Arizona Department of Environmental Quality 

Analysis of PM2.5 Exceedances in Pinal County Arizona: 

Demonstration that PM2.5 Concentrations are Driven by Local Sources of 

PM10 Near the Cowtown Monitor 

March 15, 2010 

Air Quality Division 

Arizona Department of Environmental Quality 

Phoenix, AZ 85007
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TABLE OF CONTENTS 

Page

SUMMARY.............................................................................. .......................................... 1 

INTRODUCTION........................................................................................................................ 2 

SOUCE APPORTIONMENT STUDY........................................................................................ 3 

METEOROLOGICAL ANALYSIS................................................... ....................................... 15 

 

DATA ANALYSIS ................................................................................................................... 27 

CONCLUSIONS........................................................................................................................ 35 

LIST OF FIGURES Page 

1 Chemical Fingerprint – Feedlot Dust, PM2.5 .............. .................................................... 4 

2 Chemical Fingerprint – Road Dust, PM2.5 ..........................................................5 

3 Chemical Fingerprint – Agricultural Dust, PM2.5 ................................................5 

4 Ambient Samples– Cowtown, October and November 2003....................................7 

5 Cowtown Average PM2.5 Source Contributions ...................................................9 

 

6 Coal Fired Power Plant Carbon Fraction Signatures............................................11 

 

7 Casa Grande Average PM2.5 Source Attributions................................................13 

 

8 Coolidge Average PM2.5 Source Contributions...................................................13 

9 Pinal County Housing Average PM2.5 Source Contributions...................................14 

10 Stanfield Average PM2.5 Source Contributions ..................................................14 

11a HYSPLIT Model Design Day January 11, 2006.................................................20 

11b HYSPLIT Model Design Day February 22, 2006...............................................21 

11c HYSPLIT Model Design Day June 16, 2006.....................................................22 

11d HYSPLIT Model Design Day October 2, 2006...................................................23 

11e HYSPLIT Model Design Day October 9, 2007...................................................24 

11f HYSPLIT Model Design Day March 25, 2008...................................................25 
11g HYSPLIT Model Design Day June 23, 2008......................................................26 

12 Annual Average PM2.5 Concentrations 2006-2008................................................28 

13 24-hour Average PM2.5 Concentrations 2006-2008................................................29 

14 PM2.5 as a Function of PM10 for Cowtown Monitor...........................................................35 

LIST OF TABLES Page

1 Cowtown Monitor Exceedance Days 2006-2008................................................2 

2 Pinal County Chemical Source Profiles............................................................4 

3 Effect of Meteorological Conditions on PM2.5 Concentrations at the Cowtown............ 17 

 Monitor 

 

4 Arizona Annual Average PM2.5 Concentrations 2006-2008.....................................28 

5 24-hour Average PM2.5 Concentrations on Exceedance Days.................... ................ 29 

6 Paired T-Test – Cowtown and Casa Grande Monitors .........................................30 

7 Paired T-Test – Cowtown and Apache Junction Monitors.....................................30 

8 Paired T-Test – Casa Grande and Apache Junction Monitors (2006-2008)...................31 

 

9 Paired T-Test – Casa Grande and Apache Junction Monitors (Exceedance Days)...........31 

10 Paired T-Test – Casa Grande and Apache Junction Monitors (Exceedance Days)...........32 

 

11 Paired T-Test – Casa Grande and Apache Junction Monitors (Exceedance Days)...........32 

12 Comparison of PM10 and PM2.5 Values on Exceedances Days at the Cowtown..............33 

 Monitor 

13 Linear Regression Analysis – Particulate Matter Concentrations and Meteorological......34 

 Conditions 

APPENDICES 

Appendix A Memorandum from Pinal County Air Quality Control District Regarding Data 

 Invalidation 
1

Analysis of PM2.5 Exceedances in Pinal County Arizona: 

Demonstration that PM2.5 Concentrations are Driven by Local Sources of 

PM10 Near the Cowtown Monitor

Summary 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

the monitor. 

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

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

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

a 2005 source apportionment study1

 carried out by the Pinal County Air Quality Control District 

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

meteorology data, and back trajectory analysis will each demonstrate that local sources of PM10

are the most significant contributors to elevated PM2.5 concentrations at the monitor. One of the 

major implications of this analysis is that no PM2.5 specific control measures will be necessary to 

attain the NAAQS; i.e., controls capable of attaining the PM10 NAAQS will result in attainment 

of the PM2.5 NAAQS. Further, these findings would support a recommendation to establish 

PM2.5 nonattainment area, if it is at all necessary, covering only that geographic area directly 

affecting the Cowtown monitor and nearby areas with the same primary sources of PM10 and, 

thus, PM2.5. 

 

1

 PCAQCD staff, “Pinal County Air Quality Control District Source Apportionment Study,” July 29, 2005 
2

Introduction 

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

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

understand ambient PM2.5 pollutant concentrations in Pinal County, AZ. the objective of this 

paper is to discuss the factors which have likely contributed to the 29 PM2.5 exceedances 

recorded at the Cowtown monitor during 2006, 2007, and 2008. 

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

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

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

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

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

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

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

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

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

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

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

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

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

2006, 2007, and 2008. 

RUN DATE PM2.5 RUN DATE PM2.5

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

* Hyponex fire 
3

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

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

Hwy. (Lat. N 33°63’ Long. W 111°58’). The site lies within the irrigated desert plain, with active 

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

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

southwest, and southeast. The source makeup near the Cowtown monitor suggests that nearby 

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

any boundary recommendation must take into consideration these nearby sources. Most existing 

PM2.5 nonattainment areas within the United States are affected by long-range transport of 

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

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

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

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

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

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

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

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

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

concentrations at Cowtown. 

Source Apportionment 

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

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

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

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

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

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

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

Source Apportionment Study” and are summarized in this memorandum. 

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

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

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

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

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

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

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

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

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

profiles or “fingerprints” for Pinal County soils and feedlots. 
4

Table 2: The source apportionment modeling required chemical source profiles 

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

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 

2

roadside exhaust 

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

piles 

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

AmSulf Ammonium Sulfate Secondary ammonium sulfate 

AmNitr Ammonium Nitrate Secondary ammonium nitrate 

Other Unclassified Sources not modeled 

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

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

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

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

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

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

fingerprints were acquired from existing source profiles. 

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

presented in Figures 1, 2 and 3 below. 

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

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

October and November 2003 and analyzed 

 

2

 BRAVO - Big Bend Regional Aerosol and Visibility Observational Study
5

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

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

monitor, during October and November 2003. 

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

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

monitor, during October and November 2003. 

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

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

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

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

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

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

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

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

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

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

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

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

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

behind. The OC1, OC2, OC3, OC4, OPT, EC1, EC2 and EC3 designations refer to eight 
6

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 

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

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

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

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

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

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

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

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

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

sample, are less conspicuous. 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

insufficient for apportioning sources. 

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

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

relationship based on an effective-variance least squares method (EVLS)3

, by analyzing and 

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

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

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

categories at each monitor. 

 

3

 http://www.epa.gov/scram001/receptor_cmb.htm 
7

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). 

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

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

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

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

feedlots and is the major component of the eight source types presented in Table 2. The CMB 

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

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

Cowtown monitor. 
8

The average source category contributions from the remaining four monitoring sites were 

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

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

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

for each of the remaining four monitoring sites. It is also worth mentioning, that the PM10

samples from the Cowtown monitor were also dominated by feedlot emissions, which supports 

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

PM2.5 exceeds the standard, so does PM10. 

Source Apportionment - Primary Components of Cowtown PM2.5 

Nine 24-hour ambient PM2.5 samples were collected from the Cowtown monitoring site during 

October and November 2003. The nine ambient samples were speciated and modeled using the 

CMB model to estimate the relative source category contributions at the Cowtown monitor. 

Based on the ambient sample data and CMB modeling, the most abundant source category for 

PM2.5 was feedlot soils with an average contribution of 49% of the total. The second highest 

source contribution was the geological soil category with an average contribution of 24%. 

Cowtown was the only monitor in the Pinal County study area where the geological soil category 

did not have the highest source contribution. (See Figure 5) The average 24-hour PM2.5

concentration at Cowtown was also well above any other Pinal County site at 67g/m3

, as 

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

concentration above 100 g/m3

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

all three dates, the meteorological data indicates inversion conditions with light winds 

throughout the day, which further indicates that distant sources are not likely to have been major 

contributors to the highest recorded days during this study period. 

The coal-fired power plant source category was the third most abundant category with an 

average relative contribution of 9%. This contribution from the coal power plant source category 

is a bit perplexing, however. According to the most updated state-wide emissions inventory, the 

nearest coal power plant is approximately 82 miles to the south east of the Cowtown monitor, 

and although it is possible for stack emissions to transport long distances, the stagnant 

meteorological conditions which were evident on the highest recorded PM2.5 days, would likely 

have served to mitigate or even prevent any long range pollutant transport from affecting the 

Cowtown monitor on those days. Given that several exceedances were recorded on stagnant 

days, the relative contribution from the coal power plant source category is expected to be less 

than the third most abundant. This is particularly true at the Cowtown monitor, since it is located 

near several local sources of PM10/2..5 and just 50 meters to the north of the Union Pacific 

Railroad tracks. However, although it is the third most abundant source category, the relative 

contribution from the power plant signature is only 9%, while geologic dust and feedlots together 

comprise 73% of the total PM2.5. 
9

Figure 5: Average source contributions predicted by the CMB model at the 

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

the predominant category at Cowtown. 

It is possible; however, that the coal power plant profile was confused with the locomotive 

emission profile or even with feedlot emissions, since both locomotives and feedlots, like power 

plants, can have high measures of total carbon (TC) in their speciation profiles. However, 

locomotive sources were not modeled in the 2003 CMB analysis, so a locomotive emission 

profile was not readily available for comparison to the coal fired power plant profile. In any 

event, coal power plant emissions typically have more traceable heavy metals like selenium (Se) 

in the exhaust stream, which would make it difficult to confuse diesel exhaust from locomotives 

with coal power plant emissions. In fact, locomotive emissions may be more likely confused 

with heavy duty diesel vehicle emissions. But, if the locomotive emission profile were mixed-in 

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

elevated PM2.5 concentrations, since vehicle emissions are only on average 3% of the total 

ambient PM2.5. However, no evidence appears to support that locomotive emissions have been 

confused with any of the seven source profiles, including the coal power plant source category. 

It is also unlikely that the coal fired power plant signature was confused with feedlot emissions. 

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

study are substantially different then that of feedlot signature. Figure 6 below illustrates the 

BRAVO carbon profiles for the three power plants. Each profile is different than that of the 

feedlot profile illustrated in Figure 1. The feedlot signature measures an abundance of TC and 

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

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

 
10

One explanation may be that the coal power plant category is entirely erroneous. The CMB 

modeling in the PCAQCD source apportionment report made use of both source specific 

chemical profiles for geologic and feedlot dust in addition to source profiles from the BRAVO 

study. As part of the BRAVO study, six coal power plants were profiled and all but two (plant 

numbers two and three) used lignite coal as a fuel, which is not used as a fuel in Arizona. 

However, the PCAQCD study, used three of the BRAVO coal power plants signatures, plant one 

(CFPP1), plant two (CFPP2) and plant three (CFPP3). CFPP2 and CFPP3 are described in the 

BRAVO study as using low-sulfur Western coal as fuel, similar to the fuel used in Arizona coal 

plants. However, CFPP1 has no such description, leaving some uncertainty as to what type of 

fuel CFPP1 uses. Furthermore, the PCAQCD report makes no mention of how the CMB model 

made use of the three profiles. So, the coal power plant contribution at Cowtown and in Pinal 

County should likely be considered unknown. 

ADEQ has no data that suggest or supports that emissions from gas- or coal-fired power plants 

contribute to the PM2.5 problem in Pinal County. Transport of windblown PM2.5 to the Cowtown 

monitor has not been documented in Pinal County. To the contrary, the 2005 PCAQCD source 

apportionment report estimated that, on average, 49% of PM2.5 at the Cowtown monitor 

originates from feedlots. While this study concludes that coal fired power plants may have 

contributed to the PM2.5 concentrations measured at Cowtown: 

 The coal power plant proportion is small when compared to the portion of 

particulate apportioned to dust from soil and feedlots (the feedlot proportion 

increases with increasing PM2.5 concentration) and is smaller when exceedances 

of the 24-hour average standard is exceeded; 

 The most important marker of long-range emissions transport from coal power 

plants is secondary sulfate particles, the presence of which in the ambient 

samples was not at all in proportion to the amount of particulate attributed to coal 

power plants; 

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

source apportionment analysis and the results relating to those sources (e.g., 

feedlot dust, soil, vegetative burning), but power plant signatures are poorly 

documented and significant nearby sources, such as railroad locomotives, were 

not included, which reduces the reliability of the results regarding power plants; 

 Even if one assumes the coal power plant apportionment is correct, it is 

evidenced at all of the Pinal County sites, not just the only violating monitor, 

Cowtown; and 

 Trajectory analyses described later in this paper show that for the range of 

meteorological conditions when exceedances of the 24-hour average standard are 

measured, winds in most cases would be carrying emissions from these plants (85 

to 140 miles southeast of the violating monitor) away from the monitor rather 

than towards it. 
11

Figure 6: Coal fired power plant (CFPP) carbon fraction signatures according to the 

BRAVO speciation profiles and the feedlot profile from the PCAQCD study. 

CFPP1

0

20

40

60

80

OC1 OC2 OC3 OC4 OP EC1 EC2 EC3 TC

Chemical Species

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
12

Source Apportionment - Comparison with other PM2.5 monitors 

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

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

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

Stanfield. 

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

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

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

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

this monitor. 

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

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

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

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

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

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

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

are the top three source categories contributing to this monitor. 

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

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

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

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

and feedlot emissions tied for third. 

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

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

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

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

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

source categories contributing to this monitor. 

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

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

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

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

exception, again, is the Cowtown monitor, where feedlot emissions are dominant. 
13

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

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

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

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

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

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

Figure 8 

Figure 7 
14

 Figure 9 

Figure 10 
15

Meteorological Analysis 

Site Geography

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

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

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

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

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

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

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

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

Site Climatology

Temperature 

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

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

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

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

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

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

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

Precipitation

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

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

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

with increase moisture from Mexico). 

Wind

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

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

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

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

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

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

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

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

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

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

storms can develop anywhere, winds can come from any direction. As storms become severe, 
16

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

Inversions 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

trapped near the surface for several days at a time. 

Stagnation

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

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

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

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

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

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

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

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

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

until Arizona Monsoon pattern sets up. 
17

PM2.5 Exceedance Days from 2006-2008

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

* Hyponex fire 

Exceedances by Season 

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

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

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

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

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

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

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

Exceedances by Wind Event/Stagnation 

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

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

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

relatively light horizontal winds that could be classified under stagnant conditions. The final 
18

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

neither be classified as wind or stagnation event. 

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

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

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

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

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

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

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

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

 

Back Trajectory Analysis 

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

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

Trajectory) model. The model is capable of computing air parcel trajectories in either a forward 

or backward mode and is a common tool used to examine long range transport. Forward 

trajectories can compute the path forward of an air parcel as it leaves a known point, while back 

trajectories can compute the path an air parcel traversed to get to a known point. The HYSPLIT 

model was used to compute back trajectories for each of seven design days: January 11, 2006, 

February 22, 2006, June 16, 2006, October 2, 2006, October 9, 2007, March 25, 2008, and June 

23, 2008. The model results are used to estimate which sources may have contributed to the 

PM2.5 exceedances at Cowtown on those days. The seven design dates represent seasonal high 

wind and low wind episodes for 2006, 2007 and 2008 on days when the Cowtown monitor 

exceeded the PM2.5 standard. 

For each day, HYSPLIT was used to compute a midnight, morning, afternoon and evening back 

trajectory. Each trajectory represents an air parcel as it moves through the atmosphere over 

twenty four hours, starting at 2300 hours and adjusted back every six hours for a total of 4 

trajectories for each of the seven days. Each trajectory moves backwards from the end point at 

Cowtown to the, yet unknown, starting point, twenty four hours earlier. But, for ease of 

interpretation, it may be more practical to discuss the trajectories as moving forward, even 

though the HYSPLIT model calculated the paths in reverse. 

If one were to consider only the yellow line, in Figure 11a below, and move forward, the air 

parcel begins near Chandler, AZ at 6AM on January 10, 2006 and travels south for most of the 

day. The parcel then turns toward the north near Cowtown and at 6AM January 11, 2006 (twenty 

four hours later), the air parcel arrives at Cowtown. The green trajectory is next in the series and 

also begins near Chandler, AZ. But, this trajectory begins at 12PM on January 10, 2008 and 

travels south for most of the day, until it changes direction to the west and arrives at Cowtown at 

12PM on January 11, 2006. The blue trajectory begins at 6PM on January 10, 2006 and arrives at 

Cowtown at 6PM January 11, 2006. The red trajectory begins at midnight January 10, 2006 and 

arrives at midnight January 11, 2006. Each of the trajectories for the seven design days begin at 
19

the same times (6AM, 12PM, 6PM and 12AM) and travel for 24 hours, for each date. On this 

day, most of the winds were coming from the north and east and due to stagnant conditions, the 

winds were moving very slowly. On most other days, the typical wind direction is from the 

southwest, as seen in the remaining HYSPLIT analyses. 

The January 11th modeling results indicate that the majority of 24-hour trajectories do not 

traverse any likely PM2.5 sources until the air parcel nears the Cowtown monitor. For example, 

on June 23, 2008 (Figure 11g), each trajectory begins in the south-southwest area of the state and 

travels steadily towards the Cowtown monitor. Since no obvious PM2.5 sources are located along 

the trajectory (beyond the area near Cowtown) only local PM2.5 sources could have caused the 

exceedance on that day. The same is true for June 16, 2006 (Figure 11c), October 2, 2006 

(Figure 11c), October 9, 2007 (Figure 11e), and March 25, 2008 (Figure 11f), only when the 

parcel nears the Cowtown monitor, does the air parcel traverse any PM2.5 sources. However, 

January 11, 2006 (Figure 11a) and February 22, 2006 (Figure 11b) are somewhat different from 

the other five days, because some trajectories begin near the Phoenix and Yuma, urban areas. On 

January 11, 2006 (Figure 11a), the yellow, green and red trajectories originate from the Phoenix 

metropolitan area and on February 22, 2006 (Figure 11b) the yellow and green trajectories begin 

near Yuma, AZ. The January and February 2006 trajectories may indicate some tendency for air 

pollution from these urban 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 PM2.5 source(s). 

When the air parcel nears the Cowtown monitor, however, the majority of the trajectories will 

traverse nearby feedlot and agricultural sources. 

The HYSPLIT model was also used to investigate the wind flows nearby the Cowtown monitor 

during times when the PM2.5 monitor was measuring elevated hourly concentrations. The hourly 

PM10 data profiles (Continuous PM2.5 was not available, so PM10 is used as a surrogate for PM2.5) 

are provided for each figure. Detailed HYSPLIT output images illustrating the time varying air 

parcel trajectories layered on top local and distant sources is provided for each modeled day and 

are illustrated in the following figures (11a-h): 
20
21
22
23
24
25
26
27

Data Analysis - Medium and long range transport is not occurring

Long and medium range transport of Primary PM generally creates region wide impacts due to 

down wind horizontal plume spreading. This is a well known component of dispersion and 

becomes particularly evident in ambient PM monitoring data for air pollution monitors which are 

located within the same airshed and in close proximity to each other. In Arizona, there are 

several PM2.5 monitors in the state, including: Casa Grande, Apache Junction, Douglas, Orange 

Grove, Prescott Valley, and Yuma. Of these sites, Casa Grande is the nearest to Cowtown at 

approximately 24 km while Yuma is the farthest at 253 km. The Cowtown, Casa Grande and 

Apache Junction monitors are all in the same Gila River basin airshed and in terms of potential 

impacts from long range transport, would all likely to be impacted by the same plume. 

The relative distance from the Cowtown monitor for Casa Grande and Apache Junction are 24 

km and 64 km, respectively. If one were to assume Pasquill-Gifford rural horizontal dispersion 

(see figure 12) from a source 100 km from Cowtown and south-westerly winds, which is the 

predominant wind direction for each of the seven days modeled in the back trajectory analysis, it 

is easy to see that both Cowtown and Casa Grande should be equally impacted by the distant 

source. This is because the horizontal plume spread (σy) is expected to be large enough to impact 

both sites (figure 13). Based on each meteorological stability class, A-F, a source 100 km away 

from Cowtown is likely to produce a plume with a σy ranging from 20 km to 100 km. Since the 

Casa Grande monitor is only 24 km from the Cowtown monitor, one would estimate that long 

range transport from any direction would likely impact the two monitors. Of course, for the 

impacts to be equal at each site, one would need to assume a perpendicular incident angle of 

impact to the line drawn between Cowtown and Casa Grande, but varying the incident angle in 

either direction would only likely create marginal variations in relative impacts at each site, since 

the deposition rate of PM2.5 is slow and the two monitors are close together. 

Figure 12: Pasquill-Gifford σy, rural horizontal dispersion parameters. A-F are 

meteorological stability classes, where A is extremely unstable, B is moderately 

unstable, C is slightly unstable, D is neutral, E is slightly stable, F is moderately 

stable. 
28

The Pasquill-Gifford dispersion parameters are theoretical and except for the use of stability 

classes, it does not include provisions for real world actions: like chemical conversion, 

deposition and removal, wind speed, surface roughness and a whole suite of meteorological 

variables which will directly influence the plume spread and PM concentrations downwind. 

Furthermore, the six stability classes do not represent the real atmosphere. Real dispersion varies 

continuously throughout the atmosphere, from stable to unstable and not stepwise as the A-F 

stability classes do. So, some variation in the relative PM impacts at each monitor is expected, 

but the general principal remains the same; distant sources from Cowtown will likely impact 

both Cowtown and Casa Grande, and on some occasions impact all three monitors, including 

Apache Junction. 

If long or medium range transport is the source of the exceedances at Cowtown, a statistical 

analysis of the data collected from each of the three Pinal County monitoring sites should 

demonstrate that the ambient concentrations of the three sites are similar. The following analysis 

compares the ambient PM2.5 concentrations from Cowtown to the ambient PM2.5 concentrations 

from the nearby Casa Grande monitor, and as a supplement, to the more distant monitors at: 

Apache Junction, Douglas, Orange Grove, Prescott Valley, and Yuma for three years spanning 

from 2006 to 2008. 

The comparison between the Casa Grande PM2.5 Monitor and the Cowtown monitor reveals a 

significant difference in measured concentrations on both daily average and annual average 

PM2.5. Over a period of three years from 2006 to 2008, the Cowtown monitor consistently 

measured more than double the ambient concentrations at Casa Grande, which implies that the 

impacts at that monitor are not from distant sources, but are from local sources of particulate 

pollution. Table 4 below illustrates the long term average differences between all of the PM2.5

monitors in Arizona For all days in 2006, 2007 and 2008. 

Table 4: Annual average ambient PM2.5 concentrations at the Cowtown 

monitoring site and other PM2.5 monitoring sites. In 2006 the annual average 

PM2.5 concentration at Cowtown was 22.74 g/m3, while the next highest annual 

average PM2.5 concentration was 5.31 g/m3

 from Apache Junction. This 

substantial difference in average ambient PM2.5 concentrations continues through 

2007 and 2008. 

PM2.5 Measurements (μg/m3)

Site Site ID 2006 2007 2008 3 Yr Annual Avg

Cowtown 04-021-3013 22.74 22.5 19.63 21.62

Apache Junction 04-021-3002 5.31 6.96 7.52 6.60

Casa Grande 04-021-0001 7.55 10.25 10.61 9.47

Douglas 04-003-1005 6.78 6.79 6.84 6.80

Orange Grove 04-019-0011 5.8 5.84 5.57 5.74

Prescott Valley 04-025-2002 - - 6.42 -

Yuma 04-027-0004 - - 10.18 -
29

Close inspection of monitored PM2.5 concentrations at Cowtown for days where the 

concentration exceeded the 35 g/m3 standard, again indicates highly localized PM2.5 values at 

Cowtown independent of meteorological conditions. A comparative analysis of a sample of 30 

days where the Cowtown monitor exhibits an exceedance for the three year period is shown 

Table 5 below. 

Table 5: Daily ambient PM2.5 concentrations at the Cowtown monitoring site and 

other Pinal county monitoring sites for the 30 exceedance days. For all PM2.5

monitors in the state. 

Sample of 30 Exceedance Days of PM2.5 (μg/m3)

Site Site ID Sample Average Observations*

Cowtown 04-021-3013 43.8 30

Apache Junction 04-021-3002 8.1 25

Casa Grande 04-021-0001 11.8 29

Douglas 04-003-1005 9.5 27

Orange Grove 04-019-0011 6.1 25

Prescott Valley 04-025-2002 7.2 7

Yuma 04-027-0004 13.8 5

*Not all monitors were in operation prior to 2008 and some monitors were flagged for invalid 

data on the day of the Cowtown monitor exceedance. 

A statistical analysis was performed between the Pinal County ambient PM2.5 monitored values: 

Cowtown, Casa Grande and Apache Junction. The average measured 24-hr averages are plotted 

in the Figure 13 below and indicate that the average 24-hour PM2.5 concentrations are more than 

twice that of Casa Grande and Apache Junction. 

Figure 13: A comparison of average 24-hour PM2.5 concentrations between 

Cowtown, Apache Junction and Casa Grande. 

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

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

Site

Concentration (μg/m3)

Cowtown

Apache Junction

Casa Grande
30

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

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

use all available PM2.5 data from 2006, 2007 and 2008, while the second set of three (Tables 9,10 

and 11) are paired t-tests using only the PM2.5 data from the 30 exceedance days. Results indicate 

the differences in the measured concentrations at Cowtown are statistically significant compared 

to Casa Grande and Apache Junction at the 95% confidence interval. The detailed results of the 

t-tests are: 

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

2007 and 2008. 

 Cowtown Casa Grande 

Mean 21.65 9.34 

Variance 167.99 20.30 

Observations 159 159 

Pearson Correlation 0.44 

Mean Difference 12.30 

Maximum Difference 55.26 

Minimum Difference -6.80 

df 158 

t Stat 13.263 

P(T<=t) one-tail 9.25E-28 

t Critical one-tail 1.655 

P(T<=t) two-tail 1.85E-27 

t Critical two-tail 1.975 

Table 7: Paired t-test between Cowtown and Apache Junction, for all data 2006, 

2007 and 2008. 

 Cowtown Apache Junction 

Mean 21.05 6.65 

Variance 153.69 9.94 

Observations 150 150 

Pearson Correlation 0.35

Mean Difference 14.40

Maximum Difference 55.70

Minimum Difference -6.00

df 149

t Stat 15.127

P(T<=t) one-tail 3.28E-32

t Critical one-tail 1.655

P(T<=t) two-tail 6.55E-32

t Critical two-tail 1.976 
31

Table 8: Paired t-test between Casa Grande and Apache Junction, for all data 

2006, 2007 and 2008. 

 Casa Grande Apache Junction 

Mean 9.41 6.75 

Variance 21.39 9.97 

Observations 144 144 

Pearson Correlation 0.55 

Mean Difference 2.65 

Maximum Difference 22.60 

Minimum Difference -6.50 

df 143 

t Stat 8.122 

P(T<=t) one-tail 9.70E-14 

t Critical one-tail 1.656 

P(T<=t) two-tail 1.94E-13 

t Critical two-tail 1.977 

Table 9: Paired t-test between Casa Grande and Apache Junction, for only the 30 

exceedance days. 

 Casa Grande Cowtown 

Mean 11.6034 44.0692 

Variance 27.8040 57.3325 

Observations 29 29 

Pearson Correlation -0.1982 

Mean Difference 32.4658 

Maximum Difference 55.2633 

Minimum Difference 19.2 

df 28 

t Stat -17.4001 

P(T<=t) one-tail 7.55652E-17 

t Critical one-tail 1.7011 

P(T<=t) two-tail 1.5113E-16 

t Critical two-tail 2.0484 

Table 10: Paired t-test between Casa Grande and Apache Junction, for only the 

30 exceedance days. 
32

 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) two-tail 2.57601E-18 

t Critical two-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) two-tail 0.000142657 

t Critical two-tail 2.0639 

The data analysis makes a clear distinction between the three Pinal County PM2.5 monitoring 

sites. If the assumption is made that long range transport is the source of the PM2.5 exceedances 

in Pinal County, one would also need to assume that the exceedances would be more regional in 

nature, and therefore any statistical analysis on PM2.5 monitor data from Pinal County would 

show considerable agreement among the monitors. The paired t-tests do not indicate that level of 

agreement, however. For all data, the paired t-test indicates that the Cowtown monitoring site is 

very different from both Casa Grande and Apache Junction, while the paired t-test for Casa 

Grande and Apache Junction show reasonable agreement. The same is true when looking at only 

the 30 exceedance days. 

Data Analysis - PM2.5 is a function of PM10

The PM2.5 problem in western Pinal County is relatively unique because, unlike virtually all 

other areas of the United States experiencing nonattainment of the PM2.5 NAAQS, it does not 
33

appear that primary combustion emissions and secondary particles are dominant for the site 

where the exceedances have been recorded. Both direct and statistical evidence suggests that 

PM2.5 exceedances at the Cowtown monitor are largely the result of the numerous and extreme 

exceedances of the PM10 NAAQS. 

First, with only one exception, all of the exceedances of the 24-hour average PM2.5 NAAQS of 

35 g/m3 occur only when there is also an exceedance of the PM10 NAAQS of 150 g/m3, as the 

following Table 9 illustrates: 

Table 12: Comparison of PM10 and PM2.5 Values for Valid PM2.5 Exceedances 

for the Cowtown Monitor 

2006 2007 2008 All Years 

PM10 (g/m3) 380.5 353.6 322.6 358.2 

PM2.5 (g/m3 Average ) 45.1 46.5 39.0 44.1 

Percent PM2.5 12.6% 16.1% 12.8% 13.7% 

PM10 (g/m3) 606.0 759.3 465.0 759.3 

PM2.5 (g/m3 Maximum ) 69.4 59.7 41.7 69.4 

Percent PM2.5 20.4% 32.7% 18.2% 32.7% 

PM10 (g/m3) 249.0 182.6 217.0 182.6 

PM2.5 (g/m3 Minimum ) 36.2 37.0 35.9 35.9 

Percent PM2.5 6.9% 6.0% 9.0% 6.0% 

Number of Samples 13 9 7 29 

The one exception was for August 27, 2006, for which the PM2.5 concentration was recorded as 

35.8 g/m3 and PM10 was 86 g/m3

. Closer examination of this sample revealed that the original 

filter weight was very likely erroneous. This value has been flagged in AQS as invalid. The 

supporting documentation for this is contained in Appendix A of this memorandum. 

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

to which PM10 concentrations and meteorological factors explained the variation in PM2.5

concentrations. Data from calendar years 2006 through 2008 and January through June 2009 

were included in this analysis. Meteorological data were taken from the AZMET Maricopa 

station because there was a complete record for that site, whereas the meteorological data set for 

the Cowtown monitor contains several data gaps. The AZMET Maricopa monitor (Latitude 33° 

04' 07" N, Longitude 111° 58' 18" W) is located approximately 4 miles north of the Cowtown 

monitor (Latitude 33° 00' 38" N, Longitude 111° 58' 19" W). Meteorological variables evaluated 

included: average, maximum and minimum temperature; average and maximum relative 
34

humidity; daily average, hourly average and hourly maximum wind speed; and daily average 

vector wind speed and direction, and wind direction during the maximum wind hour. 

Multiple linear regression can be done either as a least-squares fit based on the averages of the 

dependent and independent variables, which includes calculating the estimate for a non-zero 

constant (y=ax+b), or through the origin (intercept of zero, or y=ax). Because PM2.5 is a subset 

of PM10, the potential that none of the particulate matter would be in the PM2.5 fraction is highly 

unlikely; i.e., performing a linear regression with an intercept other than zero would be 

inappropriate. PM10 concentration and several meteorological variables were evaluated using 

scatter plots to determine if nonlinear relationships were evident (none were found), and then 

regressed with PM2.5 as the dependent variable. PM10, by far, explained more variation in PM2.5

than any other variable. The only other variable that explained a significant amount of variation 

in PM2.5 concentrations was relative humidity, and maximum relative humidity was a better 

predictor. This model, however, had a significant fault, in that the residuals (difference between 

the predicted and actual PM2.5 concentrations) were significantly correlated with the actual PM2.5

concentration. Consequently, the only reliable model that could be developed was for PM2.5 as a 

function of PM10. The results of that regression analysis are presented in Table 13: 

Table 13: A linear regression analysis to determine the degree to which PM10

concentrations and meteorological factors explained the variation in PM2.5

concentrations. 

Regression Statistics 

Multiple R 0.9192 

R Square 0.8448 

Adjusted R Square 0.8398 

Standard Error 9.30858 

Observations 199 

ANOVA 

 df SS MS F Significance F 

Regression 1 93417.61 93418 1078.11 7.92E-82 

Residual 198 17156.62 86.65 

Total 199 110574.23 

 Coefficients Standard Error t Statistic P-value 

Intercept 0 #N/A #N/A #N/A 

PM10 0.1051 0.00320 32.83 4.7E-82 
35

Figure 14, below, presents a scatter plot with the regression line, the 95% 

confidence intervals for the regression line and the 95% one-tailed upper 

prediction interval. 

PM2.5 as a Function of PM10 for Cowtown Monitor

January 2006 through June 2009

PM2.5 = 0.1051*PM10

Adjusted r2 = 0.8398

0

10

20

30

40

50

60

70

0 100 200 300 400 500 600 700 800

PM10 Conc. (ug/m3)

PM2.5 (ug/m3)

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

Levels of NAAQS

This figure demonstrates that there is still a lot of variability that is not explained within this 

simple statistical model, especially for the outliers – where either the percentage of PM10 that is 

PM2.5 is either much higher or much lower than the statistically estimated average of 10.5%. 

Many of these intervening factors likely include levels of activity at difference sources, wind 

direction and speed with respect to emissions from specific sources on certain days, and smoke 

from local fires (which would result in a much higher PM2.5 proportion in that sample), to name a 

few. Regardless, this model indicates that, with 95% confidence, PM2.5 concentrations are 

largely driven by the amount of PM10 in the atmosphere. 

Conclusions 

The Cowtown monitoring site lies within an irrigated desert plain, with active and retired 

agricultural operations to the north, northwest and east. Three cattle feedlot companies and a 

grain-processing complex operate to the south, southwest and southeast. Given the source mix 

near the Cowtown monitor, it is of little surprise that the major contributor from each of seven 

source categories (geologic soil, feedlot soil, motor vehicle emissions, vegetative burning, coal 

fired power plant, ammonium sulfate and ammonium nitrate) is from the feedlot source category, 

with geologic soil (including unpaved roads and agricultural soils) following second. 
36

The following list presents the major findings of this research: 

 The Cowtown monitor has a prominent chemical signature from nearby 

feedlots. 

 The major source contributors to the PM2.5 exceedances at Cowtown are 

from feedlots and geologic soil. 

 When compared to other monitors in Pinal County, only Cowtown has 

feedlots as the major source contributor; other sites are geologic soil. 

 Stagnation during the 2003 study produced high relative contributions 

from feedlot dust at Cowtown. 

 Both stagnation and non-stagnation events created elevated PM2.5 

concentrations at Cowtown in 2006, 2007 and 2008, which indicate that 

fairly strong local sources can impact the monitor under any 

meteorological condition. 

 The trajectory of winds for each of the 29 days in 2006, 2007 and 2008 

can transport emissions from local sources to the Cowtown monitor. 

 The trajectory of winds for each of the 29 days in 2006, 2007 and 2008 

indicate that there are no distant sources along the back trajectory which 

would be expected to contribute to any of the PM2.5 exceedances at 

Cowtown. 

 On a daily and annual basis, Cowtown consistently measures higher PM2.5 

then any other monitor in Pinal County. 

 PM2.5 exceedances are always concurrent with PM10 exceedances. 

 Any control measures that will achieve attainment of the PM10 NAAQS 

will also yield attainment of the PM2.5 NAAQS. 
37

REFERENCES 

 PCAQCD staff, “Pinal County Air Quality Control District Source Apportionment 

Study,” July 29, 2005 

Clements, A.L., Jia, Y., Fraser, M.P., Herckes, P., Sundblom, M., & Solomon, P.A., 

(2010). Chemical Analysis for Source Attribution of Fine and Coarse particulate Matter in Pinal 

County, AZ. [Presented to PCAQCD] 

Chow, J.C., Watson, J.G., Crow, D., Lowenthal, D.H., Merrifield, T., 

(2001). Comparison of IMPROVE and NIOSH Carbon Measurements. 

 Chow, J.C., Watson, J.G., Kuhns, H., Etyemezian, V., Lowenthal, D.H., & Crow, D., 

Kohl, S.D., Engelbrecht, J.P., Green, M.C., (2004). Source profiles for industrial, mobile, and 

area sources in the Big Bend regional Aerosol Visibility and Observational study. 
1

APPENDIX A 
2