# Arizona Air Quality Designations Technical Support Document

Boundary Recommendation for the Pinal County

24-hour Standard PM 2.5 

# Nonattainment Area

Air Quality Division

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

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

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

2.0 AREA DESIGNATI ON 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 PM 2.5 Nonattainment Area Boundaries...... ................... ES-3

2-1 EPA Area of Interest and State PM 2.5 Monitoring Network..................................... 3

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

3-2 Major Features Related to Pinal County PM 2.5 Nonattainment Area Designations .. ........5

3-3 Annual PM 2.5 Emissions by County According to EPA’s National Emissions Inventory...7

3-4 EPA Area of Interest and State PM 2.5 Monitoring Network..................................... 8

3-5 Cowtown PM 2.5 Exceedances by Max Wind Speed Category 2006-2008....................10

3-6 Pinal County PM 2.5 24-hour Averages............................................................12

3-7 Comparison of PM 2.5 and PM 10 Source Contributions at the Cowtown and Casa .........13

Grande Downtown Monitors

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

3-9 PM 2.5 24-hour Average Comparison between Sites (2006-2008) ..............................15

3-10 PM 2.5 as a Function for PM 10 for Cowtown Mon itor......................................... .........16

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

3-12 Pinal County Proposed PM 2.5 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 PM 2.5 Nonattainment Area Boundaries..........................27LIST OF TABLES Page

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

1-1 History of EPA’s PM 2.5 Particulate Matter National Ambient Air Quality Standards........ 1

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

3-1 Annual PM 2.5 Emissions by County According to EPA’s National Emissions Inventory...6

3-2 Chemical Mass Balance Modeled PM 2.5 Source Attributions for the Cowtown Monitor... 7

3-3 Pinal County 24-hour PM 2.5 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 Inventor y and Average Daily Traffic........................... 24

3-7 Summary of Nine Criteria Analysis for the Proposed Pinal County PM 2.5 .................. 26

Nonattainment Area

APPENDICES

Appendix A Ambient PM 2.5 Monitoring Data 2006-2008

Appendix B Memorandum to Ira Domsky from Leonard Montenegro

### Ariz ona Air Quality Designations T echnical Support Document Boundary Recommendation for the Pinal County 24-hour PM 2.5 

### Nonattainment Area

EXECUTIVE SUMMARY

In July 1997, the U.S. Environmental Protection Ag ency (EPA) established National Ambient Air Quality Standards (NAAQS) for particulate matter 2.5 microns or less (PM 2.5 ) setting a 24-hour average standard of 65μg/m 3 and an annual average at 15 μg/m 3 . Ambient monitoring data for PM 2.5 collected in Pinal County by the Pinal County Air Quality Control Dist rict (PCAQCD) documents numerous violations of

the both the annual and 24-hour PM 2.5 NAAQS in the western portion of Pinal County. In a letter dated October 14, 2009, EPA notified the Governor of Ariz ona that Pinal County was being redesignated for

nonattainment of the 1997 annual PM 2.5 NAAQS and requested the State to submit PM 2.5 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 PM 2.5 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 PM 2.5 nonattainment area to the Governor.

The only air quality monitor in Pinal County violating the 24-hour PM 2.5 NAAQS is known as the Cowtown monitor. The location of this “hotspot” m onitor was chosen because of its proximity to the largest cattle feedlot in the County – approximately 150 yards from the monitor. The area near themonitor includes a unique mix of sources in Pinal County – locomotive tracks, industrial processes, highdensity urban areas, agricultural activities, and c oncentrated animal feeding operations (CAFOs).

The portion of Pinal County recommended by the Arizona Department of Environmental Quality (ADEQ) for nonattainment consists of the western re gion of the County near the Cowtown monitor where

violations of the PM 2.5 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 rema inder of the County be designated as

attainment/unclassifiable. This recommendation is based on careful consideration of the PM 2.5 standard, requirements for designation of nonattainment areas, a nd options available for implementing the standard. This document demonstrates the impracticability of a nd 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

PM 2.5 NAAQS. This recommendation excludes all Indian C ountry. 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.



### 2Table ES-1 Summary of Nine Criteria Analysis for the Proposed Pinal County PM 2.5 Nonattainment Area

Criteria Factors Supporting the Recommende d Nonattainment Area Boundary

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

Arizona counties in annual PM 2.5 emissions; counties with a greater quantity of

PM 2.5 emissions have demonstrated attainment with the PM 2.5 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 PM 2.5 monitor.

The NEI is for primary PM 2.5 emissions only. Secondary PM 2.5 particles may be forming and contributing to readings at the violating monitor; the main sources of secondary particles are emissions from co mbustion and on-road mobile sources. Meteorology The recommended nonattainment area is locat ed in the agricultural basin area of Pinal County often affected by high-wind conditions and stagnation events. Geography/ Topography The recommended nonattainment area is locat ed 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 PM 2.5 NAAQS is located within the recommended nonattainment area.

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

PM 2.5 emissions from adjoining cattle feedlots.

Jurisdictional

Boundaries ADEQ’s recommendation maintains jurisdic tional cohesiveness to the extent feasible, acknowledges the State’s lack of jurisdiction over Indian Country, and requires no new institutional arrangement s 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

PM 2.5 emissions. PCAQCD and ADEQ can at any time review a permit to determine if additional control measures are necessary for PM 2.5 generating sources. PCAQCD also regulates open-bur ning and area sources. Additional control measures may be necessary for sources causing PM 2.5 exceedances. Population Density and Degree of

Urbanization The recommendation includes the population most affected by high PM 2.5 concentrations.

Traffic and Commuting

Patterns Emissions estimates developed by ADEQ i ndicate on-road sources are a relatively

minor contributor to ambient PM 2.5 concentrations in Pinal County.

Growth Rates and

Patterns The area surrounding the recommended nonattain ment area is somewhat limited in growth by Indian Country and publicly owned lands. Factors Supporting the Exclusion of the Re mainder of Pinal County and the State Those areas of the county recommended as attainmen t/unclassifiable have demonstrated attainment with ambient monitoring data. These areas include ur banized 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 reco mmended nonattainment area have not contributed to an exceedance of the NAAQS within the recommended borders and are not likely to in the future.



### 3 ES-2

### 4 Figure ES-11.0 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 pa rticulate matter known as PM 2.5 . Maximum pollution levels or limits that are based on human health are called primary standards; limit s 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 pa rticulate matter to add new standards for PM 2.5 . EPA

established annual and 24-hour standards for PM 2.5 (62 FR 38652). The prim ary and secondary annual standards were set at a level of 15 micrograms per cubic meter ( μ g/m 3 ) and were based on a three-year average of annual mean PM 2.5 concentrations. The primary and secondary 24-hour standards were set at a

level of 65 μ g/m 3 and were based on a three-year average of the 98th percentile of 24-hour PM 2.5 concentrations.

In 2006, EPA lowered the primary and secondary 24-hour PM 2.5 standards from 65 μ g/m 3 to 35 μ g/m 3

. EPA retained the 1997 primary and secondary annual PM 2.5 standards of 15 μ g/m 3 . The revised 24-hour standards became effective on December 18, 2 006; EPA did not revoke the 1997 24-hour PM 2.5 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 PM 2.5 NAAQS for the period 2006-2008, EPA began the process under CAA section 107(d)(3) to redesignate Pi nal County, and any nearby counties that may be contributing to PM 2.5 violations in Pinal County, as nonattainment for the 1997 annual PM 2.5 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 determin ation regarding violations of the 24-hour PM 2.5 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 PM 2.5 NAAQS and requested the State to submit PM 2.5 nonattainment area boundary recommendations for Pina l County within 120 days. EPA also notified the Governor that designation of Pinal County for violations of the 2006 24-hour PM 2.5 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 PM 2.5 Nonattainment Area.



### 12.0 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 th e pollutant.” In a lette r dated October 14, 2009, EPA requested that the State of Arizona dete rmine nonattainment area boundaries for a Pinal County

PM 2.5 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 PM 2.5 NAAQS,

and the 2006 PM 2.5 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) viol ations are not occurring in nearby areas that are excluded from the recommended area, and 2) the excl uded 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 PM 2.5 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 urbani zation (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 PM 2.5 

# Monitoring

Network

33.0 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 PM 2.5 NAAQS and submit the recommendations to EPA by February 8, 2010. EPA based this request on violations of the PM 2.5 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 Figur e 3-1. The Cowtown monito r recorded 30 exceedances

of the 24-hour PM 2.5 NAAQS of 35  g/m 3 during 2006-2008. To assist in the selection of boundaries for the proposed Pinal County PM 2.5 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 PM 2.5 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 criteri on 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



### 45Figure 3-23.1 EMISSIONS DATA

Due to time constraints, ADEQ was not able to make a gridded PM 2.5 emissions inventory for Pinal County. In the absence of an emissions inventory, ADEQ eval uated county-level emissions data from Version 2 of

EPA’s 2002 National Emissions Inventory (NEI) and a source apporti onment 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 PM 2.5 SIP.

Table 3-1 contains annual PM 2.5 emissions (in tons per year) for counties in Arizona according to the NEI; these emissions are depicted in Figure 3-3. The estimat e for Navajo County is elevated due to a major wildfire in that county that burned 467,066 acres of fo rest during the summer of 2002. Figure 3-4 depicts

the considered counties and the State’s PM 2.5 monitoring network.

Table 3-1

PM 2.5 Emissions by County According to EPA’s 2002 National Emissions Inventory

County PM 2.5 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/ttnchie 1/net/2002inventory.html



### 6Figure 3-3 

# Annual PM 2.5 

# 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 PazTons Per Year

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



## Figure 3-4

EPA Area of Interest

and State PM 2.5 

## Monitoring Network

● Monitors measuring no

exceedances of 24-hour NAAQS

● Monitors Measuring

exceedances

● Violating monitors



### 7While the NEI is for primary emissions of PM 2.5 , secondary particles may be forming near the Cowtown monitor or being transported to it. The major s ources of the secondary emissions are combustion byproducts and onroad mobile sources. PCAQCD’s sour ce apportionment study included chemical mass balance (CMB) analyses of samples taken at the Cowtow n monitor in October and November of 2003. The analyses determined that ammonium nitrate and ammonium sulfate accounted for less than 10 μg/m 3 . Table 3-2 contains the CMB analyses of the Cowtown monitor samples.

Table 3-2

Chemical Mass Balance Modeled PM 2.5 Source Attributions for the Cowtown Monitor ( μ g/m 3 )

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 PM 2.5 problem in Pinal County. The PCAQCD Source Apportionment Study estimates that, on average, 49% of PM 2.5 at the Cowtown monitor originates from feedlots. Eight so urce 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

PM 2.5 concentrations measured at Cowtown:



###  Th e 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 doc umented and significant nearby sources, such as railroad locomotives, were not included, which redu ces the reliability of the results regarding power plants;



###  Even if one assumes the coal power plant apportionmen t 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 viol ating monitor) away from the monitor rather than

towards it.

Other points that address this issue are detailed in Ap pendix B. ADEQ finds the conclusion that coal-fired power plants contribute to nonattainment at Cowtow n as potentially spurious and worthy of additional analysis. This effort is curren tly underway, as EPA has sited a PM 2.5 speciation monitor at the site and data from that monitor are already being analyzed.



### 83.2 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 scal e 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 sout heast 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 PM 2.5 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 hi gh 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 durati on 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 ‘drain age’ 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. Du ring 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 larg e 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 15 th and lasts through September 30 th . 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, ho wever, tend to increase coarse particle loading in the atmosphere and disperse PM 2.5 . 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 PM 2.5 arising from the same sources and as a component of PM 10 emissions.

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



### 9Figure 3-5



### Cowtown PM 2.5 

### 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

ExceedancesNumber of Exceedances

0.0 15.0 30.0 45.0 60.0 75.0PM2.5Conc. (ug/m3)

# Exceedances

Max PM2.5

Min PM2.5

Avg PM2.5

Review of PM 2.5 Exceedance Days during 2006-2008

Exceedances by Season

Of the days when the Cowt own monitor exceeded the PM 2.5 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 PM 2.5 exceedances occur during non-stagnant, light wind events.

Exceedances by Wind Event/Stagnation

As noted above, light winds due to low-pressure sy stems 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 an alysis on the impact of meteorology on PM 2.5 exceedances

in the recommended nonattainment area.



### 103 .3 GEOGRAPHY/TOPOGRAPHY

The topography of Pinal County can best be descri bed 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 betw een 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 C ounty. The area does not have geographical or topographical barriers limiting air pollution transpor t within its airshed. Although there are no topographical barriers that separate the recommended nonattainment area from the rest of Pinal County, the significantly lower PM 2.5 concentrations recorded at the Apache Junction and Casa Grande monitors demonstrate that distance prevents most of the County’s population from exposure to high PM 2.5 concentrations.

3.4 AIR QUALITY DATA

Table 3-4 contains 24-hou r PM 2.5 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 PM 2.5 NAAQS – are located in urbanized areas of the County with high population densities. The Cowtown monitor was sited to characterize localized ‘hotspot’ PM 2.5 emissions from adjoining cattle feedlots.

An area is meeting the 24-hour PM 2.5 standard when the three year average of the 98 th percentile values is

35 μ g/m 3 or less. Design values are valid only if minimum data completeness criteria are met. A descending trend in 24-hour PM 2.5 concentrations at the Cowtown m onitor is evident in Figure 3-6. Appendix A contains a complete record of ambient monitoring data for every monitor in Arizona’s PM 2.5 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 PM 2.5 Average Concentrations 2006-2008

98th Percentile Samples (in μg/m 3 ) 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



### 11Figure 3-6



# Pinal County

PM 2.5 

# 24-hour Averages

(concentrations shown in μ g/m 3 ) *



### 0 10 20 30 40 50 60

2006 2007 2008μg/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 locat ed 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 radi us of the Cowtown monitor: locomotive tracks; a two-lane highway; the City of Maricopa (popula tion approximately 45,000); a grain milling facility.

Source Apportionment Study

PCAQCD co nducted a study in 2003 to identify emissi on 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 Pina l County: Cowtown, Casa Gr ande 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 particl es accounted for 49 percent of the collected samples.

PM 10 samples collected by the Cowtown monitor were also disportionately comprised of feedlot emissions. Figure 3-7 compares PM 2.5 samples collected by the Cowtown and Casa Grande monitors –

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



### 12miles apart. PCAQCD’S study also examined the composition of PM 10 samples collected at the Casa

Grande and Cowtown monitors. The PM 10 sample collected at the Cowtown monitor was also disportionately comprised of feedlot emissions. As shown in Figure 3-7, the PM 10 sample collected by the Casa Grande monitor did contain feedlot emissions, although the amount (20 percent) was significantly

less than the PM 10 sample collected at Cowtown (59 percent). This finding supports ADEQ’s

determination that PM 2.5 emissions from feedlots largely remain within the recommended nonattainment

area.

Figure 3-7 Comparison of PM 2.5 and PM 10 Source Contributions at the

Cowtown and Casa Grande Monitors



## PM 2.5

Casa Grande Downtown Monitor Cowtown Monitor

59% 4% 1%

1% 0%

1% 2%

32%

62% 20% 1% 6% 5% 0% 3%

3%



## PM 10

Back Traject ory Analyses

Using the Cowtown monitor as a starting point, ADEQ created 7 HYSPLIT models of back trajectories to analyze PM 2.5 transport on exceedance days with a range of meteorological conditions; a back trajectory analysis for an exceedance date with stagnant conditi ons is shown below in Figure 3-8. The dates selected represent the four seasons of the year. The back traj ectories 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 monito r. Appendix B features a detailed report on the back trajectories completed by ADEQ.



### 13Figure 3-8 Back Trajectory Analysis – January 11, 2006

Data Analyses

ADEQ compared ambient PM 2.5 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 excee dances. On exceedance days, the Cowtown monitor consistently recorded PM 2.5 concentrations that were more than tw ice the concentrations recorded at the

Casa Grande monitor 15 miles to the southeast a nd 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 m onitor, but rather that the elevated PM 2.5 concentrations are highly localized and affect only the recommended nonattainment area.



### 14Figure 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

SiteConcentration (μg/m3)

Cowtown
Apache Junction

Casa Grande

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

in PM 2.5 concentrations recorded by the Cowtown m onitor. These analyses determined that high PM 10 concentrations were the only reliable predictor of high PM 2.5 concentrations. Figure 3-10 depicts a linear regression indicating, with a 95 percent confidence level, that PM 2.5 concentrations are driven by the

amount of PM 10 in the atmosphere.



### 15Figure 3-10



### PM 2.5 

### as a Function of PM 10 

### for Cowtown Monitor

January 2006 through June 2009

PM 2.5 = 0.1051*PM 10 Adjusted r 2 = 0.8398

0 10 20 30 40 50 60 70

0 100 200 300 400 500 600 700 800

PM 10 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 PM 10 and PM 2.5 Concentrations

In October 2006, PCAQCD conducted a study of hourly PM 10 and PM 2.5 concentrations recorded by continuously operating monitors at the Cowtown monito r site. The results of that study show a clear correlation among hour-by- hour variances in PM 10 and PM 2.5 concentrations. The sync hronous variations in hourly concentrations depicted in Figure 3-11 provide compelling evidence that th e same sources generate
local ambient concentrations of both PM 10 and PM 2.5 . This is further corroboratin g evidence that the nearby

feedlots are the main source of PM 10 emissions in the Cowtown monitor area. The time-correlation of PM 10 and PM 2.5 concentrations establishes that the feedlots are also the dominant local source of PM 2.5 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 PM 2.5 and PM 10 exceedances, indicating that PM 2.5 exceedances are essentially a component of high PM 10 concentrations. This

determination makes the PM 2.5 exceedances at Cowtown unique because th ey 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 PM 2.5 nonattainment area boundaries, the State considered the authorities of PCAQCD and its responsibilities to facilitate air qua lity 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 I ndian Nation, most of which is in Pima County, with approximately 275,000 acres in southwestern Pi nal County. Because the State has no jurisdiction over Indian Country, no recommendation is being made for designations relating to these three Indian

Reservations.



### 183.6 LE VEL OF CONTROL OF EMISSION SOURCES

Pinal County is authorized under the CAA and Arizon a 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 ac tivities 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 PM 2.5 emissions. PCAQCD and ADEQ can at any time review a permit to determine if additional control measures are necessary for PM 2.5 sources. PCAQCD also regulates open burning and area sources. The Count y conducts routine field inspections to verify compliance with permit conditions and operates a count y-wide monitoring network to determine if the ambient air is meeting the PM 2.5 NAAQS. Additional control measures may be necessary to control sources causing PM 2.5 exceedances.

3.7 POPULATION DENSITY AND DEGREE OF URBANIZATION

Table 3-5 features population estimates for municipa lities within Pinal County for 2000 and 2008. The population of the County has grown 82 percent in the in tervening years. Development in Pinal County is limited to privately owned areas; most of the land av ailable for development is located along the I-8 and I-10 corridors, as shown in Figure 3-12. Approximatel y 95 percent of the Pinal County population lives within this region. Figure 3-13 depicts the popula tion density of Pinal County in 2005; Figure 3-14 depicts the County’s projected population density in 203 0. It must be noted that the residential housing market in Pinal County has been particularly affect ed 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 Junction 6 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 Creek 7 6.0 168.3 929.3 1,010 5,576

5 http://www.azstarne t.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 por tion of Queen Creek, ADEQ divided the 2000 and 2008 population estimates by 23.4 percent.



### 1920Figure3-12Figure 3-13 Pinal County Population- Density 2005

Source: Arizona Departme nt of Transportation

.

Figure 3-14 Pinal County Population Density 2030



### 213.8 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 a nd percentage of those commuting to and within Pinal, Pima, and Maricopa Counties in 2000; these statistics may be dram atically higher in the 2010 decennial census. As discussed below, emissions from onroad mobile sources are a relatively minor contributor to PM 2.5 concentrations in Pinal County.

Table 3-6 Inter-County Employment Commute Statis tics 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 PM 2.5 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 f actors 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 Mon itoring 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, availa ble municipal records, and from inspection of aerial photographs. The unpaved roads covered by that invent ory included public (i.e. County), private roads, agricultural roads, and trails.



### 22For County-supplied roads, AADT volumes were assumed as shown in Table 3-7. For example, for County-maintained unpaved roads, an average daily vol ume 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 tr ips 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 determin e the appropriate emission factors for this category. An emission factor was calculated for each road segment in th e 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 w as then multiplied by its VMT to derive exhaust,

and tire and brake wear emissions.

Fugitive emissions on paved and unpaved roads:

To estimate fugitive PM 2.5 emissions for paved and unpaved roads in the HPMS database, the State utilized the methods described in EPA's AP-42 guidan ce 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 w as 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 PM 10 for the Maricopa County PM 10 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 f actor 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.



### 23Emissions 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 travel ing on the road segments and the VMT.

Conclusion

ADEQ’s estimates that PM 2.5 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 PM 2.5 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 ow ned 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 guid ance and determined that the portions of Pinal County discussed in this section meet the criteria for inclusion in a PM 2.5 nonattainment area. Table 3-8 summarizes the data and information suppor ting the nonattainment area recommendation.



### 24Table 3-8 Summary of Nine Criteria Analysis for the Proposed Pinal County PM 2.5 Nonattainment Area

Criteria Factors Supporting the Recommende d Nonattainment Area Boundary

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

Arizona counties in annual PM 2.5 emissions; counties with a greater quantity of

PM 2.5 emissions have demonstrated attainment with the PM 2.5 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 PM 2.5 monitor.

The NEI is for primary PM 2.5 emissions only. Secondary PM 2.5 particles may be forming and contributing to readings at th e violating monitor; the main sources of secondary particles are emissions from co mbustion and on-road mobile sources. Meteorology The recommended nonattainment area is locat ed in the agricultural basin area of Pinal County often affected by high-wind conditions and stagnation events. Geography/ Topography The recommended nonattainment area is locat ed 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 PM 2.5 NAAQS is located within the recommended nonattainment area.

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

PM 2.5 emissions from adjoining cattle feedlots.

Jurisdictional

Boundaries ADEQ’s recommendation maintains jurisdic tional cohesiveness to the extent feasible, acknowledges the State’s lack of jurisdiction over Indian Country, and requires no new institutional arrangement s 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

PM 2.5 emissions. PCAQCD and ADEQ can at any time review a permit to determine if additional control measures are necessary for PM 2.5 generating sources. PCAQCD also regulates open-bur ning and area sources. Additional control measures may be necessary for sources causing PM 2.5 exceedances. Population Density and Degree of

Urbanization The recommendation includes the population most affected by high PM 2.5 concentrations.

Traffic and Commuting

Patterns Emissions estimates developed by ADEQ i ndicate on-road sources are a relatively

minor contributor to ambient PM 2.5 concentrations in Pinal County.

Growth Rates and

Patterns The area surrounding the recommended nonattain ment 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 attainmen t/unclassifiable have demonstrated attainment with ambient monitoring data. These areas include ur banized 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 reco mmended nonattainment area have not contributed to an exceedance of the NAAQS within the recommended borders and are not likely to in the future.



### 254.0 AREA DESIGNATION RECOMMENDATIONS

ADEQ demonstrated in the preceding analysis that th e area outside Indian Country affected by the primary sources causing violations of the PM 2.5 NAAQS at Cowtown monitor be designated as nonattainment. The proposed Pinal County PM 2.5 nonattainment area shown in Figure 4-1 encompasses all or part of those townships listed below. T hose 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 PM 2.5 

### Monitoring Data

2006-2008This page is intentionally blank.Appendix A 2006-2008 Annual Average PM 2.5 Compliance (in μg/m 3 )
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 5.92 1 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 su mmary criteria, usually meaning less than 75 percent of valid data recovery availabl e in one or more calendar quarters.Appendix A 2006-2008 24-hour Average PM 2.5 Compliance (in μg/m 3 )
98 th Percentile Samples 1

2006 2007 2008 3-year Average

Cochise County Douglas Red Cross 2 14.0 32.2 13.6 20 Coconino County Flagstaff Middle School 2 13.7 30.2 10.3 18 Gila County Payson Well Site 2

(closed 12/31/2007) 23.4 21.9 N/A N/A

Maricopa County JLG Supersite 3 24.6 23.5 17.8 22

Mesa 3 20.1 18.3 14.5 18

South Phoenix 3 28.8 29.2 22.7 27

West Phoenix 3 28.8 27.2 24.3 27 Pima County

Children’s Park 3 12.1 12.0 11.7 12 Orange Grove 4 11.2 13.6 11.7 12 Pinal County Apache Junction Fire

Station 3 9.3 14.6 15.4 13

Casa Grande Downtown 2 15.4 22.4 22.0 20

Cowtown 2 48.9 53.9 40.7 48 Santa Cruz County Nogales Post Office 2 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 98 th percentile value will be the second highest va lue for sites on a one-in-six day schedule. The 98 th 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 sa mple days in non-leap years.



### 31

### APPENDI X B

Analysis Cowtown PM 2.5 

### Monitor ExceedancesThis 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 PM 2.5 

### Monitor Exceedances

Summary In 2006, the EPA revised the 1997 PM 2.5 

### (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 Pi nal County exceeds both the annual average and 24- hour average PM 2.5 

### NAAQS, but only requested that the State send a recommendation for a nonattainment area boundary for the annual average st andard. Further, EPA indicated that it may favor a proposal in which Pinal County and ei ght other surrounding counties (approximately 58,000 square miles) be designated as a PM 2.5 

### nonattainment area

. 

### Any proposal, however, would be based solely on violations of the PM 2.5 

### NAAQS measured at the Cowtown monitor located in the Town of Maricopa in Pinal County. The Cowtown m onitor frequently records PM 2.5 

### concentrations above the established 2006 ambient PM 2.5 

### 24-hour standard. Yet, unlike most other existing PM 2.5 

### nonattainment areas, the elevated PM 2.5 

### concentrations measured at Cowtown are not primarily from combustion, medi um- or long-range transport of emissions, or secondary particle formation. Rather, they are the result of very high localized PM 2.5 

### concentrations as a component of PM 10 

### and from emissions generated within a few kilometers of

the monitor.

This memorandum demonstrates that the monitored PM 2.5 

### exceedances are largely, if not entirely, a product of local sources — primar ily, 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 th e Pinal County Air Quality Control District (PCAQCD), as well as statisti cal analysis of recent PM 10 

### and PM 2.5 

### air quality data, study of meteorology data, and back trajectory analysis w ill each demonstrate that local sources are the most significant contributors to elevated PM 2.5 

### concentrations at the monitor. These findings support a recommendation to establish PM 2.5 

### nonattainment area boundaries in Pinal County for only that geographic area directly affecting the Cowtown monitor and regions with the same primary sources of PM 10 

### and PM 2.5 

### . The only other air-quality mon itor in Arizona which violates

the PM 2.5 

### NAAQS is the one located in Nogales, Arizona.

### Ira Domsky February 22, 2010

Introduction As a technical support document to th e Pinal County nonattainment area boundary recommendation, this paper will present several analytical methods to help regulators better

understand ambient PM 2.5 

### pollutant concentrations in Pina l County, AZ. This discussion will support a recommendation to establish a PM 2.5 

### boundary around the Cowtown monitor. More specifically, the objective of this paper is to disc uss the factors which have likely cont ributed to

the 29 PM 2.5 

### 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 PM 2.5 

### NAAQS. Any analysis of annual average data, however, would be a fruitless exercise for a larg e number of reasons. U nderstanding the nature

and causes of elevated PM 2.5 

### concentrations is best accomplis hed by looking at the smallest unit of analysis – the 24-hour averag e concentrations in this case. Evaluation of the exceedances of the 24-hour average NAAQS will shed ample light on all the issues th at 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 re duce emissions every day of the year.

The Cowtown, Pinal County, monitor has exceeded the PM 2.5 

### 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 PM 2.5 

### standard of 35 μg/m3 during 2006, 2007, and 2008.

RUN DATE PM 2.5 

### RUN DATE PM 2.5 

### 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 approxim ate ly 4 miles southeast of the City of Maricopa

and 15 miles south of the Phoenix urban area at 37580 W. Maricopa/Cas a Grande Hwy. (Lat. N 33°63’ Long. W 111°58’). The site lie s 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 dist ance of the monitor, to the south, southwest, and southeast. The source makeup near the Cowt own monitor suggests that nearby sources have a high potential to contribute to elevated PM 2.5 

### concentrations at the monitor; thus, any boundary

recommendation must take into considerat ion these nearby sources. Most existing PM 2.5 

### 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 investigatio n 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 a nd 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 monito rs. An investigation into the meteorological factors on each of the 30 exceedance days , and statistical analysis of the PM 2.5 

### data from Cowtown and other monitors, will demonstrate th at local sources are driving the elevated PM 2.5 

### concentrations at Cowtown.

Source Apportionment In 2003 PCAQD embarked on a rigorous source appor tionment study to identify which emission sources are contributing to elevated particul ate matter concentrations in the Pinal County agricultural basin. The study required extensive field work to collect ambient PM 10 

### and PM 2.5 

### 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 Ma ss Balance). The results of that study are presented in a 2005 report prepared by PCAQD title d “Pinal County Air Quality Control District Source Apportionment Study” and are summarized in this memorandum.

PCAQCD co-located filter based Mini-V ol monitors capable of measuring PM 2.5 

### and PM 10 

### at five existing monitoring sites: Casa Grande , Coolidge, Cowtown, Pinal County Housing and Stanfield. All the existing site s had at least one Federal Re ference Method (FRM) monitor configured to measure ambient PM 10 

### concentrations and one site had an existing FRM monitor configured to measure ambient PM 2.5 

### 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 surfa ce material samples be collected to characterize unpaved road dust, agricultural dus t and feedlot dust. DRI (Desert Research Institute) analyzed the material on the filters using various chem ical methods, which provided refined source profiles or “fingerprints” for Pi nal County soils and feedlots.

Table 2: The source apportionment mode ling required chemical source profiles from seven source types, the eigh t 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 di fferent locations from each of the three Pinal County sources (feedlots, unpaved ro ads and agricultural fields) a nd 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 th e CMB model, along with the other existing source profiles, to determine s ource contributions at each of five monitors. Motor vehicle, vegetative burning, coal power pa nt, ammonium sulfate and ammonium nitrate fingerprints were acquired fr om existing source profiles.

The chemical source profiles for feedlot dust, u npaved road dust and agricultural field dust are presented in figures 1, 2 and 3 below.

Fig. 1: Source specific chemical fingerpri nt 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 fingerp rint for unpaved road dust. The samples

were collected from five locations al ong an unpaved road near the Cowtown monitor, during October and November 2003.

8 BRAVO - Big Bend Regional Aerosol and Visibility Observational Study



### 4

### Ira Domsky February 22, 2010

Fig. 3: Source specific chemical fingerpri nt 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 chemi cal fingerprints from Pinal County, for fine particulate matter (PM 2.5 

### ). Shown are the percent concentrati ons of 17 chemical species and their analytical uncertainties, including water soluble nitrate, sulfate and ammonium, the eight carbon species (organic carbon fraction (OC1-4), pyrol ysis of organic carbon s 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 a ssortment of chemical analyses, including X-ray Fluorescence spectrometry (XRF) for elemental species like aluminum, silicon and iron, Ion Chromatography (IC) for sulfates and nitrat es, Atomic Absorption (AA) for potassium, Automated Calorimetry (AC) for ammonium a nd 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

5

### Ira Domsky February 22, 2010

organic carbon species associated with the feedlots, li ke cow ma nure, and distinguish them from

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

in that it contains a substa ntial amount of total carbon (T C) which is the total of the concentrations of the carbon species, part icularly of OC2, OC 3, 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 (feedlo ts, agricultural fields and unpaved roads). The chemical profiles from the Cowtown monitoring site indicate that feedlot emissions do contribute

to the ambient PM 2.5 

### sample, while unpaved road dust and agri cultural 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).

6

### Ira Domsky February 22, 2010

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

substantial amounts of total car bon (TC) with large proportions of OC2, OC3, OC4, OPT and EC1. This shows that the ambient samples do cont ain substantial amounts of the Feedlot Dust, as was confirmed by the Chemical Mass Balance (Version 8) receptor modeling results.

With the source and receptor prof iles 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 sour ce contribution analysis, since we are attempting to identify and apportion source categories to the observed PM 2.5 

### concentrations from ea ch monitoring site, but have limited source specific emissions invent ory data and meteorol ogy. Receptor-oriented models focus on the chemical profiles of ambien t samples collected at th e 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 vary ing qualities of emission invent ories, 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 i nventories 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 es tablish a quantitative measure of impacts from any particular source, but may be used to demo nstrate relative impacts from sources or source categories at each monitor.

Seven source categories (including one catego ry 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



### 7

### Ira Domsky February 22, 2010

each speciated set of samples co llected from each Pinal Cou nty monitoring site. At the Cowtown monitor, the CMB model estimate d that, on average, 49% of PM 2.5 

### at the monitor originates from feedlots and is the major component of the ei ght source types presente d in table 1. The CMB modeling results are consistent with the finding s 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 contri butions from the remaining 4 m onitoring sites were compared to the average source category contributions fr om the Cowtown monito r. That conclusion indicated that the Cowtown monitor is the only monitoring site in Pi nal County where feedlot

emissions dominate the PM 2.5 

### samples. Geologic soil was the do minate source category for each of the remaining four monitoring sites. It is also worth me ntioning, that the PM 10 

### samples from

the Cowtown monitor were also dominate d by feedlot emissions, which supports the PM 2.5 

### as a function of PM 10 

### , data analysis presented later; that analysis show s that when PM2.5 exceeds the standard, so does PM 10



### .

Source Apportionment - Primar y Components of Cowtown PM 2.5



### Nine ambient PM 2.5 

### 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 re ceptor. Based on the sample data and CMB modeling, the most abundant source category for PM 2.5 

### in each of the nine samples was the feedlot soil category with an average of 49% of the total. The second high est source contribution for all nine samples was the geological soil category with an average of 24%. This was th e only site in the Pinal County study area where the geologi cal soil category did not have the highest s ource contribution. The average 24-hour PM 2.5 

### concentration at Cowtown was also well above any other site at 67  g/m 3



### , as recorded by the Mini-Vol PM 2.5 

### sampler. Three samples had a recorded PM 2.5 

### Mini-Vol

concentration above 100  g/m 3 

### , October 21, 2003, October 27, 2003, and November 8, 2003. In all three cases the meteorological data indicate s inversion conditions wi th light winds throughout the day.

Coal-fired power plant emissions were the thir d most abundant with an average of 9%. This contribution from the coal power plant source ca tegory is a bit perplexi ng, however. According to the most updated state-wide emissions i nventory, the nearest coal power plant is approximately 82 miles away from the Cowtow n monitor, and although stack emissions are capable of transporting long distan ces, 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 abund ance of local sources and just 50 meters to the north of the Union Pacific Railroad tracks. It is possible th at the coal power plant profile is actually a variation of the locomotive emission profile or may even be conf used with feedlot emissions, since locomotives and feedlots both show high measures of tota l carbon (TC) in their speciation profiles.

However, it does not appear that locomotive sour ces were modeled in the 2003 CMB analysis, so a locomotive emission profile was not readily avai lable for comparison to the coal fired power

8

### Ira Domsky February 22, 2010

plant profile. The ambient f ilter analysis may not have identified a locomotive signature and for that reason the locomotive source group ma y 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 th e exhaust stream, which w ould make it difficult to

confuse diesel exhaust from locomotives with coal power plant emissions. In-fact, locomotive emissions may be more likely confused with he avy 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 apporti onment study, or at least were not awarded as much consideration as other sources, the loco motive emissions do not appear to have been confused with power plant emissi ons. Even if the locomotive emission profile is mixed in with vehicle exhaust profile, locomotives would only likely be marginally responsible for any

elevated PM 2.5 

### concentrations, since vehi cle 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 feed lot 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 illu strated in figure 1. The feedlot si gnature measures an abundance of TC and OC3, yet CFPP1, 2 and 3 (C oal Fired Power Plants 1, 2 an d 3) each only indicate an abundance of TC, yet OC3 remains unsubstantia l relative to the other carbon fractions.

Figure 5: Coal fired power plant (CFPP) carbon fraction signatures according to the BRAVO speciation profiles and the f eedlot profile used in the PCAQCD study.

CFPP1

0 20 40 60 80

OC1 OC2 OC3 OC4 OP EC1 EC2 EC3 TC

Chemical Species



### 9

### Ira Domsky February 22, 2010

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



### 10

### Ira Domsky February 22, 2010

Figure 6: Average source contributions predicted by the CMB model at the Cowtown monitor according to the 2003 s ource apportionment study. Feedlots are the predominant category at Cowtown.

Fig. 6

Source Apportionment - Comparis on with other PM 2.5 

### 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 indi cate 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 pr ojects in the area immediately surrounding the monitoring site. The CMB modeling results (Figure 7) indicate that geologic soil, coal power plants and auto mobile 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 resi dential 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 b ack to native desert and that landscape continues to the eastern boundary of the C ounty with increasing el evations 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.

11

### Ira Domsky February 22, 2010

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

imme diately 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 to p two source categories, with vegetative burning

and feedlot emissions tied for third.

The Stanfield site is located ne ar 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 domin ates the landscape. Siz eable feedlot operations and dairies lie approximately three miles to th e 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 surrounde d by a different source mix, soil emissions contribute the most to all monitors, except Co wtown. 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 Count y housing and Stanfield). The figures illustrate the average relative percent PM 2.5 

### source contributions for ten samples collected and analyzed at the Casa Gr ande monitoring site. (MvEmi = Motor Vehicle Emissions, VgBrn = Vegitative Burning, ColPP = Coal Fired Power Plant, AmSulf = Ammonium Sulfat e, AmNitr = Ammonium Nitrate)

Fig. 7

12

### Ira Domsky February 22, 2010

Fig. 8

Fig. 9

13

### Ira Domsky February 22, 2010

Fig. 10

Meteorolog ical 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. Th e elevation of Cowtown is approximately 1,200 feet above s ea 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 eleva tion 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 com position. 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 dur ing the winter. The normal January high temperature in Casa Grande (17 miles southeast of Co wtown) is 65°F and the normal low is 38°F. The normal July high temperature is 105°F and the low is 78°F.

14

### Ira Domsky February 22, 2010

Precipita tion 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 sy stems with attached cold fronts push through the region. This pattern is ofte n seen in the spring and fall s eason. Characteristically as the system approaches, winds increase out of the sout hwest, 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 ab sence 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 o ccur 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 fr om 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 su rface to the bottom of the troposphere (approximately 11 miles above the surface) shows temperatures decreasi ng with increasing height. Warm air naturally

rises and mixes with cooler air in an effort to reach e quilibrium. 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 incr ease with height rather than decrease. Other times, the surface cools at a much more rapid ra te than the air above. Both conditions produce the same result - a temperature inve rsion. Under an inversion, ther e 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 th e air at the surface wa rms. If the surface temperatures surpasses that of the air above it, the inversion breaks and vertical mixing ensues. Weak inversions (1-5°F) may br eak within an hour of sunrise. Sometimes, however, strong inversions (15-20°F) may not break at all, pos sibly keeping the air and its accumulated pollution trapped near the surface for se veral 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 ove r 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 verti cally and horizontally, primarily driven by

15

### Ira Domsky February 22, 2010

topography. The typical surface flow is up-sl ope during the afternoon and down-slope durin g the night time and morning hours. Calms ca n be common and persistent. Under these conditions, pollution accumulates in the lower leve ls of the atmosphere, potentially leading to

PM 10 

### and PM 2.5 

### exceedances. Summer stagnation events may occur in late June and early July until Arizona Monsoon pattern sets up.

PM 2.5 

### Exceedance Days from 2006-2008

Table 3: A total of 30 days exceeded the current PM 2.5 

### standard of 35 μg/m3 during 2006, 2007, and 2008. 9 days were subject to st agnant 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 a ssociated with high or low winds.

RUN DATE Meteorology PM 2.5 

### RUN DATE Meteorology PM 2.5



### 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 th e days that exceeded the PM 2.5 

### Federal health standard at the Cowtown monitoring site, 15 da ys (50%) occurred during the sp ring 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 seas ons, it would appear th at a large portion of

the PM 2.5 

### exceedances are a result of non-stagnant events.

16

### Ira Domsky February 22, 2010

Exceedances by Wi nd Event/Stagnation

A closer look at horizontal a nd vertical wind speeds at the Co wtown monitor on the days that

exceeded the PM 2.5 

### Federal health standards shows that 17 of the 30 days (56.3%) had relatively strong sustained winds with positive vertical mi xing. 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 relativ ely low average wind speeds but strong enough peak wind gusts to

neither be classified as wi nd or stagnation event.

As expected, 14 of the 17 (82%) hi gh-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 PM 2.5 

### 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-Partic le 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 se ven 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

PM 2.5 

### 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 PM 2.5 

### 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 ev ery 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, star ting point, twenty four hours earlier. But, for ease of interpretation, it may be more practical to di scuss the trajectories as moving forward, even though the HYSPLIT model calculate d the paths in reverse.

17

### Ira Domsky February 22, 2010

If one were to consider only the yellow line, in figure 11a below, and move forwar d, the air parcel begins near Chandler, AZ at 6AM on Ja nuary 10, 2006 and travels south for most of the day. The parcel then turns toward the north near Cowtown and at 6A M January 11, 2006 (twenty four hours later), the air parcel ar rives at Cowtown. The green trajecto ry 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 be gins at 6PM on January 10, 2006 and arrives at Cowtown at 6PM January 11, 2006. The red traject ory begins at midnight January 10, 2006 and arrives at midnight January 11, 2006. Each of the tr ajectories for the seve n 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 11 th 

### modeling results indicate that the ma jority of 24-hour trajectories do not traverse any likely PM 2.5 

### sources until the air parcel nears th e 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 Cowt own monitor. Since no obvious PM 2.5 

### sources are located along the trajectory (beyond the area near Cowtown) only local PM 2.5 

### 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 ai r parcel traverse any PM 2.5 

### sources. However, January 11, 2006 (Fig. 11a) and February 22, 2006 (Fig. 11b) are so mewhat different from the other five days, because some trajectories begin near the Phoe nix and Yuma, urban areas. On January 11, 2006 (Fig. 11a), the yellow, green and red trajectorie s 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 indi cate some tendency for ai r pollution from these

urban areas to 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 PM 2.5 

### source(s). When the air parcel nears the Cowtown monitor, however, the majority of th e trajectories will traverse nearby feedlot and agricultural sources.

The HYSPLIT model was also used to investig ate the wind flows nearby the Cowtown monitor during times when the PM 2.5 

### monitor was measuring elevated hour ly concentrations. The hourly

PM 10 

### data profiles (Continuous PM 2.5 

### was not available, so PM 10 

### is used as a surrogate for PM 2.5 

### ) are provided for each figure. Detailed HYSPLIT output images illustrating the time varying air parcel trajectories layered on top local and dist ant sources is provided for each modeled day and

are illustrated in the fo llowing figures (11a-h):

18

### Ira Domsky February 22, 2010

19

### Ira Domsky February 22, 2010

20

### Ira Domsky February 22, 2010

21

### Ira Domsky February 22, 2010

22

### Ira Domsky February 22, 2010

23

### Ira Domsky February 22, 2010

24

### Ira Domsky February 22, 2010

25

### Ira Domsky February 22, 2010

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 spr eading. This is a well known component of dispersion and becomes particularly evident in ambient PM mon itoring data for air pollution monitors which are located within the same airshed and in close pr oximity to each other. In Arizona, there are

several PM 2.5 

### 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 farthe st 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 monito r for Casa Grande and Apache Junction are 24 km and 64 km, respectively. If one were to assume Pasquill-Gifford rura l horizontal dispersion (see figure 12) from a source 100 km from Co wtown and south-westerly winds, which is the predominant wind direction for each of the seven da ys modeled in the back trajectory analysis, it is easy to see that both Cowt own and Casa Grande should be equally impacted by the distant source. This is because the horizontal plume spread ( σ y 

### ) 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 σ y 

### 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 direc tion 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 va riations in relative impacts at each site, since the deposition rate of PM 2.5 

### is slow and the two mon itors are close together.

Figure 12: Pasquill-Gifford σ y, 

### rural horizontal dispersi on 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.

26

### Ira Domsky February 22, 2010

The Pasquill-Gifford dispersion parame ters are theoretical and except fo r the use of stability classes, it does not include provisions for r eal world actions: like chemical conversion, deposition and removal, wind speed, surface r oughness and a whole suite of meteorological variables which will directly influence the pl ume spread and PM con centrations downwind. Furthermore, the six stability cl asses do not represent the real at mosphere. Real dispersion varies continuously throughout the atmosphere, from stab le to unstable and not stepwise as the A-F stability classes do. So, some variation in the re lative PM impacts at each monitor is expected, but the general principal remains the same; dist ant sources from Cowtown will likely impact both Cowtown and Casa Grande, and on some o ccasions impact all three monitors, including Apache Junction.

If long or medium range transport is the sour ce 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 th e three sites are similar. The following analysis compares the ambient PM 2.5 

### concentrations from Cowtown to the ambient PM 2.5 

### 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 PM 2.5 

### Monitor and the Cowtown monitor reveals a significant difference in measured concentrat ions on both daily average and annual average

PM 2.5 

### . Over a period of three years from 2006 to 2008, the Cowtown monitor consistently

measured more than double the ambient concentra tions at Casa Grande, which implies that the impacts at that monitor are not from distant sour ces, but are from . Table 4 below illustrates the long term average differen ces between all of the PM 2.5 

### monitors in Arizona For all days in 2006,

2007 and 2008.

Table 4: Annual average ambient PM 2.5 

### concentrations at the Cowtown monitoring site and other PM 2.5 

### monitoring sites. In 2006 the annual average

PM 2.5 

### concentration at Cowtown was 22.74  g/m 3, 

### while the next highest annual average PM 2.5 

### concentration was 5.31  g/m 3 

### from Apache Junction. This substantial difference in average ambient PM 2.5 

### concentrations continues through

2007 and 2008.

PM2.5 Measurements ( μ g/m 3 )

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 PM 2.5 

### concentrations at Cowt own for days where the

concentration exceeded the 35  g/m 3 

### standard, again indicates highly localized PM 2.5 

### values at Cowtown independent of meteorol ogical conditions. A comparative analysis of a sample of 30 days where the Cowtown monitor exhibits an exc eedance for the three year period is shown table

5 below.

Table 5: Daily ambient PM 2.5 

### concentrations at the Co wtown monitoring site and other Pinal county monitoring sites fo r the 30 exceedance days. For all PM 2.5 

### monitors in the state.

Sample of 30 Exceedance Days of PM2.5 ( μ g/m 3 )

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 20 08 and some monitors were flagged for invalid data on the day of the Cowtown monitor exceedance.



### A statistical analysis was performe d between the Pinal County ambient PM 2.5 

### monitored values: Cowtown, Casa Grande and Apache Junction. The av erage measured 24-hr averages are plotted in the figure 13 below and indica te that the average 24-hour PM 2.5 

### concentrations are more than twice that of Casa Grande and Apache Junction.

Figure 13: A comparison of average 24-hour PM 2.5 

### 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

SiteConcentration (μg/m3)

Cowtown
Apache Junction

Casa Grande



### 28

### Ira Domsky February 22, 2010

Paired t-tes ts were performed to test the statistical significance of the difference between the

measured concentrations at the three sites. The fi rst set of three paired t-tests (tables 6,7 and 7)

use all available PM 2.5 

### data from 2006, 2007 and 2008, while the second set of th ree (tables 9,10 and 11) are paired t-tests using only the PM 2.5 

### 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% c onfidence interval. The detailed results of the

t-tests are:

Table 6: Paired t-test between Cowtow n and Casa Grande, for all data 2006,

2007 and 2008.

Cowtown Casa Grande

Mean 21.65 9.34

Variance 167.99 20.30

Observation s 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 Cowtow n 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

Observation s 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 Gr ande 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 PM 2.5 

### monitoring sites. If the assumption is made that l ong range transport is the source of the PM 2.5 

### exceedances in Pinal County, one would also need to assume th at the exceedances would be more regional in nature, and therefore any statistical analysis on PM 2.5 

### monitor data from Pinal County would show considerable agreement among the monitors. Th e paired t-tests do not in dicate 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 Apach e Junction, while the paired t-test for Casa Grande and Apache Junction show reasonable agreem ent. The same is true when looking at only the 30 exceedance days.

31

### Ira Domsky February 22, 2010

Data Analysis - PM 2.5 

### is a function of PM 10 

### The PM 2.5 

### problem in western Pinal County is rela tively unique because, unlike virtually all

other areas of the United States experiencing nonattainment of the PM 2.5 

### 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

PM 2.5 

### exceedances at the Cowtown monitor are largely the result of the numerous and extreme

exceedances of the PM 10 

### NAAQS.

First, with only one exce ption, all of the exceedan ces of the 24-hour average PM 2.5 

### NAAQS of

35  g/m 3 

### occur only when there is also an exceedance of the PM 10 

### NAAQS of 150  g/m 3 

### , as the following table 9 illustrates:

Table 9: Comparison of PM 10 

### and PM 2.5 

### Values for Valid PM 2.5 

### Exceedances

for the Cowtown Monitor

2006 2007 2008 All Years

PM 10 

### (  g/m 3 

### ) 380.5 353.6 322.6 358.2

PM 2.5 

### (  g/m 3 

### ) 45.1 46.5 39.0 44.1 Average

Percent PM 2.5 

### 12.6% 16.1% 12.8% 13.7%

PM 10 

### (  g/m 3 

### ) 606.0 759.3 465.0 759.3

PM 2.5 

### (  g/m 3 

### ) 69.4 59.7 41.7 69.4 Maximum

Percent PM 2.5 

### 20.4% 32.7% 18.2% 32.7%

PM 10 

### (  g/m 3 

### ) 249.0 182.6 217.0 182.6

PM 2.5 

### (  g/m 3 

### ) 36.2 37.0 35.9 35.9 Minimum

Percent PM 2.5 

### 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 PM 2.5 

### concentration was recorded as

35.8  g/m 3 

### and PM 10 

### was 86  g/m 3 

### . 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 cont ained in Appendix A of this memorandum.

In addition to this assessment, linear regression analysis was performed to determine the degree

to which PM 10 

### concentrations and meteorological factors explained the variation in PM 2.5 

### concentrations. Data from calendar years 2006 through 2008 and Ja nuary through June 2009 were included in this analysis. Meteorologi cal data were taken from the AZMET Maricopa station because there was a complete record for th at 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 locat ed approximately 4 miles north of the Cowtown

32

### Ira Domsky February 22, 2010

monitor (Latitude 33° 00' 38" N, Longitude 111° 58' 19" W ). Meteorologic al 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 wi nd 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 vari ables, which include s calculating the estim ate for a non-zero constant (y=ax+b), or through the origin (i ntercept of zero, or y=ax). Because PM 2.5 

### is a subset

of PM 10 

### , the potential that none of the pa rticulate matter would be in the PM 2.5 

### fraction is highly unlikely; i.e., performing a lin ear regression with an interc ept other than zero would be inappropriate. PM 10 

### concentration and several meteorologi cal variables were evaluated using scatter plots to determine if nonlinear relationships were evident (non e were found), and then regressed with PM 2.5 

### as the dependent variable. PM 10 

### , by far, explained more variation in PM 2.5 

### than any other variable. The only other variable that explained a signific ant amount of variation

in PM 2.5 

### 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 PM 2.5 

### concentrations) were significantly correlat ed with the actual PM 2.5 

### concentration. Consequently, th e only reliable model that could be developed was for PM 2.5 

### as a function of PM 10 

### . The results of that regression an alysis are presented in table 10:

Table 10: A linear regression analysis to determine the degree to which PM 10 

### concentrations and meteorological f actors explained the variation in PM 2.5 

### concentrations.

Regression Statistics
Multiple R 0.9192
R Square 0.8448

Ad j usted R Square 0.8398

Standard Error 9.30858

Observations 199

AN O VA

df SS MS F Significance F
Regression 1 93417.61 93418 1078.11 7.92E-82

Residual 198 17156.62 86.65

Tot a l 199 110574.23

Co efficients Standard Error t Statistic P-value
Intercept 0 #N/A #N/A #N/A

PM 10 0.1051 0.00320 32.83 4.7E-82



### 33

### Ira Domsky February 22, 2010

Figure 15, below, presents a scatter pl ot with the regression line, the 95% confidence intervals for the regre ssion line and the 95% one-tailed upper predic tion interval.

PM 2.5 as a Function of PM 10 for Cowtown Monitor

January 2006 through June 2009

PM 2.5 = 0.1051*PM 10 Adjusted r 2 = 0.8398

0 10 20 30 40 50 60 70

0 100 200 300 400 500 600 700 800

PM 10 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 PM 10 

### that is

PM 2.5 

### 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 respec t to emissions from specific s ources on certain days, and smoke from local fires (which would result in a much higher PM 2.5 

### proportion in that sample), to name a few. Regardless, this model indicates that, with 95% confidence, PM 2.5 

### concentrations are largely driven by the amount of PM 10 

### in the atmosphere.

Conclusions

The Cowtown monitoring site lies within an ir rigated desert plain, with active and retired agricultural operations to the nor th, northwest and east. Three cattle feedlot companies and a grain-processing complex operate to the south, southwes t 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 (includi ng unpaved roads and agricultu ral soils) following second.

34

### Ira Domsky February 22, 2010

T he fo llowing 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 PM 2.5 

### 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 contribu tor; other sites are geologic soil.

 Stagnation during the 2003 study produ ced high relative contributions

from feedlot dust at Cowtown.

 Both stagnation and non-stagnati on events created elevated PM 2.5 

### 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 so urces along the back trajectory which would be expected to c ontribute to any of the PM 2.5 

### exceedances at

Cowtown.

 On a daily and annual basis, Cowtown consistently measures higher PM 2.5 

### then any other monitor in Pinal County.

 PM 2.5 

### exceedances are always concurrent with PM 10 

### exceedances.

35

### Ira Domsky February 22, 2010

REFERENCES

Pinal County Air Quality Control Dist rict Source Apportionment Study. (2005).

Clem ents, A.L., Jia, Y., Fraser, M.P., He rckes, P., Sundblom, M., & Solomon, P.A., (2010). Chemical Analysis for Source Attribution of Fine and Coarse par ticulate 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 a nd NIOSH Carbon Measurements.

Chow, J.C., Watson, J.G., Kuhns, H., Etyem ezian, 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 Ae rosol Visibility and Observational study.

36

### Ira Domsky February 22, 2010

APPENDIX A

37

### Ira Domsky February 22, 2010

38