# Appendix A :

Pinal County PM Inventory Preparation Plan

P repared 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) 444-6666Report 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 Conce ntrations Recorded at Six Pinal

County TEOM Monitoring Sites

Appendix B

– Plots of Hourly PM10 Concentrat ion, 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-Calculati on 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/m 3 ...................................25

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

Figure 2-13 Stanfield Hourly PM10 Ave rages 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 P M10 Rose of Hours Exceeding 150 μg/m 3 .............................33-ivFigure 2-17 Casa Grande Hourly P M10 Averages by Wind Azimuth (degrees) and Wind Speed (m/sec) ..............................................................................................35

Figure 2-18 Casa Grande Hourly PM10 Maxima by Wind Az imuth (degrees) and Wind Speed (m/sec) .....................................................................................................36

Figure 2-19 Casa Grande Sum of P M10 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/m 3 ..............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 Hour ly PM10 Maxima by Wind Azimuth (degrees) and Wind Speed (m/sec), All Data ...............................................................43

Figure 2-23 Pinal County Housing S um 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 Averag e and Maximum PM10, All Hours .......................32

Table 2-4 Casa Grande Hourly Averag e and Maximum PM10, Hours > 150 μg/m 3 ........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/m 3 .....................................................................................................................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 Durati on 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 nonatt ainment 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 monitorin g site exceeding the annual standard, Cowtown, was a population-oriented microsca le (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 monitor ed airborne pollutant concentrations that exceed the prevailing standard. A nonatta inment designation re quires 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 inve ntory defines which sources generate long-term
emissions of PM10 across the entire nonattainmen t area. That general understanding provides the factual basis for discussions rega rding 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 inventor y is required in order to define an annual emissions budget for transportation planning. Second, attainment demonstrations for the 24-hour PM10 NAAQS require modeling inventor ies 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 a pproved by U.S. EPA. The EPA implementation rule for preparation of PM2.5 State Impl ementation 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 Pi nal 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 P lan identifies key requirements, procedures, and data sources to be used in preparing em ission inventories for the nonattainment area and for the modeling domains of monitors sel ected to represent exceedance conditions
within the nonattainment area. Standard el ements 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 characte rize activity and emission factors, local conditions affecting emissions, and quality control procedures to be followed.

To provide a better understanding of the c onditions 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. Guida nce for the development of PM10 and
PM2.5 emission inventories is available from the documents listed below.

PM-10 Emission Inventory Requirements 4

Emissions Inventory Guidance for Implementation of Ozone and Particulate Matter National Ambient Air Quality Standards (NAAQS) and Regional Haze Regulations 5

1996 Air Quality Criteria for Particulate Matter, Vol. 1 6

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: ba se 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 c onsistent 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 necessa ry by the Administrator. A periodic inventory may be the consequence of RFP requireme nts, 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 emissi ons for model performance evaluation. For
control measure evaluations and the attainm ent 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 “allowa ble emissions” are actual emissions and attainment year “allowable emissions” will reflect base year actual emissions adj usted for the projected control efficiencies of emissi on 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 e ntire central Pinal County PM10 or PM2.5 nonattainment areas. At least under stagnation and mod erate 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 relative ly 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/eig uid/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 va rious attainment demonstrations, and inventory improvements developed in these domains wil l 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 benefi t the modeling domain inventories.

IPP Requirements

– For base year inventories, EPA require s 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 fe edback 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 disti nction and indicate dr aft and final submittal dates for each component. The final submittal dates should be consistent with the PM10 emission inventory requirements. A complete dr aft inventory is required to be submitted to the EPA Regional Offices and the EPA hea dquarters 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 di scuss 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 whe ther 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 g uidance 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, whet her rule effectiveness will be applied (and how it was determined if applied), and the ba sis 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 purpose s. 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 EP A Regional Office to determine computerized data submittal and format requirements, and must describe in i ts 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 c oncise, and provide only as much detail as is necessary to communicate to the agency how the State intends to de velop and present its inventory. However, the document must c ontain 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 guidan ce 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 resolut ion, 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. Tab le 1-1 presents an overview of the IPP and QAP planning activities recommended under EPA’s EIIP guidance.

Table 1-1 Inventory Preparation Planning a nd 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 responsibilitie s 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, audit ing, and relative “scoring” or ranking of the emission estimation, spatia l and temporal allocation, and forecasting methodologies and data sources for each individual emission source in an inventory. For agencies with significant budgetary resourc es and staff that are developing rigorous, Level I-type 8 compliance inventories in industrialized urban areas, the high degree of quality assurance (QA) auditing, rankin g, and documentation suggested in the EIIP guidance is appropriate. Since these condit ions 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 c onsidered in the development of this IPP.
Presented below is a brief review of the iss ues 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 staffi ng 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 c over all PM10 and fine particulate pollutants and precursors, ADEQ- a nd PCAQMD-sponsored CMB studies 9 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 represe ntative source specific emission estimates. Tests of alternate high wind emission estimates will be conducted to determine which method provides a more repr esentative 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 accura cy. 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/Depa rtments/AirQuality/Documents/Other% 20EPA%20Regulatory%20Actions/C owtownTechnicalWhitePaper.pdf-8- Inventory Reporting

– The final steps in Table 1-1 de scribe the reporting practices and documentation of the emission inventory. The se elements include the following:

Documentation of methods and data sources (e.g., MOVES, local travel model outputs, etc.);

Discussion and explanation of sources ex cluded 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 proc esses 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 a nd criteria pollutant inve ntories elsewhere in Arizona. The challenge is adapting a nd 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 c onditions 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 Emis sion Inventory Development,” Emission Inventory Preparation Program, May 2001.

### Available Resources Planning Status of Existing Inventory

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 averag es at each of these four stations. In the second stage, we used two-variable analyses to compute hourly P M10 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 cat egories contributing to periods of high, and low, PM10 concentrations at these stations. The data used in these analyses were collected between January 2006 and Dece mber 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/m 3 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 ca pable of achieving attainment at the initial study sites was believed to be capable of achieving attainm ent 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. Environmen tal Protection Agency, Region 9, Sept. 21, 2010

13 These initial analyses were conducted prior to final s election of design days and monitoring sites.-11- the analysis to prevent the resulting statisti cs 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 contribut ions. 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/m 3

— 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/m 3 found in the 8 to 9 m/sec wind speed bin, with an observation count of 105 hours (0.40% of all hours), sugg ests that high wind conditions are also responsible for a portion of the 24-hour PM10 exceedances at this site. The relatively high average PM10 concentra tion 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/m 3 were consistently associated with hourly average wind spee ds exceeding 12 mph. The Cowtown concentrations exceeding 10,000 μg/m 3 were predominately at night.-12- The diurnal distribution of hourly PM10 averag es 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 si te (which would conce ntrate emissions from groundlevel sources in shallow mixing la yers) 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 pe ak 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 de veloped 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 th e southeast, with the southwest quadrant contributing the next highest fra ction of wind hours. PM10 roses limited to exceedance days and hours of PM10 exceeding 150 μg/m 3 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 ba nd of PM10 concentrations represents hours exceeding 2,000 μg/m 3 , not 200 μg/m 3 . 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 apa rt, 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 high est (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 sig nificant 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 fr om 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 fig ure also shows a high PM10 cluster at 7 to 10 m/sec wind speeds and 240 to 290 degr ees 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 fre quencies 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 maxim a 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 sour ces 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 Win d 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 e l s L ab-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 PM Column 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 l e s La b

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 mil es 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 be tween 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 PM Column 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 l e s La b-19-Figure 2-7Example Cowtown PM10 Hot Spot Source Corridor-20- Diurnal trends in PM10 conce ntrations were assessed by tabulating hourly PM10 averages by wind azimuth and hours of the day. The result s, 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 heatin g 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 imp acts may also be driven by nocturnal high wind

events.

Diurnal trends in PM10 conce ntrations were also assessed by contrasting hours of the day against wind speed bins. The results of this analy sis 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-ba sed 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 ea rly evening of the summer months. The cluster of moderate PM10 averages betw een 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 possibil ities should be the focus of emission inventory development for this site. Windbl own dust generated during high wind events appears to be the next greatest source im pacting 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 init ial 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 hi gh 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 c ollecting 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 2-8Cowtown 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 ofColumn LabRow Labels 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 230 190 232 191 162 129 169 258 246 167 165 114 50 49 67 53 56 192 57 91 156 287 438 214 16310 102 305 83 102 47 60 104 301 163 205 88 68 49 42 42 46 64 36 74 154 917 129 128 13720 51 119 127 73 106 199 123 143 171 273 95 103 73 45 37 37 38 212 77 331 65 267 73 7330 119 47 49 130 78 102 147 234 370 140 106 54 45 45 33 36 24 44 91 203 478 219 117 20540 166 72 91 65 136 114 173 160 233 121 130 55 67 29 22 37 31 51 314 289 159 100 375 14150 81 56 66 165 78 171 117 192 201 201 110 54 67 29 51 15 722 50 66 362 145 686 45 19560 130 195 148 72 84 110 151 179 346 148 154 86 89 36 32 40 46 31 100 169 173 93 331 13870 205 130 65 186 100 99 179 281 349 131 115 250 88 58 120 66 45 44 153 133 432 469 213 20280 107 208 82 73 86 117 177 198 125 176 162 102 79 141 28 80 29 29 127 120 151 804 438 8090 69 102 69 121 134 153 197 151 225 106 67 95 136 89 107 41 197 71 183 131 229 71 176 111100 71 117 155 85 135 153 207 100 194 110 87 61 54 67 66 34 25 68 61 233 233 267 330 201110 161 118 140 157 110 133 164 242 233 97 72 70 61 39 35 93 31 45 95 69 320 370 213 169120 129 148 199 186 181 123 148 172 221 133 94 76 61 59 55 52 43 173 72 284 452 424 255 132130 155 209 195 197 167 187 209 260 255 146 96 65 48 38 46 28 136 64 148 328 392 321 202 178140 232 247 245 231 215 197 246 285 228 166 100 69 66 35 31 54 157 247 316 188 693 340 257 258150 219 202 195 233 215 200 237 256 256 163 142 57 54 48 37 43 58 28 90 419 683 574 345 208160 203 228 233 207 199 188 196 285 305 178 105 91 70 39 65 49 63 54 113 482 728 476 285 230170 183 191 181 154 171 175 206 260 235 162 129 94 53 37 35 174 58 65 96 444 647 480 346 285180 158 162 169 176 174 175 170 272 222 148 87 70 81 52 52 50 63 74 120 479 673 520 320 195190 149 154 134 135 212 181 205 250 256 145 103 68 151 62 63 56 54 84 190 521 585 481 357 214200 139 174 119 124 150 217 206 229 287 182 176 68 63 80 66 111 106 101 166 312 891 664 275 178210 259 132 156 159 137 119 257 243 187 179 148 76 60 73 84 62 105 198 100 244 751 876 345 158220 137 109 192 136 146 199 239 190 180 194 81 59 45 62 54 119 71 87 111 649 741 529 318 193230 221 167 106 146 125 183 284 354 313 169 106 79 63 50 58 63 133 78 195 296 625 709 270 328240 184 137 153 199 160 188 160 246 247 192 97 107 229 102 73 148 80 103 166 407 679 655 382 407250 196 153 97 102 120 105 207 310 191 155 146 200 92 90 80 73 153 131 165 256 397 662 211 212260 160 117 96 163 116 155 208 186 161 226 129 125 171 121 93 90 109 148 128 203 512 386 240 200270 118 147 73 80 135 182 153 221 259 203 98 75 154 99 144 121 123 75 153 236 420 633 437 218280 79 128 148 165 108 183 174 202 191 135 97 72 82 88 90 134 117 115 122 354 410 387 138 120290 243 107 151 145 60 93 91 225 202 151 88 94 71 54 64 67 94 54 103 246 576 553 190 184300 46 54 68 60 114 106 49 101 270 178 224 80 56 49 56 43 60 56 76 238 440 1066 200 153310 131 52 117 79 120 70 194 215 271 224 93 66 66 80 61 54 50 73 262 304 226 592 189 135320 70 106 85 65 61 161 152 180 184 99 93 76 47 61 49 54 45 43 73 206 206 177 311 126330 82 149 25 118 102 77 154 209 312 90 92 46 39 45 28 33 48 38 70 161 324 287 165 86340 82 143 64 253 94 125 210 191 166 110 79 59 58 42 39 28 31 48 74 83 273 89 82 126350 50 141 101 98 83 92 85 185 182 116 91 59 51 38 39 29 36 36 102 119 155 515 54 104360 24 29 72 52 28 82 106 338 204 158 76 62 70 43 51 62 39 46 120 270 130 259 70 55Color Scale:0 50 100 150 200 250 300elsLab-22-Figure 2-9Cowtown 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 Lab0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 230 151 190 173 171 136 123 234 240 275 149 93 68 56 77 78 58 281 83 110 228 366 518 430 1461 168 162 155 164 169 166 196 238 249 160 107 80 75 44 41 42 46 50 116 381 611 499 322 1992 200 190 192 183 174 174 200 238 240 157 104 66 57 50 44 45 60 50 96 232 499 542 281 2243 158 205 196 198 179 187 223 266 208 166 94 71 96 46 55 48 73 74 108 211 359 535 222 2604 135 119 122 171 113 172 182 252 267 124 123 102 69 82 99 98 72 113 109 222 420 385 236 1685 108 165 82 39 111 130 149 206 201 151 121 110 106 97 92 113 135 107 138 172 298 121 176 1186 111 56 36 72 84 15 237 306 240 188 197 145 94 105 160 108 189 178 140 192 587 464 215 1217 67 99 121 71 208 237 265 182 223 275 153 321 202 238 130 285 219 157 320 407 857 319 146 2058 212 120 224 108 142 83 143 234 224 198 241 526 401 219 382 343 492 2354 575 1043 950 382 759 825 102 283 49 51 271 190 402 117 200 27 193 202 1281 456 833 740 206 90 13410 312 72 519 118 31 125 692 2055 329 566 440 2250 105 12961124 21365121046 2328Color Scale: 0 50 100 150 200 250 300Labumn-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/m 3 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 maxim um PM10 values at wind speeds in this same range also suggest that episodic sources periodically impact the monitor.

Table 2-2 Stanfield Hourly Average a nd 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 inve rsions, 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 si te 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/m 3 , 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 sugge sts that the highest PM10 impacts at themonitor 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/m 3-26- Figure 2-12 Stanfield PM10 Rose of Hours with Wind Speeds > 5.0 m/sec-27- Two-Variable Analyses

– The first two-variable analy sis 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 sourc es 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 degr ees 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 betw een 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 P Column 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 e l s n L-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 PM Column 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 n La-30-Figure 2-15Stanfield 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 19 20 21 22 230 37 11 48 20 14 25 65 64 93 82 52 108 58 34 35 53 42 115 139 136 242 45 5310 51 32 17 36 31 18 109 73 85 39 101 43 37 57 34 35 44 93 99 104 182 29 5720 24 37 54 38 27 27 34 56 91 107 43 43 36 34 50 35 31 45 61 82 122 65 7530 10 10 62 32 12 75 132 54 47 43 41 36 31 30 27 24 34 71 121 88 51 7140 87 21 89 35 31 49 5 77 127 86 38 37 58 33 34 33 27 35 47 54 65 64 2650 44 28 19 19 60 20 26 46 21 66 56 40 48 41 33 25 27 34 39 58 77 50 71 4560 35 76 66 32 38 41 42 137 110 72 70 62 41 35 34 45 32 38 56 55 91 83 124 6070 58 53 66 58 46 43 47 76 92 103 77 196 47 34 79 47 56 42 64 53 68 99 197 6780 68 37 62 51 78 32 37 78 80 139 120 135 135 96 60 34 33 45 214 192 237 154 69 7290 45 43 66 61 28 43 53 86 127 115 96 59 51 164 32 38 54 40 45 83 101 146 60 60100 54 37 53 30 46 25 71 70 97 73 87 51 50 39 27 30 30 33 68 38 64 39 74 47110 83 77 52 57 39 41 57 82 105 71 55 41 38 38 26 39 6 135 215 46 88 49 229 88120 67 37 45 63 37 32 57 81 91 80 47 25 34 34 32 23 22 83 63 122 55 155 70 60130 64 47 38 33 45 43 54 84 78 65 65 35 29 36 45 73 27 85 27 37 27 118 187 49140 62 50 57 49 45 41 50 85 88 60 38 29 26 26 29 22 15 71 142 346 269 79 113 75150 54 50 34 39 50 33 70 94 90 73 49 54 41 36 17 23 11 63 43 79 122 140 75 48160 41 43 42 30 38 43 63 75 85 71 37 38 38 32 42 38 2068 39 59 414 96 59 69 70170 61 45 40 27 33 40 61 69 80 62 71 67 28 26 19 81 40 21 37 151 148 80 82 54180 36 86 29 36 28 30 42 69 75 79 31 111 36 79 69 22 36 67 63 31 69 82 62 164190 57 35 44 39 20 36 40 81 99 65 30 49 96 19 266 68 28 201 126 95 61 71 49200 22 46 19 27 39 42 34 36 112 174 329 176 56 52 52 61 32 18 42 69 463 76 47 41210 75 47 25 38 62 27 43 39 81 79 44 51 37 100 29 156 158 74 143 90 185 71 58 130220 35 63 23 46 53 38 53 61 74 46 52 43 118 51 167 202 93 121 70 78 322 110 85 58230 62 40 33 45 28 25 35 97 119 63 34 29 209 64 85 47 42 39 135 47 100 199 91 92240 35 26 55 40 41 42 61 71 35 39 39 58 28 148 71 82 93 111 39 138 191 133 98 53250 47 27 32 58 48 45 25 67 41 46 68 45 59 29 34 84 35 60 46 163 202 110 81 66260 41 54 39 39 44 36 29 42 98 88 156 137 79 45 78 80 46 250 93 167 240 189 133 66270 58 69 67 58 50 44 46 77 91 70 39 42 116 117 100 36 94 35 61 155 209 153 129 59280 50 54 63 40 56 69 60 71 88 65 50 61 52 35 43 49 89 58 123 201 228 236 94 84290 84 63 48 39 34 27 30 51 80 46 77 38 88 32 53 38 74 64 55 147 142 121 145 75300 64 41 42 47 12 28 68 53 99 62 109 196 40 41 39 76 58 38 104 201 143 241 64 69310 55 38 41 54 29 53 27 104 30 87 56 64 44 57 36 32 109 82 70 86 115 90 63 52320 48 66 42 31 18 34 45 59 90 38 38 48 41 86 34 36 34 52 43 99 197 64 109 36330 66 45 32 28 35 39 43 40 55 147 44 42 35 31 37 60 39 38 81 96 103 136 72 27340 32 59 60 48 80 25 15 15 36 71 142 40 38 65 52 34 35 52 65 105 117 120 84 74350 68 36 32 30 39 15 18 25 138 52 43 35 36 41 40 66 35 52 65 86 83 356 81 15Color Scale: 0 50 100 150 200elsnL-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 monit or 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 so urces 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 i nspection and interviews of people living or working near the monitoring station. Acti vity rates and emission characteristics of
these sources should be estimated from availa ble records or field observation. Land uses and soil characteristics lying along the b ack-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 a irport, 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 monitor s 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 ta ble 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 ev ent may occur within the urbanized area of Casa Grande. Higher hourly PM10 av erages and maxima at wind speeds above 9
m/sec indicate that this monitor is i mpacted by windblown dust sources within the surrounding region.

Confirmation of the absence of significant sour ces, and the presence of episodic emission events, in the urbanized area is shown in Ta ble 2-4. This table represents hourly PM10 averages and maxima for all hours during which hourly PM10 c oncentrations at the monitor exceeded 150 μg/m 3 . 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/m 3 (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 conce ntrations at the Casa Grande monitor exceed 150 μg/m 3 is presented in a PM10-rose format in Figure 2-16. This figure shows that during hours of elevated PM10, winds typica lly 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/m 3 (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 ur banized 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 th e 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 emissi ons from sources impacting the monitor. This pattern suggests that controls impleme nted 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 urbaniz ed portion of Casa Grande were found to impact the monitor. Periodic high impa cts 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 resear ched and compiled at this site. Although the Maricopa monitoring site was selected for an attainment demonstration, no meteorological data have been collected at t his 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 La b

e m n L-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 P Column 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 L a b e mn L-37- Figure 2-19 Casa Grande Sum of PM10 by Wind Azimu th (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 PM Column 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 n La-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 mechanic al sources within a few miles of the monitor. The moderately high maximum hourly PM10 values, up to 5,000 μg/m 3 , 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/m 3 . 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/m 3 suggests that soils in the region of Pinal County Housing are frequently disturbed a nd 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 a nd limited dispersion occur at these low wind speeds.-39- Table 2-6 Pinal County Housing Average and Ma ximum PM10, Hours Exceeding 150 μg/m 3 (No Outliers) Wind Speed Category (m/s) Observation Count (Hours > 150 μg/m 3 ) Fraction of
Total Hours Average PM10 (Hours > 150 μg/m 3 )

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/m 3 is presented in Figure 2-20. This rose shows that sources producing hourly PM10 concentrations above 150 μg/m 3 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/m 3 (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 monit or. 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 prev ailing 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 an d 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 responsi ble 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 Av erages 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 a b e mn L-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 La b

e m n L-44- Figure 2-23 Pinal County Housing Sum of Hour ly 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 PM Column 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 a b e 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 betw een 0700 and 1000 hours suggest that mechanical activities produce higher emissions from so urces 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 betw een 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 eve nts 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 mech anical 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 moni tor. Emission investigations at this site
should include an assessment of mechanical acti vities occurring during these hours of the day within one or two miles and within the ar c 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 2-24Pinal County Housing Hourly PM10 Averages by Wind Azimuth (degrees) and Hour of Day, All Data(No Outliers)Pinal County Housing Hourly PM10 Averages by Wind Azimuth (degrees) and Hour of Day, All Data (No Outliers)AverageColumnRow Lab0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Average0 43 24 70 37 37 47 71 89 130 116 97 60 37 38 37 51 22 71 174 98 110 79 88 40 7710 55 36 27 43 31 49 65 97 123 121 66 52 57 41 27 27 40 63 55 124 135 101 160 262 7720 42 41 55 40 34 32 52 79 112 105 86 57 55 41 69 34 35 36 266 116 147 62 41 58 7830 47 50 34 33 35 39 56 72 128 87 59 65 95 84 45 217 65 53 48 103 78 82 38 83 6940 46 40 43 52 28 34 44 86 104 127 260 199 94 85 170 105 63 61 118 71 90 68 62 54 8350 47 42 39 35 34 42 38 74 75 81 165 224 315 146 179 76 81 113 299 97 117 84 78 50 10660 96 64 40 35 44 37 39 90 110 81 54 74 57 53 23 32 55 416 100 160 159 83 58 28 7970 59 66 45 50 40 39 51 72 98 63 88 96 43 42 59 58 19 24 80 146 97 77 109 87 6980 58 56 99 47 44 38 58 77 70 45 36 47 53 31 24 37 34 857 222 106 115 128 85 48 8790 72 80 69 36 34 44 36 75 91 113 52 27 16 31 29 38 55 132 158 123 140 80 59 74100 285 48 48 50 47 38 54 81 81 110 39 63 16 15 4 35 42 69 136 161 139 105 66 74 88110 79 56 53 43 51 43 49 96 102 47 55 62 49 31 11 10 119 166 48 165 149 110 75 76 76120 60 69 56 48 36 40 56 70 105 91 75 28 127 34 115 32 23 410 62 175 132 103 91 75 73130 75 76 52 47 37 31 48 69 111 58 45 39 20 30 25 7 57 103 124 113 121 91 86 80 67140 65 63 50 40 43 36 45 63 106 59 46 33 28 35 38 32 54 83 98 203 103 110 88 78 65150 68 54 41 36 32 32 49 58 82 73 62 26 55 23 24 16 188 66 36 175 108 129 77 90 62160 73 55 48 35 35 35 36 64 92 84 52 35 44 20 38 57 17 125 141 85 146 113 100 68 63170 56 52 41 37 36 32 39 66 86 69 60 38 51 20 56 25 25 48 127 140 108 118 89 71 61180 60 50 36 33 36 38 36 63 66 110 72 52 85 66 141 181 46 47 83 192 147 210 102 78 80190 62 53 44 61 48 45 54 70 102 77 50 53 30 62 32 143 297 205 122 144 101 162 80 86 82200 58 58 45 36 40 39 44 66 79 75 54 71 48 86 106 160 129 180 363 164 133 80 103 80 91210 64 47 40 38 41 44 55 69 97 80 57 64 105 66 116 115 124 92 87 83 124 98 75 99 84220 68 49 43 43 31 44 40 54 91 66 54 43 29 54 48 80 164 76 77 82 91 124 118 92 73230 56 34 54 59 42 35 30 87 77 90 67 58 51 41 45 107 51 94 73 92 120 106 100 66 71240 71 27 27 41 56 56 39 63 109 75 57 45 36 49 48 52 94 91 72 107 120 138 217 70 76250 93 74 20 40 52 34 47 127 72 107 62 52 49 40 49 64 80 47 84 150 180 154 109 73 79260 40 40 39 43 28 20 45 90 83 119 80 55 49 63 64 67 52 52 75 270 98 102 109 15 74270 69 22 32 32 40 27 49 67 93 115 76 87 50 39 40 52 60 53 66 88 213 88 79 74 67280 86 43 27 58 53 45 78 95 105 120 83 83 46 35 37 34 44 53 69 122 106 76 62 55 61290 27 59 30 41 20 45 54 84 151 80 68 69 63 84 33 34 37 53 71 95 232 68 29 42 63300 59 37 32 44 31 32 59 67 97 85 101 61 53 41 32 33 39 59 91 127 94 116 61 60310 46 44 40 47 23 64 44 48 99 81 71 54 39 37 41 34 32 79 80 62 149 56 64 55 55320 28 24 30 36 37 37 41 88 62 111 87 58 43 34 28 44 32 41 112 155 93 127 43 75 58330 31 55 14 17 38 32 43 75 106 92 80 43 53 27 48 38 43 57 91 124 328 128 30 24 71340 65 69 27 40 32 36 48 97 126 135 88 46 61 39 28 30 35 46 120 110 110 61 35 42 68350 25 39 53 40 52 47 43 65 109 86 117 55 43 46 29 26 23 37 66 134 109 76 40 23 60360 37 30 50 30 28 41 54 110 109 178 113 44 56 31 40 36 33 53 54 77 106 63 51 56 63Average71 57 47 41 38 38 47 74 96 89 74 63 60 52 53 61 62 79 103 127 129 111 90 76 72Color Scale: 0 50 100 150 200 250 300Labumn-47-Figure 2-25Pinal County Housing Hourly PM10 Averages byWind Speed (m/sec) and Hour of Day, All Data(No Outliers)Pinal County Housing Hourly PM10 Averages by Wind Speed (m/sec) and Hour of Day, All DataAverageColumnRow Lab0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Average0 68 57 45 43 38 39 50 82 110 106 81 73 60 37 47 44 46 75 134 141 132 118 96 77 791 65 61 48 42 40 40 48 76 102 92 71 52 42 35 30 28 30 45 78 107 118 103 84 73 652 54 42 38 32 33 31 41 63 80 71 52 42 40 34 30 29 32 42 55 71 122 79 63 61 493 46 60 34 22 27 25 30 49 68 54 48 45 39 35 37 55 53 51 54 69 96 86 65 71 524 55 45 171 33 38 33 49 53 83 79 50 57 64 41 50 65 64 73 86 134 152 180 191 86 775 170 42 25 94 58 34 38 64 82 77 142 85 65 109 111 106 119 344 324 203 289 144 406 121 1446 91 79 83 50 56 28 17 98 42 141 271 285 185 257 263 253 336 295 834 679 225 508 140 1228 2787 8449 301 23 96 25 137 325 309 372 931 294 269 565 412 598 208 716 1308 1190 335 181 5218 95 40 289 450 784 593 395 794 586 822 543 3875 946 39 87 6319 749 447 1398 412 1660 456 2471 3300 14 1229101328 3141 570 108128711740 0370Average71 57 47 41 38 38 47 74 96 89 74 63 60 52 53 61 62 79 103 127 129 111 90 76 72Color Scale: 0 50 100 150 200 250 300Labumn-48- Summary

– Mechanical dust sources within one mile of and surrounding the Pinal County Housing monitor appear to daily p roduce moderate PM10 concentrations, between 45 and 100 μg/m 3 at the monitor. Information on candidate sources fitting this profile should be collected at this si te, possibly through interviews of residents or employees typically situated at or near the housing complex during daylight hours.
Hours of maximum PM10 conce ntrations in excess of 1000 μg/m 3 should be investigated with respect to wind speed and direction to determine which of the se continuous sources has the potential to generate high PM10 hourl y values during episodic events. High windevents 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 shoul d 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 compar ed 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 in ventory 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 proper ty 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 nonatt ainment 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 microsca le (i.e., localized hot spot) monitor that was not eligible for designation as a nonatt ainment 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 C ounty must be based on ambient air quality conditions and contributing emissions on specific design days.

Through discussions between U.S. EPA, ADEQ, a nd PCAQCD, a base year of 2008 was selected for the preparation of an emi ssion 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. EP A. During 2008, exceedances of the 24- hour PM2.5 NAAQS were reco rded 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 thes e 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 reco rd 24-hour average PM2.5 concentrations.

In an analysis of the relationship betwe en PM2.5 and PM10 concentrations at Cowtown, ADEQ concluded that any control measures that will achieve attainment of the PM10 NAAQS will also yield atta inment 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 e levated 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/m 3 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 e ach 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 wi nd speed and PM10 concentration at the four sites with longer PM10 monitoring recor ds indicated the presence of two primary meteorological scenarios that produced hourly PM10 concentrations in excess of the 24- hour NAAQS of 150 μg/m 3 : high wind conditions, and stagnation conditions. The sources responsible for high PM10 concentra tions are fundamentally different under these two scenarios. During high wind c onditions, 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 concen trations. Conversely, during stagnation conditions, when wind speeds are typically below 3 mph, wind entrainment of loose soil particles is virtually nonexistent, and mechanic al 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 hour ly 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 attainm ent 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 proxim ity 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 occurr ed on some of these exceedance days, a
substantial number of exceedan ces occurred on stagnation days with little to no wind entrainment of loose soil pa rticles 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 c ommon to most of the nonattainment PM10 monitoring sites. A control program that re duces 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 200 8 at Monitoring Sites Other Than Cowtown

Date Site 24-Hour Average PM10 Concentration (μg/m 3 )

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 stagnat ion conditions. Additionally, there were no high wind speed hours recorded at any of the thr ee 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 stagn ation conditions at each of these three monitoring sites. Because no PM10 exceedances occurred on stagnation days at the Maricopa, Casa Grande, or Combs School monit oring 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 shieldi ng 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 disturbe d 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 even ts 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 m onitoring 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 concen tration 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 tabul ated 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 screenin g 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 inc luded hourly PM10 concentrations in excess of 150 μg/m 3 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 P M10 concentration for stagnation days in Appendix B demonstrated that a final criterion fo r 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 adverse ly 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 P M10 concentrations, wind speed, wind direction, and mixing height for this date are sho wn in the plots contained in Appendix C.-54- 

### 3.6 Stanfield

During 2008, exceedances of the PM10 24- hour standard were reco rded at Stanfield on 11 days on which the hourly wind speeds exceed ed 12 mph for at least one hour. These dates, together with PM10 and wind speed stati stics, 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 fr om 11 to 6. The remaining candidate days, having at least 7 high wind hours each, we re 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 wi th 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 correlati on 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 st eadily 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 correlati on 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. P lots 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 20 08. 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 hi gh wind hours of the three candidate days. January 1, 2008, recorded the second-highe st correlation between hourly wind speeds and PM10 concentrations, and had the second-highest 24-hour aver age 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/m 3 ), but the wind speed/PM10 conc entration correlation for June 21 was 27% lower than that of January 1. Based on these c onsiderations 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 atta inment at this site would simultaneously
demonstrate attainment at the Maric opa 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 separa te 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/m 3 , but levels in both the preceding and succeeding hours were below 920 μg/m 3 . 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 concentrati ons 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 re corded 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 conc entrations 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 be tween 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 con centrations 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 conc entration 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 Oc tober 27, 2008, as the high wind design for-57- this monitoring site. Hourly plots of w ind 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 im pacting 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 conc luded that the high wind control strategies that would bring the Casa Grande site into at tainment would also provide the same benefit at the Combs School site. As a result, these sit es were not chosen for development of modeling domain emission inventories or attainment demonstrations.



### 3.10 PM2.5 Exceedance Days

The national 24-hour a mbient air quality standard for PM2.5 of 35 μg/m 3 was exceeded at only the Cowtown monitoring station during 2008. A tabulation of the 24-hour average PM2.5 concentrations, PM10 concentrati ons, 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 averag e 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 reporte d 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 repr esents 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 hour s. 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 wa s 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 wa s 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 concentrati ons, 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 approxim ately 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 summar ized above, November 20, 2008 was selected as the PM2.5 design day for the Cowtown site.

Summary

– For the purpose of demonstrating at tainment 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 quali ty monitoring stations. Separate modeling domains for stagnant design days and high wind de sign 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/m 3 )

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

PM 2.5 Day Cowtown November 20, 2008 345.0



### 3.11 Modeling Domains

Emission inventory preparation will focus on em issions generated within the attainment demonstration modeling domain surrounding each PM10 monitor for which a design day
was selected. For the central Pinal C ounty PM10 nonattainment area, these monitoring sites include Cowtown, Maricopa, Pinal Count y Housing, and Stanfield. Because separate stagnation, high wind, and PM2.5 design days were selected for each affected site, separate modeling domains were also deve loped 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 dislodg ing 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 emi ssions 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 sig nificantly exceed concentrations produced by
mechanical sources in the same area. Research has demonstrated that the flux of PM10
in the vertical direction above disturbed soi l 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 sur face matrices and lifted into the air by turbulent eddies to produce airborne PM10 conce ntrations. Below the threshold wind speed, sand particles remain at rest on soil surfaces and no significant PM10 emissions are generated. Once entrained in the a ir, 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 monit or, 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 entrainme nt. This characteristic of PM10 facilitated the process by whic h 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 point s 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 al igned 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 en closing 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 earlie r 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 cente rline 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 surface s. This finding guided the design ofhigh 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 Dom ain During the High Wind Design Day

Cowtown

– The Cowtown monitor will be the subjec t 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 demonstra tion. Each of these attainment demonstrations will be based on the modeling of controlled emissions within modeling domains whose boundaries will be set using the pr otocols 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 sim ilar. 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 3-3Cowtown-64- Maricopa

– A map of the high and low wind hour modeling domains for the high wind day (October 27, 2008) attainment demonstrati on 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 3-4Maricopa Modeling Domains-66-Figure 3-5Pinal County Housing Modeling Domains-67-Figure 3-6Stanfield 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 fr om 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 w ind, covering of soil surfaces, or the
consolidation of wind-entrainable surface particles into wind-r esistant 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 studies 20 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 em issions 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 faci lities 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., evap orative emissions from leaks in pump seals, valves, and flanges), but the presence of emitt ing 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 proce dures 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/Mon itoring%20Network/RARE_Report _v4_1.pdf-69- Land use files supplied by the Pinal Count y Air Quality Control Department (PCAQCD) identified a total of 44 industrial point sources with in 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 fa cilities can be determined from annual source emission reports, PCAQCD investigation reports, permit fil es and logs, or telephone contacts with sources. For most of the sources, materia l 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 th at facilities must submit to PCAQCD.

Annual emission inventory reports include calcula ted process-level emissions for PM10, NOx, SOx, and NH 3 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 con trol 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 inc lude recalculating emissions using methods other than those
used to make the initial emissions calculations and comparing result s. 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 repo rts. Notes concerning follow-up calls and
corrections to calculations need to be doc umented on each 2008 annual emissions report.

The QA point source coordinator should review and check calculations, identify errors, and perform completeness, rea sonableness 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 agri cultural operations, such as tillage, harvesting, and cotton ginning; offroad recreat ional 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 a nd 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 agric ultural 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 gra in 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 fie ld operations data are extremely difficult to obtain, the approach used to estimate activity nece ssarily 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 usi ng this methodology have the potential to significantly underestimate agricultural emissions when tilling occurs.-71- The second method uses tilling activity-specific emission factors from CARB 21 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/Planting a 0.8 Average 4.8

a. Discussions with CARB staff indicated that the weed ing 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 operat ions and the time of year in which they occur vary by crop type; therefore it is nece ssary 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 Plan 22 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 pro duced 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 cult ivated 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 shoul d be available from responsible government agencies, including the Arizona Departmen t of Agriculture and USDA. The results

21 http://www.arb.ca.gov/ei/a reasrc/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 representati veness and to request additional information on activities that took place on selected design days.

Harvesting

– The 2008 PEI used crop-speci fic emission factors (CARB, 2003) to
estimate harvest emissions. Table 4-2 li sts 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 Grain a 5.8

Potatoes 2.7 Vegetables b 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 harvesti ng 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 loca ted 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 B ulletin. Harvest acres for potatoes were obtained from the USDA National Agricultural Statistics Service for 2008, and vegetables and citrus were obtained from th e 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 re solution 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 deter mined that seven cotton gins are located
within the nonattainment area.

Data Sources

– Each of the seven cotton gins sho uld be contacted to determine their daily operating schedules.

Offroad Recreational Vehicle Fugitive Dust

– EPA’s NONROAD model estimates
exhaust emissions for offroad recreational vehic les. Since particulate emissions are also generated by recreational ve hicles traveling on unpaved surf aces, estimates of fleet size and activity are needed to support fugitive dust calculations. NONROAD provides estimates of annual mileage a nd 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 ca n 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 wheel ed 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 nonattainme nt area and within the modeling domains will be needed. Severa l options are available to allocate NONROAD estimates of vehicle class activity for the county to the nonatta inment 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 en forcement 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 var ied by project type (in units of tons/acremonth) by the number of months of constructi on duration, which also varied by project type.

Estimates for the duration of house and apa rtment 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 activit ies 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 developm ent activities (weighted average of residential and commercial duration) beca use the duration depends on the project type and size; and

12-month duration for temporary storage yard activities because these activities are frequently associate d 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 calculati ng 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 du ration (e.g., road construction, 12 months; residential single family, 6 mont hs; commercial, 11 months) were used to count backwards from the design date a nd 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 fo r 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, a ssumptions about whether projects are active on weekends, holidays, etc. will also be need ed. Discussions with Pinal County staff indicate that there are few multi-family projec ts, 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 de fined 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 facto r 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 avai lable 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 c ollection 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 repr esents 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 define d; again, the vehicle registration
information can be used as the source of that information.-77-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 V MT 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 ar e 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 for mulation 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 suf ficient 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) reg istration files; fuels information can be obtained from the Arizona Department of Weights and Measures; I/M program data can be obtained from the Arizona Departmen t 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 M AG’s travel demand model, which char acterizes 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 ar e classified as high-traffic arterials. The AP-42 equation for calculating paved roa d 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/m 2 ),
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 a rterials. 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 we ights 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 ar e 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 e quation has the following form:-79- 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, br ake 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 nonatta inment 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 si lt 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 emiss ion 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 representa tive 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, pave d 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 Joa quin 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 tr uck 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 dema nd model, Salt River Area truck speed was estimated to average 35 miles per hour. At t his 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

pur poses. 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 spe ed data were available from MCAQD for the two monitors during the December 2006 fi eld 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 frac tion of travel generated by heavy trucks, and location and extent of unpaved shoulde rs within the nonattainment area and within the low wind modeling domains. Unpaved shoul der lengths can be multiplied by hourly truck counts to determine bow wak e vehicle miles traveled for individual road segments. These estimates can be combi ned 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 sec ondary 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-sp ecific 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 Shoul ders 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 occupan cy, 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 exc lusively on the more frequent, higher emission activities that occur on agricultura l, residential, and commercial property.

Data Sources

– Emission factors for open burning by activity type will be derived from
AP-42 and from research studies conduc ted 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-re sistant matrices in order to reduce PM10 emissions. Control strategies for high wind sourc es 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 selec ting 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 comple tely 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 PM 10 Emissions ,” which is presented in Appendix I.

The second method replicates the approach MAG employed in quantifying high wind emissions for the Maricopa Count y’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, agricult ural activity, etc.). Maricopa County’s 2008 PEI cited local
conditions that did not reconcile with th e 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 re present 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 qua lity 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 deve lopment of the inventory and final emission report. These procedures include, but are not limited to, the following:

The development and impleme ntation of written procedures for data collection, data assessment, data handling, calcula tion 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 fa ctors 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 th at 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, ba sed 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, the y 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 fo cused 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 a nother 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 ensu re 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 sour ces. After identifying these sources, the goal will be to determi ne 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 th e data file and final report.

Representativeness Technical personnel will review all of the primary source data and compare them to previous emission resu lts and similar results from comparable regions to determi ne the reasonableness of the emissions estimates and repr esentativeness 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 At tribute 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 non attainment 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 developm ent 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 lea ding source-specific inventory development
activities.

QA/QC Coordinator

– The QA/QC Coordinator will be responsible for ensuring that adequate QA/QC procedures are incorpora ted 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 audit ed prior to generation of the emission report. The QA Coordinator will attend status meetings (by teleconference if necessary) held by the Inventory Deve lopment 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, wh en 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 audit s, 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 Mana ger 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 im plementation 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 sour ce 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 we re developed to help ensure data accuracy, completeness, representativeness, and compara bility. 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 accordin g to U.S. EPA-approved procedures. The approach and supporting documents or referen ces 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 th e 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 diffe rent parts of the state. To enhance communication and productivity, team members will be allowed to employ either approach but will be encouraged to tr ack information relative to the development of the inventory electronically. This daily log of activities will help another IDT member reproduce the emission results and all ow an evaluation of data accuracy and completeness.-88- Table 5-3 Data Collection Guidance Documents

Source Type Guidance Document Suggested Data Sources

Point EPA-450/4-91-016 a
AP -42 b

EIIP Volume II Existing inventories, state permit files, facility surveys, county business directories, telephone directory

Area EPA-45-/4-91-016 a
EIIP Volume III Existing inventories, example cases and data

sources

Nonroad Mobile EPA-450/4-81-026d c Existing inventories, example cases, and data

sources

Onroad Mobile EPA-45-/4-91-010 d

EPA -450/4-91-011 e
EIIP Volume IV Transportation or planning agency data

a.“Procedures for the Preparation of Emission In ventories for Carbon Monoxide and Precursors of Oz one,

Volume I: General Guidance for Stationary Source s,” U.S. EPA, Office of Air Quality Planning and Standards, Research Triangle Park, North Carolina, 1991. b. “Compilation of Air Pollutant Emissi on 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, Vo lume 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, Offic e 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 correcti on (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 emissi on will be described and example calculations will be provided;

The approach used to determine comple teness for each source type will be described;-89-Documents from which emission factors ar e taken will be identified and referenced; and

The source, agency, group, or company providing information by telephone will be identified (include tele phone 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 da te 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 ch ecking will be docume nted to further qualify
the emission estimates. In addition to the D ARS 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, a nd 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 understa nding 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 docume ntation 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 reque sts 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 point s in the inventory development process. These will include assessments as the inventory development process is being planned, during data collection, as emissions are be ing calculated, and when the results are reported. The primary goal of the audit pro gram is to prevent quality concerns. Opportunities to incorporate preventative meas ures will be included during each audit.

Prior to announced audits, the auditor will inf orm 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 c ompliance 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 deve lop 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 deve lopment 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 dat a 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 membe rs 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 preliminar y 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, whic h 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 Developme nt 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 shoul d 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 broug ht 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 plan ned 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 audit ors 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 conce rn. 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 submitti ng 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, ra w 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 documentati on section of the emissions inventory will provide enough detail so that the inventory de velopment described in the report can be compared to the information provided in this QA P. 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 compromi se 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 rec ommendation for correc tive 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 SitesA-1A-2A-3A-4A-5A-6A-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, 2008B-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 4500:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)PM10 (ug/m3)

Time PM10
Average Wind Speed

PM10 NAAQS (150 ug/m 3 )

Figure B2. Wind Direc tion vs. Wind Speed

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

0 90 180 270 3600:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)Wind Direction (degree)

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 4500:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Mixing Height (m)PM10 (ug/m3)

Time PM10 Mixing Height

PM10 NAAQS (150 ug/m 3 )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 7000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)PM10 (ug/m3)

Time PM10
Average Wind Speed

PM10 NAAQS (150 ug/m 3 )

Figure B5. Wind Direc tion vs. Wind Speed

0.0 0.5 1.0 1.5 2.0 2.5 3.0

0 90 180 270 3600:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)Wind Direction (degree)

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 7000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Mixing Height (m)PM10 (ug/m3)

Time PM10 Mixing Height

PM10 NAAQS (150 ug/m 3 )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 7000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)PM10 (ug/m3)

Time PM10 Average Wind Speed

PM10 NAAQS

(150 ug/m 3 )

Figure B8. Wind Di rection 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 3600:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)Wind Direction (degree)

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 7000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Mixing Height (m)PM10 (ug/m3)

Time PM10
Mixing Height

PM10 NAAQS (150 ug/m 3 )

### Appendix C

Statistics of the High Wind PM10 Exceedance Days at Cowtown in 2008C-1 Table C1. Statistics of the PM10 High Wi nd Exceedance Days at Cowtown in 2008

Date Number of High Wind

Hours Daily Average

PM10 (ug/m 3 ) 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 2C-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 1,0000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)PM10 (ug/m3)

Time PM10

Average Wind Speed

PM10 NAAQS (150 ug/m 3 )

Figure C2. Wind Direc tion vs. Wind Speed

0 2 4 6 8 10 12 14 16 18

0 90 180 270 3600:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)Wind Direction (degree)

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 1,0000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Mixing Height (m)PM10 (ug/m3)

Time PM10 Mixing Height

PM10 NAAQS (150 ug/m 3 )

### Appendix D

Statistics of the High Wind PM10 Ex ceedance Days at Stanfield in 2008D-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/m 3 ) 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 0.3838D-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 1,2000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)PM10 (ug/m3)

Time PM10

Average Wind Speed

PM10 NAAQS (150 ug/m 3 )

Figure D2. Wind Direc tion vs. Wind Speed

0 5 10 15 20 25

0 90 180 270 3600:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)Wind Direction (degree)

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 1,2000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Mixing Height (m)PM10 (ug/m3)

Time PM10
Mixing Height

PM10 NAAQS (150 ug/m 3 )

HIGH WIND DAY, 11/09/2008, STANFIELDD-3 Figure D4. PM10 vs. Wind Speed

0 5 10 15 20 25

0 500 1,000 1,500 2,000 2,5000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)PM10 (ug/m3)

Time PM10

Average Wind Speed

PM10 NAAQS (150 ug/m 3 )

Figure D5. Wind Direc tion vs. Wind Speed

0 5 10 15 20 25

0 90 180 270 3600:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)Wind Direction (degree)

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 2,5000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Mixing Height (m)PM10 (ug/m3)

Time PM10
Mixing Height

PM10 NAAQS (150 ug/m 3 )

HIGH WIND DAY, 12/13/2008, STANFIELDD-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 8000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)PM10 (ug/m3)

Time PM10

Average Wind Speed

PM10 NAAQS (150 ug/m 3 )

Figure D8. Wind Direc tion vs. Wind Speed

0 2 4 6 8 10 12 14 16 18

0 90 180 270 3600:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)Wind Direction (degree)

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 8000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Mixing Height (m)PM10 (ug/m3)

Time PM10 Mixing Height

PM10 NAAQS (150 ug/m 3 )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 7000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)PM10 (ug/m3)

Time PM10

Average Wind Speed

PM10 NAAQS (150 ug/m 3 )

Figure D11. Wind Direction vs. Wind Speed

0 2 4 6 8 10 12 14 16 18

0 90 180 270 3600:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)Wind Direction (degree)

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 7000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Mixing Height (m)PM10 (ug/m3)

Time PM10 Mixing Height

PM10 NAAQS (150 ug/m 3 )

### Appendix E

Statistics of the High Wind PM10 Exceedance Days at Pinal County Housing in 2008E-1 Table E1. Statistics of the PM10 High Wind Exceed ance Days at Pinal County Housing in 2008

Date Number of High wind
hours Daily Average

PM10 (ug/m 3 ) Daily Average

WSPD (mph) Daily Max

WSPD (mph) Correlation of

H ourly 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 0.7750E-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 1,2000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)PM10 (ug/m3)

Time PM10

Average Wind Speed

PM10 NAAQS (150 ug/m 3 )

Figure E2. Wind Di rection vs. Wind Speed

0 5 10 15 20 25

0 90 180 270 3600:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)Wind Direction (degree)

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 1,2000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Mixing Height (m)PM10 (ug/m3)

Time PM10 Mixing Height

PM10 NAAQS (150 ug/m 3 )

### Appendix F

Statistics of the PM10 Exceedance Days at Maricopa in 2008F-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/m 3 ) 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 0.6795F-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 9000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)PM10 (ug/m3)

Time PM10 Average Wind Speed

PM10 NAAQS (150 ug/m 3 )

Figure F-2. Wind Direction vs. Wind Speed

0 2 4 6 8 10 12 14 16 18 20

0 90 180 270 3600:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)Wind Direction (degree)

Time Wind Direction

Average Wind Speed N

W

S

E

N

Figure F-3. PM10 vs. Mixing HeightF-3 0 500 1000 1500 2000 2500 3000 3500

0 100 200 300 400 500 600 700 800 9000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Mixing Height (m)PM10 (ug/m3)

Time PM10
Mixing Height

PM10 NAAQS

(150 ug/m 3 )

### Appendix G

Statistics of the PM2.5 Exceed ance Days at Cowtown in 2008G-1 Table G1. Statistics of the PM2.5 Exce edance Days at Cowtown in 2008

Date Daily Average

PM2.5 (ug/m 3 ) Daily Average

PM10 (ug/m 3 ) 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 7G-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 6,0000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)PM10 (ug/m3)

Time PM10

Average Wind Speed

PM10 NAAQS (150 ug/m 3 )

Figure G2. Wind Di rection vs. Wind Speed

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

0 90 180 270 3600:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)Wind Direction (degree)

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 6,0000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Mixing Height (m)PM10 (ug/m3)

Time PM10 Mixing Height

PM10 NAAQS (150 ug/m 3 )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 4,0000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)PM10 (ug/m3)

Time PM10

Average Wind Speed

PM10 NAAQS (150 ug/m 3 )

Figure G5. Wind Direc tion vs. Wind Speed

0 2 4 6 8 10 12

0 90 180 270 3600:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)Wind Direction (degree)

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 4,0000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Mixing Height (m)PM10 (ug/m3)

Time PM10 Mixing Height

PM10 NAAQS (150 ug/m 3 )

PM2.5 DAY, 05/06/2008, COWTOWNG-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 1,6000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)PM10 (ug/m3)

Time PM10

Average Wind Speed

PM10 NAAQS (150 ug/m 3 )

Figure G8. Wind Direc tion vs. Wind Speed

0 2 4 6 8 10 12 14 16 18

0 90 180 270 3600:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)Wind Direction (degree)

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 1,6000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Mixing Height (m)PM10 (ug/m3)

Time PM10 Mixing Height

PM10 NAAQS (150 ug/m 3 )

PM2.5 DAY, 04/30/2008, COWTOWNG-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 2,0000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)PM10 (ug/m3)

Time PM10

Average Wind Speed

PM10 NAAQS (150 ug/m 3 )

Figure G11. Wind Direction vs. Wind Speed

0 2 4 6 8 10 12 14 16 18 20

0 90 180 270 3600:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)Wind Direction (degree)

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 2,0000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Mixing Height (m)PM10 (ug/m3)

Time PM10 Mixing Height

PM10 NAAQS (150 ug/m 3 )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 1,8000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)PM10 (ug/m3)

Time PM10 Average Wind Speed

PM10 NAAQS (150 ug/m 3 )

Figure G14. Wind Direction vs. Wind Speed

0 1 2 3 4 5 6 7

0 90 180 270 3600:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)Wind Direction (degree)

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 1,8000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Mixing Height (m)PM10 (ug/m3)

Time PM10 Mixing Height

PM10 NAAQS (150 ug/m 3 )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 7000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)PM10 (ug/m3)

Time PM10

Average Wind Speed

PM10 NAAQS (150 ug/m 3 )

Figure G17. Wind Direction vs. Wind Speed

0 2 4 6 8 10 12

0 90 180 270 3600:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)Wind Direction (degree)

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 7000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Mixing Height (m)PM10 (ug/m3)

Time PM10 Mixing Height

PM10 NAAQS (150 ug/m 3 )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 1,8000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)PM10 (ug/m3)

Time PM10

Average Wind Speed

PM10 NAAQS (150 ug/m 3 )

Figure G20. Wind Direction vs. Wind Speed

0 1 2 3 4 5 6 7 8

0 90 180 270 3600:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Wind Speed (mph)Wind Direction (degree)

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 1,8000:002:004:006:008:0010:0012:0014:0016:0018:0020:0022:00Mixing Height (m)PM10 (ug/m3)

Time PM10 Mixing Height

PM10 NAAQS (150 ug/m 3 )

### Appendix H

Road Count DataH-1 ROAD COUNTS CONDUCTED BY PI NAL 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 Highway June 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 174H-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 Highway September 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 Highway October 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 Di rt ~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 164H-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 165H-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 D irt ~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 DivisionI-1 A Method for the Back-Calculation of Wind -Blown Emission Factors from Field
Measured PM 10 Concentrations

Arizona Department of Environmental Quality Air Quality Division

ABSTRACT

Regional variability in soil characteristic s, meteorological trends, and surface disturbances lead to changes in wind blown emission factors (EFs) for a given land use (LU). Therefore, it is p rudent to determine accurate EF s for Pinal County in order to provide the most accurate wind blown Emission Inve ntory (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 PM 10 State Implementation Plan (SIP). This method re lies 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 Emissi on 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:

nnn EF AE * 

where E n is total emissions for each land use within a given footprint (tons/hr), A
n is the
calculated area of each land use located within each footprint (m 2 ), and EF n is the estimated emission factor for each land use located within a given footprint.

This paper presents the steps take n 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 e stimated PM10 concentrations and PM10 concentrations
measured in the field varied be tween 2% and 282% over the 24 hour period. The average
24 hour PM 10 concentration for PCH on 1/1/2008 was 264.9 μg/m 3 as measured at the monitor. The model predicted a 24 hour averag e concentration for PCH on the design day of 260.4 μg/m 3

.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 particula te matter less than 10 μm (PM 10 ). The purpose of the SIP is to protect human health and we lfare through: 1) air monitoring data and analysis, 2) PM 10 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 PM 10 and PM 2.5 standard.

Airborne particles are a serious threat to human h ealth. Major health concerns due to exposure to PM 10 include: effects on breathing and respiratory systems, damage to lung tissue, cancer, and premature de ath. The elderly, childr en, and people with chronic lung disease, influenza, or asthma, are especiall y sensitive to the effects of particulate matter.

Ambient levels of PM 10 measured at air quality monitoring stations within Pinal County show widespread, frequent, and in some instan ces, severe, violations of the PM 10 standard, d ating back to 2002. Pinal County’s PM 10 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 PM 10 ambient air quality stand ard of 150 micrograms per cubic meter (μg/m 3 ).

Ambient monitors located in the new nonattainment ar ea routinely record concentrations
two to three times the level of the standard and several monitors have recorded levels approaching or exceeding the sig nificant harm level of 600 (μg/m 3 ). After reviewing available data, EPA redesignated much of the western half of Pinal County to nonattainment for the 1987 24-hour PM 10 National Ambient Air Quality Standard (NAAQS) on May 23, 2012. See 75 FR 60680, October 1, 2010.

Redesignation of the western portion of Pinal Co unty to nonattainme nt requires the State to create a State Implementation Plan (S IP) which proposes reasonable control methods
to reduce PM 10 emissions in Pinal County to levels which comply with EPA’s PM 10 ambient air quality standard. Confo rmity with these standards are exhibited through modeling of exceedance days prior to and following control technology implementation to demonstrate adequate emi ssions 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 mecha nical activities which ca use the entrainment of particulate matter into the atmosphere . As wind speeds increase, wind blown dust
entrainment increase s resulting in a lower proportion of field measured PM10 concentrations originating from activity ba sed PM entrainment. The degree to which
wind blown emissions contribute to ambient air PM 10 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 roug hness, 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 re lying 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 pr oposes another method of estimating
windblown EFs. The Emission Factor Back Ca lculation (EFBC) method utilizes field measured meteorological and PM10 concentra tion 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 estimate s emission particle origination areas in order to determine what land uses are contributing emissions impacting the monitor during a period of interes t. 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 ta ken to implement the EFBC method. ADEQ further tests the estimated EF s to determine how well PM10 concentrations are calculated using these EFs aga inst 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 co ver 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 rem ove 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 orde r to estimate particle emission origination location. This method utilizes probability anal ysis to calculate an area from which particles (PM 10 ) are most likely to have originated. The next step requires the integration of aerial photography, calculated footprint ex tents, and land use information into ArcGIS
in order to characterize the land uses loc ated 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 PM 10 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 nnn EF AE * 

These equations cannot be solved individually, but must be solved utilizing a statistical optimization technique such as the Ne wton 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 differe nt 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) propose d design day of January 1, 2008. The following steps to the EFBC method are described:

Land Use Classification

Meteorological and PM10 Conce ntration 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 1 st was modeled to assess the accuracy of these EF s 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 PM 10 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 surr ounding 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 Stanf ield, Cowtown, Maricopa, and Pinal County Housing monitors:

Developed Urban Lands

Developed Rural Lands

Paved Roads

Unpaved Roads

Cleared AreasI-5Construction 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 discusse d 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 testi ng the PCH 1/1/2008 design day, data was processed from 4 monitors within the nonatt ainment area: Cowtown, Pinal County Housing, Stanfield, and Maricopa. These 4 monitors recorded the highest number of exceedances during the year of 2008 providing th e most conservative datasets available from a regulatory standpoint. Furthermore, processing multiple meteorological and PM10 concentration datasets al lowed ADEQ to create EFs repr esentative of the entire
nonattainment area rather than for a small section of the nonattainment area.

The first step of data proce ssing was to isolate only the hourly data which occurred during the winter of 2008 (December 22nd

– March 20 th ). Arizona Meteorological Network (AZMET) rainfall data was analyzed for all rain gauges located within the nonattainment area. Any date when a rain gaug e 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 inte rpolated. Wind speed, wind direction, and PM 10 concentration values were taken from field m easurements. Mixing heights and sensible heat fluxes were calculated by AERMET using meteorological data (including upper air sounding information) from Tucson, AZ. Hourly se asonal temperature was averaged from AZMET values for the year of 2008. Hourly data at each statio n 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 meteorolog ical and PM10 concentration data to be utilized for emission footprint creation.I-6 Estimation of Emissions Origination Area

Characterization of the land uses contribut ing 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 ba ck 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 imag e can then be imported into ArcGIS for characterization contributing land uses . The Footprint model is a windows desktop implementation of the Korman and Meixner anal ytical footprint model (2001). This
model accounts for vertical turbulence and cross-wind disper sion in order to back calculate particle motion based on a range of meteorological inputs such as: wind speed, wind direction, distance to obst ructions, air temperature, sensible heat flux, mixing height, and surrounding vegetation canopy densit y 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 determi ned from aerial photography of the region to be approximately 15 km for each of the monitors,

“Maximum Fetch” was determined by th e length of the footprint,

“Measurement height” was se t 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 “Brightnes s distribution footpr int boundary, %” was set to 90% to estimate the origination area of 90% of the emissions.

A single emission estimation origination footprint wa s created for each EF needed to model the January 1, 2008 proposed design da y 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 forI-7 each wind speed/hour of day grouping. The tot al 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 give n footprint. Images created during the Footprint processing portion of the method were loaded into ArcGIS. The receptor (i.e.
PM 10 monitor) on each footprint was geo-recti fied 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 we re created to outline the footprint boundary. When necessary, these polygons were clippe d to insure the extent of the footprint did not exceed the distance a parcel of air could tra vel in an hour. The land use polygon was then clipped to the boundary of the indi vidual footprint polygons to create a land use polygon unique to each footprint polygon. Land use areas and roa d lengths were then
calculated in ArcGIS. Road width was utilize d to estimate road area for unpaved and paved roadways.

Statistical Optimization of EFs

The final step of the emission fa ctor back calculation process involves data consolidation and statistical optimization in or der to estimate the EFs for e ach land use. For each footprint, the total area of each land use ty pe 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 tot al footprint must be
calculated from the field PM 10 average concentrations for th e period of interest. Daily average emissions at the station are calculated using the following equation:

000

, 740

, 184

, 907 **

CHA E M FP


where E is the average emissions captured by the station [tons], A FP is the area of the emissions origination footprint [m 2 ], H M is the atmospheric mixing height [m], C is the average hourly measured PM 10 concentration [μg/m 3 ], and 907,184,740,000 is the conversion factor from micrograms to tons. Once this has been calculated for each footprint, an E 80 or E 90 can be calculated so 80% or 90% of the total emissions can be attributed to the 80% or 90% footprint (percenta ge is dependant on which footprint size reasonably fit within the boundaries of the Footprint model boundaries):

EE *8.0 80  or EE *9.0 90 

The final step requires the use of statistical optimi zation 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. SolverI-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 va lue 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 xx EF A  *

where A
x is a given land use Area, EF
x is the estimated Emissi on 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 s o, 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 PM 10 entrainment.

Design Day Modeling

Due to time constraints, modeling using AERM OD could not be performed to test the validity of the back calculated EFs. There fore, a simplified model was utilized to test the accuracy of the estimated EFs. Model vali dity 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 PM 10 entrainment originating from sta gnation activities were unknown, estimated EFs created prior to the wind blown correction were utilized in this testing in order to account for unknown stagnati on emissions as well. Testing was achieved by using the meteorological da ta for each hour of the January 1 st proposed PCH design day to create 24 unique emission origination footprints using the EdiRe Footprint program. These footprints represented the estimated origination ar ea 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 em issions were then converted to PM 10 atmospheric concentrations to compare to monitor measure d concentrations on the design day by modifying the equation:

000

, 740

, 184

, 907 **

CHA E M FP


to calculate for C, whereby:I-9 M FP HA E C

* * 000

, 740

, 184

, 907



The variables A and H were based on the hourly measurements specific to the design day being tested. Hourly calculated conce ntrations were then compared to PM 10 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 da y 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 reg istered at the PCH monitor, but no wind speeds higher than this were registered during this ti meframe. 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 e stimated EFs for 9 LUs as determined using the EFBC methodology. These EFs were estimate d 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-10Table 2: Estimated EFs [tons/m2/hr] for different wind speeds and times of the day for the winter months of 2008. These EFs were calculatedusing the EF back calculation (EFBC) model. These EFsinclude emissions originatingfrom stagnation activities.LU IDABCDEFGHI&J0-3mph 000-700hrs5.45E-101.87E-101.18E-103.14E-112.07E-106.43E-102.45E-097.68E-101.37E-090-3mph 800-1500hrs6.70E-092.94E-103.13E-085.73E-117.13E-112.89E-084.84E-102.58E-102.33E-090-3mph 1600-2300hrs2.10E-101.41E-105.09E-117.12E-111.59E-105.32E-116.44E-086.86E-106.39E-093-6mph 000-700hrs1.44E-091.36E-093.53E-101.60E-101.21E-092.34E-101.62E-101.97E-092.00E-093-6mph 800-1500hrs1.32E-099.49E-101.73E-107.34E-111.84E-101.88E-102.35E-095.25E-101.63E-0915-18mph 800-1500hrs7.39E-074.57E-071.04E-074.23E-082.29E-088.48E-091.57E-097.58E-073.28E-0815-18mph 1600-2300hrs1.65E-071.45E-073.70E-089.63E-093.40E-085.67E-082.02E-091.75E-071.80E-0718+mph 800-1500hrs5.93E-071.09E-061.87E-071.12E-075.13E-071.21E-084.36E-102.07E-061.21E-0618+mph 1600-2300hrs1.35E-081.11E-072.33E-071.67E-073.95E-061.48E-06NA6.60E-082.36E-07I-11000-700 Hours EFs1.0E-115.1E-101.0E-091.5E-092.0E-092.5E-093.0E-090-3mph 000-700hrs3-6mph 000-700hrsWind Speed (mph) and Time

EFs (tons/day/m2)ABCDEFGHI&JFigure 1: Estimated Winter Emission Factors [tons/m2/hour] for all land uses between 0-700 hours for the wind speed categoriesof 0-3 mph and 3-6 mph. Wind speeds were adjusted for a 10m height.I-12800-1500 Hours EFs1.0E-111.0E-101.0E-091.0E-081.0E-071.0E-061.0E-050-3mph 800-1500hrs 3-6mph 800-1500hrs 15-18mph 800-1500hrs 18+mph 800-1500hrsWind Speed (mph) and Time

EFs (tons/day/m2)ABCDEFGHI&JFigure 2: Estimated Winter Emission Factors [tons/m2/hour]for all land uses between 800-1500 hours for the wind speed categoriesof 0-3 mph, 3-6 mph, 15-18 mph, and 18+ mph. Windspeeds were adjusted for a 10m height.I-131600-2300 Hours EFs1.0E-111.0E-101.0E-091.0E-081.0E-071.0E-061.0E-050-3mph 1600-2300hrs 15-18mph 1600-2300hrs 18+mph1600-2300hrsWind Speed (mph) and Time

EFs (tons/day/m2)ABCDEFGHI&JFigure 3: Estimated Winter EmissionFactors [tons/m2/hour] for all land uses between 1600-2300 hours for the wind speed categoriesof 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 ch ange 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 Us e type G) is the only land use which showed decreasing emission rates with increasin g wind speeds. This could be attributed to cattle movement patterns. As wind speeds in crease, 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 ha ve the highest EFs under stagnation conditions, likely due to the increased ac tivity on these lands in comparison to other land uses at the same time of day. During the mi d-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 act ivity on these lands during the middle portion of the day for winter months.

The next step in the windblown EF estimation pro cess 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 increa sing wind speeds were assumed to have negligible emissions originating from wind and were thus given wind blown EFs of 0
tons/m 2 /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 PM 10 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 c overage to reduce emission factors.

For all hours of the day, as wind speeds increa se 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 w hich 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 rese rvoir depletion may in fact be playing a significant role. Cleared Areas and C rop 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 an d Paved Road EFs were surprising due to the degree to which these land uses have coverage to reduce the reservoir size.I-15Table 3: Estimated wind blown EFs [tons/m2/hr] for different wind speeds and times of the dayfor the winter months of2008. These EFs werecalculated using the EF back calculation model, andwere corrected to remove stagnation activities.LU IDABCDEFGHI&J3-6mph 000-700hrs8.91E-101.17E-092.35E-101.28E-101.00E-090.00E+000.00E+001.20E-096.26E-103-6mph 800-1500hrs0.00E+006.55E-100.00E+001.61E-111.13E-100.00E+001.86E-092.66E-100.00E+0015-18mph 800-1500hrs7.32E-074.57E-077.28E-084.22E-082.29E-080.00E+001.08E-097.58E-073.05E-0815-18mph 1600-2300hrs1.65E-071.45E-073.69E-089.56E-093.39E-085.67E-080.00E+001.75E-071.74E-0718+mph 800-1500hrs5.86E-071.09E-061.55E-071.12E-075.13E-070.00E+000.00E+002.07E-061.21E-0618+mph 1600-2300hrs1.33E-081.11E-072.33E-071.67E-073.95E-061.48E-060.00E+006.53E-082.30E-07I-16Windblown EFs for 000-700hrs & 3-6 mph0.0E+002.0E-104.0E-106.0E-108.0E-101.0E-091.2E-091.4E-09A B C D E F G H I&JFigure 4: Estimated WinterWindblown Emission Factors [tons/m2/hour] forall land uses between 0-700 hours for the wind speedcategory of 3-6 mph. Wind speeds were adjusted for a 10m height. These EFs were corrected for stagnation, activity drivenemissions.I-17Windblown EFs for 800-1500hrs1.0E-111.0E-101.0E-091.0E-081.0E-071.0E-061.0E-05A B C D E F G H I&J3-6mph15-18mph18+mphFigure 5: Estimated Winter Windblown Emission Factors [tons/m2/hour] for all land uses between 800-1500 hours for the wind speedcategories of 3-6 mph, 15-18 mph, and 18+ mph. Wind speeds wereadjusted for a 10m height. These EFs were corrected forstagnation, activity driven emissions.I-18Windblown EFs for 1600-2300hrs1.0E-111.0E-101.0E-091.0E-081.0E-071.0E-061.0E-05A B C D E F G H I&J15-18mph18+mphFigure 6: Estimated Winter Windblown Emission Factors [tons/m2/hour] for all landuses between 1600-2300 hours for the windspeed categories of 15-18 mph and 18+ mph. Wind speeds were adjusted for a 10m height. These EFs were corrected for stagnation,activity 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 da y, and only utilizing data from the
season of interest on dates where rainf all 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 c onsecutive 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 fo r 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 xx EF A  *

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 associa ted 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 er rors 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 -17271I-20 Design Day Modeling

The estimated EFs for the winter of 2008 pr esented in Table 2 were used to test how well total hourly estimated PM10 concentrati ons 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 1 st design day had not yet been de termined. Therefore, uncorrected emission factors (i.e. EFs which account for stagnation, activity driven emissions as well as wind blown emissions) were utilized in testin g 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 measure d 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 PM 10 atmospheric concentrations. The average hourly error throughout the day is 97%, as calculated by the equation:

Measured Measured Model C CC Difference  %

Where C Model is the PM 10 concentration which was modeled (μg/m 3 ) and C Measured is the
PM 10 concentration (μg/m 3 ) measured at the monitor for that hour of the design day. The model performed very well at the dail y scale. PCAQD reported a 24 hour PM 10 concentration for PCH on 1/1/2008 of 264.9 μg/m 3 and the model predicted a 24 hour average concentration of 260.4 μg/m 3 . The estimated 24 hour average was calculated by averaging each of the individual 24 hourly estimated PM 10 concentrations, where:

24 2400

100 24 

# 

  h h C

C

C

24 is the average 24 hour concentration (μg/m 3 ) and C h (μg/m 3 ) is the estimated PM 10 concentration for each of the 24 estimated hourly concentrations.I-21PCH 1/1/2012 Design Day020040060080010001200100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400Hour

PM10 Concentration (micro-g/m3)ModelMonitorFigure 7: A comparison of field measuredPM10 concentrations (Monitor) against PM10 concentrations predicted using EFsestimated by the back calculation method (Model) foreach hour of the 1/1/2008 PCH design day.I-22Error Between Modeled Atmospheric PM10 and Measured Concentrations0%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 2100 2200 2300 2400Hour

% Difference between Model and MeasuredFigure 8: Calculated Difference between modeled and measured atmospheric PM10concentrations for eachhour of the PCH 1/1/2008design day.I-23 DISCUSSION

The EF Back Calculation Method utilizes fie ld measured PM10 concentrations and meteorological data in an attempt to accurate ly 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 preferenti al to literature re ported 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 ra infall, 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 be ing comparable. These issue s 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, re quiring a review of the land use classification layers used for the model. Despite these issue s, when the EFBC estimated EFs were modeled for the PCH January 1, 2008 design day, the results exhibited good agreement
with field measured PM 10 concentrations at the hourly scale and excellent agreement at the daily scale (i.e. 24 hour average PM 10 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 simpli fied modeling approach presented in this paper as well as account for depositi on and dispersion effects on the modeling domain not directly accounted for in the simplified approa ch presented above. However, the preliminary results presented in the model a bove using EFs estimated by the EFBC method indicate this method represents a reason able way to estimate area specific EFs from field measured meteorological a nd PM10 concentration data for PM 10 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 o riginating 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 N onattainment Area May 2012INVENTORY DEVELOPMENT

Under a rollback modeling scheme, reductions of emissions that im pact 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 inventor ies. This following descri bes in detail how the 2007 base year and 2012 control/attainment year hi gh 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 hour ly high wind domains. Land use data, with the exception of construction data, come s from the MAG information services and represents land use patterns as of 2009. Land use is held c onstant between calculation of 2007 and 2012 year inventories. A detailed write-up of how the l and 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 c onservative 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 43 rd Avenue monitor hour 15 high wind domain (June 6, 2007 design day) are explai ned 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 windbl own dust as shown in Figure V-16. They include the land uses of Active Open Spac e (i.e., parks), Agriculture, Commercial, Construction, Coreslab (a large combinati on 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, Vac ant, 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 residentia l 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 surfac e roughness of the slope severely limits the production of windblown dust.

After the land uses are identified, a 10-acre fi shnet 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 monito r; which is West 43 rd Avenue in this example. The distance of the parcel to the monitor is necessary in order to apply

V - 62the distance weighting factor to the calc ulated emissions as discussed earlier in this chapter.

Figure V-16

Land Uses Capable of Producing Windblown Dust Within the Hour 15, West 43 rd Avenue Monitor, High Wind Domain (June 6, 2007)

To be conservative, all but f our 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 ex empted from this assumption because these land uses are known to have a mix of lar ge buildings and paved parking lots/access roads in addition to areas of bare so il. Over 2500 aerial photographs of these types of parcels in the Salt River area were evaluated to det ermine the average percentage of bare soil present on each of these land uses. The result s of that analysis are listed in Table V-22. To be conservative, the mean value was c hosen to represent t he 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.

V - 63Table 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 dom ain 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. Distu rbed 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 so ils emit at higher rates, as the extra energy from the wind entrains more material from both su rfaces. Thus, the main elements needed to calculate windblown PM-10 emissions are t he 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 em ission factors were developed based upon local testing of Arizona soils. The units of the em ission 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 Fact ors (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 43 rd 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 fact ors 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 l and use and the five minute wind speeds have been determined for the example high wind domain. The one variable that is left to be

V - 64determined is how much of the land use ac reage 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 discu ssed 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 leve ls of bare soils. As an ex ample, a rule effectiveness of 80% would assume that 80% of the acreage is stable and 20% is disturbed. Maricopa County Air Quality Department per formed rule effectiveness studi es for three fugitive dust rules: Rule 310, earthmoving activities, Ru le 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 cal endar 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 th is 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 Figur e V-16 are thus assigned disturbance rates based upon the rule effectiveness rates in Tabl e 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 ar eas and industrial/commercial areas that are permitted or unpermitted. The only land use under which Ru le 310.01 does not regulate is agriculture. However, because there are no quantitative rule effectiveness or disturbance rates studies available that are applicable to agricultura l 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 how ever, 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 th rough 2012, meaning windblown dust emissions from agricultural lands in 2012 are the same as in 2007. All other land uses besides agriculture exper ience a reduction in emissions between 2007 and 2012 as a result of the benefit of increased rule effectiv eness; which is pragmatically translating as an assumption of less dist urbed soil in the windblown dust inventory.

V - 65Now that all of the requir ed elements (acreage, percent disturbed, and wind speeds) have been determined, the elements are simply mult iplied in order to obtain PM-10 windblown dust emissions for each land us e 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 hi gh wind domain. The only difference between 2007 and 2012 year emissions is that the disturbance ra tes 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 st eps above, represent maximum potential PM10 emissions from windblown dust. As ex plained 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 te xture, 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

V - 66large. However, this step is not necessary for high wind emission inventories prepared for use in an attainment demonstrati on, as what is important in this rollback modeling is the difference between 2007 and 2012 emissions, no t 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 differ ence between the two inv entories provides the reductions needed to model attainment at the monitors. The unit and scale of the inventories thus do not matte r, only that the methodology is the same between the base and controlled inventories in order to claim t he benefits of the measures in this plan as applied to the modeled concentrations.

Once high wind PM-10 emissions have been ca lculated for the land uses, one final step is required to prepare the emissions for atta inment modeling. The calculated emissions for each land use parcel are divided by the di stance (feet) from the modeled monitor. As discussed in an earlier section in this chapt er, 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 eac h 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 - 67Table V-25 Base Case 2007 and Controlled 2012 High Wind PM-10 Emissions Inventories for May 4, 2007 Design Day at the West 43 rd 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%

V - 68Table 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%

V - 69Table 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%

V - 70Table 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%

V - 71Table 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 - 72Table 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%

V - 73Table V-31 Base Case 2007 and Controlled 2012 High Wind PM-10 Emissions Inventories for June 6, 2007 Design Day at the West 43 rd 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%

V - 74