# Appendix D

Pinal County PM 10 

# Nonattainment Area 2018 Attainment Demonstration and
Controlled Emissions Inventories

Air Quality Division
December 21 , 2015 Final

## This page is intentionally blank.

### Appendix D

December 21 , 2015 Final Page i 

# Table of Contents

Table of Contents

................................ ................................ ................................ ............................. i
Index of Tables

................................ ................................ ................................ ................................ . ii Index of Figures

................................ ................................ ................................ ............................... iii Glossary o f Agencies

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

................................ ................................ ................................ ........................... iii

D1 Introduction

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

D2 Base Scenario

................................ ................................ ................................ ...................... 2

D3 Attainmen t Control Measures

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

D3.1 Agricultural Best Management Practices

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

D3.1.1 Requirements for Commercial Crop Operations

................................ ..... 6

D3.1.2 Requi rements 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 Roa ds

................................ ................................ ...................... 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 Reasonabl e 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 Operatio n 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 Table s

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 Scena rio 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 f or 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. Summ ary 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 PM 10 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 Ad ministration

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/m 3 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 PM 10 NA )
NAAQS National Ambient Air Quality Standards (herein, the 1987 primary and secondary 24

-

hour PM 10 NAAQS)

PCH Pinal County Housing or Eleven Mile Corner monitor

PM 10 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 p arts of the

Arizona SIP which apply to the West Pinal PM 10 NA)

STF Stanfield mon itor

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 refe rred to as “Exhibit s ”).

The proposed SIP revision applies to the West Pinal PM 10 Nonattainment 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 tec hnical methodologies and rationales whose results support claims made in that proposed revision. Previous appendices discuss the principles used to quantify and account for the PM 10 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 co ntrols in the proposed revision will bring the NA into attainment of the 1987 primary and secondary 24

- hour PM 10 National Ambient Air Quality Standards (NAAQS),

 the accounting choices underlying the motor vehicle PM 10 emissions budget (MVEB) for

on

- road ve hicles 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 Sectio ns 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 e xhibits

. 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 PM 10 NA strategy for attaining the NAAQS by 2018 was formulated based on an analysis of PM 10 emissions in 2008, and projections of what those emissions would look like in 2018 without intervention. Days representing the PM 10 problem around each exceeding monitor (design days) were modeled to identify which sources of PM 10 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 r elated 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 PM 10 sources

are c onsidered and how the ir 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 m onitor, 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 als o 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

201 8 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, s ummed 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 stag nation modeling, each emissions source in the appropriate domain was run through the AerMOD dispersion model, whose raw output was the concentration of PM 10 at the monitor attributable to each local source for a given hour. These values were adjusted to ap proximate 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 esti mated 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 45,105.3 1 12,352.3 3,923.6 12

, 735.7 11

, 2 41.5 4

, 3 89.9 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 1 44.4 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 1 4.0

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 13 2,751.8 20

, 804.5 5

, 656.7 1 9,639.7 26

, 9 56.4 2 8,9 15 .1 52

, 2 60.6 24

, 2 87.0

1 Note that emissions from unpaved test tracks at permitted vehicle testing facilities are not included here, but instead in th e annual total for Point Sources. However, test track emissions are modeled just like other unp aved 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 a pproximate values of the resulting scaling factors are shown in Table D2

- 2 , and were reused for 2018 modeling. For example, the Cowtown stagnation des ign day in 2008 yields

244

. 5 μg/ m 3 = 11

. 5 μg/ m 3 + c ∙ 849

. 6 μg/ m 3

so that c ≈ 27

. 42% . W hen interpreting the model results for the 2018 base scenario in Cowtown, that value of c is used:

M μg/ m 3 = 11

. 5 μg/ m 3 + 27

. 42%

∙ 645

. 1 μg/ m 3

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

sources 2 Scaling

factor 2 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 28 9.5 52. 1 % 150.8

17.5 168.3

MC 45 6.5 3 1.1 % 141.9 159.4

PCH 1

, 05 3.7 23.5% 247.4 264.9

STF 21 4.1 75. 2 % 161.1 178.6

2018 Stagnation CWT 64 5.1 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 28 0.7 52. 1 % 14 6.2

17.5 16 3.7

MC 39 4.0 3 1.1 % 122. 5 140.0

PCH 1

, 04 4.3 23.5% 245. 2 26 2.7

STF 22 4.0 75. 2 % 16 8.5 186. 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 sca ling, are in terms of (lbs/hr)/mile, whereas stagnation impacts are in μg/m 3 . In either case, the resulting scaled impact is in μg/m 3 . Therefore, the empirically

- derived scaling factor is unitless for stagnation modeling, but carries the unit (μg/m 3 )/([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 reduct ions into the 2018 modeling scenarios results in lower modeled concentrations

– as described in Section D3.3

— which indicate that the NA will attain the PM 10 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 PM 10 emissions source , from which the operator selects which controls to implement

. Whenever possible, ADEQ relied on surveys of operators to determine which of t he 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 PM 10 problems in the NA, giving the operators sufficient warning to implement more stringent controls in order to avert a PM 10 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 shortlist s of the most effective BMPs from the menus that the operator had previously been choosing from. If that operator’s ev ery

- 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

- ris k

- day BMPs would be implemented, PCAQCD evaluated the relevant data that would have been available to them each day in 2011

- 2013 3

. 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

, 04 6.5

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

, 42 6.3

### 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
coll ectively 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 PM 10 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 PM 10 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 e nsure increased PM 10 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 Cr op 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 impl ement 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

me asure/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 PM 10 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 irriga tion 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 electro nically by ADEQ from the Ag BMP Committee on May 19 th , 2014. ADEQ later verified individual survey results against the compiled survey results provided by the Ag BMP

Committee to en sure 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 determi ne 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 eff iciency 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 harvestin g control efficiencies by crop.

Table D3

- 2

. Survey Representativeness for Major Crops Cultivated in the Pinal County

PM10 Nona ttainment 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

2007 4

. 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 Me asures

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 s urvey 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 con stitute 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 a ttainment 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 impl ementing 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

r ule, the farmer was given credit for that control which had the highest control efficiency based on efficiencies researched from previous PM 10 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 usin g 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’ (4 3% 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 cott on.
Based on this information we can calculate an average non

- high risk day cotton tillage control efficiency utilizing the following equation:

CE avg = ( CE a × A a ) + ( CE b × A b ) + ( CE c × A c )

A a + A b + A c
which becomes

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

150 acres + 500 acres + 293 acres = 43%

where

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

CE x = the crop specific activity emission control efficiency assigned to farmer x given his/her survey response (where x = A, B, C...), and

A x = 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 no t 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 applie d to calculated design day tillage activity emissions and windblown emissions under the assumption that all design days would be forecasted as high risk 5

. For annual emission calculations, ADEQ relied on estimates of the number of annual days assumed to qu alify 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 ti llage 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 r eported to experience tillage activities (presented
in Exhibit DI3 ). This was determined from the crop calendar p rovided by PCAQCD (Appendix B).

5 See Section D3.1

.

### Appendix D D3

. Attainment Control Measures

December 21 , 2015 Final Page 10 Add itional 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 em ission 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 c rop 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 n on

- 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 g iven 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 requirem ents 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 pre vious PM 10 plans or studies (a list of utilized control efficiencies and their references are presented in Exhibit DI2 ). In those instances where co ntrol 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 conservati ve 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.

 Farm er 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 equa tion:

CE avg = ( CE a × A a ) + ( CE b × A b ) + ( CE c × A c )

A a + A b + A c which becomes

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

150 acres + 500 acres + 293 acres = 36%

where

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

CE x = the crop specific activity emission control efficiency assigned to farmer x given his/her survey response (where x = A, B, C...), and

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

Acceptable no n

- 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 durin g 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 windb lown 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 estimat es 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 cr op calendar provided by PCAQCD.

6 See Section D3.1

.

### Appendix D D3

. Attainment Control Measures

December 21 , 2015 Final Page 12 Additional Ass umptions/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
efficiencie s 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 contro l 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 implem ented 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 con trol 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 PM 10 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 u npaved 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 un paved roads.

Based on this information we can calculate an average non

- high risk day commercial farm road control efficiency utilizing the following equation:

CE avg = ( CE a × L a ) + ( CE b × L b ) + ( CE c × L c )

L a + L b + L c
which becomes

CE avg = ( 2% × 10 miles ) + ( 26% × 12

. 5 miles ) + ( 46% × 58 miles )

10 miles + 12

. 5 miles + 58 miles = 37%

where

CE avg = average activity emission control efficiency (%),

CE x = the activity emission control efficiency assigned to farmer x given his/her survey response (where x = A, B, C...), and

L x = 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 ac tivity 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 describe d 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 a nd 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 non

high 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 signi ficantly 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’ efficie ncy.



### 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 o ccurs 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 respo nses 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 we re 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), t he 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 adequat e in order to assure conservativ e 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% c ontrol 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 acr es 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:

CE avg = ( ( 1

− ( 1

− CE a 1 ) × ( 1

− CE a 2 ) ) × A a ) + ( ( 1

− ( 1

− CE b 1 ) × ( 1

− CE b 2 ) ) × A b )

A a + A b
which becomes

CE avg = ( ( 1

− ( 1

− 57% ) × ( 1

− 45% ) ) × 109 acres ) + ( ( 1

− ( 1

− 33% ) × ( 1

− 45% ) ) × 150 acres )

109 acres + 150 acres

= 69%

where

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

CE x = crop specific/fallow field windblown dust emission control efficiency assigned to farmer x given his/her survey responses (where x = A, B, C...), and

A x = 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 imple mentation 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 giv en 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 adequ ately 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 acrea ge 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 b y 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 f arms 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, ADE Q 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 the n able to estimate the percentage of farmland, by acreage, associated with each farm size bin ( Table D3

- 4 ). ADEQ interpreted rule penetration as the pe rcentage 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; an d

 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 agricultura l 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 st affed 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 efficienci es.

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 effect iveness 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 windbl own 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 Vege

tables 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 non

high 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 c alculations. 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

CE T

- NHR = Non

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

CE T

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

CE H

- NHR = Non

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

CE H

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

D = High risk days per year = 45

L T = Length of Tillage season (% of annual) = 19.44%

### Appendix D D3

. Attainment Control Measures

December 21 , 2015 Final Page 19 L H = Length of Harves t season (% of annual) = 38.89%

CE = ( CE T

− NHR × [ 366

− D ] + CE T

− HR × [ D ]
366 ) × L T + ( CE H

− NHR × [ 366

− D ] + CE H

− HR × [ D ]
366 ) × L H

L T + L H
which becomes

6 4

. 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

CE C

- NHR = Non

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

CE C

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

D = High risk days per year = 45

CE = CE C

− NHR ∗ ( 366

− D ) + CE C

− HR ∗ ( D )

366

which beco mes

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 co ntrol 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 m ethod 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.

Cro ps

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 ag ricultural lands.

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

- 5 was dependent on the crop season in w hich 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 emissi on 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 c rop 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 field s 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 ar ea 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 a dequate 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 co ntrol 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 emissio n 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

- hi gh 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 effectiven ess.



### 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 mode ling 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 T ribal 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 (lb s /day) Controlled

Emissions (lb s /day) Emission Reductions (lb s /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 (lb s /day) Controlled

Emissions (lb s /day) Emission

Reductions (lb s /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 3,42 2 1,886 1,53 6

Stanfield Crop Agriculture 1,058 413 645

Unpaved Agricultural Roads 7,268 4,00 4 3,26 4

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, 0 51 705



## 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 PM 10 Nonattainment Area. Controls adopted under the general permit established in this rule focus on the following comme rcial 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 con trols from a menu of ADEQ approved measures which will reduce activity related and/or windblown PM 10 emissions on all days of the year. For high risk forecast days, commercial crop farmers must

e nsure implementation of controls on unpaved roadways of >20 v ehicle 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
ab ove 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 determin e 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

. Attainme nt 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 distribute d 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 13 th , 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 PM 10 nonattainment area. This represe nts 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 al l 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,
al l 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 aren as) 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 prec eding 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 Mis ting 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:

CE avg = ( CE a × N a ) + ( CE b × N

b ) + ( CE c × N

c )

N a + N b + N c
which becomes

### Appendix D D3

. Attainment Control Measures

December 21 , 2015 Final Page 26 CE avg = ( 10% × 11

, 000 h ead ) + ( 25% × 2

, 000 h ead ) + ( 10% × 6

, 900 h ead )

11

, 000 h ead + 2

, 000 h ead + 6

, 900 h ead = 12%

where

CE avg = average dairy activity emission control efficiency (%),

CE x = t he activity emission control efficiency assigned to facility x given the survey response (where x = A, B, C...), and

N x = the head of cattle reported for f acility x (where x = A, B, C...).

BMPs for arena emission control are outlined in R18

- 2

- 611.03(D)(1). So me 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 c ontrol 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 cr edited 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 ha ndling 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 2,00 0 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 activ ity emission control efficiency for handling and transporting utilizing the following equation:

CE avg = ( CE a × N a ) + ( CE b × N

b ) + ( CE c × N

c )

N a + N b + N c
which becomes

CE avg = ( 10% × 11

, 000 h ead ) + ( 10% × 2

, 000 h ead ) + ( 10% × 6

, 900 h ead )

11

, 000 h ead + 2

, 000 h ead + 6

, 900 h ead = 10%

where

CE avg = average dairy activity emission control efficiency (%),

CE x = the activity emission control efficiency assigned to facility x given the survey response (where x = A, B, C...), and

N x = the head of cattle reported f or 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 acro ss 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
ef ficiencies. 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

- 6 11.03(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
effi ciency 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 emi ssions) 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:

CE avg = ( CE a × N a ) + ( CE b × N

b ) + ( CE c × N

c )

N a + N b + N c
which becomes

CE avg = ( 46% × 11

, 000 h ead ) + ( 23% × 2

, 000 h ead ) + ( 2% × 6

, 900 h ead )

11

, 000 h ead + 2

, 000 h ead + 6

, 900 h ead = 28%

where

CE avg = average dairy activity emission control efficiency (%),

CE x = activity emission control efficiency assigned to facility x given their survey response (where x = A, B, C...), and

N x = 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 unp aved 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 calcula ted 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 Efficie ncy 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 dete rmine 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 b e 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 cont rol efficiency for unpaved roads utilizing the following equation:

CE avg = ( CE a × N a ) + ( CE b × N

b ) + ( CE c × N

c )

N a + N b + N c
which becomes

CE avg = ( 67% × 11

, 000 h ead ) + ( 23% × 2

, 000 h ead ) + ( 2% × 6

, 900 h ead )

11

, 000 h ead + 2

, 000 h ead + 6

, 900 h ead = 40%

whe re

CE avg = average dairy activity emission control efficiency (%),

CE x = activity emission control efficiency assigned to facility x given their survey response (where x = A, B, C...), and

N x = 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 with in 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 ac tivity 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 outlin ed 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 Ariz ona ( 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 emissio ns 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

*B elow 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 as sumed 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,

A DEQ 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 Calcu lation

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 th e relative fraction each operation/sector produces. The sum product of the fraction of total emissions presented in Table

D3

- 13 was multiplied by the a ctivity 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 emission s respectively ( Table D3

- 14 ).

Table D3

- 13

. Dairy Cattle Emission Factors by Livestock Activity

Operation / Sector Emission Factor ( lb s /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 a lteration 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 Emi ssion 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 pre sents the annual emission reductions attributab le 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 in ventory. 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 Contr ol 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 R ange 3 ).

Subsections A and E of the rule require comme rcial 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 esti m a tes 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 a nd emission factor parameters prior to BMP implementation

, a s 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 Are n a, 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). PM 10 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 o r 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 t o 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 o f 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 b efore 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 (weighte d 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

Potentia l

7 Dust
Potential
Reduction 8

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%
41.84%

Frequent manure
removal 1 2 29.67% 42.15%

48

, 000 300 None (baseline) 2 2 7.38% 87.57%
17.08%

Frequent manure
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%
72.86%

Frequent manure removal, add water 1 3 44.51% 19.67%

48

, 000 300 None (baseline) 2 2 7.38% 87.57%
34.89%

Frequent manure 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 risk 9

. 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 resultin g 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, t he 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 high

wind 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 t he public paved road network . These BMPs are intended to reduce trackout from unpaved feedlot roads onto the paved road s . Trackout is incorporated into paved road emissions, and so these controls do not affect the CAFO emissions total. Moreover, the amount of trackout
from feedlot s 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 overe stimate. 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 thei r 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 u npaved 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 a ccording 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%

W e 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 a ctivity

- based

emissions are 100%

∙ 69%

∙ 22

. 48%

∙ 1

, 620

. 6 TPY ≈ 251

. 4 TPY . For windblown dust, ADEQ compared the total area of the pen s (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 penetr ation, 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
reductio ns 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 activ ity

- based emissions reductions were 738.4 lbs/day at CWT, 764.2 lbs/day for the STF stagnation d esign day, and 940.9 lbs/day for the STF high

- wind design day 11 . An additional reduction of 55.0 lbs/day of windblown dust was estimated for the STF high

- wind de sign 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 main tenance 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 activity

based modeling domains for the stagnation design day and the high

- win d 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 m aintenance 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 regulatio n 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

, 80 3 .1

- - - 84 0.3 70 5.2 2

, 7 66.0 356.6

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

, 186.4 12 10,180.6 2

, 9 22.1 8

, 901.7 9

, 3 45.3 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 1 44.4 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

, 4 99.8 3

, 651.0 1

, 131.0 2,348.0

- 3

, 667.0 935.0 1 4.0

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 1 4,525.8 20

, 9 56.4 2 3 ,9 9 5

. 4 27

, 07 6.4 15

, 33 3 .4

12 Note that emissions from unpaved test tracks at permitted vehicl e 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. A ll of the values in
Table D3

- 21 have been post processed

, as described in Section D2 and summarized in Table

D3

- 20

. W hen rounded to the nearest 10, no expected concentrat ion at the monitor exceeds 150 μg/m 3 ( though this limit is approached as closely as possible

, as discussed in Section D4.2 ). Thus,

s ince these are estimates of the 24

- hour average PM 10 concentrations monitored on the high

risk days expected in 2018, the NA is expected to comply with the PM 10 NAAQS in 2018.

Table D3

- 20 . Summary of Attainment Modeling Res ults
Design day Modeled

local impact 13 Scaling

factor 13 Local impact

only (μg/m3) Background con

centration (μg/m3) Concentration at the

monitor (μg/m3) Stag . CWT 51 4.1 27.4% 14 1.0

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 % 10 3.3

17.5 12 0.8

MC 343.6 3 1.1 % 106. 8 124. 3

PCH 50 1 .8 23.5% 11 7.8 135. 3

STF 125. 8 75. 2 % 9 4.7 112. 2

Table D3

- 21 . Attainment Modeling Results Apportioned by Source Category

Source Category 24

- Hour Average Concentrations for Modeling Domains (μg/m 3 ) 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

- - - 1 1.6 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 9 3.9 91.1 1 7.9 1.8 8.9 27. 1

Construction 1. 8 35.7 0.3

- 1 1.3

- 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 T he 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 PM 10 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 f rom 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 analy sis, ADEQ also took into account the control measures
described in Section D5.2 , and determined that this budget would provide adequate room for

an ticipated 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

PM 10 NAAQS. As discussed in Section D3.3 , modeling indicates that the NA should attain the
NAAQS a s long as this budget is not exceeded, as long as the emission s are distributed appropriately ( using planning tools such as hotspot an alyses to prevent high concentrations of

PM 10 building up in a give n place and time), and as long as no unforeseen growth occurs in

emissions from PM 10 source sectors other than those included in this budget.

The following sections describe which source ca tegories 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

PM 10 emissions. Vehicle exhaust was calculated as:

E Exhau st PM10
= VMT × EF Exhaust PM10

where E Exhaust

PM10 is the total PM 10 (in TPY) emitted as vehicle exhaust on unpaved roads in the NA,

VMT is the total number of Vehicle Miles Traveled per year, and EF Exhaust PM10 is the speed

dependent emission factor (in tons/VMT) generated by the MOV ES 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 PM 10 from the physical disturbance of the road surface by vehicles, not from exhaust. That portion of road dust was calculated as:

E Reentrainment PM10
= VMT × EF Reentrainment PM10

where EF Reentrainment

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 control s described in Chapter D3 , except for agricultural roads. The emission factors
f or some public irrigation roads are expected to be reduced by the c ontrols 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

E 2018 = E 2013 × POP 20 18 County

POP 2013 County

where E 2018 is the estimated emissions total for 2018, E 2013 is the estimated emissions total as
calculated using the 2013 traffic count, POP 2018 County is the ADOA projection of the population in
Pinal county in 2018, and POP 2013 County is the corresponding estimate for 2013 14

. 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. A ccess 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 th e 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 70 2

. 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 2 6,433.5 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 PM 10 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 onroa d 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

.

PM 10 generated from the bow wake of large vehicles traveling on roads with unpaved shoulders was calculated using the following emission rate formula E Bow wake PM10
= 0

. 03 × ( S 55

⁄ ) 2

, where

E Bow wake PM10 is the PM 10 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 shou lders 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 PM 10 to the 2 018 inventory, and so is not considered a significant enough source to include in the MVEB.

Vehicle reentrained road dust emiss ions 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)

. E mission rates were scaled by a factor of 381.59% o n 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. Followi ng the methodology of (MCAQD, 2011) , ADEQ estimated that 251.53 tons of PM 10 were emitted from these parking lots each year. Because the annual rates in that methodology are not forecasted to change over time , these emissions a re omitted from the MVEB.

### Appendix D D4

. On

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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. Al though the se 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 th os e projects is 0.29 tons/acre

- month

. The acre wise duration of each chipsealing or paving project is conservatively estimated to be 1.03 months 15

. 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 zone 16 , so that construction is expected to produce

73 ft

∙ 5

, 280 ft/mi

43

, 560 ft 2 /acre ∙ 0

. 29 tons/acre

- month

∙ 1

. 03 months ≈ 2

. 64 ton s/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 PM 10 emissions are expected from each mile of additional cont rols.

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 ft 2 /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

c onstruction
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 PM 10 are expected to be emitted, 59.6 tons of which are expected in 2018 17

. 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 w ill 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 PM 10 in 2018. For modeling purposes, these emissions were spre ad 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

. A t a growth factor of 91.4 %, each design day still showed attainment

. This was true eve n 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 th an those planned for 2016 or 2017) in developing the budget, and added 91.4 % more road work emissions
to the 2018 inventor ies , to budget for potential unforeseen road work increases. Note that th is analysis relied on assumptions about the placement and tim ing 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 2 6,433.5 Reentrained paved road dust 1,211.1 Road construction/maintenance 1 68.8
Total 2 7,987.1

Even beyond road work inflation, t he 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 PM 10 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 Pro gress

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

, 42 6.3 TPY. With a three

- year horizon from present (2015) to modeled attainment (2018), a lin ear 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
be low the linear glide path , as illustrated in Figure D5

- 1

.

Figure D5

- 1 . West Pinal PM 10 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

, 47 5.4 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 t he se 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 es timate the pursuant reductions. Those estimates are summarized in Table D5

- 1

. Note that the total, 7

, 814.5 TPY, satisfies the 7

, 47 5.4 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 instea d counted as contingency measures.



## D5.2.2 Pinal County Construction Rule

The new construction r ule for Pinal County replaces an existing rule which was taken into account
in the base scenario. Based on inspection and compliance history discussed in S ection 3.1.1. 8 of Appendix B, ADEQ assigned the baseline rule 80% rule effectiveness , near the middle of “ Non

point 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

, 6 91.7 18

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 reintrain ed pav ed road dust. Of
the 84.8 TPY of reintrained road dust from roads identified as trackout hotspots, 7 5.0 TPY are emitted on roads adjacent to agricultural land. ADEQ therefore assumed that the trackout on those roads was attributable to agriculture (not con struction). 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 Sect ion 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 a ll 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 emissi ons 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

. Be cause 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 bookkeepin g 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 m ethods 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 Effic iency 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 perf ormed to determine the

### Appendix D D5

. Reasonab le 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 activities 19 . The emis sion 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

s calable 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 det ermine 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 act ivities.

Given knowledge of control implementation within the Maricopa County PM 10 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 operatio ns 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 wa tering. 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% fo r construction site traffic related emissions was calculated by ADEQ utilizing the AP

- 42 unpaved road eq uation 21 amongst other resources.

ADEQ ca lculated 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 S outh 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 PM 10 Emission

Factor (lbs/unit) Uncontrolled

PM 10 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 equation 21

. 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 on site vehicle sp eed 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 emiss ion 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 wi th 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 ru le
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 ef fectiveness being directly applied to emissions, it was utilized to determine the percentage of disturbed lands that should
be converted to stabilize d 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 t ype 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 e ach 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 project s 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 note d, 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 an alyzed.

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 41 1.9 22 8.7 183 .2

Commercial 3,274 .3 1,81 7.7 1,45 6.6
Site Development 4,64 4.7 2,578 .4 2,066 .3

Road 1 68.8 93.7 75.1

Sub

- Total 8,4 99.8 4,7 18.5 3,7 81.3

Windblown Residential 83 7.5 430.5 407 .0

Commercial 441 .4 216.3 225 .1

Site Development 552 .2 278.9 273 .3

Sub

- Total 1,831 .1 92 5.7 905 .4

Total 10,3 30.9 5,6 44.2 4,6 86.7



## 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 wa s 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 R ange 3 ) as the
rule e ffectiveness for this provision.

### Appendix D D5

. Reasonable Further Progress

December 21 , 2015 Final Page 54 

### D5.2.3.1 Requirements for O wners 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 i n §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 l ots to 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 through out the NA 23 , 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
disturb ance level for the land use t ype ( 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 size 24

. 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

Pene

tration Rule

Effect

iveness 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

, 236.3 24 30.09% 69.00% 3.96% 1

, 554.2

Total 511

, 472.0 150

, 250.1 1

, 7 27.9

23 Digital plat maps were provided by the Pinal County Assessor’s Office on M ay 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 munic ipalities). 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 De veloped Urban/Rural and Cleared Areas, this criterion was not considered; only parcel size was used to determine qualificatio n.

### 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 stabi lized, 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 stora ge 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 la nd (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, an d instead subject
to the controls described in Section D5.2.2 ) . ADEQ therefore restricted its analysis of unpaved lots to the Developed Urban, Deve loped 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 NA 23 , 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 Lot s

Land Use Category Unpaved

Lots (acres) Uncontrolled Emissions (TPY) Rule

Penetration Activity

- B ased Windblown Dust
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 efficiency 25 ) and windblown (45% control efficiency 26 ) emissions, this control was gravel. T h us , 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 Furth er Progress

December 21 , 2015 Final Page 56 

### D5.2.3.3 Requirements for Operators of Unpaved Roads

The unpaved road provision o f 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 c alculated the average control efficienc ies . The weighted average
control efficienc ies are about 9 7.1 % for activity based emissions and 53.7% for windblown dust

, similar to the control efficienc ies 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 Pla nned for the NA, 2016

- 2018

Control type Control efficiency Miles planned to be implemented in

Activity

based W ind

blown 2016 2017 2018 2016

- 2017
Subtotal 2016

- 2018

Total Gravel/a ggregate cover 46.00% 25 45.00% 26 0.00 0.00 0.00 0.00 0.00 Synthetic dust palliative 73.00% 27 4 5

. 93 % 28 3.50 0.00 0.00 3.50 3.50 Chip/ double chip seal ing 9 9.86 % 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 diffe rence 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 basis 31 . 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 designation s 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

roa d surface. Thus, both paving and chip or double chip sealing

— which eliminate windblown dust from the travel
lanes

— have a n average control efficiency of 54.55%. Meanwhile, the windblown dust control efficiency for 100%
stabilization is calculated as 84.2% f or the NA, on average (see Section D5.2.3.4 ). Since synthetic dust suppressants must be applied in sufficient quantity to stabilize the travel lane s, 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 Pina l 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 2018 33 , resulting in an uncontrolled emission factor of nearly 44.8 TPY/mile. The 9 7.1 % control rate, therefore, would reduce approximately 4 3.5 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 (> 15 0

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

im plemented 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 sample d 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 span ned 1.6 miles of Peters R oad , 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. T he 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

p rojects 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 2018 34 . Therefore, they cannot also be credited towards contingency measures. Deducting these 3.9 4 miles from the 30 mile total leaves 26. 06 miles of 2016

- 2017 controls to be credited towards contingency measures. At a rate of 4 3.5 TPY/mi, the refore, 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 cont ingency reduction total above the needed 7

, 47 5.4 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 conservative 35 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 w ould 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 ) cal ls 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 exempt ed 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 inste ad subject to the trackout controls described in Section D5.2.2.1 ). For applicable roadways, this subsection requires the prompt removal of trackou t 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 unp aved 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 e ach bin

. The control efficiency for stabilization was calculated as the av erage reduction across all bins, 84 .2 %.

Table D5

- 10 . Windblown Dust E mission F actor 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 type s (and therefore the disturbed vs. stable proportions) throughout the NA. Using GIS to extract and process a 10

- foot buffer 37 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 747 5.4 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 TP Y 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 disturb ed 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 no t 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 B ow W ake. 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 estima ted 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 sho ulders, 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%

∙ 6 9%

∙ 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). Agricu ltural 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 exchang e). 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 Particu late 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 Op erations. 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 an d 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 Cattle

Feeding 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/Publication s/2007/Full_Report/Volume_1,_Chapter_2_ County_Level/Arizona/st04_2_001_001.pdf

### Appendix D Exhibit DI

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- 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

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- 2 

# DI1 Agricultural Crop Surveys

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- 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 t he 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 efficien cies 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 spe cific 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

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- 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
Activity Emissions Tilling Conservation Tillage Cotton 28% San Joaquin 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 Infor mation

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 Supplement al 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 win d 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 P inal County PM

- 10 Emissions for Five Episode Days

For t he 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 requir ed.

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 sit es. 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 MA G 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 m ultiplication of the two ratios became the back

casted 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, wh ich 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

- h aul 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 d erived 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 Fue l 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 202 5 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 arterial s 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 distribut ions 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 Fraction s
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 mo del 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 weekda ys 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 w as 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 vehicl e 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
fac tors 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 M ethodology 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 no n

- 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 deri ved 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 a ge 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 MOVES20 14 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 n on

- 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 t ype 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 interpol ated 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 2 007.

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 populatio n 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 v ehicle 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 anal ysis 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 betwe en the years 2015
and 2018.