Appendix A:

Pinal County

PM Inventory Preparation Plan

Prepared by:

Sierra Research, Inc.

1801 J Street

Sacramento, California 95811

February 11, 2013
Report No. SR2013-01-01 

Pinal County 

PM Inventory Preparation Plan 

Prepared for 

Arizona Department of Environmental Quality 

February 11, 2013 

prepared by: 

Sierra Research, Inc. 

1801 J Street 

Sacramento, California 95811 

(916) 444-6666
Report No. SR2013-01-01 

Pinal County PM Inventory Preparation Plan

 prepared for: 

Arizona Department of Environmental Quality 

February 11, 2013 

 Principal authors: 

 Robert G. Dulla 

 Earl Withycombe 

 

Sierra Research, Inc. 

1801 J Street 

Sacramento, CA 95811 

(916) 444-6666 
Pinal County PM Inventory Preparation Plan

Table of Contents 

Page 

1. Introduction ................................................................................................................... 1 

1.1 Emission Inventory Guidance ...............................................................................2 

1.2 Report Organization ..............................................................................................8 

2. Initial Analysis of Base Year Monitoring Data .......................................................... 10 

2.1 Cowtown .............................................................................................................11 

2.2 Stanfield ..............................................................................................................23 

2.3 Casa Grande ........................................................................................................31 

2.4 Pinal County Housing .........................................................................................38 

3. Design Day Selection .................................................................................................. 49 

3.1 Introduction .........................................................................................................49 

3.2 PM10 Exceedance Days .....................................................................................50 

3.3 Stagnation Design Days ......................................................................................51 

3.4 High Wind Design Days .....................................................................................52 

3.5 Cowtown .............................................................................................................52 

3.6 Stanfield ..............................................................................................................54 

3.7 Pinal County Housing .........................................................................................55 

3.8 Maricopa .............................................................................................................55 

3.9 Combs School and Casa Grande .........................................................................57 

3.10 PM2.5 Exceedance Days ....................................................................................57 

3.11 Modeling Domains..............................................................................................59 

4. Emission Inventory Procedures and Data Sources ..................................................... 68 

4.1 Low Wind Emissions ..........................................................................................68 

4.2 High Wind Emissions .........................................................................................81 

5. Quality Assurance ....................................................................................................... 83

5.1 Managerial Responsibilities ................................................................................85 

5.2 Schedule ..............................................................................................................86 

5.3 General QA/QC Procedures ................................................................................87 

5.4 Data Reporting ....................................................................................................92 
-iiList of Appendices 

Appendix A – 2008 24-Hour Average PM10 Concentrations Recorded at Six Pinal 

County TEOM Monitoring Sites 

Appendix B – Plots of Hourly PM10 Concentration, Wind Speed, Wind Direction, and 

Mixing Height Modeled at Cowtown, Pinal County Housing, and 

Stanfield Monitoring Sites on October 29, 2008 

Appendix C – Statistics of the High Wind PM10 Exceedance Days at Cowtown in 2008 

Appendix D – Statistics of the High Wind PM10 Exceedance Days at Stanfield in 2008 

Appendix E – Statistics of the High Wind PM10 Exceedance Days at Pinal County 

Housing in 2008 

Appendix F – Statistics of the PM10 Exceedance Days at Maricopa in 2008 

Appendix G – Statistics of the PM2.5 Exceedance Days at Cowtown in 2008 

Appendix H – Road Count Data 

Appendix I – A Method for the Back-Calculation of Wind-Blown Emission Factors 

from Field Measured PM10 Concentrations, Arizona Department of 

Environmental Quality Air Quality Division 

Appendix J – MAG 2012 Five Percent Plan for PM10 for the Maricopa County 

Nonattainment Area, May 2012
-iiiList of Figures 

Figure Page 

Figure 1-1 Overview of Inventory Preparation ....................................................................9 

Figure 2-1 Cowtown Hourly PM10 Averages by Time of Day, All Hours .......................12 

Figure 2-2 Cowtown Exceedance Day Hourly PM10 Averages by Time of Day, 

All Hours ......................................................................................................................13 

Figure 2-3 Cowtown Hourly PM10 Rose ..........................................................................14 

Figure 2-4 Cowtown Hourly PM10 Averages by Wind Speed and Azimuth, All 

Hours ............................................................................................................................16 

Figure 2-5 Cowtown Maximum Hourly PM10 Concentration by Wind Speed and 

Azimuth, All Hours ......................................................................................................17 

Figure 2-6 Cowtown Sum of Hourly PM10 by Wind Speed and Azimuth, All 

Hours ............................................................................................................................18 

Figure 2-7 Example Cowtown PM10 Hot Spot Source Corridor ......................................19 

Figure 2-8 Cowtown Hourly PM10 Averages by Azimuth and Hours of the Day, 

All Hours ......................................................................................................................21 

Figure 2-9 Cowtown Hourly PM10 Averages by Wind Speed and Hours of the 

Day, All Hours .............................................................................................................22 

Figure 2-10 Stanfield Hourly PM10 Averages by Time of Day, All Hours ......................24 

Figure 2-11 Stanfield PM10 Rose of Hours Exceeding 150 μg/m3

 ...................................25 

Figure 2-12 Stanfield PM10 Rose of Hours with Wind Speeds > 5.0 m/sec ....................26 

Figure 2-13 Stanfield Hourly PM10 Averages by Wind Speed and Azimuth, All 

Hours ............................................................................................................................28 

Figure 2-14 Stanfield Sum of Hourly PM10 by Wind Speed and Azimuth, All 

Hours ............................................................................................................................29 

Figure 2-15 Stanfield Hourly PM10 Averages by Azimuth and Hours of the Day, 

All Hours ......................................................................................................................30 

Figure 2-16 Casa Grande PM10 Rose of Hours Exceeding 150 μg/m3

 .............................33 
-ivFigure 2-17 Casa Grande Hourly PM10 Averages by Wind Azimuth (degrees) 

and Wind Speed (m/sec) ..............................................................................................35 

Figure 2-18 Casa Grande Hourly PM10 Maxima by Wind Azimuth (degrees) and 

Wind Speed (m/sec) .....................................................................................................36 

Figure 2-19 Casa Grande Sum of PM10 by Wind Azimuth (degrees) and Wind 

Speed (m/sec), All Hours .............................................................................................37 

Figure 2-20 Pinal County Housing PM10 Rose of Hours Exceeding 150 μg/m3

..............40 

Figure 2-21 Pinal County Housing Hourly PM10 Averages by Wind Azimuth 

(degrees) and Wind Speed (m/sec), All Data ...............................................................42 

Figure 2-22 Pinal County Housing Hourly PM10 Maxima by Wind Azimuth 

(degrees) and Wind Speed (m/sec), All Data ...............................................................43 

Figure 2-23 Pinal County Housing Sum of Hourly PM10 by Wind Azimuth 

(degrees) and Wind Speed (m/sec), All Data ...............................................................44 

Figure 2-24 Pinal County Housing Hourly PM10 Averages by Wind Azimuth 

(degrees) and Hour of Day, All Data ...........................................................................46 

Figure 2-25 Pinal County Housing Hourly PM10 Averages by Wind Speed 

(m/sec) and Hour of Day, All Data ..............................................................................47 

Figure 3-1 Cowtown Stagnant Design Day Modeling Domain .........................................61 

Figure 3-2 Cowtown High Wind Hour Modeling Domain During the High Wind 

Design Day...................................................................................................................62

Figure 3-3 Cowtown ..........................................................................................................63

Figure 3-4 Maricopa Modeling Domains ..........................................................................65 

Figure 3-5 Pinal County Housing Modeling Domains ......................................................66 

Figure 3-6 Stanfield Modeling Domains ...........................................................................67 
-v- 

 List of Tables 

Table Page 

Table 1-1 Inventory Preparation Planning and Quality Assurance Planning Steps .............6 

Table 2-1 Cowtown Hourly Average and Maximum PM10 – All Hours..........................11 

Table 2-2 Stanfield Hourly Average and Maximum PM10, All Hours .............................23 

Table 2-3 Casa Grande Hourly Average and Maximum PM10, All Hours .......................32 

Table 2-4 Casa Grande Hourly Average and Maximum PM10, Hours > 150 μg/m3

........32 

Table 2-5 Pinal County Housing Average and Maximum PM10, All Hours ....................38 

Table 2-6 Pinal County Housing Average and Maximum PM10, Hours Exceeding 

150 μg/m3 .....................................................................................................................39 

Table 3-1 Exceedances of 24-Hour Average PM10 and PM2.5 NAAQS at Pinal 

County Monitoring Sites in 2008 .................................................................................50 

Table 3-2 PM10 Exceedances on Stagnation Days in 2008 at Monitoring Sites 

Other Than Cowtown ...................................................................................................51 

Table 3-3 Summary of Selected Design Days ...................................................................59 

Table 4-1 CARB Land Preparation Emission Factors Used to Prepare Agricultural 

Estimate of Agricultural Fugitive Dust Emissions ......................................................71 

Table 4-2 CARB Harvest PM10 Emission Factors ...........................................................72 

Table 4-3 Average Project Duration and Emission Factor Employed in 2008 

Maricopa County PEI ..................................................................................................75 

Table 5-1 Data Quality Objectives ....................................................................................84 

Table 5-2 Data Quality Indicators......................................................................................85 

Table 5-3 Data Collection Guidance Documents ..............................................................88 
-1- 

1. INTRODUCTION 

On February 3, 2011, the U.S. Environmental Protection Agency (U.S. EPA) designated 

the central portion of Pinal County as nonattainment for the 24-hour PM2.5 National 

Ambient Air Quality Standard (NAAQS), and designated the same area as attainment or 

unclassified for the annual PM2.5 NAAQS.1

 The rationale for the latter designation was 

based on the finding that the single monitoring site exceeding the annual standard, 

Cowtown, was a population-oriented microscale (i.e., localized hot spot) monitor that 

was not eligible for designation as a nonattainment site for the annual standard. On 

May 31, 2012, U.S. EPA designated a somewhat larger portion of central Pinal County as 

nonattainment for the 24-hour PM10 NAAQS.2

 The annual PM10 NAAQS was revoked 

by U.S. EPA on October 17, 2006. As a result of these decisions, the attainment 

demonstration for both PM2.5 and PM10 in Pinal County must be based on ambient air 

quality conditions and contributing emissions on specific design days. 

A nonattainment designation reflects monitored airborne pollutant concentrations that 

exceed the prevailing standard. A nonattainment designation requires a curative 

amendment to the State Implementation Plan, configured to reduce concentrations below 

the standard. For purposes of developing that plan, emission inventories define the 

sources of the offending pollutant. Inventories constitute the practical and regulatory 

foundation for a plan amendment. Long-term and short-term inventories serve different 

primary functions. First, an annual inventory defines which sources generate long-term 

emissions of PM10 across the entire nonattainment area. That general understanding 

provides the factual basis for discussions regarding long-term emission trends based on 

changes in underlying activity rates and the impacts of control strategy implementation. 

In addition, an annual on-road emission inventory is required in order to define an annual 

emissions budget for transportation planning. Second, attainment demonstrations for the 

24-hour PM10 NAAQS require modeling inventories defining the local, short-term 

emissions that caused specific violations of the 24-hour standard. This document defines 

a plan for developing those inventories. 

Through discussions between U.S. EPA, the Arizona Department of Environmental 

Quality (ADEQ), and the Pinal County Air Quality Control District (PCAQCD), a base 

year of 2008 was selected for the preparation of an emission inventory and an attainment 

demonstration. This is the year of the most recent triennial National Emission Inventory 

prepared by ADEQ and PCAQCD and approved by U.S. EPA. The EPA implementation 

rule for preparation of PM2.5 State Implementation Plans (SIP) requires the use of this 

 

1

 Federal Register, Vol. 76, No. 23, Thursday, February 3, 2011 

2

 Federal Register, Vol. 77, No. 105, Thursday, May 31, 2012 
-2- 

most recent year for which a complete inventory was required to be submitted to EPA.3

During 2008, exceedances of the 24-hour PM2.5 NAAQS were recorded at Cowtown; 

exceedances of the 24-hour PM10 NAAQS were recorded at Maricopa, Cowtown, 

Stanfield, Casa Grande, Combs School, and Pinal County Housing. At each of these 

latter sites, continuously recording TEOMs are used to record hourly averaged PM10 

ambient concentrations. At the Cowtown site, a filter-based instrument is used to record 

24-hour average PM2.5 concentrations. 

To support the preparation of PM2.5 and PM10 State Implementation Plans (SIPs), 

ADEQ commissioned the preparation of an Inventory Preparation Plan (IPP) and an 

emission inventory based on the IPP. The Plan identifies key requirements, procedures, 

and data sources to be used in preparing emission inventories for the nonattainment area 

and for the modeling domains of monitors selected to represent exceedance conditions 

within the nonattainment area. Standard elements of an IPP include the presentation of 

EPA guidance and related requirements, a summary of the sources to be addressed and 

those not to be included, documentation of the procedures to be followed in computing 

emissions, data sources available to characterize activity and emission factors, local 

conditions affecting emissions, and quality control procedures to be followed. 

To provide a better understanding of the conditions influencing fugitive dust production 

in the nonattainment area, additional tasks were undertaken (1) to gauge the influence of 

local versus regional emission sources impacting nonattainment monitors and (2) to 

select design days and modeling domains of monitors representative of exceedance 

conditions. Presented below is a summary of EPA guidance on IPP development. 

1.1 Emission Inventory Guidance 

EPA emission inventory guidance is summarized at the Clearinghouse for Inventories 

and Emission Factors (CHIEF) website. Guidance for the development of PM10 and 

PM2.5 emission inventories is available from the documents listed below. 

PM-10 Emission Inventory Requirements4

Emissions Inventory Guidance for Implementation of Ozone and Particulate 

Matter National Ambient Air Quality Standards (NAAQS) and Regional Haze 

Regulations5

1996 Air Quality Criteria for Particulate Matter, Vol. 16

 

 

3

 Federal Register, Vol. 72, No. 79, Wednesday, April 25, 2007 

4 http://www.epa.gov/ttn/chief/eidocs/PM10eir.pdf

5 http://www.epa.gov/ttn/chief/eiinformation.html 6

 1996 Air Quality Criteria for Particulate Matter, Vol. 1, EPA/600/P-95-001aF, (Washington, D.C.: U.S. 

Environmental Protection Agency, April 1996), Sections 5.2.1, 5.5.1, and 5.5.3 
-3- 

For PM10 nonattainment areas, EPA identifies three basic kinds of inventories that States 

may need to develop under the CAA: base year inventories, periodic inventories, and 

modeling inventories. Presented below is EPA’s description of the information required 

for each of the inventory submittals and related IPP requirements. 

Base Year Inventory – This is the primary inventory from which all other inventories are 

derived. All inventories are required to be consistent with data provided in the base year 

inventory. Section 172(c)(3) of the CAA requires States to ensure that this inventory is 

comprehensive, accurate, and current for all actual emissions of PM10. The base year 

inventory must include emissions from all point, area, and mobile sources.

Periodic Emissions Inventory – The CAA states that the emission inventories may be 

periodically updated as deemed necessary by the Administrator. A periodic inventory

may be the consequence of RFP requirements, a maintenance plan for an attainment area, 

or for other reasons deemed necessary by EPA. The Administrator has determined that 

states should submit statewide annual and three-year cycle inventories for PM10 and 

PM2.5.7

Modeling Emissions Inventory – The modeling emissions inventory may be used as a 

tool for a number of purposes, including model performance evaluations, projection to 

future years, evaluation of the impact of rulemaking, evaluation of control measures and 

technology, receptor modeling reconciliation, and determination of design 

concentrations. One of the major roles for modeling inventories is for use in attainment 

demonstrations. Modeling inventories can be based upon either allowable or actual 

emissions, depending on the purpose of the modeling. For instance, modeling inventories 

should be based on the actual daily emissions for model performance evaluation. For 

control measure evaluations and the attainment demonstration, the modeling emission 

inventory should consist of allowable emissions for the base year and projected allowable 

emissions for the attainment year. The phrase “allowable emissions” is a term of art 

related to permitting and modeling the impacts of stationary sources. The bulk of 

emissions here are fugitive emissions that are not subject to a stationary source permitting 

program—for those emissions, base year “allowable emissions” are actual emissions and 

attainment year “allowable emissions” will reflect base year actual emissions adjusted for 

the projected control efficiencies of emission reduction measure commitments. 

As will be discussed later in this Plan, the emissions inventories to be used in attainment 

demonstrations will be design-day-specific and specific to the modeling domains 

computed separately for each design day and monitoring site. None of the modeling 

domains covers either the entire central Pinal County PM10 or PM2.5 nonattainment 

areas. At least under stagnation and moderate wind conditions, the spatial and temporal 

variability in PM10 and PM2.5 exceedances demonstrates that sources within a few miles 

of each violating monitor are responsible for exceedances, not the totality of sources 

contained within the boundaries of the nonattainment areas. However, to the extent that 

land utilization patterns are relatively homogenous across the relevant area, modeling 

 

7

 Emissions Inventory Guidance for Implementation of Ozone and Particulate Matter National Ambient Air 

Quality Standards (NAAQS) and Regional Haze Regulations, EPA-454/R-05-001, August 2005, 

http://www.epa.gov/ttn/chief/eidocs/eiguid/eiguidfinal_nov2005.pdf
-4- 

inventory analyses are expected to fairly represent similar conditions across the 

nonattainment area. As a result, emission inventory preparation under this plan will 

focus on the modeling needs of the various attainment demonstrations, and inventory 

improvements developed in these domains will be extended to the applicable source 

categories in the baseline inventories for the two nonattainment areas. However, lower 

priority will be given under this Plan to improving other emission inventory categories in 

the nonattainment area baselines that do not benefit the modeling domain inventories. 

IPP Requirements – For base year inventories, EPA requires States to prepare a brief IPP 

that specifies how they intend to develop, document, and submit their inventories. The 

plans provide States the opportunity to notify EPA how they plan to compile the required 

inventories and to allow EPA to provide important feedback to prevent the use of 

approaches that are not consistent with the EPA requirements. 

In addition to technical data, the IPPs should contain a schedule showing when the State 

plans to submit the draft and final inventories to EPA. If the State plans to submit an 

inventory in component pieces (e.g., point source component, area source component, 

etc.), the IPP must clearly make this distinction and indicate draft and final submittal 

dates for each component. The final submittal dates should be consistent with the PM10 

emission inventory requirements. A complete draft inventory is required to be submitted 

to the EPA Regional Offices and the EPA headquarters within 12 months after the date 

that an area is designated nonattainment. The IPP is also required to detail the year 

chosen as the base year for the inventory, and explain the basis for the selection. 

Source Category Requirements – States are required to define how all pertinent emission 

sources will be identified and located. 

Point Sources – States are required to describe how point source activity levels and 

associated parameters will be developed, and how these data will be used to calculate 

emission estimates. States must also discuss how and when statewide point source 

reporting to the EPA will be conducted and how it will be coordinated with reporting for 

area and mobile sources. States must indicate whether rule effectiveness was applied and, 

if it was, the basis for determining control efficiencies. States must describe any source 

surveys that are planned, and if they intend to use existing data contained in the 

Emissions Inventory System (EIS), individual State emissions systems, or State 

permitting files. 

 

Area and Mobile Sources – States must explicitly identify in the IPP which source 

categories will be addressed and which will not be addressed (with justification for 

exclusion). For included categories, the plan must indicate what calculation methods will 

be used to estimate emissions. If a State plans on using EPA’s inventory guidance for all 

categories, the IPP must summarize the EPA guidance and contain reference(s) for the 

guidance. If the EPA guidance offers alternative methods for a category, the IPP shall 

clearly indicate which method the State intends to use in its inventory. Particular 

emphasis shall be given to categories for which the State plans to use an approach other 

than that recommended in the EPA guidance. Any major assumptions that may affect the 

development of emission estimates in a category must be clearly stated. The IPP must 
-5- 

also identify the sources of activity data, whether rule effectiveness will be applied (and 

how it was determined if applied), and the basis for determining control efficiencies. 

For on-road mobile sources, the IPP must include a discussion of how the State intends to 

develop vehicle miles traveled (VMT) estimates, and how PM10 emission factors for 

mobile sources will be determined. EPA’s original guidance required the use of a mobile 

source particulate model (PART5), which has been superseded by AP-42 guidance for 

paved and unpaved roads. 

The IPP must also clearly describe how the State plans to present, document, and submit 

the inventory to the EPA. The types of documentation that will be provided and the form 

of this documentation must be described to the extent that EPA can judge if it would be 

satisfactory for inventory review purposes. The IPP shall specify the written and 

computerized methods that the State plans to use to compile and submit its emission data. 

The State must contact its EPA Regional Office to determine computerized data submittal 

and format requirements, and must describe in its IPP how its data system (e.g., AIRS, 

State system) will be used to submit the data to EPA. 

States are required to submit Quality Assurance (QA) plans as an initial step in their 

inventory development work and receive EPA approval on their plans early on in the 

process. The QA plans must be submitted as part of a State’s IPP. The content and 

general form of QA plans must be consistent with previously issued guidance. This plan 

shall describe the overall QA program that the State intends to use during the compilation 

of the inventory. 

In summary, PM10 IPPs are required to be concise, and provide only as much detail as is 

necessary to communicate to the agency how the State intends to develop and present its 

inventory. However, the document must contain sufficient information to enable the 

EPA to make a judgment that the State’s intended inventory approach is sound and 

consistent with EPA’s guidance and requirements. 

For PM2.5 nonattainment areas, the guidance is rather general and stresses the need to 

begin with a description of inventory objectives (e.g., geographic area covered, base year, 

pollutants included, temporal resolution, etc.). It states that the IPP should contain the 

following sections that address portions of the inventory: 

Introduction, 

Point Source Inventory, 

Area Source Inventory, 

Nonroad Mobile Source Inventory, 

Mobile Source Inventory, 

Biogenic Source Inventory, 

Documentation Approach, and 

Quality Assurance Plan. 
-6- 

The primary source of guidance referenced for developing an Inventory Preparation Plan 

(IPP) and related Quality Assurance Plan (QAP) is EPA’s Emissions Inventory 

Improvement Program (EIIP) documentation. Table 1-1 presents an overview of the IPP 

and QAP planning activities recommended under EPA’s EIIP guidance. 

Table 1-1 

Inventory Preparation Planning and Quality Assurance Planning Steps 

Inventory Preparation Quality Assurance

1. Preliminary Planning Activities:

1. Define purpose and scope of inventory

2. Define organization and staffing roles 

(including agency and contractor roles)

3. Define and document data quality 

objectives

4. Identify quality assurance (QA) coordinator 

and assign QA responsibilities to inventory 

staff

2. Prepare Technical Work Plan:

5. Identify geographical area

6. Delineate pollutants to inventory

7. Establish point/area source cutoffs

8. Prioritize source categories for inclusion in 

inventory

9. Prioritize data sources

10. Delineate emissions estimation procedures

11. Prepare QA plan concurrently with or after 

technical work plan

12. Document data-gathering methods in QA 

plan

13. Select QA procedures to be used 

14. Determine indicators that will be used to 

measure data quality

3. Prepare Inventory:

15. Data collection

16. Data handling

17. Estimate emissions

18. Document inventory development 

activities

19. Follow data handling procedures as 

documented in QA plan

20. Conduct routine QA activities and 

independent audits

21. Document QA steps coinciding with 

inventory development activities

4. Inventory Reporting:

22. Document methods, data sources, 

adjustments

23. Discuss sources excluded and explain why

24. Present estimated emissions

25. Prepare QA audit reports

26. Document QA findings and resolution of 

problems

27. Discuss QA in final inventory report and 

prepare separate QA report

Source: EPA EIIP Document Series, Volume VI: Chapter 2 
-7- 

The guidance and “blueprints” for inventory development specified in the EIIP are 

necessarily broad and comprehensive so that they can be used and adopted by individual 

States based on prioritized objectives, staffing, and resources. Some of the guidance, 

however, demands substantial rigor. For example, the QAP guidance in the EIIP 

provides for an extremely high degree of review, auditing, and relative “scoring” or 

ranking of the emission estimation, spatial and temporal allocation, and forecasting 

methodologies and data sources for each individual emission source in an inventory. For 

agencies with significant budgetary resources and staff that are developing rigorous, 

Level I-type8

 compliance inventories in industrialized urban areas, the high degree of 

quality assurance (QA) auditing, ranking, and documentation suggested in the EIIP 

guidance is appropriate. Since these conditions do not apply throughout the central Pinal 

County nonattainment area, judgment will be applied in establishing data collection, 

analysis, and related QA requirements. 

Each of the steps outlined in Table 1-1 was considered in the development of this IPP. 

Presented below is a brief review of the issues considered and addressed within the four 

main categories of effort. 

Preliminary Planning Activities – The work plan for this effort must define the purpose 

and scope of the inventory. It must address the development of source-specific estimates 

for PM10 and PM2.5 and precursor emissions. Where options are available for the 

development of emission inventory estimates for specific source categories, these options 

are identified and discussed. Since the selection of inventory methodologies will be 

made subsequent to approval of this IPP, only limited information on the organizational 

structure and assigned staffing roles relative to inventory preparation is contained in this 

IPP. 

Technical Work Plan Development – While EPA guidance calls for the development of 

an emissions inventory system that will cover all PM10 and fine particulate pollutants 

and precursors, ADEQ- and PCAQMD-sponsored CMB studies9

 have shown that PM10 

and PM2.5 peaks are dominated by crustal source emissions. For this reason, the 

inventory will focus on crustal sources and not secondary aerosol precursor emissions. 

Prepare Inventory – Activity data will be collected for key emission sources following 

procedures outlined in this report. The activity data will be combined with emission 

factors also outlined in this report to prepare representative source specific emission 

estimates. Tests of alternate high wind emission estimates will be conducted to 

determine which method provides a more representative estimate of concentrations 

recorded at selected monitoring sites. QA procedures outlined in Table 1-1 will be 

followed to ensure that emission estimates are reliable and can be replicated. 

 

8

The EIIP guidance categorizes emission inventories into four groups (Levels I through IV) based on uses, 

requirements, and rigor or accuracy. A Level I inventory refers to that developed for enforcement, 

compliance, or litigation uses with a high degree of accuracy. 

9

 Analysis of PM2.5 Exceedances in Pinal County Arizona: Demonstration that PM2.5 Concentrations are 

Driven by Local Sources of PM10 Near the Cowtown Monitor. 

http://pinalcountyaz.gov/Departments/AirQuality/Documents/Other%20EPA%20Regulatory%20Actions/C

owtownTechnicalWhitePaper.pdf
-8- 

Inventory Reporting – The final steps in Table 1-1 describe the reporting practices and 

documentation of the emission inventory. These elements include the following: 

Documentation of methods and data sources (e.g., MOVES, local travel model 

outputs, etc.); 

Discussion and explanation of sources excluded from the inventory (e.g., space 

heating emissions, etc.); and 

Summary reports of the emissions inventory (for both AQ and inventory submittal 

purposes). 

 

Figure 1-1 illustrates an overview of the processes and issues that were considered in the 

development of this document. As previously discussed, these processes were largely 

followed in the development of PM10 SIPs and criteria pollutant inventories elsewhere in 

Arizona. The challenge is adapting and prioritizing those processes for the central Pinal 

County nonattainment area and selected modeling domains. 

1.2 Report Organization 

Following this introduction, Section 2 provides an initial assessment of the diurnal trends 

and relationships between PM10 hourly averages and meteorological parameters at select 

Pinal County monitoring stations conducted to gauge the influence of local versus 

regional sources at these sites. Section 3 presents a summary of design day and modeling 

domain selections for monitors determined to be representative of exceedance conditions 

within the nonattainment area. Section 4 presents a review of the emission inventory 

development procedures for key emission source categories and a summary of data 

available to characterize activity and conditions within the nonattainment area and 

selected modeling domains. Section 5 presents a summary of QAPs to be implemented 

to ensure quality assurance requirements are satisfied. 
-9- 

Figure 1-1 

Overview of Inventory Preparation 

Source: “Introduction to Stationary Point Source Emission Inventory Development,” Emission Inventory 

Preparation Program, May 2001. 

### 

Available

Resources Planning Status of ExistingInventory

Identify Inventory

Resolution and

Objectives

Define Point

Source/Area Source

Categories

Identify Data Needs, Available

Information, Data Collection Procedures,

and Emission Estimation Methods 

Quality Assurance/

Quality Control and

Documentation

Data Collection

Data Handling

System for

Compilation, Analysis

And Reporting

Calculate

Emissions

Fill Data Gaps

Report Emissions
-10- 

2. INITIAL ANALYSIS OF BASE YEAR 

MONITORING DATA 

As the first step in developing site-specific, short-term inventories to support attainment 

analyses, an initial assessment of the diurnal trends and relationships between PM10 

hourly averages and meteorological parameters at select Pinal County monitoring stations 

was conducted to gauge the influence of local versus regional sources at these sites. The 

monitoring stations at which hourly data were available for this assessment were 

Cowtown, Stanfield, Casa Grande/Airport, and Pinal County Housing.10 Prior analysis 

by both the Arizona Department of Environmental Quality (ADEQ)11 and the U.S. 

Environmental Protection Agency (U.S. EPA)12 demonstrated that windblown dust, 

generated during high wind events caused by thunderstorm outflows and frontal system 

passages, was responsible for some of the higher 24-hour average PM10 recordings. This 

subsequent assessment was designed to determine the prevailing directions from which 

windblown dust originated and whether higher 24-hour readings were also occurring 

during low wind conditions and, if so, the relative proximity of sources that might be 

contributing to these concentrations.13

The assessment was conducted in two stages. In the first stage, single-variable analyses 

were used to rank hourly PM10 averages by wind speed bin and examined diurnal 

profiles of PM10 hourly averages at each of these four stations. In the second stage, we 

used two-variable analyses to compute hourly PM10 averages by combinations of wind 

speed, wind direction, and hour of the day to develop more information about the relative 

locations and contributions of potential source categories contributing to periods of high, 

and low, PM10 concentrations at these stations. The data used in these analyses were 

collected between January 2006 and December 2008 at the Cowtown and Pinal County 

Housing sites, between March 2007 and December 2008 at the Casa Grande/Airport site, 

and between July 2007 and December 2008 at the Stanfield site. Because of the 

influence of very sporadic, but very high, hourly PM10 concentrations, we removed 

hours during which the average 1-hour PM10 concentration exceeded 10,000 μg/m3

 from 

 

10 Although limited hourly data were also available at the Maricopa and Combs School monitoring sites, 

these data were not included in the initial analyses as the frequencies and magnitudes of PM10 exceedances 

were lower at these sites, and the control strategy capable of achieving attainment at the initial study sites 

was believed to be capable of achieving attainment at the Maricopa and Combs Schools sites also. 11 Boundary Recommendations for the Pinal County 24-hour PM10 Nonattainment Area, Arizona Air 

Quality Designations Technical Support Document, Arizona Department of Environmental Quality, 

March 15, 2010 

12 Pinal County, Arizona Area Redesignation for the 1987 24-hour PM10 National Ambient Air Quality 

Standard, Technical Support Document, U.S. Environmental Protection Agency, Region 9, Sept. 21, 2010 13 These initial analyses were conducted prior to final selection of design days and monitoring sites. 
-11- 

the analysis to prevent the resulting statistics from being unduly influenced by outlier 

data.14

2.1 Cowtown 

Single-Variable Analyses – Table 2-1 summarizes hourly PM10 average and maximum 

concentrations ranked by wind speed bin. A review of these data suggests several 

relationships that shed light on source contributions. First, the combination of the 

relatively uniform magnitude of PM10 average concentrations between 0 and 7 meterper-second (m/sec) wind speeds—at levels in excess of the federal 24-hour standard of 

150 μg/m3—and the very high maximum concentrations across this range indicates that 

local sources produce significant and persistent emissions of PM10. The elevated PM10 

average of 536 μg/m3

 found in the 8 to 9 m/sec wind speed bin, with an observation 

count of 105 hours (0.40% of all hours), suggests that high wind conditions are also 

responsible for a portion of the 24-hour PM10 exceedances at this site. The relatively 

high average PM10 concentration in this wind speed bin, in comparison to those of other 

wind speed bins below 8 m/sec, suggests that this is the threshold velocity for a land use 

category or soil type producing significant emissions upwind of the station. 

 

Table 2-1 

Cowtown Hourly Average and Maximum PM10 – All Hours 

(No Outliers) 

Wind Speed 

Category (m/s)

Observation 

Count

Average 

PM10

Max 

PM10

0~1 3,018 216 6,373

1~2 10,657 196 7,511

2~3 6,655 171 6,175

3~4 2,859 159 7,300

4~5 1,301 146 5,316

5~6 667 146 3,182

6~7 391 195 3,083

7~8 222 292 4,047

8~9 105 536 4,989

9~10 33 403 2,250

10~11 9 446 1,084

11~12 1 2,328 2,328

12~13 1 1,046 1,046

Total Count 25,943 N/A N/A 

 

14 At Pinal County Housing, Casa Grande and Stanfield, the hours exceeding 10,000 μg/m3

 were 

consistently associated with hourly average wind speeds exceeding 12 mph. The Cowtown concentrations 

exceeding 10,000 μg/m3

 were predominately at night. 
-12- 

The diurnal distribution of hourly PM10 averages at Cowtown was examined, but did not 

reveal significant insights with respect to potential source contributions. The diurnal 

distribution for this site is shown in Figure 2-1. The early-morning smaller peak between 

0600 and 0900 hours and much higher peak in PM10 between 1800 and 2400 hours may 

result from several different phenomena that warrant evaluation in the preparation of the 

emission inventory. These phenomena include, but are not limited to, the timing and 

severity of nocturnal inversions at this site (which would concentrate emissions from 

groundlevel sources in shallow mixing layers) and diurnal variations in mechanical 

source activity (such as traffic flows on nearby paved and unpaved roads). 

Figure 2-1 

Cowtown Hourly PM10 Averages by Time of Day, All Hours 

(No Outliers)

A refinement of the diurnal distribution pattern at Cowtown, limited to exceedance days, 

is shown in Figure 2-2. This time plot again shows a low early-morning peak in PM10 

and a corresponding evening peak between 1800 and 2400 hours. 
-13- 

Figure 2-2 

Cowtown Exceedance Day Hourly PM10 Averages by Time of Day, All Hours 

(No Outliers) 

A PM10 rose was constructed of the hourly PM10 and wind direction data from the 

Cowtown monitor using techniques originally developed to generate wind speed and 

direction roses. This rose is shown in Figure 2-3.15 The lengths of wind direction radii 

reflect the fractions of the total number of hours during the monitoring period that the 

wind blew from the directions to which the wedges point. This plot shows that the 

prevailing winds at Cowtown are from the southeast, with the southwest quadrant 

contributing the next highest fraction of wind hours. PM10 roses limited to exceedance 

days and hours of PM10 exceeding 150 μg/m3

 show very similar patterns (not shown in 

the figures), suggesting that the sources most significantly impacting this monitor lie in 

the same arc as the prevailing winds. 

 

15 Because of limitations of the windrose software used to generate this plot, the values displayed in the 

legend in the lower right need to be multiplied by 10 to reflect the ranges of PM10 concentrations on which 

this plot is based. For example, the light green band of PM10 concentrations represents hours exceeding 

2,000 μg/m3

, not 200 μg/m3. Similar multiples should be applied to values displayed in the other PM10 

roses, i.e., Figures 2-11, 2-12, 2-17, and 2-21. 
-14- 

Figure 2-3 

Cowtown Hourly PM10 Rose 
-15- 

Two-Variable Analyses – The first two-variable analysis of Cowtown data grouped 

hourly PM10 averages by wind speed and wind azimuth (direction). Wind speed data 

were grouped in bins spaced 1.0 m/sec apart, and wind azimuth data were grouped in bins 

spanning 10 degrees of compass arc. The hourly Cowtown PM10 averages by wind 

speed and azimuth are presented in Figure 2-4. In this figure and the other two-variable 

tables, data are color-coded to indicate highest (red) through lowest (dark green) values to 

highlight data clusters. The numerical ranges included in each color band are shown in a 

legend just below each data table. 

In Figure 2-4, the presence of highest PM10 averages at 0-1 m/sec wind speed and 

multiple wind directions suggests that significant nearby sources impact the monitor 

during stagnant wind conditions. Wind meander that occurs during these very low wind 

speeds may be responsible for the multi-directionality of high averages, meaning that 

significant sources may not lie in these directions, but rather that emissions from any 

significant nearby sources approach the monitor from a variety of directions as a result of 

winds meandering between source and monitor. The cluster of moderate PM10 averages 

at 1-2 m/sec wind speeds between 140 and 260 degrees suggests that the most significant 

nearby sources lie within this quadrant. This figure also shows a high PM10 cluster at 7 

to 10 m/sec wind speeds and 240 to 290 degrees wind direction. This cluster suggests 

that disturbed soils lying upwind of the monitor within this wind direction arc produce 

high impacts at the monitor at higher frequencies than disturbed soils in other directions 

during high wind conditions. This tendency may be due to soils having lower threshold 

velocities for generating windblown dust in this arc, soils being more continuously 

disturbed, or greater quantities of deposited dust available for re-entrainment in these 

upwind directions. 

A subsequent analysis examined the relationship of peak hour PM10 concentrations to 

the same wind speed and direction bins. The resulting table of values is presented in 

Figure 2-5, showing that high PM10 maxima at 1 to 3 m/sec wind speeds occur in most 

wind directions, suggesting the presence of multiple high-strength episodic sources 

surrounding the monitor. Because episodic sources generate high emission rates for short 

periods of time, initial identification of these sources will depend more on interviews of 

people who spend time in areas near the monitor than on further statistical analysis of the 

hourly PM10 and meteorological data. Once potential candidate sources are identified, 

followup inquiries will be made of any parties responsible for source operation and 

emission rates will be estimated using reverse dispersion modeling techniques. 
-16- 

Figure 2-4 

Cowtown Hourly PM10 Averages by Wind Speed and Azimuth, All Hours 

(No Outliers) 

Cowtown Hourly PM10 Averages by Wind Azimuth (degrees) and Speed (m/sec), All Data (No Outliers)

Average of Column Lab

Row Labels 0 1 2 3 4 5 6 7 8 9 10 11 12 Count Subto

0 183 109 50 80 320 88 18 493

10 111 138 84 102 85 93 143 25 463

20 84 122 88 54 294 224 191 276 433

30 338 102 99 62 116 107 298 2279 23 381

40 96 134 90 154 84 26 239 33 1307 326

50 455 123 168 217 82 22 25 255 251

60 147 151 104 98 104 99 84 108 550 22 267

70 132 156 157 99 137 122 146 368 136 151 475 365 328

80 432 167 124 80 100 94 133 197 182 330 339

90 142 156 72 149 71 159 127 233 146 98 281

100 136 144 93 99 283 146 83 253 102 403 741 334

110 151 186 127 100 128 100 86 188 95 362 460

120 242 228 168 134 109 74 137 459 186 1214 717

130 494 188 172 200 223 187 246 27 70 158 1580

140 91 253 233 242 176 195 222 25 149 795 1982

150 548 307 235 213 150 148 99 145 131 159 1759

160 166 306 242 188 169 98 190 129 65 2261 1445

170 121 302 200 176 150 199 95 47 1189 1253

180 500 278 190 147 134 141 171 165 31 66 976

190 90 254 225 163 209 146 115 98 58 58 776 822

200 364 261 179 166 181 84 602 127 279 357 716 24 1046 799

210 118 292 184 143 136 95 273 140 174 2328 744

220 171 263 248 161 323 87 113 238 154 440 635

230 135 244 249 229 228 197 360 208 769 679

240 136 314 246 224 177 231 150 261 560 508 736

250 396 254 200 184 134 128 179 96 786 844

260 246 284 179 180 119 130 180 206 685 386 392 1011

270 464 234 134 184 163 130 163 312 528 1558 2055 976

280 163 160 150 145 137 124 137 466 422 784

290 125 180 105 115 88 131 220 323 155 0 596

300 223 144 129 93 79 117 162 599 51 21 564

310 202 144 87 83 149 115 129 130 2803 24 609

320 139 126 70 49 70 119 43 161 217 571

330 297 115 70 73 118 214 42 409 612 547

340 135 101 66 60 78 47 17 255 515

350 126 95 55 96 102 36 37 33 83 53 474

360 124 102 53 77 285 42 374 58 61 233

Count Subto 662 7866 9499 4322 1848 960 510 294 159 60 22 3 2 26207

Color Scale: 0 50 100 150 200 300 400

els

Lab
-17- 

Figure 2-5 

Cowtown Maximum Hourly PM10 Concentration by Wind Speed 

and Azimuth, All Hours 

(No Outliers) 

Cowtown Hourly PM10 Maxima by Wind Azimuth (degrees) and Speed (m/sec), All Data (No Outliers)

Max of PMColumn Lab

Row Labels 0 1 2 3 4 5 6 7 8 9 10 11 12

0 3703 879 276 287 1468 118 18

10 481 4262 916 1132 492 173 283 31

20 493 3639 1364 439 3604 1437 995 1041

30 1273 625 1903 941 488 497 929 4047 23

40 448 1436 1825 2535 837 72 887 33 4722

50 2156 1642 6068 2513 475 80 38 372

60 310 1921 722 712 631 531 288 234 550 22

70 516 3264 1650 582 1185 686 818 3083 349 484 718 365

80 2308 4830 917 550 414 548 707 1017 794 976

90 335 1682 269 552 632 916 741 666 233 134

100 242 1629 734 574 2776 847 184 253 102 1336 1296

110 838 3482 874 766 1132 361 217 188 119 362

120 1099 3547 1903 833 807 271 346 1020 349 1214

130 1236 3979 1954 2005 5316 735 1009 39 89 158

140 253 3648 1851 6175 553 1400 652 65 252 795

150 4391 3493 4927 2958 825 954 253 264 262 213

160 467 6373 3921 1414 1155 463 640 346 101 2261

170 365 3228 3565 864 1217 1309 442 123 4989

180 2863 4429 2365 1730 822 464 814 732 71 90

190 548 5066 4223 2319 1789 439 286 301 118 206 2250

200 1894 5580 2989 1049 1039 451 3182 278 1199 1166 1084 24 1046

210 368 3739 5457 2274 1058 318 1627 396 415 2328

220 819 6137 6439 1587 2462 236 361 352 214 440

230 355 1789 6140 3307 1511 547 1269 373 1028

240 323 4607 3095 7300 1285 1090 475 1249 2901 508

250 1274 4369 1821 3713 932 454 546 346 1513

260 684 4887 2871 3329 619 485 1014 1130 4102 787 392

270 2476 3776 1997 2355 2677 643 911 1615 1975 2900 2055

280 641 1245 7511 2065 875 1306 557 2400 1607

290 234 3127 3270 1527 938 584 622 1335 155 0

300 716 1763 3552 2492 693 520 201 599 51 21

310 752 2591 1053 639 966 350 255 406 4658 31

320 566 1290 1236 258 433 481 162 369 393

330 1086 3714 820 404 500 725 42 409 612

340 419 1649 741 426 510 209 29 255

350 483 967 676 1590 864 100 195 59 102 53

360 281 802 418 429 632 63 723 58 83

Color Scale 0 100 200 400 800 1000 1500 2000

b les

Lab

An analysis that accounts for both PM10 average concentrations and the number of hours 

contributing to each average was performed by grouping sums of hourly PM10 

concentrations by wind speed and direction. The results of this analysis are displayed in 

Figure 2-6. This plot reflects the wind speeds and azimuths associated with the highest 

cumulative PM10 impacts at the monitor over the period of the data record. As a rough 

indicator, high PM10 sums within a single wind speed range can be attributed to sources 

lying along the upwind direction up to X miles of the monitor, where X = (upper end of 

wind speed range, m/sec)* (2.24 mph/m/sec). Based on this approach, for example, 

sources that impact the monitor when wind speeds are between 0.0 and 1.0 m/sec are 

assumed to lie primarily within 2.2 miles of the monitor (= 1.0 m/sec * 2.24 mph/m/sec). 

Figure 2-6 shows a hot spot at wind speeds between 2.0 and 3.0 m/sec and within the arc 
-18- 

of 140 to 160 degrees southwest of the monitor. Figure 2-7 shows an aerial photograph 

from May 2011 of the Cowtown monitor with a radius extending up to 6.7 miles away on 

an azimuth of 150 degrees. Emission sources anywhere along this line are potentially 

responsible for the higher PM10 sum shown in Figure 2-6 for this wind speed and 

direction. 

Figure 2-6 

Cowtown Sum of Hourly PM10 by Wind Speed and Azimuth, All Hours 

(No Outliers) 

Cowtown Sum of Hourly PM10 by Wind Azimuth (degrees) and Speed (m/sec), All Data

Sum of PMColumn Lab

Row Labels 0 1 2 3 4 5 6 7 8 9 10 11 12 Grand Total

0 55007 9243 3770 1599 2561 176 18 72374

10 1548 27522 13203 6306 1872 187 429 74 51141

20 925 22156 13039 2719 5881 2464 1143 1378 49705

30 3040 19158 10398 2249 2210 1178 2978 4559 23 45793

40 1442 19074 8552 5066 1846 155 1913 33 5227 43308

50 3184 14069 12789 5857 1318 110 75 765 38167

60 1616 17954 5420 2745 2400 1979 671 432 550 22 33790

70 926 14678 8792 2667 4920 3176 5706 7735 1496 1204 951 365 52616

80 3022 19519 7051 2410 2191 3006 2918 5135 2736 3635 51621

90 1132 19183 4767 3433 1345 2073 1654 2097 730 195 36609

100 1087 18776 10611 3269 5947 1457 831 253 102 1611 1481 45424

110 1361 30549 18730 6587 5514 1897 687 188 190 362 66064

120 1692 41704 44670 22008 6767 1471 960 2295 371 1214 123151

130 2963 52005 88710 106623 43293 7491 1475 107 211 158 303035

140 732 99430 195506 134749 21174 9157 1998 124 598 795 464263

150 4933 134309 201788 65853 12295 6196 1089 724 262 318 427767

160 1165 144369 157551 40770 8458 2648 2280 775 194 2261 360470

170 1336 140942 108806 22901 7824 4786 1238 375 5943 294150

180 6504 110844 69829 13639 4963 2828 3247 3295 153 133 215433

190 1440 85629 67450 13513 7513 2044 1149 1080 292 351 3103 183563

200 4001 77450 52500 14276 7222 2515 9637 888 2233 2144 2148 24 1046 176082

210 1302 72717 53565 11704 6656 2764 3279 1535 1564 2328 157414

220 1705 57089 65759 11426 10010 1830 1354 715 616 440 150945

230 2027 55864 70839 21096 5476 3738 3240 833 1538 164652

240 1491 65715 64787 29807 8337 4856 3593 4432 5600 508 189125

250 5143 39930 56273 34430 14490 6271 5002 1250 5503 168292

260 1473 37465 48395 42321 19134 14445 8820 4736 9593 3089 392 189863

270 7420 35111 31332 37581 27274 13401 8312 10921 6331 4675 2055 184414

280 1627 20269 25683 26603 17127 10673 6556 10723 4225 123485

290 751 25923 19687 14200 5886 5508 4405 1613 155 0 78127

300 1782 25340 29063 8497 3080 2337 325 599 51 21 71093

310 2015 30488 22085 7784 2836 923 257 652 8410 49 75498

320 1672 25388 17292 3024 1404 1542 304 804 651 52081

330 3863 24959 16820 4294 1175 856 42 409 612 53028

340 1480 22258 14677 2321 1165 285 35 255 42474

350 1389 19317 10534 3460 1425 181 261 99 166 53 36885

360 495 10747 4407 1859 2280 85 1122 116 123 21233

Grand Tota 134690 1687141 1655130 739644 285264 126688 88983 71576 66456 21614 12166 410 3374 4893136

Color Scale 0 40000 80000 120000 160000 200000

b les

Lab
-19- 

Figure 2-7 

Example Cowtown PM10 Hot Spot Source Corridor 
-20- 

Diurnal trends in PM10 concentrations were assessed by tabulating hourly PM10 averages 

by wind azimuth and hours of the day. The results, presented in Figure 2-8, show a cluster 

of high PM10 averages during 1900 to 2300 hours in the wind directions of 100 to 300 

degrees. As noted above (on page 12), these elevated concentrations could be due to the 

onset of strong nocturnal inversions as ground heating ceases after sunset or to variations in 

diurnal activity rates of contributing mechanical sources. The presence of moderate PM10 

averages between 0000 and 0600 hours and 120 to 190 degrees from the monitor suggests 

nocturnal mechanical sources may lie in this arc, which is also the arc occupied by the 

prevailing winds at this site. These impacts may also be driven by nocturnal high wind 

events. 

Diurnal trends in PM10 concentrations were also assessed by contrasting hours of the day 

against wind speed bins. The results of this analysis appear in a table in Figure 2-9. High 

PM10 averages between 2000 and 2300 hours and 0 to 5 m/sec wind speeds again 

suggest the possibility that strong ground-based inversions are forming during these 

hours while some mechanical source activity is occurring or that significant local sources 

with pronounced diurnal variability are causing higher emissions during this period of the 

day. High PM10 averages between 1500 and 2000 hours at wind speeds between 7 and 

12 m/sec suggest the predominance of late-afternoon high wind episodes that transport 

significant windblown dust to the monitor, possibly from thunderstorm outflows that 

typically occur in the late afternoon and early evening of the summer months. The 

cluster of moderate PM10 averages between 0600 and 0900 hours at wind speeds from 0 

to 4 m/sec indicate monitor impacts from nearby sources that exhibit significant 

emissions from early morning activity. 

Summary – The Cowtown monitor is impacted primarily by emissions from nearby 

sources lying between 100 and 300 degrees of the monitor and/or the presence of strong 

nocturnal inversions. Both of these possibilities should be the focus of emission 

inventory development for this site. Windblown dust generated during high wind events 

appears to be the next greatest source impacting the monitor. Thunderstorm outflows are 

suspected of producing a number of these high wind episodes. As a somewhat repeatable 

occurrence, these flows may follow wind trajectories that are co-axial, crossing the same 

source areas in transporting dust to the monitor. If further investigation of outflow backtrajectories demonstrates the dominance of a few wind directions, the emissivity of lands 

along these trajectories should be a focus of inventory development for this site. 

Inventory Focus – The emission sources that this initial analysis points to as significant 

contributors to high PM10 hours at the Cowtown monitor on low wind days appear to lie 

within a few miles of the monitor across a fairly wide arc from the southeast to the west. 

Because of the frequency and magnitude of high hourly PM10 concentrations recorded at 

this monitor, substantial effort should be directed toward identifying all potentially 

significant emission sources in the area identified in this analysis and collecting activity 

and emission characteristic data from these identified sources. For the designated high 

wind design day, hourly wind back-trajectories ending at the monitor on high wind hours 

should be identified and the soil attributes related to windblown dust emission rates 

should be evaluated for each combination of land use and soil type found along these 

back-trajectories. 
-21- 

Figure 2-8 

Cowtown Hourly PM10 Averages by Azimuth and Hours of the Day, All Hours 

(No Outliers) 

Cowtown Hourly PM10 Averages by Wind Azimuth (degrees) and Hour of Day, All Data (No Outliers)Average of Column LabRow Labels 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 0 190 232 191 162 129 169 258 246 167 165 114 50 49 67 53 56 192 57 91 10 102 305 83 102 47 60 104 301 163 205 88 68 49 42 42 46 64 36 74 20 51 119 127 73 106 199 123 143 171 273 95 103 73 45 37 37 38 212 77 30 119 47 49 130 78 102 147 234 370 140 106 54 45 45 33 36 24 44 91 40 166 72 91 65 136 114 173 160 233 121 130 55 67 29 22 37 31 51 314 50 81 56 66 165 78 171 117 192 201 201 110 54 67 29 51 15 722 50 66 60 130 195 148 72 84 110 151 179 346 148 154 86 89 36 32 40 46 31 100 70 205 130 65 186 100 99 179 281 349 131 115 250 88 58 120 66 45 44 153 80 107 208 82 73 86 117 177 198 125 176 162 102 79 141 28 80 29 29 127 90 69 102 69 121 134 153 197 151 225 106 67 95 136 89 107 41 197 71 183 100 71 117 155 85 135 153 207 100 194 110 87 61 54 67 66 34 25 68 61 110 161 118 140 157 110 133 164 242 233 97 72 70 61 39 35 93 31 45 95 120 129 148 199 186 181 123 148 172 221 133 94 76 61 59 55 52 43 173 72 130 155 209 195 197 167 187 209 260 255 146 96 65 48 38 46 28 136 64 148 140 232 247 245 231 215 197 246 285 228 166 100 69 66 35 31 54 157 247 316 150 219 202 195 233 215 200 237 256 256 163 142 57 54 48 37 43 58 28 90 160 203 228 233 207 199 188 196 285 305 178 105 91 70 39 65 49 63 54 113 170 183 191 181 154 171 175 206 260 235 162 129 94 53 37 35 174 58 65 96 180 158 162 169 176 174 175 170 272 222 148 87 70 81 52 52 50 63 74 120 190 149 154 134 135 212 181 205 250 256 145 103 68 151 62 63 56 54 84 190 200 139 174 119 124 150 217 206 229 287 182 176 68 63 80 66 111 106 101 166 210 259 132 156 159 137 119 257 243 187 179 148 76 60 73 84 62 105 198 100 220 137 109 192 136 146 199 239 190 180 194 81 59 45 62 54 119 71 87 111 230 221 167 106 146 125 183 284 354 313 169 106 79 63 50 58 63 133 78 195 240 184 137 153 199 160 188 160 246 247 192 97 107 229 102 73 148 80 103 166 250 196 153 97 102 120 105 207 310 191 155 146 200 92 90 80 73 153 131 165 260 160 117 96 163 116 155 208 186 161 226 129 125 171 121 93 90 109 148 128 270 118 147 73 80 135 182 153 221 259 203 98 75 154 99 144 121 123 75 153 280 79 128 148 165 108 183 174 202 191 135 97 72 82 88 90 134 117 115 122 290 243 107 151 145 60 93 91 225 202 151 88 94 71 54 64 67 94 54 103 300 46 54 68 60 114 106 49 101 270 178 224 80 56 49 56 43 60 56 76 310 131 52 117 79 120 70 194 215 271 224 93 66 66 80 61 54 50 73 262 320 70 106 85 65 61 161 152 180 184 99 93 76 47 61 49 54 45 43 73 330 82 149 25 118 102 77 154 209 312 90 92 46 39 45 28 33 48 38 70 340 82 143 64 253 94 125 210 191 166 110 79 59 58 42 39 28 31 48 74 350 50 141 101 98 83 92 85 185 182 116 91 59 51 38 39 29 36 36 102 360 24 29 72 52 28 82 106 338 204 158 76 62 70 43 51 62 39 46 120 Color Scale: 0 50 100 150 200 250 300

els

Lab
-22- 

Figure 2-9 

Cowtown Hourly PM10 Averages by Wind Speed and Hours of the Day, All Hours 

(No Outliers) 

Cowtown Hourly PM10 Averages by Wind Speed (m/sec) and Hour of Day, All Data (No Outliers)AverageColumnRow Lab 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 

0 151 190 173 171 136 123 234 240 275 149 93 68 56 77 78 58 281 83 110 1 168 162 155 164 169 166 196 238 249 160 107 80 75 44 41 42 46 50 116 2 200 190 192 183 174 174 200 238 240 157 104 66 57 50 44 45 60 50 96 3 158 205 196 198 179 187 223 266 208 166 94 71 96 46 55 48 73 74 108 4 135 119 122 171 113 172 182 252 267 124 123 102 69 82 99 98 72 113 109 5 108 165 82 39 111 130 149 206 201 151 121 110 106 97 92 113 135 107 138 6 111 56 36 72 84 15 237 306 240 188 197 145 94 105 160 108 189 178 140 7 67 99 121 71 208 237 265 182 223 275 153 321 202 238 130 285 219 157 320 8 212 120 224 108 142 83 143 234 224 198 241 526 401 219 382 343 492 2354 9 825 102 283 49 51 271 190 402 117 200 27 193 202 1281 456 833 10 312 72 519 118 31 125 692 2055 329 566 11 24 21 

12 1046 2328Color Scale: 0 50 100 150 200 250 300

Lab

umn
-23- 

2.2 Stanfield 

Single-Variable Analysis – Table 2-2 presents hourly PM10 average and maximum 

concentrations ranked by wind speed bin for the Stanfield site. This table reveals a 

progression of increasing PM10 average values with increasing wind speed, suggesting 

that there are no locally significant sources near the monitor. The averages, however, 

which range from 50 to 85 μg/m3

 at wind speeds between 0 and 5 m/sec, also suggest that 

a matrix of comparatively low emission sources surrounds the monitor, producing an 

elevated background level. The high maximum PM10 values at wind speeds in this same 

range also suggest that episodic sources periodically impact the monitor. 

Table 2-2 

Stanfield Hourly Average and Maximum PM10, All Hours 

(No Outliers) 

Wind speed 

Category (m/s) 

Observation 

Count 

Average 

PM10 

Max 

PM10 

0~1 2,225 71 1,284 

1~2 6,319 62 1,820 

2~3 2,336 50 1,979 

3~4 802 52 1,548 

4~5 475 85 1,553 

5~6 252 195 4,063 

6~7 136 277 3,485 

7~8 46 453 2,497 

8~9 20 664 1,770 

9~10 8 1,196 2,528 

10~11 1 2,098 2,098 

12~13 1 3,384 3,384 

Total Count 12,622 N/A N/A 
-24- 

A plot of hourly PM10 averages by hour of the day is shown in Figure 2-10. This plot 

shows a low morning PM10 peak that possibly represents an initial increase in 

anthropogenic emissions from nocturnal levels and, after 0800 hours, a decline in these 

groundlevel concentrations as inversion heights increase with corresponding increases in 

morning ground heating from solar insolation. The evening peak in PM10 average at 

2000 hours could be due to either an increase in emissions from diurnally varying sources 

and/or the onset of strong nocturnal inversions, as was also possible at the Cowtown 

monitor. 

Figure 2-10 

Stanfield Hourly PM10 Averages by Time of Day, All Hours 

(No Outliers) 

Examination of PM10 roses for this site provides limited information. An example PM10 

rose is presented in Figure 2-11. This plot is limited to hours during which the PM10 

concentration at the monitor exceeded 150 μg/m3

, which is the federal 24-hour PM10 

standard. This plot shows that prevailing directions of winds transporting high PM10 

concentrations are primarily from the west and, to a much lesser extent, from the 

southeast. A second PM10 rose, limited to hours when wind speeds exceeded 5.0 m/sec, 

is presented in Figure 2-12. This plot suggests that the highest PM10 impacts at the 

monitor during high wind conditions are generated by sources to the east and eastnortheast. 
-25- 

Figure 2-11 

Stanfield PM10 Rose of Hours Exceeding 150 μg/m3 
-26- 

Figure 2-12 

Stanfield PM10 Rose of Hours with Wind Speeds > 5.0 m/sec 
-27- 

Two-Variable Analyses – The first two-variable analysis of Stanfield monitoring data 

sorted hourly PM10 averages by wind speed and wind direction. The table showing this 

distribution appears in Figure 2-13. High PM10 averages at the highest wind speeds in 

several directions suggest that episodic windblown dust sources regionally surround the 

monitoring site. A plot of PM10 hourly counts by wind direction when wind speeds 

exceed 5.0 m/sec, shown in Figure 2-12 as a PM10 rose, suggest that winds producing 

high PM10 hour values blow towards the monitor most frequently from the east and eastnortheast. Low PM10 averages at low wind speeds in most directions, as shown in 

Figure 2-13, suggest that few significant PM10 sources are close to the monitor. A 

review of hourly PM10 maxima by wind speed and azimuth shows that high PM10 

concentrations during low wind speeds originate from episodic sources lying in an arc 

from the south through the northwest of the monitor. 

The sums of hourly PM10 concentrations grouped by wind speed and direction are 

presented in Figure 2-14. This plot suggests that (1) sources within 2.2 miles of the 

monitor do not significantly contribute to cumulative PM10 concentrations; (2) sources 

located between 2.2 and 4.5 miles of the monitor and within the arcs of 140-170 degrees 

and 250-300 degrees produce the highest cumulative impacts at the monitor; and (3) high 

wind sources in the arcs of 70-90 degrees and 200-240 degrees cumulatively contribute to 

PM10 measured at the monitor at somewhat higher frequencies or levels than sources 

lying in other directions. Because the meteorological regimes at Stanfield and Cowtown 

are somewhat similar, given a separation distance between the two monitors of 8.9 miles 

over flat terrain, the differences in patterns of PM10 concentrations between the two 

stations can be attributed almost entirely to the differences in locations, diurnal 

variabilities, and strengths of the emission sources separately impacting each monitor. 

An examination of PM10 averages by hour of day (Figure 2-15) shows that the higher 

hourly averages of PM10 concentrations occur during the evening hours between 1900 

and 2200 hours, with winds from 200 to 330 degrees of the monitor producing higher 

averages during these hours. Comparison to Figure 2-14 suggests that these higher 

averages occur more when wind speeds are between 1 and 3 m/sec, suggesting impacts 

from mechanical sources located within this arc and the possibility of enhancement by 

the onset of nocturnal inversions. The higher PM10 averages recorded between 0700 and 

1000 hours, in comparison with lower averages in hours of 0000 to 0700, suggest that 

nearby sources related to human activity contribute to daytime background levels at the 

monitor. 
-28- 

Figure 2-13 

Stanfield Hourly PM10 Averages by Wind Speed and Azimuth, All Hours 

(No Outliers) 

Stanfield Hourly PM10 Average by Wind Azimuth (degrees) and Speed (m/sec), All Data (No Outliers)

Average of PColumn L

Row Labels 0 1 2 3 4 5 6 7 8 9 10 12 Count Sub

0 41 70 53 40 107 89 413 1517 303

10 100 67 44 43 46 35 841 278

20 79 50 44 38 67 237

30 24 51 36 47 38 74 188

40 39 51 41 48 48 194

50 41 41 40 34 30 33 28 85 208

60 47 51 41 46 46 37 184 306 235

70 75 54 51 40 107 108 76 528 422 1070 293

80 90 59 51 75 42 123 417 576 973 302

90 20 56 60 61 62 52 85 294 412 1477 378

100 47 57 50 38 23 181 159 122 339

110 51 60 66 94 43 404 237 327

120 45 64 54 40 85 95 371

130 68 55 54 40 236 1020 473

140 73 61 56 62 79 396 568

150 79 62 55 33 59 224 551

160 54 56 57 39 63 737 683

170 69 55 42 72 80 78 424 141 521

180 66 53 41 27 61 258 1002 347

190 102 55 43 34 85 127 322 52 1099 286

200 35 55 37 33 132 90 720 30 111 1683 224

210 85 80 39 39 52 209 151 123 144 539 296

220 177 68 56 34 56 128 318 1077 323

230 82 71 43 40 63 121 355 64 2528 296

240 95 90 69 36 57 68 287 365 467 311

250 103 89 86 38 52 96 197 110 365

260 86 121 97 95 47 76 172 282 218 2098 410

270 81 117 92 45 74 66 126 135 315 842 434

280 53 97 124 90 41 88 123 288 545 427

290 71 85 71 43 77 46 79 151 396 522 494

300 29 92 69 50 47 50 159 575 2497 3384 382

310 100 70 49 44 48 274 89 131 1374 323

320 106 68 45 34 83 105 274 310

330 30 70 48 33 39 233 340 362 322

340 65 63 51 33 71 1060 373 288

350 65 53 46 45 150 120 943 334

Count Subto 185 5583 4309 1294 570 357 202 80 24 15 1 1 12621

Color Scale: 0 50 100 150 200 300 400

els

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-29- 

Figure 2-14 

Stanfield Sum of Hourly PM10 by Wind Speed and Azimuth, All Hours 

(No Outliers) 

Stanfield Sum of Hourly PM10 Concentrations by Wind Azimuth (degrees) and Speed (m/sec), All Data

Sum of PMColumn La

Row Label 0 1 2 3 4 5 6 7 8 9 10 12 Grand Total

0 124 6747 8327 1304 645 267 1238 1517 20169

10 300 7621 5508 1194 232 35 1683 16573

20 787 5013 4710 646 201 11357

30 122 4434 2898 520 113 148 8235

40 78 4735 3122 772 337 9044

50 206 3967 3628 238 179 65 28 85 8396

60 141 5217 3522 731 599 183 1103 1223 12718

70 523 6947 4294 850 1499 1844 760 3696 1267 1070 22749

80 720 8613 4145 1726 464 1722 4167 2303 4867 28727

90 59 10164 7103 1772 986 465 851 2056 1236 2954 27646

100 235 10097 6074 938 46 1088 478 122 19077

110 257 10360 7934 1873 214 1212 474 22323

120 179 11498 8444 969 513 190 21793

130 136 13851 10287 998 707 1020 26999

140 511 17679 12611 2402 158 792 34152

150 394 18915 10620 1223 294 897 32341

160 324 20750 13622 1605 1252 4421 41973

170 689 14743 7685 2897 1037 233 1696 141 29120

180 266 9004 5083 655 797 2067 4010 21882

190 407 6591 3856 1159 1694 1652 1287 104 1099 17850

200 248 4773 1838 1337 2635 719 4323 91 222 1683 17869

210 255 8444 2492 2124 1550 3756 1818 492 288 1078 22295

220 530 8813 3722 1476 2089 2694 6050 4307 29681

230 574 8252 2825 1701 2005 2669 3547 64 2528 24163

240 570 13410 4346 1424 1309 1283 1150 2188 467 26146

250 724 16376 8220 1290 1355 1056 1180 221 30422

260 778 21089 11113 4181 1035 1745 2584 1695 218 2098 46536

270 651 18713 10997 2818 2073 2115 2014 406 629 1683 42098

280 265 17961 15010 4326 942 2123 863 3173 1090 45751

290 282 14560 10530 2626 3010 1623 1738 1058 1187 1044 37656

300 117 12105 8429 3077 1281 891 2064 1725 2497 3384 35570

310 401 7476 5221 2763 1247 3016 268 261 1374 22027

320 423 7655 5487 1754 1327 315 822 17783

330 30 7235 6922 1622 541 1629 1019 362 19359

340 326 6767 5743 1285 1495 1060 746 17422

350 456 5894 6757 2253 1952 481 943 18734

Grand Tot 13084 376470 243122 60525 37809 44453 49923 27165 13907 14696 2098 3384 886634

Color Scale 0 4000 8000 12000 16000 20000

bel

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-30- 

Figure 2-15 

Stanfield Hourly PM10 Averages by Azimuth and Hours of the Day, All Hours 

(No Outliers) 

Stanfield Hourly PM10 Averages by Wind Azimuth (degrees) and Hour of Day, All Data (No Outliers)Average of PColumn LRow Labels 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 0 37 11 48 20 14 25 65 64 93 82 52 108 58 34 35 53 42 115 10 51 32 17 36 31 18 109 73 85 39 101 43 37 57 34 35 44 93 20 24 37 54 38 27 27 34 56 91 107 43 43 36 34 50 35 31 45 61 30 10 10 62 32 12 75 132 54 47 43 41 36 31 30 27 24 34 71 40 87 21 89 35 31 49 5 77 127 86 38 37 58 33 34 33 27 35 47 50 44 28 19 19 60 20 26 46 21 66 56 40 48 41 33 25 27 34 39 60 35 76 66 32 38 41 42 137 110 72 70 62 41 35 34 45 32 38 56 70 58 53 66 58 46 43 47 76 92 103 77 196 47 34 79 47 56 42 64 80 68 37 62 51 78 32 37 78 80 139 120 135 135 96 60 34 33 45 214 90 45 43 66 61 28 43 53 86 127 115 96 59 51 164 32 38 54 40 45 100 54 37 53 30 46 25 71 70 97 73 87 51 50 39 27 30 30 33 68 110 83 77 52 57 39 41 57 82 105 71 55 41 38 38 26 39 6 135 215 120 67 37 45 63 37 32 57 81 91 80 47 25 34 34 32 23 22 83 63 130 64 47 38 33 45 43 54 84 78 65 65 35 29 36 45 73 27 85 27 140 62 50 57 49 45 41 50 85 88 60 38 29 26 26 29 22 15 71 142 150 54 50 34 39 50 33 70 94 90 73 49 54 41 36 17 23 11 63 43 160 41 43 42 30 38 43 63 75 85 71 37 38 38 32 42 38 2068 39 59 170 61 45 40 27 33 40 61 69 80 62 71 67 28 26 19 81 40 21 37 180 36 86 29 36 28 30 42 69 75 79 31 111 36 79 69 22 36 67 63 190 57 35 44 39 20 36 40 81 99 65 30 49 96 19 266 68 28 201 200 22 46 19 27 39 42 34 36 112 174 329 176 56 52 52 61 32 18 42 210 75 47 25 38 62 27 43 39 81 79 44 51 37 100 29 156 158 74 143 220 35 63 23 46 53 38 53 61 74 46 52 43 118 51 167 202 93 121 70 230 62 40 33 45 28 25 35 97 119 63 34 29 209 64 85 47 42 39 135 240 35 26 55 40 41 42 61 71 35 39 39 58 28 148 71 82 93 111 39 250 47 27 32 58 48 45 25 67 41 46 68 45 59 29 34 84 35 60 46 260 41 54 39 39 44 36 29 42 98 88 156 137 79 45 78 80 46 250 93 270 58 69 67 58 50 44 46 77 91 70 39 42 116 117 100 36 94 35 61 280 50 54 63 40 56 69 60 71 88 65 50 61 52 35 43 49 89 58 123 290 84 63 48 39 34 27 30 51 80 46 77 38 88 32 53 38 74 64 55 300 64 41 42 47 12 28 68 53 99 62 109 196 40 41 39 76 58 38 104 310 55 38 41 54 29 53 27 104 30 87 56 64 44 57 36 32 109 82 70 320 48 66 42 31 18 34 45 59 90 38 38 48 41 86 34 36 34 52 43 330 66 45 32 28 35 39 43 40 55 147 44 42 35 31 37 60 39 38 81 340 32 59 60 48 80 25 15 15 36 71 142 40 38 65 52 34 35 52 65 350 68 36 32 30 39 15 18 25 138 52 43 35 36 41 40 66 35 52 65 Color Scale: 0 50 100 150 200

els

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-31- 

Summary – A cluster of sources lying within 4.5 miles to the south through northwest of 

the monitor appears to produce the highest impacts at the monitor during low wind 

conditions. No sources within 2.2 miles of the monitor appear to produce high impacts. 

Windblown dust sources producing the highest impacts at the monitor appear to lie to the 

east and east-northeast of the monitor. Mechanical source activity occurring in the late 

evening should be investigated to determine the sources of high PM10 impacts during 

these hours. 

Inventory Focus – Mechanical sources lying within 4.5 miles and 200 to 330 degrees of 

the monitor should be identified through field inspection and interviews of people living 

or working near the monitoring station. Activity rates and emission characteristics of 

these sources should be estimated from available records or field observation. Land uses 

and soil characteristics lying along the back-trajectories of high wind hours on the 

selected high wind design day should be identified from record data or evaluated from 

sample collection and analysis. 

2.3 Casa Grande 

The hourly PM10 data were collected at an urban site within the city of Casa Grande, 

while the meteorological data were collected at the city’s airport, approximately 4 miles 

to the north. For this analysis, we assumed that the wind patterns at the downtown site 

were very similar to those recorded at the airport. Because of this separation distance, 

however, the relative locations of some sources, especially those close to the monitor 

producing impacts during low wind “meander” conditions, cannot be estimated with the 

same accuracy as those near other monitors where meteorological monitors are colocated with PM10 instruments. 

Single-Variable Analyses – A table of hourly PM10 averages and maxima grouped by 

wind speed is shown in Table 2-3. This table suggests that there are no significant 

sources operating in a continuous fashion within a few kilometers of the monitor. An 

elevated maxima PM10 hour when wind speeds are between 3 and 4 m/sec, however, 

suggests that an occasional episodic emission event may occur within the urbanized area 

of Casa Grande. Higher hourly PM10 averages and maxima at wind speeds above 9 

m/sec indicate that this monitor is impacted by windblown dust sources within the 

surrounding region. 

Confirmation of the absence of significant sources, and the presence of episodic emission 

events, in the urbanized area is shown in Table 2-4. This table represents hourly PM10 

averages and maxima for all hours during which hourly PM10 concentrations at the 

monitor exceeded 150 μg/m3

. The relatively low numbers of hours shown in the 

Observation Count column for wind speeds less than 5 m/sec suggests that high PM10 

hours at the monitor at low and moderate wind speeds are relatively infrequent and that 

local and nearby sources are not continuously significant in emission strength, but rather 

may be significant on an episodic basis. 
-32- 

Table 2-3 

Casa Grande Hourly Average and Maximum PM10, All Hours 

(No Outliers) 

Wind Speed 

Category (m/s) 

Observation 

Count 

Average 

PM10 

Max 

PM10 

0~1 1,771 52 417 

1~2 5,101 48 400 

2~3 3,419 42 561 

3~4 2,109 42 2,172 

4~5 1,309 44 757 

5~6 736 57 1,972 

6~7 458 73 1,698 

7~8 254 76 693 

8~9 151 101 902 

9~10 85 202 3,565 

10~11 44 290 1,603 

11~12 11 562 4,173 

12~13 6 463 1,453 

13~14 1 38 38 

Total Count 15,455 N/A N/A 

Table 2-4 

Casa Grande Hourly Average and Maximum PM10, Hours > 150 μg/m3

(No Outliers) 

Wind Speed 

Category (m/s) 

Observation 

Count 

Average 

PM10 

Max 

PM10 

0~1 43 188 417 

1~2 96 196 400 

2~3 47 206 561 

3~4 34 316 2,172 

4~5 32 233 757 

5~6 36 327 1,972 

6~7 27 470 1,698 

7~8 27 275 693 

8~9 27 275 902 

9~10 30 444 3,565 

10~11 29 409 1,603 

11~12 8 760 4,173 

12~13 5 527 1,453 

Total Count 442 N/A N/A 
-33- 

The count of PM10 hours during which concentrations at the Casa Grande monitor 

exceed 150 μg/m3

 is presented in a PM10-rose format in Figure 2-16. This figure shows 

that during hours of elevated PM10, winds typically blow from the east through southeast 

and the west through west-northwest quadrants. 

Figure 2-16 

Casa Grande PM10 Rose of Hours Exceeding 150 μg/m3

(No Outliers) 
-34- 

Two-Variable Analyses – The distribution of hourly PM10 averages by wind direction 

and wind speed at the Casa Grande monitor is shown in Figure 2-17. Relatively uniform 

and low PM10 averages at wind speeds of less than 5 m/sec suggest the absence of 

continuously significant sources within a few miles of the monitor. Scattered high PM10 

averages at wind speeds above 7 m/sec suggest variability in emissions rates from 

windblown dust sources in the region surrounding the urbanized portion of Casa Grande. 

This variability could be due to emission seasonality resulting from high wind seasonality 

driven by monsoon conditions. 

Emission variability is also borne out by the distribution of hourly PM10 maxima by 

wind direction and speed, as presented in Figure 2-18. Scattered hot spots in this figure 

suggest the episodic nature of PM10 emissions from sources impacting the monitor. This 

pattern suggests that controls implemented to bring the Cowtown and Stanfield monitors 

into attainment will probably reduce high episodic emission rates in the area surrounding 

the Casa Grande monitor and obviate the need to conduct inventory research focused on 

sources near this monitor. 

The sum of hourly PM10 distributed by wind direction and wind speed is displayed in 

Figure 2-19. This plot shows that emission sources lying in an arc between 60 and 140 

degrees and within 6.7 miles of the monitor are responsible for the highest cumulative 

impacts at the monitor. 

Summary – No significant sources within the urbanized portion of Casa Grande were 

found to impact the monitor. Periodic high impacts from surrounding rural areas suggest 

the generation of significant emissions during high events entraining dust from disturbed 

lands. 

Inventory Focus – As the Casa Grande monitoring site was not selected for a design day 

attainment demonstration, no modeling domains that included this site were developed, 

and no focused emission inventory will be researched and compiled at this site. Although 

the Maricopa monitoring site was selected for an attainment demonstration, no 

meteorological data have been collected at this site and, as a result, this site was not 

included in initial study analyses. 
-35- 

Figure 2-17 

Casa Grande Hourly PM10 Averages by Wind Azimuth (degrees) 

and Wind Speed (m/sec) 

Casa Grande Hourly PM10 Average by Wind Azimuth (degrees) and Speed (m/sec), All Data

Average Column L

Row Labe 0 1 2 3 4 5 6 7 8 9 10 11 12 13

0 58 61 43 38 48 19 48

10 71 42 40 40 33 55 27 537 48

20 51 53 49 44 27 41 11 29

30 45 49 49 34 25 65 23 27 40

40 32 48 42 34 24 22 41 23 34 17

50 55 54 50 36 40 31 116 380 76 47

60 63 56 49 31 37 22 74 68 20 78 25

70 46 49 44 46 35 34 45 39 83 68 40 28

80 68 51 38 34 40 37 47 142 58 292 1603

90 57 53 44 44 40 49 37 46 82 163 452 422

100 35 53 48 37 46 50 47 100 62 66 212 21

110 92 57 51 48 47 49 64 206 247 547 849

120 51 58 55 42 68 41 35 57 126 64

130 39 59 58 51 37 175 23 98 156

140 46 57 58 47 43 69 64 273

150 46 54 51 47 35 42 113 294

160 49 52 47 47 24 117 92 92 302 151

170 50 52 48 41 44 77 58 33 28 533

180 33 54 43 43 51 41 307 42 340

190 49 51 47 35 23 45 100 88 395 281 152 56

200 44 46 42 37 41 41 56 43 42 67 228 594

210 68 52 39 40 32 71 84 82 109 407 287

220 67 52 41 36 80 63 244 138 68 37

230 40 42 39 47 64 69 97 124

240 39 41 45 40 38 80 98 67 131

250 44 46 38 38 47 85 51 81 103 105

260 35 47 41 48 43 49 43 67 52 84 71 126 301

270 207 38 41 42 42 51 57 111 67 128 116 313 142 420

280 95 47 36 42 51 42 57 52 81 74 112 215 1247 38

290 44 40 37 41 47 47 46 55 83 66 95 103 673

300 33 41 40 35 39 39 43 49 77 85 277 264

310 89 50 37 35 32 56 36 60 116 71 252 687

320 74 54 38 35 36 48 50 37 38 191

330 59 48 31 34 38 22 96 30 36 158

340 52 48 38 34 34 28 44 19 56 263

350 34 50 37 38 42 27 29 45 255 118 242

360 21 47 43 42 41 17 29 178 171 105

Color Sca 0 50 100 150 200 250 300

Labe

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-36- 

Figure 2-18 

Casa Grande Hourly PM10 Maxima by Wind Azimuth (degrees) 

and Wind Speed (m/sec) 

Casa Grande Hourly PM10 Maxima by Wind Azimuth (degrees) and Speed (m/sec), All Data

Max of PColumn L

Row Labe 0 1 2 3 4 5 6 7 8 9 10 11 12 13 (blank)

0 67 236 170 147 161 28 120

10 182 180 154 109 62 276 27 1556 48

20 75 173 156 111 41 109 12 29

30 45 208 197 94 45 109 34 42 40

40 62 261 124 97 73 44 67 27 34 17

50 81 292 178 153 170 87 583 693 103 47

60 133 230 167 95 187 44 120 87 20 124 25

70 61 203 159 163 138 118 99 68 83 74 40 28

80 217 165 196 105 99 96 121 635 129 292 1603

90 122 188 152 515 159 161 93 112 348 384 3565 704

100 90 157 166 120 328 152 209 750 153 142 330 21

110 158 384 377 561 339 236 199 411 247 547 849

120 119 263 382 164 486 100 67 61 200 64

130 55 400 223 234 137 892 31 98 287

140 102 244 329 195 308 205 152 550

150 73 246 513 179 108 122 343 548

160 81 176 183 139 43 757 310 148 721 187

170 114 164 248 186 125 281 98 60 44 533

180 65 235 236 267 148 84 1972 119 340

190 126 203 322 88 93 242 1461 409 902 281 152 56

200 133 143 286 148 266 120 385 106 192 140 284 954

210 118 170 120 339 89 934 262 240 281 801 552

220 167 376 176 259 2172 312 1698 205 68 37

230 64 192 176 331 325 283 284 166

240 43 204 291 199 124 204 167 159 259

250 116 152 172 200 174 251 166 107 185 146

260 92 264 197 847 136 103 92 196 56 84 71 126 301

270 207 121 164 150 344 512 326 1031 175 335 239 733 142 420

280 145 137 203 215 264 168 700 152 241 175 291 301 4173 38

290 123 146 116 178 323 185 127 203 220 123 143 186 1453

300 44 157 210 183 135 169 112 130 180 152 277 449

310 157 141 122 132 91 285 64 89 116 73 272 1140

320 117 202 148 135 139 187 130 37 38 191

330 70 207 108 164 131 46 226 53 36 179

340 99 417 142 112 131 59 56 19 56 263

350 61 241 122 116 151 58 34 70 397 118 242

360 39 138 164 132 109 31 43 259 182 105

(blank)

Color Sca 0 200 400 600 800

Labe

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-37- 

Figure 2-19 

Casa Grande Sum of PM10 by Wind Azimuth (degrees) and Wind Speed (m/sec), All Hours 

(No Outliers) 

Casa Grande Sum of Hourly PM10 by Wind Azimuth (degrees) and Wind Speed (m/sec)

Sum of PMColumn La

Row Label 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Grand Tota

0 115 2581 2212 2071 1348 116 193 8636

10 284 3182 2963 2091 685 549 27 1612 48 11443

20 303 4183 2931 1051 216 204 21 29 8939

30 45 4406 2989 615 150 196 69 82 40 8590

40 192 4515 2650 509 341 223 82 46 34 17 8608

50 218 7608 3002 572 802 307 926 761 153 47 14394

60 252 9387 3884 645 675 130 444 137 20 156 25 15754

70 185 9146 4931 1459 700 372 627 117 83 137 40 28 17826

80 613 9811 5527 1545 1555 1227 793 1418 174 292 1603 24556

90 687 12353 8850 2929 2748 2639 1850 1904 3831 3106 7235 1687 49817

100 176 12968 14015 2610 2381 1643 1083 1296 868 856 1061 21 38979

110 461 13683 15045 4344 932 728 514 617 247 547 849 37969

120 717 10920 12783 4088 2248 497 69 114 253 64 31752

130 195 7798 12582 4850 1205 1403 68 98 312 28509

140 229 8441 9839 3717 904 625 192 819 24765

150 138 5756 6737 2270 451 250 563 588 16754

160 438 5165 4654 1424 291 1055 462 184 907 302 14881

170 198 5635 4593 1452 614 919 231 99 55 533 14328

180 134 5865 4465 2065 1019 493 2457 167 340 17003

190 388 6005 5454 1356 551 675 2507 791 1581 281 152 56 19796

200 309 5790 4763 2210 1738 1176 1340 639 466 269 456 1188 20344

210 408 6406 5204 3285 1938 3314 2521 1639 1307 814 575 27410

220 606 7910 7048 2486 4620 2384 4395 691 68 37 30243

230 359 5156 5082 3041 2869 1652 773 495 19427

240 77 5548 5361 2590 1531 1202 294 734 523 17860

250 266 4774 4597 2928 1545 1105 560 243 517 210 16744

260 176 4555 5065 5500 2637 1481 894 535 313 84 71 126 301 21738

270 207 2829 4815 5589 5410 3058 2577 2101 1473 1156 696 1251 142 420 31724

280 285 2333 3590 6667 8609 5048 4808 2387 2023 1327 1121 858 4987 38 44078

290 218 1660 3180 5041 8429 7558 3284 2825 1571 461 851 206 2019 37303

300 201 2371 3157 3370 4042 3043 1899 976 692 510 277 792 21328

310 178 2379 2513 2751 1396 1841 287 480 116 141 503 1374 13961

320 221 2483 3792 3114 1860 913 351 37 38 191 13000

330 235 2761 2715 3169 1604 200 288 91 36 475 11574

340 156 3045 4182 3616 1488 389 133 19 56 263 13347

350 68 3131 3465 5881 2319 384 117 90 1020 118 242 16834

360 62 1075 2052 2794 1424 100 115 356 342 105 8424

Grand Tota 10001 213612 200684 105692 73271 49097 37813 24860 19416 11732 16704 7773 7225 758 778638

Color Scale 0 2500 5000 7500 10000

bel

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-38- 

2.4 Pinal County Housing 

Single-Variable Analyses – An analysis of PM10 impacts grouped by wind speed is 

presented in Table 2-5. The low averages for wind speeds less than 5 m/sec indicate the 

absence of continuously significant mechanical sources within a few miles of the 

monitor. The moderately high maximum hourly PM10 values, up to 5,000 μg/m3

, at 

wind speeds of 0 to 2 m/sec suggest, however, the presence of significant episodic 

sources near to the monitor that periodically contribute to elevated 24-hour average 

PM10 concentrations. 

Table 2-5 

Pinal County Housing Average and Maximum PM10, All Hours 

(No Outliers)

Wind Speed 

Category (m/s) 

Observation 

Count 

Average 

PM10 

Max 

PM10 

0~1 10808 74 2292 

1~2 8564 56 5004 

2~3 3486 47 937 

3~4 1593 63 2914 

4~5 869 105 8608 

5~6 481 213 5591 

6~7 212 408 8449 

7~8 81 475 2311 

8~9 24 954 5774 

9~10 8 1098 4281 

10~11 4 1302 3141 

Total Count 26130 N/A N/A 

The relationships of hourly PM10 impacts and wind speeds were further studied by 

limiting the above analysis to those hours during which the monitor recorded hourly 

PM10 concentrations greater than 150 μg/m3

. The results of this analysis are shown in 

Table 2-6. The relationship between increasing wind speed and increasing fraction of 

hours when PM10 is greater than 150 μg/m3 suggests that soils in the region of Pinal 

County Housing are frequently disturbed and available to contribute entrained PM10 

emissions during high wind events. The higher fraction of elevated PM10 hours when 

wind speeds are less than 1 m/sec, compared to hourly fractions when wind speeds are 

between 1 and 4 m/sec, suggests the presence of PM10 sources close to the monitor that 

produce impacts when wind meander and limited dispersion occur at these low wind 

speeds. 
-39- 

Table 2-6 

Pinal County Housing Average and Maximum PM10, Hours Exceeding 150 μg/m3

(No Outliers)

Wind Speed 

Category (m/s) 

Observation Count 

(Hours > 150 μg/m3) 

Fraction of 

Total Hours 

Average PM10 

(Hours > 150 μg/m3) 

0~1 1202 51% 241 

1~2 475 20% 240 

2~3 104 4% 259 

3~4 103 4% 351 

4~5 119 5% 446 

5~6 152 7% 541 

6~7 106 5% 748 

7~8 52 2% 707 

8~9 15 1% 1498 

9~10 6 <1% 1442 

10~11 3 <1% 1736 

Total Count 2337 100% N/A 

A PM10 rose based on hours when PM10 concentrations exceed 150 μg/m3

 is presented 

in Figure 2-20. This rose shows that sources producing hourly PM10 concentrations 

above 150 μg/m3 appear to surround the monitor. Sources producing the highest hourly 

concentrations appear to lie to the northeast, south, and south-southwest of the monitor. 
-40- 

Figure 2-20 

Pinal County Housing PM10 Rose of Hours Exceeding 150 μg/m3

(No Outliers)
-41- 

Two-Variable Analyses – Hourly PM10 averages grouped by wind speed (m/sec) and 

direction (degrees) at Pinal County Housing are displayed in Figure 2-21. Uniform and 

moderate hourly PM10 averages at wind speeds less than 5 m/sec suggest that 

continuously emitting sources surround the monitor. These sources appear to produce an 

elevated background concentration of PM10 at the monitor, but not at levels that exceed 

the 24-hour standard. The uniformity of hourly averages within this range of wind 

speeds and in all directions also suggests the presence of mechanical dust sources of 

uniform emission strength. High hourly PM10 averages at wind speeds greater than 6 

m/sec in many directions also suggests that disturbed soil surfaces surround the monitor, 

with sources centered on 50, 200, and 250 degrees from the monitor producing the 

highest impacts primarily because of the prevailing directions of highest wind speeds. 

A corresponding table of hourly PM10 maxima by wind speed (m/sec) and direction 

(degrees) is presented in Figure 2-22. The occasional elevated PM10 maxima at wind 

speeds of less 6 m/sec indicate that sources relatively close and within arcs spanning 0 to 

80 degrees and 180 to 270 degrees of the monitor periodically generate high levels of 

emissions. 

The combinations of wind speeds and wind directions responsible for the greatest 

cumulative PM10 impacts are shown in Figure 2-23, which presents the sums of hourly 

PM10 concentrations grouped by wind speeds and directions. The highest cumulative 

subtotals at wind speeds of 1 to 2 m/sec suggest that sources within 4.5 miles, and in 

almost all directions, of the monitor are responsible for a majority of PM10 measured. At 

these wind speeds, mechanical generation will be the primary mechanism for emission 

generation. 
-42- 

Figure 2-21 

Pinal County Housing Hourly PM10 Averages by Wind Azimuth (degrees) 

and Wind Speed (m/sec), All Data 

(No Outliers) 

Pinal County Housing Hourly PM10 Averages by Wind Azimuth (degrees) and Speed (m/sec), All Data (No Outliers)

Average Column L

Row Labe 0 1 2 3 4 5 6 7 8 9 10 11 Average

0 108 65 45 60 29 77 77

10 76 62 51 60 182 71 1002 77

20 81 61 61 54 60 92 2065 57 78

30 65 58 51 103 72 115 348 300 69

40 77 54 50 50 80 215 406 1839 83

50 61 57 53 61 60 280 187 780 1016 919 106

60 68 69 60 56 55 71 288 223 302 79

70 73 55 46 55 137 168 179 464 69

80 72 61 47 56 250 1237 406 87

90 82 71 60 87 25 156 95 74

100 84 69 47 75 213 140 8449 88

110 86 71 60 70 198 304 250 76

120 75 73 48 58 267 64 73

130 72 72 45 49 42 112 67

140 73 64 45 77 81 167 108 410 90 65

150 65 61 48 43 78 184 413 655 62

160 76 64 46 41 87 392 257 108 63

170 79 60 55 36 81 48 176 100 61

180 73 66 56 44 79 47 218 727 744 800 80

190 80 74 54 74 70 116 194 235 239 510 82

200 87 69 55 53 55 77 213 281 320 1484 2235 91

210 68 71 53 48 62 84 208 495 525 413 370 84

220 84 67 49 48 62 99 364 122 73

230 84 74 48 53 61 59 496 409 71

240 73 68 49 56 53 290 174 1040 118 76

250 96 70 45 47 78 107 161 721 2198 661 570 79

260 95 66 41 47 66 102 277 576 234 5774 74

270 99 61 43 53 84 152 265 264 67

280 87 59 44 46 62 121 318 690 61

290 72 64 40 48 224 86 653 700 63

300 93 59 44 59 118 41 60

310 78 53 39 48 45 157 55

320 91 56 37 36 75 358 58

330 84 59 82 42 170 197 412 71

340 78 70 58 33 125 182 68

350 73 61 41 40 96 169 60

360 73 60 58 39 74 137 63

Average 79 65 49 52 77 144 278 521 631 1229 1287 370 72

Color Scale: 0 50 100 150 200 300 400

abe

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-43- 

Figure 2-22 

Pinal County Housing Hourly PM10 Maxima by Wind Azimuth (degrees) 

and Wind Speed (m/sec), All Data 

(No Outliers)

Pinal County Housing Hourly PM10 Maxima by Wind Azimuth (degrees) and Speed (m/sec), All Data (No Outliers)

Max of P Column L

Row Labe 0 1 2 3 4 5 6 7 8 9 10 11 Max

0 2052 554 237 344 69 146 2052

10 578 355 374 376 2234 359 3619 3619

20 633 455 417 171 444 219 5591 82 5591

30 498 697 379 2914 425 432 696 300 2914

40 976 463 224 357 597 1208 2922 1839 2922

50 369 450 288 235 321 2518 479 3731 2316 919 3731

60 588 505 1444 296 322 381 3883 1285 460 3883

70 503 336 301 198 543 730 382 622 730

80 490 431 180 184 1279 8608 1069 8608

90 454 531 371 636 42 217 95 636

100 522 442 188 511 629 259 8449 8449

110 565 667 837 192 449 558 250 837

120 395 622 168 410 1421 92 1421

130 436 540 159 203 248 396 540

140 387 557 583 1074 386 869 187 443 90 1074

150 310 441 524 417 711 850 1467 655 1467

160 457 386 376 255 617 1436 363 108 1436

170 341 568 422 163 1269 443 1402 190 1402

180 290 400 400 320 997 349 2265 3771 1880 800 3771

190 389 1049 379 2193 437 1134 2471 997 964 856 2471

200 587 721 670 317 552 765 2806 1439 814 4281 3141 4281

210 308 475 658 322 251 412 1108 2400 1579 826 740 2400

220 562 320 444 192 444 457 2033 250 2033

230 490 490 191 323 223 120 1737 781 1737

240 298 478 706 1139 216 3714 541 2311 118 3714

250 790 626 261 288 1104 722 732 3555 3875 661 570 3875

260 474 526 408 169 328 538 1589 718 252 5774 5774

270 2292 433 277 314 776 808 1088 264 2292

280 478 294 343 151 310 403 457 1568 1568

290 620 675 292 142 1289 86 2863 700 2863

300 648 421 619 506 215 41 648

310 435 949 313 163 104 296 949

320 484 341 133 118 127 498 498

330 613 549 5004 209 352 487 412 5004

340 457 1133 937 172 265 182 1133

350 452 1145 252 135 169 203 1145

360 319 479 372 187 206 202 479

Max 2292 1145 5004 2914 2234 8608 5591 8449 3875 5774 3141 740 8608

Color Scale: 0 100 200 400 800 1000 1500 2000

Labe

mn L
-44- 

Figure 2-23 

Pinal County Housing Sum of Hourly PM10 by Wind Azimuth (degrees) 

and Wind Speed (m/sec), All Data 

(No Outliers)

Pinal County Housing Sum of Hourly Average PM10 by Wind Azimuth (degrees) and Speed (m/sec), All Data

Sum of PMColumn L

Row Labe 0 1 2 3 4 5 6 7 8 9 10 11 Sum

0 11853 7454 2113 1324 116 385 23245

10 11996 12886 4791 1750 4188 498 4006 40114

20 8964 11448 5741 2381 1797 1008 6195 114 37647

30 7647 11314 6312 6208 2367 2177 1739 300 38064

40 10431 10321 5752 4116 3857 7542 8518 1839 52376

50 6739 11466 4754 2078 2693 12037 5801 7802 6094 919 60384

60 8285 15428 3963 2031 1430 2066 5751 2895 603 42452

70 10225 11967 3024 1315 2459 1179 1076 1857 33102

80 11205 13933 3120 1070 2002 9893 1217 42439

90 12781 20054 3083 1040 76 312 95 37441

100 14204 23042 2503 1346 1276 700 8449 51520

110 17234 28992 3947 1048 988 609 250 53069

120 19553 39826 3867 1339 2402 128 67115

130 19455 40666 6283 1435 883 670 69392

140 14088 35070 7247 2678 2267 1499 216 820 90 63975

150 11192 31643 10415 2631 2956 1104 2480 655 63075

160 14686 31841 14009 4917 3137 3924 771 108 73393

170 13466 28077 16701 4498 3624 1104 2819 200 70488

180 9653 22096 12614 3995 4169 932 4584 7265 5209 800 71316

190 10005 22903 10572 8034 3877 3833 5614 3989 1194 2040 72060

200 9106 17006 9479 6983 5702 4993 8962 5900 2241 7419 4469 82260

210 9040 20432 11316 4326 5321 4791 6446 8905 5777 826 740 77920

220 10615 21524 9644 4429 4602 4929 7998 611 64353

230 9317 21636 9037 5517 1880 892 3473 1638 53389

240 7996 17191 10755 6582 2472 5800 2614 6242 118 59770

250 9613 15910 11680 8193 7161 4267 3210 5045 6593 661 570 72902

260 7490 15436 9878 7497 5804 4194 5822 1727 467 5774 64088

270 9105 14833 10070 5928 4044 4110 2918 264 51271

280 7739 15803 10367 3894 1799 1692 955 2071 44319

290 7483 16923 9145 1967 1795 86 3265 700 41363

300 9353 18480 7907 1818 355 41 37954

310 9650 14583 5842 869 136 315 31394

320 10170 15702 5577 1229 226 716 33620

330 8702 16278 10748 1093 681 590 412 38502

340 7672 16886 6731 756 627 182 32854

350 8698 14902 3292 889 383 338 28501

360 4642 5997 2211 815 517 410 14591

Sum 390051 709945 274486 118017 90068 89946 97205 69284 28388 18439 5147 740 1891716

Color Scale: 0 5000 10000 15000 20000

abe

n L
-45- 

Diurnal variations in PM10 impacts were analyzed by grouping hourly PM10 

concentrations by hour of the day and wind speed or wind direction. The table showing 

hourly PM10 averages by wind direction and hour of the day is presented in Figure 2-24. 

Increasing hourly PM10 averages between 0700 and 1000 hours suggest that mechanical 

activities produce higher emissions from sources near the monitor than occur between 

0100 and 0700 hours. Decreasing hourly PM10 averages between 1000 and 1500 hours, 

compared with averages between 0700 and 1000 hours, suggest either lower emission 

rates or, more probably, increasing mixing height overcoming higher morning emissions. 

Scattered elevated PM10 averages between 1600 and 2100 hours suggest episodic source 

impacts, possibly resulting from high wind events. 

Figure 2-25 shows the hourly PM10 averages by wind speed and hour of the day. The 

cluster of highest averages between 0900 and 2300 hours and between 6 and 11 m/sec 

wind speeds suggests that high wind events produce the highest consistent concentrations 

at the monitor, and that high wind events tend to occur during these hours and not during 

nocturnal hours after 2300 hours. 

Figure 2-25 also shows the increase in PM10 averages from 0700 to 1000 hours at all 

wind speeds, possibly due to increases in mechanical source emissions near the monitor. 

Correspondingly, averages are seen to rise between 1900 and 2200 hours, especially at 

very low wind speeds, but these increases occur after the evening traffic peak. Looking 

back at Figure 2-24, evening increases in PM10 appear when winds are blowing from 

some, but not all, directions toward the monitor. Emission investigations at this site 

should include an assessment of mechanical activities occurring during these hours of the 

day within one or two miles and within the arc of 60 to 200 degrees of the monitor. 

Finally, fairly low PM10 averages are seen at wind speeds between 6 and 8 m/sec during 

the nocturnal hours of 0400 to 0800. The lack of windblown dust at these wind speeds 

and during these hours suggests the presence of a mitigation factor unique to this time 

period. The source of this mitigating influence should also be included in emission 

investigations at this site. 
-46- 

Figure 2-24 

Pinal County Housing Hourly PM10 Averages by Wind Azimuth (degrees) and Hour of Day, 

(No Outliers) 

Pinal County Housing Hourly PM10 Averages by Wind Azimuth (degrees) and Hour of Day, All Data (No Outliers)AverageColumnRow Lab 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 

0 43 24 70 37 37 47 71 89 130 116 97 60 37 38 37 51 22 71 174 98 110 10 55 36 27 43 31 49 65 97 123 121 66 52 57 41 27 27 40 63 55 124 135 20 42 41 55 40 34 32 52 79 112 105 86 57 55 41 69 34 35 36 266 116 147 30 47 50 34 33 35 39 56 72 128 87 59 65 95 84 45 217 65 53 48 103 78 40 46 40 43 52 28 34 44 86 104 127 260 199 94 85 170 105 63 61 118 71 90 50 47 42 39 35 34 42 38 74 75 81 165 224 315 146 179 76 81 113 299 97 117 60 96 64 40 35 44 37 39 90 110 81 54 74 57 53 23 32 55 416 100 160 159 70 59 66 45 50 40 39 51 72 98 63 88 96 43 42 59 58 19 24 80 146 97 80 58 56 99 47 44 38 58 77 70 45 36 47 53 31 24 37 34 857 222 106 115 90 72 80 69 36 34 44 36 75 91 113 52 27 16 31 29 38 55 132 158 123 100 285 48 48 50 47 38 54 81 81 110 39 63 16 15 4 35 42 69 136 161 139 110 79 56 53 43 51 43 49 96 102 47 55 62 49 31 11 10 119 166 48 165 149 120 60 69 56 48 36 40 56 70 105 91 75 28 127 34 115 32 23 410 62 175 132 130 75 76 52 47 37 31 48 69 111 58 45 39 20 30 25 7 57 103 124 113 121 140 65 63 50 40 43 36 45 63 106 59 46 33 28 35 38 32 54 83 98 203 103 150 68 54 41 36 32 32 49 58 82 73 62 26 55 23 24 16 188 66 36 175 108 160 73 55 48 35 35 35 36 64 92 84 52 35 44 20 38 57 17 125 141 85 146 170 56 52 41 37 36 32 39 66 86 69 60 38 51 20 56 25 25 48 127 140 108 180 60 50 36 33 36 38 36 63 66 110 72 52 85 66 141 181 46 47 83 192 147 190 62 53 44 61 48 45 54 70 102 77 50 53 30 62 32 143 297 205 122 144 101 200 58 58 45 36 40 39 44 66 79 75 54 71 48 86 106 160 129 180 363 164 133 210 64 47 40 38 41 44 55 69 97 80 57 64 105 66 116 115 124 92 87 83 124 220 68 49 43 43 31 44 40 54 91 66 54 43 29 54 48 80 164 76 77 82 91 230 56 34 54 59 42 35 30 87 77 90 67 58 51 41 45 107 51 94 73 92 120 240 71 27 27 41 56 56 39 63 109 75 57 45 36 49 48 52 94 91 72 107 120 250 93 74 20 40 52 34 47 127 72 107 62 52 49 40 49 64 80 47 84 150 180 260 40 40 39 43 28 20 45 90 83 119 80 55 49 63 64 67 52 52 75 270 98 270 69 22 32 32 40 27 49 67 93 115 76 87 50 39 40 52 60 53 66 88 213 280 86 43 27 58 53 45 78 95 105 120 83 83 46 35 37 34 44 53 69 122 106 290 27 59 30 41 20 45 54 84 151 80 68 69 63 84 33 34 37 53 71 95 232 300 59 37 32 44 31 32 59 67 97 85 101 61 53 41 32 33 39 59 91 127 94 310 46 44 40 47 23 64 44 48 99 81 71 54 39 37 41 34 32 79 80 62 149 320 28 24 30 36 37 37 41 88 62 111 87 58 43 34 28 44 32 41 112 155 93 330 31 55 14 17 38 32 43 75 106 92 80 43 53 27 48 38 43 57 91 124 328 340 65 69 27 40 32 36 48 97 126 135 88 46 61 39 28 30 35 46 120 110 110 350 25 39 53 40 52 47 43 65 109 86 117 55 43 46 29 26 23 37 66 134 109 360 37 30 50 30 28 41 54 110 109 178 113 44 56 31 40 36 33 53 54 77 106 

Average 71 57 47 41 38 38 47 74 96 89 74 63 60 52 53 61 62 79 103 127 129 

Color Scale: 0 50 100 150 200 250 300

Lab

umn
-47- 

Figure 2-25 

Pinal County Housing Hourly PM10 Averages by Wind Speed (m/sec) and Hour of Day, Al

(No Outliers)

Pinal County Housing Hourly PM10 Averages by Wind Speed (m/sec) and Hour of Day, All DataAverageColumnRow Lab 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 

0 68 57 45 43 38 39 50 82 110 106 81 73 60 37 47 44 46 75 134 141 132 1 65 61 48 42 40 40 48 76 102 92 71 52 42 35 30 28 30 45 78 107 118 2 54 42 38 32 33 31 41 63 80 71 52 42 40 34 30 29 32 42 55 71 122 3 46 60 34 22 27 25 30 49 68 54 48 45 39 35 37 55 53 51 54 69 96 4 55 45 171 33 38 33 49 53 83 79 50 57 64 41 50 65 64 73 86 134 152 5 170 42 25 94 58 34 38 64 82 77 142 85 65 109 111 106 119 344 324 203 289 6 91 79 83 50 56 28 17 98 42 141 271 285 185 257 263 253 336 295 834 679 225 7 8449 301 23 96 25 137 325 309 372 931 294 269 565 412 598 208 716 1308 8 95 40 289 450 784 593 395 794 586 822 543 3875 946 9 749 447 1398 412 1660 456 2471 3300 14 10 1328 3141 570 11 740 0 

Average 71 57 47 41 38 38 47 74 96 89 74 63 60 52 53 61 62 79 103 127 129 

Color Scale: 0 50 100 150 200 250 300

Lab

umn
-48- 

Summary – Mechanical dust sources within one mile of and surrounding the Pinal 

County Housing monitor appear to daily produce moderate PM10 concentrations, 

between 45 and 100 μg/m3 at the monitor. Information on candidate sources fitting this 

profile should be collected at this site, possibly through interviews of residents or 

employees typically situated at or near the housing complex during daylight hours. 

Hours of maximum PM10 concentrations in excess of 1000 μg/m3

 should be investigated 

with respect to wind speed and direction to determine which of these continuous sources 

has the potential to generate high PM10 hourly values during episodic events. High wind 

events also appear to be responsible for high PM10 hours at the monitor. Backtrajectories of high wind hours on design days should be computed and the disturbance 

status of lands along these back-trajectories should be determined on these days through 

interviews of property owners or leasees. Land uses within 4 miles of the monitor in an 

arc from 90 to 230 degrees should be compared to those in the complementary arc to 

identify any differences that may contribute to the cumulatively higher impacts being 

generated in the former range of directions. Finally, the cause of nocturnal mitigation of 

emissions during wind speeds of 6 to 8 m/sec should be probed. 

Inventory Focus – Mechanical source activity in areas immediately surrounding the Pinal 

County Housing monitor should be the focus of inventory data collection and analysis for 

the low wind day modeling inventory. On the high wind design day of January 1, 2008, 

the disturbance status of lands along the high wind hour back-trajectories should be 

assessed through interviews of property owners or lessees. 

### 
-49- 

3. DESIGN DAY SELECTION 

3.1 Introduction 

On February 3, 2011, the U.S. Environmental Protection Agency (U.S. EPA) designated 

the central portion of Pinal County as nonattainment for the 24-hour PM2.5 National 

Ambient Air Quality Standard (NAAQS), and designated the same area as attainment or 

unclassified for the annual PM2.5 NAAQS.16 The rationale for the latter designation 

was based on the finding that the single monitoring site exceeding the annual standard, 

Cowtown, was a population-oriented microscale (i.e., localized hot spot) monitor that 

was not eligible for designation as a nonattainment site for the annual standard. On May 

31, 2012, U.S. EPA designated a somewhat larger portion of central Pinal County as 

nonattainment for the 24-hour PM10 NAAQS.17 The annual PM10 NAAQS was revoked 

by U.S. EPA on October 17, 2006. As a result of these decisions, the attainment 

demonstration for both PM2.5 and PM10 in Pinal County must be based on ambient air 

quality conditions and contributing emissions on specific design days. 

Through discussions between U.S. EPA, ADEQ, and PCAQCD, a base year of 2008 was 

selected for the preparation of an emission inventory and an attainment demonstration. 

This is the year of the most recent triennial National Emission Inventory prepared by 

ADEQ and PCAQCD and approved by U.S. EPA. During 2008, exceedances of the 24-

hour PM2.5 NAAQS were recorded at Cowtown, and exceedances of the 24-hour PM10 

NAAQS were recorded at Maricopa, Cowtown, Stanfield, Casa Grande, Combs School, 

and Pinal County Housing. At each of these latter sites, continuously recording TEOMs 

are used to record hourly averaged PM10 ambient concentrations. At the Cowtown site, 

a filter-based instrument is used to record 24-hour average PM2.5 concentrations. 

In an analysis of the relationship between PM2.5 and PM10 concentrations at Cowtown, 

ADEQ concluded that any control measures that will achieve attainment of the PM10 

NAAQS will also yield attainment of the PM2.5 NAAQS.18 This conclusion was based 

on the predominance of local source emissions, primarily from four nearby feedlots and 

disturbed soil surfaces, on elevated concentrations of both PM2.5 and PM10 measured at 

the Cowtown monitor. The monitoring data upon which this analysis was based were 

collected at the Cowtown station between 2006 and 2008. 

 

16 Federal Register, Vol. 76, No. 23, Thursday, February 3, 2011 17 Federal Register, Vol. 77, No. 105, Thursday 

18 Analysis of PM2.5 Exceedances in Pinal County Arizona: Demonstration that PM2.5 Concentrations are 

Driven by Local Sources of PM10 Near the Cowtown Monitor, Arizona Department of Environmental 

Quality, March 15, 2010 
-50- 

3.2 PM10 Exceedance Days 

Each of the six monitoring sites deemed to be nonattainment for the 24-hour PM10 

NAAQS recorded several exceedances of the 150 μg/m3 standard. The numbers of 

exceedances recorded at each site in 2008 are presented in Table 3-1. The 24-hour 

average PM10 concentrations measured at each site on each monitoring day in 2008 are 

tabulated in Appendix A. 

Table 3-1 

Exceedances of 24-Hour Average PM10 and PM2.5 NAAQS at 

Pinal County Monitoring Sites in 2008 

Monitoring Site 

Number of Exceedances 

PM10 PM2.5 

Casa Grande 3 0 

Combs School 4 N/A 

Cowtown 173 7 

Maricopa 6 N/A 

Pinal County Housing 10 N/A 

Stanfield 14 N/A 

Initial evaluation of the relationships of hourly wind speed and PM10 concentration at the 

four sites with longer PM10 monitoring records indicated the presence of two primary 

meteorological scenarios that produced hourly PM10 concentrations in excess of the 24-

hour NAAQS of 150 μg/m3

: high wind conditions, and stagnation conditions. The 

sources responsible for high PM10 concentrations are fundamentally different under 

these two scenarios. During high wind conditions, disturbed soil surfaces typically 

dominate the emission inventory and, because of the tendency of wind trajectories to 

remain somewhat constant during these periods, sources within a narrow band along the 

upwind trajectory and within one hour’s transport distance of a monitor tend to 

significantly contribute to recorded PM10 concentrations. Conversely, during stagnation 

conditions, when wind speeds are typically below 3 mph, wind entrainment of loose soil 

particles is virtually nonexistent, and mechanical sources that lift particles into the air 

through combustion, vehicle movement, and other anthropogenic activities are primarily 

responsible for recorded PM10 concentrations. Additionally, because transport distances 

are relatively short under low wind conditions, only those sources within one mile in any 

direction of a monitor contribute to high hourly PM10 concentrations during stagnation 

conditions. Because of these differences in source mix and the need to demonstrate 

attainment under the range of meteorological conditions found at Pinal County monitors, 

a committee providing technical assistance to the development of the Pinal County 

PM2.5/PM10 State Implementation Plan (SIP) advised that at least one stagnation and 
-51- 

one high wind design day be selected for each nonattainment monitor ultimately selected 

for emission inventory and attainment demonstration development. 

3.3 Stagnation Design Days 

Table 3-1 shows that Cowtown is unique among the Pinal County monitoring stations 

with respect to PM10 exceedances. The proximity of stationary sources under permit to 

PCAQCD and transportation corridors to the Cowtown monitor in 2008 caused or 

significantly contributed to the very high number of PM10 exceedances recorded there in 

that year. While high wind conditions occurred on some of these exceedance days, a 

substantial number of exceedances occurred on stagnation days with little to no wind 

entrainment of loose soil particles nor wind transport of suspended PM10 over extended 

distances. Instead, mechanical sources caused the elevated PM10 impacts at the 

Cowtown monitor on stagnation days. 

Some of these mechanical sources are common to most of the nonattainment PM10 

monitoring sites. A control program that reduces emissions from these sources sufficient 

to attain the PM10 24-hour NAAQS at the stations other than Cowtown will provide air 

quality benefits to Cowtown and help define the reductions needed from sources that 

uniquely impact the Cowtown site. When this approach was endorsed by the SIP 

advisory committee, a list of PM10 exceedances occurring on stagnation days for stations 

other than Cowtown was assembled. This list is provided in Table 3-2. 

Table 3-2 indicates that two monitoring sites—Pinal County Housing and Stanfield—

exceeded the 24-hour PM10 NAAQS on one of the stagnation days, October 29, 2008. 

Table A3 in Appendix A shows that the Cowtown site also exceeded the 24-hour PM10 

NAAQS on this date, and analysis of the meteorological data shows that October 29, 

2008, was also a stagnation day at this site. Plots of hourly PM10 concentration, wind 

speed, wind direction, and mixing height recorded or modeled at each of these three 

monitoring sites on October 29, 2008, are presented in Appendix B. As each of the 

PM10 vs. Wind Speed plots show, wind speeds were low (typically less than 4 mph) and 

fluctuations in PM10 concentration did not correlate well with fluctuations in wind speed. 

Table 3-2 

PM10 Exceedances on Stagnation Days in 2008 at Monitoring Sites Other Than 

Cowtown 

Date Site 24-Hour Average PM10 Concentration (μg/m3) 

4/26/08 Stanfield 207.5 

5/10/08 Stanfield 161.7 

10/29/08 

PCH 178.0 

Stanfield 162.1 

 
-52- 

These conditions are representative of stagnation conditions. Additionally, there were no 

high wind speed hours recorded at any of the three sites on this day. Because of these 

factors, October 29, 2008, was selected by the SIP advisory committee as the design day 

for the attainment demonstration under stagnation conditions at each of these three 

monitoring sites. Because no PM10 exceedances occurred on stagnation days at the 

Maricopa, Casa Grande, or Combs School monitoring sites, these sites were not included 

in the list of low wind speed attainment demonstrations to be addressed in the SIP. 

3.4 High Wind Design Days 

The control strategies needed to reduce emissions from high wind sources are different 

from those applicable to stagnation sources. High wind sources typically consist of 

disturbed soil surfaces that require shielding from the wind, covering of soil surfaces, or 

the consolidation of wind-entrainable surface particles into wind-resistant matrices in 

order to reduce PM10 emissions. High wind source emission strength is directly related 

to the magnitudes of wind velocity and disturbed surface area, and the soil texture. 

Stagnation sources typically involve mechanical or combustion processes from which 

PM10 emissions can be controlled through either pre-treatment of emission-generating 

materials or the capture of PM10 released by these processes. In sum, PM10 

concentrations from wind events are primarily proportional to the areas of wind-affected 

soils, while concentrations from stagnation events are proportional to activity rates of 

mechanical sources. 

Control strategies for high wind sources are best developed from the analysis and 

modeling of design days during which only these sources are contributing to ambient 

PM10 concentrations. To identify days on which these conditions existed, the PM10 

exceedance days recorded at each of the six nonattainment monitoring sites were 

screened for hourly average wind speeds during hours of high PM10 concentrations. 

From limited analysis of the relationships between hourly wind speeds and PM10 

concentrations over the 2008 baseline year at the monitoring stations continuously 

sampling these parameters, an average wind speed of 12 mph was estimated to be the 

threshold velocity for the generation of significant PM10 emissions from disturbed soil 

surfaces and was chosen as the definitional cut point for the identification of high wind 

hours in the 2008 meteorological record. 

3.5 Cowtown 

Several criteria were used to select the high wind day at the Cowtown monitoring site. 

These included the 24-hour average PM10 concentration for the day, the number of hours 

during which the hourly average wind speed exceeded 12 mph, the number of hours 

during which the high winds blew from areas other than the directions of the nearby 

stationary and transportation sources, and the correlation between hourly wind speeds and 

PM10 concentrations. These statistics are tabulated for the candidate high wind days at 

Cowtown in 2008 in Table C1 in Appendix C. The initial list of high wind days shown in 

the table represents those days on which the hourly wind speed exceeded 12 mph for at 

least one hour during the day. 
-53- 

The high wind PM10 exceedance days in the Appendix C table are ranked by 24-hour 

PM10 concentration. The screening criteria enumerated above were used sequentially to 

eliminate days from consideration, starting at the top of the table and working down. The 

average number of high wind hours per day was calculated to be 4.03 on PM10 

exceedances days. Days reporting 4 or fewer high wind hours were removed from 

consideration as these days either included hourly PM10 concentrations in excess of 150 

μg/m3

 during low wind hours (suggesting the influence of stagnation day sources), or 

resulted from very high PM10 concentrations for four or fewer hours in a day, such as 

result from passage of monsoon winds, which would normally qualify as an exceptional 

event because of the overpowering of emission controls qualifying as best available 

control measures. This screen reduced the number of candidate days from 36 to 11, but 

retained the top two highest PM10 days: May 21 and November 9, 2008. 

Emission reductions at the stationary sources and transportation corridors south and west 

of the Cowtown monitor are expected to be critical to the attainment of the 24-hour 

PM10 NAAQS on stagnation days. The controls developed to achieve these reductions 

are also expected to significantly reduce windblown PM10 emissions from these 

facilities. In order to develop a control strategy that will be effective on all high wind 

days, a high wind design day during which high winds blow from sources other than 

those to the south and west is desirable. In the second screening of candidate days, the 

number of high wind hours during which the wind blew from sources other than those to 

the south and west—i.e., when the wind was blowing from a direction greater than 

303 degrees or less than 134 degrees—were tabulated and days with 0 high wind hours 

meeting this condition were eliminated from consideration. As the table in Appendix C 

shows, this condition reduced the candidate days from 11 to 4: October 27, September 

29, April 27, and November 21, 2008. 

Review of hourly plots of wind speed and PM10 concentration for stagnation days in 

Appendix B demonstrated that a final criterion for screening high wind days should be 

the correlation of hourly PM10 concentration to wind speed. This was deemed to be 

useful since, in view of the many elevated PM10 concentrations recorded during low 

wind conditions at Cowtown, the recording of a high PM10 concentration during a low 

wind hour could be due to emissions from mechanical sources observed frequently by 

PCAQCD inspectors at Cowtown that would adversely skew the analysis of high wind 

source impacts at the monitoring site. The correlation between hourly wind speed and 

PM10 concentration was computed for each day at Cowtown in 2008, and the resulting 

values were included in Table C1 in Appendix C. Review of these values shows negative 

correlations on October 27 and September 29, and positive correlations on April 27 and 

November 21, 2008. Of these latter two days, April 27 has the higher correlation value 

and the higher 24-hour PM10 concentration. On this basis, April 27, 2008 was selected 

by the SIP advisory committee as the high wind design day for the Cowtown monitoring 

site. The hourly profiles of PM10 concentrations, wind speed, wind direction, and 

mixing height for this date are shown in the plots contained in Appendix C. 
-54- 

3.6 Stanfield 

During 2008, exceedances of the PM10 24-hour standard were recorded at Stanfield on 

11 days on which the hourly wind speeds exceeded 12 mph for at least one hour. These 

dates, together with PM10 and wind speed statistics, are presented in Table D1 in 

Appendix D. The dates are ranked by highest-to-lowest 24-hour average PM10 

concentration measured at the Stanfield monitor. 

In the first screening of these data, days with fewer than the average number of high wind 

hours for the days listed were eliminated from consideration as the design day should 

include a greater-than-average number of high wind hours. The average number of high 

wind hours calculated for these exceedances is 6.2 hours per day. This initial screen 

reduced the candidate exceedances days from 11 to 6. The remaining candidate days, 

having at least 7 high wind hours each, were November 9, May 21, January 1, November 

15, November 21, and December 13, 2008. 

These remaining days were further screened by the correlation of hourly PM10 

concentration to wind speed. The date with the highest correlation, May 21, 2008, was 

further evaluated by reviewing the hourly plots of PM10 concentration, wind speed, wind 

direction, and mixing height. These plots are also presented in Appendix D. The plot of 

PM10 concentration and wind speed, Figure D1, shows a significant drop in PM10 

concentration at 1400 hours and then a return to previous hourly values without any 

corresponding drop in wind speed. It is unclear what might have caused this anomalous 

fluctuation in PM10 concentration, but the lack of a change in wind speed causes this day 

to be questionable as a high wind design day candidate. 

The day with the next-highest correlation between PM10 concentration and wind speed 

was November 9, 2008. Plots of hourly PM concentrations, wind speeds, wind 

directions, and mixing heights for this day appear in Figures D4 through D6. Like May 

21, this candidate design day also shows a significant decline in PM10 concentration at 

1200 hours and a slow return while wind speeds steadily increase. This precipitous drop 

in hourly PM10 concentration and later return similarly cannot be explained. The use of 

these data to calculate and model windblown dust emission rates, however, would lead to 

both errors in the emission rates and the lack of good correlation between modeled and 

monitored PM10 concentrations at the Stanfield monitor. For this reason, November 9, 

2008, was questioned as a design day candidate. 

The day with the third-highest correlation between hourly PM10 concentrations and wind 

speeds was December 13, 2008. Plots of hourly PM10 concentrations, wind speeds, wind 

directions, and mixing heights for this day are presented in Figures D7 through D9. 

Figured D7 shows a short-term drop and return in PM10 concentration commencing at 

1200 hours without any decline, and in fact an increase, in hourly wind speed, which 

raises the same questions of modeling accuracy discussed above for May 21 and 

November 9, 2008. Like these two latter candidate days, December 13, 2008, was 

questioned as a candidate for the high wind design day for the Stanfield site. 

The day with the fourth-highest correlation between hourly PM10 concentrations and 

wind speeds was November 21, 2008. Plots of hourly PM10 concentrations, wind 
-55- 

speeds, wind directions, and mixing heights for this day are presented in Figures D10 

through D12. Although this day shows a small decline in PM10 concentration at 0800 

hours, the drop is far less dramatic than is evidenced in the three candidate days with 

higher PM10 concentration-to-wind speed correlations. As a result of this smoother 

hourly PM10 profile and the relatively good statistics in the categories evaluated, 

November 21, 2008 was chosen as the high wind design for the Stanfield site. 

3.7 Pinal County Housing 

The monitoring station at Pinal County Housing recorded seven exceedances of the 

PM10 24-hour standard on high wind days in 2008. These dates, together with PM10 

and wind speed statistics, are presented in Table E1 in Appendix E. 

The process of choosing a high wind design day for the Pinal County Housing site 

followed a process similar to that used at the Cowtown site. First, high wind days were 

sorted into those that recorded more than 4 hours of hourly average wind speeds greater 

than 12 mph. This had the effect of excluding short duration, monsoonal-related, high 

wind days from the analysis, and reduced the list from seven candidate days to three: 

June 21, January 1, and October 27, 2008. The correlation values of hourly wind speed 

to PM10 concentration were then compared for these three days. Although October 27, 

2008, recorded the highest correlation, this date had the lowest 24-hour average PM10 

concentration and the lowest number of high wind hours of the three candidate days. 

January 1, 2008, recorded the second-highest correlation between hourly wind speeds and 

PM10 concentrations, and had the second-highest 24-hour average PM10 concentration. 

The 24-hour average PM10 concentration on June 21, 2008, was only 4% higher than that 

of January 1 (271 vs. 260 μg/m3

), but the wind speed/PM10 concentration correlation for 

June 21 was 27% lower than that of January 1. Based on these considerations for the two 

top candidate days, January 1, 2008, was selected as the high wind design day for Pinal 

County Housing. Plots of hourly PM10 concentration, wind speed, wind direction, and 

mixing height for January 1 are also presented in Appendix E. 

3.8 Maricopa 

In 2008, six exceedances of the PM10 24-hour standard were recorded at the Maricopa 

monitor. All of these exceedances occurred on high wind days. Wind and PM10 

statistics for each of these days are presented in Table F1 in Appendix F. 

Until the modeling domains for low wind and high wind days at the Cowtown monitoring 

site were established, the working assumption of the SIP steering committee was that the 

emission control strategies demonstrating attainment at this site would simultaneously 

demonstrate attainment at the Maricopa site. When the Cowtown modeling domains 

were selected, the committee realized that the Maricopa site was sufficiently distant from 

these study areas as to invalidate this working assumption. Upon that realization, the 

committee agreed to develop a separate emission inventory focus and attainment 

demonstration based on a modeling domain specific to the Maricopa site. 
-56- 

The selection of a high wind design day for the Maricopa site was based primarily on the 

number of high PM10 hours recorded on each candidate day. On May 21, 2008, hourly 

PM10 concentrations spiked at 1300 hours at an hourly average value of 1704 μg/m3

, but 

levels in both the preceding and succeeding hours were below 920 μg/m3. During the 

same hours, hourly average wind speeds remained fairly constant, suggesting a poor 

correlation between wind speed and PM10 concentration during 1200 to 1400 hours. 

Because of this lack of good correlation, this date was not considered to be a good 

candidate for a design day. 

On August 7, 2008, hourly PM10 concentrations also spiked for a three-hour period in 

the late evening. Hourly wind speeds followed a similar trend, suggesting the presence of 

a thunderstorm outflow on this day. Because hourly PM10 concentrations exceeded the 

24-hour standard for only three hours, this day was also deemed not to be a good 

candidate for a design day. 

Another three-hour PM10 spike was recorded on September 11, 2008. Although this 

spike corresponded to a similar rise and fall in hourly average wind speed, the very 

limited number of hours of high PM10 concentrations also eliminated this day from 

consideration as a design day. 

Hourly PM10 concentrations exceeded the 24-hour standard for a period of seven noncontiguous hours on October 11, 2008. Although hourly wind speeds were close to or 

exceeded 12 mph during these hours, there is little correlation between wind speed and 

PM10 concentration during the high PM10 hours. As a result, this day was also not 

considered to be a good candidate for a design day. 

The 24-hour PM10 standard was exceeded on six successive hours on October 27, 2008. 

The rise of hourly PM10 concentrations between 0700 and 0900 hours on that day closely 

followed the rise in hourly wind speeds. Although hourly average wind speeds remained 

elevated through 1600 hours, hourly PM10 concentrations fell between 0900 and 1200 

hours back to the level of the 24-hour standard and continued to decline slowly through 

the remainder of the day. This relationship suggests that contributing windblown dust 

sources contained limited reservoirs of abradable material, and that these reservoirs were 

exhausted by hourly wind speeds peaking at 18 mph at 0900 hours. The good correlation 

between hourly wind speed and PM10 concentration during the rise in PM10 and the 

continuing decline in hourly PM10 concentration after the daily peak make this day a 

reasonable candidate for a high wind design day. 

On the last PM10 exceedance day in 2008 at the Maricopa monitor, which occurred on 

November 9, 2008, hourly PM10 concentrations rose and fell in two spikes of two hours 

duration each, while hourly wind speeds gradually rose from the beginning of the first 

spike at 1,000 hours to the peak for the second hourly PM10 spike at 1600 hours. The 

poor correlation between hourly wind speed and PM10 concentrations during this interval 

makes this day a poor candidate for a design day. 

Based on the analyses of each PM10 exceedance day recorded in 2008 at the Maricopa 

monitor, the advisory committee selected October 27, 2008, as the high wind design for 
-57- 

this monitoring site. Hourly plots of wind speed, wind direction, mixing height, and 

PM10 concentration for this day are shown in Figures F-1 through F-3 in Appendix F. 

3.9 Combs School and Casa Grande 

All of the PM10 exceedances recorded at the Combs School and Casa Grande sites in 

2008 occurred on high wind days. Like the Maricopa site, the SIP steering committee 

assumed that a high wind day attainment demonstration developed for the Casa Grande 

site would also demonstrate PM10 attainment at the Combs School site due to proximity 

and a similar set of disturbed soil land uses impacting each site. The Casa Grande site 

was subsequently determined to be impacted not by local emission sources, but only by 

regional windblown dust sources that would be subject to control under the attainment 

strategies developed for the Cowtown, Stanfield, Maricopa, and Pinal County Housing 

sites. At this point, the committee conducted a limited analysis of hourly PM10 and wind 

relationships on exceedance days, and concluded that the high wind control strategies that 

would bring the Casa Grande site into attainment would also provide the same benefit at 

the Combs School site. As a result, these sites were not chosen for development of 

modeling domain emission inventories or attainment demonstrations. 

 

3.10 PM2.5 Exceedance Days 

The national 24-hour ambient air quality standard for PM2.5 of 35 μg/m3 was exceeded 

at only the Cowtown monitoring station during 2008. A tabulation of the 24-hour 

average PM2.5 concentrations, PM10 concentrations, and wind speed statistics for these 

days is shown in Table G1 in Appendix G. The exceedance days are ranked in the table 

from highest to lowest 24-hour average PM2.5 concentration for each day. 

PM2.5 is collected by a filter-based sampler that produces only 24-hour average 

concentrations at the Cowtown station. Hourly average values of PM2.5 are not available 

from this type of monitoring. As a result, fluctuations in hourly PM10 concentrations 

were evaluated as a surrogate for hourly PM2.5 values, based on the close correlation 

between PM10 and PM2.5 concentrations reported in an analysis conducted by ADEQ. 

At the Cowtown monitoring site, PM10 concentrations are dominated by emissions from 

south and west of the monitor. Because of the close correlation between 24-hour average 

PM10 and PM2.5 concentrations recorded at this site, it is logical that PM2.5 

concentrations are also heavily influenced by emissions from the stationary sources and 

transportation corridors in these directions. As a result, the PM2.5 design day should not 

be a high wind day, but rather a day in which wind directions are from the south or west 

to the monitor at wind speeds less than 12 mph. The hourly PM10 concentrations, wind 

speeds, wind directions, and mixing heights of the days listed in Table G1 were plotted 

and reviewed using these criteria in selecting a PM2.5 design day. These plots are 

presented in Figures G1 through G21 in Appendix G. 

The day with the highest PM2.5 concentration—March 25, 2008—has a single hour with 

a very high PM10 concentration that represents approximately 40% of the 24-hour 
-58- 

average for that day. Because of the very small number of high PM10 concentration 

hours, this date was not considered to be a good candidate for the PM2.5 design day. 

The day with the second-highest PM2.5 concentration—April 12, 2008—contains a 

similar evening PM10 spike spanning three hours. This spike represents approximately 

79% of the 24-hour average for this day. Again, because of the small number of high 

PM10 hours, this date was not considered to be a good candidate for the PM2.5 design 

day. 

The day with the third-highest PM2.5 concentration—May 6, 2008—includes two hours 

with the highest PM10 concentrations when wind speeds exceeded 12 mph. Because of 

the desire to avoid days when high winds caused high PM10 concentrations that 

dominated the 24-hour average, this day was not considered to be a good candidate for 

the PM2.5 design day. 

The day with the fourth-highest PM2.5 concentration—April 30, 2008—also contains a 

single high PM10 hour during which the wind speed exceeded 12 mph. Because of this 

high wind contribution, and the significant contribution made by this single hour to the 

24-hour average concentration, this day was also not considered to be a good candidate 

for the PM2.5 design day. 

The day with the fifth-highest PM2.5 concentration—November 20, 2008—includes a 

major PM10 peak spanning four hours in the evening and a minor PM10 peak in the early 

morning spanning two hours. None of the hours spanned by these peaks exceeded the 

assumed high wind speed threshold of 12 mph. In the absence of concerns raised in the 

days with higher PM2.5 24-hour concentrations, this day was considered to be a 

reasonable candidate for the PM2.5 design day. 

The day with the sixth-highest PM2.5 concentration—June 23, 2008—contains several 

PM10 peaks spread throughout the day. The most significant of these peaks, spanning 

two hours in the late evening, occurs when hourly average wind speeds are between 10 

and 12 mph, approaching the high wind threshold. Because of the presence of these two 

hours with elevated wind speeds, this day was considered to be a marginal candidate for 

the PM2.5 design day. 

The day with the seventh-highest PM2.5 concentration—April 18, 2008, has an evening 

PM10 spike spanning three hours and a missing hour of PM10 data at 0900 hours. The 

evening PM10 spike represents approximately 40% of the 24-hour average PM10 

concentration for this day. Because of the missing PM10 hour and the significant 

contribution of the evening PM10 spike, this day was not considered to be a good 

candidate for the PM2.5 design day. 

As a result of the screening analysis summarized above, November 20, 2008 was selected 

as the PM2.5 design day for the Cowtown site. 

Summary – For the purpose of demonstrating attainment with the national 24-hour PM10 

ambient air quality in central Pinal County, emission inventories will be prepared for 

modeling use in the source areas surrounding each of the Cowtown, Maricopa, Pinal 
-59- 

County Housing, and Stanfield air quality monitoring stations. Separate modeling 

domains for stagnant design days and high wind design days will serve as the basis for 

modeling inventory preparation at each of the Cowtown, Pinal County Housing, and 

Stanfield monitoring sites. For the Maricopa site, only a high wind design day modeling 

domain will be developed. Finally, a PM2.5 design day modeling domain will be 

prescribed for the Cowtown site. Table 3-3 lists the design days for which inventory 

preparation will also focus at each attainment demonstration site. 

Table 3-3 

Summary of Selected Design Days 

Design Day 

Character Site Date 

24-Hour Average 

PM10 Concentration 

(μg/m3

) 

High Wind Days 

Cowtown April 27, 2008 168.3 

Pinal County 

Housing January 1, 2008 264.9 

Stanfield November 21, 2008 178.6 

Maricopa October 27, 2008 159.4 

Stagnant Days

Cowtown October 29, 2008 188.8 

Pinal County 

Housing October 29, 2008 178.0 

Stanfield October 29, 2008 162.1 

PM2.5 Day Cowtown November 20, 2008 345.0 

3.11 Modeling Domains 

Emission inventory preparation will focus on emissions generated within the attainment 

demonstration modeling domain surrounding each PM10 monitor for which a design day 

was selected. For the central Pinal County PM10 nonattainment area, these monitoring 

sites include Cowtown, Maricopa, Pinal County Housing, and Stanfield. Because 

separate stagnation, high wind, and PM2.5 design days were selected for each affected 

site, separate modeling domains were also developed to correspond to each design day. 

Different modeling domains were developed for stagnation and high wind design days 

because the source categories significantly impacting monitors under each of these of 

meteorological conditions are quite different. On stagnation days, PM10 is entrained into 

the air only through the action of mechanical sources—such as motor vehicles, farm 

equipment, and industrial machinery—either releasing quantities of fine particulate 

matter through production processes or dislodging fine particulate lying on soil surfaces, 

with sufficient force to discharge the particulate into the air. Once suspended in the air, 

these particles travel limited distances—because of the lack of lateral air movement 

during stagnation conditions—before settling out the air again within an hour or so. This 
-60- 

condition of limited travel means that sources whose emissions impact a monitoring site 

on such a day are typically located quite close to the monitor. 

The types and locations of sources whose emissions significantly impact PM10 monitors 

on high wind days are considerably different from significant stagnation day sources. 

During high wind hours, when hourly average wind speeds nominally exceed 12 mph, 

windblown dust sources—most of which are disturbed soil surfaces—produce high 

concentrations of ambient PM10 that significantly exceed concentrations produced by 

mechanical sources in the same area. Research has demonstrated that the flux of PM10 

in the vertical direction above disturbed soil surfaces is proportional to the speed of the 

wind, above a certain threshold velocity, blowing horizontally over the soil. This 

research has also demonstrated that below this threshold wind speed, windblown dust 

emissions from disturbed soil surfaces are much lower.19 This condition is explained by 

the fact that the saltation (bouncing) of sand particles over soil surfaces is the primary 

cause of fine particles being dislodged from surface matrices and lifted into the air by 

turbulent eddies to produce airborne PM10 concentrations. Below the threshold wind 

speed, sand particles remain at rest on soil surfaces and no significant PM10 emissions 

are generated. Once entrained in the air, PM10 particles can be blown considerable 

distances depending on the velocity of the wind during transport. With time, however, 

suspended PM10 particles settle out of the moving air mass and are also widely dispersed 

laterally and, to a less extent, vertically due to increased groundlevel turbulence at higher 

wind speeds. As a result, windblown dust sources located a number of miles upwind can 

contribute to measured concentrations at a monitor, but those that significantly impact a 

PM10 monitor during a high wind hour are more typically within a few miles upwind of 

the monitor. 

The settling velocities of PM10 particles cause a majority of particles entrained from soil 

surfaces to settle out of the air within an hour after initial entrainment. This characteristic 

of PM10 facilitated the process by which modeling domains for stagnation and high wind 

days were selected. On stagnation days, when wind velocities were typically less than 

4 mph throughout the day, particles arriving at the monitor were assumed to have 

traveled no more than one hour at the average wind velocity recorded at the monitoring 

station for that hour. The starting point for each hourly trajectory of emissions from 

windblown dust sources to the monitor was determined by plotting the back-trajectory of 

the hourly wind parcel from the monitor to the emission source on the basis of that hour’s 

wind speed and direction. The starting points of each hourly wind trajectory ending at 

the monitor were plotted on an aerial photograph of the area surrounding each stagnation 

design day, and rectangles with sides aligned with the cardinal compass directions were 

drawn to bound all of the trajectory origin points. A diagram showing each hourly wind 

trajectories and the rectangular boundary enclosing all of the origin points for the 

Cowtown monitor on the stagnation design day of October 29, 2008, is presented in 

Figure 3-1. 

 

19 The Physics of Blown Sand and Sand Dunes, Bagnold, Ralph A., Dover Publications, Mineola, 

New York, 2005 
-61- 

Figure 3-1 

Cowtown Stagnant Design Day Modeling Domain 

During high wind hours, wind directions tend to be less variable than those recorded 

hourly on stagnation days. Analyses of area source plume behavior contained in 

technical support documents of earlier PM10 attainment planning in Arizona, indicated 

that windblown dust sources lying more than one mile laterally from the centerline of the 

one hour wind back-trajectory centerline contributed less than 1% to the PM10 

concentrations measured at a monitor, assuming that windblown dust emission factors 

were uniform across all upwind disturbed soil surfaces. This finding guided the design of 

high wind hour modeling domains that were centered over one-hour wind backtrajectories and extended one mile laterally from each centerline. An example of this 

type of modeling domain for high wind hours on April 27, 2008, at the Cowtown monitor 

is shown in Figure 3-2. 
-62- 

Figure 3-2 

Cowtown High Wind Hour Modeling Domain During the High Wind Design Day 

Cowtown – The Cowtown monitor will be the subject of three attainment demonstrations 

in the Pinal County PM SIP: a stagnation day PM10 demonstration, a high wind day 

PM10 demonstration, and a peak day PM2.5 demonstration. Each of these attainment 

demonstrations will be based on the modeling of controlled emissions within modeling 

domains whose boundaries will be set using the protocols described above. A map of the 

various modeling domains proposed for the Cowtown demonstrations is presented in 

Figure 3-3. 

In Figure 3-3, the modeling domains for the stagnation day, October 29, 2008, and the 

PM2.5 peak day, November 20, 2008, are very similar. As a result, the larger modeling 

domain for the PM2.5 peak day will be used for both attainment demonstrations, and 

emission data from sources of both PM10 and PM2.5 will be collected simultaneously in 

this domain to serve both modeling efforts. 
-63- 

Figure 3-3 

Cowtown 
-64- 

 

Maricopa – A map of the high and low wind hour modeling domains for the high wind 

day (October 27, 2008) attainment demonstration is provided in Figure 3-4. Because a 

substantial portion of the high wind hour modeling domain for the Maricopa 

demonstration overlays the high wind hour modeling domain for the Cowtown monitor, a 

single data collection effort will be undertaken to quantify windblown PM10 emissions 

from exposed soil surfaces common to both domains. 

Pinal County Housing – The modeling domains for the Pinal County Housing (PCH) 

attainment demonstration do not overlay those of any other PM10 monitoring site. The 

location of these domains in relation to the PCH site is displayed in Figure 3-5. 

Emissions data will be collected in these domains following the same protocols used for 

the other design day monitoring sites. 

Stanfield – The high and low wind modeling domains to be used in the Stanfield 

attainment demonstration lie just to the south of those to be used for the Cowtown 

modeling. As a result, the modeling domains share similar emission source mixes, and 

may well share similar emission factors for significant source categories. A map showing 

the location of the Stanfield modeling domains is presented in Figure 3-6. 
-65- 

Figure 3-4 

Maricopa Modeling Domains 
-66- 

Figure 3-5 

Pinal County Housing Modeling Domains 
-67- 

Figure 3-6 

Stanfield Modeling Domains 

###
-68- 

4. EMISSION INVENTORY PROCEDURES AND 

DATA SOURCES 

As discussed in Section 3, for the purpose of design day selection, PM10 exceedance 

days in 2008 were divided into separate low and high wind event categories based on the 

12 mph threshold velocity for generation of windblown dust. This threshold was 

estimated from limited analysis of the relationships between hourly wind speeds and 

PM10 concentrations over the 2008 baseline year at the monitoring stations continuously 

sampling these parameters. Low wind or stagnation event sources typically involve 

mechanical or combustion processes from which PM10 emissions can be controlled 

through either pre-treatment of emission-generating materials or the capture of PM10 

released by these processes. High wind sources typically consist of disturbed soil 

surfaces that require shielding from the wind, covering of soil surfaces, or the 

consolidation of wind-entrainable surface particles into wind-resistant matrices in order 

to reduce PM10 emissions. Separate discussions of methods and data sources available 

to quantify emissions under low and high wind conditions are presented below. These 

discussions presented in this section provide a greater emphasis on area sources as 

previous studies20 indicate that the crustal component of area source emissions comprise 

a significant portion of the overall inventory. As a result, available resources will be 

more heavily focused on these area source emissions calculations, whereas point and 

mobile source emissions calculations will utilize more standard methodologies that 

require less discussion. 

4.1 Low Wind Emissions 

Point Sources – Point sources refer to stationary facilities that release emissions to the air 

primarily through stacks or flues. Such facilities may also release “fugitive” emissions to 

the air from non-stack sources (e.g., evaporative emissions from leaks in pump seals, 

valves, and flanges), but the presence of emitting stacks places such facilities in the 

“point” source category. (Facilities with stack sources and facility-wide emissions below 

a certain limit or “cutoff” are treated as area sources. This section of the IPP discusses 

the key issues, available data, and procedures for the development of a point source 

emissions inventory.) 

 

20 Summary of Findings Report for the RARE Pinal County Source Apportionment Study. 

http://www.pinalcountyaz.gov/Departments/AirQuality/Documents/Monitoring%20Network/RARE_Report

_v4_1.pdf
-69- 

Land use files supplied by the Pinal County Air Quality Control Department (PCAQCD) 

identified a total of 44 industrial point sources within the nonattainment area in 2008 (see 

the land use discussion later in this section). All facilities emitting 5.5 lbs/day or 1 ton 

per year from stacks or vents are required to obtain permits. Annual and typical daily 

emission estimates for these facilities can be determined from annual source emission 

reports, PCAQCD investigation reports, permit files and logs, or telephone contacts with 

sources. For most of the sources, material balance methods will be used to determine 

activity rates for vented sources. Emissions will be calculated using the emission factors 

from AP–42, source tests, engineering calculations, or manufacturers’ specifications. 

PCAQCD distributes annual emissions survey forms to nearly all facilities to which 

operating permits have been issued. Facilities are required to report detailed information 

on stacks, control devices, operating schedules, and process-level information concerning 

their annual activities. These instructions include examples and explanations on how to 

complete the annual emissions reporting forms that facilities must submit to PCAQCD. 

Annual emission inventory reports include calculated process-level emissions for PM10, 

NOx, SOx, and NH3 from each vented source at each facility. Actual emissions for these 

pollutants are calculated using cited emission factors (from AP–42 or source test results) 

and estimated efficiencies of any control devices installed. PM2.5 emissions are typically 

calculated as a fraction of PM10 emissions, depending on the Source Classification Code 

(SCC) of the process reported, as outlined below. 

For those SCCs and control device combinations included in EPA’s WebFIRE, 

this database will be used to calculate PM2.5 on the basis of EPA-recommended 

emission factors and typical control efficiencies. 

For processes with no PM10 controls, emission factors for PM2.5 published by 

the California Air Resources Board (CARB, 2004) will be used where available. 

For all other processes (where neither of the above resources provided guidance), 

PM2.5 will be assumed equal to PM10 as a conservative estimate. 

Air quality planners are required to check inventory accuracy and reasonableness, and 

assure that all point sources have been identified and that the methodology applied to 

calculate emissions is appropriate and that the calculations are correct. Other 

reasonableness checks include recalculating emissions using methods other than those 

used to make the initial emissions calculations and comparing results. QA needs to be 

conducted by checking all emissions reports submitted to PCAQD for the year 2008 for 

missing and questionable data and by checking the accuracy and reasonableness of all 

emissions calculations made for such reports. Notes concerning follow-up calls and 

corrections to calculations need to be documented on each 2008 annual emissions report. 

The QA point source coordinator should review and check calculations, identify errors, 

and perform completeness, reasonableness and accuracy checks. 
-70- 

Area Sources – Area sources are facilities or activities whose individual emissions do not 

qualify them as point sources. Area sources represent numerous facilities or activities 

that individually release small amounts of a given pollutant, but collectively can release 

significant amounts of a pollutant. Emissions from stationary sources that were not 

identified as point sources in this report have been included in the area source inventory. 

Examples of area source categories include agricultural operations, such as tillage, 

harvesting, and cotton ginning; offroad recreational vehicle use; construction activities; 

and windblown dust from disturbed soil areas. 

Tillage – The use of mechanical operations such as discing, digging, ripping, overturning 

and land planing is generally referred to as tillage, which produces fugitive dust. Several 

methods are available to estimate tillage emissions. The first was used in the 2008 

Periodic Emission Inventory PEI for Maricopa County. Tillage emissions were estimated 

using the tillage emission factor equation and Maricopa County specific soil silt content 

for agricultural land (URS and ERG, 2001). The agricultural tillage emission factor was 

calculated as follows: 

EF = k (4.8) s

0.6

where: 

EF = Agricultural emission tillage factor (lbs PM10/acre-pass) 

k = Particle size multiplier (value of 0.15 for PM10) 

s = Silt content of soil (%) = 35.2% (URS and ERG, 2001) 

Thus: EF = 0.15 × 4.8 × (35.2)0.6 = 6.10 lbs PM10/acre-pass. This estimate does not 

take into account reductions for agricultural best management practices. 

According to the 2008 PEI documentation, data on the majority of planted acres were 

obtained from the 2008 Arizona Agricultural Statistics Bulletin (AASS, 2009). Planted 

acres for potatoes and sorghum for grain were obtained from the USDA National 

Agricultural Statistics Service for 2008 (USDA, 2008b) and vegetables and citrus acreage 

were obtained from the 2007 Census of Agriculture (USDA, 2007a). Crop-specific 

annual land preparation operations data were obtained from the Technical Support 

Document for Quantification of Agricultural Best Management Practices (URS and ERG, 

2001). 

The key uncertainty in this emissions calculation is the estimate of activity that occurred 

on the design day. Since date-specific field operations data are extremely difficult to 

obtain, the approach used to estimate activity necessarily assumes that tilling operations 

can occur on any of the days during a crop’s tilling season. This has the effect of 

producing an estimate of activity that, while statistically correct, significantly 

underestimates the level of activity on days when the activity occurs (i.e., since on many 

days no activity occurs). Thus, estimates developed using this methodology have the 

potential to significantly underestimate agricultural emissions when tilling occurs. 
-71- 

The second method uses tilling activity-specific emission factors from CARB21 for 

Agricultural Land Preparation. Those values are summarized in Table 4-1. 

Table 4-1 

CARB Land Preparation Emission Factors Used to Prepare 

Agricultural Estimate of Agricultural Fugitive Dust Emissions 

Land Preparation Operation Emission Factor (lbs/acre pass) 

Discing, Tilling, Chiseling 1.2 

Ripping, Subsoiling 4.6 

Land Planing & Floating 12.5 

Forming/Plantinga 0.8 

Average 4.8 

a. Discussions with CARB staff indicated that the weeding emission factor would be appropriate for this 

activity. 

Additional information on plow-down speeds is needed to determine the acre passes per 

operation (e.g., disc implement width and speed can be used to determine the number of 

acres tilled per hour). The number of operations and the time of year in which they occur 

vary by crop type; therefore it is necessary to determine not only the acres of crop under 

cultivation, but the mix of crops being farmed on the selected design days. 

MAG’s 2007 Five Percent Plan22 contrasted emission estimates using the two methods 

and found the above method produced an estimate of emissions for corn tillage that was 

an order of magnitude higher than produced by the first methodology. AERMOD 

performance evaluations determined that the lower values provided a better fit with 

concentration measurements. This indicates that care should be taken in determining 

which methodology to employ. 

Data Sources – The following data are needed: 

Acreage under cultivation within modeling domains selected for low and high 

wind design days; 

The distribution of crops being cultivated within the selected modeling domains, 

either on average or within finer spatial domains; and 

The number of operations being conducted by crop type on the design days. 

Information on the first two categories should be available from responsible government 

agencies, including the Arizona Department of Agriculture and USDA. The results 

 

21 http://www.arb.ca.gov/ei/areasrc/fullpdf/full7-4.pdf 22 Air Quality Modeling for the Salt River Area in Support of the Five Percent Plan for PM-10, prepared for 

Maricopa Association of Governments, by Sierra Research, November 2007 
-72- 

should be reviewed by the County Agricultural Agent to confirm their representativeness. 

Data on the number of operations are available only from farmers who were active on the 

design days. It is recommended that the results of the above analysis be shared with the 

local farm organizations to determine representativeness and to request additional 

information on activities that took place on selected design days. 

Harvesting – The 2008 PEI used crop-specific emission factors (CARB, 2003) to 

estimate harvest emissions. Table 4-2 lists the crop types and associated PM10 emission 

factors used to calculate emissions from agricultural harvesting. 

Table 4-2 

CARB Harvest PM10 Emission Factors 

(lb/acre-yr) 

Crop Type PM10 Emission Factor 

Cotton 3.4 

Wheat 5.8 

Barley 5.8 

Alfalfa Hay 0.0 

Other Hay 1.68 

Corn 1.68 

Sorghum for Graina 5.8 

Potatoes 2.7 

Vegetablesb 0.08 

Citrus 0.08 

a. Assumed same emission factor, control efficiency, and number of harvest days per year as wheat and 

barley.

b. Includes melons, excludes potatoes. 

Annual PM10 emissions from agricultural harvesting were calculated using the following 

equation: 

Uncontrolled annual harvestCrop emissions = EFcrop × ACrop × ton / 2,000 lb 

where: 

harvestCrop = harvest emissions for each crop type (tons PM10/yr) 

EFCrop = harvest emission factor (lbs PM10/acre) 

ACrop = number of harvested acres for each crop type per year 

Example: 

EFCotton = 3.4 lbs PM10/acre for cotton 

ACotton = 18,800 acres of cotton 
-73- 

Uncontrolled annual harvestCotton emissions = 

3.4 lbs PM10/acre ×18,800 acres × 1 ton/2,000 lbs 

= 31.96 tons PM10/yr 

Key issues to be resolved in estimating harvest emissions are the control efficiency 

produced by applicable agricultural BMPs and whether fields located within the selected 

modeling domains were harvested on the selected design days. 

Data Sources – Maricopa County obtained the majority of crop-specific harvest acreage 

from the 2008 Arizona Agricultural Statistics Bulletin. Harvest acres for potatoes were 

obtained from the USDA National Agricultural Statistics Service for 2008, and 

vegetables and citrus were obtained from the 2007 Census of Agriculture. These sources 

should be reviewed to determine if similar information is available for Pinal County. The 

data will be available only at a county level of disaggregation; if possible, more detailed 

data should be pursued to address the distribution of crops being cultivated within the 

selected modeling domains. It is suggested that the County Agricultural Agent be 

contacted to determine if a finer spatial resolution of cultivation is available. 

Cotton Ginning – Annual emission reports are available from all permitted cotton gins in 

Pinal County. Daily emission levels can be calculated from facility-specific operating 

schedules. A review of the coordinates determined that seven cotton gins are located 

within the nonattainment area. 

Data Sources – Each of the seven cotton gins should be contacted to determine their daily 

operating schedules. 

Offroad Recreational Vehicle Fugitive Dust – EPA’s NONROAD model estimates 

exhaust emissions for offroad recreational vehicles. Since particulate emissions are also 

generated by recreational vehicles traveling on unpaved surfaces, estimates of fleet size 

and activity are needed to support fugitive dust calculations. NONROAD provides 

estimates of annual mileage and number of vehicles by county for all-terrain vehicles 

(ATVs), offroad motorcycles (ORMs), and specialty vehicles/carts (SVCs). Daily 

estimates of travel can be calculated from the annual mileage and number of vehicles by 

category. These estimates of travel can be combined with emission factors for unpaved 

industrial roads, discussed in the section addressing fugitive dust from unpaved roads. 

The Maricopa County PEI assumed the average vehicle weight for offroad recreational 

vehicles was one half of a ton. ORMs (two wheeled vehicles) are assumed to emit at half 

the level of ATVs and SVCs (both four wheeled vehicles). 

Data Sources – In addition to the variables required for estimating unpaved road fugitive 

dust emissions, estimates of vehicle activity in both the nonattainment area and within the 

modeling domains will be needed. Several options are available to allocate NONROAD 

estimates of vehicle class activity for the county to the nonattainment area and the 

individual modeling domains, including allocation by population, allocation by share of 

acreage, consideration of where offroad activity parks are located, etc. Contacts should 

be established with knowledgeable park and law enforcement officials to determine the 

best method for allocating activity. 
-74- 

Construction – Unlike most emission sources, the locations of construction activities and 

emissions move within modeling domains from year to year. Therefore, it is necessary to 

obtain information on dust control permits issued during the years of interest. Permits 

provide information on the project type, acreage, date issued, permit period (one year) 

and project location. Project types are listed below. 

Sand & Gravel 

Construction 

Unknown 

Commercial Construction 

Site Development 

Vacant 

Low Density Residential 

 

To estimate emissions for each permit for the 2008 PEI, Maricopa County multiplied the 

number of acres by an emission factor that varied by project type (in units of tons/acremonth) by the number of months of construction duration, which also varied by project 

type. 

Estimates for the duration of house and apartment construction were obtained from EIIP 

guidance (U.S. EPA, 2002). Estimates for the duration of nonresidential construction and 

road construction were obtained from the WRAP Fugitive Dust Handbook (WRAP, 

2006a). No estimates for the duration of trenching, demolition, weed control, site 

prep/land development, and temporary storage yard activities were available; thus, the 

following activity periods are assumed, as previously cited in the MCAQD PEI: 

1-month duration for trenching, demolition, and weed control; 

8-month duration for site prep/land development activities (weighted average of 

residential and commercial duration) because the duration depends on the project 

type and size; and 

12-month duration for temporary storage yard activities because these activities 

are frequently associated with road construction. 

Emission estimates for residential (single-family and multi-family), nonresidential, road 

and general construction were obtained from the Western Regional Air Partnership 

(WRAP) Fugitive Dust Handbook. Estimates for road construction were obtained from 

Clark County and general construction emission factors from the WRAP Handbook were 

used for trenching, demolition, weed control, and temporary storage yard activities. The 

average duration of construction activity and emission factors for each project type are 

shown below in Table 4-3. 
-75- 

Table 4-3 

Average Project Duration and Emission Factor Employed in 

2008 Maricopa County PEI 

Project Type 

Duration 

(months) 

Emission Factor 

(tons PM10/acre-month) 

Residential: single-family 6 0.032 

Residential: multi-family 12 0.11 

Commercial 11 0.19 

Road Construction 12 0.265 

Trenching 1 0.11 

Demolition 1 0.11 

Trenching 1 0.11 

Weed Control 1 0.11 

Site prep/land development 8 0.11 

Temp. Storage Yard 12 0.11 

Two key assumptions employed in calculating emissions are described below. 

Acreage – No permits were included unless the area of disturbance was equal to 

or greater than an acre. 

Dates – The County estimates of project duration (e.g., road construction, 12 

months; residential single family, 6 months; commercial, 11 months) were used to 

count backwards from the design date and determine if a permitted site would 

have been active on the design day. Thus, for example, any road construction 

project started more than 11 months prior to the day to be modeled was excluded 

from the inventory of sources that impacted that date. 

Data Sources – Since the county issues permits for a 12-month period and many projects 

have durations that are shorter than 12 months, assumptions will need to be developed 

about when projects start so that it can be determined whether those projects are active on 

the selected design days. Similarly, assumptions about whether projects are active on 

weekends, holidays, etc. will also be needed. Discussions with Pinal County staff 

indicate that there are few multi-family projects, so a decision will be needed as to 

whether separate estimates of single- versus multi-family construction emissions are 

warranted. Similarly, estimates of the duration of the construction period on active days 

will also need to be developed (e.g., 10-hour days, 8-hour days, etc.), along with start and 

ending times. 

On-Road Mobile Sources – On-road mobile sources are defined as those vehicles that are 

licensed for and operated on public roads (i.e., on highways, arterials, and other local 
-76- 

roads) and include cars, motorcycles, trucks, and buses. For the purpose of this IPP, onroad mobile sources consist of the following types of emissions: 

Exhaust, tire, and brake wear; 

Fugitive dust from vehicles operating on paved roads; 

Fugitive dust from vehicles operating on unpaved roads; and 

Fugitive dust from unpaved shoulders. 

EPA’s most recent motor vehicle emission factor model, MOVES (Motor Vehicle 

Emissions Simulator), is required to characterize emissions for the first category. AP-42, 

which is EPA’s Compilation of Air Pollutant Emission Factors, provides specific 

methodologies for quantifying fugitive dust emissions for the second and third categories. 

The general approach for computing on-road mobile source emissions consists of 

multiplying vehicle activity expressed as vehicle miles traveled (VMT) by vehicle 

emission factors (in units of grams/mile) obtained from the methods noted above. In 

large urban areas, VMT data are generally available at the roadway link level from travel 

demand model outputs. Outside urban areas, VMT is typically compiled at the county or 

regional level from sources such as the Highway Performance Monitoring System 

(HPMS) maintained by the Federal Highway Administration or fuel consumption and 

sales data (although use of fuel sales data at the county level can be problematic in 

regions of heavy inter-county travel). An outline of the methods, inputs required to 

characterize conditions within the nonattainment area and data sources for preparing 

inputs is outlined below. 

Exhaust, Tire & Brake Wear – EPA’s MOVES model requires the collection of local data 

to characterize the vehicle fleet operating in the nonattainment area. This is 

accomplished by preparing input fields to represent vehicle population, activity, road 

types, inspection and maintenance, fuel supply, and meteorology for the region and time 

period being simulated through MOVES. Each of the individual inputs once obtained 

must then be properly formatted and imported through the provided MOVES importer 

tools into a MYSQL database. 

Described below are the inputs needed to represent the nonattainment area. 

Vehicle Population – The vehicle population field represents the regional fleet of 

vehicles for each of the 13 MOVES vehicle types; these data can typically be 

obtained from vehicle registration information. Using a VIN decoder software 

tool, the raw registration information can then be parsed into the appropriate 

MOVES vehicle type fields. 

Age Distribution – A separate input field that also relates to the vehicle 

population is the age distribution. Each of the 13 vehicle types in MOVES must 

also have the age of the vehicles defined; again, the vehicle registration 

information can be used as the source of that information. 
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Alternative Vehicle Fuels and Technologies (AVFT) – The Alternative Vehicle 

Fuels and Technologies input defines the fraction of each vehicle type belonging 

to separate fuel and engine technology groups such as diesel, gasoline, natural gas 

and electric vehicles. VIN-decoded DMV data are one source for deriving these 

data. 

VMT by HPMS Vehicle Type – Annual Vehicle Miles Traveled organized by 

HPMS vehicle types are required inputs for the MOVES model. Where available 

the output from a travel demand model for a given region is used as the source of 

this information. 

VMT Fraction – In order to resolve the annual VMT by HPMS into finer temporal 

resolution three temporal breakdowns of VMT by each MOVES vehicle type are 

developed. These fields represent VMT fractions by month, day, and hour. At 

present, MOVES differentiates only between weekdays and weekends for daily 

VMT breakdowns. Traffic counter data are commonly used as the source of data 

for the VMT fraction fields. 

Road Type Distribution – Each of the vehicle types has VMT further resolved 

into the 5 distinct MOVES road types. A source for this data can be the outputs 

from a regional travel demand model. 

Ramp Fraction – The fraction of vehicle hours traveled on ramps is calculated 

using VHT data from a regional travel demand model. MOVES defines two 

distinct ramp categories: rural restricted and urban restricted roads. 

Speed Distribution – Average speed distribution can be calculated using VHT 

outputs from a regional travel demand model. Speed distributions are broken into 

16 different bins in MOVES and distributed across vehicle type, road type, day, 

and hour. 

Inspection and Maintenance Programs – I/M data tables should reflect the 

vehicles in the region that are required to comply with the local I/M programs 

along with an estimate of the compliance fractions. The table is organized by 

pollutant process, state, county, vehicle types, fuel types, and model year. 

Fuel Data – The fuel supply and fuel formulation should be specified as MOVES 

inputs reflecting the local blend of fuels available. These inputs can be derived 

from local fuel inspection results where available. Additionally, MOVES does 

provide a default set of values that may be sufficient in the absence of local data. 

Meteorology – Average hourly temperature and relative humidity by month 

should be obtained from local meteorology stations and used in place of the 

MOVES default values. 
-78- 

Data Sources – Inputs for characterizing the vehicle fleet can be obtained from Arizona 

Department of Transportation (ADOT) registration files; fuels information can be 

obtained from the Arizona Department of Weights and Measures; I/M program data can 

be obtained from the Arizona Department of Environmental Quality (ADEQ); and 

vehicle activity data can be obtained from several sources, including ADOT, 2008 

Highway Performance Monitoring System (HPMS) records (which can be obtained from 

ADOT), and MAG’s travel demand model, which characterizes travel on portions of 

Pinal County. An alternate option for configuring MOVES to represent the 

nonattainment area is to use inputs developed by MAG to quantify Pinal County 

emissions for the ozone plan. 

Fugitive Dust, Paved Roads – The AP-42 methodology computes paved road emissions 

as a function of silt loading values and the average weight of vehicles traveling on paved 

road surfaces. Maricopa County classifies roads as freeways, high-traffic arterials, and 

low-traffic arterials to reflect different silt loading assumptions. An arterial carrying a 

traffic volume of less than 10,000 vehicles per average weekday is classified as lowtraffic; all other roads that are not freeways are classified as high-traffic arterials. The 

AP-42 equation for calculating paved road fugitive dust emission factors is as follows: 

=  × 0.91 ×1.02 × (1 − ⁄4) 

where: E = annual average particulate emission factor (g/mile), 

 k = particle size multiplier for particle size range (1.0 g/mile for PM10 and 

0.25 g/mile for PM2.5), 

 sL = road surface silt loading (g/m2

), 

 W = average weight of the vehicles traveling on the roads (tons), 

 P = annual number of “wet” days with at least 0.254 mm (0.01 in) of 

precipitation, and 

 N = annual number of days (366 days in 2008). 

Data Sources – Silt loadings are available from multiple sources. Maricopa County 

derived them from samples collected by an EPA contractor in 1993. The values, in 

grams per meter squared, derived from those samples are 0.02 for freeways, 0.067 for 

high-traffic arterials, and 0.23 for low-traffic arterials. Alternately, measurements can be 

obtained for selected roads from ADOT or those values can be augmented with 

measurements collected by ADEQ and/or Pinal County staff from a representative 

sample of roadways. Average vehicle weights can be derived from ADOT vehicle 

registration data for Pinal County. 

Fugitive Dust, Unpaved Roads – The emission equations for calculating fugitive dust 

from unpaved road travel developed by EPA are published in AP-42.2. The equation 

used in this analysis is designed to estimate particulate matter emissions from light-duty 

vehicle travel on unpaved roads. This equation has the following form: 
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E = [(k)(s/12)a

(S/30)d/(M/0.5)c – C][(365 – P)/365] 

where: E = particulate matter emission rate, pound per vehicle miles traveled (lb/VMT), 

k = particulate size factor (dimensionless) = 1.8 for PM10, 

s = surface material silt content (%), 

S = mean vehicle speed, miles per hour (mph), 

M = surface material moisture content (%), 

a = empirical constant = 0.2 for PM10, 

d = empirical constant = 0.5 for PM10, 

C = PM10 emissions from vehicle exhaust, brake wear, and tire wear (lb/VMT), 

and 

P = number of precipitation days per year on which 0.01 inches or more rain falls 

(days/yr). 

Data Sources – The three variables in this equation—silt content, moisture content, and 

vehicle speed—vary significantly from one unpaved road to another. Because of this 

variability, measurements of these parameters are needed to increase the accuracy of the 

representation of roads within the nonattainment area. Options to collect these values 

include use of values employed by Maricopa County in the 2008 PEI (silt content, 11.9%; 

moisture content, 0.5%; and vehicle speed, 25 mph on unpaved roads and 10 mph on 

alleys). Another source of information on silt content and moisture content is an ADOT 

report entitled Identification of Emissions Sources for Pinal County ADOT, which 

provides values for five separate unpaved roads along with information on average 

vehicle speed and average daily traffic counts collected in 2005. This information was 

used to prepare separate PM emission factors for each of these roads. Lacking any other 

data sources, these values could be extrapolated to similar roads located within the 

nonattainment area and modeling domains. Alternately, ADEQ and/or Pinal County staff 

can collect measurements from a representative sample of roadways following AP-42 

data collection and measurement procedures. 

With regard to vehicle activity, PCAQCD collected daily counts of selected roads 

between October 2001 and August 2011. Counts were generally collected to respond to 

complaints and employed a single tube counter for a seven-day period. Road surface 

distinctions include dirt, dirt w/soil stabilizer, paved and dirt/gravel. Most of the roads 

counted were dirt roads. Some roads were sampled multiple times and could provide a 

basis from tracking trends in growth. A copy of the road counts is presented in 

Appendix H. 

Fugitive Dust, Unpaved Shoulders – The 2006 field study in the Salt River Area of 

Maricopa County observed blowing dust emissions on unpaved arterial road and 

secondary road shoulders as a result of bow wake passage. Because emissions from this 

source can be controlled through programs that pave or stabilize shoulders and were 

found to be significant in the MAG PM SIP plans, they should be addressed in both the 

nonattainment area and modeling PM emissions inventories. 

Although the literature reports very few truck bow wake emission studies, MAG used one 

good study from the San Joaquin Valley to estimate bow wake emissions in the Salt 
-80- 

River Area. The San Joaquin Valley study measured wind energy levels and particulate 

flux away from a paved road on which trucks were traveling at an average speed of 60 

miles per hour.23 The PM10 emission factor was computed to be 7.75 grams per vehicle 

kilometer traveled, or 12.47 g/mi. This emission factor cannot be directly applied to 

Pinal County traffic because of the lower truck speeds on arterial roads found in the 

nonattainment area. 

Truck bow wake emissions were assumed in this study to vary with the square of the 

truck speed, very much as aerodynamic drag and bow wake energy vary with the square 

of vehicle speed. From the MAG travel demand model, Salt River Area truck speed was 

estimated to average 35 miles per hour. At this speed, the bow wake emission factor was 

computed to be 4.24 g/mi (= 12.47 g/mi x {35 mph/65 mph}2

) at 35 miles per hour. 

MAG also computed windblown PM10 emissions from unpaved shoulders for modeling 

purposes. For this calculation, MAG used the “All Sites” wind erosion equation 

published in the 1986 Nickling and Gillies study.24 Because the emission factor predicted 

by this equation is a function of the wind speed raised to the 4.355 power, it was 

concluded that the average hourly emission factor based on the average hourly wind 

speed would be less than the factor based on wind speeds measured during sub-hour 

increments. Because five-minute average wind speed data were available from MCAQD 

for the two monitors during the December 2006 field study period, a statistical analysis of 

this relationship found the use of five-minute wind data produced an hourly emission 

factor that was roughly 10% higher than the factor calculated on the basis of the hourly 

average wind speed. As a result, the statistical model developed from the December 

2006 high wind data was used to adjust the Nickling and Gillies equation to account for 

this phenomenon. The adjusted equation was then used to compute the windblown 

emission factor for each high wind hour on the high wind design day. 

Emission estimates require information on the fraction of travel generated by heavy 

trucks, and location and extent of unpaved shoulders within the nonattainment area and 

within the low wind modeling domains. Unpaved shoulder lengths can be multiplied by 

hourly truck counts to determine bow wake vehicle miles traveled for individual road 

segments. These estimates can be combined with g/mi bow wake emission factors to 

produce estimates of fugitive dust emissions. 

Data Sources – Visual analysis of aerial photographs and Google Earth images can be 

used to determine the extent of unpaved shoulders on arterial and secondary roads within 

the nonattainment area and the modeling domains. Vehicle class count data will be 

needed for a representative sample of arterial and secondary roads within the 

nonattainment area and modeling domains. If not available, these data can be collected 

by either visual observation or through size-specific tube counts. Alternately, the values 

employed in the MAG analysis can be used. 

 

23 “Effectiveness Demonstration of Fugitive Dust Control Measures on Public Unpaved Roads and 

Unpaved Shoulders on Paved Roads,” prepared for the San Joaquin Valley Unified Air Pollution Control 

District by Desert Research Institute, 1996. 

24 “Evaluation of Aerosol Production Potential of Type Surfaces in Arizona,” prepared for 

Engineering-Science by W.G. Nickling and J.A. Gillies, 1986. 
-81- 

Open Burning – Open burning is a waste disposal tool on central Pinal County lands used 

for agricultural production, residential occupancy, and commercial activities. Open 

burning is also used with less frequency to provide fire protection training, building 

demolition, and destruction of certain hazardous materials. Because of the very small 

quantity of daily or annual emissions generated by these latter uses, the open burning 

portion of the emission inventory will focus exclusively on the more frequent, higher 

emission activities that occur on agricultural, residential, and commercial property. 

Data Sources – Emission factors for open burning by activity type will be derived from 

AP-42 and from research studies conducted in field experiments and burn chamber tests 

in the western United States over the past 30 years. Activity and location data for each 

open burning event in 2008 will be extracted from the open burning permit database 

maintained by PCAQCD. 

4.2 High Wind Emissions 

As discussed in Section 3, high wind sources typically consist of disturbed soil surfaces 

that require shielding from the wind, covering of soil surfaces, or the consolidation of 

wind-entrainable surface particles into wind-resistant matrices in order to reduce PM10 

emissions. Control strategies for high wind sources are best developed from the analysis 

and modeling of design days during which only these sources are contributing to ambient 

PM10 concentrations. For the purpose of selecting high wind design days, high wind 

hours were defined as those exceeding a threshold velocity of 12 mph. Additional 

analyses may be warranted to determine a more precise wind speed threshold velocity for 

the purpose of determining high wind emissions. 

At this point, ADEQ is considering two completely different methods for quantifying 

emissions under high wind conditions. The first back calculates emission factors for 

upwind lands by establishing a relationship between monitored concentrations and lands 

located within footprints determined to be impacting the monitors, based on an analysis 

of meteorological conditions for specific days and conditions; this method is summarized 

in a report entitled “A Method for the Back-Calculation of Wind-Blown Emission Factors 

from Field Measured PM10 Emissions,” which is presented in Appendix I. 

The second method replicates the approach MAG employed in quantifying high wind 

emissions for the Maricopa County’s 2008 Periodic Emission Inventory (PEI) for PM10 

and MAG’s 2012 Five Percent Plan; a copy of MAG’s methodology is presented in 

Appendix J. Effort will be required to adjust the methodology to represent conditions in 

the Pinal County nonattainment area (e.g., meteorology, land use, precipitation, silt 

content, soil texture, agricultural activity, etc.). Maricopa County’s 2008 PEI cited local 

conditions that did not reconcile with the certain underlying wind tunnel studies, and 

relied upon a long-established low-wind inventory to justify a “standardization” to reduce 

the calculated annual windblown PM10 inventory. Prevailing conditions in Pinal County 

may not justify such “standardization” of wind-generated emissions. In general, activity 

rates and emission factors for a number of areally dispersed fugitive dust sources are 
-82- 

extremely difficult to quantify.25 As such, EPA has requested that a modeling analysis be 

prepared to assess how well the two methods represent monitored concentrations. 

### 

 

25 Air Quality Criteria for Particulate Matter, Vol. 1, EPA/600/P-99/002aF, U.S. Environmental Protection 

Agency, October 2004 
-83- 

5. QUALITY ASSURANCE 

This section presents a review of the QA procedures to be employed during the 

development of the emission inventories. It includes all of the critical elements 

recommended in the U.S. EPA documents cited in the Introduction, as well as guidance 

provided through the Emission Inventory Improvement Program (EIIP). It also provides 

written instructions for the technical and quality aspects associated with development of 

the new emission inventories. It is designed so that QA and quality control (QC) 

procedures are implemented throughout the entire inventory development process. This 

will ensure that the inventories are as complete, accurate, comparable, and representative 

as possible. 

Inventory tasks and QC procedures will include data checking by the inventory 

development team (IDT) throughout the development of the inventory and final emission 

report. These procedures include, but are not limited to, the following: 

The development and implementation of written procedures for data collection, 

data assessment, data handling, calculation of emissions, and reporting; 

Adequate management and supervision of the work; 

Review of all calculations for technical soundness and accuracy, including 

verification that the appropriate emission factors were used and the impacts of 

controls were correctly addressed; 

Correct assignment of Source Category Codes; 

Assignment of DARS scores; 

Use of technically sound approaches when developing results based on 

engineering judgment; 

Documentation of the data in a manner that will allow reconstruction of all 

inventory development activities; and 

Maintenance of an orderly master file of all the data gathered and a copy-ready 

version of the final inventory submitted to the U.S. EPA. 
-84- 

The emission inventories developed in accordance with this plan are for SIP development 

and are considered Level II inventories, based on guidance provided by the 1996 EIIP. 

The estimates contained in the inventories will be used to make decisions about the need 

for and types of control strategies required to ensure short-term attainment and long-term 

compliance with the NAAQS. As a result, they must satisfy applicable QA requirements. 

The first step in this process is establishing the data quality objectives (DQO) for the new 

inventories. Table 5-1 summarizes the procedures to be employed in meeting the DQOs, 

showing that considerable effort will be focused on meeting accuracy, completeness, 

representativeness, and comparability objectives. 

Table 5-1 

Data Quality Objectives 

DQO Procedure for Achieving Objective 

Accuracy For point and onroad mobile sources, the data generator will check 

100% of the calculations, and another equally qualified inventory 

development team (IDT) member will check 20% of the 

calculations. For area and nonroad mobile sources, the data 

generator will check 100% of the calculations, and another equally 

qualified IDT member will check 10% of the calculations. In all 

cases, the data validator will develop a written summary of his or 

her activities, and will conduct follow-up activities to ensure that 

data are corrected as needed. If more than 5% of the calculations 

checked by the data validator need to be revised, then 100% of the 

calculations will be checked. 

Completeness Extensive planning will be conducted prior to data collection to 

identify all applicable emission sources. After identifying these 

sources, the goal will be to determine 100% of the emissions from 

the largest emitting sources from each source category and as 

many of the minor sources as possible within the time frame 

allotted for the work. Those sources identified but not included in 

the inventory will be identified in the data file and final report. 

Representativeness Technical personnel will review all of the primary source data and 

compare them to previous emission results and similar results from 

comparable regions to determine the reasonableness of the 

emissions estimates and representativeness of the data. 

Comparability To ensure that the data are comparable, standard procedures will be 

followed and results for the NAA will be presented in the same 

units that were used in the NEI inventories. 
-85- 

Table 5-2 shows the data quality indicators (DQIs) that will be used to measure progress 

towards the DQOs. The Data Attribute Rating System (DARS)26 will be used to verify 

the desired inventory accuracy. 

Table 5-2 

Data Quality Indicators 

DQO Inventory DQI Target Values 

Accuracy Achieve DARS score >= 0.7 for all area sources contributing 

>10% of total emissions

Achieve DARS score >=0.8 for all point sources >=100 tons per 

year (TPY)

Achieve DARS score >=0.7 for onroad mobile source inventory 

Achieve DARS score <=0.5 for nonroad mobile source inventory. 

Completeness 100% of all point sources >=100 tpy 

90% of all other point sources 

Representativeness Monitor specific low and high wind source characterization 

Comparability Results for the NAA will be compared to NEI pollutant 

inventories. 

5.1 Managerial Responsibilities 

ADEQ will lead the preparation of the nonattainment area emission inventories. Key 

assignments shall include those outlined below. 

State Inventory Coordinator – The State Inventory Coordinator will be responsible for 

planning and managing all inventory development activities, including the development 

of the QAP and the final emissions report. 

Source Inventory Development Managers – Source Inventory Development Managers 

will be responsible for planning and leading source-specific inventory development 

activities. 

QA/QC Coordinator – The QA/QC Coordinator will be responsible for ensuring that 

adequate QA/QC procedures are incorporated into the inventory development process. 

The Coordinator will conduct QA training and revise the audit schedule as needed so that 

all critical phases of the inventory development process are audited prior to generation of 

the emission report. The QA Coordinator will attend status meetings (by teleconference 

if necessary) held by the Inventory Development Managers and use the information from 

 

26 Beck, L.L., R.L Peer, L.A. Bravo, and Y. Yan, “A Data Attribute Rating System,” presented at the Air & 

Waste Management Association Specialty Conference on Emission Inventory Issues, Raleigh, North 

Carolina, November 1994 
-86- 

these meetings to revise the audit schedule, when appropriate, to ensure that the audit 

objectives are met. 

The QA Coordinator will categorize and use the audit findings to evaluate the 

effectiveness of QC measures and QA audits. The QA/QC program will be revised to 

address trends that suggest that the technical and data quality objectives are not being 

achieved. 

The QA Coordinator’s responsibilities and activities are as follows:

Help develop the QAP; 

Develop the audit checklist and audit schedule; 

Provide QA training to inventory development and QA personnel; 

Attend inventory status meetings; 

Schedule audits, conduct audits, and report findings; 

Evaluate audit findings to determine if trends exist, and keep management 

informed of results; 

Follow up on recommendation for corrective actions; 

Keep the Inventory Development Manager informed of audit results; 

Work with the SIP Program Manager to resolve any quality concerns that cannot 

be resolved at the inventory management level; and 

Maintain a file of audit findings and corresponding corrective actions. 

The QA Coordinator reports directly to the State Inventory Coordinator and indirectly to 

the managers overseeing the development of the inventory. These reporting lines help 

provide an objective approach to the implementation of the QA program and reporting of 

quality issues. 

5.2 Schedule 

The schedule includes the following tasks/completion dates: 

Data collection, including field measurements – January 31, 2013 

Inventory development, including source specific emission estimates and 

selection of high wind estimation method – March 29, 2013 
-87- 

5.3 General QA/QC Procedures 

QA/QC procedures described in this QAP were developed to help ensure data accuracy, 

completeness, representativeness, and comparability. These procedures have been 

incorporated in the technical procedures, where applicable, and will be implemented by 

the IDT throughout the planning, data collection, emission estimation, and reporting 

phases of the inventory development program. 

QC procedures will be implemented by the IDT during inventory development to meet 

the technical objectives and DQOs. These activities will be conducted at the following 

steps in the inventory development process: 

Data collection; 

Data documentation; 

Calculation of emissions; 

Data checking and DARS scoring; 

Reporting; and 

Maintenance of the master file. 

Data collection will be conducted according to U.S. EPA-approved procedures. The 

approach and supporting documents or references will be thoroughly documented and 

included in the emissions report. The documents identified in Table 5-3 will be used to 

determine the best data-collection approach for each emission source type. Some data 

sources identified in these documents are also listed in Table 5-3. All data sources will 

be thoroughly documented by the IDT. The IDT will also document when required data 

needed for specific source categories cannot be obtained or do not apply. The reason for 

not including a source or source category in the inventory will be clearly explained in the 

documentation. 

All activities conducted by the IDT will be documented. The traditional approach is to 

use bound notebooks with indices to facilitate the retrieval of recorded information. An 

alternate approach is to record activities electronically and make this information 

available to team members located in different parts of the state. To enhance 

communication and productivity, team members will be allowed to employ either 

approach but will be encouraged to track information relative to the development of the 

inventory electronically. This daily log of activities will help another IDT member 

reproduce the emission results and allow an evaluation of data accuracy and 

completeness. 
-88- 

Table 5-3 

Data Collection Guidance Documents 

Source Type Guidance Document Suggested Data Sources 

Point

EPA-450/4-91-016a

AP-42b

EIIP Volume II 

Existing inventories, state permit files, facility 

surveys, county business directories, telephone 

directory 

Area EPA-45-/4-91-016a

EIIP Volume III 

Existing inventories, example cases and data 

sources 

Nonroad Mobile EPA-450/4-81-026dc Existing inventories, example cases, and data 

sources 

Onroad Mobile 

EPA-45-/4-91-010d

EPA-450/4-91-011e

EIIP Volume IV 

Transportation or planning agency data 

a.“Procedures for the Preparation of Emission Inventories for Carbon Monoxide and Precursors of Ozone, 

Volume I: General Guidance for Stationary Sources,” U.S. EPA, Office of Air Quality Planning and 

Standards, Research Triangle Park, North Carolina, 1991. 

b. “Compilation of Air Pollutant Emission Factors, 5th Edition and Supplements, AP-42,” U.S. EPA, Office of 

Air Quality Planning and Standards, Research Triangle Park, North Carolina,1995. 

c. “Procedures for Emission Inventory Preparation, Volume IV: Mobile Sources,” U.S. EPA, Office of Mobile 

Sources, Ann Arbor, Michigan, 1992. 

d. “Emission Inventory Requirements for Ozone State Implementation Plans,” U.S. EPA, Office of Air Quality 

Planning and Standards, Research Triangle Park, North Carolina, 1991. 

e. “Emission Inventory Requirements for Carbon Monoxide State Implementation Plans, U.S. EPA, Office of 

Air Quality Planning and Standards, Research Triangle Park, North Carolina, 1992.

The following procedures are to be followed when documenting data in notebooks: 

Data will recorded legibly and in black ink; 

Entries will be corrected by drawing a single line through the data and writing the 

correct data above or below the correction (with initials, date, and explanation of 

corrections to allow reconstruction of the work); 

Complete descriptions of all data sources will be included (references to be 

included in final inventory report); 

Units of measurements will provided for emission sources that are omitted from 

the final inventory (justification required in report); 

The procedures used to calculate emission will be described and example 

calculations will be provided; 

The approach used to determine completeness for each source type will be 

described; 
-89- 

Documents from which emission factors are taken will be identified and 

referenced; and 

The source, agency, group, or company providing information by telephone will 

be identified (include telephone number and date information was provided). 

Worksheets and contact reports may also be used to maintain records of data sources or 

calculations; however, the same guidelines must be followed when recording information 

on them. A file will be developed specifically for these forms to ensure that they are 

retained and are easily located when the data are needed to calculate emissions. A 

contact report should include the date of contact; originator name, title, organization, and 

address of person contacted; and a summary. All worksheets, electronic spreadsheets, 

and notebooks will be reviewed periodically by the inventory development task leaders to 

determine whether the procedures described above are being followed. This review 

should be evidenced by a dated signature on the notebook pages or worksheets reviewed 

(i.e., reviewed by ________ on _______). 

Data used in calculation emissions should be checked for data accuracy, reasonableness, 

and completeness. The results from data checking will be documented to further qualify 

the emission estimates. In addition to the DARS scores assigned, the number of data 

points checked assists reviewers in evaluating the accuracy of the completed emissions 

report. Documentation of DARS scoring and data checking should include descriptions 

of the rationale for scoring, the data checked, and the dated signature of the reviewer. An 

example area source form that will be used to document the data checked and the 

findings can be found in EIIP Volume VI, Chapter 5, Section 5, Figure 5.2. 

QA activities are not directly involved in the development of the inventory. These 

include assessments of the effectiveness and appropriateness of the systems established 

by management to control data quality. This includes evaluations of the management and 

supervision of the work. 

QA activities will include QA training and the conduct of a series of independent audits 

to assess the effectiveness of the QC system and management of inventory development 

activities. The QA coordinator and other trained personnel who are not involved in the 

inventory development process will conduct these activities. 

Training/Education – Initial training sessions for the ADEC IDT will be conducted to 

discuss the items on the audit checklist and QA/QC requirements specified in this 

document. The QA Coordinator will conduct additional training when audits reveal the 

need for more QC measures or revision of existing procedures. 

Occurrences that may lead to additional QA training include those listed below. 

An audit reveals a lack of understanding of QA/QC requirements or the need to 

develop additional QC measures. 
-90- 

An audit reveals the need to provide guidance on acceptable data handling 

procedures because data are not maintained in a manner that allows easy 

verification of the accuracy of emission results and source of the supporting data. 

An audit reveals unacceptable data documentation practices that lead to entry 

errors or an inability to recreate emission results. 

An audit reveals that the internal data reviews do not adequately control data 

entry and calculation errors. 

The Inventory Development Manager requests QA training for new IDT member. 

The QA coordinator will conduct the training and maintain records of each training 

session. 

Audits – Audits will be conducted at key points in the inventory development process. 

These will include assessments as the inventory development process is being planned, 

during data collection, as emissions are being calculated, and when the results are 

reported. The primary goal of the audit program is to prevent quality concerns. 

Opportunities to incorporate preventative measures will be included during each audit. 

Prior to announced audits, the auditor will inform the persons to be interviewed of the 

date and time of the audit and data systems to be reviewed. All of the personnel involved 

in inventory activities being audited will be asked to be available to respond to questions 

about their duties. The responses will then be compared to the requirements specified in 

this document and other referenced documents to determine compliance with approved 

procedures. 

Questions that will be asked by the auditor and data to be evaluated during the audits vary 

by source type. The QA Coordinator will use the results of previous audits and work 

with the Inventory Development Manager to develop source-specific audit checklists. 

Example checklists for technical systems and data audits are provided in EIIP, 

Volume VI, Chapter 5, Appendices A, B, and C. After developing the checklists, QA 

training will be held to inform the IDT of the steps in the inventory development process 

that are considered to be of concern. These meetings will be used to increase the team’s 

awareness of the points in the inventory development process that may require more QC 

and managerial oversight. 

Data audits will be conducted after major data transcriptions and calculations. The 

auditor will evaluate consistency in data entry and manipulation between IDT members. 

The results from the audits will be immediately shared with the data generator and the 

Inventory Development Manager. IDT members involved in the audit will be asked to 

respond immediately to the findings that will be informally discussed after the audit. 

Audit findings will be documented on the audit checklist. The audit checklist and notes 

will be used to summarize the preliminary findings for the IDT and Inventory 

Development Manager after the audit. The checklist and notes will also be used to 
-91- 

develop the audit report, which will be included in the QA documentation section in the 

inventory report. 

The audit report will describe each deviation from approved procedures or finding that 

could compromise the successful outcome of the inventory. Documentation of each 

finding will include a description of the action or data reviewed that led to the quality 

concern and recommendation for corrective action. 

At a minimum, the audit report will include the following: 

Name of auditor, Inventory Development Manager, and IDT members audited; 

Audit date; 

Audit type; 

Audit objectives; 

Audit findings; and 

Recommendations for corrective actions. 

Audit reports will be distributed within two weeks of the completion of each audit to the 

persons interviewed and the Inventory Development Manager. A summary of the types 

of quality concerns found will be periodically forwarded to the Section Manager to keep 

her informed of the quality issues found and actions being taken to resolve them. Audit 

reports will be retained in a QA file and used to conduct subsequent audits and plan 

follow-up activities. 

Recommendations for corrective actions will be presented in the audit report. Findings 

and actions implemented in response to each recommendation should be documented and 

included in the audit file on a response form. The response form should include the date, 

originator, problem identification, priority of problem, recommended action, problem 

resolution, and QA verification (a sample form is included in EIIP Volume VI, Chapter 5, 

Figure 2-3). The auditor will use the information on the response form to monitor the 

types of problems found and the phases of the inventory development process that may 

need additional QC to eliminate recurring quality concerns. 

The urgency of the response is determined by the category of the finding, as listed below. 

Priority 1: Potential for major revisions needed 

Priority 2: Potential for failure to achieve DQOs 

Priority 3: Suggested improvements. 

Priority 1 and 2 will be immediately brought to the attention of upper management and 

the planned implementation dates for the corrective actions will be as soon as possible 

after the audit. The planned implementation date for Priority 3 findings may be later than 

the dates proposed for implementing actions taken in response to Priority 1 and 2 

findings. 
-92- 

Follow-up activities will be conducted as frequently as required by the Inventory 

Development Manager and auditors to determine whether the recommended actions are 

taken. Each effort to assess the implementation of the corrective action will be 

documented and maintained by the QA Coordinator in an audit file established for these 

records. 

Follow-up activities could include the conduct of additional audits or informal 

assessments of the data or system of concern. The type of reevaluation will be 

determined by considering the impact that the quality concern could have on the technical 

objectives and DQOs. 

5.4 Data Reporting 

Reporting will be accomplished by submitting written documentation and emissions 

summaries to the U.S. EPA. All supporting documentation, project notebooks, data 

sheets, and calculations shall be submitted for review. 

The report will include summary tables, raw listings of equipment, activity levels and 

emissions from individual sources, and a QA documentation section. A detailed 

inventory report allows baseline inventories to be compared between one area and 

another and the impact of control strategies to be evaluated, and also facilitates updates to 

the inventory and development of projection inventories. 

In addition to EIIP guidance, the U.S. EPA reports cited in the introduction will be 

followed. These documents provide guidance for presenting and documenting SIP 

emissions inventories, and contain examples of how to present and verify inventory 

development efforts. The QA documentation section of the emissions inventory will 

provide enough detail so that the inventory development described in the report can be 

compared to the information provided in this QAP. Any discrepancies will be identified 

and explained. 

At a minimum, documentation should describe in general terms how the inventory data 

were collected and where they came from. Specific details for documentation 

requirements should be negotiated with the EPA regional office. The report will include 

the components listed below. 

A description of the geographic area included in the inventory, including 

documentation for any adjustments made to the original designated area. 

Documentation shall reference all sources of current or projected data, and 

include maps of boundaries for excluded areas. 

The base year of the emissions inventory. 

The population of the area, and the source of the population data. 

Efforts taken as part of QA program. 
-93- 

Procedures used to temporally allocate each source category (e.g., selection of the 

months comprising the seasons, seasonal variations in activity levels at sources, 

daily variation in activity levels, etc.). 

Procedures used to spatially allocate the emissions inventory. If a dispersion 

model will be used for control strategy demonstrations, a map of the geographic 

area with the modeling domain and grid squares overlaid shall be included. The 

grid square sizes need to be indicated on the map. 

The QA documentation section of the inventory report will also include the audit report. 

As discussed previously, the audit report will describe each deviation from approved 

procedures or findings that could compromise the successful outcome of the inventory. 

Documentation of each finding will include a description of the action or data reviewed 

that led to the quality concern, along with a recommendation for corrective action. The 

QA documentation section of the inventory report will then discuss how the 

recommended corrective actions were implemented. 

### 
 

Appendix A 

2008 24-Hour Average PM10 Concentrations 

Recorded at Six Pinal County TEOM Monitoring Sites
A-1 
A-2 
A-3 
A-4 
A-5 
A-6 
A-7 
 

Appendix B 

Plots of Hourly PM10 Concentration, Wind Speed, Wind Direction, 

and Mixing Height Recorded or Modeled at Cowtown, Pinal County Housing, and 

Stanfield Monitoring Sites on October 29, 2008 
B-1 

STAGNATION DAY, 10/29/2008, COWTOWN 

Figure B1. PM10 vs. Wind Speed 

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0

50

100

150

200

250

300

350

400

450

0:002:004:006:008:00

10:00

12:0014:0016:0018:0020:0022:00

Wind Speed (mph)

PM10 (ug/m3)

Time

PM10 

Average Wind Speed

PM10 NAAQS

(150 ug/m3)

Figure B2. Wind Direction vs. Wind Speed 

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0

90

180

270

360

0:002:004:006:008:00

10:0012:0014:0016:00

18:0020:0022:00

Wind Direction (degree)

Wind Speed (mph)

Time

Wind Direction 

Average Wind Speed

N

W

S

E

N

Figure B3. PM10 vs. Mixing Height 

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

200

250

300

350

400

450

0:002:004:006:008:00

10:00

12:0014:0016:0018:0020:0022:00

Mixing Height (m)

PM10 (ug/m3)

Time

PM10 

Mixing Height

PM10 NAAQS

(150 ug/m3

)
B-2 

STAGNATION DAY, 10/29/2008, PINAL COUNTY HOUSING 

Figure B4. PM10 vs. Wind Speed 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0

100

200

300

400

500

600

700

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Wind Speed (mph)

PM10 (ug/m3)

Time

PM10 

Average Wind Speed

PM10 NAAQS

(150 ug/m3)

Figure B5. Wind Direction vs. Wind Speed 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0

90

180

270

360

0:002:004:006:008:00

10:0012:0014:0016:00

18:0020:0022:00

Wind Direction (degree)

Wind Speed (mph)

Time

Wind Direction 

Average Wind Speed

N

W

S

E

N

Figure B6. PM10 vs. Mixing Height 

0

200

400

600

800

1000

1200

1400

1600

1800

0

100

200

300

400

500

600

700

0:002:004:006:008:00

10:00

12:0014:0016:0018:0020:0022:00

Mixing Height (m)

PM10 (ug/m3)

Time

PM10 

Mixing Height

PM10 NAAQS

(150 ug/m3

)
B-3 

STAGNATION DAY, 10/29/2008, STANFIELD 

Figure B7. PM10 vs. Wind Speed 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0

100

200

300

400

500

600

700

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Wind Speed (mph)

PM10 (ug/m3)

Time

PM10 

Average Wind Speed

PM10 NAAQS

(150 ug/m3

)

Figure B8. Wind Direction vs. Wind Speed

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0

90

180

270

360

0:002:004:006:008:00

10:0012:0014:0016:00

18:0020:0022:00

Wind Direction (degree)

Wind Speed (mph)

Time

Wind Direction 

Average Wind Speed

N

W

S

E

N

Figure B9. PM10 vs. Mixing Height 

0

200

400

600

800

1000

1200

1400

1600

0

100

200

300

400

500

600

700

0:002:004:006:008:00

10:00

12:0014:0016:0018:0020:0022:00

Mixing Height (m)

PM10 (ug/m3)

Time

PM10 

Mixing Height

PM10 NAAQS

(150 ug/m3)
 

Appendix C 

Statistics of the High Wind PM10 Exceedance Days at Cowtown in 2008 
C-1 

Table C1. Statistics of the PM10 High Wind Exceedance Days at Cowtown in 2008 

Date 

Number of 

High Wind 

Hours 

Daily 

Average 

PM10 

(ug/m3

) 

Daily 

Average 

WSPD 

(mph) 

Daily Max 

WSPD 

(mph) 

Correlation 

of Hourly

Wind Speed 

and PM10

Number of 

High Wind 

Non-feedlot 

Hours 

5/21/2008 12 609 11 19 0.643 0 

11/9/2008 10 528 12 22 0.540 0 

4/8/2008 1 385 6 12 -0.118 0 

4/16/2008 4 376 4 16 0.432 0 

4/15/2008 2 326 7 14 -0.464 0 

8/7/2008 3 336 6 22 0.498 0 

10/11/2008 12 326 11 18 0.462 0 

3/14/2008 5 310 8 13 -0.237 0 

6/18/2008 2 306 6 13 -0.326 0 

8/5/2008 1 306 5 16 0.384 1 

5/6/2008 3 304 7 16 -0.034 0 

5/12/2008 3 304 8 15 0.236 0 

10/27/2008 9 280 10 20 -0.076 9 

5/9/2008 1 272 7 13 -0.300 0 

4/30/2008 6 285 10 18 0.110 0 

4/9/2008 1 277 8 12 -0.401 0 

5/15/2008 1 265 5 19 0.250 1 

3/30/2008 2 268 7 15 0.309 0 

5/16/2008 3 269 7 14 -0.427 3 

10/4/2008 2 238 7 14 -0.098 0 

5/5/2008 6 243 9 16 0.012 0 

10/28/2008 3 248 6 17 -0.470 3 

4/5/2008 2 241 7 13 -0.353 0 

6/30/2008 1 242 5 13 -0.288 1 

4/2/2008 2 230 6 13 0.217 0 

6/4/2008 8 216 10 17 0.476 0 

5/1/2008 4 219 8 14 -0.122 0 

9/29/2008 5 210 8 18 -0.010 5 

7/1/2008 2 200 6 15 0.125 2 

7/8/2008 2 200 5 13 0.321 1 

5/13/2008 2 180 8 17 -0.112 1 

5/22/2008 4 168 9 14 0.577 0 

10/10/2008 4 177 9 15 0.096 0 

4/27/2008 7 168 7 16 0.191 7 

11/21/2008 8 160 8 19 0.127 6 

4/13/2008 2 164 5 13 0.038 2 
C-2 

HIGH WIND DAY, 04/27/2008, COWTOWN 

Figure C1. PM10 vs. Wind Speed 

0

2

4

6

8

10

12

14

16

18

0

100

200

300

400

500

600

700

800

900

1,000

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Wind Speed (mph)

PM10 (ug/m3)

Time

PM10 

Average Wind Speed

PM10 NAAQS

(150 ug/m3)

Figure C2. Wind Direction vs. Wind Speed 

0

2

4

6

8

10

12

14

16

18

0

90

180

270

360

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Wind Direction (degree)

Wind Speed (mph)

Time

Wind Direction 

Average Wind Speed

N

W

S

E

N

Figure C3. PM10 vs. Mixing Height 

0

100

200

300

400

500

600

700

800

900

1000

0

100

200

300

400

500

600

700

800

900

1,000

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Mixing Height (m)

PM10 (ug/m3)

Time

PM10 

Mixing Height

PM10 NAAQS

(150 ug/m3

)
 

Appendix D 

Statistics of the High Wind PM10 Exceedance Days at Stanfield in 2008 
D-1 

Table D1. Statistics of the PM10 High Wind Exceedance Days at Stanfield in 2008 

Date 

Number of 

High wind 

hours 

Daily 

Average 

PM10 

(ug/m3) 

Daily 

Average 

WSPD 

(mph) 

Daily Max 

WSPD 

(mph) 

Correlation of 

Hourly Wind 

Speed and 

PM10 

11/9/2008 7 371 9 22 0.7494 

8/7/2008 3 365 6 21 0.6961 

5/21/2008 15 317 14 21 0.8545 

1/1/2008 11 255 10 21 0.5550 

11/15/2008 7 244 8 18 0.5942 

10/27/2008 5 211 7 16 0.4814 

11/21/2008 7 178 7 16 0.6289 

9/11/2008 2 177 6 27 0.8111 

12/13/2008 7 177 8 15 0.6429 

7/3/2008 1 159 5 12 0.5470 

4/13/2008 3 158 6 18 0.3838 
D-2 

HIGH WIND DAY, 05/21/2008, STANFIELD 

Figure D1. PM10 vs. Wind Speed 

0

5

10

15

20

25

0

200

400

600

800

1,000

1,200

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Wind Speed (mph)

PM10 (ug/m3)

Time

PM10

Average Wind Speed

PM10 NAAQS

(150 ug/m3)

Figure D2. Wind Direction vs. Wind Speed 

0

5

10

15

20

25

0

90

180

270

360

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Wind Direction (degree)

Wind Speed (mph)

Time

Wind Direction

Average Wind Speed

N

W

S

E

N

Figure D3. PM10 vs. Mixing Height 

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0

200

400

600

800

1,000

1,200

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Mixing Height (m)

PM10 (ug/m3)

Time

PM10

Mixing Height

PM10 NAAQS

(150 ug/m3)

HIGH WIND DAY, 11/09/2008, STANFIELD 
D-3 

Figure D4. PM10 vs. Wind Speed 

0

5

10

15

20

25

0

500

1,000

1,500

2,000

2,500

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Wind Speed (mph)

PM10 (ug/m3)

Time

PM10

Average Wind Speed

PM10 NAAQS

(150 ug/m3)

Figure D5. Wind Direction vs. Wind Speed 

0

5

10

15

20

25

0

90

180

270

360

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Wind Direction (degree)

Wind Speed (mph)

Time

Wind Direction

Average Wind Speed

N

W

S

E

N

Figure D6. PM10 vs. Mixing Height 

0

500

1000

1500

2000

2500

3000

0

500

1,000

1,500

2,000

2,500

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Mixing Height (m)

PM10 (ug/m3)

Time

PM10

Mixing Height

PM10 NAAQS

(150 ug/m3)

HIGH WIND DAY, 12/13/2008, STANFIELD 
D-4 

Figure D7. PM10 vs. Wind Speed 

0

2

4

6

8

10

12

14

16

18

0

100

200

300

400

500

600

700

800

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Wind Speed (mph)

PM10 (ug/m3)

Time

PM10 

Average Wind Speed

PM10 NAAQS

(150 ug/m3)

Figure D8. Wind Direction vs. Wind Speed 

0

2

4

6

8

10

12

14

16

18

0

90

180

270

360

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Wind Direction (degree)

Wind Speed (mph)

Time

Wind Direction 

Average Wind Speed

N

W

S

E

N

Figure D9. PM10 vs. Mixing Height 

0

200

400

600

800

1000

1200

0

100

200

300

400

500

600

700

800

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Mixing Height (m)

PM10 (ug/m3)

Time

PM10 

Mixing Height

PM10 NAAQS

(150 ug/m3)
D-5 

HIGH WIND DAY, 11/21/2008, STANFIELD 

Figure D10. PM10 vs. Wind Speed 

0

2

4

6

8

10

12

14

16

18

0

100

200

300

400

500

600

700

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Wind Speed (mph)

PM10 (ug/m3)

Time

PM10 

Average Wind Speed

PM10 NAAQS

(150 ug/m3

)

Figure D11. Wind Direction vs. Wind Speed 

0

2

4

6

8

10

12

14

16

18

0

90

180

270

360

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Wind Direction (degree)

Wind Speed (mph)

Time

Wind Direction 

Average Wind Speed

N

W

S

E

N

Figure D12. PM10 vs. Mixing Height 

0

200

400

600

800

1000

1200

1400

1600

1800

0

100

200

300

400

500

600

700

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Mixing Height (m)

PM10 (ug/m3)

Time

PM10 

Mixing Height

PM10 NAAQS

(150 ug/m3)
Appendix E 

Statistics of the High Wind PM10 Exceedance 

Days at Pinal County Housing in 2008 
E-1 

Table E1. Statistics of the PM10 High Wind Exceedance Days at Pinal County Housing in 2008 

Date 

Number of 

High wind 

hours 

Daily 

Average 

PM10 

(ug/m3) 

Daily 

Average 

WSPD 

(mph) 

Daily Max 

WSPD 

(mph) 

Correlation of 

Hourly Wind 

Speed and 

PM10 

11/9/2008 4 282 5 17 0.7954 

5/21/2008 13 271 11 17 0.4827 

1/1/2008 15 260 11 20 0.6639 

5/15/2008 2 222 5 15 0.5432 

9/11/2008 2 213 4 18 0.7246 

8/15/2008 1 172 4 14 0.7661 

10/27/2008 7 156 7 15 0.7750 
E-2 

HIGH WIND DAY, 01/01/2008, PINAL COUNTY HOUSING 

Figure E1. PM10 vs. Wind Speed 

0

5

10

15

20

25

0

200

400

600

800

1,000

1,200

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Wind Speed (mph)

PM10 (ug/m3)

Time

PM10 

Average Wind Speed

PM10 NAAQS

(150 ug/m3)

Figure E2. Wind Direction vs. Wind Speed 

0

5

10

15

20

25

0

90

180

270

360

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Wind Direction (degree)

Wind Speed (mph)

Time

Wind Direction 

Average Wind Speed

N

W

S

E

N

Figure E3. PM10 vs. Mixing Height 

0

200

400

600

800

1000

1200

1400

1600

1800

0

200

400

600

800

1,000

1,200

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Mixing Height (m)

PM10 (ug/m3)

Time

PM10 

Mixing Height

PM10 NAAQS

(150 ug/m3

)
Appendix F 

Statistics of the PM10 Exceedance Days at Maricopa in 2008 
F-1 

Table F1. Statistics of the PM10 High Wind Exceedance Days at Maricopa in 2008 

Date 

Number of 

High wind 

hours 

Daily Average 

PM10 (ug/m3

) 

Daily Average 

WSPD (mph) 

Daily Max 

WSPD (mph) 

Correlation of 

Hourly Wind 

Speed and 

PM10 

1/1/2008 12 317 11 19 0.7112

8/7/2008 3 213 6 22 0.7717

9/11/2008 2 170 6 21 0.6398

10/11/2008 7 157 8 14 0.5433

10/27/2008 9 159 9 18 0.4384

11/9/2008 6 520 8 18 0.6795
F-2 

HIGH WIND DAY, 10/27/2008, MARICOPA 

Figure F-1. PM10 vs. Wind Speed 

0

2

4

6

8

10

12

14

16

18

20

0

100

200

300

400

500

600

700

800

900

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Wind Speed (mph)

PM10 (ug/m3)

Time

PM10

Average Wind Speed

PM10 NAAQS

(150 ug/m3)

Figure F-2. Wind Direction vs. Wind Speed 

0

2

4

6

8

10

12

14

16

18

20

0

90

180

270

360

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Wind Direction (degree)

Wind Speed (mph)

Time

Wind Direction

Average Wind Speed

N

W

S

E

N

Figure F-3. PM10 vs. Mixing Height 
F-3 

0

500

1000

1500

2000

2500

3000

3500

0

100

200

300

400

500

600

700

800

900

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Mixing Height (m)

PM10 (ug/m3)

Time

PM10

Mixing Height

PM10 NAAQS

(150 ug/m3)
Appendix G 

Statistics of the PM2.5 Exceedance Days at Cowtown in 2008 
G-1 

Table G1. Statistics of the PM2.5 Exceedance Days at Cowtown in 2008 

 Date

Daily 

Average 

PM2.5 

(ug/m3

) 

Daily 

Average 

PM10 

(ug/m3

) 

Daily 

Average 

WSPD 

(mph) 

Daily Max 

WSPD 

(mph) 

3/25/2008 42 497 3 5 

4/12/2008 41 370 4 10 

5/6/2008 40 304 7 16 

4/30/2008 40 285 10 18 

11/20/2008 38 345 3 6 

6/23/2008 37 249 6 11 

4/18/2008 36 362 3 7 
G-2 

PM2.5 DAY, 03/25/2008, COWTOWN 

Figure G1. PM10 vs. Wind Speed 

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0

1,000

2,000

3,000

4,000

5,000

6,000

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Wind Speed (mph)

PM10 (ug/m3)

Time

PM10

Average Wind Speed

PM10 NAAQS

(150 ug/m3)

Figure G2. Wind Direction vs. Wind Speed 

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0

90

180

270

360

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Wind Direction (degree)

Wind Speed (mph)

Time

Wind Direction

Average Wind Speed

N

W

S

E

N

Figure G3. PM10 vs. Mixing Height 

0

500

1000

1500

2000

2500

0

1,000

2,000

3,000

4,000

5,000

6,000

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Mixing Height (m)

PM10 (ug/m3)

Time

PM10

Mixing Height

PM10 NAAQS

(150 ug/m3

)
G-3 

PM2.5 DAY, 04/12/2008, COWTOWN 

Figure G4. PM10 vs. Wind Speed 

0

2

4

6

8

10

12

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Wind Speed (mph)

PM10 (ug/m3)

Time

PM10

Average Wind Speed

PM10 NAAQS

(150 ug/m3

)

Figure G5. Wind Direction vs. Wind Speed 

0

2

4

6

8

10

12

0

90

180

270

360

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Wind Direction (degree)

Wind Speed (mph)

Time

Wind Direction

Average Wind Speed

N

W

S

E

N

Figure G6. PM10 vs. Mixing Height 

0

100

200

300

400

500

600

700

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Mixing Height (m)

PM10 (ug/m3)

Time

PM10

Mixing Height

PM10 NAAQS

(150 ug/m3)

PM2.5 DAY, 05/06/2008, COWTOWN 
G-4 

Figure G7. PM10 vs. Wind Speed 

0

2

4

6

8

10

12

14

16

18

0

200

400

600

800

1,000

1,200

1,400

1,600

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Wind Speed (mph)

PM10 (ug/m3)

Time

PM10

Average Wind Speed

PM10 NAAQS

(150 ug/m3)

Figure G8. Wind Direction vs. Wind Speed 

0

2

4

6

8

10

12

14

16

18

0

90

180

270

360

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Wind Direction (degree)

Wind Speed (mph)

Time

Wind Direction

Average Wind Speed

N

W

S

E

N

Figure G9. PM10 vs. Mixing Height 

0

200

400

600

800

1000

1200

1400

1600

0

200

400

600

800

1,000

1,200

1,400

1,600

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Mixing Height (m)

PM10 (ug/m3)

Time

PM10

Mixing Height

PM10 NAAQS

(150 ug/m3

)

PM2.5 DAY, 04/30/2008, COWTOWN 
G-5 

Figure G10. PM10 vs. Wind Speed 

0

2

4

6

8

10

12

14

16

18

20

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Wind Speed (mph)

PM10 (ug/m3)

Time

PM10 

Average Wind Speed

PM10 NAAQS

(150 ug/m3)

Figure G11. Wind Direction vs. Wind Speed 

0

2

4

6

8

10

12

14

16

18

20

0

90

180

270

360

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Wind Direction (degree)

Wind Speed (mph)

Time

Wind Direction 

Average Wind Speed

N

W

S

E

N

Figure G12. PM10 vs. Mixing Height 

0

200

400

600

800

1000

1200

1400

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Mixing Height (m)

PM10 (ug/m3)

Time

PM10 

Mixing Height

PM10 NAAQS

(150 ug/m3)
G-6 

PM2.5 DAY, 11/20/2008, COWTOWN 

Figure G13. PM10 vs. Wind Speed 

0

1

2

3

4

5

6

7

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Wind Speed (mph)

PM10 (ug/m3)

Time

PM10 

Average Wind Speed

PM10 NAAQS

(150 ug/m3)

Figure G14. Wind Direction vs. Wind Speed 

0

1

2

3

4

5

6

7

0

90

180

270

360

0:002:004:006:008:00

10:0012:0014:0016:0018:00

20:0022:00

Wind Direction (degree)

Wind Speed (mph)

Time

Wind Direction 

Average Wind Speed

N

W

S

E

N

Figure G15. PM10 vs. Mixing Height 

0

200

400

600

800

1000

1200

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Mixing Height (m)

PM10 (ug/m3)

Time

PM10 

Mixing Height

PM10 NAAQS

(150 ug/m3)
G-7 

PM2.5 DAY, 06/23/2008, COWTOWN 

Figure G16. PM10 vs. Wind Speed 

0

2

4

6

8

10

12

0

100

200

300

400

500

600

700

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Wind Speed (mph)

PM10 (ug/m3)

Time

PM10

Average Wind Speed

PM10 NAAQS

(150 ug/m3

)

Figure G17. Wind Direction vs. Wind Speed 

0

2

4

6

8

10

12

0

90

180

270

360

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Wind Direction (degree)

Wind Speed (mph)

Time

Wind Direction

Average Wind Speed

N

W

S

E

N

Figure G18. PM10 vs. Mixing Height 

0

500

1000

1500

2000

2500

3000

3500

4000

0

100

200

300

400

500

600

700

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Mixing Height (m)

PM10 (ug/m3)

Time

PM10

Mixing Height

PM10 NAAQS

(150 ug/m3

)
G-8 

PM2.5 DAY, 04/18/2008, COWTOWN 

Figure G19. PM10 vs. Wind Speed 

0

1

2

3

4

5

6

7

8

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Wind Speed (mph)

PM10 (ug/m3)

Time

PM10

Average Wind Speed

PM10 NAAQS

(150 ug/m3)

Figure G20. Wind Direction vs. Wind Speed 

0

1

2

3

4

5

6

7

8

0

90

180

270

360

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Wind Direction (degree)

Wind Speed (mph)

Time

Wind Direction

Average Wind Speed

N

W

S

E

N

Figure G21. PM10 vs. Mixing Height 

0

50

100

150

200

250

300

350

400

450

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

0:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00

Mixing Height (m)

PM10 (ug/m3)

Time

PM10

Mixing Height

PM10 NAAQS

(150 ug/m3

)
 

Appendix H 

Road Count Data 
H-1 

ROAD COUNTS CONDUCTED BY PINAL COUNTY AIR QUALITY 

Road Name 

Road 

Surface Location of Count 

Month/Year of 

Count 

Cars 

per day 

Signal Peak Road Dirt ~1/2 mile south of Hwy 87 October 2001 228

Diversion Dam Road Dirt At the end of the pavement November 2001 127

Gary Road Dirt Between Lind Rd and Gail Rd November 2001 904

Tweedy Road Dirt Just south of Kleck Road December 2001 133

Bartlett Road Dirt Just west of Kenworthy Road January 2002 139

Arizona Western Road Dirt ~1/4 mile west of Eleven Mile 

Corner Road

January 2002 83

Judd Road Dirt ~1/4 mile west of Gary Road March 2002 154

Sossaman Road Dirt ~1/2 mile south of Empire Blvd April 2002 117

Ellsworth Road Dirt ~1/4 mile south of Hunt Hwy May 2002 193

White and Parker Road Dirt ~0.1 mile north of Farrell Road June 2002 456

Hartman Road Dirt ~0.1 mile north of MaricopaCasa Grande HighwayJune 2002 88

Warren Road Dirt ~0.1 mile south of Farrell Road July 2002 80

Amarillo Valley Road Dirt ~0.1 mile south of Clayton Road July 2002 643

Christensen Road Dirt ~2 miles north of Highway 287 August 2002 21

Peralta Road Dirt ~0.1 mile northeast of Fransisco 

de Coronado Road

September 2002 86

San Juan Road Dirt ~0.1 mile north of Solano Road September 2002 343

Ivar Road Dirt ~0.1 mile west of Gary Road September 2002 200

Sherry Lane Dirt ~0.1 mile west of Schnepf Road October 2002 65

Hash Knife Road Dirt ~0.2 mile east of Schnepf Road November 2002 416

Mammoth Road Dirt ~0.5 mile north of Selma Hwy November 2002 50

Honeycutt Road Dirt ~1.0 mile east of Hwy 387 December 2002 423

Barnes Road Dirt ~0.6 mile east of Ralston Road December 2002 115

Warren Road Dirt ~0.1 mile south of Century January 2003 340

Houston Road Dirt ~0.4 mile west of Ganzel February 2003 1042

Southwood Road Dirt Near the intersection with 

Country Lane

February 2003 81

Combs Road Dirt ~0.2 mile east of Schnepf Road February 2003 94

Macrae Road Dirt ~0.1 mile south of Vah Ki Inn March 2003 166

Jacob Waltz Road Dirt ~0.1 mile west of Mountain 

View Road

March 2003 268

Mountain View Road Dirt ~0.1 mile south of Lost 

Dutchman Road

April 2003 494

Lost Dutchman Road Dirt ~0.1 mile west of Mountain 

View

April 2003 312

Price Road Dirt w/ 

soil 

stabilizer

~0.3 mile east of Highway 79

(Friday thru Monday of Country 

Thunder)

April 2003 

6676

Price Road Dirt w/ 

soil 

stabilizer

~0.3 mile east of Highway 79

(Friday thru Wednesday of 

Country Thunder)

April 2003 

4061

Navajo Road Paved ~50 feet mile south of Papago 

Road (next to Stanfield monitor)

May 2003 91

Macrae Road Dirt ~0.1 mile south of Vah Ki Inn May 2003 174
H-2 

Foothill Road Dirt ~0.1 mile east of Cortez Road June 2003 110

Signal Peak Road Dirt ~0.1 mile north of Woodruff August 2003 206

Signal Peak Road Dirt ~0.4 mile south of Highway 87 August 2003 127

Hidalgo Street Dirt ~0.1 mile west of Val Vista August 2003 91

Lost Dutchman Road Dirt ~0.1 mile east of Val Vista September 2003 129

Hartman Road Dirt ~0.1 mile north of MaricopaCasa Grande HighwaySeptember 2003 96

Farrell Road Dirt ~0.9 mile east of White and 

Parker Road

October 2003 134

White an Parker Road Dirt ~0.1 mile north of MaricopaCasa Grande HighwayOctober 2003 343

Maricopa-Casa Grande 

Highway Paved

~0.1 mile southeast of White and 

Parker Road (next to the 

Cowtown monitor)

October 2003

2717

Cowtown Road Dirt ~0.1 mile northwest of Hartman 

Road

November 2003 146

Saddle Butte Street Dirt ~0.1 mile east of Ironwood 

Drive

November 2003 319

Plaza Drive Dirt ~0.1 mile south of Smoketree St December 2003 114

Combs Road Paved ~0.2 mile east of Ganzel Road 

(next to Combs monitor)

December 2003 2459

Wagon Wheel Road Dirt ~0.1 mile north of Dove Roost 

Road

January 2004 173

Magma Road Dirt ~0.1 mile east of Gary Road January 2004 84

Davis Ranch Road Dirt ~0.6 mile west of Bootleg Road February 2004 220

Grande Valley Road Dirt ~0.1 mile east of Way Out Road February 2004 83

Tomahawk Road Dirt ~0.1 mile north of McKellips 

Road

February 2004 231

Cactus Road Dirt ~0.1 mile north of McKellips 

Road

March 2004 184

Blanco Drive Dirt ~ 0.1 north of Val Vista Road March 2004 126

Greasewood Street Dirt ~ 0.1 mile east of San Marcos March 2004 41

Desierto Road Dirt/Grav

el

~0.1 mile eat of Estrella Road April 2004 118

Toluca Drive Dirt/Grav

el

~0.1 mile eat of Estrella Road April 2004 51

Price Road Dirt w/ 

soil 

stabilizer

~0.3 mile east of Highway 79

(Thur. thru Mon. of Country 

Thunder)

April 2004 

5737

Caballero Road Dirt/Grav

el

~0.1 mile eat of Estrella Road May 2004 122

Eleven Mile Corner Road Paved ~0.1 mile south of Storey Road 

(next to PCH monitor)

June 2004 653

Gary Road Dirt ~0.3 mile south of Judd June 2004 141

Judd Road Dirt ~0.1 mile west of Bryce Trail July 2004 245

Brenner Pass Road Dirt ~0.1 mile south of Phillips Road July 2004 347

Bianco Road Dirt ~0.1 mile south of Peters Road August 2004 66

Florence-Kelvin Hwy Paved ~1.1 mile south of Hwy 177 August 2004 75

Arroya Road Dirt ~0.1 mile south of Superstition 

Blvd.

August 2004 17

Pacific Street Paved ~0.1 mile north of Broadway October 2004 164
H-3 

Ave

Rolling Ridge Road Dirt ~0.1 mile east of Schnepf Road October 2004 362

Queen Anne Drive Paved ~0.2 mile south of Cavendish 

Drive

November 2004 785

Arizona Hwy 88

(conducted by ADOT) Paved ~0.1 mile southwest of 

Superstition Blvd

November 2004 3848

Arizona Hwy 347

(conducted by ADOT) Paved ~0.5 mile south of AZ Hwy 238 June 2004 6787

Price Road

Dirt

~0.2 mile east of Hwy 79 

(non-event weekend)

February 2005

2/25 AM thru 2/28 

AM

213

Price Road

Dirt

~0.2 mile east of Hwy 79 

(non-event weekend)

March 2005

3/4 PM thru 3/7 

AM

175

Price Road

Dirt

~0.2 mile east of Hwy 79 (Girl 

Scout Jamboree weekend)

March 2005

3/11 AM thru

3/14 AM

541

Selma Highway Dirt ~0.1 mile west of Peart Rd. March 2005 89

Price Road Dirt w/ 

soil 

stabilizer

~1.3 mile east of Highway 79

(Fri. thru Mon. of Country 

Thunder)

April 2005

6243

Hidden Valley Dirt ~0.1 mile north of Farrell Rd. April 2005 Road tube 

cut twice

Evans Road Dirt ~0.1 north of Warren Rd. July 2005 73

Southern Avenue Dirt ~0.1 mile east of Geronimo Rd. July 2005 120

Moon Vista Road Dirt ~0.1 mile east of Ironwood Rd. September 2005 105

Hunt to Empire Short 

Cut

T3SR7E between S2&3

Private 

Property 

– not a 

road –

Dirt

~0.1 mile north of Hunt Hwy September 2005

216

Hash Knife Road Dirt ~0.1 mile east of Schnepf October 2005 424

Cooper Road Dirt ~0.1 mile north of Arizona 

Farms Rd

October 2005 370

Scorpio Road Dirt ~0.1 mile west of Henness Road October 2005 25

Tweedy Road Dirt ~0.1 south of Hwy 287 December 2005 95

Varnum Road Dirt ~0.1 mile south of Judd Road January 2006 71

Libra Road Dirt ~0.1 mile east of Sagittarius 

Road

February 2006 55

Hewitt Station Road Dirt ~1.8 mile north of Hwy 60 March 2006 125

Reddington Road Dirt ~ 0.1 mile north of the County 

line

March 2006 112

Reddington Road Dirt At the end of the pavement March 2006 162

Price Road Dirt w/ 

soil 

stabilizer

~1.3 mile east of Highway 79

(Thu. thru Mon. of Country 

Thunder)

April 2006

5952

Price Road

Dirt

~1.3 mile east of Highway 79

(Fri. thru Mon. of non-event 

weekend)

April 2006

4/14/06 AM thru

4/17/06 AM

212

Mitchell Trail Dirt ~0.1 mile south of Arizona 

Farms Rd.

April 2006 165
H-4 

Southwood Road Dirt ~0.1 mile east of Kenworthy May 2006 174

Sherry Lane Dirt ~0.1 mile east of Kenworthy May 2006 213

Hartman Road Dirt ~0.1 mile north of Honeycutt 

Rd.

July 2006 81

Cooper Road Dirt ~0.1 mile south of Judd Rd. September 2006 225

Gary Road Dirt ~0.1 mile north of Silverdale October 2006 742

Hopi Road Dirt ~0.1 mile east of Trekell November 2006 170

Arabian Road Dirt ~0.2 mile south of Organ Pipe 

Rd.

December 2006 61

Dune Shadow Road Dirt ~0.1 mile west of Amarillo 

Valley Rd.

January 2007 291

Phillips Road Dirt ~0.1 mile east of Sunland Gin 

Road

January 2007 100

Geronimo Road Dirt ~0.1 mile north of Broadway 

Road

January 2007 270

Arabian Road Dirt ~0.2 mile south of Organ Pipe 

Rd.

January 2007 60

Magma Road alignment 

(utility easement) Dirt ~0.1 mile east of Edwards Rd. February 2007 37

Mountain View Road Dirt ~0.1 mile south of Lost 

Dutchman Rd

March 2007 214

Mountain View Road Dirt ~0.1 mile north of Lost 

Dutchman Rd

March 2007 74

Mountain View Road Dirt ~0.1 mile north of Jacob Waltz 

Rd

April 2007 206

Mountain View Road Dirt ~0.1 mile east of Mountain View 

Rd

May 2007 181

Price Road Dirt w/ 

soil 

stabilizer

~1.3 mile east of Highway 79

(Thu. thru Mon. of Country 

Thunder)

April 2008

4/17/08 AM thru

4/21/09 AM

9150

Clayton Road Dirt ~0.1 mile east of Ethington Rd. June 2008 103

Bianco Road Dirt ~0.1 mile south of Peters Rd. June 2008 115

Ethington Road Dirt ~0.1 mile south of Clayton Rd. June 2008 126

Price Road Dirt w/ 

soil 

stabilizer

~1.3 mile east of Highway 79

(Wed. thru Mon. of Country 

Thunder)

April 2009

4/1/09 AM thru

4/6/09 AM

6681

Price Road

Dirt w/ 

soil 

stabilizer

~1.3 mile east of Highway 79

(Tue. thru Mon. of Country 

Thunder)

April 2010

4/13/09 AM thru

4/19/09 AM

(4/15 6pm thru 

4/16 8am lost)

6760

Cooper Road Dirt 0.1 mile south of Magma Rd. August 2011

(8/12-19/11) 383

Cooper Road Dirt 0.1 mile south of Magma Rd. August 2011

(8/22-29/11) 310

 
Appendix I 

A Method for the Back-Calculation of Wind-Blown Emission Factors from 

Field Measured PM10 Concentrations 

Arizona Department of Environmental Quality 

Air Quality Division 
I-1

A Method for the Back-Calculation of Wind-Blown Emission Factors from Field 

Measured PM10 Concentrations

Arizona Department of Environmental Quality 

Air Quality Division 

ABSTRACT 

Regional variability in soil characteristics, meteorological trends, and surface 

disturbances lead to changes in wind blown emission factors (EFs) for a given land use 

(LU). Therefore, it is prudent to determine accurate EFs for Pinal County in order to 

provide the most accurate wind blown Emission Inventory (EI) for the completion of the 

Pinal County State Implementation Plan (SIP). Emission Factor Back Calculation 

(EFBC) is currently being proposed as a method of wind blown EF estimation in place of 

wind blown EF literature values for the creation of the Pinal County PM10 State 

Implementation Plan (SIP). This method relies on the use of field measured PM10 

concentrations and meteorological data to back-calculate EFs for different land uses. The 

method depends on accurate completion of the following steps: 1) Land Use 

Classification, 2) Meteorological and Emission Data Processing, 3) Estimation of 

Emission Origination Area, 4) ArcGIS Data Integration, and 5) EF Statistical 

Optimization. Furthermore, the model uses the following simple linear approach during 

the statistical optimization step: 

 AE *EFnnn

where En is total emissions for each land use within a given footprint (tons/hr), An is the 

calculated area of each land use located within each footprint (m2

), and EFn is the 

estimated emission factor for each land use located within a given footprint. 

This paper presents the steps taken in the EFBC process and further presents a testing of 

the model, where the proposed SIP design day for the Pinal County Housing monitor of 

1/1/2008 was modeled to estimate EFs for 9 different land uses. The estimated hourly 

EFs were then used to calculate hourly emissions for the design day. Individual hour 

variability between model estimated PM10 concentrations and PM10 concentrations 

measured in the field varied between 2% and 282% over the 24 hour period. The average 

24 hour PM10 concentration for PCH on 1/1/2008 was 264.9 μg/m3

 as measured at the 

monitor. The model predicted a 24 hour average concentration for PCH on the design 

day of 260.4 μg/m3

. 

 
I-2

INTRODUCTION 

The Arizona Department of Environmental Quality (ADEQ) in cooperation with the 

Pinal County Air Quality Department is in the process of preparing a State 

Implementation Plan (SIP) for particulate matter less than 10 μm (PM10). The purpose of 

the SIP is to protect human health and welfare through: 1) air monitoring data and 

analysis, 2) PM10 emission inventory, 2) air dispersion modeling to determine maximum 

concentrations of pollutants, and 4) control measures used in controlling particulate 

emissions in order to attain the PM10 and PM2.5 standard. 

Airborne particles are a serious threat to human health. Major health concerns due to 

exposure to PM10 include: effects on breathing and respiratory systems, damage to lung 

tissue, cancer, and premature death. The elderly, children, and people with chronic lung 

disease, influenza, or asthma, are especially sensitive to the effects of particulate matter. 

Ambient levels of PM10 measured at air quality monitoring stations within Pinal County 

show widespread, frequent, and in some instances, severe, violations of the PM10

standard, dating back to 2002. Pinal County’s PM10 levels are among the highest in the 

country. Based on 2009 – 2011 certified air quality data, the Pinal County Housing 

monitor, located approximately 11 miles east of Casa Grande, averaged over 14 recorded 

exceedances per year of the 1987 PM10 ambient air quality standard of 150 micrograms 

per cubic meter (μg/m3). 

Ambient monitors located in the new nonattainment area routinely record concentrations 

two to three times the level of the standard and several monitors have recorded levels 

approaching or exceeding the significant harm level of 600 (μg/m3

). After reviewing 

available data, EPA redesignated much of the western half of Pinal County to 

nonattainment for the 1987 24-hour PM10 National Ambient Air Quality Standard 

(NAAQS) on May 23, 2012. See 75 FR 60680, October 1, 2010. 

Redesignation of the western portion of Pinal County to nonattainment requires the State 

to create a State Implementation Plan (SIP) which proposes reasonable control methods 

to reduce PM10 emissions in Pinal County to levels which comply with EPA’s PM10

ambient air quality standard. Conformity with these standards are exhibited through 

modeling of exceedance days prior to and following control technology implementation 

to demonstrate adequate emissions reductions have been estimated. Modeling is highly 

dependant on an intimate understanding of the factors within a region that contribute to 

emissions during low wind, stagnation, and high wind periods. 

 

Low wind emissions are dominated by mechanical activities which cause the entrainment 

of particulate matter into the atmosphere. As wind speeds increase, wind blown dust 

entrainment increases resulting in a lower proportion of field measured PM10 

concentrations originating from activity based PM entrainment. The degree to which 

wind blown emissions contribute to ambient air PM10 concentrations is dependant on the 

land use being analyzed. Different land uses can exhibit varying degrees of soil moisture, 
I-3

soil texture, soil disturbance, surface roughness, and impermeable layer coverage. 

Furthermore, these characteristics can change for land uses within different regions of the 

US, State, or even County. For this reason, it is important to identify site specific 

windblown emission factors (EFs) rather than relying on literature based EFs which may 

originate from measurements taken from a variety of different locations. 

 

Since performing field measurements of windblown EFs for a variety of land uses using a 

wind tunnel is resource intensive, ADEQ proposes another method of estimating 

windblown EFs. The Emission Factor Back Calculation (EFBC) method utilizes field 

measured meteorological and PM10 concentration data for the sites and periods of 

interest in order to estimate EFs for land uses that are characteristic for that region. 

Using field measured meteorological data, the EFBC method estimates emission particle 

origination areas in order to determine what land uses are contributing emissions 

impacting the monitor during a period of interest. The method then employs a statistical 

optimization approach to estimate the most logical EF for each land use within the 

contributing area. 

 

In this document, ADEQ presents the steps taken to implement the EFBC method. 

ADEQ further tests the estimated EFs to determine how well PM10 concentrations are 

calculated using these EFs against the field measured PM10 concentrations for the 

proposed model design day and location of 1/1/2008 for the Pinal County Housing 

monitor. 

METHODS 

The process behind EF back-calculation first relies on land use classification into distinct 

categories. These categories should cover all land uses necessary to perform a 

comprehensive EI calculation for the non-attainment area. The second step of the EF 

back-calculation process is data processing of meteorological and PM10 concentration 

field data in order to remove periods of incomplete data. This step further requires wind 

speed, wind direction, mixing height (the height at which there is negligible vertical 

atmospheric mixing), air temperature, sensible heat flux (the conductive heat flux from 

the Earth’s surface to the atmosphere), and PM10 concentration data be averaged at the 

daily scale and for some categorizes to be analyzed for variability to insure a group of 

representative sample periods are utilized for analysis. The third step utilizes the Korman 

and Meixner analytical footprint model in order to estimate particle emission origination 

location. This method utilizes probability analysis to calculate an area from which 

particles (PM10) are most likely to have originated. The next step requires the integration 

of aerial photography, calculated footprint extents, and land use information into ArcGIS 

in order to characterize the land uses located within each footprint surrounding the 

monitors of interest. One footprint analysis is performed for each land use classification 

previously categorized. The idea is to use the total emissions (as determined from PM10

monitor measurements) and the calculated area of each land use (as determined from 

ArcGIS) within a footprint to back calculate the unknown emission factors utilizing the 

equation: 
I-4

 AE *EFnnn

These equations cannot be solved individually, but must be solved utilizing a statistical 

optimization technique such as the Newton Method. The Newton Method requires an 

equal number of equations as unknowns. Therefore, if nine land use types have been 

identified for EF estimation, nine different equations and thus nine individual footprints 

must be described. Once all footprints have been created, the Newton Method can be 

used to solve for all EF’s simultaneously by using the Excel Addin known as Solver.

 

The following sections will outline the steps which were taken to implement this method 

for the Pinal County Housing (PCH) proposed design day of January 1, 2008. The 

following steps to the EFBC method are described: 

Land Use Classification 

Meteorological and PM10 Concentration Data Processing 

Estimation of Emissions Origination Area 

ArcGIS Data Integration 

EF Statistical Optimization 

Following the estimation of EFs, the proposed design day for PCH of January 1st

 was 

modeled to assess the accuracy of these EFs to produce field collected PM10 

concentration values. 

Land Use Classification 

 

The first step in the EFBC method is to identify and classify the land use types which 

require EF determination. This will be limited to those land use types within 

approximately 6 km of the PM10 monitors (the distance from the monitors will change 

with the wind speed examined). For all meteorological conditions tested, land use 

classification for a 6 km radius area surrounding the monitor was adequate since the 

Footprint estimation model only identifies those land uses contributing 90% of total 

emissions to the monitor. Therefore, the footprint emissions origination model predicts 

that for the meteorological extremes tested in Pinal County, emissions originating from 

>6 km from the monitor will always comprise < 10% of the total emissions impacting 

the monitor. Aerial photography and activity records supplied by Pinal County Air 

Quality Department (PCAQD) were used to classify land uses. The following land uses 

were identified within 6 km of the Stanfield, Cowtown, Maricopa, and Pinal County 

Housing monitors: 

Developed Urban Lands 

Developed Rural Lands 

Paved Roads 

Unpaved Roads 

Cleared Areas 
I-5

Construction Areas 

Concentrated Animal Feeding Operations - CAFOs (Feedlots & Dairies) 

Desert Shrubland 

Cropland (Fallow & Vegetated) 

Characterization of land use for the 1/1/2008 design day was achieved through the 

examination of an aerial photograph taken of the western portion of Pinal County in early 

April of 2008 (LandisCor), PCAQD and ADEQ road maps, and 2008 active CAFO 

location maps and active construction areas provided by PCAQD. Each land use required 

the creation of 1 emission footprint (as discussed later in this document). Therefore, 9 

land use classifications will require 9 footprints with each footprint’s extent covering a 

unique land use classification. 

Meteorological and Emission Data processing 

 

The next step requires the acquisition and processing of wind speed, wind direction, 

mixing height, sensible heat flux, and field measured PM10 concentration data from the 

stations of interest. For the purposes of testing the PCH 1/1/2008 design day, data was 

processed from 4 monitors within the nonattainment area: Cowtown, Pinal County 

Housing, Stanfield, and Maricopa. These 4 monitors recorded the highest number of 

exceedances during the year of 2008 providing the most conservative datasets available 

from a regulatory standpoint. Furthermore, processing multiple meteorological and 

PM10 concentration datasets allowed ADEQ to create EFs representative of the entire 

nonattainment area rather than for a small section of the nonattainment area. 

 

The first step of data processing was to isolate only the hourly data which occurred 

during the winter of 2008 (December 22nd – March 20th). Arizona Meteorological 

Network (AZMET) rainfall data was analyzed for all rain gauges located within the 

nonattainment area. Any date when a rain gauge registered greater than 2 mm for the day 

was removed from the data pool to minimize the effects of precipitation. Hourly periods 

where data were missing were interpolated. Wind speed, wind direction, and PM10 

concentration values were taken from field measurements. Mixing heights and sensible 

heat fluxes were calculated by AERMET using meteorological data (including upper air 

sounding information) from Tucson, AZ. Hourly seasonal temperature was averaged 

from AZMET values for the year of 2008. Hourly data at each station was then separated 

by hour of the day, wind direction, and wind speed at a 10 m height into the following 

groupings: 

Hour periods: 0-700, 800-1500, 1600-2300 

Wind Direction: 20 degrees increments 

Wind speed periods (mph): 0-3. 3-6, 6-9, 9-12, 12-15, 15-18, 18+ 

Data was then averaged in each group. Averaging of similar data allows the model to 

create sample sizes greater than 1 to reduce the effect of outliers. The averaged data 

created a representative dataset of meteorological and PM10 concentration data to be 

utilized for emission footprint creation. 
I-6

Estimation of Emissions Origination Area 

Characterization of the land uses contributing to emissions impacting the monitor 

requires the estimation of the area from which the emissions originate. Back trajectories 

were considered for this purpose; however, the use of back trajectories does not account 

for particle deposition rates. In order to account for this, emission footprints were 

estimated using the EdiTools program known as “Footprint”

(http://www.geos.ed.ac.uk/abs/research/micromet/EdiTools/). Footprint creates an image 

representative of the measurement site of interest dependent on the meteorological 

characteristics input into the model. This image can then be imported into ArcGIS for 

characterization contributing land uses. The Footprint model is a windows desktop 

implementation of the Korman and Meixner analytical footprint model (2001). This 

model accounts for vertical turbulence and cross-wind dispersion in order to back 

calculate particle motion based on a range of meteorological inputs such as: wind speed, 

wind direction, distance to obstructions, air temperature, sensible heat flux, mixing 

height, and surrounding vegetation canopy density and height. The model originates 

from the advection/diffusion equation but has shown good agreement with a Lagrangian 

stochastic particle dispersion model (Kljun et al. 2003). 

The following information was utilized for creation of footprints for the modeling of the 

PCH 1/1/2008 design day: 

“Map X/Y dimension, meters” is the distance from the station to the nearest large 

obstruction (i.e. hill, mountain, etc) in the direction from which the wind flow 

originates, which was determined from aerial photography of the region to be 

approximately 15 km for each of the monitors, 

“Maximum Fetch” was determined by the length of the footprint, 

“Measurement height” was set to the height at which the PM10 concentration 

measurements were taken and varied for each monitor, 

“Canopy Height” was estimated as 0.6m, 

“Canopy Area Density” was estimated as 1.0, 

“Boundary Layer Depth” is the average mixing height estimated by AERMET, 

“Sensible Heat Flux” was estimated by AERMET, 

“Air Temperature” was estimated to hourly averages for the season based on 

AZMET data from 2008, 

“Wind Speed” was obtained from field measurements at the monitors,

“Wind Direction” was obtained from field measurements at the monitors, 

And the “Brightness distribution footprint boundary, %” was set to 90% to 

estimate the origination area of 90% of the emissions. 

A single emission estimation origination footprint was created for each EF needed to 

model the January 1, 2008 proposed design day for PCH. For each unique wind speed 

and hour of day grouping it was necessary to create 9 footprints. Each of these footprints 

contained a unique land use type so that EFs could be created for all 9 land use types for 
I-7

each wind speed/hour of day grouping. The total number of footprints created for the 

testing of the PCH 1/1/2008 design day was 80. 

ArcGIS Data Integration 

The next step of the method utilized ArcGIS (Esri) to plot each footprint and to determine 

the boundaries for each land use within a given footprint. Images created during the 

Footprint processing portion of the method were loaded into ArcGIS. The receptor (i.e. 

PM10 monitor) on each footprint was geo-rectified to match the physical location of the 

monitor station of interest and the scale of the footprints were adjusted to match that of 

the land use data layer. New polygons were created to outline the footprint boundary. 

When necessary, these polygons were clipped to insure the extent of the footprint did not 

exceed the distance a parcel of air could travel in an hour. The land use polygon was 

then clipped to the boundary of the individual footprint polygons to create a land use 

polygon unique to each footprint polygon. Land use areas and road lengths were then 

calculated in ArcGIS. Road width was utilized to estimate road area for unpaved and 

paved roadways. 

Statistical Optimization of EFs 

The final step of the emission factor back calculation process involves data consolidation 

and statistical optimization in order to estimate the EFs for each land use. For each 

footprint, the total area of each land use type was summed in ArcGIS. After calculating 

the total land use area for each footprint, the data was compiled in MS Excel. For the 

optimization process each land use must be contained within at least one unique footprint. 

 

Following the consolidation of data, the emissions from the total footprint must be 

calculated from the field PM10 average concentrations for the period of interest. Daily 

average emissions at the station are calculated using the following equation: 

907,184,740,000

** CHA E FP M 

where E is the average emissions captured by the station [tons], AFP is the area of the 

emissions origination footprint [m2], HM is the atmospheric mixing height [m], C is the 

average hourly measured PM10 concentration [μg/m3

], and 907,184,740,000 is the 

conversion factor from micrograms to tons. Once this has been calculated for each 

footprint, an E80 or E90 can be calculated so 80% or 90% of the total emissions can be 

attributed to the 80% or 90% footprint (percentage is dependant on which footprint size 

reasonably fit within the boundaries of the Footprint model boundaries): 

80  *8.0 EE or 90  *9.0 EE

The final step requires the use of statistical optimization in order to simultaneously back 

calculate all 9 EFs for each wind speed/diurnal temporal grouping. In order to complete 

this process, the Excel Addin - Solver was utilized to apply the Newton Method. Solver 
I-8

was set up to simultaneously calculate all EFs and the difference between the calculated 

PM10 concentrations using those EFs for each footprint and the field measured PM10 

concentrations for each footprint (this value is known as the Error). Solver was then 

instructed to minimize the sum of the individual Errors squared for all footprints. Using 

this method, Solver determined the most appropriate EF values for each land use across 

all footprints in order to minimize Error using the equation: 





o

ax

A EFxx * 

where Ax is a given land use Area, EFx is the estimated Emission Factor of land use x, 

and ε is the Error associated with the footprint of interest. 

 

After the creation of individual EFs for all land uses at all wind speeds for the 3 periods 

of the day, wind blown EFs were determined by subtracting low wind EFs (i.e. 0-3 mph 

winds) from all higher wind speed emission factors (i.e. 3-6, 6-9, 9-12, 12-15, 15-18, and 

18+ mph) for all 9 land uses during each of the 3 temporal periods of the day (0-700, 

800-1500, and 1600-2300 hours). By doing so, activity driven emissions could be 

separated from wind blown emissions in order to adequately calculate EFs which were 

only dependant on emissions originating from windblown PM10 entrainment. 

Design Day Modeling 

Due to time constraints, modeling using AERMOD could not be performed to test the 

validity of the back calculated EFs. Therefore, a simplified model was utilized to test the 

accuracy of the estimated EFs. Model validity was tested using the EFs estimated for 

each land use by the EFBC method and applying the appropriate EFs to each hour of the 

design day. Since PM10 entrainment originating from stagnation activities were 

unknown, estimated EFs created prior to the wind blown correction were utilized in this 

testing in order to account for unknown stagnation emissions as well. Testing was 

achieved by using the meteorological data for each hour of the January 1st proposed PCH 

design day to create 24 unique emission origination footprints using the EdiRe Footprint 

program. These footprints represented the estimated origination area of 90% of the 

emissions impacting the station for a given hour of the design day. These 24 footprints 

were used in the same method described on the “ArcGIS Data Integration” section to 

determine the total land use area contributing emissions for each hour of the day. Each 

hour was modeled separately, multiplying EFs by the corresponding land use area to 

determine the emissions from each land use type and then summing all land use 

emissions for the hour. Total emissions were then converted to PM10 atmospheric 

concentrations to compare to monitor measured concentrations on the design day by 

modifying the equation: 

907,184,740,000

** CHA E FP M 

to calculate for C, whereby: 
I-9

FP HA M

E C

*

907,184,740,000* 

 

The variables A and H were based on the hourly measurements specific to the design day 

being tested. Hourly calculated concentrations were then compared to PM10

concentrations measured at the monitor for the design day. 

RESULTS 

 

EF Estimation 

 

The EFBC method relies upon adequate characterization and classification of emission 

contributing lands into discrete categories. For the purposes of the initial test of this method, land 

uses were classified into 9 categories. Table 1 gives a description of each of these Land Use (LU) 

codes. These LU codes will be used in subsequent graphs and tables. 

Table 1: Land use code categories 

Landuse Code Description 

A Developed Urban Lands 

B Developed Rural Lands (low density residential) 

C Paved Roads 

D Unpaved Roads 

E Cleared Areas 

F Construction Area 

G CAFOs and Dairies 

H Desert Shrubland 

I&J Fallow and Active Crop Fields 

For the purposes of creating the necessary EFs for all land uses during the PCH January 

1, 2008 design day, nine wind speed/period of day combinations were required. Table 2 

shows these combinations. For instance, on January 1stfor 000-700 hours winds speeds 

between 0-3 mph and 3-6 mph were registered at the PCH monitor, but no wind speeds 

higher than this were registered during this timeframe. Therefore, EFs were developed 

for these 2 wind speed bins, but not for any higher wind speeds for this time of day. 

Altogether, Table 2 gives 80 estimated EFs for 9 LUs as determined using the EFBC 

methodology. These EFs were estimated during the winter period of 2008 (Dec 21 – Mar 

20). These EFs have not been corrected to remove stagnation, activity driven emissions. 

An emission factor for land use G (CAFOs and Dairies) could not be determined for the 

winter of 2008 for wind speeds over 18 mph at a 10m height between the hours of 1600 

and 2300 due to lack of available data. This EF was not necessary to test the PCH 

1/1/2008 proposed design day. 
I-10

Table 2: Estimated EFs [tons/m2/hr] for different wind speeds and times of the day for the winter months of 2008. Th

using the EF back calculation (EFBC) model. These EFs include emissions originating from stagnation activities. 

LU ID A B C D E F G H 0-3mph 000-700hrs 5.45E-10 1.87E-10 1.18E-10 3.14E-11 2.07E-10 6.43E-10 2.45E-09 7.680-3mph 800-1500hrs 6.70E-09 2.94E-10 3.13E-08 5.73E-11 7.13E-11 2.89E-08 4.84E-10 2.580-3mph 1600-2300hrs 2.10E-10 1.41E-10 5.09E-11 7.12E-11 1.59E-10 5.32E-11 6.44E-08 6.863-6mph 000-700hrs 1.44E-09 1.36E-09 3.53E-10 1.60E-10 1.21E-09 2.34E-10 1.62E-10 1.973-6mph 800-1500hrs 1.32E-09 9.49E-10 1.73E-10 7.34E-11 1.84E-10 1.88E-10 2.35E-09 5.2515-18mph 800-1500hrs 7.39E-07 4.57E-07 1.04E-07 4.23E-08 2.29E-08 8.48E-09 1.57E-09 7.5815-18mph 1600-2300hrs 1.65E-07 1.45E-07 3.70E-08 9.63E-09 3.40E-08 5.67E-08 2.02E-09 1.7518+mph 800-1500hrs 5.93E-07 1.09E-06 1.87E-07 1.12E-07 5.13E-07 1.21E-08 4.36E-10 2.0718+mph 1600-2300hrs 1.35E-08 1.11E-07 2.33E-07 1.67E-07 3.95E-06 1.48E-06 NA 6.60
I-11

000-700 Hours EFs

1.0E-115.1E-101.0E-091.5E-092.0E-092.5E-093.0E-09

0-3mph 000-700hrs 3-6mph 000-700hrs

Wind Speed (mph) and Time

EFs (tons/day/m2)

A B C D E F G H I&J

Figure 1: Estimated Winter Emission Factors [tons/m2/hour] for all land uses between 0-700 hours for the wind 

3 mph and 3-6 mph. Wind speeds were adjusted for a 10m height. 
I-12

800-1500 Hours EFs

1.0E-111.0E-101.0E-091.0E-081.0E-071.0E-061.0E-05

0-3mph 800-1500hrs 3-6mph 800-1500hrs 15-18mph 800-1500hrs 18+mph 800-1

Wind Speed (mph) and Time

EFs (tons/day/m2)

A B C D E F G H I&J

Figure 2: Estimated Winter Emission Factors [tons/m2/hour] for all land uses between 800-1500 hours for the 

of 0-3 mph, 3-6 mph, 15-18 mph, and 18+ mph. Wind speeds were adjusted for a 10m height. 
I-13

1600-2300 Hours EFs

1.0E-111.0E-101.0E-091.0E-081.0E-071.0E-061.0E-05

0-3mph 1600-2300hrs 15-18mph 1600-2300hrs 18+mph 1600-230

Wind Speed (mph) and Time

EFs (tons/day/m2)

A B C D E F G H I&J

Figure 3: Estimated Winter Emission Factors [tons/m2/hour] for all land uses between 1600-2300 hours for the of 0-3 mph, 15-18 mph, and 18+ mph. Wind speeds were adjusted for a 10m height. 
I-14

Figures 1-3 show how these estimated EFs change with varying wind speeds for the three 

periods of the day for which they were calculated (0-700 hours, 800-1500 hours, and 

1600-2300hours). CAFOs and Dairies (Land Use type G) is the only land use which 

showed decreasing emission rates with increasing wind speeds. This could be attributed 

to cattle movement patterns. As wind speeds increase, especially into extreme speeds 

such as 15+ mph, cattle may be less likely to move around which would reduce 

mechanical entrainment not related to windblown emissions. During the early and late 

hours of the day, Croplands and CAFOs have the highest EFs under stagnation 

conditions, likely due to the increased activity on these lands in comparison to other land 

uses at the same time of day. During the mid-hours of the day, construction areas, paved 

roads, and urban developed lands have much higher stagnation related emission rates. 

This is also attributable to the increased activity on these lands during the middle portion 

of the day for winter months. 

The next step in the windblown EF estimation process was to correct the EFs listed above 

for stagnation, activity driven emissions. In order to do so, EFs for wind speeds less than 

3 mph were subtracted from wind speeds greater than 3 mph. In this way, windblown 

EFs were created through the removal of activity driven emissions. Land uses which 

exhibited decreasing emission factors with increasing wind speeds were assumed to have 

negligible emissions originating from wind and were thus given wind blown EFs of 0 

tons/m2

/hour. Table 3 presents wind blown EFs after correcting for stagnation, activity 

driven emissions. 

For the early hours of the day (000-700 hours) low speeds (i.e. 0-3 mph) exhibited the 

highest windblown EFs for Desert Shrublands, Developed Rural Lands, and Cleared 

Areas followed by Developed Urban Lands and Crop Fields. The first 3 land use types 

are likely to have large reservoirs for PM10 entrainment with the possibility of being 

highly disturbed. Developed Urban Lands and a majority of Crop Fields are going to 

have a high degree of land coverage to reduce emission factors. 

For all hours of the day, as wind speeds increase Desert Shrubland EFs increase with the 

exception of the late hours of the day. As wind speeds transition from 15-18 mph to 18+ 

mph Desert Shrubland EFs decrease slightly which could be an artifact of reservoir 

depletion in the late hours of the day. In fact, 3 of the 4 instances where EFs decreased 

when transitioning from 15-18 mph to 18+ mph for a given land use occurred in the later 

hours of the day (1600-2300 hours), indicating reservoir depletion may in fact be playing 

a significant role. Cleared Areas and Crop Fields showed drastic increases in EFs during 

the middle of the day (800-1500 hours) with increasing wind speed, while in the late 

hours of the day (1600-2300 hours) Unpaved Roads, Cleared Areas, and Construction 

Areas showed the greatest increases in emission factors with increasing wind speed. 

Developed Urban and Rural Lands had consistently high EFs as did Paved Roads. The 

elevated Developed Urban Lands and Paved Road EFs were surprising due to the degree 

to which these land uses have coverage to reduce the reservoir size.
I-15

Table 3: Estimated wind blown EFs [tons/m2/hr] for different wind speeds and times of the day for the winter months of 

calculated using the EF back calculation model, and were corrected to remove stagnation activities. 

LU ID A B C D E F G H I&J 3-6mph 000-700hrs 8.91E-10 1.17E-09 2.35E-10 1.28E-10 1.00E-09 0.00E+00 0.00E+00 1.20E-09 6.26E-10 3-6mph 800-1500hrs 0.00E+00 6.55E-10 0.00E+00 1.61E-11 1.13E-10 0.00E+00 1.86E-09 2.66E-10 0.00E+015-18mph 800-1500hrs 7.32E-07 4.57E-07 7.28E-08 4.22E-08 2.29E-08 0.00E+00 1.08E-09 7.58E-07 3.05E-08 15-18mph 1600-2300hrs 1.65E-07 1.45E-07 3.69E-08 9.56E-09 3.39E-08 5.67E-08 0.00E+00 1.75E-07 1.74E-07 18+mph 800-1500hrs 5.86E-07 1.09E-06 1.55E-07 1.12E-07 5.13E-07 0.00E+00 0.00E+00 2.07E-06 1.21E-06 18+mph 1600-2300hrs 1.33E-08 1.11E-07 2.33E-07 1.67E-07 3.95E-06 1.48E-06 0.00E+00 6.53E-08 2.30E-07 
I-16

Windblown EFs for 000-700hrs & 3-6 mph

0.0E+00

2.0E-104.0E-106.0E-108.0E-101.0E-091.2E-091.4E-09

A B C D E F G H 

Figure 4: Estimated Winter Windblown Emission Factors [tons/m2/hour] for all land uses between 0-700 houcategory of 3-6 mph. Wind speeds were adjusted for a 10m height. These EFs were corrected for stagnemissions. 
I-17

Windblown EFs for 800-1500hrs

1.0E-111.0E-101.0E-091.0E-081.0E-071.0E-061.0E-05

A B C D E F G H I&

3-6mph 15-18mph 18+mph

Figure 5: Estimated Winter Windblown Emission Factors [tons/m2/hour] for all land uses between 800-1500 hocategories of 3-6 mph, 15-18 mph, and 18+ mph. Wind speeds were adjusted for a 10m height. These Estagnation, activity driven emissions. 
I-18

Windblown EFs for 1600-2300hrs

1.0E-111.0E-101.0E-091.0E-081.0E-071.0E-061.0E-05

A B C D E F G H I&J

15-18mph 18+m

Figure 6: Estimated Winter Windblown Emission Factors [tons/m2/hour] for all land uses between 1600-230

speed categories of 15-18 mph and 18+ mph. Wind speeds were adjusted for a 10m height. These EFs were coactivity driven emissions. 
I-19

EF estimation Variability and Error 

The EF back calculation method attempts to minimize data variability by grouping 

similar wind speeds, wind directions, hours of the day, and only utilizing data from the 

season of interest on dates where rainfall was less than 2 mm. However, natural 

variability will still occur in emissions data due to soil texture variability within the 

modeled region; reservoir depletion over consecutive high wind hours; variability in soil 

moisture over the modeled region; variability in stagnation, activity related emissions; 

etc. This variability is reflected in Table 3. Table 3 presents the error matrix which 

calculates the percent error for each footprint when back calculating the EFs for each 

wind speed/diurnal temporal grouping (separated by rows in the table). This error was 

calculated using the following equation: 

 

E

E 

 	 % 

where E is the field measured emissions [tons/hour] and  is the absolute value of the 

error from the previously mentioned equation: 





o

ax

A EFxx * 

The variability in this error accentuates the variability which can be seen in field 

measured PM10 concentrations under similar meteorological conditions. 

Table 3: This error matrix shows the percent errors associated with each footprint during 

the back calculation portion for determining EFs. Positive errors indicate that the 

measured field PM10 concentrations are larger than the errors for that footprint during 

statistical optimization. Negative errors indicate that the measured field PM10 

concentrations are larger than the errors for that footprint during statistical optimization. 

Strongly negative values show poor correlation resulting from highly variable data. Only 

8 footprints were calculated for the last row of data since CAFOs and Dairies were not 

found to contribute during any hours with these meteorological characteristics. 

EF Back Calculation Error Matrix (%) 

 1 2 3 4 5 6 7 8 9 

0-3mph 000-700hrs 72 102 -51 -63 105 119 23 -131 124 

0-3mph 800-1500hrs 198 179 197 156 198 198 98 95 100 

0-3mph 1600-2300hrs -108 25 -245 -704 -573 124 116 71 -25 

3-6mph 000-700hrs 83 88 -239 -66 69 70 91 100 88 

3-6mph 800-1500hrs 116 98 23 124 15 100 -149 -140 100 

15-18mph 800-1500hrs 86 -335 -1862 -251 -1594 -575 -2297 -890 -3447 

15-18mph 1600-2300hrs -449 -191 34 -125 -155 -2937 -523 -322 -252 

18+mph 800-1500hrs 48 -3601 93 -3519 -2673 -1240 -3693 -1543 -33 

18+mph 1600-2300hrs -133 40 -388 -333 100 -1 -2755 -17271 
I-20

Design Day Modeling 

The estimated EFs for the winter of 2008 presented in Table 2 were used to test how well 

total hourly estimated PM10 concentrations using a simplified modeling approach would 

compare to field measured PM10 concentrations for the Pinal County Housing design day 

of January 1, 2008. Wind blown EFs could not be used since activity driven emissions 

from the January 1st design day had not yet been determined. Therefore, uncorrected 

emission factors (i.e. EFs which account for stagnation, activity driven emissions as well 

as wind blown emissions) were utilized in testing the model performance against field 

measured PM10 concentrations. Figure 7 presents this comparison and shows fairly good 

model results against field measured values. Modeled peak PM10 concentrations are 

delayed by 3 hours in comparison to field measured data; however, the overall trend is 

captured and for most hours of the day there is only a small amount of error between field 

measured and model estimated PM10 atmospheric concentrations. The average hourly 

error throughout the day is 97%, as calculated by the equation: 

Measured

Model Measured

C

CC

Difference 	 % 

Where CModel is the PM10 concentration which was modeled (μg/m3) and CMeasured is the 

PM10 concentration (μg/m3

) measured at the monitor for that hour of the design day. The 

model performed very well at the daily scale. PCAQD reported a 24 hour PM10

concentration for PCH on 1/1/2008 of 264.9 μg/m3

 and the model predicted a 24 hour 

average concentration of 260.4 μg/m3. The estimated 24 hour average was calculated by 

averaging each of the individual 24 hourly estimated PM10 concentrations, where: 

24

2400

100

24

  h

Ch

C

C24 is the average 24 hour concentration (μg/m3

) and Ch (μg/m3) is the estimated PM10

concentration for each of the 24 estimated hourly concentrations. 
I-21

PCH 1/1/2012 Design Day

0

200400600800

10001200

100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 

Hour

PM10 Concentration (micro-g/m3)

Model 

Figure 7: A comparison of field measured PM10 concentrations (Monitor) against PM10 concentrations 

estimated by the back calculation method (Model) for each hour of the 1/1/2008 PCH design day. 
I-22

Error Between Modeled Atmospheric PM10 and Measured Concentratio

0%

50%

100%150%200%250%300%

100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 

Hour

% Difference between Model and Measured

Figure 8: Calculated Difference between modeled and measured atmospheric PM10 concentrations for each hou

design day. 
I-23

DISCUSSION 

The EF Back Calculation Method utilizes field measured PM10 concentrations and 

meteorological data in an attempt to accurately estimate EFs for a variety of land use 

types within a given area. The estimated EFs using this method are specific to the area 

for which the model is run. This is preferential to literature reported EFs which may be 

calculated in an environment uniquely different from the modeled area. Pinal County is a 

region of high soil disturbance, low annual rainfall, and low surface friction due to a lack 

of vegetative cover. For these reasons, EFs specific to the region were needed and the 

EFBC method was employed. In this document, the methodology behind the EF back 

calculation method is presented. In addition, the resulting estimated EFs from a test run 

were utilized in a simplified model to gauge the accuracy of these EFs for one of the 

design days proposed for modeling in the Pinal County State Implementation Plan. The 

EFBC method provided some individual EFs which were counter intuitive, such as EFs 

from paved and unpaved roads being comparable. These issues could be due to statistical 

optimization problems with these land use types due to the small relative area they 

exhibit in relation to other land use types used in the model. This could also be due to 

incorrect characterization of land uses, requiring a review of the land use classification 

layers used for the model. Despite these issues, when the EFBC estimated EFs were 

modeled for the PCH January 1, 2008 design day, the results exhibited good agreement 

with field measured PM10 concentrations at the hourly scale and excellent agreement at 

the daily scale (i.e. 24 hour average PM10 concentrations) using a simplified modeling 

approach. Further work will need to be done to test validity of these estimated EFs in an 

EPA approved model such as AERMOD. The use of AERMOD will provide a different 

modeling domain than that used in the simplified modeling approach presented in this 

paper as well as account for deposition and dispersion effects on the modeling domain 

not directly accounted for in the simplified approach presented above. However, the 

preliminary results presented in the model above using EFs estimated by the EFBC 

method indicate this method represents a reasonable way to estimate area specific EFs 

from field measured meteorological and PM10 concentration data for PM10 concentration 

modeling. While WRAP or AP-42 regionally or nationally based EF values and 

equations present more generalized values, this method allows the user to specify the land 

use types of interest and develop EFs originating from locally sampled data. 

REFERENCES

Kljun, N.; Kormann, R.; Rotach, M.W.; Meixner, F.X. Comparison of the Lagrangian 

Footprint Model LPDM-B with an Analytical Footprint Model. 2003. Boundary-Layer 

Meteorology, 106: 349-355. 

Kormann, R.; Meixner, F.X. An Analytical Footprint Model for Non-neutral 

Stratification. 2001. Boundary-Layer Meteorology, 99: 207-224. 
 

Appendix J 

MAG 2012 Five Percent Plan for PM10 for the 

Maricopa County Nonattainment Area 

May 2012 
INVENTORY DEVELOPMENT

Under a rollback modeling scheme, reductions of emissions that impact the monitor are

assumed to result in an equal reduction in monitor concentrations. As such, both base

case (2007) low and high wind inventories must be developed along with control case

(2012) low and high wind inventories. This following describes in detail how the 2007 base

year and 2012 control/attainment year high and low wind inventories were developed. 

Details on how the base and attainment year inventories are developed for the entire

nonattainment area are presented in Chapters II and III of this TSD.

High Wind Inventories

For the high wind hours, hourly windblown PM-10 emissions are calculated based upon

the land uses that fall within each of the hourly high wind domains. Land use data, with

the exception of construction data, comes from the MAG information services and

represents land use patterns as of 2009. Land use is held constant between calculation

of 2007 and 2012 year inventories. A detailed write-up of how the land use data is created

is included in the 2008 PM-10 Periodic Emissions Inventory as an Appendix to the

windblown dust section. Construction data was provided by the Maricopa County Air

Quality Department for the year 2007. To be conservative this data is also held constant

through 2012 even though there is substantially less construction occurring due to the

economic recession. Windblown PM-10 emissions for the subject land uses are calculated

per the methodology described in the 2008 PM-10 Periodic Emissions Inventory (Appendix

A, Exhibit 1). As an example, emissions from the West 43rd Avenue monitor hour 15 high

wind domain (June 6, 2007 design day) are explained below. All of the hourly high wind

emissions are calculated in the same manner shown in this example.

Initially, through the use of ArcGIS, the land uses subject to windblown dust emissions

within the high wind domain are identified. For the hour 15 example, 11 unique land uses

within the high wind domain are subject to windblown dust as shown in Figure V-16. They

include the land uses of Active Open Space (i.e., parks), Agriculture, Commercial,

Construction, Coreslab (a large combination of sand and gravel and industrial facility),

Industrial, Passive/Restricted Open Space (i.e., preserves), Public/Military Space (i.e.,

jails, bases), Sand and Gravel facilities, Vacant, and Dry Washes. These land uses are

chosen because either all or some of their acreage is bare soil from which windblown dust

can be generated. Land uses such as residential or golf courses are not included as land

uses subject to windblown dust since none (or a very limited amount) of the acreage

associated with these land uses is bare soil. Land uses on the steep slopes of mountain

preserves are also excluded as the surface roughness of the slope severely limits the

production of windblown dust. 

After the land uses are identified, a 10-acre fishnet grid is applied to the land uses, so that

no individual land use parcel is larger than ten acres. This allows for calculating the

distance from each parcel boundary to the modeled monitor; which is West 43rd Avenue

in this example. The distance of the parcel to the monitor is necessary in order to apply

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the distance weighting factor to the calculated emissions as discussed earlier in this

chapter.

Figure V-16

Land Uses Capable of Producing Windblown Dust Within the Hour 15, West 43rd

Avenue Monitor, High Wind Domain (June 6, 2007)

To be conservative, all but four of the land uses shown above are assumed to have 100%

of their acreage capable of emitting windblown dust. The land uses of Commercial,

Coreslab, Industrial and Public/Military are exempted from this assumption because these

land uses are known to have a mix of large buildings and paved parking lots/access roads

in addition to areas of bare soil. Over 2500 aerial photographs of these types of parcels

in the Salt River area were evaluated to determine the average percentage of bare soil

present on each of these land uses. The results of that analysis are listed in Table V-22. 

To be conservative, the mean value was chosen to represent the average percentage of

bare soil on these land uses, as this value was higher than the median in all cases. Note

that Coreslab is considered an industrial parcel, and as such is assigned the average of

24% for the amount of bare soil present. 

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J-2
Table V-22

Results of Aerial Photography Analysis of Percentage of Bare Soil on Land Uses

with Both Bare and Covered Surfaces

Land Use Mean Median Std. Deviation

Commercial 26% 20% 23%

Industrial 24% 17% 21%

Public/Military 35% 21% 35%

After the land uses within the high wind domain have been classified and selected, the

process for calculating windblown emissions from these land uses can begin. The 2008

PM-10 Periodic Emissions Inventory developed emission factors for two classes of bare

soil: disturbed and stable. Disturbed soils emit at a much higher rate than stabilized soils

because there is more loose soil available to be entrained by the wind. As wind speeds

increase, both disturbed and stable soils emit at higher rates, as the extra energy from the

wind entrains more material from both surfaces. Thus, the main elements needed to

calculate windblown PM-10 emissions are the number of acres of bare soil, the wind

speeds over that soil, and the percentage of the acres that are disturbed or stable.

Table V-23 displays the emission factors by wind speed bins as developed in the 2008

Periodic Emissions Inventory. These emission factors were developed based upon local

testing of Arizona soils. The units of the emission factors are tons per acre per fiveminute, as the input wind speed data for most of the high wind domains is a five-minute

average wind speed. 

Table V-23

Windblown PM-10 Emission Factors (tons /acre-five-minute)

by 10-Meter Wind Speed Bin and Soil Stabilization

Soil

Stabilization

Wind Speed

12-15 mph

Wind Speed

15-20 mph

Wind Speed

20-25 mph

Wind Speed

25-30 mph

Wind Speed

30-35 mph

Disturbed 5.44E-05 1.69E-04 5.14E-04 1.24E-03 2.57E-03

Stable 1.10E-05 2.93E-05 7.68E-05 1.64E-04 3.10E-04

Continuing with the use of the hour 15, West 43rd Avenue, high wind domain example, the

hourly average wind speed for that hour was 18.1 mph. An examination of the five-minute

wind speed data for that hour reveals that ten of the five minute periods had wind speeds

between 15-20 mph and two of the five minute periods had wind speeds between 20-25

mph. This means that the emissions factors from only two wind speed bins (15-20 and

20-25) will be used to calculate emissions for this hour.

At this point, the amount of acreage by land use and the five minute wind speeds have

been determined for the example high wind domain. The one variable that is left to be

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J-3
determined is how much of the land use acreage is disturbed or stable. A direct

measurement of this variable is not possible, as stabilization rates change over time with

meteorological and anthropogenic activities unique to each individual parcel. Therefore,

a surrogate variable is needed to assume an average disturbance level for each land use

subject to producing windblown dust. As discussed in Chapter Five of the main plan and

the 2008 PM-10 Periodic Emissions Inventory, rule effectiveness has been chosen as the

surrogate for the disturbance levels of bare soils. As an example, a rule effectiveness of

80% would assume that 80% of the acreage is stable and 20% is disturbed. Maricopa

County Air Quality Department performed rule effectiveness studies for three fugitive dust

rules: Rule 310, earthmoving activities, Rule 310.01, open and vacant areas, and Rule

316, sand and gravel sites. These studies were done on an annual basis and are shown

in Table V-24. The values for calendar years 2007, 2008 and 2010 were all derived per

the methodology listed in the 2008 PM-10 Periodic Emissions Inventory. The increases

in rule effectiveness through time is a direct result of the implementation of the control

measures in this plan. The value for 2012 is the assumed value achieved as a result of

the control measures in this plan projected out through 2012 (See Chapter Five of main

plan for more explanation).

Table V-24

Calendar Year Rule Effectiveness Rates as Surrogates

for Soil Disturbance Rates

Rule 2007 2008 2010 2012

310 76% 90% 94% 94%

310.01 85% 95% 96% 97%

316 40% 65% 73% 73%

The 11 land uses identified earlier in Figure V-16 are thus assigned disturbance rates

based upon the rule effectiveness rates in Table V-24. Rule 310 serves as a surrogate

value for construction land uses, Rule 316 serves as a surrogate for sand and gravel land

uses, and Rule 310.01 serves as a surrogate for all remaining land uses. Rule 310.01

directly regulates all open areas and industrial/commercial areas that are permitted or unpermitted. The only land use under which Rule 310.01 does not regulate is agriculture. 

However, because there are no quantitative rule effectiveness or disturbance rates studies

available that are applicable to agricultural land, Rule 310.01 disturbance rates are

assumed to apply, as was assumed in the 2008 PM-10 Periodic Emissions Inventory. It

is important to point out however, that because there are no benefits for agricultural land

taken as part of the attainment demonstration or the five percent reductions, the assumed

2007 rule 310.01 effectiveness rate of 85% is held constant through 2012, meaning

windblown dust emissions from agricultural lands in 2012 are the same as in 2007. All

other land uses besides agriculture experience a reduction in emissions between 2007

and 2012 as a result of the benefit of increased rule effectiveness; which is pragmatically

translating as an assumption of less disturbed soil in the windblown dust inventory.

V - 65

J-4
Now that all of the required elements (acreage, percent disturbed, and wind speeds) have

been determined, the elements are simply multiplied in order to obtain PM-10 windblown

dust emissions for each land use within the high wind domain. An example equation using

data from the hour 15 example high wind domain is shown below to detail how high wind

emissions are calculated:

Example for calculating base year (2007) windblown dust emissions from Commercial land

uses:

(1) Land use * % acreage * 15-20 mph wind * % Stable * Stable 15-20 mph = Stable Emissions

Acreage of bare soil speed periods emission factor

(250.2) * (26%) * (10) * (85%) * (2.93E-05) = 1.62E-02 Tons

+

(2) Land use * % acreage * 20-25 mph wind * % Stable * Stable 20-25 mph = Stable Emissions

Acreage of bare soil speed periods emission factor

(250.2) * (26%) * (2) * (85%) * (7.68E-05) = 8.49E-03 Tons

 +

(3) Land use * % acreage * 15-20 mph wind * % Disturbed * Disturbed 15-20 mph = Disturbed

Emissions

Acreage of bare soil speed periods emission factor

(250.2) * (26%) * (10) * (15%) * (1.69E-04) = 1.65E-02 Tons 

+

(4) Land use * % acreage * 20-25 mph wind * % Disturbed * Disturbed 20-25 mph = Disturbed

Emissions

Acreage of bare soil speed periods emission factor

(250.2) * (26%) * (2) * (15%) * (5.14E-04) = 1.00E-02 Tons

Sum of above equations (1 through 4) = 5.12E-02 Tons of PM-10 emissions from Commercial land uses.

This process is repeated for all land uses and then summed to calculate total PM-10

emissions from all land uses within the high wind domain. The only difference between

2007 and 2012 year emissions is that the disturbance rates change based upon gains in

rule effectiveness, thus the reductions in emissions between 2007 and 2012 are solely

attributable to increases in rule effectiveness.

The emissions that are calculated per the steps above, represent maximum potential PM10 emissions from windblown dust. As explained in the 2008 PM-10 Periodic Emissions

Inventory, a whole host of other factors including supply limitations, surface roughness,

soil moisture content, vegetative cover, soil texture, etc. and would likely limit the PM-10

emissions from windblown dust to levels below what is calculated in each of the high wind

domains. The 2008 PM-10 Emissions Inventory corrects for these limiters by

standardizing PM-10 emissions as compared to concentrations seen at the monitor under

high wind conditions. This helps to ensure the scale of the emissions estimate is not too

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J-5
large. However, this step is not necessary for high wind emission inventories prepared for

use in an attainment demonstration, as what is important in this rollback modeling is the

difference between 2007 and 2012 emissions, not the scale of those emissions as

compared to low wind emission sources. Since both 2007 and 2012 high wind emissions

are calculated in the same way, the difference between the two inventories provides the

reductions needed to model attainment at the monitors. The unit and scale of the

inventories thus do not matter, only that the methodology is the same between the base

and controlled inventories in order to claim the benefits of the measures in this plan as

applied to the modeled concentrations.

Once high wind PM-10 emissions have been calculated for the land uses, one final step

is required to prepare the emissions for attainment modeling. The calculated emissions

for each land use parcel are divided by the distance (feet) from the modeled monitor. As

discussed in an earlier section in this chapter, distance weighting is applied to this

emissions in order to account for their diminishing impact on monitor concentrations with

distance. The best weighting function was determined to be a simple inverse distance

ratio (1/d) through analysis of dispersion behavior provided by sample AERMOD model

runs (see earlier sections on source weighting and temporary monitor insights).

Tables V-25 through V-31 provide the results of the high wind emissions inventories for

the high wind domains of each modeled monitor, based upon the process outlined above. 

They list both the calculated emissions and the inverse-distance weighted emissions for

the base and controlled year inventories.

V - 67

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Table V-25

Base Case 2007 and Controlled 2012 High Wind PM-10 Emissions Inventories

for May 4, 2007 Design Day at the West 43rd Avenue Monitor

High Wind

Hour

Un-weighted Emissions

(tons) 

Distance-Weighted Emissions

(tons/feet from monitor)

% Reduction

of Weighted

Emissions 2007 2012 2007 2012

12 3.05 2.09 2.872E-04 1.900E-04 33.8%

13 9.49 6.36 6.529E-04 4.245E-04 35.0%

14 9.94 6.67 6.866E-04 4.454E-04 35.1%

15 13.94 9.19 8.721E-04 5.581E-04 36.0%

16 13.36 8.81 8.921E-04 5.699E-04 36.1%

17 19.01 12.39 1.190E-03 7.505E-04 36.9%

18 11.84 7.86 7.837E-04 5.033E-04 35.8%

19 6.58 4.44 5.158E-04 3.364E-04 34.8%

21 4.02 2.72 3.236E-04 2.129E-04 34.2%

22 6.63 4.48 5.104E-04 3.330E-04 34.8%

Total 97.86 65.02 6.714E-03 4.324E-03 35.6%

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Table V-26

Base Case 2007 and Controlled 2012 High Wind PM-10 Emissions Inventories

for June 6, 2007 Design Day at the Central Phoenix Monitor

High Wind

Hour

Un-weighted Emissions

(tons) 

Distance-Weighted Emissions

(tons/feet from monitor)

% Reduction

of Weighted

Emissions 2007 2012 2007 2012

0 1.90 1.32 7.549E-05 5.230E-05 30.7%

1 0.99 0.74 5.184E-05 3.687E-05 28.9%

6 1.69 1.18 6.873E-05 4.797E-05 30.2%

7 0.99 0.74 5.254E-05 3.730E-05 29.0%

10 0.88 0.64 4.939E-05 3.485E-05 29.4%

11 2.01 1.41 7.319E-05 5.123E-05 30.0%

12 4.01 2.91 1.706E-04 1.166E-04 31.7%

13 7.52 5.04 2.448E-04 1.625E-04 33.6%

14 7.28 4.96 2.265E-04 1.520E-04 32.9%

15 4.33 3.04 1.657E-04 1.121E-04 32.3%

16 4.27 3.13 1.737E-04 1.192E-04 31.4%

17 2.29 1.60 8.494E-05 5.855E-05 31.1%

18 2.21 1.55 7.971E-05 5.545E-05 30.4%

Total 40.39 28.25 1.517E-03 1.037E-03 31.7%

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Table V-27

Base Case 2007 and Controlled 2012 High Wind PM-10 Emissions Inventories

for June 6, 2007 Design Day at the Durango Complex Monitor

High Wind

Hour

Un-weighted Emissions

(tons) 

Distance-Weighted Emissions

(tons/feet from monitor)

% Reduction

of Weighted

Emissions 2007 2012 2007 2012

11 1.99 1.37 1.340E-04 9.011E-05 32.8%

12 3.69 2.55 2.267E-04 1.543E-04 31.9%

13 3.12 2.15 1.876E-04 1.281E-04 31.7%

14 8.02 5.49 4.672E-04 3.093E-04 33.8%

15 9.84 7.07 4.992E-04 3.379E-04 32.3%

16 5.62 3.79 3.247E-04 2.160E-04 33.5%

17 2.47 1.72 1.619E-04 1.110E-04 31.4%

Total 34.74 24.13 2.001E-03 1.347E-03 32.7%

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Table V-28

Base Case 2007 and Controlled 2012 High Wind PM-10 Emissions Inventories

for June 6, 2007 Design Day at the Greenwood Monitor

High Wind

Hour

Un-weighted Emissions

(tons) 

Distance-Weighted Emissions

(tons/feet from monitor)

% Reduction

of Weighted

Emissions 2007 2012 2007 2012

12 1.05 0.77 5.872E-05 4.191E-05 28.6%

13 1.03 0.80 6.482E-05 4.701E-05 27.5%

14 3.03 2.16 1.618E-04 1.110E-04 31.4%

15 2.34 1.75 1.266E-04 8.868E-05 30.0%

Total 7.45 5.49 4.119E-04 2.886E-04 29.9%

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Table V-29

Base Case 2007 and Controlled 2012 High Wind PM-10 Emissions Inventories

for June 6, 2007 Design Day at the Higley Monitor

High Wind

Hour

Un-weighted Emissions

(tons) 

Distance-Weighted Emissions

(tons/feet from monitor)

% Reduction

of Weighted

Emissions 2007 2012 2007 2012

10 0.74 0.55 6.103E-05 4.481E-05 26.6%

11 3.00 2.05 1.944E-04 1.330E-04 31.6%

12 2.15 1.54 1.805E-04 1.270E-04 29.6%

13 2.41 1.69 1.824E-04 1.268E-04 30.4%

14 5.61 3.83 2.282E-04 1.553E-04 31.9%

15 5.70 3.98 2.113E-04 1.442E-04 31.8%

16 5.65 3.90 2.065E-04 1.403E-04 32.1%

17 1.50 1.11 6.687E-05 4.791E-05 28.4%

Total 26.76 18.64 1.331E-03 9.195E-04 30.9%

V - 72

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Table V-30

Base Case 2007 and Controlled 2012 High Wind PM-10 Emissions Inventories

for June 6, 2007 Design Day at the West Phoenix Monitor

High Wind

Hour

Un-weighted Emissions

(tons) 

Distance-Weighted Emissions

(tons/feet from monitor)

% Reduction

of Weighted

Emissions 2007 2012 2007 2012

11 0.65 0.53 2.164E-05 1.680E-05 22.4%

12 1.78 1.33 5.329E-05 3.930E-05 26.2%

13 3.35 2.45 8.177E-05 5.933E-05 27.4%

14 4.27 3.08 1.123E-04 8.105E-05 27.8%

15 1.63 1.21 4.623E-05 3.396E-05 26.5%

Total 11.67 8.60 3.152E-04 2.304E-04 26.9%

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Table V-31

Base Case 2007 and Controlled 2012 High Wind PM-10 Emissions Inventories

for June 6, 2007 Design Day at the West 43rd Avenue Monitor

High Wind

Hour

Un-weighted Emissions

(tons) 

Distance-Weighted Emissions

(tons/feet from monitor)

% Reduction

of Weighted

Emissions 2007 2012 2007 2012

0 2.29 1.59 2.064E-04 1.388E-04 32.8%

5 3.31 2.29 2.894E-04 1.924E-04 33.5%

6 5.05 3.43 4.359E-04 2.860E-04 34.4%

10 3.44 2.38 2.835E-04 1.898E-04 33.1%

11 6.67 4.51 4.937E-04 3.237E-04 34.4%

12 6.02 4.06 5.697E-04 3.704E-04 35.0%

13 5.66 3.82 4.721E-04 3.088E-04 34.6%

14 9.67 6.48 6.830E-04 4.425E-04 35.2%

15 13.99 9.12 9.717E-04 6.168E-04 36.5%

16 9.67 6.48 6.630E-04 4.304E-04 35.1%

17 5.10 3.45 4.285E-04 2.812E-04 34.4%

18 4.94 3.34 3.857E-04 2.526E-04 34.5%

Total 75.81 50.97 5.883E-03 3.833E-03 34.8%

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