest Management Cotton 9% San Joaquin

Harvest Integrated Pest Management Wheat 14% San Joaquin

Harvest Integrated Pest Management Alfalfa 10% Assumed

Windblown Emissions

Harvest Integrated Pest Management Barley 10% Assumed

Harvest Integrated Pest Management Corn 10% Assumed

Harvest Integrated Pest Management Cotton 10% Assumed
Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-17

Table DI2-1. Crop Control Efficiencies

Category Practice Crop Control 

Efficiency Reference Comments

Harvest Integrated Pest Management Wheat 10% Assumed Windblown Emissions

Harvest Limited Harvest Activity Alfalfa 100% Assumed

Activity Emissions - This requirement 

prevents ANY harvest the day before and 

day of a high risk event.

Harvest Limited Harvest Activity Barley 100% Assumed

Harvest Limited Harvest Activity Corn 100% Assumed

Harvest Limited Harvest Activity Cotton 100% Assumed

Harvest Limited Harvest Activity Vegetables 100% Assumed

Harvest Limited Harvest Activity Wheat 100% Assumed

Harvest Limited Harvest Activity Alfalfa 10% Assumed

Windblown Emissions

Harvest Limited Harvest Activity Barley 10% Assumed

Harvest Limited Harvest Activity Corn 10% Assumed

Harvest Limited Harvest Activity Cotton 10% Assumed

Harvest Limited Harvest Activity Vegetables 10% Assumed

Harvest Limited Harvest Activity Wheat 10% Assumed

Harvest Precision Farming Alfalfa 15% San Joaquin

Harvest Precision Farming Barley 15% San Joaquin

Harvest Precision Farming Corn 15% San Joaquin

Harvest Precision Farming Cotton 15% San Joaquin

Harvest Precision Farming Wheat 15% San Joaquin

Harvest Reduced Harvest Activity Barley 50% ADEQ 2001

Used midpoint of CE’s reported

Harvest Reduced Harvest Activity Corn 50% ADEQ 2001

Harvest Reduced Harvest Activity Cotton 50% ADEQ 2001

Harvest Reduced Harvest Activity Wheat 2% ADEQ 2001

Harvest Reduced Tillage System Alfalfa 63% Walters & Jasa / ADEQ 2001

Windblown Emissions - Assumed RTS was 

equivalent to residue management with 

20% ground cover.

Harvest Reduced Tillage System Barley 63% Walters & Jasa / ADEQ 2001

Harvest Reduced Tillage System Cotton 63% Walters & Jasa / ADEQ 2001

Harvest Reduced Tillage System Vegetables 63% Walters & Jasa / ADEQ 2001

Harvest Reduced Tillage System Wheat 63% Walters & Jasa / ADEQ 2001
Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-18

Table DI2-1. Crop Control Efficiencies

Category Practice Crop Control 

Efficiency Reference Comments

Harvest Shuttle System / Large Carrier Alfalfa 17% San Joaquin

Harvest Shuttle System / Large Carrier Barley 20% San Joaquin

Harvest Shuttle System / Large Carrier Corn 17% San Joaquin

Harvest Shuttle System / Large Carrier Cotton 40% San Joaquin

Harvest Shuttle System / Large Carrier Wheat 20% San Joaquin

Harvest Transgenic Crops Alfalfa 8% San Joaquin

Harvest Transgenic Crops Corn 12% San Joaquin

Harvest Transgenic Crops Cotton 9% San Joaquin

Tilling Cessation of Night Tilling Alfalfa 0%

ADEQ assumed 0% CE, as these emissions 

will only be displaced to a subsequent day 

or hour of the day. Design days already 

assume no night tilling.

Tilling Cessation of Night Tilling Barley 0%

Tilling Cessation of Night Tilling Corn 0%

Tilling Cessation of Night Tilling Cotton 0%

Tilling Cessation of Night Tilling Vegetables 0%

Tilling Cessation of Night Tilling Wheat 0%

Tilling Combining Tractor Operations Alfalfa 43% ADEQ 2001

Tilling Combining Tractor Operations Barley 43% ADEQ 2001

Tilling Combining Tractor Operations Corn 43% ADEQ 2001

Tilling Combining Tractor Operations Cotton 43% ADEQ 2001

Tilling Combining Tractor Operations Wheat 43% ADEQ 2001

Tilling Conservation Tillage Barley 19% San Joaquin

Tilling Conservation Tillage Cotton 28% San Joaquin Activity Emissions

Tilling Conservation Tillage Wheat 19% San Joaquin

Tilling Conservation Tillage Barley 63% ADEQ 2001

Windblown Emissions - Assumed 20% 

ground cover. Tilling Conservation Tillage Cotton 63% ADEQ 2001

Tilling Conservation Tillage Wheat 63% ADEQ 2001

Tilling Equipment Modification Alfalfa 35% San Joaquin

Tilling Equipment Modification Barley 50% ADEQ 2010
Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-19

Table DI2-1. Crop Control Efficiencies

Category Practice Crop Control 

Efficiency Reference Comments

Tilling Equipment Modification Corn 50% ADEQ 2010

Tilling Equipment Modification Cotton 50% ADEQ 2010

Tilling Equipment Modification Wheat 50% ADEQ 2010

Tilling Limited Activity during High Risk Alfalfa 100% Calculated

Activity Emissions - This requirement 

prevents ANY tillage the day before and 

day of a high risk event.

Tilling Limited Activity during High Risk Barley 100% Calculated

Tilling Limited Activity during High Risk Corn 100% Calculated

Tilling Limited Activity during High Risk Cotton 100% Calculated

Tilling Limited Activity during High Risk Wheat 100% Calculated

Tilling Limited Activity during High Risk Alfalfa 10% Assumed Windblown - This control will prevent 

some farmers from destabilizing the land 

on a high risk day, but due to uncertainties 

regarding the ground’s previous level of 

stability, ADEQ assumes a conservative 

10%.

Tilling Limited Activity during High Risk Barley 10% Assumed

Tilling Limited Activity during High Risk Corn 10% Assumed

Tilling Limited Activity during High Risk Cotton 10% Assumed

Tilling Limited Activity during High Risk Wheat 10% Assumed

Tilling Planting based on Soil Moisture Alfalfa 30% ADEQ 2001 Activity Emissions

Tilling Planting based on Soil Moisture Barley 30% ADEQ 2001 Activity Emissions

Tilling Planting based on Soil Moisture Corn 30% ADEQ 2001 Activity Emissions

Tilling Planting based on Soil Moisture Cotton 30% ADEQ 2001 Activity Emissions

Tilling Planting based on Soil Moisture Wheat 30% ADEQ 2001 Activity Emissions

Tilling Planting based on Soil Moisture Alfalfa 81% Calculated Windblown Emissions - Assumed fully 

stabilized since soil is wet to 60% of field 

capacity (modeled as average reduction 

between base EF and stabilized EF). Based 

on STF monitor design day taking a plot 

from fully disturbed to fully stabilized.

Tilling Planting based on Soil Moisture Barley 81% Calculated

Tilling Planting based on Soil Moisture Corn 81% Calculated

Tilling Planting based on Soil Moisture Cotton 81% Calculated

Tilling Planting based on Soil Moisture Wheat 81% Calculated

Tilling Precision Farming Alfalfa 15% San Joaquin

Tilling Precision Farming Barley 15% San Joaquin

Tilling Precision Farming Corn 15% San Joaquin
Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-20

Table DI2-1. Crop Control Efficiencies

Category Practice Crop Control 

Efficiency Reference Comments

Tilling Precision Farming Cotton 15% San Joaquin

Tilling Precision Farming Vegetables 15% San Joaquin

Tilling Precision Farming Wheat 15% San Joaquin

Tilling Reduced Tillage System Alfalfa 25% San Joaquin

Activity Emissions - Assumed RTS was 

equivalent to Conservation Tillage 

reported in San Joaquin.

Tilling Reduced Tillage System Barley 19% San Joaquin

Tilling Reduced Tillage System Cotton 28% San Joaquin

Tilling Reduced Tillage System Vegetables 25% San Joaquin

Tilling Reduced Tillage System Wheat 19% San Joaquin

Tilling Reduced Tillage System Alfalfa 63% Walters & Jasa / ADEQ 2001

Windblown Emissions - Assumed RTS was 

equivalent to residue management with 

20% ground cover.

Tilling Reduced Tillage System Barley 63% Walters & Jasa / ADEQ 2001

Tilling Reduced Tillage System Cotton 63% Walters & Jasa / ADEQ 2001

Tilling Reduced Tillage System Vegetables 63% Walters & Jasa / ADEQ 2001

Tilling Reduced Tillage System Wheat 63% Walters & Jasa / ADEQ 2001

Tilling Tillage Based on Soil Moisture Alfalfa 15% ADEQ 2010

Activity Emissions - San Joaquin CE 

reported in ADEQ 2010

Tilling Tillage Based on Soil Moisture Cotton 15% ADEQ 2010

Tilling Tillage Based on Soil Moisture Vegetables 15% ADEQ 2010

Tilling Tillage Based on Soil Moisture Wheat 15% ADEQ 2010

Tilling Tillage Based on Soil Moisture Alfalfa 81% Calculated Windblown Emissions - Assumed fully 

stabilized since soil is wet to 40-60% of 

field capacity (modeled as average 

reduction between base EF and stabilized 

EF). Based on STF monitor design day 

taking a plot from fully disturbed to fully 

stabilized.

Tilling Tillage Based on Soil Moisture Cotton 81% Calculated

Tilling Tillage Based on Soil Moisture Vegetables 81% Calculated

Tilling Tillage Based on Soil Moisture Wheat 81% Calculated

Tilling Timing of a Tillage Operation Alfalfa 60% ADEQ 2001

Tilling Timing of a Tillage Operation Barley 60% ADEQ 2001

Tilling Timing of a Tillage Operation Corn 60% ADEQ 2001

Tilling Timing of a Tillage Operation Cotton 50% ADEQ 2001
Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-21

Table DI2-1. Crop Control Efficiencies

Category Practice Crop Control 

Efficiency Reference Comments

Tilling Timing of a Tillage Operation Wheat 60% ADEQ 2001

Tilling Transgenic Crops Alfalfa 8% San Joaquin

Tilling Transgenic Crops Corn 12% San Joaquin

Tilling Transgenic Crops Cotton 9% San Joaquin

Tilling Transplanting Vegetables 6% San Joaquin

Unpaved 

Roads Access Restriction N/A 2% ADEQ 2001 Activity Emissions

Unpaved 

Roads Access Restriction N/A 0%

Windblown Emissions, assumed same 

stabilization as more frequently traveled 

roads.

Unpaved 

Roads Aggregate Cover N/A 46% San Joaquin Activity Emissions

Unpaved 

Roads Aggregate Cover N/A 45% Li 2001

Windblown Emissions - ADEQ assumed the 

average CE from Li 2001 was reduced by 

50% due to travel on the aggregate.

Unpaved 

Roads Reduce Vehicle Speed N/A 26% Calculated Activity Emissions - Calculated using the AP-42 equation

Unpaved 

Roads Reduce Vehicle Speed N/A 0% Windblown emissions

Unpaved 

Roads

Synthetic Particulate 

Suppressant N/A 73% ADEQ 2010 Activity Emissions

Unpaved 

Roads

Synthetic Particulate 

Suppressant N/A 65% Calculated

Windblown Emissions - Assumed 

conversion to fully stabilized soil (modeled 

as average reduction between base EF and 

stabilized EF) resulting in a CE of 81%.

Conservatively, ADEQ then assumed this 

CE to only by 80% effective due to 

repeated disturbance due to traffic 

resulting in a 65% CE.
Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-22

Table DI2-1. Crop Control Efficiencies

Category Practice Crop Control 

Efficiency Reference Comments

Unpaved 

Roads Watering N/A 67% Calculated

Activity Emissions - Assumed the same 

control efficiency as for the new 

construction rule, which assumes regular 

watering to maintain soil moisture >6%.

Unpaved 

Roads Watering N/A 65% Calculated

Windblown Emissions - Assumed 

conversion to fully stabilized soil (modeled 

as average reduction between base EF and 

stabilized EF) resulting in a CE of 81%.

Conservatively, ADEQ then assumed this 

CE to only by 80% effective due to 

repeated disturbance due to traffic 

resulting in a 65% CE.

Unpaved 

Roads Wind Barrier Alfalfa 54% Grantz 1998

Windblown Emissions - Minimum reported 

control efficiency from Grantz 1998 for 

wind barriers of 55% porosity

DI2.1 References

ADEQ. (2001). Technical Support Document for Quanitification of Agricultural Best Management Practices. Appendix B.

ADEQ. (2010). Agricultural Best Management Practices Technical Support Document.

Grantz, D. A., Vaughn, D. L., Farber, R. J., Kim, B., VanCuren, T., & Cambpell, R. (1998). Wind barriers offer short-term solution to 

fugitive dust. California Agriculture, 52(4), 14-18.

Li, X.-Y., Liu, L.-Y., & Gong, J.-D. (2001). Influence of pebble mulch on soil erosion by wind and trapping capacity for windblown 

sediment. Soil & Tillage Research, 59, 137-142.

San Joaquin Valley Air Pollution Control District. (2006). Conservation Management Practices Program Report for 2005.

Walters, D., & Jasa, P. Conservation Tillage in the United States: An Overview. Retrieved July 8, 2013, from: 

http://agecon.okstate.edu/isct/labranza/walters/conservation.doc.
Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-23

DI3 Crop Season Distribution

The following table shows the calculated relative length of each crop season (i.e., tillage, harvest, 

cropland, and growing), as determined as a percent of the entire year. This information was used 

in the calculation of annual control effectivenesses for windblown dust from crop fields. The 

distributions were calculated based on the Pinal County crop calendar (Appendix B, Exhibit BIII).

Table DI3-1. Crop Season Length (% of the year)

Crop Tillage Harvest Cropland Growing

Alfalfa 0.0% 80.6% 0.0% 19.4%

Cotton 19.4% 38.9% 11.1% 30.6%

Forage 0.0% 80.6% 0.0% 19.4%

Vegetables 19.4% 44.4% 36.1% 0.0%

Corn 22.2% 28.4% 25.0% 24.4%

Grain 25.0% 28.5% 33.3% 13.2%
Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-24

DI4 Dairy Operation Survey
Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-25

DI5 Dairy Operation Control Efficiencies

Table DI5-1 presents the control efficiencies used to calculate dairy control effectivenesses.

Unless otherwise specified, activity and windblown control efficiencies are assumed equivalent.

Table DI5-1. Dairy Operation Control Efficiencies

Operation / 

Sector BMP Control Efficiency Reference Comments

Arenas, 

Corrals, Pens

Apply a Fibrous Layer 10%

Ag BMP 

Committee

Activity

Cement Dairy Cattle 

Walkways to Milk Barn 25% Activity

Do Not Run Cattle 10% Activity

Feed Green Chop 10%

Free Stall Housing 60%

Groom Manure Surface 10%

Keep Calves in Barns or 

Hutches 10% Activity

Pile Manure Between 

Cleanings 10%

Provide Shade in Corral 10% Activity

Provide Cooling in Corral 10% Activity

Use Drag Equipment to 

Maintain Pens 10% Activity

Water Misting Systems 10%

Wind Barrier 10% Activity

Apply a Fibrous Layer 33% San Joaquin

Windblown. Control efficiency 

assumed equivalent to 

Chips/Mulch CE in Table DI2-1.

Cement Dairy Cattle 

Walkways to Milk Barn 10% Ag BMP Committee Windblown

Do Not Run Cattle 0% Assumed

Windblown, assumed to not 

affect windblown dust 

emissions.

Keep Calves in Barns or

Hutches 0% Assumed

Provide Shade in Corral 0% Assumed

Provide Cooling in Corral 0% Assumed

Use Drag Equipment to 

Maintain Pens 0% Assumed

Wind Barrier 45% ADEQ 2010

Windblown. Control efficiency 

assumed equivalent to Wind 

Barrier CE in Table DI2-1.
Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-26

Table DI5-1. Dairy Operation Control Efficiencies

Operation / 

Sector BMP Control Efficiency Reference Comments

Animal 

Waste (and 

Feed) 

Handling & 

Transport

Cover Manure Hauling 

Trucks 10%

Ag BMP 

Committee

Covers for Silage 10%

Feed High Moisture Feed 10%

Increase Manure Moisture 10%

Do Not Load Dry Manure 

when Wind > 15mph 10%

Store & Maintain Feed Stock 10%

Store Silage in Bunkers 10%

Unpaved 

Access 

Connections

Apply & Maintain 

Pavement in High Traffic 

Areas

8% Ag BMP 

Committee

Apply & Maintain Aggregate 

Cover 46% San Joaquin

Activity. Control efficiency 

assumed equivalent to that 

reported in Table DI2-1.

Apply & Maintain Synthetic 

Particulate Suppressants 73% ADEQ 2010

Apply & Maintain Water as a 

Dust Suppressant 67% Calculated

Install Signage to Limit 

Vehicle Speed to 15mph 23% Calculated This BMP was calculated by 

Sierra Research utilizing EPA's 

AP-42 equation for unpaved 

roads. Install Speed Control Devices 23% Calculated

Install & Maintain a Trackout 

Control Device

This BMP was not reported by 

any respondents as a preferred 

control; therefore, no control 

efficiency was researched.

Restrict Access to Through 

Traffic 2% ADEQ 2001

Control efficiency assumed 

equivalent to that in reported 

Table DI2-1.

Apply & Maintain Aggregate 

Cover 45% Li 2001

Windblown. Control efficiency 

assumed equivalent to that 

reported in Table DI2-1.

Apply & Maintain Synthetic 

Particulate Suppressants 65% Calculated

Apply & Maintain Water as a 

Dust Suppressant 65% Calculated
Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-27

Table DI5-1. Dairy Operation Control Efficiencies

Operation / 

Sector BMP Control Efficiency Reference Comments

Unpaved 

Roads or 

Feed Lanes

Apply & Maintain Aggregate 

Cover 46% San Joaquin

Activity. Control efficiency 

assumed equivalent to that 

reported in Table DI2-1.

Apply & Maintain Oil on 

Roads with Approved 

Products

76% Ag BMP 

Committee

Apply & Maintain Pavement 

in High Traffic Areas 8% Calculated

Calculated based on input from 

the Agricultural BMP 

committee on the average 

mileage of roadways with >20 

ADT as compared to that of 

total mileage and assuming a

paving control efficiency of 

99%.

Apply & Maintain Synthetic 

Particulate Suppressants 73% ADEQ 2010 Activity. Control efficiency 

assumed equivalent to that 

reported in Table DI2-1.

Apply & Maintain Water as a 

Dust Suppressant 67% Calculated

Install Engine Speed 

Governors on Feed Truck to 

15mph

23% Calculated This BMP was calculated by 

Sierra Research utilizing EPA's 

AP-42 equation for unpaved 

roads.

Install Speed Control Devices 23% Calculated

Install Signage to Limit 

Vehicle Speed to 15mph 23% Calculated

Restrict Access to Through 

Traffic 2% ADEQ 2001

Control efficiency assumed 

equivalent to that reported in 

Table DI2-1.

Use Appropriate Vehicles 20% Ag BMP 

Committee

Apply & Maintain Aggregate 

Cover 45% Li 2001

Windblown. Control efficiency 

assumed equivalent to that 

reported in Table DI2-1.

Apply & Maintain Synthetic 

Particulate Suppressants 65% Calculated

Apply & Maintain Water as a 

Dust Suppressant 65% Calculated
Appendix D Exhibit DI. Agricultural Supplemental Information

December 21, 2015 Final Page DI-28

DI5.1 References

ADEQ. (2001). Technical Support Document for Quanitification of Agricultural Best Management 

Practices. Appendix B.

ADEQ. (2010). Agricultural Best Management Practices Technical Support Document.

Governor's Agricultural BMP Committee. (2010). Agricultural Best Management Practices for 

Livestock Operations.

Li, X.-Y., Liu, L.-Y., & Gong, J.-D. (2001). Influence of pebble mulch on soil erosion by wind and 

trapping capacity for windblown sediment. Soil & Tillage Research, 59, 137-142.

San Joaquin Valley Air Pollution Control District. (2006). Conservation Management Practices 

Program Report for 2005.
Appendix D Exhibit DII. MAG Methodology for MOVES2014

December 21, 2015 Final Page DII-1

Exhibit DII MAG Methodology 

for MOVES2014

Contents

DII1 Pinal County PM-10 Emissions for Five Episode Days..........................................DII-2

DII1.1 Year 2008.................................................................................................DII-2

DII1.2 Year 2018.................................................................................................DII-3

DII2 Pinal County PM-10 Annual Emissions.................................................................DII-5

DII2.1 Year 2008.................................................................................................DII-5

DII2.2 Year 2018.................................................................................................DII-7
Appendix D Exhibit DII. MAG Methodology for MOVES2014

December 21, 2015 Final Page DII-2

DII1 Pinal County PM-10 Emissions for Five 

Episode Days

For the five episode days, vehicle exhaust, tire wear, and brake wear emissions were calculated 

for the four modeling domains (City of Maricopa, Cowtown, Stanfield, and Pinal County Housing 

Complex) using the latest onroad model approved by EPA, MOVES2014. The MOVES2014 

simulations were configured to operate in both inventory and rate modes for all five episode 

days.

The four modeling domains are located within Area C where I/M programs are not required.

In September 2014, MAG transportation division generated a reference year 2015 detailed traffic 

assignment without centroid (called “2015_MAG_no_Centroid”) for four non-centroid areas, 

each representing the TAZ zones surrounding the four monitoring sites. The detailed traffic 

assignment data for each area are scaled individually for the year 2008 and 2018. The scaled 

traffic assignment data for the four areas are used to calculate link-based mobile emissions.

Information or input data for the MOVES2014 simulation is described below.

DII1.1 Year 2008

Scaling factors for the traffic assignment data without centroid 

For each non-centroid area, the first ratio was calculated between the total VMTs in the 2012 

MAG traffic assignment (with centroids) and the 2015 MAG traffic assignment (with centroids). 

The second ratio was MAG’s estimated human population between the years 2008 and 2012. 

Note that an average population ratio was used for the Cowtown area, because human 

population was zero in 2008 in that area. The multiplication of the two ratios became the backcasted ratio between 2008 and 2015 for the same non-centroid area. The four ratios were applied 

individually to each area of the “2015_MAG_no_Centroid” traffic assignment to generate the 

“2008_MAG_no_Centroid”.

Average Speed Distribution

Using the “2008_MAG_no_Centroid” traffic assignment, speed distributions by vehicle types, 

road types, and hours for weekdays were estimated for the union of the four areas. For weekend 

days, nighttime’s speed distribution, which is a 12-hour span, is applied for another 12-hour span.

Annual VMT

The 2008 annual VMT by HPMS vehicle type was developed using the scaled 

“2008_MAG_no_Centroid” traffic assignment.

VMT Fractions

Monthly, daily, and hourly VMT fractions were derived from the traffic volume adjustment 

factors for freeways and arterials based on data recorded by continuous traffic counters on 

freeways (ADOT Freeway Management System) and arterials (Phoenix Automatic Traffic 

Recorders) during the year 2007.
Appendix D Exhibit DII. MAG Methodology for MOVES2014

December 21, 2015 Final Page DII-3

Road Type Distribution

The VMT by vehicle, area, and facility types from the scaled “2008_MAG_no_Centroid” traffic 

assignment was translated into road VMT fractions for each vehicle source type using a mapping 

table derived by MOVES2014 default VMT fraction for 2008. 

Source Type Population

Based on ADOT’s 2008 vehicle registration information for Pinal County broken down by source 

type, source type populations for the union of the four non-centroid areas were scaled using 

ratios derived by population data covered by each non-centroid area and Pinal County in 2008. 

The 2008 population data for Pinal County and the four non-centroid area were provided by the 

MAG GIS group. Note that no single unit or combination long-haul trucks were included, because 

they are assumed to operate on interstate highways. 

Source Type Age Distribution

As used by Sierra Research, the same source type age distributions provided by ADOT were 

applied. These source type age distributions were derived from the 2008 vehicle registration data 

for Pinal County.

Meteorology

The 24-hour temperature and relative humidity data for each of the five episode days were 

imported from Casa Grande in 2008.

Fuel Data

Based on MOVES2014 default values for 2008, gasoline and diesel fuel formulations were 

updated with the 2008 fuel inspection data provided by the Arizona Department of Weights and 

Measures. 

I/M Programs

No I/M programs were applied for the union of the four non-centroid areas.

Alternate Vehicle Fuel Technology (AVFT) 

Based on MOVES2014 default values, transit bus data were updated with the local year 2010 

information provided by the Valley Metro.

Ramp Fractions

No ramp fractions calculations were applied, because there was no ramp road types within the 

modeling domains.

DII1.2 Year 2018

Scaling factors for the traffic assignment data without centroid 

For each non-centroid area, total VMT for the year 2018 was calculated through interpolation 

between 2015 MAG traffic assignment (with centroids) and the 2025 MAG traffic assignment 

(with centroids). Then four ratios of the total VMT between 2018 and 2015 for each area were 
Appendix D Exhibit DII. MAG Methodology for MOVES2014

December 21, 2015 Final Page DII-4

used to project “2018_MAG_no_Centroid” traffic assignment based on the referenced 

“2015_MAG_no_Centroid”.

Average Speed Distribution

Using the “2018_MAG_no_Centroid” traffic assignment, speed distributions by vehicle types, 

road types, and hours for weekdays were estimated. For weekend days, nighttime’s speed 

distribution, which is a 12-hour span, is applied for another 12-hour span. 

Annual VMT

The 2018 annual VMT by HPMS vehicle type was developed using the scaled 

“2018_MAG_no_Centroid” traffic assignment.

VMT Fractions

Monthly, daily, and hourly VMT fractions were derived from the traffic volume adjustment 

factors for freeways and arterials based on data collected in the Phoenix area in 2007.

Road Type Distribution

The VMT by vehicle, area, and facility types from the “2018_MAG_no_Centroid” traffic 

assignment was translated into road VMT fractions for each vehicle source type using a mapping 

table derived by MOVES2014 default VMT fraction for 2018.

Source Type Population

Based on ADOT’s 2008 vehicle registration information for Pinal County broken down by source 

type, the 2018 source type population for Pinal County was scaled up from the 2008 source type 

population with MAG’s population projections. The 2018 source type populations for the union 

of the four non-centroid area were scaled using ratios derived by population data covered by 

each non-centroid area and Pinal County in 2018. The 2018 population data were provided by 

the MAG GIS group. Note that no single unit or combination long-haul trucks were included, 

because they are assumed to operate on interstate highways.

Source Type Age Distribution

The 2008 source type age distributions were held constant for the 2018 projections.

Meteorology

The 2008 meteorology inputs were held constant for the 2018 projections.

Fuel Data

To reflect Tier 3 standards, MOVES2014 default fuel data for Pinal County in 2018 were used.

I/M Programs

No I/M programs were applied for the union of the four non-centroid area.

Alternate Vehicle Fuel Technology (AVFT) 

Based on MOVES2014 default values, transit bus data were updated with the local year 2010 

information provided by the Valley Metro.
Appendix D Exhibit DII. MAG Methodology for MOVES2014

December 21, 2015 Final Page DII-5

Ramp Fractions

No ramp fractions calculations were applied, because there was no ramp road types within the 

modeling domains.

DII2 Pinal County PM-10 Annual Emissions

The annual total vehicle exhaust, tire, and brake wear emissions were calculated using the latest 

onroad model approved by EPA, MOVES2014. The MOVES2014 simulations were configured to 

operate in inventory mode for all 12 months and weekend/weekdays with the output option of 

annual aggregation.

The Pinal County PM-10 nonattainment area (NAA) needs to be divided into two areas in terms 

of implementation of I/M programs. One is Area A in which I/M programs are mandatory and the 

other is non-Area A in which I/M programs are not required. Therefore, MOVES2014 simulations 

were conducted for both Area A and non-Area A within the Pinal County PM-10 NAA.

Information on input data for the MOVES2014 simulation is described below.

DII2.1 Year 2008

Average Speed Distribution

Using the 2012 MAG traffic assignment, speed distributions by vehicle types, road types, and 

hours for weekdays were estimated for each of Area A and non-Area A within the Pinal County 

PM-10 NAA. For weekend days, nighttime’s speed distribution, which is a 12-hour span, is applied 

for another 12-hour span. Speed distribution from the 2012 MAG traffic assignment was used in 

the 2008 emission calculations.

Annual VMT

The 2012 annual VMTs by HPMS vehicle type were developed for Area A and non-Area A within 

the Pinal County PM-10 NAA using the 2012 MAG traffic assignment. For non-Area A, additional 

adjustment factors for road type and vehicle type were applied to reflect the local road traffic 

volumes on behalf of the centroid connector traffic volumes. The adjustment factors were 

derived from the MAG 2015 traffic assignment. Based on the 2012 annual VMT by HPMS vehicle 

type, the 2008 annual VMTs for Area A and non-Area A were back-casted using population 

estimates between 2008 and 2012 within each geographic area.

VMT Fractions

Monthly, daily, and hourly VMT fractions were derived from the traffic volume adjustment 

factors for freeways and arterials based on data recorded by continuous traffic counters on 

freeways (ADOT Freeway Management System) and arterials (Phoenix Automatic Traffic 

Recorders) during the year 2007.
Appendix D Exhibit DII. MAG Methodology for MOVES2014

December 21, 2015 Final Page DII-6

Road Type Distribution

The VMT by vehicle, area, and facility types from the 2012 MAG traffic assignment was translated 

into road VMT fractions for each vehicle source type using a mapping table derived by 

MOVES2014 default VMT fraction for 2008. The road type distribution was developed for Area A 

and non-Area A separately.

Source Type Population

Based on ADOT’s 2008 vehicle registration information for Pinal County broken down by source 

type, source type populations for Area A and non-Area A within the Pinal County PM-10 NAA 

were scaled using ratios derived by population data covered by the PM-10 NAA and Pinal County 

in 2008. The 2008 population data for Pinal County and geographic coverages were provided by 

the MAG GIS group.

Source Type Age Distribution

As used by Sierra Research, the same source type age distributions provided by ADOT were 

applied. These source type age distributions were derived from the 2008 vehicle registration data 

for Pinal County. It is assumed that the Pinal County PM-10 NAA has the same source type age 

distribution regardless of Area A or non-Area A.

Meteorology

The meteorological conditions were imported as monthly average temperature and relative 

humidity data for each hour from Casa Grande in 2008.

Fuel Data

Based on MOVES2014 default values for 2008, gasoline and diesel fuel formulations were 

updated with the 2008 fuel inspection data provided by the Arizona Department of Weights and 

Measures. It is assumed that the Pinal County PM-10 NAA has the same fuel data for Area A and 

non-Area A.

I/M Programs

The I/M programs developed for the Maricopa County conformity analysis were applied for Area 

A within the Pinal County PM-10 NAA, while no I/M programs were applied for non- Area A within 

the Pinal County PM-10 NAA.

Alternative Vehicle Fuels and Technologies (AVFT)

Based on MOVES2014 default values, transit bus data were updated with the local 2011 

information provided by the Valley Metro.

Ramp Fractions

Using the 2012 MAG traffic assignment, ramp fractions were estimated for Area A and non-Area 

A separately. It is assumed that the same ramp fractions are maintained between the years 2008 

and 2012.
Appendix D Exhibit DII. MAG Methodology for MOVES2014

December 21, 2015 Final Page DII-7

DII2.2 Year 2018

Average Speed Distribution

Using the 2015 MAG traffic assignment, speed distributions by vehicle types, road types, and 

hours for weekdays were estimated for each of Area A and non-Area A within the Pinal County 

PM-10 NAA. For weekend days, nighttime’s speed distribution, which is a 12-hour span, is applied 

for another 12-hour span. Speed distribution from the 2015 MAG traffic assignment was used in 

the 2018 emission calculations.

Annual VMT

The 2015 and 2025 annual VMTs by HPMS vehicle type were developed for Area A and non-Area 

A within the Pinal County PM-10 NAA using the 2015 and 2025 MAG traffic assignment, 

respectively. For non-Area A, additional adjustment factors for road type and vehicle type were 

applied to reflect the local road traffic volumes on behalf of the centroid connector traffic 

volumes. The adjustment factors were derived from the MAG 2015 traffic assignment. The 2018 

VMTs for Area A and non-Area A were interpolated using the 2015 and 2025 annual VMTs within 

each geographic area.

VMT Fractions

Monthly, daily, and hourly VMT fractions were derived from the traffic volume adjustment 

factors for freeways and arterials based on data collected in the Phoenix area in 2007.

Road Type Distribution

The VMT by vehicle, area, and facility types from the 2015 MAG traffic assignment was translated 

into road VMT fractions for each vehicle source type using a mapping table derived by 

MOVES2014 default VMT fraction for 2018. The road type distribution was developed for Area A 

and non-Area A separately.

Source Type Population

Based on ADOT’s 2008 vehicle registration information for Pinal County broken down by source 

type, the 2018 source type population for Pinal County was scaled up from the 2008 source type 

population with MAG’s population projections. The 2018 source type populations for Area A and 

non-Area A were scaled using ratios derived by population data covered by the PM-10 NAA and 

Pinal County in 2018. The 2018 population data were provided by the MAG GIS group.

Source Type Age Distribution

The 2008 source type age distributions were held constant for the 2018 projections.

Meteorology

The meteorological conditions were imported as monthly average temperature and relative 

humidity data for each hour from Casa Grande in 2008. The 2008 meteorology inputs were held 

constant for the 2018 projections.
Appendix D Exhibit DII. MAG Methodology for MOVES2014

December 21, 2015 Final Page DII-8

Fuel Data

The 2018 fuel data for Pinal County were obtained from the MOVES2014 default fuel data in 

order to address the Tier 3 vehicle and fuel standards, which will begin phasing in with the 2017 

model year. It is assumed that the Pinal County PM-10 NAA has the same fuel data for Area A 

and non-Area A.

I/M Programs

The I/M programs developed for the Maricopa County conformity analysis were applied for Area 

A within the Pinal County PM-10 NAA, while no I/M programs were applied for non- Area A within 

the Pinal County PM-10 NAA.

Alternative Vehicle Fuels and Technologies (AVFT)

Based on MOVES2014 default values, transit bus data were updated with the local 2011 

information provided by the Valley Metro.

Ramp Fractions

Using the 2015 MAG traffic assignment, ramp fractions were estimated for Area A and non-Area 

A separately. It is assumed that the same ramp fractions are maintained between the years 2015 

and 2018.