Arizona Air Quality Designations 

Technical Support Document 

Boundary Recommendation for the Pinal County 

24-hour Standard PM2.5 Nonattainment Area 

Air Quality Division 

July 13, 2010 
This page is intentionally blank. 
TABLE OF CONTENTS 

Page

EXECUTIVE SUMMARY......................................................................................................... ES-1 

1.0 BACKGROUND....................................... ......................................................................... 1 

2.0 AREA DESIGNATION CRITERIA................ .......................................................................... 2 

3.0 AREA DESIGNATION CRITERIA ANALYSIS FOR THOSE AREAS..............................3 

 RECOMMENDED AS NONATTAINMENT 

 

4.0 AREA DESIGNATION RECOMMENDATIONS ....................................................................... 27 

LIST OF FIGURES Page

ES-1 Recommended Pinal County PM2.5 Nonattainment Area Boundaries...... ................... ES-3 

2-1 EPA Area of Interest and State PM2.5 Monitoring Network..................................... 3 

3-1 Satellite Image of the Cowtown Monitor Area ................................... .................... 4 

3-2 Major Features Related to Pinal County PM2.5 Nonattainment Area Designations .. ........5 

3-3 Annual PM2.5 Emissions by County According to EPA’s National Emissions Inventory...7 

3-4 EPA Area of Interest and State PM2.5 Monitoring Network..................................... 8 

3-5 Cowtown PM2.5 Exceedances by Max Wind Speed Category 2006-2008....................10 

3-6 Pinal County PM2.5 24-hour Averages............................................................12 

3-7 Comparison of PM2.5 and PM10 Source Contributions at the Cowtown and Casa .........13 

 Grande Downtown Monitors 

3-8 Back Trajectory Analysis for January 11, 2006..........................................................14 

3-9 PM2.5 24-hour Average Comparison between Sites (2006-2008) ..............................15 

3-10 PM2.5 as a Function for PM10 for Cowtown Monitor..................................................16 

3-11 October 2006 Temporal Analysis of Cowtown Monitoring Data................................17

 

3-12 Pinal County Proposed PM2.5 Nonattainment Area Land Ownership........................20 

3-13 Pinal County Population Density 2005..........................................................21 

3-14 Pinal County Population Density 2030...........................................................21 

4-1 Recommended Pinal County PM2.5 Nonattainment Area Boundaries..........................27
LIST OF TABLES Page

ES1 Criteria for Nonattainment Boundary Recommendations ............................................ ES-1 

1-1 History of EPA’s PM2.5 Particulate Matter National Ambient Air Quality Standards........ 1 

2-1 Criteria for Nonattainment Area Boundary Recommendations.......................................... 2 

3-1 Annual PM2.5 Emissions by County According to EPA’s National Emissions Inventory...6 

3-2 Chemical Mass Balance Modeled PM2.5 Source Attributions for the Cowtown Monitor... 7 

 

3-3 Pinal County 24-hour PM2.5 Averages 2006-2008.................... .................................. 11 

3-4 Population Growth and Density within Pinal County..........................................20 

3-5 Inter-County Employment Commute Statistics for Maricopa and Pinal ....................23 

 Counties from U.S. Census Bureau, April 2000 Census 

3-6 Pinal County-Supplied Road Inventory and Average Daily Traffic........................... 24 

3-7 Summary of Nine Criteria Analysis for the Proposed Pinal County PM2.5.................. 26 

 Nonattainment Area 

APPENDICES 

Appendix A Ambient PM2.5 Monitoring Data 2006-2008 

Appendix B Memorandum to Ira Domsky from Leonard Montenegro 
Arizona Air Quality Designations Technical Support Document Boundary 

Recommendation for the Pinal County 24-hour PM2.5 Nonattainment Area 

EXECUTIVE SUMMARY 

In July 1997, the U.S. Environmental Protection Agency (EPA) established National Ambient Air Quality 

Standards (NAAQS) for particulate matter 2.5 microns or less (PM2.5) setting a 24-hour average standard 

of 65μg/m3 and an annual average at 15 μg/m3

. Ambient monitoring data for PM2.5 collected in Pinal 

County by the Pinal County Air Quality Control District (PCAQCD) documents numerous violations of 

the both the annual and 24-hour PM2.5 NAAQS in the western portion of Pinal County. In a letter dated 

October 14, 2009, EPA notified the Governor of Arizona that Pinal County was being redesignated for 

nonattainment of the 1997 annual PM2.5 NAAQS and requested the State to submit PM2.5 nonattainment 

area boundary recommendations for Pinal County within 120 days. EPA also notified the Governor that 

designation of Pinal County for violations of the 2006 24-hour PM2.5 NAAQS was being deferred while 

further review of monitoring data was completed. On May 10, 2010, EPA presented its boundary 

recommendation for the Pinal County PM2.5 nonattainment area to the Governor. 

The only air quality monitor in Pinal County violating the 24-hour PM2.5 NAAQS is known as the 

Cowtown monitor. The location of this “hotspot” monitor was chosen because of its proximity to the 

largest cattle feedlot in the County – approximately 150 yards from the monitor. The area near the 

monitor includes a unique mix of sources in Pinal County – locomotive tracks, industrial processes, highdensity urban areas, agricultural activities, and concentrated animal feeding operations (CAFOs). 

The portion of Pinal County recommended by the Arizona Department of Environmental Quality 

(ADEQ) for nonattainment consists of the western region of the County near the Cowtown monitor where 

violations of the PM2.5 NAAQS have occurred and where emissions sources and activities contributing to 

those violations are located. The recommendation does not include portions of western Pinal County 

included in EPA’s boundary recommendation that are not contributing to violations at the Cowtown 

monitor; these areas include Stanfield, the City of Casa Grande, and the Table Top Wilderness Area south 

of Interstate 8. ADEQ recommends the remainder of the County be designated as 

attainment/unclassifiable. This recommendation is based on careful consideration of the PM2.5 standard, 

requirements for designation of nonattainment areas, and options available for implementing the standard. 

This document demonstrates the impracticability of and lack of environmental benefit from designating 

the nonattainment area based on EPA’s boundary recommendation. Further, this recommendation 

addresses the 9 criteria listed in Table ES-1 as required by EPA guidance.1

 The State is recommending 

those portions of Pinal County shown in Figure ES-1 as the nonattainment planning area for the 24-hour 

PM2.5 NAAQS. This recommendation excludes all Indian Country. Those townships in Pinal County that 

not listed are recommended as attainment/unclassifiable. 

T4S, R3E – R4E 

T5S, R3E – R4E (excluding sections 12, 13, 24, and 25) 

 

1 Memorandum from Robert J. Meyers, Acting Assistant Administrator, ``Area Designations for the Revised 24Hour Fine 

Particle National Ambient Air Quality Standard,'' June 8, 2007.

2
Table ES-1 

Summary of Nine Criteria Analysis for the Proposed Pinal County PM2.5 Nonattainment Area 

Criteria Factors Supporting the Recommended Nonattainment Area Boundary 

Emissions Data EPA’s National Emissions Inventory (NEI) ranked Pinal County fourth among 

Arizona counties in annual PM2.5 emissions; counties with a greater quantity of 

PM2.5 emissions have demonstrated attainment with the PM2.5 NAAQS. The 

recommended nonattainment area includes all of the emissions sources that caused 

or contributed to exceedances at the violating and similar sources in existence 

where there is no PM2.5 monitor. 

The NEI is for primary PM2.5 emissions only. Secondary PM2.5 particles may be 

forming and contributing to readings at the violating monitor; the main sources of 

secondary particles are emissions from combustion and on-road mobile sources. 

Meteorology The recommended nonattainment area is located in the agricultural basin area of 

Pinal County often affected by high-wind conditions and stagnation events. 

Geography/ 

Topography 

The recommended nonattainment area is located in the basin area of Pinal County 

characterized by open-ended valleys with few topographic barriers. 

Air Quality Data The only ambient air quality monitor in the State (excluding Nogales) violating 

the PM2.5 NAAQS is located within the recommended nonattainment area. 

The violating monitor was sited to characterize the impact of localized ‘hot-spot’ 

PM2.5 emissions from adjoining cattle feedlots. 

Jurisdictional 

Boundaries 

ADEQ’s recommendation maintains jurisdictional cohesiveness to the extent 

feasible, acknowledges the State’s lack of jurisdiction over Indian Country, and 

requires no new institutional arrangements for accomplishing required tasks. 

Level of Control 

of Emission 

Sources 

The operating permits issued by PCAQCD and ADEQ for sources in the 

recommended nonattainment area include control measures designed to reduce 

PM2.5 emissions. PCAQCD and ADEQ can at any time review a permit to 

determine if additional control measures are necessary for PM2.5 generating 

sources. PCAQCD also regulates open-burning and area sources. Additional 

control measures may be necessary for sources causing PM2.5 exceedances. 

Population 

Density and 

Degree of 

Urbanization 

The recommendation includes the population most affected by high PM2.5 

concentrations. 

Traffic and 

Commuting 

Patterns 

Emissions estimates developed by ADEQ indicate on-road sources are a relatively 

minor contributor to ambient PM2.5 concentrations in Pinal County. 

Growth Rates and 

Patterns 

The area surrounding the recommended nonattainment area is somewhat limited in 

growth by Indian Country and publicly owned lands. 

Factors Supporting the Exclusion of the Remainder of Pinal County and the State

Those areas of the county recommended as attainment/unclassifiable have demonstrated attainment 

with ambient monitoring data. These areas include urbanized areas with the largest populations, highest 

annual average daily traffic, and the highest growth rates in the State. As demonstrated in this 

document, emissions from sources outside the recommended nonattainment area have not contributed 

to an exceedance of the NAAQS within the recommended borders and are not likely to in the future.

ES-2 3
4

Figure ES-1 
1.0 BACKGROUND 

The U.S. Environmental Protection Agency (EPA) is charged with developing air quality standards for 

the protection of human health and the environment. As required by the Clean Air Act (CAA), EPA sets 

primary and secondary National Ambient Air Quality Standards (NAAQS) for 6 common air pollutants, 

one of which is fine particulate matter known as PM2.5. Maximum pollution levels or limits that are based 

on human health are called primary standards; limits intended to prevent environmental and property 

damage are called secondary standards. The CAA requires EPA to assess the latest scientific information 

and review the NAAQS every 5 years. 

On July 18, 1997, EPA revised the NAAQS for particulate matter to add new standards for PM2.5. EPA 

established annual and 24-hour standards for PM2.5 (62 FR 38652). The primary and secondary annual 

standards were set at a level of 15 micrograms per cubic meter (μg/m3) and were based on a three-year 

average of annual mean PM2.5 concentrations. The primary and secondary 24-hour standards were set at a 

level of 65 μg/m3

 and were based on a three-year average of the 98th percentile of 24-hour PM2.5 

concentrations. 

In 2006, EPA lowered the primary and secondary 24-hour PM2.5 standards from 65 μg/m3 to 35 μg/m3

. 

EPA retained the 1997 primary and secondary annual PM2.5 standards of 15 μg/m3

. The revised 24-hour 

standards became effective on December 18, 2006; EPA did not revoke the 1997 24-hour PM2.5 NAAQS. 

On October 8, 2009, EPA informed the Governor that ambient monitoring data recorded in Pinal County 

was in violation of the annual and 24-hour PM2.5 NAAQS for the period 2006-2008, EPA began the 

process under CAA section 107(d)(3) to redesignate Pinal County, and any nearby counties that may be 

contributing to PM2.5 violations in Pinal County, as nonattainment for the 1997 annual PM2.5 NAAQS. 

EPA also notified the State that it is in the process of collecting additional information for Pinal County 

and adjacent counties before making a determination regarding violations of the 24-hour PM2.5 NAAQS. 

In a letter dated October 14, 2009, EPA notified the Governor of Arizona that Pinal County was being 

redesignated for nonattainment of the 1997 annual PM2.5 NAAQS and requested the State to submit PM2.5 

nonattainment area boundary recommendations for Pinal County within 120 days. EPA also notified the 

Governor that designation of Pinal County for violations of the 2006 24-hour PM2.5 NAAQS was being 

deferred while further review of monitoring data was completed. On May 10, 2010, EPA presented its 

boundary recommendation for the Pinal County PM2.5 Nonattainment Area. 

1
2.0 AREA DESIGNATION CRITERIA 

Section 107(d)(1)(A)(i) of the Clean Air Act (CAA) defines a nonattainment area as “any area that does 

not meet (or that contributes to ambient air quality in a nearby area that does not meet) the national 

primary or secondary ambient air quality standard for the pollutant.” In a letter dated October 14, 2009, 

EPA requested that the State of Arizona determine nonattainment area boundaries for a Pinal County 

PM2.5 Nonattainment Area. ADEQ conducted a nine-factor analysis based on the analytical approach 

established by EPA’s guidance pertaining to the 1997 8-hour ozone NAAQS, the 1997 PM2.5 NAAQS, 

and the 2006 PM2.5 NAAQS. ADEQ assessed these 9 factors with respect to the areas of the County 

identified by EPA as contributing to violations in the recommended nonattainment area. 

To refine nonattainment boundaries to be either larger or smaller boundaries than EPA’s default, the State 

must perform an area-specific analysis that addresses the 9 criteria discussed in the EPA guidance cited 

above and listed below in Table 2-1 (74 FR 58687). In addition, the State should affirm that the 

nonattainment area analysis demonstrates that: 1) violations are not occurring in nearby areas that are 

excluded from the recommended area, and 2) the excluded nearby areas do not contain emission sources 

that contribute meaningfully to the observed violations.1

Table 2-1 

Criteria for Nonattainment Boundary Recommendations 

1. Emissions data (location of sources and contribution to PM2.5 concentrations) 

2. Meteorology (weather and air movement patterns) 

3. Geography/topography (mountain ranges or other air basin boundaries) 

4. Air quality data 

5. Jurisdictional boundaries (e.g., counties, air districts, existing nonattainment areas, etc.) 

6. Level of control of emission sources 

7. Population density and degree of urbanization (including commercial development) 

8. Traffic and commuting patterns 

9. Growth rates and patterns 

Figure 2-1 

 

1

Memorandum from Robert J. Meyers, Acting Assistant Administrator, ``Area Designations for the Revised 24Hour Fine 

Particle National Ambient Air Quality Standard,'' June 8, 2007.

2
 Monitor Locations 

 With No Exceedances 

 With Exceedances 

 With Violations 

EPA Area of 

Interest and 

State PM2.5

Monitoring 

Network 

3
3.0 ANALYSIS OF FACTORS FOR DETERMINING NONATTAINMENT AREA 

 BOUNDARIES 

INTRODUCTION 

 

EPA requested that the Governor of Arizona recommend areas of the State for designation as 

nonattainment area under the 1997 annual PM2.5 NAAQS and submit the recommendations to EPA by 

February 8, 2010. EPA based this request on violations of the PM2.5 NAAQS recorded by the Cowtown 

air quality monitor in Pinal County. The Cowtown monitor is located across a two-lane road near the 

largest CAFO in Pinal County, as depicted in Figure 3-1. The Cowtown monitor recorded 30 exceedances 

of the 24-hour PM2.5 NAAQS of 35 g/m3 during 2006-2008. To assist in the selection of boundaries for 

the proposed Pinal County PM2.5 Nonattainment Area, ADEQ examined the following 9 criteria listed in 

EPA’s guidance. The recommended nonattainment area includes the emission sources that contributed to 

those violations at the Cowtown monitor. The following demonstrations justify a PM2.5 nonattainment 

area that is satisfactory for this portion of Pinal County and appropriate for the emissions sources. 

What follows is an explanation of how each criterion was addressed in Arizona’s recommendation. Figure 

3-2 illustrates the major features considered when assessing the 9 factors for Pinal County. 

Figure 3-1 

Satellite Image of Cowtown Monitor Area 

4
Figure 3-2 
3.1 EMISSIONS DATA 

Due to time constraints, ADEQ was not able to make a gridded PM2.5 emissions inventory for Pinal County. 

In the absence of an emissions inventory, ADEQ evaluated county-level emissions data from Version 2 of 

EPA’s 2002 National Emissions Inventory (NEI) and a source apportionment study conducted by PCAQCD 

in 2003 to assist in the boundary recommendation process.3

 The State will characterize and quantify 

emissions estimates when developing an inventory for the PM2.5 SIP. 

Table 3-1 contains annual PM2.5 emissions (in tons per year) for counties in Arizona according to the NEI; 

these emissions are depicted in Figure 3-3. The estimate for Navajo County is elevated due to a major 

wildfire in that county that burned 467,066 acres of forest during the summer of 2002. Figure 3-4 depicts 

the considered counties and the State’s PM2.5 monitoring network. 

Table 3-1 

PM2.5 Emissions by County According to EPA’s 

2002 National Emissions Inventory 

 County 

PM2.5 

Emissions 

(tpy) 

Recommended as 

Nonattainment 

 Navajo 82,062 No 

Maricopa 13,772 No 

Pima 6,973 No 

Coconino 5,652 No 

Pinal 4,813 Yes – partial 

Yavapai 3,271 No 

Cochise 2,813 No 

Mohave 2,393 No 

Santa Cruz 1,889 No 

Yuma 1,734 No 

Greenlee 1,635 No 

Gila 1,349 No 

Graham 825 No 

La Paz 134 No 

 

3 http://www.epa.gov/ttnchie1/net/2002inventory.html 

6
Figure 3-3 

Annual PM2.5 Emisisons by County According 

to EPA's 2002 National Emissions Inventory

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Maricopa Pima Coconino Pinal Yavapai Cochise Mohave Santa Cruz Yuma Greenlee Gila Graham La Paz

Tons Per Year

Emissions estimates for Navajo County are elevated due to the Rodeo-Chediski wildfire, and therefore, were not included in this graph. 

Figure 3-4 

EPA Area of Interest 

and State PM2.5

Monitoring Network

● Monitors measuring no 

exceedances of 24-hour 

NAAQS 

● Monitors Measuring 

exceedances 

● Violating monitors 

7
While the NEI is for primary emissions of PM2.5, secondary particles may be forming near the Cowtown 

monitor or being transported to it. The major sources of the secondary emissions are combustion byproducts and onroad mobile sources. PCAQCD’s source apportionment study included chemical mass 

balance (CMB) analyses of samples taken at the Cowtown monitor in October and November of 2003. The 

analyses determined that ammonium nitrate and ammonium sulfate accounted for less than 10 μg/m3

. Table 

3-2 contains the CMB analyses of the Cowtown monitor samples. 

Table 3-2 

Chemical Mass Balance Modeled PM2.5 Source Attributions for the Cowtown Monitor (μg/m3

) 

Note that the source attributions listed in Table 3-2 includes coal-fired power plants. ADEQ, however, has 

no data that suggest or support emissions from coal-fired power plants contribute to the PM2.5 problem in 

Pinal County. The PCAQCD Source Apportionment Study estimates that, on average, 49% of PM2.5 at the 

Cowtown monitor originates from feedlots. Eight source types were evaluated by the Desert Research 

Institute in that study. While this study concludes that coal fired power plants may have contributed to the 

PM2.5 concentrations measured at Cowtown: 

 

 The most important marker of long-range emissions transport is secondary sulfate particles, the 

presence of which in the ambient samples was not at all in proportion to the amount of particulate 

attributed to coal power plants; 

 ADEQ has very high confidence in the other “chemical fingerprints” used for the source 

apportionment analysis and the results relating to those sources (e.g., feedlot dust, soil, vegetative 

burning), but power plant signatures are poorly documented and significant nearby sources, such as 

railroad locomotives, were not included, which reduces the reliability of the results regarding power 

plants; 

 Even if one assumes the coal power plant apportionment is correct, it is evidenced at all of the Pinal 

County sites, not just the only violating monitor, Cowtown; and 

 Trajectory analyses show that for the range of meteorological conditions when exceedances of the 

24-hour average standard are measured, winds in most cases would be carrying emissions from 

these plants (85 to 140 miles southeast of the violating monitor) away from the monitor rather than 

towards it. 

Other points that address this issue are detailed in Appendix B. ADEQ finds the conclusion that coal-fired 

power plants contribute to nonattainment at Cowtown as potentially spurious and worthy of additional 

analysis. This effort is currently underway, as EPA has sited a PM2.5 speciation monitor at the site and data 

from that monitor are already being analyzed. 

8
3.2 METEOROLOGY 

Pinal County experiences periods of significant winds associated with frontal passages, troughs of low 

pressure, summer monsoon storms, and occasional strong pressure gradients. The meteorology associated 

with winds in Pinal County ranges from synoptic scale systems such as frontal passages, strong pressure 

gradients, Mesoscale Convective System (MCS)4

 and regional monsoon storms to micro scale storm cells 

that form locally. 

The frontal passages are typically associated with strong Pacific Northwest low-pressure systems that 

develop over the northern Pacific Ocean and move southeast into the western United States. Strong winds 

in advance of cold fronts can reach speeds over 30 miles per hour (mph), which cause significant 

quantities of blowing dust in central Arizona. The duration of the strong, gusty winds can last up to 8 

hours and result in elevated hourly PM2.5 concentrations. 

Pressure gradient exceptional/natural events result from strong high-pressure building over the western 

United States and low pressure to the east. As the high pressure builds, a pressure differential is created 

that causes strong winds over Arizona. The result is locally developed blowing dust and dust transported 

from areas surrounding Pinal County. As with frontal passages, duration of strong, gusty winds can last 

several hours. The combination of the long duration of transported dust and locally derived dust 

overwhelm the air quality monitors. 

The monsoon is a seasonal wind that takes place in the southwestern United States and Northern Mexico 

during the summer months. The typical diurnal winds in central Arizona are ‘drainage’ in nature; in the 

morning, easterly winds that originate in the mountains switch direction to westerly winds in the 

afternoon due to the heating of the desert floor. During a monsoon, however, winds will shift to an 

easterly to southeasterly direction. This is due to a ridge of high pressure that sets up over the Four 

Corners region in northeastern Arizona. The result is an influx of atmospheric moisture from the south 

and east, and storm development which can be synoptic in nature as large lines of storms form either over 

the northern Arizona or northern Mexico/southern Arizona and move into the central area of Pinal 

County. 

Monsoon thunderstorms can also be local in nature, with the formation of localized monsoon-supported 

storm cells. Both monsoon setups can pack significant winds (reaching gusts over 60 mph) that cause dust 

storms to develop, transporting a wall of dust (haboob) up to hundreds of miles. These storms have dustcausing effects similar to frontal passages and strong pressure gradients. The monsoon season, as defined 

by the National Weather Service, starts on June 15th and lasts through September 30th. Haboobs are 

frequent at the beginning of the monsoon and subside as the storm progresses, when measurable rainfall 

normally occurs. Monsoon storm cell(s), however, can create strong enough wind gusts to cause blowing 

dust even after recent precipitation. These events, however, tend to increase coarse particle loading in the 

atmosphere and disperse PM2.5. The Cowtown monitor, however, can exceed the 24-hour average 

NAAQS regardless of wind conditions, as shown in Figure 3-5. As will be demonstrated later in this 

document, this is a result of PM2.5 arising from the same sources and as a component of PM10 emissions. 

 

4 http://www.weather.gov/glossary/index.php?letter=m 

9
Figure 3-5 

Cowtown PM2.5 Exceedances by Max Wind Speed 

Category, 2006-2008

0

3

6

9

12

15

<10 mph 10-15 mph 15-24 mph 24-30 mph All

Exceedances

Number of Exceedance

s

0.0

15.0

30.0

45.0

60.0

75.0

PM2.5 Conc. (ug/m3)

# Exceedances

Max PM2.5

Min PM2.5

Avg PM2.5

Review of PM2.5 Exceedance Days during 2006-2008 

Exceedances by Season 

Of the days when the Cowtown monitor exceeded the PM2.5 standard, 15 exceedance days occurred 

during the springtime, 10 in the fall, 4 in the winter, and only 1 in the summer. It is apparent that with 25 

of 30 exceedance days occurring during these two seasons, the majority of PM2.5 exceedances occur 

during non-stagnant, light wind events. 

Exceedances by Wind Event/Stagnation 

As noted above, light winds due to low-pressure systems accompanied by cold fronts are typically seen 

during the spring and fall in Arizona. Only 5 exceedances can be attributed to inversions under stagnant 

conditions. Appendix B contains a detailed analysis on the impact of meteorology on PM2.5 exceedances 

in the recommended nonattainment area. 

10
3.3 GEOGRAPHY/TOPOGRAPHY 

The topography of Pinal County can best be described as a broad basin surrounded in each direction by 

mountain ranges. The Estrella Mountains in the northwest portion of the County reach 4,125 feet in 

elevation and provide a buffer between Pinal and Maricopa Counties. In the northern portion of Pinal 

County, the Superstition and San Tan Mountains rise to a height of 5,036 and 3,054 feet, respectively. 

Near the western border of the County, the Table Top Mountains reach 3,392 feet in elevation. To the 

south, the Black Mountains reach 5,577 feet. The Pinal Mountains in western Gila County, near Pinal 

County’s eastern border, reach 7,848 feet in elevation. 

The elevation of the basin area of Pinal County is approximately 1,200 feet above sea level. Open-ended 

valleys characterize the western portion of the County. The area does not have geographical or 

topographical barriers limiting air pollution transport within its airshed. Although there are no 

topographical barriers that separate the recommended nonattainment area from the rest of Pinal County, 

the significantly lower PM2.5 concentrations recorded at the Apache Junction and Casa Grande monitors 

demonstrate that distance prevents most of the County’s population from exposure to high PM2.5 

concentrations. 

3.4 AIR QUALITY DATA 

Table 3-4 contains 24-hour PM2.5 average concentrations for monitors in Pinal County during 2006-2008. 

The locations of these monitors are depicted in Figure 3-4 on Page 9. The Apache Junction and Casa 

Grande monitors – which are not violating the PM2.5 NAAQS – are located in urbanized areas of the 

County with high population densities. The Cowtown monitor was sited to characterize localized ‘hotspot’ PM2.5 emissions from adjoining cattle feedlots. 

An area is meeting the 24-hour PM2.5 standard when the three year average of the 98th percentile values is 

35 μg/m3 or less. Design values are valid only if minimum data completeness criteria are met. A 

descending trend in 24-hour PM2.5 concentrations at the Cowtown monitor is evident in Figure 3-6. 

Appendix A contains a complete record of ambient monitoring data for every monitor in Arizona’s PM2.5 

monitoring network during 2006-2008. It should be noted that as of July 12, 2010, there has been only 

one exceedance of the 24-hour PM2.5 NAAQS in calendar year 2010. 

Table 3-4 

Pinal County 24-hour PM2.5 Average Concentrations 2006-2008 

98th Percentile 

Samples 

(in μg/m3) Site Name 

2006 2007 2008 

ThreeYear Avg. 

Apache Junction Firehouse 9.3 13.0 15.4 12.6 

Casa Grande Downtown 15.4 20.0 22.0 19.1 

Cowtown 48.9 48.0 40.7 45.9 

11
Figure 3-6 

Pinal County 

 PM2.5 24-hour Averages 

(concentrations shown in μg/m3)*

0

10

20

30

40

50

60

2006 2007 2008

μg/m3

Cowtown

Casa Grande Downtown

Apache Junction Fire

Station

3.4.1 Analysis of the Cowtown Monitor and Ambient Monitoring Data 

Monitor Location 

The Cowtown monitoring site is located approximately 4 miles southeast of the City of Maricopa and 

approximately 30 miles south-southeast of downtown Phoenix. The site lies within an irrigated desert 

plain, surrounded by active and retired agricultural operations to the north, northwest and east. Three 

cattle feedlots and a grain-processing complex are located to the south, southwest, and southeast of the 

monitor. The largest cattle feedlot in the County is located across a two-lane road from the monitor. 

The following sources are found within a four mile radius of the Cowtown monitor: locomotive tracks; a 

two-lane highway; the City of Maricopa (population approximately 45,000); a grain milling facility. 

Source Apportionment Study 

PCAQCD conducted a study in 2003 to identify emission sources contributing to elevated particulate 

matter concentrations in the western portion of Pinal County. Although some years have passed since the 

study was conducted, the source mix near the monitors has changed very little since that time. Samples 

were collected at 5 monitor sites in western Pinal County: Cowtown, Casa Grande Downtown, Coolidge, 

Pinal County Housing, and Stanfield. The particulate matter collected by the monitors was then analyzed 

using a chemical mass balance that essentially created a chemical fingerprint that was then used to trace 

the origins of collected particles. 

Comparing Monitor Samples 

The chemical analyses indicated that soil particles constituted the majority of emissions at each monitor – 

except at the Cowtown monitor, where feedlot particles accounted for 49 percent of the collected samples. 

PM10 samples collected by the Cowtown monitor were also disportionately comprised of feedlot 

emissions. Figure 3-7 compares PM2.5 samples collected by the Cowtown and Casa Grande monitors – 

note the absence of feedlot emissions at the Casa Grande monitor, even though the monitors are just 15 

12
miles apart. PCAQCD’S study also examined the composition of PM10 samples collected at the Casa 

Grande and Cowtown monitors. The PM10 sample collected at the Cowtown monitor was also 

disportionately comprised of feedlot emissions. As shown in Figure 3-7, the PM10 sample collected by the 

Casa Grande monitor did contain feedlot emissions, although the amount (20 percent) was significantly 

less than the PM10 sample collected at Cowtown (59 percent). This finding supports ADEQ’s 

determination that PM2.5 emissions from feedlots largely remain within the recommended nonattainment 

area. 

Figure 3-7 

Comparison of PM2.5 and PM10 Source Contributions at the 

Cowtown and Casa Grande Monitors 

PM2.5

Cowtown Monitor Casa Grande Downtown Monitor 

59%

4%

1%

1%

0%

1%

2%

32%

62%

20%

1%

6%

5%

0% 3%

3%

PM10

 

Back Trajectory Analyses 

Using the Cowtown monitor as a starting point, ADEQ created 7 HYSPLIT models of back trajectories to 

analyze PM2.5 transport on exceedance days with a range of meteorological conditions; a back trajectory 

analysis for an exceedance date with stagnant conditions is shown below in Figure 3-8. The dates selected 

represent the four seasons of the year. The back trajectories demonstrate that on the selected exceedance 

days, winds were blowing from a south-southwesterly direction. Wind blowing from these directions 

likely re-entrained particulate matter from nearby feedlots, agricultural fields, and unpaved roads, and 

directed those emissions toward the Cowtown monitor. Appendix B features a detailed report on the back 

trajectories completed by ADEQ. 

13
Figure 3-8 

Back Trajectory Analysis – January 11, 2006 

Data Analyses 

ADEQ compared ambient PM2.5 monitoring data recorded on exceedance days by the Cowtown monitor 

with data recorded by monitors in Apache Junction, Casa Grande, Douglas, Tucson, Prescott Valley, and 

Yuma, and found that there were no correlating exceedances. On exceedance days, the Cowtown monitor 

consistently recorded PM2.5 concentrations that were more than twice the concentrations recorded at the 

Casa Grande monitor 15 miles to the southeast and the Apache Junction monitor 30 miles northeast of 

Cowtown, as shown in Figure 3-9. This analysis demonstrates that medium and long-range transport is 

not causing the exceedances at the Cowtown monitor, but rather that the elevated PM2.5 concentrations are 

highly localized and affect only the recommended nonattainment area. 

14
Figure 3-9 

PM2.5 24-hr Avg Comparison Between Sites (2006-2008)

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

Site

Concentration (μg/m3)

Cowtown

Apache Junction

Casa Grande

ADEQ also conducted linear regression analyses using several different variables to explain the variation 

in PM2.5 concentrations recorded by the Cowtown monitor. These analyses determined that high PM10

concentrations were the only reliable predictor of high PM2.5 concentrations. Figure 3-10 depicts a linear 

regression indicating, with a 95 percent confidence level, that PM2.5 concentrations are driven by the 

amount of PM10 in the atmosphere.

15
Figure 3-10 

PM2.5 as a Function of PM10 for Cowtown Monitor

January 2006 through June 2009

PM2.5 = 0.1051*PM10

Adjusted r2 = 0.8398

0

10

20

30

40

50

60

70

0 100 200 300 400 500 600 700 800

PM10 Conc. (ug/m3)

PM2.5 (ug/m3)

Observed 95% Hi Conf. Int. 95% Lo Conf. Int. 95% Hi Predict. Int. Regression

Levels of NAAQS

Study of Hourly PM10 and PM2.5 Concentrations 

In October 2006, PCAQCD conducted a study of hourly PM10 and PM2.5 concentrations recorded by 

continuously operating monitors at the Cowtown monitor site. The results of that study show a clear 

correlation among hour-by-hour variances in PM10 and PM2.5 concentrations. The synchronous variations in 

hourly concentrations depicted in Figure 3-11 provide compelling evidence that the same sources generate 

local ambient concentrations of both PM10 and PM2.5. This is further corroborating evidence that the nearby 

feedlots are the main source of PM10 emissions in the Cowtown monitor area. The time-correlation of PM10

and PM2.5 concentrations establishes that the feedlots are also the dominant local source of PM2.5 emissions. 

16
17

Figure 3-11 

October 2006 Temporal Analysis of Cowtown Monitoring Data 

(Each data point reflects the average of each valid hourly average value during October) 

Conclusion 

The analyses conducted by ADEQ and PCAQCD point to localized particulate matter concentrations 

caused by feedlots and agricultural activities in the immediate area around the Cowtown monitor. 

ADEQ’s analyses also demonstrated a strong relationship between PM2.5 and PM10 exceedances, 

indicating that PM2.5 exceedances are essentially a component of high PM10 concentrations. This 

determination makes the PM2.5 exceedances at Cowtown unique because they are not the result of primary 

combustion emissions and secondary particles as they are in most of the U.S. 
3.5 JURISDICTIONAL BOUNDARIES

 

When recommending PM2.5 nonattainment area boundaries, the State considered the authorities of 

PCAQCD and its responsibilities to facilitate air quality planning and implement control measures to 

attain the standard. Two cities are located in the western portion of Pinal County: Casa Grande and the 

City of Maricopa. Since the violating monitor is located within boundaries of the City of Maricopa and 

near Casa Grande, the incorporated boundaries of these municipalities were taken into account. The 

recommended nonattainment area includes almost all of the City of Maricopa and excludes all of Casa 

Grande. 

Three Indian Reservations occupy portions of Pinal County: Gila River Indian Community, covering 

372,000 acres, 280,000 acres of which are located in the northwestern part of the County; the nearly 

22,000 acre Ak Chin Indian Community, all of which is located near the City of Maricopa in western 

Pinal County; and the 2.7 million acre Tohono O’odham Indian Nation, most of which is in Pima County, 

with approximately 275,000 acres in southwestern Pinal County. Because the State has no jurisdiction 

over Indian Country, no recommendation is being made for designations relating to these three Indian 

Reservations. 

18
3.6 LEVEL OF CONTROL OF EMISSION SOURCES 

Pinal County is authorized under the CAA and Arizona Revised Statutes as one of three Arizona counties 

with designated authority for air quality monitoring and permitting. Prior to issuing a permit, PCAQCD 

uses computer modeling to assess the impact of emissions on local ambient air quality. PCAQCD issues 

permits for a variety of fugitive dust-producing activities and has an approved Title V program. The 

County also has an approved program in place for the prevention of significant deterioration. The 

operating permits issued by PCAQCD and ADEQ for sources in the recommended nonattainment area 

include control measures designed to reduce PM2.5 emissions. PCAQCD and ADEQ can at any time 

review a permit to determine if additional control measures are necessary for PM2.5 sources. PCAQCD 

also regulates open burning and area sources. The County conducts routine field inspections to verify 

compliance with permit conditions and operates a county-wide monitoring network to determine if the 

ambient air is meeting the PM2.5 NAAQS. Additional control measures may be necessary to control 

sources causing PM2.5 exceedances. 

3.7 POPULATION DENSITY AND DEGREE OF URBANIZATION 

Table 3-5 features population estimates for municipalities within Pinal County for 2000 and 2008. The 

population of the County has grown 82 percent in the intervening years. Development in Pinal County is 

limited to privately owned areas; most of the land available for development is located along the I-8 and 

I-10 corridors, as shown in Figure 3-12. Approximately 95 percent of the Pinal County population lives 

within this region. Figure 3-13 depicts the population density of Pinal County in 2005; Figure 3-14 

depicts the County’s projected population density in 2030. It must be noted that the residential housing 

market in Pinal County has been particularly affected by the national economic recession, with 1 in 35 

homes in foreclosure in 2009.5

 Construction of new homes and commercial buildings has slowed 

considerably. 

Source: Arizona Department of Economic Security – Population Statistics Unit. 

Table 3-5 

Population Growth and Density within Pinal County 

Entity Area in 

Square 

Miles 

2000 

Population 

Density 

2008 

Population 

Density 

2000 

Population 

2008 

Population 

Pinal County Total 5,369.6 33.5 65.3 169,475 350,558

Apache Junction6 34.2 756.8 1,100.6 25,880 37,641

Casa Grande 48.2 549.6 936 26,490 45,116

Coolidge 5.03 1,495 2,447.5 7,520 12,311

Eloy 71.7 153 225.4 10,970 16,163

Florence 8.3 1,753 2,903.1 14,550 24,096

City of Maricopa 31.9 0 1,167.8 0 37,252

Queen Creek7 6.0 168.3 929.3 1,010 5,576

 

5 http://www.azstarnet.com/business/313423 6

 A small amount of acreage of Apache Junction falls within the borders of Maricopa County. 

7

 Queen Creek straddles the Maricopa/Pinal County border; approximately 23.4 percent of the city’s square miles are within Pinal

 County. To estimate a population for the Pinal County portion of Queen Creek, ADEQ divided the 2000 and 2008 population 

 estimates by 23.4 percent. 

19
Figure

3-12
Figure 3-13 

Pinal County Population- Density 2005 

Source: Arizona Department of Transportation. 

Figure 3-14 

Pinal County Population Density 2030 

 

21
3.8 TRAFFIC AND COMMUTING PATTERNS 

 

This factor considers commuting patterns of residents and commuters to Pinal County. Nearly 80 percent 

of those commuting to work in Pinal County are residents. For the 2006-2008 period, the U.S. Census 

Bureau estimated that Pinal County had a work force population of 101,887. As of July 2008, 226,208 

vehicles were registered in Pinal County; this figure represents an increase of 7 percent from 2007. Table 

3-6 contains data on the number and percentage of those commuting to and within Pinal, Pima, and 

Maricopa Counties in 2000; these statistics may be dramatically higher in the 2010 decennial census. As 

discussed below, emissions from onroad mobile sources are a relatively minor contributor to PM2.5 

concentrations in Pinal County. 

Table 3-6 

Inter-County Employment Commute Statistics for Maricopa and Pinal Counties 

from U.S. Census Bureau, April, 2000 Census 

Employment County Residence County Number of Commuters 

Maricopa Pinal 19,918 

Apache 7 

Cochise 43 

Coconino 0 

Gila 333 

Graham 3 

Greenlee 6 

La Paz 0 

Maricopa 7,751 

Mohave 7 

Navajo 7 

Pima 1,974 

Pinal 35,961 

Santa Cruz 11 

Yavapai 25 

Yuma 13 

Pinal 

Total 46,141 

Pima Pinal 2,601 

3.8.1 Onroad PM2.5 Emissions 

Emissions from onroad sources include three categories: 1) emissions from exhaust, and tire and brake 

wear on paved and unpaved roads; 2) fugitive emissions from paved and unpaved roads; and 3) emissions 

from unpaved shoulders on paved roads. Emission factors for each category and the number of vehicle 

miles traveled (VMT) for all the roads in Pinal County are required in order to estimate emissions. 

The inventory of all the roads in Pinal County come from two sources, Arizona Department of 

Transportation's (ADOT) Highway Performance Monitoring System (HPMS) and a road inventory 

supplied by PCAQCD. The HPMS database provides the annual average daily traffic (AADT) for each 

road segment which can be used to calculate VMT. The road inventory compiled by PCAQCD was 

developed from the County’s internal records, available municipal records, and from inspection of aerial 

photographs. The unpaved roads covered by that inventory included public (i.e. County), private roads, 

agricultural roads, and trails. 

22
For County-supplied roads, AADT volumes were assumed as shown in Table 3-7. For example, for 

County-maintained unpaved roads, an average daily volume of 100 trips was used to develop estimates. 

This figure was selected based on traffic counts conducted by PCAQCD on highly-traveled unpaved 

roads and a study conducted in a high-traffic area of Pinal County in 2009. 

 

Table 3-7 

Pinal County-Supplied Road Inventory 

and Average Daily Traffic 

Surface Type ADT 

Paved 100 

County-maintained Unpaved 100 

Asphalt Rock Dust Palliative 100 

Trail 10 

Private Unpaved 50 

Agricultural 4 

For private unpaved roads, an AADT volume of 50 was selected using an estimate of 5 residential 

dwellings per private unpaved road; the number of trips per dwelling was derived from a study conducted 

by the Texas Traffic Institute that determined that one residence generates an average of 10 trips per day. 

The emissions for each category will be described next. 

Emissions from Exhaust, and Tire and Brake Wear: 

EPA's MOBILE6 was used to determine the appropriate emission factors for this category. An emission 

factor was calculated for each road segment in the HPMS database based on the road classification and 

the vehicle mix. For the County road inventory, the average emission factor of HPMS road segments was 

assumed. The emission factor for each road segment was then multiplied by its VMT to derive exhaust, 

and tire and brake wear emissions. 

 

Fugitive emissions on paved and unpaved roads: 

To estimate fugitive PM2.5 emissions for paved and unpaved roads in the HPMS database, the State 

utilized the methods described in EPA's AP-42 guidance to determine the emission factors. The emission 

factor for each paved road segment in the HPMS database was determined based on silt loading and 

vehicle weight derived from the vehicle mix. The emission factor for each unpaved road segment in the 

HPMS database was calculated based on silt content and average speed. 

The fugitive emission factor for each road segment was then multiplied by its VMT to derive the fugitive 

emissions. The silt loading value for paved roads was extracted from the Maricopa Association of 

Governments' (MAG) 2007 Five Percent Plan for PM10 for the Maricopa County PM10 Nonattainment 

Area. The silt content value for unpaved roads was extracted from ADOT's 2006 Final Report: 

Identification of Emissions Sources for Pinal County. 

For the paved roads inventory supplied by PCAQCD, the average emission factor of paved HPMS 

segments was assumed, while the average emission factor of unpaved HPMS segments was assumed for 

the unpaved roads supplied by PCAQCD. The emission factor for each road segment was then multiplied 

by the VMT to derive the fugitive emissions. 

23
Emissions from unpaved shoulders on paved roads 

Fugitive emission estimates for unpaved shoulders on paved roads were calculated based on the 

percentage of heavy-duty vehicles traveling on the road segments and the VMT. 

Conclusion 

ADEQ’s estimates that PM2.5 emissions from onroad mobile sources in Pinal County total 4,951 tons per 

year. This estimate is greater than EPA’s annual estimate for Pinal County in the NEI – likely due to an 

increase in the number of vehicles in the County. Regardless, on-road vehicle emissions have not been 

demonstrated to be a significant contributor to PM2.5 exceedances at the Cowtown monitor. 

3.9 GROWTH RATES AND PATTERNS 

Most of the population growth in Pinal County since the year 2000 occurred in the central and western 

region of the County; the City of Maricopa, which is included in the recommended nonattainment area, 

experienced the highest growth rate in the County. The recommended nonattainment area is bordered on 

three sides with Indian country and publicly owned lands that provide a buffer from contiguous 

development, as shown in Figure 3-12 on page 20. 

3.10 NONATTAINMENT AREA SUMMARY OF CRITERIA ANALYSIS 

ADEQ reviewed the 9 criteria outlined in EPA's guidance and determined that the portions of Pinal 

County discussed in this section meet the criteria for inclusion in a PM2.5 nonattainment area. Table 3-8 

summarizes the data and information supporting the nonattainment area recommendation.

24
Table 3-8 

Summary of Nine Criteria Analysis for the Proposed Pinal County PM2.5 Nonattainment Area 

Criteria Factors Supporting the Recommended Nonattainment Area Boundary 

Emissions Data EPA’s National Emissions Inventory (NEI) ranked Pinal County fourth among 

Arizona counties in annual PM2.5 emissions; counties with a greater quantity of 

PM2.5 emissions have demonstrated attainment with the PM2.5 NAAQS. The 

recommended nonattainment area includes all of the emissions sources that caused 

or contributed to exceedances at the violating and similar sources in existence 

where there is no PM2.5 monitor. 

The NEI is for primary PM2.5 emissions only. Secondary PM2.5 particles may be 

forming and contributing to readings at the violating monitor; the main sources of 

secondary particles are emissions from combustion and on-road mobile sources. 

Meteorology The recommended nonattainment area is located in the agricultural basin area of 

Pinal County often affected by high-wind conditions and stagnation events. 

Geography/ 

Topography 

The recommended nonattainment area is located in the basin area of Pinal County 

characterized by open-ended valleys with few topographic barriers. 

Air Quality Data The only ambient air quality monitor in the State (excluding Nogales) violating 

the PM2.5 NAAQS is located within the recommended nonattainment area. 

The violating monitor was sited to characterize the impact of localized ‘hot-spot’ 

PM2.5 emissions from adjoining cattle feedlots. 

Jurisdictional 

Boundaries 

ADEQ’s recommendation maintains jurisdictional cohesiveness to the extent 

feasible, acknowledges the State’s lack of jurisdiction over Indian Country, and 

requires no new institutional arrangements for accomplishing required tasks. 

Level of Control 

of Emission 

Sources 

The operating permits issued by PCAQCD and ADEQ for sources in the 

recommended nonattainment area include control measures designed to reduce 

PM2.5 emissions. PCAQCD and ADEQ can at any time review a permit to 

determine if additional control measures are necessary for PM2.5 generating 

sources. PCAQCD also regulates open-burning and area sources. Additional 

control measures may be necessary for sources causing PM2.5 exceedances. 

Population 

Density and 

Degree of 

Urbanization 

The recommendation includes the population most affected by high PM2.5 

concentrations. 

Traffic and 

Commuting 

Patterns 

Emissions estimates developed by ADEQ indicate on-road sources are a relatively 

minor contributor to ambient PM2.5 concentrations in Pinal County. 

Growth Rates and 

Patterns 

The area surrounding the recommended nonattainment area is somewhat limited in 

growth by Indian Country and publicly owned lands. 

Factors Supporting the Exclusion of the Remainder of Pinal County

Those areas of the county recommended as attainment/unclassifiable have demonstrated attainment 

with ambient monitoring data. These areas include urbanized areas with the largest populations, highest 

annual average daily traffic, and the highest growth rates in the State. As demonstrated in this 

document, emissions from sources outside the recommended nonattainment area have not contributed 

to an exceedance of the NAAQS within the recommended borders and are not likely to in the future. 

25
4.0 AREA DESIGNATION RECOMMENDATIONS 

ADEQ demonstrated in the preceding analysis that the area outside Indian Country affected by the 

primary sources causing violations of the PM2.5 NAAQS at Cowtown monitor be designated as 

nonattainment. The proposed Pinal County PM2.5 nonattainment area shown in Figure 4-1 encompasses 

all or part of those townships listed below. Those townships in Pinal County that not listed are 

recommended as attainment/unclassifiable. 

T4S, R3E – R4E 

T5S, R3E – R4E (excluding sections 12, 13, 24, and 25) 

26
27

Figure 4-1 
APPENDIX A 

Ambient PM2.5 Monitoring Data 

2006-2008
This page is intentionally blank.
Appendix A 

2006-2008 Annual Average PM2.5 Compliance (in μg/m3) 

2006 2007 2008 3-year Average 

Cochise County 

Douglas Red Cross 6.78 7.69 6.98 7.15 

Coconino County 

Flagstaff Middle School 6.61 8.00 5.921 N/A 

Gila County 

Payson Well Site 

(closed 12/31/2007) 

9.04 9.38 N/A N/A 

Maricopa County 

JLG Supersite 10.22 9.48 8.88 9.53 

Mesa 9.66 9.72 8.45 9.28 

South Phoenix 12.69 12.27 10.93 11.96 

West Phoenix 13.52 10.89 10.62 11.68 

Pima County 

Children’s Park 5.79 5.71 5.38 5.63 

Orange Grove 5.80 5.84 5.72 5.79 

Pinal County 

Apache Junction Fire 

Station 

5.31 6.96 7.52 6.60 

Casa Grande Downtown 7.55 10.25 10.61 9.47 

Cowtown 22.74 22.50 19.63 21.61 

Santa Cruz County 

Nogales Post Office 15.59 12.30 13.08 13.66 

Yavapai County 

Prescott Valley 

(opened 1/1/2008) 

N/A N/A 5.91 N/A 

Yuma County 

Yuma Courthouse 

(opened 1/1/2008) 

N/A N/A 9.93 N/A 

Bold denotes a value above the standard. 

 

1

 Indicates the data do not satisfy EPA’s summary criteria, usually meaning less than 75 percent of valid data recovery available 

in one or more calendar quarters. 
Appendix A 

2006-2008 24-hour Average PM2.5 Compliance (in μg/m3)

98th Percentile Samples1

2006 2007 2008 

3-year Average 

Cochise County 

Douglas Red Cross2 14.0 32.2 13.6 20 

Coconino County 

Flagstaff Middle School2 13.7 30.2 10.3 18 

Gila County 

Payson Well Site2 

(closed 12/31/2007) 

23.4 21.9 N/A N/A 

Maricopa County 

JLG Supersite3 24.6 23.5 17.8 22 

Mesa3 20.1 18.3 14.5 18 

South Phoenix3 28.8 29.2 22.7 27 

West Phoenix3 28.8 27.2 24.3 27 

Pima County 

Children’s Park3 12.1 12.0 11.7 12 

Orange Grove4 11.2 13.6 11.7 12 

Pinal County 

Apache Junction Fire 

Station 3

9.3 14.6 15.4 13 

Casa Grande Downtown2 15.4 22.4 22.0 20 

Cowtown2 48.9 53.9 40.7 48 

Santa Cruz County 

Nogales Post Office2 56.2 28.2 35.8 40 

Yavapai County 

Prescott Valley 

(opened 1/1/2008) 

N/A N/A 12.4 N/A 

Yuma County 

Yuma Courthouse 

(opened 1/1/2008) 

N/A N/A 21.1 N/A 

 Bold denotes a value above the standard.

 

1

 The 98th percentile value will be the second highest value for sites on a one-in-six day schedule. The 98th percentile 

 value will be the third highest value for sites on an every third day schedule. 

2 Samples collected every sixth day – 61 sample days in non-leap years. 

3 Samples collected every third day – 122 sample days in non-leap years.

4 Samples collected every day – 365 sample days in non-leap years. 

31
APPENDIX B 

Analysis Cowtown PM2.5 Monitor Exceedances 
This page is intentionally blank. 
Memorandum

Date: February 23, 2010 

To: Ira Domsky, Acting Director 

From: Leonard Montenegro, Supervisor 

Evaluation Unit 

Thru: Steve Peplau, Manager 

Air Assessment Section 

Subject: Analysis Cowtown PM2.5 Monitor Exceedances

Summary 

In 2006, the EPA revised the 1997 PM2.5 (particulate matter smaller then 2.5 microns in size) 

National Ambient Air Quality Standard (NAAQS), setting the 24-hour average standard to 35 

μg/m3, but leaving the annual average standard at 15 μg/m3. Since then, the EPA has sent notice 

to the State that the Cowtown monitor in Pinal County exceeds both the annual average and 24-

hour average PM2.5 NAAQS, but only requested that the State send a recommendation for a 

nonattainment area boundary for the annual average standard. Further, EPA indicated that it may 

favor a proposal in which Pinal County and eight other surrounding counties (approximately 

58,000 square miles) be designated as a PM2.5 nonattainment area. Any proposal, however, would 

be based solely on violations of the PM2.5 NAAQS measured at the Cowtown monitor located in 

the Town of Maricopa in Pinal County. The Cowtown monitor frequently records PM2.5 

concentrations above the established 2006 ambient PM2.5 24-hour standard. Yet, unlike most 

other existing PM2.5 nonattainment areas, the elevated PM2.5 concentrations measured at 

Cowtown are not primarily from combustion, medium- or long-range transport of emissions, or 

secondary particle formation. Rather, they are the result of very high localized PM2.5

concentrations as a component of PM10 and from emissions generated within a few kilometers of 

the monitor. 

This memorandum demonstrates that the monitored PM2.5 exceedances are largely, if not 

entirely, a product of local sources — primarily, nearby agriculture and related activities, 

including geologic dust, manure particles, and, on occasion, smoke and soot. Results presented in 

a 2003 source apportionment study carried out by the Pinal County Air Quality Control District 

(PCAQCD), as well as statistical analysis of recent PM10 and PM2.5 air quality data, study of 

meteorology data, and back trajectory analysis will each demonstrate that local sources are the 

most significant contributors to elevated PM2.5 concentrations at the monitor. These findings 

support a recommendation to establish PM2.5 nonattainment area boundaries in Pinal County for 

only that geographic area directly affecting the Cowtown monitor and regions with the same 

primary sources of PM10 and PM2.5. The only other air-quality monitor in Arizona which violates 

the PM2.5 NAAQS is the one located in Nogales, Arizona. 
Ira Domsky 

February 22, 2010 

Introduction

As a technical support document to the Pinal County nonattainment area boundary 

recommendation, this paper will present several analytical methods to help regulators better 

understand ambient PM2.5 pollutant concentrations in Pinal County, AZ. This discussion will 

support a recommendation to establish a PM2.5 boundary around the Cowtown monitor. More 

specifically, the objective of this paper is to discuss the factors which have likely contributed to 

the 29 PM2.5 exceedances recorded at the Cowtown monitor during 2006, 2007, and 2008 and to 

make any recommendations based on those findings. 

It should be noted that EPA has notified the State that a nonattainment designation needs to 

address violations of the annual average PM2.5 NAAQS. Any analysis of annual average data, 

however, would be a fruitless exercise for a large number of reasons. Understanding the nature 

and causes of elevated PM2.5 concentrations is best accomplished by looking at the smallest unit 

of analysis – the 24-hour average concentrations in this case. Evaluation of the exceedances of 

the 24-hour average NAAQS will shed ample light on all the issues that affect the violations of 

the annual standard, and, further, all control measures that are implemented to address the annual 

average will, by necessity, need to reduce emissions every day of the year. 

The Cowtown, Pinal County, monitor has exceeded the PM2.5 standard 30 times during calendar 

years 2006, 2007, and 2008. On April 17, 2006 there was a fire at the Hyponex Corporation 

facility, located in the town of Maricopa, AZ, which may have impacted the Cowtown monitor. 

The 30 days are listed in chronological order in Table 1. 

Table 1: A total of 30 days exceeded the current PM2.5 standard of 35 μg/m3 during 

2006, 2007, and 2008. 

RUN DATE PM2.5 RUN DATE PM2.5

1/11/2006 36.2 4/12/2007 45.4 

1/17/2006 39.4 4/30/2007 53.9 

2/22/2006 44.3 5/30/2007 37.0 

4/17/2006 * 48.5 6/5/2007 44.2 

5/5/2006 48.9 6/17/2007 48.2 

5/23/2006 46.2 10/9/2007 38.3 

5/29/2006 38.2 11/8/2007 44.7 

6/16/2006 36.4 11/26/2007 46.8 

10/2/2006 35.0 3/25/2008 41.7 

10/20/2006 47.9 4/12/2008 40.7 

11/1/2006 37.0 4/18/2008 35.9 

12/1/2006 69.4 4/30/2008 39.6 

12/7/2006 47.9 5/6/2008 40.4 

12/13/2006 46.3 6/23/2008 36.8 

2/23/2007 59.7 11/20/2008 38.1 

* Hyponex fire 

2
Ira Domsky 

February 22, 2010 

The Cowtown monitoring site is located approximately 4 miles southeast of the City of Maricopa 

and 15 miles south of the Phoenix urban area at 37580 W. Maricopa/Casa Grande Hwy. (Lat. N 

33°63’ Long. W 111°58’). The site lies within the irrigated desert plain, with active and retired 

agricultural operations to the north, northwest, and east. A grain-processing complex and three 

cattle-feedlot companies operate within a short distance of the monitor, to the south, southwest, 

and southeast. The source makeup near the Cowtown monitor suggests that nearby sources have 

a high potential to contribute to elevated PM2.5 concentrations at the monitor; thus, any boundary 

recommendation must take into consideration these nearby sources. Most existing PM2.5 

nonattainment areas within the United States are affected by long-range transport of primary and 

secondary particles from combustion, as well as locally generated primary and secondary 

particulates. However, after an investigation into the primary causes of the Cowtown 

exceedances, this does not appear to be the case for the Cowtown monitor. 

Results from a 2003 source apportionment study and from recent ongoing source attribution 

work at the Cowtown monitoring site will be used to evaluate sources near the Cowtown 

monitor. CMB (Chemical Mass Balance) modeling will determine relative source contributions 

at both the Cowtown and other Pinal County monitors. An investigation into the meteorological 

factors on each of the 30 exceedance days, and statistical analysis of the PM2.5 data from 

Cowtown and other monitors, will demonstrate that local sources are driving the elevated PM2.5 

concentrations at Cowtown. 

Source Apportionment 

In 2003 PCAQD embarked on a rigorous source apportionment study to identify which emission 

sources are contributing to elevated particulate matter concentrations in the Pinal County 

agricultural basin. The study required extensive field work to collect ambient PM10 and PM2.5 

samples from five monitoring sites and three local source types, within Pinal county. The 

ambient and source-specific samples were chemically speciated and source-receptor chemical 

profiles were modeled with CMB (Chemical Mass Balance). The results of that study are 

presented in a 2005 report prepared by PCAQD titled “Pinal County Air Quality Control District 

Source Apportionment Study” and are summarized in this memorandum. 

PCAQCD co-located filter based Mini-Vol monitors capable of measuring PM2.5 and PM10 at 

five existing monitoring sites: Casa Grande, Coolidge, Cowtown, Pinal County Housing and 

Stanfield. All the existing sites had at least one Federal Reference Method (FRM) monitor 

configured to measure ambient PM10 concentrations and one site had an existing FRM monitor 

configured to measure ambient PM2.5 concentrations. The Mini-Vol monitors operated in parallel 

with existing FRM monitors. Data collection occurred in October and November of 2003. The 

fieldwork also required that soil and feedlot surface material samples be collected to characterize 

unpaved road dust, agricultural dust and feedlot dust. DRI (Desert Research Institute) analyzed 

the material on the filters using various chemical methods, which provided refined source 

profiles or “fingerprints” for Pinal County soils and feedlots. 

Table 2: The source apportionment modeling required chemical source profiles 

from seven source types, the eight source type was unclassifiable. 

3
Ira Domsky 

February 22, 2010 

Source ID Source Description Notes 

Soil Geologic Soil Existing 1990 Phoenix/Tucson - ag, unpaved roads, desert 

soils (Pinal County soils were updated for 2003) 

Feedlot Feedlot Soil 5 samples collected from nearby Cowtown feedlot soil 

MvEmi Motor vehicle Sources Phoenix motor vehicle composite / Texas BRAVO 

8

roadside exhaust 

VgBrn Vegetative Burning BRAVO open burning – mesquite/shrubs/brush/dry grass 

piles 

ColPP Coal Fired Power Plant BRAVO stack emissions from Texas electric utility 

AmSulf Ammonium Sulfate Secondary ammonium sulfate 

AmNitr Ammonium Nitrate Secondary ammonium nitrate 

Other Unclassified Sources not modeled 

Fine dust samples were collected from five different locations from each of the three Pinal 

County sources (feedlots, unpaved roads and agricultural fields) and were then shipped to the 

laboratory for chemical analysis. The new source specific chemical fingerprints for feedlots, 

unpaved roadways and agricultural fields, were then used as input to the CMB model, along with 

the other existing source profiles, to determine source contributions at each of five monitors. 

Motor vehicle, vegetative burning, coal power pant, ammonium sulfate and ammonium nitrate 

fingerprints were acquired from existing source profiles. 

The chemical source profiles for feedlot dust, unpaved road dust and agricultural field dust are 

presented in figures 1, 2 and 3 below. 

Fig. 1: Source specific chemical fingerprint for feedlot dust. The samples were 

collected from five locations at a feedlot near the Cowtown monitor, during 

October and November 2003 and analyzed 

Fig. 2: Source specific chemical fingerprint for unpaved road dust. The samples 

were collected from five locations along an unpaved road near the Cowtown 

monitor, during October and November 2003. 

 

8

 BRAVO - Big Bend Regional Aerosol and Visibility Observational Study

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Ira Domsky 

February 22, 2010 

Fig. 3: Source specific chemical fingerprint for agricultural dust. The samples 

were collected from five locations within an agricultural field near the Cowtown 

monitor, during October and November 2003. 

Figures 1-3, illustrate the comparative chemical fingerprints from Pinal County, for fine 

particulate matter (PM2.5 ). Shown are the percent concentrations of 17 chemical species and their 

analytical uncertainties, including water soluble nitrate, sulfate and ammonium, the eight carbon 

species (organic carbon fraction (OC1-4), pyrolysis of organic carbons to elemental carbons 

(OPT) and elemental carbon fraction (EC1-3), as well as the total carbon (TC), and five metals 

typical of most soils (aluminum, silicon, potassium, calcium and iron). 

The chemical fingerprints are derived from an assortment of chemical analyses, including X-ray 

Fluorescence spectrometry (XRF) for elemental species like aluminum, silicon and iron, Ion 

Chromatography (IC) for sulfates and nitrates, Atomic Absorption (AA) for potassium, 

Automated Calorimetry (AC) for ammonium and Thermal/Optical Reflectance (TOR) for the 

carbon fractions (OC1-4, OPT, EC1-3 and TC). The relative abundance of each compound or 

compound group makes up the source signature. For example, the feedlot dust has a substantial 

amount of Total Carbon (TC) and OC3, with OC2, OC4, OPT and EC1 shares following close 

behind. The OC1, OC2, OC3, OC4, OPT, EC1, EC2 and EC3 designations refer to eight 

separate carbon fractions, defined by temperature, oxidation atmosphere, and He-Ne laser light 

(J.C. Chow, 2001). One purpose of the TOR analysis is to distinguish, for example, diesel 

combustion from wood smoke particulate sources or in the case of Cowtown, to help catalog any 

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Ira Domsky 

February 22, 2010 

organic carbon species associated with the feedlots, like cow manure, and distinguish them from 

other carbon rich sources. The Feedlot Dust sample differs from the Agricultural and Road Dusts 

in that it contains a substantial amount of total carbon (TC) which is the total of the 

concentrations of the carbon species, particularly of OC2, OC3, OC4, OPT and EC1. 

Three ambient samples collected on high wind days on October 18 and 21 2003 and November 8 

2003, from the Cowtown monitoring site were also analyzed using the same analytical methods 

used to profile the three nearby sources (feedlots, agricultural fields and unpaved roads). The 

chemical profiles from the Cowtown monitoring site indicate that feedlot emissions do contribute 

to the ambient PM2.5 sample, while unpaved road dust and agricultural dust, albeit evident in the 

sample, are less conspicuous. 

Figure 4: The sample source profiles from Cowtown for three high concentration 

days in 2003 (October 18 2003, October 21 2003 and November 8 2003). 

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February 22, 2010 

 

The three ambient samples are similar to the Feedlot Dust fingerprint since they all contain 

substantial amounts of total carbon (TC) with large proportions of OC2, OC3, OC4, OPT and 

EC1. This shows that the ambient samples do contain substantial amounts of the Feedlot Dust, as 

was confirmed by the Chemical Mass Balance (Version 8) receptor modeling results. 

With the source and receptor profiles created, the CMB model was then used to evaluate source 

contributions at each Pinal County monitoring site. This receptor-oriented model is a good 

choice for this type of source contribution analysis, since we are attempting to identify and 

apportion source categories to the observed PM2.5 concentrations from each monitoring site, but 

have limited source specific emissions inventory data and meteorology. Receptor-oriented 

models focus on the chemical profiles of ambient samples collected at the receptor and apply a 

mass balance approach to identify and apportion the sources. Source-oriented dispersion models, 

on the other hand, rely on real-world meteorology to transport the pollutant from the source to 

the receptor. Although dispersion models are generally capable of estimating quantitative 

impacts from sources, limitations in the model’s ability to simulate complex meteorological 

conditions, like stagnation and varying qualities of emission inventories, make dispersion models 

insufficient for apportioning sources. 

Fortunately, the CMB model can overcome the need to simulate complex meteorology and 

temporally resolved source specific emission inventories by establishing a source / receptor 

relationship based on an effective-variance least squares method (EVLS)9

, by analyzing and 

comparing the chemical characteristics of each ambient sample and source profile. The CMB 

model however, may not be the best choice to establish a quantitative measure of impacts from 

any particular source, but may be used to demonstrate relative impacts from sources or source 

categories at each monitor. 

Seven source categories (including one category for unclassifiable compounds), each with 

speciated source profiles from potential nearby and distant sources, were modeled along with 

 

9

 http://www.epa.gov/scram001/receptor_cmb.htm 

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each speciated set of samples collected from each Pinal County monitoring site. At the Cowtown 

monitor, the CMB model estimated that, on average, 49% of PM2.5 at the monitor originates from 

feedlots and is the major component of the eight source types presented in table 1. The CMB 

modeling results are consistent with the findings from the speciated source profile analyses, 

whereby feedlot emissions appeared prominent in the three ambient samples collected from the 

Cowtown monitor. 

The average source category contributions from the remaining 4 monitoring sites were compared 

to the average source category contributions from the Cowtown monitor. That conclusion 

indicated that the Cowtown monitor is the only monitoring site in Pinal County where feedlot 

emissions dominate the PM2.5 samples. Geologic soil was the dominate source category for each 

of the remaining four monitoring sites. It is also worth mentioning, that the PM10 samples from 

the Cowtown monitor were also dominated by feedlot emissions, which supports the PM2.5 as a 

function of PM10, data analysis presented later; that analysis shows that when PM2.5 exceeds the 

standard, so does PM10. 

Source Apportionment - Primary Components of Cowtown PM2.5 

Nine ambient PM2.5 samples were collected from the Cowtown monitoring site during October 

and November 2003. The nine ambient samples were speciated and each speciation profile was 

used as input, along with the source profiles, to the CMB model to estimate the relative source 

category contributions at the receptor. Based on the sample data and CMB modeling, the most 

abundant source category for PM2.5 in each of the nine samples was the feedlot soil category with 

an average of 49% of the total. The second highest source contribution for all nine samples was 

the geological soil category with an average of 24%. This was the only site in the Pinal County 

study area where the geological soil category did not have the highest source contribution. The 

average 24-hour PM2.5 concentration at Cowtown was also well above any other site at 67g/m3, 

as recorded by the Mini-Vol PM2.5 sampler. Three samples had a recorded PM2.5 Mini-Vol 

concentration above 100 g/m3

, October 21, 2003, October 27, 2003, and November 8, 2003. In 

all three cases the meteorological data indicates inversion conditions with light winds throughout 

the day. 

Coal-fired power plant emissions were the third most abundant with an average of 9%. This 

contribution from the coal power plant source category is a bit perplexing, however. According 

to the most updated state-wide emissions inventory, the nearest coal power plant is 

approximately 82 miles away from the Cowtown monitor, and although stack emissions are 

capable of transporting long distances, the relative contribution from any coal power plant(s) is 

expected to be less than the third most abundant source; particularly since the Cowtown monitor 

is located near an abundance of local sources and just 50 meters to the north of the Union Pacific 

Railroad tracks. It is possible that the coal power plant profile is actually a variation of the 

locomotive emission profile or may even be confused with feedlot emissions, since locomotives 

and feedlots both show high measures of total carbon (TC) in their speciation profiles. 

However, it does not appear that locomotive sources were modeled in the 2003 CMB analysis, so 

a locomotive emission profile was not readily available for comparison to the coal fired power 

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plant profile. The ambient filter analysis may not have identified a locomotive signature and for 

that reason the locomotive source group may have been left out of the modeling. Either way, 

According to the BRAVO source profiles, coal power plant emissions typically have more 

traceable heavy metals like selenium (Se) in the exhaust stream, which would make it difficult to 

confuse diesel exhaust from locomotives with coal power plant emissions. In-fact, locomotive 

emissions may be more likely confused with heavy duty diesel vehicle emissions, yet vehicle 

emissions, as a whole, contribute only 3% on average at Cowtown, as shown by the Pinal County 

speciation study. This would imply that although it appears that locomotive emissions may not 

have been considered in the 2003 source apportionment study, or at least were not awarded as 

much consideration as other sources, the locomotive emissions do not appear to have been 

confused with power plant emissions. Even if the locomotive emission profile is mixed in with 

vehicle exhaust profile, locomotives would only likely be marginally responsible for any 

elevated PM2.5 concentrations, since vehicle emissions are only 3% of the total; although no 

evidence appears to support any assumption that locomotive emissions have in-fact been 

confused with any of the seven source profiles. 

It is also not likely that the coal fired power plant emissions were confused with feedlot 

emissions. The carbon fraction profiles for the three power plants used as source profiles in the 

PCAQCD study are substantially different then that of feedlot signature. Figure 5 below 

illustrates the BRAVO OC/EC profiles for the three power plants. Each profile is different than 

that of the feedlot profile illustrated in figure 1. The feedlot signature measures an abundance of 

TC and OC3, yet CFPP1, 2 and 3 (Coal Fired Power Plants 1, 2 and 3) each only indicate an 

abundance of TC, yet OC3 remains unsubstantial relative to the other carbon fractions. 

Figure 5: Coal fired power plant (CFPP) carbon fraction signatures according to 

the BRAVO speciation profiles and the feedlot profile used in the PCAQCD 

study. 

CFPP1

0

20

40

60

80

OC1 OC2 OC3 OC4 OP EC1 EC2 EC3 TC

Chemical Species

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CFPP2

0

20

40

60

80

OC1 OC2 OC3 OC4 OP EC1 EC2 EC3 TC

chemical Specias

CFPP3

0

5

10

15

20

25

OC1 OC2 OC3 OC4 OP EC1 EC2 EC3 TC

Chemical Species

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February 22, 2010 

Figure 6: Average source contributions predicted by the CMB model at the 

Cowtown monitor according to the 2003 source apportionment study. Feedlots are 

the predominant category at Cowtown. 

 Fig. 6 

Source Apportionment - Comparison with other PM2.5 monitors 

Samples collected at each of the five Pinal County monitoring sites were analyzed and their 

chemical profiles were modeled with CMB. The CMB modeling results indicate that soil dust is 

the major contributor at each monitor – Casa Grande, Coolidge, Pinal County housing and 

Stanfield. 

The Casa Grande monitoring site is located in the downtown area of Casa Grande. There are no 

agricultural fields, dirt roads, feedlots or large construction projects in the area immediately 

surrounding the monitoring site. The CMB modeling results (Figure 7) indicate that geologic 

soil, coal power plants and automobile exhaust are the top three source categories contributing to 

this monitor. 

The Coolidge monitoring site is located on the eastern side of the agricultural basin and is 

located near the edge of town. The site is located in a residential neighborhood and 

approximately 1⁄4 of a mile west of active agricultural fields. The agricultural basin extends north 

of the Coolidge site a substantial distance. Although, approximately six miles east of the 

Coolidge site the landscape generally changes back to native desert and that landscape continues 

to the eastern boundary of the County with increasing elevations further to the east. The CMB 

modeling results (Figure 8) indicate that geologic soil, coal power plants and vegetative burning 

are the top three source categories contributing to this monitor. 

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February 22, 2010 

The Pinal County Housing site is located in the heart of the agricultural basin. The site is 

immediately surrounded by native desert; beyond that open field agriculture dominates the 

landscape for at least ten miles in all directions. The CMB modeling results (Figure 9) indicate 

that geologic soil, coal power plants are the top two source categories, with vegetative burning 

and feedlot emissions tied for third. 

The Stanfield site is located near several feedlots and dairies and is generally surrounded by open 

field agriculture. The community of Stanfield only extends 1⁄4 to 1⁄2 a mile in all directions from 

the site, beyond that open field agriculture dominates the landscape. Sizeable feedlot operations 

and dairies lie approximately three miles to the north, east, and west. The CMB modeling results 

(Figure 10) indicate that geologic soil, coal power plants and vegetative burning are the top three 

source categories contributing to this monitor. 

Even though each monitoring site is surrounded by a different source mix, soil emissions 

contribute the most to all monitors, except Cowtown. Coal power plants, vegetative burning and 

automobile exhaust are also prominent at each of the sites and although each site is relatively 

distinct from the other, the source profiles predicted by CMB are generally similar. The 

exception, again, is the Cowtown monitor, where feedlot emissions are dominant. 

Figures 7, 8, 9 and 10: The CMB modeling results for each of the four monitoring 

sites (Casa Grande, Coolidge, Pinal County housing and Stanfield). The figures 

illustrate the average relative percent PM2.5 source contributions for ten samples 

collected and analyzed at the Casa Grande monitoring site. (MvEmi = Motor 

Vehicle Emissions, VgBrn = Vegitative Burning, ColPP = Coal Fired Power 

Plant, AmSulf = Ammonium Sulfate, AmNitr = Ammonium Nitrate) 

Fig. 7 

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Fig. 8

Fig. 9 

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Fig. 10 

Meteorological Analysis 

Site Geography 

The Cowtown monitor is situated about 17 miles northwest of Casa Grande and 30 miles south 

of downtown Phoenix, Arizona. It is in the northern part of the Sonora Desert. The elevation of 

Cowtown is approximately 1,200 feet above sea level and separated from Phoenix by two 

mountain ranges – the Estrella Mountains (peak of 4,511 feet) and Phoenix’s South Mountains 

(peak of 2,330 feet). Natural drainage is to the north via the Gila River. The Salt River merges 

with this drainage, which continues to run east to west on the north side of the above-mentioned 

mountain ranges. The Gila River then flows south to Gila Bend, eventually colliding with the 

Colorado River in Yuma, Arizona, an elevation near 250 feet above sea level. 

Site Climatology 

Temperature 

Daily temperatures can fluctuate as much as 40 to 50 degrees Fahrenheit daily depending on 

atmospheric moisture, cloud cover and surface composition. The large fluctuations can play a 

role in the strength of afternoon surface breezes under high pressure. They can also increase the 

likelihood of morning surface inversions (where the surface becomes significantly colder than 

the air above it), especially during the winter. The normal January high temperature in Casa 

Grande (17 miles southeast of Cowtown) is 65°F and the normal low is 38°F. The normal July 

high temperature is 105°F and the low is 78°F. 

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Precipitation 

The northern part of the Sonora Desert receives very little rain, typically between 5 to 10 inches 

annually. There are two distinct rain seasons in this part of the Sonoran Desert – winter 

(associate with low pressure systems and attached cold fronts) and summer (Monsoon wind shift 

with increase moisture from Mexico). 

Wind 

Strong winds typically occur when low pressure systems with attached cold fronts push through 

the region. This pattern is often seen in the spring and fall season. Characteristically as the 

system approaches, winds increase out of the southwest, moisture levels increase (depending on 

how much moisture is available) and atmospheric pressure decreases, resulting in increased 

vertical mixing. This turbulent atmosphere can trigger wide-spread areas of blowing 

particulates, especially in the absence of rain. After the front passes, decreasing winds shift out 

of the west and much cooler, drier air fills in behind the system. This can produce strong 

morning inversions that trap particles near the surface in shallow mixing depths. 

Another scenario where strong winds may occur is during the summer Monsoon season where 

thunderstorms typically develop over mountainous areas as well as the open desert. Because the 

storms can develop anywhere, winds can come from any direction. As storms become severe, 

microburst outflow winds can exceed 60 mph along the surface, again producing local and widespread areas of blowing particles. 

Inversions 

The typical profile from the surface to the bottom of the troposphere (approximately 11 miles 

above the surface) shows temperatures decreasing with increasing height. Warm air naturally 

rises and mixes with cooler air in an effort to reach equilibrium. The rate at which temperatures 

decrease is dependant on moisture. A dry air mass has a temperature decrease of about 5.5°F per 

1,000ft in elevation (dry adiabatic lapse rate), and a saturated air mass has a temperature 

decrease of 3°F per 1,000ft (moist adiabatic lapse rate). Sometimes there can be a surge of 

warmer air aloft that cause temperatures to increase with height rather than decrease. Other 

times, the surface cools at a much more rapid rate than the air above. Both conditions produce 

the same result - a temperature inversion. Under an inversion, there is very little vertical mixing 

and typically weak horizontal winds due to high pressure. As the sun heats the surface in the 

morning, the temperature inversion weakens as the air at the surface warms. If the surface 

temperatures surpasses that of the air above it, the inversion breaks and vertical mixing ensues. 

Weak inversions (1-5°F) may break within an hour of sunrise. Sometimes, however, strong 

inversions (15-20°F) may not break at all, possibly keeping the air and its accumulated pollution 

trapped near the surface for several days at a time. 

Stagnation 

Stagnation conditions occur when a stable air mass remains over an area for an extended period 

of time, which can last as long as several days. In Arizona, this most often occurs during the 

winter months when a large ridge of high pressure sets up over most of the western United 

States. Afternoon desert temperatures may reach 70-80°F during these episodes under sunny 

conditions. Winds tend to be light both vertically and horizontally, primarily driven by 

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topography. The typical surface flow is up-slope during the afternoon and down-slope during 

the night time and morning hours. Calms can be common and persistent. Under these 

conditions, pollution accumulates in the lower levels of the atmosphere, potentially leading to 

PM10 and PM2.5 exceedances. Summer stagnation events may occur in late June and early July 

until Arizona Monsoon pattern sets up. 

PM2.5 Exceedance Days from 2006-2008 

Table 3: A total of 30 days exceeded the current PM2.5 standard of 35 μg/m3 during 

2006, 2007, and 2008. 9 days were subject to stagnant conditions, 16 days had high 

winds and 5 days cannot be classified as windy or stagnant. This suggests that the 

PM2.5 exceedances are not directly associated with high or low winds. 

RUN DATE Meteorology PM2.5 RUN DATE Meteorology PM2.5

1/11/2006 STAGNANT 36.2 4/12/2007 WIND 45.4 

1/17/2006 STAGNANT 39.4 4/30/2007 WIND 53.9 

2/22/2006 WIND 44.3 5/30/2007 37 

4/17/2006 * WIND 48.5 6/5/2007 WIND 44.2 

5/5/2006 WIND 48.9 6/17/2007 WIND 48.2 

5/23/2006 WIND 46.2 10/9/2007 38.3 

5/29/2006 WIND 38.2 11/8/2007 STAGNANT 44.7 

6/16/2006 WIND 36.4 11/26/2007 STAGNANT 46.8 

10/2/2006 WIND 35 3/25/2008 STAGNANT 41.7 

10/20/2006 47.9 4/12/2008 40.7 

11/1/2006 STAGNANT 37 4/18/2008 35.9 

12/1/2006 STAGNANT 69.4 4/30/2008 WIND 39.6 

12/7/2006 WIND 47.9 5/6/2008 WIND 40.4 

12/13/2006 STAGNANT 46.3 6/23/2008 WIND 36.8 

2/23/2007 WIND 59.7 11/20/2008 STAGNANT 38.1 

* Hyponex fire 

Exceedances by Season 

Looking at the weather pattern on the days that exceeded the PM2.5 Federal health standard at the 

Cowtown monitoring site, 15 days (50%) occurred during the spring time. There were ten 

exceedance days (33%) in the fall. Four exceedance days occurred in the winter and just one in 

the summer. As noted above, strong winds due to low pressure systems accompanied by cold 

fronts are typically seen during the spring and fall months in Arizona. With 25 of the 30 

exceedance days (88%) occurring during these seasons, it would appear that a large portion of 

the PM2.5 exceedances are a result of non-stagnant events. 

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Exceedances by Wind Event/Stagnation 

A closer look at horizontal and vertical wind speeds at the Cowtown monitor on the days that 

exceeded the PM2.5 Federal health standards shows that 17 of the 30 days (56.3%) had relatively 

strong sustained winds with positive vertical mixing. Nine exceedance days (30.3%) were due to 

relatively light horizontal winds that could be classified under stagnant conditions. The final 

four days (13.3%) have relatively low average wind speeds but strong enough peak wind gusts to 

neither be classified as wind or stagnation event. 

As expected, 14 of the 17 (82%) high-wind exceedance days occurred in either the spring or fall. 

Two were in the winter (Feb. 22, 2006 and Feb. 23, 2007) and the last one occurring on the third 

day of summer (June 23, 2008). Seven of the nine stagnant days (77%) occurred in the spring or 

fall where only two (Jan. 11 and 17, 2006) were in the winter. The final four days that were 

neither classified as high-wind or stagnant all occurred in either the spring or fall. 

The Cowtown PM2.5 location is susceptible to exceeding the Federal health standard primarily 

during the spring and fall months. The majority of those exceedances are due strong winds while 

a large minority of exceedance days are due to inversions under stagnant conditions. 

 

Back Trajectory Analysis 

A series of back trajectory analyses were conducted using the NOAA (National Oceanographic 

and Atmospheric Administration) HYSPLIT (HYbrid Single-Particle Lagrangian Integrated 

Trajectory) model. The model is capable of computing air parcel trajectories in either a forward 

or backward mode and is a common tool used to examine long range transport. Forward 

trajectories can compute the path forward of an air parcel as it leaves a known point, while back 

trajectories can compute the path an air parcel traversed to get to a known point. The HYSPLIT 

model was used to compute back trajectories for each of seven design days: January 11, 2006, 

February 22, 2006, June 16, 2006, October 2, 2006, October 9, 2007, March 25, 2008, and June 

23, 2008. The model results are used to estimate which sources may have contributed to the 

PM2.5 exceedances at Cowtown on those days. The seven design dates represent seasonal high 

wind and low wind episodes for 2006, 2007 and 2008 on days when the Cowtown monitor 

exceeded the PM2.5 standard. 

For each day, HYSPLIT was used to compute a midnight, morning, afternoon and evening back 

trajectory. Each trajectory represents an air parcel as it moves through the atmosphere over 

twenty four hours, starting at 2300 hours and adjusted back every six hours for a total of 4 

trajectories for each of the seven days. Each trajectory moves backwards from the end point at 

Cowtown to the, yet unknown, starting point, twenty four hours earlier. But, for ease of 

interpretation, it may be more practical to discuss the trajectories as moving forward, even 

though the HYSPLIT model calculated the paths in reverse. 

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February 22, 2010 

If one were to consider only the yellow line, in figure 11a below, and move forward, the air 

parcel begins near Chandler, AZ at 6AM on January 10, 2006 and travels south for most of the 

day. The parcel then turns toward the north near Cowtown and at 6AM January 11, 2006 (twenty 

four hours later), the air parcel arrives at Cowtown. The green trajectory is next in the series and 

also begins near Chandler, AZ. But, this trajectory begins at 12PM on January 10, 2008 and 

travels south for most of the day, until it changes direction to the west and arrives at Cowtown at 

12PM on January 11, 2006. The blue trajectory begins at 6PM on January 10, 2006 and arrives at 

Cowtown at 6PM January 11, 2006. The red trajectory begins at midnight January 10, 2006 and 

arrives at midnight January 11, 2006. Each of the trajectories for the seven design days begin at 

the same times (6AM, 12PM, 6PM and 12AM) and travel for 24 hours, for each date. On this 

day, most of the winds were coming from the north and east and due to stagnant conditions, the 

winds were moving very slowly. On most other days, the typical wind direction is from the 

southwest, as seen in the remaining HYSPLIT analyses. 

The January 11th modeling results indicate that the majority of 24-hour trajectories do not 

traverse any likely PM2.5 sources until the air parcel nears the Cowtown monitor. For example, 

on June 23, 2008 (Fig. 11g), each trajectory begins in the south-southwest area of the state and 

travels steadily towards the Cowtown monitor. Since no obvious PM2.5 sources are located along 

the trajectory (beyond the area near Cowtown) only local PM2.5 sources could have caused the 

exceedance on that day. The same is true for June 16, 2006 (Fig. 11c), October 2, 2006 (Fig. 

11c), October 9, 2007 (Fig. 11e), and March 25, 2008 (Fig. 11f), only when the parcel nears the 

Cowtown monitor, does the air parcel traverse any PM2.5 sources. However, January 11, 2006 

(Fig. 11a) and February 22, 2006 (Fig. 11b) are somewhat different from the other five days, 

because some trajectories begin near the Phoenix and Yuma, urban areas. On January 11, 2006 

(Fig. 11a), the yellow, green and red trajectories originate from the Phoenix metropolitan area 

and on February 22, 2006 (Fig. 11b) the yellow and green trajectories begin near Yuma, AZ. The 

January and February 2006 trajectories may indicate some tendency for air pollution from these 

urban areasto migrate towards Cowtown at those select times, but during the vast majority of the 

24 hour trip, the air parcel did not pass over any likely PM2.5 source(s). When the air parcel nears 

the Cowtown monitor, however, the majority of the trajectories will traverse nearby feedlot and 

agricultural sources. 

The HYSPLIT model was also used to investigate the wind flows nearby the Cowtown monitor 

during times when the PM2.5 monitor was measuring elevated hourly concentrations. The hourly 

PM10 data profiles (Continuous PM2.5 was not available, so PM10 is used as a surrogate for PM2.5 

) are provided for each figure. Detailed HYSPLIT output images illustrating the time varying air 

parcel trajectories layered on top local and distant sources is provided for each modeled day and 

are illustrated in the following figures (11a-h): 

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Data Analysis - Medium and long range transport is not occurring

Long and medium range transport of Primary PM generally creates region wide impacts due to 

down wind horizontal plume spreading. This is a well known component of dispersion and 

becomes particularly evident in ambient PM monitoring data for air pollution monitors which are 

located within the same airshed and in close proximity to each other. In Arizona, there are 

several PM2.5 monitors in the state, including: Casa Grande, Apache Junction, Douglas, Orange 

Grove, Prescott Valley, and Yuma. Of these sites, Casa Grande is the nearest to Cowtown at 

approximately 24 km while Yuma is the farthest at 253 km. The Cowtown, Casa Grande and 

Apache Junction monitors are all in the same Gila River basin airshed and in terms of potential 

impacts from long range transport, would all likely to be impacted by the same plume. 

The relative distance from the Cowtown monitor for Casa Grande and Apache Junction are 24 

km and 64 km, respectively. If one were to assume Pasquill-Gifford rural horizontal dispersion 

(see figure 12) from a source 100 km from Cowtown and south-westerly winds, which is the 

predominant wind direction for each of the seven days modeled in the back trajectory analysis, it 

is easy to see that both Cowtown and Casa Grande should be equally impacted by the distant 

source. This is because the horizontal plume spread (σy) is expected to be large enough to impact 

both sites (figure 13). Based on each meteorological stability class, A-F, a source 100 km away 

from Cowtown is likely to produce a plume with a σy ranging from 20 km to 100 km. Since the 

Casa Grande monitor is only 24 km from the Cowtown monitor, one would estimate that long 

range transport from any direction would likely impact the two monitors. Of course, for the 

impacts to be equal at each site, one would need to assume a perpendicular incident angle of 

impact to the line drawn between Cowtown and Casa Grande, but varying the incident angle in 

either direction would only likely create marginal variations in relative impacts at each site, since 

the deposition rate of PM2.5 is slow and the two monitors are close together. 

Figure 12: Pasquill-Gifford σy, rural horizontal dispersion parameters. A-F are 

meteorological stability classes, where A is extremely unstable, B is moderately 

unstable, C is slightly unstable, D is neutral, E is slightly stable, F is moderately 

stable. 

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February 22, 2010 

The Pasquill-Gifford dispersion parameters are theoretical and except for the use of stability 

classes, it does not include provisions for real world actions: like chemical conversion, 

deposition and removal, wind speed, surface roughness and a whole suite of meteorological 

variables which will directly influence the plume spread and PM concentrations downwind. 

Furthermore, the six stability classes do not represent the real atmosphere. Real dispersion varies 

continuously throughout the atmosphere, from stable to unstable and not stepwise as the A-F 

stability classes do. So, some variation in the relative PM impacts at each monitor is expected, 

but the general principal remains the same; distant sources from Cowtown will likely impact 

both Cowtown and Casa Grande, and on some occasions impact all three monitors, including 

Apache Junction. 

If long or medium range transport is the source of the exceedances at Cowtown, a statistical 

analysis of the data collected from each of the three Pinal County monitoring sites should 

demonstrate that the ambient concentrations of the three sites are similar. The following analysis 

compares the ambient PM2.5 concentrations from Cowtown to the ambient PM2.5 concentrations 

from the nearby Casa Grande monitor, and as a supplement, to the more distant monitors at: 

Apache Junction, Douglas, Orange Grove, Prescott Valley, and Yuma for three years spanning 

from 2006 to 2008. 

The comparison between the Casa Grande PM2.5 Monitor and the Cowtown monitor reveals a 

significant difference in measured concentrations on both daily average and annual average 

PM2.5. Over a period of three years from 2006 to 2008, the Cowtown monitor consistently 

measured more than double the ambient concentrations at Casa Grande, which implies that the 

impacts at that monitor are not from distant sources, but are from . Table 4 below illustrates the 

long term average differences between all of the PM2.5 monitors in Arizona For all days in 2006, 

2007 and 2008. 

Table 4: Annual average ambient PM2.5 concentrations at the Cowtown 

monitoring site and other PM2.5 monitoring sites. In 2006 the annual average 

PM2.5 concentration at Cowtown was 22.74 g/m3, while the next highest annual 

average PM2.5 concentration was 5.31 g/m3

 from Apache Junction. This 

substantial difference in average ambient PM2.5 concentrations continues through 

2007 and 2008. 

PM2.5 Measurements (μg/m3)

Site Site ID 2006 2007 2008 3 Yr Annual Avg

Cowtown 04-021-3013 22.74 22.5 19.63 21.62

Apache Junction 04-021-3002 5.31 6.96 7.52 6.60

Casa Grande 04-021-0001 7.55 10.25 10.61 9.47

Douglas 04-003-1005 6.78 6.79 6.84 6.80

Orange Grove 04-019-0011 5.8 5.84 5.57 5.74

Prescott Valley 04-025-2002 - - 6.42 -

Yuma 04-027-0004 - - 10.18 -

27
Ira Domsky 

February 22, 2010 

Close inspection of monitored PM2.5 concentrations at Cowtown for days where the 

concentration exceeded the 35 g/m3 standard, again indicates highly localized PM2.5 values at 

Cowtown independent of meteorological conditions. A comparative analysis of a sample of 30 

days where the Cowtown monitor exhibits an exceedance for the three year period is shown table 

5 below. 

Table 5: Daily ambient PM2.5 concentrations at the Cowtown monitoring site and 

other Pinal county monitoring sites for the 30 exceedance days. For all PM2.5

monitors in the state. 

Sample of 30 Exceedance Days of PM2.5 (μg/m3)

Site Site ID Sample Average Observations*

Cowtown 04-021-3013 43.8 30

Apache Junction 04-021-3002 8.1 25

Casa Grande 04-021-0001 11.8 29

Douglas 04-003-1005 9.5 27

Orange Grove 04-019-0011 6.1 25

Prescott Valley 04-025-2002 7.2 7

Yuma 04-027-0004 13.8 5

*Not all monitors were in operation prior to 2008 and some monitors were flagged for invalid 

data on the day of the Cowtown monitor exceedance. 

A statistical analysis was performed between the Pinal County ambient PM2.5 monitored values: 

Cowtown, Casa Grande and Apache Junction. The average measured 24-hr averages are plotted 

in the figure 13 below and indicate that the average 24-hour PM2.5 concentrations are more than 

twice that of Casa Grande and Apache Junction. 

Figure 13: A comparison of average 24-hour PM2.5 concentrations between 

Cowtown, Apache Junction and Casa Grande. 

PM2.5 24-hr Avg Comparison Between Sites (2006-2008)

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

Site

Concentration (μg/m3)

Cowtown

Apache Junction

Casa Grande

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Ira Domsky 

February 22, 2010 

Paired t-tests were performed to test the statistical significance of the difference between the 

measured concentrations at the three sites. The first set of three paired t-tests (tables 6,7 and 7) 

use all available PM2.5 data from 2006, 2007 and 2008, while the second set of three (tables 9,10 

and 11) are paired t-tests using only the PM2.5 data from the 30 exceedance days. Results indicate 

the differences in the measured concentrations at Cowtown are statistically significant compared 

to Casa Grande and Apache Junction at the 95% confidence interval. The detailed results of the 

t-tests are: 

Table 6: Paired t-test between Cowtown and Casa Grande, for all data 2006, 

2007 and 2008. 

 Cowtown Casa Grande 

Mean 21.65 9.34 

Variance 167.99 20.30 

Observations 159 159 

Pearson Correlation 0.44

Mean Difference 12.30

Maximum Difference 55.26

Minimum Difference -6.80

df 158

t Stat 13.263

P(T<=t) one-tail 9.25E-28

t Critical one-tail 1.655

P(T<=t) 2-tail 1.85E-27

t Critical 2-tail 1.975 

Table 7: Paired t-test between Cowtown and Apache Junction, for all data 2006, 

2007 and 2008. 

 Cowtown Apache Junction 

Mean 21.05 6.65 

Variance 153.69 9.94 

Observations 150 150 

Pearson Correlation 0.35

Mean Difference 14.40

Maximum Difference 55.70

Minimum Difference -6.00

df 149

t Stat 15.127

P(T<=t) one-tail 3.28E-32

t Critical one-tail 1.655

P(T<=t) 2-tail 6.55E-32

t Critical 2-tail 1.976 

29
Ira Domsky 

February 22, 2010 

Table 8: Paired t-test between Casa Grande and Apache Junction, for all data 

2006, 2007 and 2008. 

 Casa Grande 

Apache 

Junction 

Mean 9.41 6.75 

Variance 21.39 9.97 

Observations 144 144 

Pearson Correlation 0.55

Mean Difference 2.65

Maximum Difference 22.60

Minimum Difference -6.50

df 143

t Stat 8.122

P(T<=t) one-tail 9.70E-14

t Critical one-tail 1.656

P(T<=t) 2-tail 1.94E-13

t Critical 2-tail 1.977 

Table 9: Paired t-test between Casa Grande and Apache Junction, for only the 30 

exceedance days. 

 Casa Grande Cowtown 

Mean 11.6034 44.0692 

Variance 27.8040 57.3325 

Observations 29 29 

Pearson Correlation -0.1982 

Mean Difference 32.4658 

Maximum Difference 55.2633 

Minimum Difference 19.2 

df 28

t Stat -17.4001 

P(T<=t) one-tail 7.55652E-17 

t Critical one-tail 1.7011 

P(T<=t) 2-tail 1.5113E-16 

t Critical 2-tail 2.0484 

30
Ira Domsky 

February 22, 2010 

Table 10: Paired t-test between Casa Grande and Apache Junction, for only the 

30 exceedance days. 

 Apache Junction Cowtown 

Mean 8.105 42.9657 

Variance 9.1113 32.4848 

Observations 25 25 

Pearson Correlation -0.3151 

Mean Difference 34.8607 

Maximum Difference 55.7 

Minimum Difference 21.6 

df 24

t Stat -24.0702 

P(T<=t) one-tail 1.28801E-18 

t Critical one-tail 1.7109 

P(T<=t) 2-tail 2.57601E-18 

t Critical 2-tail 2.0639 

Table 11: Paired t-test between Casa Grande and Apache Junction, for only the 

30 exceedance days. 

 

Casa 

Grande Apache Junction 

Mean 11.78 8.1050 

Variance 30.9350 9.1113 

Observations 25 25 

Pearson Correlation 0.6993 

Mean Difference 3.675 

Maximum Difference 11.7 

Minimum Difference -5 

df 24

t Stat 4.5147 

P(T<=t) one-tail 7.13287E-05 

t Critical one-tail 1.7109 

P(T<=t) 2-tail 0.000142657 

t Critical 2-tail 2.0639 

The data analysis makes a clear distinction between the three Pinal County PM2.5 monitoring 

sites. If the assumption is made that long range transport is the source of the PM2.5 exceedances 

in Pinal County, one would also need to assume that the exceedances would be more regional in 

nature, and therefore any statistical analysis on PM2.5 monitor data from Pinal County would 

show considerable agreement among the monitors. The paired t-tests do not indicate that level of 

agreement, however. For all data, the paired t-test indicates that the Cowtown monitoring site is 

very different from both Casa Grande and Apache Junction, while the paired t-test for Casa 

Grande and Apache Junction show reasonable agreement. The same is true when looking at only 

the 30 exceedance days. 

31
Ira Domsky 

February 22, 2010 

Data Analysis - PM2.5 is a function of PM10

The PM2.5 problem in western Pinal County is relatively unique because, unlike virtually all 

other areas of the United States experiencing nonattainment of the PM2.5 NAAQS, it does not 

appear that primary combustion emissions and secondary particles are dominant for the site 

where the exceedances have been recorded. Both direct and statistical evidence suggests that 

PM2.5 exceedances at the Cowtown monitor are largely the result of the numerous and extreme 

exceedances of the PM10 NAAQS. 

First, with only one exception, all of the exceedances of the 24-hour average PM2.5 NAAQS of 

35 g/m3 occur only when there is also an exceedance of the PM10 NAAQS of 150 g/m3

, as the 

following table 9 illustrates: 

Table 9: Comparison of PM10 and PM2.5 Values for Valid PM2.5 Exceedances 

for the Cowtown Monitor 

2006 2007 2008 All Years 

PM10 (g/m3) 380.5 353.6 322.6 358.2 

PM2.5 (g/m3 Average ) 45.1 46.5 39.0 44.1 

Percent PM2.5 12.6% 16.1% 12.8% 13.7% 

PM10 (g/m3) 606.0 759.3 465.0 759.3 

PM2.5 (g/m3 Maximum ) 69.4 59.7 41.7 69.4 

Percent PM2.5 20.4% 32.7% 18.2% 32.7% 

PM10 (g/m3) 249.0 182.6 217.0 182.6 

PM2.5 (g/m3 Minimum ) 36.2 37.0 35.9 35.9 

Percent PM2.5 6.9% 6.0% 9.0% 6.0% 

Number of Samples 13 9 7 29 

The one exception was for August 27, 2006, for which the PM2.5 concentration was recorded as 

35.8 g/m3 and PM10 was 86 g/m3

. Closer examination of this sample revealed that the original 

filter weight was very likely erroneous. This value has been flagged in AQS as invalid. The 

supporting documentation for this is contained in Appendix A of this memorandum. 

In addition to this assessment, linear regression analysis was performed to determine the degree 

to which PM10 concentrations and meteorological factors explained the variation in PM2.5

concentrations. Data from calendar years 2006 through 2008 and January through June 2009 

were included in this analysis. Meteorological data were taken from the AZMET Maricopa 

station because there was a complete record for that site, whereas the meteorological data set for 

the Cowtown monitor contains several data gaps. The AZMET Maricopa monitor (Latitude 33° 

04' 07" N, Longitude 111° 58' 18" W) is located approximately 4 miles north of the Cowtown 

32
Ira Domsky 

February 22, 2010 

monitor (Latitude 33° 00' 38" N, Longitude 111° 58' 19" W). Meteorological variables evaluated 

included: average, maximum and minimum temperature; average and maximum relative 

humidity; daily average, hourly average and hourly maximum wind speed; and daily average 

vector wind speed and direction, and wind direction during the maximum wind hour. 

Multiple linear regression can be done either as a least-squares fit based on the averages of the 

dependent and independent variables, which includes calculating the estimate for a non-zero 

constant (y=ax+b), or through the origin (intercept of zero, or y=ax). Because PM2.5 is a subset 

of PM10, the potential that none of the particulate matter would be in the PM2.5 fraction is highly 

unlikely; i.e., performing a linear regression with an intercept other than zero would be 

inappropriate. PM10 concentration and several meteorological variables were evaluated using 

scatter plots to determine if nonlinear relationships were evident (none were found), and then 

regressed with PM2.5 as the dependent variable. PM10, by far, explained more variation in PM2.5

than any other variable. The only other variable that explained a significant amount of variation 

in PM2.5 concentrations was relative humidity, and maximum relative humidity was a better 

predictor. This model, however, had a significant fault, in that the residuals (difference between 

the predicted and actual PM2.5 concentrations) were significantly correlated with the actual PM2.5

concentration. Consequently, the only reliable model that could be developed was for PM2.5 as a 

function of PM10. The results of that regression analysis are presented in table 10: 

Table 10: A linear regression analysis to determine the degree to which PM10

concentrations and meteorological factors explained the variation in PM2.5

concentrations. 

Regression Statistics 

Multiple R 0.9192 

R Square 0.8448 

Adjusted R Square 0.8398 

Standard Error 9.30858 

Observations 199 

ANOVA 

 df SS MS F Significance F 

Regression 1 93417.61 93418 1078.11 7.92E-82 

Residual 198 17156.62 86.65 

Total 199 110574.23 

 Coefficients Standard Error t Statistic P-value 

Intercept 0 #N/A #N/A #N/A 

PM10 0.1051 0.00320 32.83 4.7E-82 

33
Ira Domsky 

February 22, 2010 

Figure 15, below, presents a scatter plot with the regression line, the 95% 

confidence intervals for the regression line and the 95% one-tailed upper 

prediction interval. 

PM2.5 as a Function of PM10 for Cowtown Monitor

January 2006 through June 2009

PM2.5 = 0.1051*PM10

Adjusted r2 = 0.8398

0

10

20

30

40

50

60

70

0 100 200 300 400 500 600 700 800

PM10 Conc. (ug/m3)

PM2.5 (ug/m3)

Observed 95% Hi Conf. Int. 95% Lo Conf. Int. 95% Hi Predict. Int. Regression

Levels of NAAQS

This figure demonstrates that there is still a lot of variability that is not explained within this 

simple statistical model, especially for the outliers – where either the percentage of PM10 that is 

PM2.5 is either much higher or much lower than the statistically estimated average of 10.5%. 

Many of these intervening factors likely include levels of activity at difference sources, wind 

direction and speed with respect to emissions from specific sources on certain days, and smoke 

from local fires (which would result in a much higher PM2.5 proportion in that sample), to name a 

few. Regardless, this model indicates that, with 95% confidence, PM2.5 concentrations are 

largely driven by the amount of PM10 in the atmosphere. 

Conclusions 

The Cowtown monitoring site lies within an irrigated desert plain, with active and retired 

agricultural operations to the north, northwest and east. Three cattle feedlot companies and a 

grain-processing complex operate to the south, southwest and southeast. Given the source mix 

near the Cowtown monitor, it is of little surprise that the major contributor from each of seven 

source categories (geologic soil, feedlot soil, motor vehicle emissions, vegetative burning, coal 

fired power plant, ammonium sulfate and ammonium nitrate) is from the feedlot source category, 

with geologic soil (including unpaved roads and agricultural soils) following second. 

34
Ira Domsky 

February 22, 2010 

The following list presents the major findings of this research: 

 The Cowtown monitor has a prominent chemical signature from nearby 

feedlots. 

 The major source contributors to the PM2.5 exceedances at Cowtown are 

from feedlots and geologic soil. 

 When compared to other monitors in Pinal County, only Cowtown has 

feedlots as the major source contributor; other sites are geologic soil. 

 Stagnation during the 2003 study produced high relative contributions 

from feedlot dust at Cowtown. 

 Both stagnation and non-stagnation events created elevated PM2.5 

concentrations at Cowtown in 2006, 2007 and 2008, which indicate that 

fairly strong local sources can impact the monitor under any 

meteorological condition. 

 The trajectory of winds for each of the 29 days in 2006, 2007 and 2008 

can transport emissions from local sources to the Cowtown monitor. 

 The trajectory of winds for each of the 29 days in 2006, 2007 and 2008 

indicate that there are no distant sources along the back trajectory which 

would be expected to contribute to any of the PM2.5 exceedances at 

Cowtown. 

 On a daily and annual basis, Cowtown consistently measures higher PM2.5 

then any other monitor in Pinal County. 

 PM2.5 exceedances are always concurrent with PM10 exceedances. 

35
Ira Domsky 

February 22, 2010 

REFERENCES 

 Pinal County Air Quality Control District Source Apportionment Study. (2005). 

Clements, A.L., Jia, Y., Fraser, M.P., Herckes, P., Sundblom, M., & Solomon, P.A., 

(2010). Chemical Analysis for Source Attribution of Fine and Coarse particulate Matter in Pinal 

County, AZ. [Presented to PCAQCD] 

Chow, J.C., Watson, J.G., Crow, D., Lowenthal, D.H., Merrifield, T., 

(2001). Comparison of IMPROVE and NIOSH Carbon Measurements. 

 Chow, J.C., Watson, J.G., Kuhns, H., Etyemezian, V., Lowenthal, D.H., & Crow, D., 

Kohl, S.D., Engelbrecht, J.P., Green, M.C., (2004). Source profiles for industrial, mobile, and 

area sources in the Big Bend regional Aerosol Visibility and Observational study. 

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Ira Domsky 

February 22, 2010 

APPENDIX A 

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Ira Domsky 

February 22, 2010 

38