# Arizona Department of Environmental Quality

Analysis of PM 2.5 

### Exceedances in Pinal County Arizona:

Demonstration that PM 2.5 

### Concentrations are Driven by Local Sources of

PM 10 

### Near the Cowtown Monitor



## March 15, 2010

Air Quality Division Arizona Department of Environmental Quality Phoenix, AZ 85007This page is intentionally blank.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, PM 2.5 .............. .................................................... 4

2 Chemical Fingerprint – Road Dust, PM 2.5 ..........................................................5

3 Chemical Fingerprint – Agricultural Dust, PM 2.5 ................................................5

4 Ambient Samples– Cowtown, Oc tober and November 2003....................................7

5 Cowtown Average PM 2.5 Source Contributions ...................................................9

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

7 Casa Grande Average PM 2.5 Source Attributions................................................13

8 Coolidge Average PM 2.5 Source Contributions...................................................13

9 Pinal County Housing Average PM 2.5 Source Contributions...................................14

10 Stanfield Average PM 2.5 Source Contributions ..................................................14

11a HYSPLIT Model Design Day Ja nuary 11, 2006.................................................20

11b HYSPLIT Model Design Day Fe bruary 22, 2006...............................................21

11c HYSPLIT Model Design Day J une 16, 2006.....................................................22

11d HYSPLIT Model Design Day Oc tober 2, 2006...................................................23

11e HYSPLIT Model Design Day Oc tober 9, 2007...................................................24

11f HYSPLIT Model Design Day Ma rch 25, 2008...................................................2511g HYSPLIT Model Design Day J une 23, 2008......................................................26

12 Annual Average PM 2.5 Concentrations 2006-2008................................................28

13 24-hour Average PM 2.5 Concentrations 2006-2008................................................29

14 PM 2.5 as a Function of PM 10 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 PM 2.5 Concentrations at the Cowtown............ 17
Monitor

4 Arizona Annual Average PM 2.5 Concentrations 2006-2008.....................................28

5 24-hour Average PM 2.5 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 Ap ache Junction Monitors (2006-2008)...................31

9 Paired T-Test – Casa Grande and Apach e 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 Apach e Junction Monitors (Exceedance Days)...........32

12 Comparison of PM 10 and PM 2.5 Values on Exceedances Days at the Cowtown..............33
Monitor

13 Linear Regression Analysis – Particulate Matter Concentrations a nd Meteorological......34
Conditions

APPENDICES

Appendix A Memorandum from Pinal County Air Qualit y Control District Regarding Data
Invalidation1



### Analysis of PM 2.5 

### Exceedances in Pinal County Arizona:

Demonstration that PM 2.5 

### Concentrations are Driven by Local Sources of
PM 10 

### Near the Cowtown Monitor

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 2005 source apportionment study 1 carried out by the 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 will each demonstrate that local sources of PM 10 are the most significant c ontributors to elevated PM 2.5 concentrations at the monitor. One of the major implications of this analysis is that no PM 2.5 specific control measures will be necessary to attain the NAAQS; i.e., controls capable of attaining the PM 10 NAAQS will result in attainment
of the PM 2.5 NAAQS. Further, these findings would support a recommendation to establish
PM 2.5 nonattainment area, if it is at all necessar y, covering only that ge ographic area directly affecting the Cowtown monitor and nearby ar eas with the same primary sources of PM 10 and, thus, PM 2.5

.

1 PCAQCD staff, “Pinal County Air Quality Control Di strict Source Apportionment Study,” July 29, 20052

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. the objective of this paper is to discuss the factors which have likely contributed to the 29 PM 2.5 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 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 fire3

The Cowtown monitoring site is located approxima tely 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 wi thin 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 n ear the Cowtown 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 cons ideration 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 combusti on, as well as locally generated primary and secondary particulates. However, after an invest igation 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 ch emical methods, which provided refined source profiles or “fingerprints” for Pi nal County soils and feedlots.4

Table 2: The source apportionment mode ling required chemical source profiles from seven source types, the eigh t 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 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.

Figure 1: Source specific chemical fingerp rint 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 Study5

Figure 2: Source specific ch emical fingerprint 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.

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 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 eight6

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, li ke cow manure, 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.

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 meteorology. 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) 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 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.

3 http://www.epa.gov/scram001/receptor_cmb.htm7

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 co llected from each Pinal County 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 presented in Table 2. 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.8

The average source category contributions from the remaining four monitoring sites were compared to the average source category cont ributions from the Co wtown monitor. That
conclusion indicated that the Cowtown monitor is the only monitoring site in Pinal County where
feedlot emissions dominate the PM 2.5 samples. Geologic soil was the dominate source category for each of the remaining four monitoring site s. It is also worth mentioning, that the PM 10 samples from the Cowtown monitor were also dominated by feedlot emissions, which supports
the PM 2.5 as a function of PM 10 , data analysis presented later; that analysis shows that when
PM 2.5 exceeds the standard, so does PM 10

.

Source Apportionment - Primar y Components of Cowtown PM 2.5
Nine 24-hour ambient PM 2.5 samples were collected from the Cowtown monitoring site during
October and November 2003. The nine ambient sa mples were speciated and modeled using the
CMB model to estimate the relative source cate gory contributions at the Cowtown monitor. Based on the ambient sample data and CMB m odeling, the most abundant source category for
PM 2.5 was feedlot soils with an average contribu tion of 49% of the total. The second highest source contribution was the geol ogical soil category with an average contribution of 24%. Cowtown was the only monitor in the Pinal C ounty study area where the geological soil category
did not have the highest source contribution. (See Figure 5) The average 24-hour PM 2.5 concentration at Cowtown was also well above any other Pinal County 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. On all three dates, the meteorological data indi cates inversion conditions with light winds throughout the day, which further indicates that dist ant sources are not likely to have been major contributors to the highest record ed days during this study period.

The coal-fired power plant source category wa s the third most abundant category with an average relative contributi on of 9%. This contribution from th e 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 appr oximately 82 miles to the south east of the Cowtown monitor, and although it is possible for stack emissions to transport long di stances, the stagnant meteorological conditions which were evident on the highest recorded PM 2.5 days, would likely have served to mitigate or even prevent any l ong range pollutant transport from affecting the Cowtown monitor on those days. Gi ven 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 PM 10/2..5 and just 50 meters to the north of the Union Pacific Railroad tracks. However, although it is the third most abundant source ca tegory, the relative contribution from the power plant signature is on ly 9%, while geologic dust and feedlots together comprise 73% of the total PM 2.5

.9

Figure 5: 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.

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 speciat ion profiles. However,
locomotive sources were not modeled in the 2003 CMB analysis, so a locomotive emission profile was not readily availa ble for comparison to the co al 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 difficu lt to confuse diesel exhaust from locomotives with coal power plant emissions. In fact, loco motive emissions may be more likely confused with heavy duty diesel vehicle em issions. But, if the locomotive emission profile were mixed-in with vehicle exhaust profile, lo comotives would only likely be ma rginally responsible for any
elevated PM 2.5 concentrations, since vehicle emissions are only on average 3% of the total
ambient PM 2.5. However, no evidence appears to suppor t that locomotive emissions have been confused with any of the seven source profile s, including the coal pow er 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 plan ts used as source profiles in the PCAQCD study are substantially different th en that of feedlot signature. Figure 6 below illustrates the BRAVO carbon profiles for the three power plants. Ea ch profile is different than that of the feedlot profile illustrated in Figure 1. The feed lot 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 re lative 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 West ern coal as fuel, similar to th e fuel used in Arizona coal plants. However, CFPP1 has no such description, l eaving 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 pow er plant contribution at Cowtown and in Pinal County should likely be considered unknown.

ADEQ has no data that suggest or supports that em issions from gas- or co al-fired power plants
contribute to the PM 2.5 problem in Pinal County. Transport of windblown PM 2.5 to the Cowtown
monitor has not been documented in Pinal C ounty. To the contrary, the 2005 PCAQCD source apportionment report estimated that, on average, 49% of PM 2.5 at the Cowtown monitor originates from feedlots. While this study concludes that coal fired power plants may have
contributed to the PM 2.5 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 PM 2.5 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 sulfat e 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 othe r “chemical fingerprints” used for the source apportionment analysis and the re sults 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 site s, 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 feed lot 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 Species12

Source Apportionment - Comparison 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.

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 directi ons. The CMB modeling resu lts (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.13

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)

Figure 8

Figure 714

Figure 9

Figure 1015

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. 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 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 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.

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 sy stems with attached cold fronts push through the region. This pattern is of ten seen in the spring and fall season. 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,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 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 brea k at all, possibly 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 topography. The typical surface flow is up-sl ope during the afternoon and down-slope during 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.17

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 the 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.

Exceedances by Wind 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 final18

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.

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 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 at19

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 (Figure 11g), each trajectory begins in the south-southwes t 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 (Figure 11c), October 2, 2006 (Figure 11c), October 9, 2007 (F igure 11e), and March 25, 2008 (Figure 11f), only when the parcel nears the Cowtown monitor, does the air parcel traverse any PM 2.5 sources. However, January 11, 2006 (Figure 11a) and February 22, 2006 (Figure 11b) ar e somewhat different from the other five days, because some trajectories be gin near the Phoenix a nd Yuma, urban areas. On January 11, 2006 (Figure 11a), the yellow, green and red traject ories 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 tr ajectories 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, th e 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 the 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 illustrati ng 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):2021222324252627

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.28

The Pasquill-Gifford dispersion parameters are th eoretical and except for 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 the 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 so urces, but are from local sources of particulate pollution. Table 4 below illustrates the long term average differences 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

-29

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 ex ceedance 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 indi cate 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 Grande30

Paired t-tests were performed to test the statistical significance of the difference between the
measured concentrations at the th ree sites. The first set of three paired t-tests (T ables 6,7 and 7)
use all available PM 2.5 data from 2006, 2007 and 2008, while the second set of three (Tables 9,10 and 11) are paired t-tests using only the 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
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 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) two-tail 6.55E-32

t Critical two-tail 1.97631

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 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) 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 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.

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 not33

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 12: 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 monitor (Latitude 33° 00' 38" N, Longitude 111° 58' 19" W). Meteorologi cal variables evaluated included: average, maximum and minimum temperature; average and maximum relative34

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 intercep t 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 13:

Table 13: 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 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

PM 10 0.1051 0.00320 32.83 4.7E-8235

Figure 14, 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 prediction 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.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 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.

 Any control measures that will achieve attainment of the PM 10 NAAQS will also yield attainment of the PM 2.5 NAAQS.37

REFERENCES

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

Clements, 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.1

APPENDIX A2