PM-10 Source Attribution 

and Deposition Study 

Maricopa Association of Governments 

March 2008 
Report No. 2008-03-01

PM-10 Source Attribution and 

Deposition Study 

prepared for:

Maricopa Association of Governments

March 2008

prepared by: 

Sierra Research, Inc. 

1801 J Street 

Sacramento, California 95811 

(916) 444-6666
Report No. 2008-03-01 

PM-10 SOURCE ATTRIBUTION 

AND DEPOSITION STUDY

 prepared for: 

Maricopa Association of Governments 

 March 2008 

 Principal authors: 

 Robert G. Dulla 

Wei Liu 

 Earl Withycombe, Consultant 

 

Sierra Research, Inc. 

1801 J Street 

Sacramento, CA 95811 

(916) 444-6666
-iPM-10 SOURCE ATTRIBUTION 

AND DEPOSITION STUDY

Table of Contents

Page

1. Introduction ................................................................................................................. 1 

1.1 Background ......................................................................................................... 1 

1.2 Study Goals ......................................................................................................... 4 

1.3 Approach ............................................................................................................. 6 

1.4 Report Organization .......................................................................................... 10 

2. Literature Review...................................................................................................... 11 

2.1 Attainment Demonstrations .............................................................................. 11 

2.2 Fugitive Dust Monitoring Research .................................................................. 15 

2.3 Source-Receptor Modeling Approaches ........................................................... 16 

2.4 Conclusions ....................................................................................................... 17 

3. Data Analysis – Ambient Measurements .................................................................. 19 

3.1 Particle Size Distribution .................................................................................. 19 

3.2 Back Trajectory Analysis .................................................................................. 23 

3.3 Particle Deposition Rate Modeling ................................................................... 25 

3.4 Particle Deposition Monitoring......................................................................... 29 

3.5 Transport Monitoring ........................................................................................ 32 

3.6 Non-Parametric Regression .............................................................................. 35 

3.7 Non-Parametric Results for the One Dimensional Case ................................... 39 

3.8 Non-Parametric Results for the Two-Dimensional Case .................................. 42 

3.9 Field Observations ............................................................................................ 45 

4. Data Analysis – Source Characteristics .................................................................... 50 

4.1 Travel ................................................................................................................ 50 

4.2 Silt Loadings ..................................................................................................... 52 

4.3 Industrial ........................................................................................................... 57 

4.4 Light Industrial (Vacant and Unpaved Parking Lots) ....................................... 58 

4.5 Construction ...................................................................................................... 59 

4.6 Agriculture ........................................................................................................ 59 

4.7 Local Truck Yard .............................................................................................. 60 
-ii5. Air Quality Modeling ................................................................................................ 61 

5.1 Episode Selection .............................................................................................. 62 

5.2 Emission Inventory Development..................................................................... 67 

5.3 Meteorological Data.......................................................................................... 81 

5.4 Model Performance ........................................................................................... 86 

5.5 Modeled Impacts of Nearby Sources ................................................................ 92 

5.6 Transport Considerations .................................................................................. 94 

6. Conclusions ............................................................................................................... 99 

6.1 Recommendations for Future Studies ............................................................. 100 

Appendix A – T&B Field Monitoring Study 

Appendix B – T&B Field Study/Mobile Monitoring Plots 

Appendix C – AERMOD/CALPUFF Comparison 

Appendix D – Summary of Collection & Analysis of Silt Samples from Paved Roads in 

Maricopa County 

Appendix E – Particle Analysis Report 

 List of Figures

Figure Page

Figure 1-1 Location of Fixed Monitoring Sites Within the Salt River Area ..................... 7 

Figure 3-1 Average Ratio of PM-2.5/ PM-10 for Hourly PM-10 Concentrations 

Exceeding Threshold Value Durango Complex – January 17, 2006 to February 

28, 2006............................................................................................................................ 20 

Figure 3-2 Average Ratio of PM-2.5/ PM-10 for Hourly PM-10 Concentrations 

Exceeding Threshold Value West Phoenix – December 1, 2005 to February 28, 

2006.................................................................................................................................. 20 

Figure 3-3 Hourly PM-10 at Durango Complex and West Phoenix January 17, 

2006 to February 28, 2006 ............................................................................................... 21 

Figure 3-4 Hourly PM-2.5 at Durango Complex and West Phoenix January 17, 

2006 to February 28, 2006 ............................................................................................... 21 

Figure 3-5 Average Particle Diameter Ranges at Salt River Area Sampling 

Locations .......................................................................................................................... 23 

Figure 3-6 Back Trajectory of Winds Impacting the West 43rd Avenue Monitor 

(December 6, 2006 at 9 a.m.) ........................................................................................... 25 
-iiiFigure 3-7 Summary of Monitoring Conditions at West 43rd (December 5-6, 

2006) ................................................................................................................................ 26 

Figure 3-8 Forward Trajectory of Winds Aloft Starting at the West 43rd Avenue 

Monitor at 5:00 p.m., December 6, 2006 ......................................................................... 28 

Figure 3-9 Durango Complex Dustfall Jar Network........................................................ 30 

Figure 3-10 West 43rd Avenue Dustfall Jar Network ...................................................... 31 

Figure 3-11 Summary of PM-10 Mobile Monitoring Data on November 15, 2006........ 33 

Figure 3-12 Summary of PM-10 Monitoring Data on November 16, 2006 – After 

8 a.m. ................................................................................................................................ 34 

Figure 3-13 PM-10 Concentration versus Wind Direction Durango Complex 

Monitor, 11/15 – 12/15/06 ............................................................................................... 36 

Figure 3-14 PM-10 Concentration versus Wind Direction West 43rd Avenue 

Monitor, 11/15 – 12/15/06 ............................................................................................... 36 

Figure 3-15 PM-10 Concentration versus Wind Speed Durango Complex 

Monitor, 11/15 – 12/15/06 ............................................................................................... 37 

Figure 3-16 PM-10 Concentration versus Wind Speed West 43rd Avenue Monitor, 

11/15 – 12/15/06 .............................................................................................................. 37 

Figure 3-17 Average PM-10 Concentration by Wind Speed and Direction 

Durango Complex Monitor, 11/15 – 12/15/06 ................................................................ 38 

Figure 3-18 Average PM-10 Concentration by Wind Speed and Direction West 

43rd Avenue Monitor, 11/15 – 12/15/06 .......................................................................... 38 

Figure 3-19 One-Dimensional Non-Parametric Regression of PM-10 

Concentrations and Wind Direction at Durango Complex Monitor, Nov. 15 – Dec. 

15, 2006............................................................................................................................ 39 

Figure 3-20 One-Dimensional Non-Parametric Regression of PM-10 

Concentrations and Wind Direction at West 43rd Avenue Monitor, Nov. 15 – Dec. 

15, 2006............................................................................................................................ 40 

Figure 3-21 Two-Dimensional Non-Parametric Regression of PM-10 

Concentrations, Wind Direction, and Wind Speed at Durango Complex Monitor, 

Nov. 15 – Dec. 15, 2006 .................................................................................................. 42 

Figure 3-22 Two-Dimensional Non-Parametric Regression of PM-10 

Concentrations, Wind Direction, and Wind Speed at West 43rd Avenue Monitor, 

Nov. 15 – Dec. 15, 2006 .................................................................................................. 44 
-ivFigure 3-23 Agricultural Field Plowing in the Salt River Area December 5, 2006 ........ 46 

Figure 3-24 Reentrainment of Paved Road Trackout in the Salt River Area 

December 12, 2006 .......................................................................................................... 46

Figure 3-25 Entrained Particulate in a Truck Plume Being Dragged Onto a Paved 

Road in the Salt River Area - December 14, 2006 .......................................................... 47 

Figure 3-26 Truck Bow Wake Plume in the Salt River Area December 12, 2006 .......... 48 

Figure 3-27 Unpaved Shoulder Travel Plume in the Salt River Area December 12, 

2006.................................................................................................................................. 48 

Figure 3-28 Open Burning of Scrap Wooden Poles Near Broadway Road at West 

43rd Avenue December 6, 2006 ....................................................................................... 49 

Figure 4-1 Traffic Counter Locations in the Salt River Area December 4-8, 2006 ........ 51 

Figure 4-2 Average Hourly Traffic on 27th Avenue Between Durango and Lower 

Buckeye (December 5-7, 2006) ...................................................................................... 51 

Figure 4-3 Distribution of SCAMPER Emission Factors for 2006 ................................. 53 

Figure 4-4 SCAMPER Mobile Monitoring Routes ......................................................... 55 

Figure 5-1 Summary of Monitoring Conditions at Durango Complex During 

December Episode ........................................................................................................... 64

Figure 5-2 Summary of Monitoring Conditions at West 43rd Ave. During 

December Episode ........................................................................................................... 65

Figure 5-3 Summary of Monitoring Conditions at Durango Complex and West 

43rd Ave. on High Wind Day During December Episode ............................................... 66 

Figure 5-4 Salt River Area Modeling Domain ................................................................ 68 

Figure 5-5 Arterial and Local Roads Included in 2006 Analysis .................................... 70 

Figure 5-6 Comparison of Diurnal Distribution of Measured Concentrations and 

AERMOD Predicted Source Concentrations for Durango Complex (December 5, 

2006) ................................................................................................................................ 87 

Figure 5-8 Comparison of Diurnal Distribution of Measured Concentrations and 

AERMOD Predicted Source Concentrations for Durango Complex (December 6, 

2006) ................................................................................................................................ 88 

Figure 5-9 Comparison of Diurnal Distribution of Measured Concentrations and 

AERMOD Predicted Source Concentrations for West 43rd Avenue (December 6, 

2006) ................................................................................................................................ 88 
-vFigure 5-10 Comparison of Diurnal Distribution of Measured Concentrations and 

AERMOD Predicted Source Concentrations for Durango Complex (December 7, 

2006) ................................................................................................................................ 89 

Figure 5-11 Comparison of Diurnal Distribution of Measured Concentrations and 

AERMOD Predicted Source Concentrations for West 43rd Avenue (December 7, 

2006) ................................................................................................................................ 89 

List of Tables

Table Page

Table 1-1 Summary of PM-10 Measurements Collected at MCAQDa

 Monitoring 

Sites in 2005 & 2006 (24-hour NAAQS) .......................................................................... 3 

Table 1-2 Summary of Measurement Equipment Installed in Sampling Vans ................. 8 

Table 3-1 Particle Residence Times (hr) ......................................................................... 27 

Table 3-2 Size Fraction of Dustfall Collected Near Durango Complex and West 

43rd Avenue Monitors ..................................................................................................... 30 

Table 3-3 Azimuth of Non-Regression Peaks at Durango Complex Monitor 

November 15, 2006 – December 15, 2006 ...................................................................... 41 

Table 3-4 Azimuth of Non-Regression Peaks at West 43rd Avenue Monitor 

November 15, 2006 – December 15, 2006 ...................................................................... 41 

Table 4-1 Silt Sampling Results Organized by SCAMPER Emission Rates (g/m2) ....... 56 

Table 4-2 Comparison Between Salt River Silt Measurements Collected in 2003 

and 2007 ........................................................................................................................... 57 

Table 4-3 Impact of Location Assumption on Permits and Acreage Included in the 

Construction Emissions Inventory ................................................................................... 59 

Table 5-1 24-Hour Average PM-10 Concentrations and Wind Speeds During 

Field Study ....................................................................................................................... 62 

Table 5-2 Differences in Road Network Emission Calculations Between Salt 

River Studies .................................................................................................................... 69 

Table 5-3 Differences in Trackout Emission Calculations Between Salt River 

Studies .............................................................................................................................. 72 
-viTable 5-4 Differences in Industrial Emission Calculations Between Salt River 

Studies .............................................................................................................................. 74 

Table 5-5 Differences in Construction Emission Calculations Between Salt River 

Studies .............................................................................................................................. 75 

Table 5-6 Differences in Agriculture Emission Calculations Between Salt River 

Studies .............................................................................................................................. 76 

Table 5-7 Differences in Vacant Lot Source Data Collection and Analysis 

Methods for the Salt River Area ...................................................................................... 78 

Table 5-8 Differences in Unpaved Parking Lot Source Data Collection and 

Analysis Methods for the Salt River Area ....................................................................... 79 

Table 5-9 Differences in Windblown Soil Source Data Collection and Analysis 

Methods for the Salt River Area ...................................................................................... 80 

Table 5-10 Summary of Source Specific PM-10 Emissions for Salt River Area 

Modeling Domain And Design Day Conditions (tons/day) ........................................... 81 

Table 5-11 Meteorological Data File Used for December 2006 Modeling at 

Durango Complex ............................................................................................................ 82

Table 5-12 Meteorological Data File used for December, 2006 Modeling at West 

43rd ................................................................................................................................... 84 

Table 5-14 Share of Predicted Concentrations from Sources Located Within 

Selected Distances from the Durango Complex Monitor ................................................ 93 

Table 5-15 Share of Predicted Concentrations from Sources Located Within 

Selected Distances from the West 43rd Avenue Monitor ................................................. 94 

Table 5-16 Share of Predicted Design Day Contributions From Transport For Salt 

River Monitors ................................................................................................................. 97 

Table 5-17 Comparison of Afternoon Wind Speeds and Concentrations Recorded 

During High Wind Events at the West 43rd Avenue Monitor on February 15, 2006 

and December 7, 2006 ..................................................................................................... 98 
-1- 

1. INTRODUCTION 

Maricopa County has worked for over 20 years to identify the mix of control measures 

needed to attain the ambient air quality standard for particulate matter with an 

aerodynamic diameter of 10 micrometers or less (PM-10). Despite the completion of 

increasingly comprehensive planning cycles (i.e., inventory development, control 

measure evaluation, implementation of progressively more stringent controls, modeling 

of attainment, plan submission to EPA and approval), violations of the PM-10 standards 

have continued to occur. The result of the continued attainment shortfall has been a 

“bump up” in nonattainment classification severity and increasingly restrictive planning 

requirements. In late 2005 and early 2006, numerous exceedances were recorded and it 

became clear that the previously extended attainment date of December 31, 2006, could 

not be met. At that point, the nonattainment area became subject to section 189(d) of the 

Clean Air Act, which requires the preparation of a new State Implementation Plan (SIP) 

that shows reductions in PM-10 emissions of five percent per year until attainment is 

reached at all monitors. The new “Five Percent Plan” was due by the end of 2007, one 

year after the previously scheduled attainment date. 

Recognizing the severity of the problem, the impact of the five percent per annum 

reduction requirement on the community, and the limitations of past planning efforts, the 

designated Air Quality Planning agency for the region—the Maricopa Association of 

Governments (MAG)—issued a procurement to (1) identify sources of emissions 

contributing to high PM-10 concentrations at monitors in the nonattainment area and 

(2) characterize the deposition of PM-10 particles emitted from those sources. Two 

respondents were selected to conduct these efforts: Sierra Research, Inc. (Sierra) and 

Technical and Business Systems, Inc. (T&B Systems). T&B Systems was responsible 

for ambient data collection efforts and Sierra was responsible for analysis, deposition 

characterization and air quality modeling. This report documents the results of those 

efforts. 

1.1 Background

PM-10 in the nonattainment area is composed primarily of coarse particles (with an 

aerodynamic diameter ranging between 2.5 and 10 microns) that are typically crustal in 

nature. Key sources include windblown dust, resuspended road dust (from both paved 

and unpaved roads), unpaved parking lots, disturbed vacant land, mining operations, 

construction and agricultural activities (e.g., tilling, harvesting, travel on unpaved farm 

roads, etc.). While fugitive dust is the dominant source of emissions impacting monitors 
-2- 

within the nonattainment area, other sources contributing to PM-10 concentrations 

include directly emitted PM-10 (e.g., Diesel soot, etc.) and secondary particulates (i.e., 

particles formed through atmospheric chemical reactions from precursor gases, primarily 

oxides of nitrogen, oxides of sulfur, and ammonia). Combustion-related particulates 

such as sulfates, nitrates, and organic and elemental carbons (OC and EC) are typically 

found in the fine fraction of particulates (i.e., aerodynamic diameter of less than or equal 

to 2.5 microns). 

Previous analyses of PM-2.5 data in the Phoenix area have shown that mobile source 

exhaust, burning, and industrial sources are important constituents of PM-2.5. EPA 

designated Maricopa County as an attainment area for PM-2.5 in September 2005. Local 

monitoring by co-located PM-10 and PM-2.5 monitors confirms that PM-2.5 on high PM10 days is a small fraction of the PM-10 concentrations. Therefore, the PM-10 problem 

in the Maricopa County nonattainment area is largely attributable to coarse particles, 

comprised primarily of geologic material. 

High PM-10 concentrations generally occur during fall and winter months (i.e., 

September through March) on days with stagnant or near-stagnant conditions. Under 

these conditions, when winds are extremely light and variable, the contributions of local 

sources are significant. Past efforts to characterize the level of material emitted from 

these sources have relied largely upon the preparation of detailed emission inventories 

and the use of air quality modeling to assess the significance of individual sources to 

impacts recorded at modeling sites exceeding the ambient 24-hour PM-10 standard. 

Those efforts, however, reached varying conclusions about the significance of local 

sources versus the transport of material emitted outside of the modeling domain. Since 

these conclusions directly affect the control measures selected to demonstrate attainment 

and past efforts incorrectly modeled attainment, MAG determined that a field study was 

needed to collect measurements of ambient concentrations at locations throughout the 

area that routinely record the most frequent and highest PM-10 concentrations in the 

nonattainment area. These measurements could then be combined with meteorological, 

emissions, and monitoring data to perform air quality modeling and identify which 

sources impacted monitors under design day conditions. This information in turn could 

be used to support the selection of the control measures needed to demonstrate 

attainment. 

Insight into which monitors consistently exceeded the 24-hour PM-10 standard is 

provided in Table 1-1. In 2005 and 2006, most of the PM-10 concentrations that 

exceeded the 24-hour federal standard occurred on fall and winter days (i.e., November 

through February) with stagnant or near-stagnant conditions. All but one of the 

exceedances in 2005 occurred in November and December during a regional drought 

(i.e., no measurable rain was recorded after October 18, 2005) and stagnant conditions. 

These monitors, Durango Complex and West 43rd Ave, are located in the Salt River area, 

a 29-square-mile area that has experienced the highest and most frequent violations of the 

ambient PM-10 standard. As a result of the history of these monitors and recent 

exceedances, MAG determined that the study should focus on the collection of 

measurement, activity, and monitoring data within the Salt River area. 
-3- 

Table 1-1 

Summary of PM-10 Measurements Collected at MCAQDa Monitoring Sites in 2005 & 2006 

(24-hour NAAQS) 

Site 

Name 

2005 2006 

Max 

(μg/m3

) 

Average 

2nd High 

(μg/m3

) 

# of 

Exceedances 

Max 

(μg/m3) 

Average 

2nd High 

(μg/m3

) 

# of 

Exceedances 

Bethune Elementaryb 198 - 1 140 - 0 

Buckeyea 169 158 2 272 192 3 

Central Phoenix 125 76 0 - - - 

Central Phoenixc 116 104 0 134 99 0 

Chandler 130 115 0 - - - 

Durango Complexc 206 200 13 240d 183 9 

Dysart 76 68 0 67 55 0 

Glendale 84 56 0 60 59 0 

Greenwood 173 95 1 166 141 1 

Higleyc 142 121 0 170 166 2 

Mesa 86 55 0 75 59 0 

North Phoenix 81 72 0 79 62 0 

South Phoenix 147 107 0 132 100 0 

South Scottsdale 121 96 0 76 60 0 

West Chandler 94 68 0 77 68 0 

West 43rd Avec 233 200 13 260d 204 18 

West Phoenix 155 103 1 147 122 0 

Total - - 31 - - 33 

a

 Maricopa County Air Quality Department 

b

 Bethune Elementary School is an ADEQ special-purpose monitor located within the Salt River Area. 

c

 Indicates a continuous particulate monitor. 

d

 Indicates a natural event. 

The Durango Complex and West 43rd Ave. monitors were the subject of an extensive 

Arizona Department of Environmental Quality (ADEQ) report entitled “Revised PM-10 

State Implementation Plan for the Salt River Area.”1

 The technical documentation for 

that plan, referred to hereafter as the Salt River Area TSD (for Technical Support 

Document), described the conduct of an intensive air quality monitoring study in 2002, 

the development of a detailed emissions inventory, and an extensive modeling analysis of 

emission sources and control measure commitments that demonstrated attainment of the 

ambient PM-10 standard in 2006. While exceedances recorded in 2005 and 2006 mooted 

the need for EPA review and approval of the Salt River Area Plan,2

 the information 

 

1

 Revised PM-10 State Implementation Plan for the Salt River Area, Technical Support Document, Air 

Quality Division, Arizona Department of Environmental Quality, June 2005 

2

 While the ADEQ plan was not approved by EPA, the control measure commitments in the ADEQ Plan 

were approved by EPA in the Federal Register August 21, 2007. 
-4- 

assembled in the supporting TSD provide an excellent starting point for developing the 

Five Percent Plan and the modeling analysis of data collected in this study. 

1.2 Study Goals

The principal goal of this project was to quantify the ambient contributions of significant 

sources to elevated PM-10 concentrations being measured at monitoring stations in the 

Salt River area during low wind conditions such as occurred during the fall and winter of 

2005/06. During that time the jet stream so continuously followed a northerly track that 

it prevented storm fronts from passing through the Pacific Southwest and produced very 

stagnant air conditions in Maricopa County. During these low wind conditions, the 

atmospheric loading of PM-10 accumulated near significant sources, producing 

concentrations at nearby monitors that exceeded air quality standards at frequencies not 

seen in previous winters. The challenge was to determine which sources contributed to 

these elevated readings and to prepare the way for the implementation of control 

measures designed to prevent these exceedances from reoccurring. 

A second goal of quantifying significant source impacts during high wind conditions was 

added to the project after commencement. During the late winter and early spring of 

2006, exceedances of the federal 24-hour PM-10 standard were recorded on days when 

high wind conditions coincided with peak hourly ambient concentrations. Because of 

this relationship, and the fact that meteorological conditions on these days were 

significantly different from the stagnant conditions recorded on exceedance days earlier 

in the winter, identifying source contributions—especially those of windblown dust—on 

high wind days was deemed essential to attainment of the federal standard. 

Another goal of the project was to investigate significant findings of the ADEQ’s Salt 

River Area TSD. One of the key conclusions of that study was that a significant fraction 

of PM-10 measured in the Salt River Area came from sources located outside of the 

modeling domain. An early task of the project was to determine whether this hypothesis 

was correct. Correspondingly, questions arose as to whether morning drainage flows 

down the Salt River (i.e., from east to west) carried suspended particulate from upstream 

sources to the Salt River modeling domain. Evaluating this possibility was also an early 

goal of the project. 

Another objective of the field study was to examine the causes of the ADEQ modeling 

shortfall on low wind days. Regression statistics showed very poor correlation between 

the modeled plus background values versus the monitored concentrations (the mean R2

for all hours modeled was 0.27). This suggested potential problems with the modeling 

methodology. One issue was the method used to distribute area source emissions, such as 

those from paved roads and vacant lots, uniformly across 400 meter x 400 meter (0.25 

mile x 0.25 mile) grid cells used to represent the modeling domain. It was suspected, due 

to the low release height of fugitive dust and the settling velocity of PM-10, that the 

sources producing significant impacts at the two Salt River monitors were sources located 

very close to the monitors, and that the distribution of emissions from road links spread 
-5- 

evenly across grid cells would interfere with accurately quantifying the impacts of roads, 

unpaved work areas, and agricultural fields near the monitors. Thus, another goal was to 

determine whether the use of a more spatially accurate emission inventory would 

improve the correlation between modeled impacts and measured concentrations at each 

monitor. 

Initial review of the hourly PM-10 data collected on low wind design days at both 

Durango Complex and West 43rd Avenue sites showed that peaks occurred during early 

morning and late evening periods. This dual peak profile was similar to diurnal traffic 

flow patterns in the area, suggesting that road travel was a significant source. The 

morning peaks were substantially higher than those of the evening, suggesting higher silt 

levels on roads in the morning than in the evening. The evaluation of diurnal silt levels 

on Salt River roads to determine why evening PM-10 peaks were lower than morning 

peaks became another goal of the project. 

Because the project included an intensive field study phase during similar low wind 

conditions in the fall and winter of 2006/07 another goal was to use this field study to 

improve the accuracy of emission factors used in the emission inventory. Since the 

development of the ADEQ study in 2005, improved technologies for continuously 

sampling roadway emission rates have been developed. The University of California 

Riverside (UC Riverside) has employed a SCAMPER (System for Continuous Aerosol 

Measurement of Particulate Emissions from Roadways) to continuously measure vehicle 

paved road emission PM-10 emissions in several urban areas in the Pacific Southwest. 

Additionally, the improving ability of portable continuous particle counters with PM-10 

sizing inlets to accurately measure ambient PM-10 concentrations offered new 

opportunities to isolate source plumes in ways that are impossible with fixed based 

continuous monitors. As a result it was determined these emerging technologies should 

be brought into play to add depth and accuracy to the emission inventories of the areas 

immediately surrounding the Salt River monitors. 

A final goal of the project was to quantify the deposition rates of particulate matter at the 

Salt River monitors, based on the belief that a portion of the paved road silt levels 

resulted from deposition of fugitive dust emitted by industrial, commercial, and 

agricultural sources near these roads. The quantification of deposition would also 

provide a reality check on the deposition rates estimated by the dispersion models used to 

simulate PM transport from nearby sources to the PM-10 monitors. 

Other initial goals included efforts to: 

• Interview source operators and property owners for activity data on design days; 

• Visually evaluate soil disturbance levels (from Google Earth); 

• Collect traffic data; 

• Obtain new measurements of roadway emissions via SCAMPER or manual 

collection method from targeted roads; 

• Use results to prepare hourly link-specific emission estimates; 
-6- 

• Install/operate network of deposition monitors during field study period; and 

• Contrast deposition gradients with T&B measurements and AERMOD estimates. 

1.3 Approach

To assist in the planning of monitoring and modeling studies aimed at identifying the 

significant sources or source categories of PM-10 emissions, a literature search of 

relevant research documentation was conducted. Past studies of PM-10 emissions in the 

Salt River were reviewed to determine the extent of available data and conclusions 

relevant to this study. Similar studies of other fugitive dust areas such as Clark County, 

Nevada, and the San Joaquin Valley of California were reviewed to evaluate other 

research methodologies and results. 

A field monitoring program was conducted from mid November through mid December 

of 2006 to respond to the questions raised about the relative contributions of PM-10 

sources to high concentrations measured at Salt River area monitors. The monitoring 

program was conducted in two phases after a review of existing emission inventory and 

monitoring data. In the first phase, mobile monitoring and visual surveys were conducted 

within and beyond the boundaries of the proposed modeling domain. In the second 

phase, plume concentrations downwind of significant sources within the modeling 

domain were monitored to improve the accuracy of the emission inventory used for 

modeling. 

The monitoring sites selected for analyses in the field study were the Durango Complex 

and West 43rd Avenue sites operated by the Maricopa County Air Quality Department. 

Initial modeling of source impacts using the ADEQ modeling files indicated that sources 

within approximately 2 miles of each monitor were responsible for the vast majority of 

concentrations recorded. This area generally conformed to the modeling domain selected 

by ADEQ for analyses published in the 2005 PM-10 SIP Technical Support Document. 

As a result, this modeling domain was selected as the area in which an intensive field 

monitoring study would be performed during November and December of 2006. 

Described below is the instrumentation that was assembled and operated to collect 

ambient measurements of PM and meteorological activity. Appendix A provides a more 

detailed description of the field instrumentation, the procedures used to quality assure the 

equipment employed in the study, and a log of field observations made during both 

phases of the study. 

Maricopa County Monitoring Network Enhancements – To better understand low wind 

speed conditions, the Maricopa County monitoring group added higher time resolution 

time measurements with 5-minute averages (instead of hourly average values). 

Modifications were also made to the PM-10 measurements to record 5-minute averages 

from the TEOM samplers. These modifications were made to the Durango Complex, 

West 43rd Avenue, West Phoenix, Central Phoenix, South Phoenix and Greenwood 

stations. 
-7- 

West 43rd Monitoring Site Enhancements – In addition to those described above, the 

ADEQ provided an AeroVironment model 4000 minSodar for measurement of winds and 

mixing in the lowest 200 meters of the atmosphere. The system was installed at the 

outset of the project and operation was verified by T&B Systems prior to the start of the 

data collection effort. The system was programmed to collect and report 10-minute 

average winds and a facsimile display of the acoustic backscatter. An automated digital 

camera was also installed at the site to document the environment surrounding the West 

43rd Ave station. High temporal resolution pictures were taken at intervals of 3-30 

seconds depending on the pointing direction of the camera. More than 400,000 pictures 

were taken over the course of the study. 

During the last week of the second phase of the field study, Arizona State University 

installed a particle LIDAR unit at the West 43rd Avenue station. The LIDAR performed 

horizontal and vertical scans of the atmosphere to provide relative concentrations of 

particulate matter around the site. It also estimated the wind field through an analysis of 

Doppler shifted signals from LIDAR pulses. 

Temporary Fixed Monitor at 35th Avenue – A key issue that arose after the first phase 

was whether the high PM-10 concentrations recorded at West 43rd Avenue and Durango 

Complex monitors were the result of local sources adjacent to the sites or sources located 

farther away, but within the Salt River area. To help answer this question, a monitoring 

site was established near the Salt River, east of 35th Avenue and away from any local 

sources. Figure 1-1 displays the location of this site as well as the location of the West 

43rd Avenue, Durango Complex, and Bethune Elementary School monitors. 

Figure 1-1 

Location of Fixed Monitoring Sites Within the Salt River Area 
-8- 

The 35th Avenue monitoring site was equipped with a DustTrak configured to measure 

PM-10 concentrations at 5-minute and 60-minute intervals. Wind measurements were 

made with an RM Young Wind Monitor AQ wind speed and wind direction sensor. 

Meteorological measurements were averaged over 5-minute intervals. 

Mobile Monitoring – Two vehicles were equipped to collect ambient measurements. The 

primary vehicle, referred to as Van 1 or the Pilot, performed measurements during both 

phases of the study. The second vehicle, referred to as Van 2 or the Kia, was added for 

sampling in the second phase. Table 1-2 provides a summary of the instrumentation 

installed in each of the vehicles. The Pilot collected data in both mobile (while driving) 

and fixed (at pre-selected sites) mode operation. The primary use was to map the region 

and to obtain relative concentrations throughout the Salt River area and identify “hot 

spot” areas to be studied in more depth. The Kia was used to obtain the particle size 

distribution measurements. Data were collected and stored in two-second intervals, with 

all values stored in the data logger and backup computers installed in each of the vans. 

Table 1-2 

Summary of Measurement Equipment Installed in Sampling Vans 

Measurement Equipment Pilot 

(Van 1) 

Kia 

(Van 2) 

TSI 8520 DustTrak (PM-10) X X 

TSI 8520 DustTrak (PM-2.5) X X 

TSI 3321 Aerodynamic Particle Sizer X 

Airmetrics minivol (PM-10) X 

Airmetrics minivol (PM-2.5) X 

Garmin GPS receiver (van 3-D position) X X 

Campbell Scientific CR1000 (data logging) X X 

RM Young Wind Monitor (wind speed & direction) X 

RTD temperature sensor (outside air temperature) X 

RTD Young Electronic Compass (van direction) X 

Intellinet Network IP camera with recording laptop X 

Traffic Measurements – Under a separate procurement MAG had contracted with Field 

Data Services to collect traffic counts (some of the counters also provided information on 

vehicle size) on roads traversed by UC Riverside’s SCAMPER vehicle. As part of this 

contract, MAG also tasked Field Data Services to collect traffic count data on a large 

number of road links within the Salt River modeling domain. These counts were also 

collected in December 2006. 

Activity Measurements – Two Sierra subcontractors with offices in or near Phoenix − 

SOTA Environmental, Inc., and Applied Environmental Consultants, Inc. (AEC) – 
-9- 

conducted interviews of specific facilities, collected data on off-road vehicle activity, 

recorded opacity measurements of facility plumes, and obtained copies of permit files, 

among other tasks. 

Particle Deposition – AEC staff constructed stands to support dust jars that were sited at 

four locations surrounding each of the Durango Complex and the West 43rd Avenue 

monitoring sites. Once approval for locating the stands was received from responsible 

agencies, dustfall jars were installed on these stands for a week at each location. AEC 

retrieved the jars and used distilled water to remove deposited dust from each dust jar at 

the two network locations. The samples were sent to Particle Measurement Technology 

Co. for measurement using an electro-optical particle analysis system. 

Silt Measurements – Under a separate procurement, MAG had contracted with the 

University of California (UC) at Riverside to operate its mobile monitoring SCAMPER 

vehicle to measure PM-10 emissions from paved roads over a 100+ mile route that 

traversed the PM-10 nonattainment area. Separate traverses of this route were conducted 

in each of the seasons in 2006. Since only a small portion of the route fell within the 

boundaries of the Salt River modeling domain, a separate subcontract was established 

with UC Riverside to operate the SCAMPER vehicle multiple times over five preselected routes on roads located within the Salt River Area during a one-week period in 

December 2006. 

Subsequent analysis of the SCAMPER measurements, which are based on contrasting 

PM-10 measurements at front and rear of the vehicle, determined that this measurement 

method was fundamentally different from the vacuuming method employed in past 

ADEQ Salt River road silt measurements. For this reason, AEC was tasked with 

collecting silt measurements on a representative sample of roads within the Salt River 

Area using the traditional vacuuming methodology. 

As can be seen from the description above, a variety of ambient, meteorological, 

emission factor, activity, and visual measurements were collected by a mixture of 

contractors and agencies. The data were transmitted to Sierra where it was organized into 

appropriate databases for analysis. The data were analyzed using a variety of techniques. 

One of the analytical tools used to identify the locations of significant PM-10 sources 

impacting the two monitoring sites was non-parametric regression. This work was 

performed by Rincon Ranch Consulting (RRC), a statistical analysis subcontractor to 

Sierra. Preliminary results were forwarded to appropriate contractors and the Air Quality 

Planning Team for review and comment. Presentations were also made to MAG’s 

Technical Advisory Committee and interested parties identified by MAG. 

The next step was to use the results of the analysis to construct revised emission 

inventory estimates for sources located within the Salt River modeling domain. The 

results were contrasted with those prepared in ADEQ’s Salt River Area TSD to assess the 

impacts of the revisions. 
-10- 

A review of available air quality models meeting EPA guidelines for dispersion modeling 

was conducted. Two models were evaluated and configured to represent a subset of 

sources located within the Salt River modeling domain. A review of the concentrations 

recorded during the field study and related meteorological conditions was performed and 

an episode representative of low wind conditions was selected for modeling. The 

performance of the two models under these conditions was prepared and AERMOD, an 

EPA regulatory model, was selected to evaluate source-specific contributions to the 

Durango Complex and West 43rd Avenue monitoring sites. 

1.4 Report Organization

Following this introduction, Chapter 2 provides a summary of the literature search of past 

research studies of PM-10 emissions in the Salt River, and other areas with fugitive dust 

problems such as Clark County, Nevada, and the San Joaquin Valley of California. The 

results of the analyses of ambient measurements are presented in Chapter 3. Similarly, a 

summary of the analysis of data collected to characterize individual source emissions is 

presented in Chapter 4. Chapter 5 presents the results of the air quality modeling, 

including episode selection, inventory development, and model performance. The study 

conclusions are presented in Chapter 6. The appendices include a more detailed 

summary of the T&B field study, the air quality modeling comparison, silt 

measurements, and particle measurements. 

### 
-11- 

2. LITERATURE REVIEW 

Numerous violations of the 24-hour PM-10 ambient air quality standard were recorded in 

the Salt River area of south central Phoenix during the fall and winter of 2005/06. From 

past studies, fugitive dust sources were the apparent dominant sources responsible for 

causing these violations. The area contains a mix of sand and gravel operations, 

agricultural lands, exposed dry river bed, industrial operations, trucking centers, 

residential and commercial construction sites, and landfills. Area roads are used by high 

fractions of heavy-duty trucks that access unpaved areas and transfer soils onto paved 

surfaces. Within the Salt River area, sources of disturbed soil particles are ubiquitous. 

To assist in the planning of monitoring and modeling studies aimed at identifying the 

significant sources or source categories of PM-10 emissions, a literature search of 

relevant research documentation was conducted. Past studies of PM-10 emissions in the 

Salt River were reviewed to determine the extent of available data and conclusions 

relevant to this study. Similar studies of other fugitive dust areas such as Clark County, 

Nevada, and the San Joaquin Valley of California were reviewed to evaluate other 

research methodologies and results. Finally, research reports relating to alternative 

monitoring and modeling approaches not previously used in regional PM-10 monitoring 

and modeling efforts were reviewed to assess the feasibility and efficacy of applying new 

methods to solve the Salt River source-receptor relationship questions. 

2.1 Attainment Demonstrations

Attainment demonstrations are the analyses required by the federal Clean Air Act to 

explain how air quality violations occur in each designated nonattainment area, and how 

proposed control programs will reduce emissions to levels that will assure attainment of 

ambient air quality standards. To identify promising methodologies for determining the 

significant sources causing PM-10 violations in the Salt River area, the attainment 

demonstrations of three serious PM-10 nonattainment areas were reviewed: Maricopa 

County, Arizona; Clark County, Nevada; and the San Joaquin Valley in California. 

2005 ADEQ Study – In 1999, U.S. EPA required Arizona to submit an amended State 

Implementation Plan for the Salt River portion of metropolitan Phoenix demonstrating 

attainment of PM-10 ambient air quality standards by 2006. To assist in the analysis of 

PM-10 violations and design of appropriate control measures, the Arizona Department of 

Environmental Quality conducted an extensive study of Salt River sources and air 
-12- 

quality. A final report, published in 2005, describes the analyses conducted and the 

conclusions reached.1

The 2005 Salt River PM-10 study consisted of three primary tasks: 

• An ambient air quality study, including monitoring from April to December 2002; 

• A complete emission inventory of the study area for calendar year 2002; and 

• Air quality modeling of current and future year emissions. 

A review of historical ambient air quality data in the Salt River area indicated that 

elevated concentrations occur during high wind conditions, typically during the spring, 

and during winter stagnant air conditions.2

 During the low wind speed design days, 

hourly PM-10 concentrations tended to be highest during the four hours after sunrise and 

during four hours after sunset. This diurnal trend closely matches traffic flow patterns in 

the Phoenix area and suggests that traffic flows may contribute significantly to measured 

PM-10 peaks. 

In the modeling effort undertaken, emission rates for an extensive set of sources were 

estimated during each of the spring and winter of 2002, and one design day in each 

season was studied extensively to evaluate contributions from significant sources at 

nonattainment monitoring sites. Background PM-10 levels were estimated from 

concurrent data recorded at monitoring sites outside the Salt River modeling domain and 

added to the modeling results. The modeled results at two monitoring sites—Durango 

Complex and West 43d Avenue—were compared with continuous PM-10 monitoring 

data on an hour-by-hour basis to evaluate the accuracy of the modeling effort. For the 

high wind days, the correlation coefficient (R2

) between modeled and monitored 

concentrations was 0.58. On these days, the model predicted roughly 20% of the 

measured concentrations and the remaining 80% was estimated from upwind monitoring 

data to be urban background. Based on several parameters, the modeling of high wind 

days was judged to be a success. 

On the low wind days, the R2

 correlation coefficient between modeled and measured PM10 concentrations was 0.03, almost indicative of no relationship at all. This result 

suggests that either significant problems existed with the emission inventory or that the 

ISCST dispersion model failed to accurately represent air quality impacts during low 

wind speed conditions. On the low wind design day, background PM-10 concentrations 

were also estimated to be several times greater than modeled impacts at the two monitors. 

Unlike the high wind data, the variations in background were not at all in syncopation 

with the modeled impacts. The fact that the background was so variable on this design 

 

1

 “Revised PM-10 State Implementation Plan for the Salt River Area, Technical Support Document,” 

Arizona Department of Environmental Quality, Air Quality Division, September 2005. 

2

 Because the current study focuses primarily on PM-10 exceedances that occurred during stagnant winter 

conditions in December 2005 through March 2006, the literature review likewise focuses primarily on low 

wind speed conditions. 
-13- 

day was unexpected and also raised significant questions about source-receptor 

relationship understandings. 

Clark County PM-10 Nonattainment Plan – The Clark County, Nevada PM-10 

nonattainment plan was approved by EPA on May 3, 2004. The nonattainment plan used 

an emission inventory rollback method to demonstrate attainment of the annual and 

24-hour PM-10 standards. Only one monitoring site, at the J.D. Smith School, recorded 

an exceedance of the annual standard during the baseline period (1997–1999). This site 

and five others reported exceedances of the 24-hour standard. All of the exceedances of 

the 24-hour standard occurred on relatively high wind days. Clark County Department of 

Comprehensive Planning (CCDCP) concluded from analysis of monitoring data that PM10 impacts at violating monitoring sites were driven by sources located within 2 

kilometers of each monitoring site. CCDCP assumed that the non-background portion of 

measured PM-10 was proportional to the individual emission contributions of sources 

within the 2-kilometer microinventory area surrounding each nonattainment monitoring 

site. Background was assumed to be equal to the lowest PM-10 measurement recorded at 

any monitoring site on the design day or year plus an annual average aerosol contribution 

of 3.5 μg/m3 as determined by Desert Research Institute through chemical mass balance 

modeling. The emissions reductions estimated for application of candidate control 

measures, as a fraction of the total emission inventory of each microinventory area, were 

applied to the design day and year PM-10 concentrations to demonstrate attainment. The 

absence of exceedances during low wind, stagnant winter conditions means that the 

conclusions drawn and the control measure proposed in the nonattainment plan were not 

of significant use in resolving winter exceedances in the Salt River area. 

The assumption in the plan that significant fugitive PM-10 sources impacting monitors 

were located within a 2-kilometer radius of affected monitors was based on a dispersion 

modeling analysis. This analysis, conducted by Desert Research Institute for the Clark 

County Health District in 1997, concluded that sources more than 5 miles away from a 

monitor had minimal impact at the monitor site and that sources within 2 kilometers of a 

monitor drove measured concentrations.1 Interestingly, this analysis also reported that 

modeled PM-10 concentrations at monitors exceeded measured values by factors of 2 to 

4. This result was attributed to uncertainties in emissions and emission variability not 

captured by the model. The fact that modeled impacts exceeded monitored impacts in 

this study suggests that dispersion models do not invariably underpredict impacts as was 

the experience in the Salt River study, and thus should not be eliminated as analytical 

tools in identifying significant sources during the fall and winter of 2005/06 episode in 

the Salt River area. 

San Joaquin Valley PM-10 Nonattainment Plan – PM-10 air quality in the San Joaquin 

Valley is driven by a combination of emissions and meteorological conditions. During 

the summer and early fall, fugitive dust sources dominate the emission inventory and 

PM-10 measurements. In the late fall and winter, substantial secondary aerosol is formed 

and regional emissions of fine particles, primarily composed of organic and elemental 

 

1

 “Middle- and Neighborhood-Scale Variations of PM-10 Source Contributions in Las Vegas, Nevada,” J. 

Chow et al, Journal of the Air & Waste Management Association, 49:641-564, June 1999. 
-14- 

carbon, tend to accumulate under conditions of low inversion heights and stagnant wind 

conditions. 

Two areas in which fugitive dust emissions have contributed significantly to measured 

violations of the federal 24-hour PM-10 standard include Bakersfield and Corcoran.1 

Both of these sites were subjects of a series of studies in 1995 and 1999. In 1995, 

saturation monitoring of fine and coarse PM, and their constituents, was conducted in 

Corcoran, a small city surrounded by agricultural lands having high silt contents. 

Agricultural activities within the community, including a large cotton gin, and in the 

surrounding area caused Corcoran to register some of the highest PM-10 concentrations 

in the San Joaquin Valley. A saturation study in 1995 concluded that local sources within 

1 km of the central community monitor dominated PM-10 readings during the fall, but 

that regional sources of fugitive dust also provided significant contributions.2

A second effort to determine local versus regional contributions to crustal PM-10 

concentrations in Corcoran was conducted in the fall of 2000.3

 In this study, samples of 

ambient PM-10 and bulk soils in and about Corcoran were analyzed using scanning 

electron microscopy. Particle photographs were taken and the elemental composition of 

scanned particles was determined. The ambient particle and bulk soil elemental 

signatures were imported into a chemical mass balance model and evaluated to determine 

the soil source of ambient PM-10. The conclusions were that local and regional soils 

were equally dominant at the Corcoran site recording the highest PM-10 concentrations, 

but that regional soils were the dominant source at the other non-industrial monitoring 

sites in the community. 

More recently, a plan for evaluating the relative contribution of local and regional 

fugitive dust sources to PM-10 concentrations measured in central Bakersfield was 

submitted to the San Joaquin Valley Unified Air Pollution Control District 

(SJVUAPCD).4

 This survey-level evaluation of 10 years of data from two monitors 

located in the metro-Bakersfield PM-10 area concluded that emissions from agricultural 

operations were the key source impacting regional PM-10 levels. Seasonal polygons, 

scaled to airport windroses, were constructed to represent 24-hour “airshed catchments” 

to define the average maximal area from which a PM-10 particle could be transported to 

impact each monitor. Analysis of activities from selected sources (not including unpaved 

roads or combustion emissions) within the polygons was used to assess source 

significance. The principal working hypothesis to be tested in Phase 2 was that 

conveyance of PM-10 emissions into the metro area from agricultural sources located 

 

1

 “2003 PM-10 Plan,” San Joaquin Valley Unified Air Pollution Control District, December 2003, 

http://www.valleyair.org/Air_Quality_Plans/2003%20PM-10%20Plan%20Amended.pdf

2

 “California Regional PM-10/PM-2.5 Air Quality Study, 1995 Integrated Monitoring Study Data Analysis, 

Spatial Representativeness of Monitoring Sites and Zones of Influence of Emission Sources,” prepared for 

the San Joaquin Valleywide Air Pollution Study Agency by Envair, July 1998. 

3

 “Apportionment of Ambient PM-10 Crustal Component Using SEM Data – Corcoran Fall 2000 Study,” 

prepared for California Air Resources Board by RJ Lee Group, March 2003. 

4

 “Phase One Spatial Analysis of Metro-Bakersfield PM-10,” Prepared by Hydro Bio Advanced Remote 

Sensing for Southern California Edison, Kern Council of Governments and San Joaquin Valley Air 

Pollution Control District, March 2005. 
-15- 

within the polygons increased summer to fall. This resulted in “enrichment” of particles 

in the metro area that could be readily entrained. Vehicles were thought to be the 

principal cause of entrainment. 

This study has limited utility to the Salt River area. First, the analysis failed to assess the 

contribution of several potentially significant sources. Second, the methodology of 

assessing source significance (i.e., use of satellite data to quantify potential emissions 

from different land uses) is not directly applicable, particularly since the critical issue in 

the Salt River is establishing a link between source activity and localized diurnal 

emissions. 

2.2 Fugitive Dust Monitoring Research

Several studies of fugitive PM-10 transport have been published in the past several years. 

This work has been motivated by the need to reconcile the contributions of fugitive dust 

sources to emission inventories and to speciated ambient PM-10 measurements. In 

several analyses, the portion of ambient PM-10 consisting of soil contributions has been 

found to be approximately 25% of the fractional contribution of fugitive dust sources to 

PM-10 emission inventories.1

A Desert Research Institute (DRI) study conducted at a southwest military base shows 

that surface roughness plays a significant role in the deposition and removal of fugitive 

dust during transport.2

 Over undulating dunes no greater than 0.3 meters in height, and in 

neutral stability atmospheric conditions, less than 9.5% of PM-10 was removed from 

plumes at 100 meters downwind of an unpaved road. The PM-10 deposition rate 

predicted by a Gaussian plume model, such as ISCST3, for the same transport distance is 

approximately 5% for the meteorological conditions existing during the field study. 

In a comparison study by Veranth conducted over artificially created rough terrain and 

stable atmospheric conditions, 86% to 89% of PM-10 generated by unpaved road travel 

was removed within 95 meters of transport downwind from the source.3 The rough 

terrain was created by placing 10 cargo containers, spaced 6.3 meters apart, in a row 

perpendicular to the roadway to simulate an urban housing environment. This caused 

particulate entrained in eddies downwind of each container to settle out of the air due to 

the stilling effect on air movement of the cavities between the containers. The deposition 

rate predicted by ISCST3 for these calmer conditions, and for a 95-meter transport 

distance, was approximately 30%. This type of plume model is not configured to account 

for roughness height (the height below which the horizontal wind velocity is essentially 

 

1

 “Reconciling Urban Fugitive Dust Emissions Inventory and Ambient Source Contribution Estimates: 

Summary of Current Knowledge and Needed Research,” prepared by for the U.S. Environmental 

Protection Agency by Desert Research Institute, May 2000. 

2

 “Deposition and Removal of Fugitive Dust in the Arid Southwestern United States: Measurements and 

Model Results,” Etyemezian, V. et al, Journal of the Air & Waste Management Association, Vol. 43: 1099-

1111, September 2004. 

3

 “Vehicle-Generated Fugitive Dust Transport: Analytic Models and Field Study,” Veranth, J.M. et al, 

Atmospheric Environment, Vol. 37:2295-2303, 2003. 
-16- 

zero). These studies suggest that PM-10 removal in the field is greater than that predicted 

by ISCST3, and varies significantly with roughness height and atmospheric stability 

conditions. With respect to the transport of PM-10 in developed urban areas, the Veranth 

study indicates that regional transport of fugitive dust generated by sources within the 

developed area would not be transported any substantial distance due to impaction and 

deposition. This result suggests that transport of coarse fraction PM-10 across the 

developed portion of Maricopa County to the Salt River area is very improbable. 

A literature review conducted by DRI to assess the differences between soil contributions 

to PM-10 emission inventories and ambient concentrations also supports limits on the 

transport distance of fugitive dust.1

 After identifying an insufficient accounting in the 

research literature for injection heights, deposition losses, and horizontal impaction losses 

in dispersion models, the authors suggest that a first approximation for transportable dust 

is an assumption that all particles within 2 meters above ground level do not travel more 

than a few kilometers from emission sources. Such particles will generally be produced 

by groundlevel fugitive dust sources during stable atmospheric conditions, such as those 

found in the Salt River area during last winter’s series of exceedances. 

2.3 Source-Receptor Modeling Approaches

The use of Gaussian dispersion models to assess fugitive dust impacts during low wind 

conditions is problematical at best, based on the lack of correlation with measured 

concentrations reported in the ADEQ Salt River study and the analyses of ambient air 

quality data in Corcoran, California. Chemical mass balance models are also severely 

limited in identifying separate fugitive dust source-receptor relationships due to the 

homogeneity of soil elemental signatures over the limited influence zones near 

monitoring sites. As a result, other analytical tools were reviewed in this literature 

review for usefulness in quantifying individual source impacts at monitoring sites. 

Positive Matrix Factorization – Positive matrix factorization (PMF) is a statistical 

approach used to identify the elemental signatures of significant sources.2

 Because this 

method assumes that the elemental signatures of significant sources are sufficiently 

unique to be separately distinguishable, the use of this method has very limited 

applicability to PM-10 monitoring datasets dominated by fugitive dust source soil 

signatures of limited variability. Also, no PM-10 elemental data were collected during 

the episodes in the fall and winter of 2005/06 in the Salt River area and no comparable 

data were collected during the 2006/07 field study period. As a result, use of this 

analytical approach will not be useful in identifying significant fugitive PM-10 sources in 

the Salt River area at this time. 

 

1

 “Reconciling Urban Fugitive Dust Emissions Inventory and Ambient Source Contribution Estimates: 

Summary of Current Knowledge and Needed Research,” prepared by for the U.S. Environmental 

Protection Agency by Desert Research Institute, May 2000. 

2

 “Initial Exploration of Advanced Data Analysis Methods to Assist Air Quality Management,” prepared by 

Philip K. Hopke for the California Air Resources Board, 2005. 
-17- 

Nonparametric Regression – Nonparametric regression is a form of regression analysis in 

which the predictor does not take a predetermined form but is constructed according to 

information derived from the data. In an initial study, nonparametric regression was used 

to identify the contribution of emissions from a large petrochemical facility to ambient 

cyclohexane concentrations measured in an industrial area.1 In subsequent studies, the 

authors have evaluated contributions of large industrial and area sources on ambient 

concentrations of several non-reactive pollutants measured near large airports in Hong 

Kong and Los Angeles.2 While this approach appears to offer promise in evaluating the 

impacts from nearby fugitive dust sources in the Salt River area, it is limited to the 

identification of sources lying less than one hour’s distance by wind transport of a 

monitor as the approach cannot account for multi-hour wind trajectories from a source to 

a receptor. 

A number of source-receptor studies of PM-10 were reviewed in this literature search. 

Many rely on chemical analyses to provide useful signatures of significant sources. 

These studies typically involve areas in which emission inventories are dominated by one 

or more large industrial sources. Such studies have limited applicability to the Salt River 

area, as emissions in this area are dominated by fugitive dust sources that cannot be 

distinguished from each other by chemical signature. 

Trajectory Models – Trajectory models are used to describe the paths air parcels take, and 

have served to identify the potential locations of significant sources impacting monitoring 

sites. In many studies, these models have been run over domains of several hundred 

kilometers to identify regional or continental air quality influences. No study of 

neighborhood scale impacts being evaluated by trajectory model was found in the 

literature search. Also, most of the studies reviewed relied on three-dimensional wind 

field models to identify the vertical component of long distance transport. Such wind 

field data are not currently available for the design day period in the fall of 2005. The 

use of a simplified trajectory model using two-dimensional wind data from the Salt River 

monitoring sites may assist in identifying potentially significant sources of fugitive PM, 

as vertical mixing on the neighborhood scale and during winter inversion conditions is 

very limited. 

2.4 Conclusions

The literature review produced mixed results with respect to the challenges posed by 

fugitive dust sources in the Salt River area. First, with respect to the domain over which 

significant emission sources affecting a particular monitoring site may be deployed, the 

literature suggests both that very localized PM-10 sources dominate nearby air quality on 

low wind days, but also that regional sources play a role in contributing to exceedances of 

 

1

 “Locating Nearby Sources Of Air Pollution By Nonparametric Regression Of Atmospheric 

Concentrations On Wind Direction,” R.C. Henry et al, Atmospheric Environment, Vol. 36 (2002) 2237-

2244. 

2

 “Identifying The Impact Of Large Urban Airports On Local Air Quality By Nonparametric Regression,” 

K.N. Yu et al, Atmospheric Environment, Vol. 38 (2004) 4501-4507. 
-18- 

the federal 24-hour ambient air quality standard, especially under higher wind conditions. 

Second, no single analytical tool appears to have the power to correctly and separately 

identify the significant sources impacting a PM-10 monitor. As a result, several 

analytical approaches were employed in this study and the weight of evidence from these 

approaches was used to quantify PM-10 source-receptor relationships in the Salt River 

area. 

### 
-19- 

3. DATA ANALYSIS – AMBIENT MEASUREMENTS 

As discussed in Chapter 1, the field study conducted in November/December 2006 was 

designed to collect data on meteorology, ambient concentrations, particle size 

distributions, particle deposition, and source-specific activity within the Salt River area. 

This required a coordinated effort from Sierra, T&B Systems, MAG, Maricopa County, 

and several subcontractors. The collected data were subjected to a variety of analytical 

techniques to gain insight into the causes and conditions producing elevated 

concentrations as well as the behavior of suspended particles during exceedance 

conditions. Presented below is a summary of the results of the analyses of ambient 

measurements. A summary of the results of analyses of data needed to characterize 

source specific activity and emissions is presented in Chapter 4. 

3.1 Particle Size Distribution

To initially assess the contribution of secondary aerosol to peak PM-10 concentrations, 

the continuous PM-2.5 and PM-10 data collected by MCAQD in the Salt River area were 

reviewed. MCAQD monitored PM-2.5 continuously using TEOMs at the Durango 

Complex and West Phoenix sites in 2005 and 2006. Because of a calibration problem, 

PM-2.5 data collected prior to January 17, 2006, at the Durango Complex monitor were 

invalidated by MCAQD. Analysis of the remaining data at the Durango Complex site 

revealed ratios between PM-2.5 and PM-10 hourly concentrations that are shown in 

Figure 3-1. 

This figure shows that PM-2.5 to PM-10 ratios decline as PM-10 concentrations rise, 

suggesting that secondary aerosol and combustion particulate do not constitute significant 

components of PM-10 on winter exceedance days. 

To examine the same relationship just outside the Salt River modeling domain, similar 

data were compiled for the West Phoenix site. At this location, MCAQD collected valid 

hourly PM-2.5 and PM-10 data throughout the three months of stagnant weather that 

were the focus of this study—December 2005 through February 2006. The PM-2.5 to 

PM-10 relationships at this site are shown in Figure 3-2. 

The data from the West Phoenix site show much lower contributions of PM-2.5 to PM-10 

when PM-10 concentrations are high, and that PM-10 peaks occur less frequently than at 

the Durango Complex. A plot of PM-10 concentrations between January 17 and 

February 29, 2006, at the two sites is shown in Figure 3-3. 
-20- 

Figure 3-1 

Average Ratio of PM-2.5/ PM-10 for 

Hourly PM-10 Concentrations Exceeding Threshold Value 

Durango Complex – January 17, 2006 to February 28, 2006 

0

0.05

0.1

0.15

0.2

0.25

0.3

0 50 100 150 200 250 300 350 400 450 500 550 600

PM10 Threshold

Average PM2.5/PM10 Ratio

Figure 3-2 

Average Ratio of PM-2.5/ PM-10 for 

Hourly PM-10 Concentrations Exceeding Threshold Value 

West Phoenix – December 1, 2005 to February 28, 2006 

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 50 100 150 200 250 300 350 400 450 500 550 600

PM10 Threshold

Average PM2.5/PM10 Ratio
-21- 

Figure 3-3 

Hourly PM-10 at Durango Complex and West Phoenix 

January 17, 2006 to February 28, 2006 

0

100

200

300

400

500

600

700

800

900

1/17/06 1/24/06 1/31/06 2/7/06 2/14/06 2/21/06 2/28/06

PM10, ug/m3

DC PM10

WP PM10

Similarly, PM-2.5 concentrations at the West Phoenix site are lower than those recorded 

at Durango Complex during winter months. Plots of hourly PM-2.5 concentrations at the 

two sites for the January 17 – February 28, 2006 period when valid data were being 

recorded at both sites are presented in Figure 3-4. 

Figure 3-4 

Hourly PM-2.5 at Durango Complex and West Phoenix 

January 17, 2006 to February 28, 2006 

0

20

40

60

80

100

120

140

1/17/06 1/24/06 1/31/06 2/7/06 2/14/06 2/21/06 2/28/06

PM2.5, ug/m3

DC PM2.5

WP PM2.5
-22- 

The monitoring data from this period suggest that PM-2.5 concentrations in the Salt River 

area are locally generated and are not the result of emission transport from outside the 

modeling domain. By virtue of the higher PM-10 concentrations at Durango Complex in 

relation to those at the West Phoenix, the same can be said of PM-10 transport from north 

of the modeling domain. 

During the December 2006 portion of the intensive Salt River Area field study, T&B 

Systems used a multichannel particle counter to sample ambient particulate 

concentrations by particle size range in source plumes. The instrument used to conduct 

this monitoring, a TSI Aerodynamic Particle Size (APS) Counter, recorded particle 

counts in 52 diameter ranges extending from 0.5 to 20.0 microns. For this study, only the 

counts of particles less than or equal to 10 microns were analyzed. 

The APS particle counter does not operate well while in motion. As a result, particle 

counts were conducted only when the T&B vehicle carrying the instrument was 

stationary at discrete sampling locations. Particle size distributions were measured at the 

following locations in the Salt River Area: 

• West and east of 51st Avenue near Lower Buckeye Road; 

• Downwind of an agricultural tilling operation near 43rd Avenue and Elwood 

Street; 

• North and south of Lower Buckeye Road at 38th Avenue; 

• East and south of the Durango Complex monitoring site near 27th Avenue and 

Durango Street; 

• North of Lower Buckeye at 27th Avenue; and 

• West of 22nd Avenue near the City of Phoenix Fire Department Training Facility. 

A map of these monitoring locations, showing the ranges in average particle diameters 

recorded at sites near each monitoring location, is shown in Figure 3-5. 

The particle size distributions measured at these locations were strikingly similar to each 

other with one exception. One of three locations downwind of the agricultural tilling 

operation had a distribution weighted more toward larger particle sizes, probably because 

this one location—of all of the locations monitored—was directly in the downwind 

plume of a source producing substantial visible dust emissions. The average diameter of 

particles less than or equal to 10 microns, at locations other than in the agricultural tilling 

plume, varied between 3.8 and 5.0 microns. The average particle diameter in the tilling 

plume was 6.3 microns. These results suggest that particles above about 7 microns in 

diameter settle out of the air relatively quickly in the Salt River and that PM-10 ambient 

concentrations are dominated by particles from 3 to 7 microns in diameter. 
-23- 

Figure 3-5 

Average Particle Diameter Ranges at Salt River Area Sampling Locations 

As discussed in Section 3.3, particles in this size range are expected to remain aloft for 1 

to 5 hours. Under stagnant conditions, when wind speeds are less than 1 mile per hour 

and wind directions are shifting, this suggests local sources are responsible for the 

majority of the impacts recorded at the Salt River monitoring sites. 

3.2 Back Trajectory Analysis

To better determine the areas to be encompassed in the modeling domains for each of the 

Durango Complex and West 43rd Avenue monitors, back trajectory wind analyses were 

conducted to identify the areas traversed by winds blowing from contributing sources to 

the monitors. The datasets involved in these analyses were the five-minute average wind 

direction, wind speed, and continuous PM-10 data obtained from the MCAQD and the 

deposition times computed from empirical research. 

During the November-December 2006 field study, MCAQD configured the data 

recorders at the two monitoring sites to store five-minute averages of meteorological 

parameters and continuous PM-10 measurements. These hourly PM-10 data were 

reviewed to select times of peak PM-10 concentrations on days with low wind speeds. 

The highest PM-10 concentrations recorded during the field study occurred during 
-24- 

December 5-7, 2006. The federal 24-hour PM-10 standard was exceeded on all three of 

these days at the West 43rd Avenue site, and on December 6 and 7 at the Durango 

Complex site. Because these days were also characterized by very low winds, they were 

selected as being most representative of the meteorological conditions that occurred 

during the December 11-13, 2005 design day period. 

Particulate matter in the coarse fraction range, from 2.5 to 10 μm in aerodynamic 

diameter, is unique among air pollutants with respect to deposition properties. Because 

gravitational settling forces on particles in this size range are greater than the buoyant 

forces provided by air turbulence and Brownian motion, these particles settle out of the 

air within hours of being emitted and entrained into the ambient air. To estimate these 

settling times, a standard particle deposition velocity model was used to compute settling 

velocities of particles between 2.5 and 10 μm in diameter, and settling times given 

maximum entrainment heights above the ground were then computed. A description of 

the inputs used in the deposition velocity model, and the reported results, is presented in 

Chapter 3.5 below. 

From these data, three hours were selected as the interval over which to evaluate back 

trajectories of particles arriving at the two monitors at times of high PM-10 readings. 

The back trajectories were computed by calculating the prior locations every five minutes 

for three hours of the air parcel that arrived at the monitor at the target time. For each 

monitoring site, the five-minute average wind speed and direction data measured at that 

site were used to compute the back trajectories, assuming a uniform wind field. An 

example plot of back trajectory analytical results for the West 43rd Avenue monitor is 

presented in Figure 3-6. This plot represents the trajectory every five minutes of an air 

parcel that arrived at the monitor at 9:00 am on December 6, 2006. This time represents 

the peak hourly PM-10 concentration recorded at the monitoring site on this day. 

The analysis of three-hour back trajectories indicated that during low wind hours, air 

parcels were traveling no more than about 2 miles before arriving at either of the two 

monitors in the Salt River area. This result confirmed the view that the initial modeling 

domain of significant sources should extend out to two miles from each monitor, and this 

was therefore the radius in which emission inventory improvements and enhancements 

during the November-December 2006 field study were targeted. 

Significant PM-10 emission sources within a 2-mile radius of each monitor included 

several riverbed quarries, sand and gravel processing facilities, unpaved truck parking 

lots, concrete casting facilities, agricultural fields, and paved arterial streets. The 

numbers and sizes of these facilities were sufficiently large to provide a rich emission 

inventory with respect to producing significant impacts at each monitor. 
-25- 

Figure 3-6 

Back Trajectory of Winds Impacting the West 43rd Avenue Monitor 

 (December 6, 2006 at 9 a.m.) 

3.3 Particle Deposition Rate Modeling

To inform the analysis of back trajectories with respect to typical residence times of 

particles in the air between source and monitor, particle deposition velocities and rates 

were modeled under the meteorological conditions recorded on stagnant fall and winter 

days in the Salt River area. 

Hourly surface meteorological data are collected at each Salt River monitoring site by 

MCAQD. Mixing height data are collected by the National Oceanographic and 

Atmospheric Administration (NOAA) in Tucson. Using these two datasets, mixing 

heights were computed for the Salt River area during the design period. These mixing 

heights, together with PM-10 concentrations, at the West 43rd Avenue monitor on 

December 5 and 6, 2006, are shown in Figure 3-7. The nocturnal mixing heights during 

this period were low, on the order of 30 meters above ground level. These conditions 

kept fugitive dust emitted in the Salt River region trapped in a layer of air near the ground 

and near the site of emission for several hours each day. 
-26- 

Figure 3-7 

Summary of Monitoring Conditions at West 43rd

 (December 5-6, 2006) 

Time

 0:00 4:00 8:00 12:00 16:00 20:00 0:00

PM10 (ug/m3

)

0

100

200

300

400

500

600

700

800

900

0

100

200

300

400

500

600

700

800

900

PM10 24 hr average: 176 ug/m3

Mixing height

Mixing Height (m)

Time

 0:00 4:00 8:00 12:00 16:00 20:00 0:00

PM10 (ug/m3

)

0

100

200

300

400

500

600

700

800

900

0

100

200

300

400

500

600

700

800

900

PM10 24 hr Average: 160 ug/m3

Mixing Height 

Mixing Height (m)

DEC 5TH

DEC 6TH

Coarse PM-10, with particle diameters greater than 2.5 μm and smaller than 10 μm, 

settles out from the atmosphere within a few hours, depending on release height above 

the ground. To estimate the maximum residence time of coarse PM during the design 

period, particle deposition velocities were computed as a function of particle diameter 

and release height. An online deposition velocity model was used to perform this 

calculation.1

 Inputs to the model include fluid density, fluid viscosity, particle diameter, 

and particle density. The density of air at a temperature of 53°F (the average daily 

temperature recorded at the two monitoring sites on December 5-6, 2006) and an 

elevation of 1,030 ft (the elevation of the two monitors as reported by the Google Earth 

website2) was found to be 0.0742 lb/ft3 (1.189 kg/m3), as computed by an online gas 

density calculator.3 The viscosity of air at this temperature and elevation was computed 

 

1

 http://www.filtration-and-separation.com/settling/settling.htm, accessed on October 15, 2006. 

2

 http://www.earth.google.com, accessed on October 15, 2006. 

3

 http://www.denysschen.com/catalogue/density.asp, accessed on October 15. 2006. 
-27- 

on another online fluid dynamics calculator to be 1.80x10-5 kg/m-sec.1 Particle release 

height was assumed to be the mixing height, which conservatively overestimated 

residence time since entrained particles are mixed throughout the mixing layer, not 

simply concentrated at the top of the layer. Particle density was assumed to be 2.65 

gm/cc, the average soil particle density as reported in a soils science syllabus.2 The 

calculated settling velocities and the residence times as functions of particle diameter and 

inversion height are presented in Table 3-1. 

Table 3-1 

Particle Residence Times (hr) 

Particle 

Diameter 

(μm microns) 

Settling 

Velocity 

(m/hr) 

Inversion/Release Height (m) 

20 30 40 

10 28.9 0.69 1.04 1.39 

8 18.5 1.08 1.35 1.89 

6 10.4 1.92 2.41 3.37 

4 4.6 4.33 5.41 7.58 

2.5 1.8 11.08 13.85 19.40 

Assuming a uniform distribution of particles within each particle size range between PM2.5 and PM-10, the mass average particle diameter within this size range was computed 

to be 7.98 μm (= [(10^3 + 2.5^3)/2]^(1/3)). For this particle diameter, at a release height 

of 40 meters, the deposition time was calculated to be 1.89 hours. On the basis of this 

result, a two-hour deposition time was selected in computing the radius of the modeling 

domain around each monitor. 

As Figure 3-7 indicates, PM-10 concentrations remained above 100 μg/m3

 at the West 

43rd monitor after 4:00 pm on December 5 and 6 when the inversion height dropped. 

Because industrial activity and truck traffic levels were low and continued to decline after 

4:00 pm, it was expected that PM-10 concentrations would also decline. However, as 

these plots show, PM-10 concentrations rose to 300 μg/m3

 (5-minute average) at about 

6:00 pm and then slowly declined to about 100 μg/m3 (5-minute average) at midnight. 

After several possible sources of this elevated PM-10 were considered and discounted, it 

was hypothesized that particulate mixed into the deeper surface mixing layer during the 

day was settling out of the atmosphere in the evening after vertical mixing resulting from 

solar radiation ceased and the mixing height dropped. To evaluate the possibility of this 

phenomenon, particle trajectories were plotted at an elevation of 50 meters above the 

ground using SODAR measures of wind aloft measured at the West 43rd Avenue site. 

 

1

 http://www.lmnoeng.com/Flow/GasViscosity.htm, accessed on October 15, 2006. 

2

 http://www.ju.edu.jo/ecourse/Lw%20Environment/Materials/lecture%2003.htm, accessed on October 15, 

2006. 
-28- 

This analysis was not a back trajectory, but a forward trajectory as it documented where 

the air parcels move in the 8-hour period following the mixing height drop. The results 

of that analysis are displayed in Figure 3-8. 

Figure 3-8 

Forward Trajectory of Winds Aloft Starting at the 

West 43rd Avenue Monitor at 5:00 p.m., December 6, 2006 

This plot shows that winds just above the inversion height tend to transport particulate 

mixed aloft during the day quickly out of the modeling domain. Similar results were seen 

from forward trajectory analysis of radar data of higher altitude winds. Thus, this 

particulate does not deposit within the modeling domain and fails to contribute to 

elevated groundlevel concentrations during nocturnal hours. In truth, the analysis was 

unable to fully explain how nocturnal PM-10 levels were kept elevated during stagnant 

days.1

 

1

 The lack of transport combined with low mixing heights and wind speeds suggests sources operating 

nearby at night are most likely to be the cause of the elevated nocturnal PM-10 levels. 
-29- 

3.4 Particle Deposition Monitoring

To better understand particle deposition dynamics in the Salt River Area, MAG asked 

Sierra to study dust fallout near the Durango Complex and West 43rd Avenue monitors. 

To conduct this study, particulate matter deposition was monitored using dust fall jars 

over one-week periods at four locations surrounding each monitor by Applied 

Environmental Consultants (AEC), a subcontractor to Sierra Research. Generally, one jar 

was placed between the monitor and the nearest arterial road, one monitor was placed on 

the opposite side of the monitor, and two were placed at other locations of interest near 

the monitor. 

The jars consisted of polyethylene tubs approximately 18 inches in diameter and 6 inches 

deep, mounted on top of portable wooden stands 6 feet in height. Jars were prewashed 

with deionized water and transported to and from the sampling locations with plastic 

covers to avoid contamination or loss of sample during transport. Upon return of each jar 

to AEC laboratories, the jar was rinsed with deionized water using a rubber policeman to 

remove particulate from the jar, and the aqueous solution was labeled and stored. 

Since the mass of particulate in each solution was very small, the use of standard soil test 

methods for determining particle size was ineffective. After discussion with several 

Phoenix-area soils laboratories, Sierra learned of a particle counting method that offered 

the ability to quantify trace levels of particulate in aqueous solutions by particle diameter 

range. Particle Measurement Technology in Ventura, California, was retained to conduct 

particle counts using a laser counting technology. Only a portion of each solution was 

used in each count, allowing for the use of duplicate counts to quantify instrumental 

precision. 

The particle counts were converted to particle mass using standard conversion methods. 

All particles were assumed to be spherical with an average density of 2.65 grams per 

cubic centimeter.1

 The results of the jar analyses are shown in Table 3-2. 

The size distributions of particles collected by the dustfall jars were weighted more 

toward coarser particle diameters than the ambient samples analyzed by the T&B 

Systems APS counter. This could result from the jars being placed closer to significant 

emissions sources (e.g., arterial roads) than was the case for the APS sampling locations. 

Plots of the dustfall jar networks and the rates of PM-10 collected at each jar at the 

Durango Complex and West 43rd monitoring sites are shown in Figures 3-9 and 3-10, 

respectively. 

 

1

 http://www.ju.edu.jo/ecourse/Lw%20Environment/Materials/lecture%2003.htm, accessed on October 15, 

2006. 
-30- 

Table 3-2 

Size Fraction of Dustfall Collected Near 

Durango Complex and West 43rd Avenue Monitors 

Size Range Durango Complex West 43rd

 Avenue 

#1 #2 #3 #4 #1 #2 #3 #4 

0-2.5 μm 8.0% 7.0% 10.8% 17.2% 18.4% 23.7% 20.7% 9.8%

2.5-5.0 μm 15.5% 15.7% 15.3% 18.1% 18.2% 21.0% 20.4% 17.7%

5.0-7.5 μm 30.3% 31.9% 28.2% 25.9% 26.0% 24.4% 24.9% 31.0%

7.5-10.0 μm 46.2% 45.5% 45.7% 38.8% 37.3% 30.9% 33.9% 41.5%

Mean Dia. μm 6.6 6.7 6.5 5.9 5.8 5.3 5.6 6.4 

Figure 3-9 

Durango Complex Dustfall Jar Network 
-31- 

Figure 3-10 

West 43rd Avenue Dustfall Jar Network 

Review of the dustfall data from each of the Durango Complex and West 43rd Avenue 

monitoring sites shows significant gradients in particle deposition, but fails to provide 

useful information with respect to sources of emitted particles. At the Durango Complex 

site, for example, the highest deposition site is the northern-most jar, which collected 

almost three times as much PM-10 as the jar co-located with the MCAQD continuous 

monitors. This gradient suggests that a strong emission source lies to the north of the 

dustfall network, and yet no significant sources were found in this direction during site 

inspections. The results from jars #1 through #3 also suggest that particle deposition is 

greater at sites away from 27th Avenue than near it, which is somewhat counterintuitive. 

In the area surrounding the dustfall network, paved road emissions on 27th Avenue and 

unpaved road emissions from the truck yard to the east of 27th Avenue were found 

through emission analysis to be the most significant local sources of PM-10. 

The dustfall data from the West 43rd Avenue dustfall network raise similar questions. 

The gradient shown by the dustfall mass at jars #1 and #2 suggests paved road emissions 

on Broadway Road to be the dominant source impacting these sites. The mass collected 

at jar #3, which is the same distance from the road as jar #2, however, shows lower 

dustfall levels than were measured at jars #1 or #2. The dustfall mass at jar #4, which is 

located closer to the Vulcan Materials quarry site—the most significant stationary source 

of PM-10 emissions near the West 43rd Avenue monitor—is lower than the mass 
-32- 

collected at jar #2. Again, the dustfall mass data raise a number of questions that require 

additional monitoring to answer. 

3.5 Transport Monitoring

To identify PM-10 gradients within the modeling domain, to assess the significance of 

pollutant transport into the modeling domain, and to identify the plumes of significant 

sources near the Salt River monitors, traverses across the modeling domain were 

conducted during the field study using vehicles equipped with continuous monitors. 

These mobile monitoring systems were constructed by T&B Systems to include 

DustTraks and MiniVols separately sampling PM-10 and PM-2.5; a GPS system to 

monitor vehicle position, speed, and altitude; a compass system for monitoring vehicle 

orientation; and a roof-mounted system for monitoring wind speed, direction, and 

temperature. This monitoring was conducted in two phases: an initial assessment of 

pollutant gradients using long longitudinal and latitudinal traverses, and a follow-up 

assessment of specific source impacts using close proximity upwind/downwind traverses. 

During the first field study period, between November 13 and 19, 2006, T&B Systems 

operated one van-based mobile monitoring platform, collecting PM, meteorological, 

location, and photographic data in a series of traverses in the Salt River area. During this 

phase, the following initial monitoring objectives were pursued: 

• Locating the van at each of the two Salt River monitors for a few hours to 

calibrate the DustTraks. 

• Driving a circumferential route around the proposed modeling domain to assess 

boundary conditions, the modeling domain was bounded by Buckeye Road, 7th

Avenue, Southern Avenue, and 51st Avenue. 

• Driving north-south transects across the modeling domain (on 19th and 35th

Avenues) to identify PM-10 gradients over distance and time. 

• Driving east-west transects (along Lower Buckeye Road and West Broadway) to 

identify PM-10 gradients. 

• Driving east-west in the area to the north of the modeling domain to assess 

gradients and evaluating the potential for transport into the Salt River area from 

the north. 

• Driving north-south in the area to the east of the modeling domain to assess 

gradients and evaluating the potential of transport into Salt River from the east, 

especially during drainage flows in the early morning. 

At the conclusion of the first phase, the collected data were analyzed by T&B Systems 

and Sierra Research for use in planning the second phase of the field study. Because the 

first phase of monitoring showed no evidence of significant PM-10 transport into the 

modeling domain from upwind areas, the second phase was designed to study the plumes 

of sources within the modeling domain. During the first phase, T&B Systems learned 

that the van-based platform was sufficiently automated as to enable its operation by a 
-33- 

single vehicle occupant. Thus, a second van was instrumented and operated much like 

the first and the data collection activities were doubled during the second phase. A laserbased particle counter capable of characterizing particulate within 53 size ranges between 

0.0 and 20 μm in diameter was added to one of the vans to further research particle size 

signatures of various sources. 

The DustTraks were programmed to record ambient concentrations over 2-second 

averaging times. At an average speed of 30 miles per hour, this setting allowed for the 

recording of an ambient concentration every 88 feet along the traveled route. In this 

mode, the mobile DustTraks were used as a survey tool to map PM-10 and PM-2.5 

gradients within and near the modeling domain. The DustTraks were calibrated by 

comparison to filter-based MiniVol PM-10 and PM-2.5 monitors operated in the van 

during the hours of travel. The MiniVols were also equipped with extended inlets located 

just below the DustTrak inlets. At the end of several testing periods, the MiniVol 

concentrations were compared to the average DustTrak concentrations, and the DustTrak 

2-second averages were adjusted by the MiniVol:DustTrak ratio of average daily 

concentrations. 

An example of the data collected in the first phase during an internal monitoring domain 

traverse series is presented in Figure 3-11. In this plot, the lowest PM-10 concentrations 

are represented by black dots. These concentrations appear in areas that were not close to 

significant sources and on roads that did not have elevated silt loadings. Elevated road 

Figure 3-11 

Summary of PM-10 Mobile Monitoring Data 

on November 15, 2006 
-34- 

silt loadings in heavily trafficked areas are suspected of increasing the ambient 

concentrations recorded by the monitoring vans independent of stationary source plumes 

near roadways. These roadway background emissions resulted from traffic resuspending 

road silt in vehicle trailing vortex plumes that remained over roadways for minutes at a 

time during low wind conditions. The light blue dots, recording ambient concentrations 

between 150 and 400 ug/m3, appear to represent roads with higher road silt and higher 

traffic levels due to their extent. The infrequent concentration peaks represented as 

yellow and red dots appear to represent the presence of stationary sources or very heavily 

soiled trackout areas where heavy-duty trucks enter arterial roads from unpaved areas. 

An example of monitoring traverses conducted outside of the modeling domain in search 

of elevated PM-10 concentrations being transported into the modeling domain is 

presented in Figure 3-12. This plot shows that in the early morning, when inversion 

heights are very low prior to groundheating, ambient PM-10 concentrations north of the 

modeling domain are low and very uniform. These results suggest that on this particular 

day, PM-10 concentrations north of the modeling domain were generally lower than 

those within the modeling domain and, thus, no plumes of PM-10 being transported into 

the Salt River area from the north were observed. 

A full set of domain-wide traverse plots recorded during the first phase of the field 

monitoring program is contained in Appendix B. 

 

Figure 3-12 

Summary of PM-10 Monitoring Data 

on November 16, 2006 – After 8 a.m. 
-35- 

3.6 Non-Parametric Regression

Non-parametric regression, one of the analytical tools described in Chapter 2, was used to 

identify the locations of significant PM-10 sources impacting each of the two monitoring 

sites. This work was performed by Rincon Ranch Consulting (RRC), a statistical 

analysis subcontractor to Sierra. 

The regression analyses developed by RRC used 5-minute average PM and 

meteorological measurements from the Durango Complex and West 43rd Avenue sites 

from the field study time period of November 15 through December 15, 2006. Data 

points missing any of the three values (PM-10 concentration, wind direction, wind speed) 

were omitted from the analyses. Data points with the wind speed below 1.0 mph were 

also omitted because the wind direction was poorly defined at such low speeds. The 

resulting data sets used in the analysis for each site included approximately 6,000 data 

points. 

The distribution of PM-10 concentrations versus wind direction for the Durango 

Complex and West 43rd Avenue sites is presented in Figures 3-13 and 3-14. These plots 

suggest that winds are observed for all directions, but that the predominant wind 

directions are easterly and westerly. 

The distribution of PM-10 concentration versus wind speed for Durango Complex and 

West 43rd Avenue monitors is shown in Figures 3-15 and 3-16, respectively. The 

Durango Complex plots show the effect of winds in excess of 6 mph in creating blowing 

dust by the absence of low PM-10 data points above this threshold velocity. This effect 

occurs at a higher wind speed of 14 mph at the West 43rd Avenue monitor. 

The meteorological datasets were further combined to identify the sample size of 

combinations of wind speed and direction. This analysis was conducted to assess the 

distribution of wind speed versus direction and inform the subsequent analysis of PM-10 

concentrations in relation to these meteorological parameters. The merging of wind 

speed and direction data was performed by compiling data counts within bins that 

spanned 5 degrees of wind direction and 2 mph of wind speed. The resulting plots of the 

samples sizes within these bins are shown in Figures 3-17 and 3-18 for the Durango 

Complex and West 43rd Avenue sites, respectively. 

The highest sample size counts were in the vicinity of azimuth 70-110 degrees for 

Durango Complex and 270 degrees for West 43rd Avenue sites at wind speeds of 4 to 6 

mph. These plots show that there were very few, if any, high wind conditions from the 

south and from the north. The upper boundaries of the contour plot were useful in 

gauging results of the non-parametric analysis because they show the directions in which 

there are no data under high-wind conditions. 
-36- 

Figure 3-13 

PM-10 Concentration versus Wind Direction 

Durango Complex Monitor, 11/15 – 12/15/06 

Figure 3-14 

PM-10 Concentration versus Wind Direction 

West 43rd Avenue Monitor, 11/15 – 12/15/06 
-37- 

Figure 3-15 

PM-10 Concentration versus Wind Speed 

Durango Complex Monitor, 11/15 – 12/15/06 

Figure 3-16 

PM-10 Concentration versus Wind Speed 

West 43rd Avenue Monitor, 11/15 – 12/15/06 
-38- 

Figure 3-17 

Average PM-10 Concentration by Wind Speed and Direction 

Durango Complex Monitor, 11/15 – 12/15/06 

Figure 3-18 

Average PM-10 Concentration by Wind Speed and Direction 

West 43rd Avenue Monitor, 11/15 – 12/15/06 
-39- 

3.7 Non-Parametric Results for the One Dimensional Case

One dimensional non-parametric analysis was used with wind direction data to identify 

significant sources. In this type of analysis, data are smoothed using a filtering technique 

that weights PM-10 concentrations within a narrow band on each side of the wind 

direction being studied. 

The results of this analysis for the Durango Complex and West 43rd Avenue sites are 

presented in Figures 3-19 and 3-20, respectively. In this analysis, only low wind 

conditions, defined as wind speeds less than or equal to 6 mph, were evaluated. The red 

line in each figure represents the average PM-10 concentration estimated by the nonparametric technique. The black lines are the upper and lower 95 percent confidence 

limits. The averages are based on a window of size ±7.5 deg in azimuth. Note that the 

averages over the one month period are never below about 75 μg/m3 at Durango Complex 

and 55 μg/m3

 at West 43rd Avenue. All of the average values are statistically significant, 

showing that these monitors had elevated PM-10 readings no matter which direction the 

wind was blowing. 

Figure 3-19 

One-Dimensional Non-Parametric Regression of 

PM-10 Concentrations and Wind Direction at 

Durango Complex Monitor, Nov. 15 – Dec. 15, 2006 
-40- 

Figure 3-20 

One-Dimensional Non-Parametric Regression of 

PM-10 Concentrations and Wind Direction at 

West 43rd Avenue Monitor, Nov. 15 – Dec. 15, 2006 

Although the figures are optimally smoothed, there is still a fair amount of variability in 

the lines. To decide whether two adjacent peaks are likely to represent different sources, 

or are more likely from the same source, the 95% confidence limits must be examined. If 

the lower confidence limit of one peak approaches the upper confidence limit of the next 

peak (or vice versa), then the sources are likely distinct. In Figure 3-19, the first peak (at 

about azimuth 25) may or may not be distinct from the broad sequence of peaks from 

about azimuth 35 to 105. In fact, the azimuth range from 25 to 105 takes in essentially all 

of the truck yard to the east of the Durango Complex monitor, with azimuth 25 being the 

northwest corner of the lot at the intersection of 27th Avenue and Durango Street. 

The peaks evident in Figure 3-19 and the potential sources near the Durango Complex 

monitor are listed in Table 3-3. 
-41- 

Table 3-3 

Azimuth of Non-Regression Peaks at Durango Complex Monitor 

November 15, 2006 – December 15, 2006 

Source 

No. 

Azimuth Range 

(degrees) 

Peak Azimuth 

(degrees) Comments 

1 12-31 22 Probably part of source 2 

2 32-105 broad Truck yard 

3 130-205 130, 167, 174, 199 

4 264-285 broad May be part of source 5 

5 313-322 315 

6 341-358 344 May be part of source 5 

The corresponding peaks evident in Figure 3-20 and the potential sources near the West 

43rd Avenue monitor are listed in Table 3-4. 

Table 3-4 

Azimuth of Non-Regression Peaks at West 43rd Avenue Monitor 

November 15, 2006 – December 15, 2006 

Source 

No. 

Azimuth Range 

(degrees) 

Peak Azimuth 

(degrees) Comments 

 1 354 - 29 21 Broadly elevated. 

Peak = 87 ug/m3 

 2 29 – 56 41 Peak = 93 ug/m3 

 3 59 – 89 71 Peak = 99 ug/m3 

 4 90 – 126 104 Broadly elevated. 

Peak = 70-75 ug/m3 

 5 128 – 172 136-164 Broadly elevated. 

Peak = 105-115 ug/m3 

 6 174 – 209 190-196 Peak = 170-180 ug/m3 

 7 211 – 228 213 – 218 

Peak = 90-95 ug/m3 

May be shoulder between larger peaks, 

not new source 

 8 229 – 251 239-241 Peak = 145 ug/m3 

 9 261 – 270 263 Peak = 102 ug/m3 

10 271 – 300 --- PM levels are elevated, but tail off 

Probably shoulder for sources in SW-W 

11 304 – 347 334-335 Peak = 76 ug/m3 
-42- 

While the one-dimensional regression method is useful as a screening tool, the method 

does not well isolate contributions from nearby sources in a manner that allows for the 

sources to be accurately identified. This probably reflects the fact that there are a number 

of potential sources surrounding both the Durango Complex and West 43rd Avenue sites. 

3.8 Non-Parametric Results for the Two-Dimensional Case

In subsequent analyses, a two-dimensional non-parametric method was used to improve 

on the results found in the one-dimensional approach. In these analyses, PM-10 

concentrations were grouped by wind speed and direction, and then filtered within 

narrow bands to allow for the concentrations found in adjacent bands of wind speed and 

direction to be weighted and contribute to the band being analyzed. Figure 3-21 shows a 

contour plot for the average PM-10 concentration versus wind direction and speed at the 

Durango Complex monitor. These averages are outputs of the two-dimensional nonparametric regression using smoothing windows of ±5 deg in azimuth and ±1 mph in 

Figure 3-21 

Two-Dimensional Non-Parametric Regression of 

PM-10 Concentrations, Wind Direction, and Wind Speed at 

Durango Complex Monitor, Nov. 15 – Dec. 15, 2006 
-43- 

wind speed.1 Only average values that exceed their standard deviations by enough to 

achieve 95% statistical confidence of being non-zero have been plotted. The empty areas 

below the dark blue contour line are largely areas where there are no (or nearly no) data. 

The highest concentrations in this plot (above 400 μg/m3) are at high wind speeds in the 

following directions: 

• Azimuth 40-50 degrees; 

• Azimuth 250-280 degrees; and 

• Azimuth 340 degrees. 

The first azimuth points directly to the center of the truck yard to the east of the Durango 

Complex monitor. This hot spot suggests that the highest emissions from the truck yard 

are generated by windblown dust and not by truck movement during low wind 

conditions. Azimuth 250-280 appears to point toward agricultural fields to the west 2.0 

to 2.5 miles away, and azimuth 340 appears to point in a direction that has a number of 

plots of bare land. Because the hot spots at these azimuths occur at relatively high wind 

speeds, these sources may have greater surface roughnesses, which would raise the 

threshold wind speed for dust entrainment, than that of the truck yard. The lesser peak at 

azimuth 70 points toward the bend in I-17. 

The highest concentrations at the West 43rd Avenue monitor in Figure 3-22 (above 400 

μg/m3) are at high wind speeds in the following directions: 

• Azimuth 220-260 degrees; and 

• Azimuth 290-300 degrees. 

There is also have one spot above 400 μg/m3

 near azimuth 190 at wind speeds of about 

6 mph. There are no other extreme PM areas at low wind speeds. 

The first azimuth points to the Vulcan Materials quarry, the Coreslab Structures concrete 

casting facility, and the Vulcan Materials processing facility southwest of the monitor. 

The second azimuth points toward the Salt River channel and the Arizona Materials 

quarry to the northwest of the monitor. 

 

1

 These smoothing parameters were determined to be optimal by the cross-validation algorithm discussed 

in the two published papers referenced in Chapter 2. 
-44- 

Figure 3-22 

Two-Dimensional Non-Parametric Regression of 

PM-10 Concentrations, Wind Direction, and Wind Speed at 

West 43rd Avenue Monitor, Nov. 15 – Dec. 15, 2006 

Note two points, however, with respect to this method: 

• The method does not provide information in every azimuth, because high winds 

occurred only in some directions during the period of the data. The preceding list 

is likely to miss many sources. 

• It is easy to generate possible explanations, because disturbed undeveloped lands 

extend in almost every direction from the monitor. In this case, correlation does 

not prove causation. 

From these results, it appears that the non-parametric technique may be more precise in 

identifying the directions to strong sources under high wind conditions than at low wind 

conditions. This is because the wind direction will be well-defined and an air cell will be 

blown to the monitor relatively quickly, even from greater distances, before settling and 

dispersal can have much effect. 
-45- 

A further comment about wind speed is warranted. This parameter is a useful second 

axis in the analysis, but one that is not easy to interpret. Hot spots at high wind speeds 

should not be interpreted as a rough measure of distance to source. Distance may be part 

of its interpretation, but only in the sense of setting an outer envelope within which 

sources can be observed; this envelop is larger (more distant) with higher winds, and 

smaller (more localized) with light winds because the air parcels would disperse or settle 

before reaching the monitor. 

Overall, the use of non-parametric regression to identify the locations of significant 

sources is limited when sources surround the monitor at varying distances. 

3.9 Field Observations

During the November-December 2006 field study, the source strengths of potentially 

significant sources were visually recorded using still photography and video. Some 

images were recorded before dawn as the data collection effort of T&B Systems was 

conducted during the period between 4:00 am and noon each day during the study period. 

This period was selected because peak hourly PM-10 concentrations recorded during the 

design day interval of December 11-13, 2005, were recorded during these hours. Most 

photographed sources were episodic in duration, as continuous sources with significant 

emissions generally would have been cited by MCAQD inspectors and fined. 

Examples of significant episodic sources observed during the field study included 

agricultural plowing, trackout reentrainment, dragout plumes, and open burning, among 

others. During the field study, cotton fields were being plowed under to prevent pink 

bollworm attack, and field preparation activities were being conducted almost around the 

clock. A photograph of the plume generated by one plowdown operation is shown in 

Figure 3-23. 

The reentrainment of soil tracked out onto paved roads from unpaved surfaces was 

readily visible during the field study at heavily loaded intersections. A view of one of 

these intersections—at the unpaved entrance to a construction site near Southern Avenue 

at 27th Avenue—is shown in Figure 3-24. 

When large trucks exit from unpaved areas onto paved roads without stopping for traffic, 

the plume entrained in the trailing vortices of the truck is drawn onto the paved road. 

When this happens, the entrained particulate in the plume deposits onto the roadway 

where it is ground into smaller particles and reentrained into the air by passing traffic. A 

photo of truck “dragout” appears in Figure 3-25. 

Truck movement on paved roads with unpaved shoulders also entrains particulate matter 

from the shoulder surface by action of the truck’s bow wake. The strong vortices created 

near the front of a moving truck entrain loose surface particles on that portion of the 

unpaved shoulder near the paved lane and deposit some of that particulate onto the road 
-46- 

Figure 3-23 

Agricultural Field Plowing in the Salt River Area 

December 5, 2006 

Figure 3-24 

Reentrainment of Paved Road Trackout in the Salt River Area 

December 12, 2006 
-47- 

Figure 3-25 

Entrained Particulate in a Truck Plume Being Dragged Onto a Paved Road in the 

Salt River Area - December 14, 2006 

surface, where it also is ground into smaller particles and reentrained by passing traffic. 

A photograph of a truck bow wake plume is shown in Figure 3-26. 

Vehicle travel over unpaved shoulders is also an episodic source of PM-10 emissions. 

These emissions can be seen in the photograph in Figure 3-27, which was taken just 

across 27th Avenue from the Durango Complex monitor. 

The open burning of scrap wooden poles may have influenced PM-10 concentrations 

recorded by the West 43rd Avenue monitoring site. A photograph of this activity, taken 

from the platform on which the monitor is located, is shown in Figure 3-28. AEC noted 

that unique black particles were captured in the dustfall jar located on the southeast 

corner of the intersection of Broadway Road and West 43rd Avenue, across Broadway 

Road from the site of the open burning. 

All of these sources are episodic, with the exception of the open burning shown in 

Figure 3-28, which means that emissions are difficult to quantify because of the short 

duration. Because the frequencies of emissions from these sources are high in the Salt 

River area, however, the emissions contribute to background air quality and to 

concentrations measured at monitoring sites. 
-48- 

Figure 3-26 

Truck Bow Wake Plume in the Salt River Area 

December 12, 2006 

Figure 3-27 

Unpaved Shoulder Travel Plume in the Salt River Area 

December 12, 2006 
-49- 

Figure 3-28 

Open Burning of Scrap Wooden Poles Near Broadway Road at West 43rd Avenue 

December 6, 2006 

### 
-50- 

4. DATA ANALYSIS – SOURCE CHARACTERISTICS 

4.1 Travel

Because the hourly PM-10 concentrations during the December 11-13, 2005 design 

period showed daily maximum levels occur in mid-morning, preceded by a steady rampup starting at about 4:00 am, Sierra initially concluded that morning traffic over primary 

roads within 2 miles of each monitor was a significant source impacting the two 

monitors. As a result, Sierra recommended that traffic counts be conducted on primary 

roads in the Salt River area during the field study period. MAG tasked Field Data 

Services (FDS) to conduct these counts during the period of December 4-8, 2006. 

Traffic counts were conducted on 57 road sections simultaneously during this period. 

Many of the traffic counters were capable of differentiating between three axle-class 

groupings (2 axles, 3 to 4 axles, and 5 and more axles). Some counters only counted total 

traffic. The two different types of counters were designated as class counters and traffic 

counters, respectively. A map of the counter locations is shown in Figure 4-1. 

The instruments summed counts every 15 minutes for the five days of operation. Counts 

were recorded separately for each direction of travel. For the Sierra analysis, the 15-

minute traffic counts were summed into hourly counts, and the hourly counts in each 

direction were summed to determine total hourly counts per counting location. 

An example of the class count results is presented in Figure 4-2. This plot illustrates the 

average hourly counts on the portion of 27th Avenue between Durango Road and Lower 

Buckeye Road on December 5 through 7, 2006. Each hourly bar is divided into counts 

for light-duty vehicles (LDV), medium-duty trucks (MDT), and heavy-duty trucks 

(HDT). These vehicle class terms are used to identify the three ranges of axle classes 

measured in the traffic counts. 

The diurnal distribution of travel on the monitored arterials reflected the rush hour peaks 

and profile shown in Figure 4-2. Sharp increases in traffic occurred between 4:00 am and 

7:00 am, when mixing heights were low, corresponding to similar increases in PM-10 

recorded at the two monitors. This relationship reinforced the hypothesis that paved road 

traffic was a primary source of reentrained PM-10 on stagnant winter days when 

exceedances were recorded.
-51- 

Figure 4-1 

Traffic Counter Locations in the Salt River Area 

December 4-8, 2006 

Figure 4-2 

Average Hourly Traffic on 27th Avenue 

Between Durango and Lower Buckeye 

 (December 5-7, 2006) 
-52- 

The axle-class traffic distribution also confirmed video observations recorded during the 

field study by T&B Systems. Heavy duty truck trips increased dramatically after 6:00 am 

on winter days. Many of these trips began at industrial sites and truck yards with 

unpaved surfaces, resulting in the trackout of soil onto paved roads. This trackout 

material was then rapidly reentrained by passing traffic and suspended into the limited 

mixing layer, adding to the increasing concentrations of PM-10 contributed by industrial 

stationary sources and unpaved yard and road travel before the solar groundheating 

caused the inversion to break. 

The collection of traffic in three axle classes was also important in the estimation of 

paved road travel emissions for modeling purposes. Vehicle weight is a key determinant 

of fugitive dust emissions on paved roads as it is raised to the 1.5 power in the AP-42 

equation; paved road travel PM-10 emissions from one 80,000 truck and trailer 

combination are equal to those of almost ninety 2-ton light-duty vehicles. 

4.2 Silt Loadings

To better characterize the emissions of individual vehicles traveling over Salt River 

arterial roads during the field study period, Sierra Research subcontracted with the 

University of California Riverside (UCR) to collect paved road dust emission data using 

a trailer-based mobile sampler. The mobile sampler, known as the System for 

Continuous Aerosol Monitoring of Particulate Emissions from Roadways (SCAMPER), 

consists of a pair of DustTraks mounted on a van-trailer platform to monitor ambient PM10 ahead of and behind the van in motion. The use of DustTraks allows for continuous 

monitoring of PM-10 while traversing a roadway circuit. This system is especially useful 

in quickly surveying variations in roadway emission rates over significant lengths of 

roadway. These roadway emission rates correlate to silt loadings on the roads being 

traversed through use of the AP-42 emission factor equation for paved road travel. To 

evaluate seasonal changes in roadway emissions, MAG contracted with UCR to conduct 

urban-wide street traverses during March, June, September, and December of 2006. 1 For 

the second phase of the field study (December 4-8, 2006), the SCAMPER was used to 

map the variability of roadway emission rates in traverses across the modeling domain 

and on single road links within the domain from hour to hour. 

The emission measurements collected in the urban traverses for MAG and within the 

modeling domain for Sierra were reported by UCR in units of milligrams of PM-10 per 

meter of roadway travel (mg/m) and were converted by Sierra to g/mi. The data from all 

of the routes were summarized in the form of a histogram recording the number of 

roadway segments on which average emission factors for each segment/month were 

reported. These data, showing the overall distribution for the full calendar year, are 

presented in Figure 1. 

 

1

 U.C. Riverside, MAG Silt Loading Study, February 2007. 
-53- 

Figure 4-3 

Distribution of SCAMPER Emission Factors for 2006 

Past silt measurement studies conducted in the Salt River area typically focused on 

representing roads with trackout (i.e., visible trackout). Thus, there are few data to 

profile roads that do not have visible trackout. Despite the lack of information, the 

expectation is that roads with visible trackout occur much less frequently than roads with 

no visible trackout. The SCAMPER data are consistent with this expectation. 

As seen in the Figure 4-3, there are a large number of roadway segments with emission 

factors between 0.00 and 0.20 g/mi, while a relatively smaller number of roadways have 

factors that extend up to 5 times as high (1 g/mi). From these data it is reasonable to infer 

that the more numerous roadways at the lower end of the emission factor distribution 

correspond to lightly silted roads with no visible trackout, while roadways with 

substantially higher emission factors correspond to heavily silted roads. 

A quantitative cutpoint between the two roadway groups was estimated by fitting a 

normal distribution to the peak at the lower end of the emission factor distribution. The 

roadways with no trackout can be summarized as a population of 216 segments with a 

mean emission factor of 0.10 g/mi and a standard deviation of 0.04 g/mi (the dotted line 

in the figure). The counts of roadway segments begin to consistently exceed the 

population distribution for lightly silted roadways with no visible trackout at 0.20 g/mi 

and above. Thus, a SCAMPER emission factor of 0.20 g/mi was chosen as the cutpoint 

to distinguish between roads with and without visible trackout. 
-54- 

The SCAMPER emission factors were converted to equivalent roadway silt levels using 

the AP-42 paved road emission factor equation. For the SCAMPER vehicle weighing 2.6 

tons, this emission factor translates to a baseline silt loading of 0.011 g/m2

, which is 

significantly below any loading measured by the AP-42-recommended sweep sampling 

method in the Salt River area. In a recent study conducted in Las Vegas, the SCAMPER 

was found to underestimate road silt levels in comparison to those measured by swept 

sample analysis.1

 As a result, Sierra used the SCAMPER road section emission factors in 

a relative sense to compute true road section emission factors. To do this, a baseline road 

silt level of 0.3 g/m2

 on roads without visible trackout was assumed, based on road 

sweeping analyses reported in the ADEQ Salt River TSD. This baseline silt level 

produced an emission factor of 1.72 g/mi for a 2.6 ton vehicle using the AP-42 paved 

road emission equation, when the SCAMPER measured a PM-10 emission factor of 0.2 

g/mi on roads without visible trackout. As a result, Sierra multiplied the SCAMPER road 

section emission factors by the ratio of 1.72 to 0.2 to convert SCAMPER factors to AP42-equivalent factors. 

The road segment-average emission factors measured by the SCAMPER system ranged 

from 0.044 to 0.728 g/mi. For those road segments with an average emission factor of 

0.20 g/mi or less, Sierra used the adjusted SCAMPER emission factors to characterize 

baseline emissions. On these road segments, it was assumed that no trackout occurred. 

For the road segments with SCAMPER average emission factors greater than 0.20 g/mi, 

it was assumed that the increase in emission factor above 0.20 g/mi was due to trackout, 

and a baseline SCAMPER emission factor of 0.20 g/mi was assigned to these segments. 

For these segments, the excess emission factor was assigned to the trackout emission 

category, which is discussed in a separate section. As described above, the SCAMPER 

baseline emission factor of 0.2 g/mi was assumed to be measured on roads with the 

baseline silt loading of 0.3 g/m2

. 

A map of the road sections traversed by the SCAMPER system within the Salt River area 

is shown in Figure 4-4. The road links shown in yellow are those surveyed every three 

months under a contract with MAG. The road links shown in blue are those surveyed 

during the week of December 4-8, 2006, under a subcontract to Sierra Research. 

To confirm the validity of using the SCAMPER-derived silt loadings in a relative sense, 

instead of using these data directly in refining the Salt River area modeling inventory, 

Sierra contracted with Applied Environmental Consultants (AEC) to collect vacuum 

samples from several Salt River arterials that were sampled by the SCAMPER. Because 

of the time interval between road sampling by the SCAMPER and the delivery of verified 

data from testing to Sierra, the vacuum sampling was not performed until several months 

later in August 2007. The season of this vacuum sampling, however, coincided with that 

 

1

 The Preferred Alternative Method for Measuring Paved Road Emissions for Emissions Inventories: 

Mobile Technologies vs. the Traditional AP-42 Methodologies, Langston et al, 16th Annual International 

Emission Inventory Conference, Raleigh, NC, May 2007, 

http://www.epa.gov/ttn/chief/conference/ei16/session11/langston.pdf, accessed on October 27, 2007. 
-55- 

Figure 4-4 

SCAMPER Mobile Monitoring Routes 

of the most recent previous vacuum sampling prior to the field study, which was 

conducted by ADEQ in September 2003. The ADEQ sampling, however, was performed 

on a Salt River arterial that was not traversed by the SCAMPER. 

MAG used the results of the SCAMPER measurements to select road segments for AEC 

to collect silt samples. A total of 10 segments were selected for sampling—5 with 

SCAMPER emission rates consistently above 0.2 g/mi (i.e., higher rates) and 5 with 

SCAMPER emission rates equal to or lower than 0.2 g/mi (i.e., lower rates). To ensure 

consistency between the sampling strata, traffic volume was also considered in the 

selection of segments included in each strata. The results, however, as presented in 

Table 4-1, show that the higher silt loadings occur in the segments selected to represent 

lower emission rates. The correlation between the two data sets is poor. Differences in 

seasons when the data were collected, however, may account for part of the inconsistency 

as the SCAMPER data were collected in each season of 2006 and the silt samples were 

collected in August 2007. Another contributing factor is that silt measurements are 

heavily influenced by daily changes in deposition from trackout, carryout, etc. Thus, 

differences between the measurements may simply be the result of the luck of the draw. 
-56- 

Table 4-1 

Silt Sampling Results Organized by SCAMPER Emission Rates 

(g/m2) 

Location Traffic 

Group Silt Loading 

SCAMPER Segments with Higher PM-10 Emission Rates 

Broadway between 35th Ave & 43rd Ave l 0.08 

27th Ave between Lower Buckeye & Durango l 0.16 

Ray Rdbetween Santan Village Pkw & Higley Rd l 0.30 

Higley Rd between Ray Rd & Williams Field Rd ll 0.90 

19th Ave between Broadway & Lower Buckeye lll 0.23 

SCAMPER Segments with Lower PM-10 Emission Rates

Roosevelt St between 7th St & 16th St l 0.17 

39th Ave between Thomas Rd & Osborne Rd l 0.31 

27th Ave between Van Buren & McDowell ll 0.12 

Santan Village Pkw between Williams Field Rd & Ray Rd ll 3.50 

Central Ave between Broadway & Southern lll 1.10 

Five of the locations that AEC sampled were determined to be within (or directly 

adjacent to) the borders of the Salt River modeling domain. A comparison of the 

measurements obtained for those locations is contrasted with the 2003 measurements in 

Table 4-2. As can be seen, both studies collected measurements at 5 locations and both 

studies determined one of those locations to be representative of trackout. The criterion 

used to distinguish between “clean streets” and trackout in 2003 was whether silt levels 

were “visual.” This resulted in an average clean street silt loading of 0.3 g/m2 and an 

average assigned trackout silt loading of 1.5 g/m2

. 

Information on whether trackout was visible was not included in the AEC report. 

Follow-up discussions with AEC indicated that while trackout was not generally visible, 

notes on visibility were not recorded for individual sampling locations.1

 Lacking this 

information, the 2003 study findings were used to assess whether a location had trackout. 

Given the uniformly low silt values recorded in 2007, the only measurement considered 

to be the result of trackout was Central Avenue (as it was clearly an outlier relative to the 

rest of the Salt River measurements). Since the 1.10 g/m2

 value is much more likely to 

fall within the distribution of values producing the average trackout value of 1.5 g/m2

than the distribution of values producing the average clean street value of 0.3 g/m2, 

Central Avenue was determined to represent a location with trackout. The remaining 

Salt River silt measurements produce an average value of 0.15 g/m2

, which represents a 

reduction of 50% from the values recorded in 2003. 

 

1

 AEC collected measurements at three locations within each of the road links selected for sampling. 
-57- 

Table 4-2 

Comparison Between Salt River Silt Measurements 

Collected in 2003 and 2007 

Location Silt Loading 

(g/m2

) Trackout 

2003 ADEQ Measurements 

19th Ave S Lower Buckeye 0.38 - 

19th Ave river N Broadway 0.57 - 

W. Broadway 38th Drive 0.24 - 

51st Ave S of bridge 0.12 - 

Lower Buckeye W 35th Ave 2.10 X 

Non Trackout Mean 0.33 

2007 MAG Measurements 

27th Ave between 

Van Buren & McDowell 0.12 - 

27the Ave between 

Lower Buckeye & Durango 0.16 - 

Broadway Rd between 

35th Ave & 43rd Ave 0.08 - 

19th Ave between 

Broadway & Lower Buckeye 0.23 - 

Central Ave between 

Broadway & Southern 1.10 X 

Non Trackout Mean 0.15 

The importance of silt measurement cannot be overstated given its influence on emission 

inventory estimates and its value as a marker in tracking the progress of control programs 

towards attainment. With the benefit of hindsight, the sample sizes of silt measurements 

used to make important judgments about changes over time and the contribution of 

control programs are too limited and larger sample sizes are needed to improve 

confidence in these estimates. 

4.3 Industrial

After identifying an episode in December 2006 for analysis (see Chapter 5 for a 

description of the days selected), requests for day-specific activity data were submitted to 

the larger aggregate operators located within the Salt River modeling domain. To assist 

in the preparation of diurnal profiles, a spreadsheet was prepared that provided space for 

an operator to mark the hours of operation for the following categories of operation: 

• Quarrying; 

• Aggregate processing; 
-58- 

• Aggregate loadout; 

• Ready-mix concrete; 

• Asphalt concrete; and 

• Other. 

Space was also provided for contact information, comments on other processes, etc. A 

copy of the spreadsheet was submitted to individual operators in some cases and to the 

Arizona Rock Products Association for distribution to member companies. Follow-up 

telephone conversations with the respondents found that most operators maintained 

computerized records of their operating hours so that they could track progress towards 

permitted emission levels. Through these discussions it was also determined that the 

operating hours of these companies were demand driven and as a result were highly 

variable. This finding reinforced the view that use of typical daily profiles or annual 

average hours of activity could significantly bias emission estimates and disadvantage air 

quality analysis of source-specific contributions to exceedances. Through discussions 

with operators it was found that activity levels in 2006 were generally lower than those 

that occurred in 2005 when construction activity was at a peak. 

The results of the survey were used to configure the emission estimates for each of the 

respondents (roughly 10 companies provided responses). The approach used was to 

quantify the average annual hourly emission rate for each process using facility-specific 

emission estimates for 2005 obtained from the Maricopa County Air Quality Department 

(MCAQD). Those values were then applied uniformly to the hours of operation obtained 

in the surveys. The approach used to configure the remaining sources was to assume that 

facility-specific annual hourly emission rate obtained from Maricopa County for calendar 

year 2005 were uniformly distributed over the hours between 7:00 am to 5:00 pm.1

 

4.4 Light Industrial (Vacant and Unpaved Parking Lots)

During an early assessment of potential sources impacting the Durango Complex and 

West 43rd Avenue monitoring sites, Sierra identified several light industrial sites with 

unpaved yards that were within 100 meters of each monitor. Because vehicle travel on 

these unpaved yards could produce measurable impacts at a monitor, a monitoring 

subcontractor—AEC—was tasked with visually monitoring vehicle movements within 

approximately ten designated light industrial facilities over three hour periods. During 

the surveillance periods, which were generally scheduled between 5:00 and 8:00 am 

during peak PM-10 levels on winter stagnant days, AEC staff parked outside these 

facilities and counted vehicle trips within facility boundaries. For each trip on unpaved 

surfaces, data on vehicle type, travel distance, and vehicle speed were recorded. Vehicle 

speed could only be estimated by the observers, and vehicle weights were later estimated 

by Sierra staff on the basis of the vehicle descriptions recorded by AEC staff. 

 

1

 This approach overestimates the contribution of these facilities to the extent that industrial operations 

declined in 2006 relative to 2005. 
-59- 

The collected data were used to estimate PM-10 emissions for each trip. Trip-specific 

emission factors were computed using the AP-42 equation for unpaved road travel at 

industrial sites.1

 Emissions per trip were calculated by multiplying the emission factor by 

the observed trip distance in units of miles. The emissions from all trips observed during 

the surveillance period were summed and divided by the number of hours of surveillance 

to compute average hourly PM-10 emissions from each site. Daily operating hours for 

each facility were obtained by AEC staff through telephone interview of facility 

operators. 

4.5 Construction

Because the locations of construction activities and emissions shift within the modeling 

domain from year to year, separate estimates of construction emissions were prepared for 

December 2005 (low wind design days), February 2006 (high wind design days), and 

December 2006 (the modeling episode selected for analysis in this report). A description 

of the process used to prepare these estimates is presented in the TSD for the Five Percent 

Plan.2

 Using a combination of assumptions employed in the MCAQD inventory 

regarding the duration of construction activity and a listing of earth moving permits 

provided by Maricopa County for projects located within the Salt River modeling 

domain, a logic was constructed to determine which sites were active in each of the 

episodes analyzed. A summary of the results of that analysis is presented in Table 4-3. It 

shows that the use of different location assumptions significantly impacted the number of 

permits and acres disturbed for each of the episodes of interest. 

 

Table 4-3 

Impact of Location Assumption 

on Permits and Acreage Included in the Construction Emissions Inventory 

Date Location Criteria Permits Acreage 

December, 2005 2 miles 36 684.6 

December, 2005 Salt River Area 57 905.8 

February, 2006 2 miles 51 966.3 

February, 2006 Salt River Area 72 1,226.6 

December, 2006 2 miles 42 661.3 

4.6 Agriculture

As discussed in the TSD for the Five Percent Plan, the key issue in calculating 

agricultural emissions is to estimate the level of activity that occurred on the design day. 

One end of the spectrum spreads all of the tilling (e.g., rip, disc, etc.) required to produce 

 

1

 Compilation of Air Pollutant Emission Factors, AP-42, 5th Edition, January 1995, U.S. Environmental 

Protection Agency, Section 13.2.2 Unpaved Roads 

2

 MAG, Five Percent Plan for PM-10 for the Maricopa County Nonattainment Area, December 2007. 
-60- 

a crop across all of the days included in a crop’s lifespan. The result is an estimate of 

activity that significantly underestimates the level of activity on days when activity 

occurs (i.e., because there is no activity on many days of a crop’s lifecycle). On the other 

end of the spectrum, there are days when farmers are operating almost continuously to 

comply with regulatory dates when operations have been delayed because of severe 

weather, etc. 

Discussions with the Arizona Farm Bureau confirmed that during the December 2006 

episode, farmers in the Salt River modeling domain were in the process of converting 

fields from cotton to wheat and operating continuously (i.e., 24-hours/day with different 

crews). Using this information, along with related estimates of tractor speed and acres 

located within the modeling domain, it was possible to determine the hours per day that 

tractors operated on each field. 

4.7 Local Truck Yard

During the initial field survey of sources located near the two Salt River monitors, T&B 

Systems identified a truck yard with unusually high activity levels operating near the 

Durango Complex. Mobile measurements and video records showed that significant 

levels of PM-10 were being emitted from vehicles operating at the facility. Sierra 

contacted the operator and determined that the facility was being used as a school to train 

truck drivers. The operator did not maintain records of daily operation, but was able to 

provide information on the level of activity that typically occurred when the school was 

in operation, the days of the week the school operated, holidays observed, and the impact 

of holidays on weekly enrollment and activity occurring in the yard. 

### 
-61- 

5. AIR QUALITY MODELING 

Air quality modeling analyses are frequently hobbled because day-specific activity 

information is not available to support the development of source-specific emissions for 

the day(s) being modeled. Methods used to overcome this limitation typically involve the 

use of annual average activity levels, seasonal profiles, etc. The problem with these 

approaches is that the unique activity conditions of the day are not reflected in the 

emission estimates (e.g., unusual congestion caused by accidents, construction, etc., 

peaks in agricultural and industrial production, etc.) and the accuracy of the resulting 

inventory and estimated concentrations can be severely under- or overestimated. The 

collection of source specific activity on days when exceedances of the ambient PM-10 

standard were recorded during December 2006 provided an opportunity to eliminate this 

layer of uncertainty in emission estimates and evaluate its impact on the ability of air 

quality modeling to represent concentrations recorded on those days. Thus, while the 

design days selected for the Five Percent Plan occurred in December 2005 and February 

2006 (for low and high wind conditions, respectively), an initial air quality analysis was 

conducted for selected days in December 2006 when exceedances of the PM-10 standard 

were recorded. The goal of the analysis was to take advantage of the improved accuracy 

of the emission inventory estimates to assess relative source contributions, the 

significance of transport, and the ability of air quality modeling to represent 

concentrations recorded under exceedance conditions. 

Since many of the air quality modeling steps have already been detailed in the TSD for 

the Five Percent Plan, those discussions will not be repeated here. Instead, the discussion 

will focus on differences in the following: 

• Design day selection; 

• Emission inventory development; 

• Meteorological data; 

• Background estimates; and 

• Model performance 

Since the Air Quality Modeling Team and MAG determined that grid-based dispersion 

modeling represented the best option for evaluating source-specific contributions 

impacting the Salt River monitors, the model choices were limited. Based on a review of 

EPA guidelines, AERMOD was determined to be the most suitable dispersion model for 

evaluating hourly source contributions to PM-10 exceedances recorded at the Salt River 

monitors (i.e., Durango Complex and West 43rd Ave.). Despite AERMOD’s advantages, 
-62- 

there was concern about how well a steady-state Gaussian plume dispersion model could 

represent low wind, stagnant conditions. Therefore, before focusing exclusively on 

AERMOD, a puff model was evaluated because of its advantage over plume models in 

representing dispersion under low wind conditions. Appendix C provides a summary of a 

comparison between AERMOD and CALPUFF in representing selected sources in the 

Salt River. The results showed no discernable difference in the results between the two 

models. They also showed that considerable effort would be required to properly 

configure CALPUFF to represent the Salt River modeling domain. For these reasons, a 

decision was made to use AERMOD for the air quality modeling analysis. 

5.1 Episode Selection

The episode was selected on the basis on 24-hour average PM-10 concentrations recorded 

at the Durango Complex and West 43rd Avenue monitors during the intensive field study. 

The field study commenced on November 15, 2006, and was completed on December 15, 

2006. Intensive field study data collection was not conducted on every day during this 

interval. Table 5-1 tabulates the 24-hour average PM-10 concentrations during this 

period, and the days of intensive data collection are shown in shaded blocks. 

Table 5-1 

24-Hour Average PM-10 Concentrations and Wind Speeds During Field Study 

Date 

PM-10, μg/m3

 Wind Speed, mph 

Durango 

Complex 

West 43rd

Avenue 

Durango 

Complex 

West 43rd

Avenue 

11/15/2006 104 129 1.8 4.2 

11/16/2006 139 164 1.2 2.5 

11/17/2006 123 175 1.1 2.5 

11/18/2006 97 118 1.8 3.3 

11/19/2006 84 72 2.2 2.5 

11/20/2006 123 120 3.6 5.7 

11/21/2006 100 93 3.7 2.4 

11/22/2006 134 151 1.6 3.3 

11/23/2006 75 108 1.6 3.3 

11/24/2006 88 94 1.8 2.8 

11/25/2006 71 74 1.3 4.1 

11/26/2006 52 54 1.6 3.4 

11/27/2006 95 164 2.5 4.0 

11/28/2006 44 98 3.4 3.5 

11/29/2006 100 138 7.7 8.9 

11/30/2006 58 88 1.6 4.0 

12/1/2006 117 115 1.4 2.3 

12/2/2006 84 127 1.7 4.4 

12/3/2006 40 51 3.0 10.4 
-63- 

Table 5-1 

24-Hour Average PM-10 Concentrations and Wind Speeds During Field Study 

Date 

PM-10, μg/m3

 Wind Speed, mph 

Durango 

Complex 

West 43rd

Avenue 

Durango 

Complex 

West 43rd

Avenue 

12/4/2006 84 93 4.0 2.6 

12/5/2006 114 174 1.8 1.6 

12/6/2006 167 160 1.3 2.2 

12/7/2006 175 160 4.6 5.4 

12/8/2006 72 80 5.3 5.4 

12/9/2006 50 52 3.5 3.3 

12/10/2006 90 127 4.8 5.3 

12/11/2006 103 105 1.9 3.3 

12/12/2006 108 138 1.8 2.9 

12/13/2006 136 138 1.8 3.1 

12/14/2006 148 164 1.2 3.2 

12/15/2006 139 178 1.5 3.1 

Note: Shaded blocks denote days of intensive data collection. 

During the first intensive field study work, between November 15 and November 19, 

2006, T&B Systems traversed the modeling domain with a single mobile monitoring 

platform and measured PM-10 profiles looking for hotspots. Daily PM-10 averages 

exceeded the federal 24-hour standard on November 16 and 17 at the West 43rd Avenue 

site during this period, but no exceedances were recorded at the Durango Complex 

monitor. These data suggested that localized sources were impacting the West 43rd

Avenue, but that the remainder of the Salt River area was being sufficiently ventilated to 

avoid exceedances. 

During the second intensive data collection period, between November 30 and 

December 8, 2006, a stagnation event occurred that caused simultaneous exceedances to 

be recorded at both monitoring sites. This period of low wind conditions commenced at 

about 2:00 a.m. on December 5, and extended through about 1:00 p.m. on December 7. 

The stagnation conditions caused locally emitted PM-10 to be trapped in the area of 

origin and to accumulate under low inversion ceilings. This stagnation event occurred 

coincidentally during the week of peak data collection activity in the field study program. 

As a result, the days when PM-10 exceedances were recorded at both the Durango 

Complex (December 6-7) and West 43rd Avenue (December 5-7) monitors was selected 

as the design day period for intensive emission inventory refinement and dispersion 

modeling. 

The analysis of design day conditions began with an examination of 5-minute average 

PM-10 concentrations and hourly average meteorological conditions that were recorded 

during the three day period. Plots of these data are presented in Figures 5-1 through 5-3. 
-64- 

Figure 5-1 

Summary of Monitoring Conditions at Durango Complex 

During December Episode 

December 5, 2006

Time

 0:00 4:00 8:00 12:00 16:00 20:00 0:00

PM10 (ug/m3

)

0

100

200

300

400

500

600

700

0

100

200

300

400

500

600

700

PM10 24 hr average: 115 ug/m3

Mixing height

Mixing Height (m)

December 6, 2006

Time

 0:00 4:00 8:00 12:00 16:00 20:00 0:00

PM10 (ug/m3

)

0

100

200

300

400

500

600

0

100

200

300

400

500

600

PM10 24 hr Average: 170 ug/m3

Mixing Height 

Mixing Height (m)

December 7, 2006

Time

 0:00 4:00 8:00 12:00 16:00 20:00 0:00

PM10 (ug/m3

)

0

100

200

300

400

500

600

700

0

100

200

300

400

500

600

700

PM10 24 hr average: 175 ug/m3

Mixing Height

Mixing Height (m)

Note: In some cases the 24-hour values reported above are different from those displayed in Table 5-1. 

This is because the values displayed above are based on averages of 5-minute values, whereas those 

reported in Table 5-1 are based on an hourly average values. 
-65- 

Figure 5-2 

Summary of Monitoring Conditions at West 43rd Ave. 

During December Episode 

December 5, 2006

Time

 0:00 4:00 8:00 12:00 16:00 20:00 0:00

PM10 (ug/m3

)

0

100

200

300

400

500

600

700

800

900

0

100

200

300

400

500

600

700

800

900

PM10 24 hr average: 176 ug/m3

Mixing height

Mixing Height (m)

December 6, 2006

Time

 0:00 4:00 8:00 12:00 16:00 20:00 0:00

PM10 (ug/m3

)

0

100

200

300

400

500

600

700

800

900

0

100

200

300

400

500

600

700

800

900

PM10 24 hr Average: 160 ug/m3

Mixing Height 

Mixing Height (m)

December 7, 2006

Time

 0:00 4:00 8:00 12:00 16:00 20:00 0:00

PM10 (ug/m3

)

0

100

200

300

400

500

600

700

800

900

0

100

200

300

400

500

600

700

800

900

PM10 24 hr average: 160 ug/m3

Mixing Height

Mixing Height (m)

Note: In some cases the 24-hour values reported above are different from those displayed in Table 5-1. 

This is because the values displayed above are based on averages of 5-minute values, whereas those 

reported in Table 5-1 are based on an hourly average values.
-66- 

Figure 5-3 

Summary of Monitoring Conditions at 

Durango Complex and West 43rd Ave. on High Wind Day 

During December Episode 

Durango Complex – December 7, 2006 

Time

 0:00 4:00 8:00 12:00 16:00 20:00 0:00

PM10 (ug/m3

)

0

100

200

300

400

500

600

700

0

100

200

300

400

500

600

700

PM10 24 hour average: 171 ug/m3

Wind speed (cm/s)

Wind speed (cm/s)

West 43rd Ave. – December 7, 2006 

Time

 0:00 4:00 8:00 12:00 16:00 20:00 0:00

PM10 (ug/m3

)

0

100

200

300

400

500

600

700

800

900

0

100

200

300

400

500

600

700

800

900

PM10 24 hour average: 160 ug/m3

Wind Speed (cm/s)

Wind speed (cm/s)

Note: In some cases the 24-hour values reported above are different from those displayed in Table 5-1. 

This is because the values displayed above are based on averages of 5-minute values, whereas those 

reported in Table 5-1 are based on an hourly average values.

Figure 5-1 shows PM-10 concentrations at the Durango Complex monitor together with 

local mixing heights as computed by the AERMET program for 24-hour periods on 

December 5 through 7, 2006. In a validation test using mixing heights recorded at the 

West 43rd Avenue monitoring site by a miniSODAR system, as reported in the TSD for 

the Five Percent Plan, the AERMET program demonstrated close agreement with 

physical data. The plots for each day show very low mixing heights during nighttime 

hours with gradual rises with groundheating in the mornings and sharp declines in the 

evenings after sunset. The exception to the evening decline in mixing height occurred on 
-67- 

December 7 when elevated wind speeds produced significant mixing for the remainder of 

the day after 11 a.m. 

On all three days shown, PM-10 concentrations rose in the early morning and then 

dropped substantially after the inversion lifted. The early morning ramp-up appeared to 

be due to the onset of industrial activity and heavy duty truck traffic after about 4:00 am. 

The morning decline in PM-10 also appeared to result from the rise in inversion height 

and mixing of groundlevel concentrations into the expanding mixing zone. After sunset, 

the inversion height was seen to drop dramatically, and PM-10 concentrations were seen 

to rise. In the absence of any significant sources that could be identified, however, it is 

unclear why PM-10 concentrations remained high during the nights of December 5/6 and 

6/7.1 

In Figure 5-2, the same morning rise and decline of PM-10 concentrations can be seen at 

the West 43rd Avenue monitoring site. As shown by these plots, these peaks were 

sufficiently high and broad as to cause the 24-hour average concentrations to exceed the 

federal PM-10 standard. At the West 43rd Avenue site, nighttime PM-10 concentrations 

were seen to steadily decline on December 5/6 and 6/7, suggesting that the initial rises 

caused by the rapid drop in inversion height were not sustained by continuing local 

emissions. 

Figure 5-3 shows a comparison of PM-10 concentrations to wind speeds at each 

monitoring site on December 7. This day began under stagnant conditions, but a passing 

front caused a rise in wind speed that was sustained over the second half of the day. 

Afternoon and evening winds at both sites appeared to produce sufficient ventilation to 

keep PM-10 concentrations below 100 μg/m3 over most of this period at both sites. 

5.2 Emission Inventory Development

This section provides a review of differences in the modeling domain, source 

representation within the modeling domain and inventory development. Because several 

studies have been prepared to evaluate source contributions within the Salt River, key 

differences are noted between assumptions and methods employed in the following 

recent efforts: 

• ADEQ’s Salt River TSD; 

• MAG’s TSD for the Five Percent Plan; and 

• MAG’s 2006 Modeling Analysis (the analysis documented in this report) 

 

1

 This suggests the concentrations were the result of an unknown source activity. Sierra checked accident 

statistics, night-time construction activity (e.g., road construction that forced vehicles to operate on 

shoulders, etc.) and notices of violation (NOVs) issued by the County on the dates of interest. No activity 

explaining these conditions could be identified. 
-68- 

A summary of the source specific emission estimates for the 2006 Modeling Analysis is 

presented at the end of the section. 

Model Domain – As noted in the Introduction to this section, the domain used to 

represent the Salt River area is essentially the same for all three studies.1 As shown in 

Figure 5-4, the domain is bounded by Van Buren Street to the north, Baseline Road to the 

South, 59th Ave to the west and 7th Street to the east. ADEQ selected this domain in the 

TSD for the 2005 Attainment Plan and identified the location and borders of all 

significant sources (e.g., vacant lots, agricultural land, industrial facilities, etc.) located 

within these boundaries. The information developed in that effort represented an 

excellent starting point for inventory development in the subsequent MAG analyses. 

Figure 5-4 

Salt River Area Modeling Domain 

The Salt River TSD divided the modeling domain into 400 x 400 meter grid cells. Both 

area and on-road source emissions were distributed evenly throughout each grid cell in 

 

1

 A review of the Salt River TSD shows that the boundaries of the modeling domain extended slightly 

beyond those displayed in Figure 5-4 (roughly one grid cell to the east and one grid cell to the north). This 

increment accounts for the 3.3 square mile difference between the 38.9 square mile size of the Salt River 

TSD and the 35.6 square mile size of the area displayed in Figure 5-4. 
-69- 

which sources were located. As noted earlier, Sierra suspected that this approach would 

eliminate any opportunity to accurately quantify the relative impacts of sources located 

near the monitors because the size of the area within which pollutants were emitted was 

enlarged and the emission density was diminished. For example, the geographical 

distribution of street sources disappears when distributed within grid cells, despite the 

fact that emissions from significant streets within the grid cell containing a monitor can 

impact modeled concentrations at the monitor independent of the wind direction. In light 

of this concern, the MAG studies (both the Five Percent Plan and the modeling analysis 

presented in this report) identified the boundaries of each source and distributed the 

emissions within those boundaries as uniform volume sources. Thus, emissions for each 

road link were distributed as series of volume sources located within the borders of the 

road (i.e., the pavement edge plus 3 meters on each side). Similarly, for construction, the 

boundaries of individual sites, which could range between 1 to over 100 acres, were 

identified and the emissions were uniformly distributed as volume sources located within 

those boundaries. Similar approaches were employed to represent other area sources, 

including unpaved parking lots, agricultural fields, etc. 

Presented below is a review of differences in the approaches used to characterize sources 

in the three recent Salt River Area studies. 

On-Road Emissions – Paved road fugitive dust is the largest source of PM-10 emissions 

in the Salt River modeling domain. A summary of differences among the three Salt 

River studies in preparing on-road emission estimates is presented in Table 5-2. It shows 

that in each study separate methods were used to characterize emissions from freeways, 

arterial and local roads. One significant difference is that while the Salt River TSD and 

the Five Percent Plan TSD prepared emission estimates for all paved roads located within 

the modeling domain, the 2006 Analysis prepared emission estimates for only a subset of 

the arterials and local roads (i.e., the higher volume arterials and the local roads located 

adjacent to the Durango Complex and West 43rd Ave. monitors, are highlighted in Figure 

5-5). It should be noted that publicly accessible unpaved roads were not represented in 

any of the studies. That is because emissions from these roads were found to be 

negligible and emissions from unpaved haul roads on private lands were included in the 

industrial emission estimates. 

Table 5-2 

Differences in Road Network Emission Calculations 

Between Salt River Studies 

Study Roads VMT Diurnal 

Profile Silt Vehicle Weight Emission Configuration 

ADEQ Salt 

River TSD 

Freeway ADOT 

Counts 

ADOT 

Counts 

0.3 g/m2 3 tons 

Distributed 

Across GridCells Arterial 

City of 

Phoenix 

Counts 

Unclear 

Local 10% of 

Counts Unclear 
-70- 

Table 5-2 

Differences in Road Network Emission Calculations 

Between Salt River Studies 

Study Roads VMT Diurnal 

Profile Silt Vehicle Weight Emission Configuration 

MAG Five 

Percent 

Plan TSD 

Freeway 

MAG Model 

MAG 

Contractor 

Hourly 

Counts 

0.3 g/m2 4.1 tons 

Volume Sources 

Arterial Volume Sources 

Local 

Distributed 

Across Grid 

Cells 

MAG 2006 

Analysis 

Freeway - - - - - 

Arterial 

MAG 

Contractor 

Counts for 

Selected 

Roads 

MAG 

Contractor 

Hourly 

Counts 

0.3 g/m2 

Light-duty 

( 3 tons) 

Medium-duty 

(13.8 tons) 

Heavy-duty 

(27.5 tons) 

Volume Sources 

Local 

Counts for 

Roads 

Adjacent to 

Monitors 

MAG 

Contractor 

Hourly 

Counts 

0.3 g/m2 

Light-duty 

( 3 tons) 

Medium-duty 

(13.8 tons) 

Heavy-duty 

(27.5 tons) 

Volume Sources 

Figure 5-5 

Arterial and Local Roads Included in 2006 Analysis 
-71- 

The approaches used to estimate travel and diurnally distribute emissions also differed in 

that the Salt River TSD and the 2006 Analysis relied upon vehicle counts (i.e., counts 

multiplied by the length of the link in which the counts were collected provide an 

estimate of vehicle miles traveled, or VMT), whereas the Five Percent Plan TSD used 

estimates of VMT from MAG’s model. MAG’s model provides estimates of travel for 

multiple time periods of the day (i.e., am, pm, mid-day, and off-peak). These estimates 

were disaggregated to hourly traffic levels based on hourly profiles represented for each 

time period, which were derived from the traffic count data. While the silt levels used to 

compute the baseline (i.e., non-trackout) estimate of fugitive dust emissions were 

consistent across the studies, the assumptions about vehicle weight were not. The Salt 

River TSD relied upon a “national average” weight of 3 tons; the 2006 analysis prepared 

separate estimates for each vehicle class and estimated representative class average 

weights. When preparing to model paved road emissions for the 2005 TSD, it was 

determined that insufficient data was available to quantify average weight by vehicle 

class in the modeling domain. As discussed in the Five Percent Plan TSD, a review of 

the available data indicated that average vehicle weight was higher than 3 tons; therefore 

an EPA-referenced value of 4.1 tons was used for the Salt River area. 

Arguably the most significant difference among the studies was the method used to 

represent road emissions within the modeling domain. As noted earlier, the Salt River 

TSD distributed emissions for roads located within each grid cell throughout the grid cell. 

Since this diminished the density of the emissions and the signature of the source, both 

the Five Percent Plan and the 2006 Analysis employed a method that provided a more 

representative source location. This was accomplished by representing each road link as 

a set of volume sources in dispersion modeling analysis. The volume source release 

heights and vertical dispersion parameters (σz) were taken from the CALINE-4 manual1

formula 5-12 (σz = 4.0/2.15 =1.86). The lateral dispersion parameter (σy) was set to 

L/2.15 meters, where L = (n*12+20)*0.3048 meters (length of volume source used to 

represent a piece of the freeway segment or the distance between separated volume 

sources used to represent the freeway segment, where n is the number of lanes on the 

segment of the road). The UTM coordinates for relevant freeways and freeway segments 

were identified and combined with the other dispersion parameters to prepare the 

AERMOD input files. The emissions of traffic (and, as will subsequently be discussed, 

the trackout) from each road segment were uniformly distributed among the total number 

of volume sources in that segment. 

Trackout Emissions – While trackout emissions are produced by the same activity that 

produces paved road emissions, the controls applied to reduce the silt levels causing these 

emissions are different from those used to reduce baseline paved road emissions. For this 

reason, trackout emissions were estimated separately. Table 5-3 summarizes the 

differences in the methodologies used to estimate trackout emissions. As can be seen, the 

data and assumptions used to estimate and diurnally distribute the traffic are identical to 

 

1

 CALINE4 – A Dispersion Model for Predicting Air Pollutant Concentrations Near Road Ways, California 

Department of Transportation, June 1989 Update. 
-72- 

Table 5-3 

Differences in Trackout Emission Calculations 

Between Salt River Studies 

Study Roads VMT Diurnal 

Profile 

Silt 

Range 

Silt 

Measurement 

Method 

Vehicle 

Weight 

Emission 

Configuration 

ADEQ 

Salt 

River 

TSD 

Freeway ADOT 

Counts 

ADOT 

Counts 

0.75 – 

3.06 g/m2 

Limited sample 

collection + 

Visual 

Observation 

3 tons 

Distributed 

Across Grid 

Cells 

Arterial 

City of 

Phoenix 

Counts 

Unclear 

Local 10% of 

Counts Unclear 

MAG 

Five 

Percent 

Plan 

TSD 

Freeway 

MAG 

Model 

Adjusted 

SCAMPER 

Data 

4.1 tons 

Volume Sources

Arterial MAG 

Contractor 

Hourly 

Counts 

0.00 - 

1.34 g/m2

Volume Sources

Local 

Distributed 

Across Grid 

Cells 

MAG 

2006 

Analysis 

Freeway - - - - - - 

Arterial 

MAG 

Contractor 

Counts for 

Selected 

Roads 

MAG 

Contractor 

Hourly 

Counts 

0.00 - 

1.34 g/m2 

Adjusted 

SCAMPER 

Data 

Light-duty 

( 3 tons) 

Mediumduty 

(13.8 tons) 

Heavyduty 

(27.5 tons) 

Volume Sources

Local 

Counts for 

Roads 

Adjacent to 

Monitors 

those used to estimate paved road emissions. Similarly, the assumptions about vehicle 

weight and the source representation methods are also the same. Significant differences, 

however, can be seen in the ranges of silt values and the measurement methods used to 

estimate those silt values. 

The Salt River TSD relied upon data collected using the EPA silt measurement 

methodology, which requires traffic to be blocked on the road to be measured and a predefined sampling area to be vacuumed from the edge of the road to the center of the road 

and then back to the edge of the road. The sample is collected on a pre-weighed vacuum 

bag, which is then extracted and weighed. A sieve analysis is then used to determine the 

silt content of the sample. Samples were collected from eight separate roads; analysis of 

the data determined that the silt loading on the road section with no visible trackout was 

0.3 g/m2. Silt loadings from areas with different amounts of trackout were found to range 

from a high of 11.2 g/m2 to a low of 0.4 g/m2. 

In contrast to the Salt River TSD, which relied upon measurements collected in 2003, the 

2006 Analysis obtained two separate measurements of silt loadings on Salt River roads. 
-73- 

As described in Section 4, the first measurements were collected with a trailer-based 

mobile sampler known as SCAMPER. The second used the EPA-approved vacuum 

procedure to collect measurements from 10 separate roads in the Salt River area in 2007. 

The SCAMPER measurements were recorded in units of grams of PM-10 per mile and 

statistical analysis of the results revealed that the minimum emission factor was 0.20 

grams per mile, suggesting that this was the baseline without trackout. As explained in 

the Five Percent Plan TSD, this SCAMPER emission factor was equated to the minimum 

silt loading of 0.3 gm/m2

 reported by ADEQ. Any road segment measured by the 

SCAMPER to have an emission factor greater than 0.2 gm/mi was assumed to have 

trackout, and the excess emissions above 0.2 gm/mi were attributed to trackout 

contributions. The trackout silt level was backcalculated from the SCAMPER trackout 

emission factor using the AP-42 equation and the 0.3 gm/m2 to 0.2 gm/mi relationship 

discussed above. 

 

A description of the procedures used to collect the vacuum measurements of silt loadings 

from Salt River roads in 2007 is presented in Appendix D. Those values were used to 

determine the effectiveness of trackout control measures implemented since the 

2005/2006 winter. 

Industrial Emissions – A summary of data and methods used to estimate industrial 

emissions is presented separately for point and area sources for the each of the three Salt 

River studies in Table 5-4. It shows the definitions used to distinguish between point and 

area sources differed between the Salt River TSD and the two MAG studies. The Salt 

River TSD selected 36 sources to be modeled separately as point sources based on 

ranking and distance from the air quality monitors. The remaining 45 sources were 

modeled as area sources. The MCAQD 2005 inventory, which was used to quantify 

emissions from industrial sources in both MAG studies, defined point sources on the 

basis of the level of pollutants emitted: 

• 25 short tons of carbon monoxide (CO); or 

• 10 tons of volatile organic compounds (VOC), oxides of nitrogen (NOx) or sulfur 

oxides (SOx); or 

• 5 tons of PM-10 or ammonia compounds (NHx). 

Using these criteria, 38 sources were identified and modeled as point sources in the 2006 

Analysis and 27 were modeled as area sources. 

The treatment of stationary and area source emissions at industrial facilities also differed 

among the three studies. In the ADEQ TSD, emission and stack data were derived from 

the MCAQD inventory and modeling files representative of the 2002 emission inventory 

year. Area sources emissions at 36 larger facilities were uniformly distributed within 

facility boundaries, and area source emissions within 45 smaller facilities were uniformly 

distributed within the grid cells in which the facilities were located. 

 
-74- 

Table 5-4 

Differences in Industrial Emission Calculations 

Between Salt River Studies 

Study Source 

Category Definition Location EmissionRate 

Diurnal 

Profile 

Emission 

Configuration 

ADEQ 

Salt River 

TSD 

Point 

Largest & 

Closest to 

Monitors 

MCAQD 

Permit 

Records and 

GIS 

Analysis 

MCAQD 

2002 

Permit 

Records 

Operating 

hours by day 

of week 

Stack Coordinates & 

Facility Boundaries 

Area 

Remaining 

Industry 

Sources 

7 am – 5 pm Distributed Across 

Grid Cells 

MAG Five 

Percent 

Plan TSD 

Point 

MCAQD 

pollutant 

specific tons 

per year 

definition 

MCAQD 

Address, 

Coordinates 

& Google 

Earth 

Inspection 

MCAQD 

2005 

Inventory 

Day-Specific 

for Largest 

7 am – 5 pm 

for 

Remaining 

Stack Coordinates & 

Facility Boundaries 

Area 

Remaining 

Sources 

That Do Not 

Qualify As 

Point 

Sources 

7 am – 5 pm Distributed Within 

Facility Boundaries 

MAG 

2006 

Analysis 

Point 

MCAQD 

pollutant 

specific tons 

per year 

definition 

MCAQD 

Address, 

Coordinates 

& Google 

Earth 

Inspection 

MCAQD 

2005 

Inventory 

Day-Specific 

for Largest 

7 am – 5 pm 

for 

Remaining 

Stack Coordinates & 

Facility Boundaries 

Area 

Remaining 

Sources 

That Do Not 

Qualify As 

Point 

Sources 

7 am – 5 pm Distributed Across 

Grid Cells 

In this study, Sierra used the same stack data and updated the emission data from 2006 

MCAQD permit records. For stack sources not included in the ADEQ modeling files, 

stack parameters were derived from process descriptions in the MCAQD permit records. 

Area source emissions at 33 larger facilities were uniformly distributed within the 

actively disturbed portions of each facility as determined through Google Earth aerial 

photographs. Emissions from extended haul roads at two mineral processing facilities 

were represented as series of juxtaposed volume sources. Area source emissions at 44 

smaller facilities were uniformly distributed within the grid cells in which the facilities 

were located. Temporal variability in emissions at 7 of the facilities with highest PM-10 

emissions were based on design day-specific operating information gathered through 

telephone and email surveys of facility operators. 

In the MAG Five Percent Plan TSD, this work was further refined to increase areal 

accuracy. The ADEQ TSD treated emissions from non-stack stationary sources, such as 
-75- 

crushing and screening operations at mineral processing facilities, as area sources. In the 

Five Percent Plan, emissions from these sources were assigned quasi-stack parameters 

based on physical proximity, estimated emission release heights, and a default stack 

velocity of 0.1 meters per second representative of unventilated sources. In this manner, 

significant PM-10 emissions from mineral processing were more appropriately located 

with respect to areas of emission release and release heights. To facilitate model run 

times with the increased number of sources, however, sources reported to have annual 

PM-10 emissions of less than 0.5 tons in the 2006 MCAQD emission inventory were 

deleted from the model input files. 

Construction Emissions – As shown in Table 5-5, the Salt River TSD used a variety of 

methods to identify construction sites located within the modeling domain. In contrast, 

the MAG studies relied upon information in the earthmoving permits issued by the 

Maricopa County Air Quality Department in 2005 and 2006 to determine which sites 

were active on the episode days being modeled. As discussed in Section 4, the number of 

active construction sites modeled in the Five Percent Plan TSD and the 2006 Analysis 

varied because of differences in the number of permits issued each year. These MAG 

studies also screened out sites that were smaller than an acre (equivalent to 8.8 lbs of PM10/day). The 2006 Analysis also screened out sites that were located more than 2 miles 

from the Durango Complex and West 43rd Avenue monitors. All studies used similar 

methods to quantify emissions and identical assumptions for diurnal profiles (discussions 

with the construction industry confirmed the representativeness of this assumption). One 

significant difference between the studies is that the Salt River TSD distributed emissions 

Table 5-5 

Differences in Construction Emission Calculations 

Between Salt River Studies 

Study Location Emission 

Rate 

Activity 

Factor 

Diurnal 

Profile 

Emission 

Configuration 

ADEQ Salt 

River TSD 

Satellite Image 

Analysis 

MCAQD 

Records 

Site Visits & 

Aerial Photos 

AP-42 

Emission 

Factors 

South Coast 

AQMD 7 am – 5 pm Distributed Across Grid Cells

MAG Five 

Percent 

Plan TSD 

Maricopa County 

Earthmoving 

Permit Records 

For Sites ≥ 1Acre 

Located Within 

Salt River Area 

MCAQD 

2005 

Inventory 

MCAQD 

2005 

Inventory 

7 am – 5 pm 

Distributed 

Within Site 

Boundary 

MAG 

2006 

Analysis 

Permits for Sites 

≥1 Acre Located 

Within 2 Miles of 

Monitors 

MCAQD 

2005 

Inventory 

MCAQD 

2005 

 Inventory 

7 am – 5 pm 

Distributed 

Within Site 

Boundary 
-76- 

within the grid cells in which the sites were located. The MAG studies used the 

information in the permit records to identify the location of the site and Google Earth to 

determine the coordinates of the boundaries of the actively disturbed areas. Emissions 

for each site were then distributed within these boundaries. 

Agricultural Emissions – Unlike the previous source categories where there is a steady 

trend of increasing source representation accuracy from the Salt River TSD, to the 

Analysis to the Five Percent Plan (i.e., the sequence in which the studies were actually 

conducted), this trend did not continue in the calculation of agricultural emissions. That 

is because the primary insight needed to improve the representation accuracy of the 

emissions estimate, the hours of operation for the days of interest, were unavailable for 

the design days addressed in the Five Percent Plan; see Table 5-6 for a summary of the 

data and assumptions used in preparing the agricultural emission estimates. Without this 

information it was not possible to prepare a more accurate estimate of emissions and the 

diurnal profile. As discussed in Section 4, the Arizona Farm Bureau provided invaluable 

insight into the activity levels that occurred on fields in December 2006. Without the 

activity insight for the December 2005 and February 2006 design days the options for 

improving representation of the inventory were limited to merging the CARB emission 

rates and the boundaries identified for selected fields in the 2006 Analysis1

 with the 

emission estimates from the Salt River TSD. This was not a simple task because the 

emission values available from the Salt River TSD were distributed across the 400 x 400 

meter grid cells and no information was available on the acreage within grid cell in which 

Table 5-6 

Differences in Agriculture Emission Calculations 

Between Salt River Studies 

Study Location Emission 

Rate 

Activity 

Factor 

Diurnal 

Profile 

Emission 

Configuration 

ADEQ Salt 

River TSD 

Satellite 

Image 

Analysis 

Ag. BMP 

Report 

Fraction of 

Annual Tillage 

Occurring on 

Design Day 

Daylight 

Hours 

8-Hours/day 

Distributed Across 

Grid Cells 

MAG Five 

Percent 

Plan TSD 

ADEQ 

Selection 

CARB 

Methodology 

Fraction of 

Annual Tillage 

Occurring on 

Design Day 

Daylight 

Hours 

8-Hours/day 

Distributed Across 

Grid Cells 

MAG 

2006 

Analysis 

ADEQ 

Update With 

Google Earth 

CARB 

Methodology 

Time Required 

to Till a Grid 

Cell with Ag 

Land 

Day-Specific 

From Arizona 

Farm Bureau 

Mixture of Field 

Boundaries and 

Grid Cells 

 

1

 A survey of Google Earth images of the modeling domain identified five agricultural fields located in the 

northeast corner of the modeling domain, beneath the I-17 which were not included in the Salt River TSD 

(i.e., grid cells in that area had no agricultural emissions). 
-77- 

agricultural operations were occurring. For this reason, the Five Percent Plan TSD 

adopted the Salt River TSD agriculture modeling parameters, including area size and 

operating hours from 2002 TSD modeling files. The emission rates from 2002 were 

reduced by the 4.6% annualized attrition rate detailed in the Five Percent Plan TSD to 

produce the 2005 inventory. 

Vacant Lots – This source category typically encompasses only undeveloped lots that are 

periodically disturbed by vehicle trespass. Limited PM-10 emissions are generated when 

vehicle trespass occurs in low wind conditions, and more significant emissions are 

produced during high wind conditions when disturbed surface soils are entrained into the 

air. Because trespass rates are low on vacant lots in the Salt River area, due primarily to 

the high visibility of such trespass activities in this urbanized area, the ADEQ Salt River 

TSD did not include vacant lots as an emission source category in attainment 

demonstration modeling under low wind conditions. In the two MAG studies, however, 

the definition of vacant lot was expanded to include more emission-generating activities, 

and activity data were collected to support emission estimates in this category. 

During initial surveys of emissions sources near the West 43rd Avenue monitoring site, 

Sierra noted vehicle activity on unpaved surfaces within several storage, recycling, and 

light industrial yards very close to the monitor. Investigation of the Salt River TSD 

showed that no emissions had been attributed to these properties. As a result, activity 

levels were monitored and emissions calculated for these vehicle operations and included 

in the modeling inventory for both MAG studies as vacant lot emissions in lieu of 

including them in any other category. 

Activity levels on these yards were monitored by AEC, a subcontractor to Sierra, on the 

mornings of two consecutive days in April and May, 2007, by visual observation. 

Vehicle type, vehicle speed, and trip length of both intra-yard and inter-yard trips were 

recorded over two hour periods. The soil silt level was assumed to be the default of Salt 

River area soils used in the Five Percent Plan TSD. Daily activity hours were determined 

through telephone interviews of facility managers. PM-10 emissions were calculated 

using the AP-42 equation for unpaved road travel. For modeling purposes, emissions 

were uniformly distributed over areas of disturbance as visually mapped using the Google 

Earth aerial photographs. A comparison of vacant lot emission protocols among the three 

studies is presented in Table 5-7. 

 

Unpaved Parking Lots – With limited exception, the same methodology was used to 

model unpaved parking lot emissions in each of the ADEQ Salt River TSD and the two 

MAG studies. ADEQ identified the area devoted to unpaved parking within each 

modeling grid cell, calculated emissions on the basis of estimated activity rates, and 

uniformly distributed these emission over the grid cells in which unpaved parking lots 

were located. Sierra used the ADEQ modeling files for this source category without 

change except where unpaved parking lot emission were found to significantly impact a 

PM-10 monitor. 
-78- 

Table 5-7 

Differences in Vacant Lot Source Data Collection and Analysis Methods 

for the Salt River Area 

Study Boundary 

Determination Activity Hours Activity Rates Emission Factor 

ADEQ Salt 

River TSD -- -- -- -- 

MAG Five 

Percent Plan 

TSD Google Earth 

Plotting 

Property Owner 

Interview 

Visual 

Observation 

AP-42 

Emission 

MAG 2006 Equation 

Analysis 

The major exception to the use of ADEQ files was the characterization of emissions from 

an unpaved truck yard directly east and across 27th Avenue from the Durango Complex 

monitor. Initial observations of this yard suggested that significant intra-yard travel by 

heavy duty Diesel truck and trailer combinations was occurring. T&B Systems filmed a 

video of these activities on November 18, 2006. Upon further investigation, Sierra 

learned that this unpaved parking lot was being used by a truck driving school, and that 

novice drivers started their training program by learning to drive continuously over this 

lot at engine idle speed (i.e., about 2 miles per hour). At any one time, up to twelve 

tractor-trailer rigs were traversing the lot simultaneously for several hours per day. Initial 

emission and modeling analyses revealed that emissions from this lot produced 

significant impacts at the Durango Complex monitor when winds were from the east. As 

a result, design day-specific information on hours of operation and the numbers of trucks 

engaged in intra-yard and inter-yard driving were collected through telephone interview 

of the yard manager. The boundaries of the disturbed areas were identified from Google 

Earth aerial photographs, and emissions were calculated using the AP-42 unpaved road 

emission factor equation using the default soil silt level used in the Five Percent Plan 

TSD. Plume intensity was verified by comparing modeled concentrations over 27th

Avenue with PM-10 concentrations recorded by T&B Systems during their driving 

passes on this portion of the road. 

The methodologies used to quantify activity rates and emissions from unpaved parking 

lots in the three studies is summarized in Table 5-8. 

Windblown Soil Sources – Emission inventory estimates presented in the ADEQ Salt 

River TSD indicated that windblown soil sources dominated modeling domain emissions 

on high wind days. As a result, Sierra chose to improve the emissions estimates and 

source configurations for this source in the two MAG studies in order to increase the 

accuracy of impact estimates for this source category. 

The ADEQ Salt River TSD uniformly distributed windblown soil emissions over 

individual grid cells. Emissions within each grid cell were calculated on the basis of the 

land area disturbed, as determined from visual inspection of aerial photographs of the 
-79- 

Table 5-8 

Differences in Unpaved Parking Lot Source Data Collection and Analysis Methods 

for the Salt River Area 

Source Study Boundary 

Determination 

Activity 

Hours Activity Rates Emission Factor 

All other 

lots 

ADEQ 

Salt River 

TSD 

Aerial photograph Assumed to be 

7am to 5 pm 

Assumed 20% 

turnover per hour 

AP-42, unpaved 

road travel 

(1988) 

MAG 

Five 

Percent 

Plan TSD 

MAG 

2006 

Analysis 

Truck 

yard at 

Durango 

Road & 

W. 27th

Avenue 

MAG 

Five 

Percent 

Plan TSD Google Earth 

plotting 

Property owner 

interview 

Property owner 

interview 

AP-42, unpaved 

road travel 

MAG (2006) 

2006 

Analysis 

modeling domain, and the use of an emission equation for windblown soil emissions at 

construction sites as reported in a study of Maricopa area soils conducted by Nickling and 

Gillies.1 ADEQ computed emissions rates for a 15 mph wind speed and applied this rate 

to all disturbed areas for every hour during which the hourly average wind speed was 

greater than or equal to 15 mph. 

Because Nickling and Gillies tested and reported on emission factors for disturbed soils 

on a variety of land uses in the Maricopa area, Sierra chose to use the different land usespecific equations reported in this study. Also, because the Nickling and Gillies emission 

equations used the wind speed raised to powers between 1.46 and 6.92, Sierra concluded 

that hourly average emission rates would be significantly influenced by peak wind gust 

velocities, and conducted a statistical analysis using 5-minute wind speed averages 

recorded at the two Salt River area monitors on high wind days to compute an adjustment 

factor for the Nickling and Gillies equations. This adjustment factor turned out to be 

about a 10% increase in effective wind speed for the construction soil equation that 

contained a wind speed exponent of 4.355. 

Emitting areas were also treated differently in the MAG studies than in the ADEQ Salt 

River TSD. Instead of uniformly distributing emissions across grid cells in which 

disturbed areas were located, Sierra mapped the boundaries of each disturbed area using 

Google Earth aerial photographs. Near the two monitoring sites, Sierra included unpaved 

 

1

 Evaluation of Aerosol Production Potential of Type Surfaces in Arizona, prepared for EngineeringScience by W.G. Nickling and J.A. Gillies, 1986. 
-80- 

road shoulders among the mapped sources. In the Salt River TSD, ADEQ concluded that 

unpaved shoulders would be insignificant sources due to their occupying only 0.2% of 

the land area within the modeling domain. 

A summary of the differences between emission and modeling methodologies among the 

three studies is presented in Table 5-9.

In summary, it should be evident from the above discussion that there was a steady 

progression in improving the representation of source-specific emissions between the 

three Salt River studies. Where day-specific activity data were available, emission 

estimates were configured to specific days. In other cases, activity estimates were 

available to characterize emissions for the time of year (e.g., construction). In still other 

cases, the emission estimates were the same for all of the days addressed under both low 

and high wind conditions (e.g., travel related). A summary of the resulting source 

specific PM-10 emission estimates is presented in Table 5-10. As can be seen the 

dominant sources are related to travel (both traffic and trackout), industry and agricultural 

operations. Lacking access to the day specific activity data the latter would typically be 

an order of magnitude lower. This illustrates the potential loss of insight that comes from 

using annualized operating profiles instead of day specific activity data. 

The day-specific inventories developed for the 2006 Analysis were designed to include 

only those sources thought to be capable of impacting the Durango Complex and 

West 43rd Ave. monitors. As a result, many of the sources located within the Salt River 

modeling domain (e.g., roads, construction sites, etc.) were not included. Thus, the 

emission estimates in this inventory are not directly comparable with either the Salt River 

TSD or the Five Percent Plan TSD. 

Table 5-9 

Differences in Windblown Soil Source Data Collection and Analysis Methods 

for the Salt River Area 

Study Boundary 

Selection 

Land Use Emission 

Factor Equation 

Used 

Wind Speed Used 

in Emission Factor 

Equation 

ADEQ Salt River 

TSD Grid cell boundaries Construction 

15 mph during 

hours when hourly 

average speeds were 

≥ 15 mph 

MAG Five Percent 

Plan TSD Disturbed area 

boundaries from 

Google Earth aerial 

photographs 

Construction, 

agriculture, 

alluvial, and 

all site average 

Actual recorded 

hourly average wind 

speed as adjusted 

for gust speed 

contributions 

MAG 2006 

Analysis 
-81- 

Table 5-10 

Summary of Source Specific PM-10 Emissions for 

Salt River Area Modeling Domain And Design Day Conditions 

 (tons/day) 

Source Category 12/5/2006 12/6/2006 12/7/2006 

Traffic 4.64 4.72 4.68 

Trackout 1.42 1.43 1.35 

Industrial Area Sources 2.29 2.29 2.29 

Industrial Point Sources 0.75 0.75 0.75 

Vacant Lots 0.13 0.13 0.13 

Unpaved Parking Lots 0.04 0.04 0.04 

Agricultural Operations 4.09 4.09 4.09 

Construction Activities 2.01 2.01 2.01 

Local Truck Yard 0.04 0.04 0.04 

Total 15.36 15.45 15.33 

5.3 Meteorological Data

AERMET, a preprocessor that converts raw meteorological data into an AERMOD-ready 

format, was used to prepare meteorological input files for the Durango Complex and 

West 43rd monitoring sites for selected design days in December, 2006. Outlined below 

are the data sources that were used to configure AERMET to produce these files. 

• The on-site met data came from West 43rd and Durango met data, with 

parameters of wind direction, wind speed, and temperature. A wind speed 

threshold of 0.0 meters/second was specified. 

• Upper air met data were derived from Tucson 1996-2006 twice-daily sounding 

data. 

• National Weather Service (NWS) met data (specifically, cloud cover data) came 

from the Phoenix NWS met station at Sky Harbor Airport. 

AERMET also requires information on three site-specific land use parameters: the 

Bowen ratio (a measure of moisture available for evaporation), Albedo (portion of 

sunlight that is reflected), and surface roughness length. The same values for these three 

site-specific parameters are used in Salt River TSD study and 2006 Analysis. AERMET 

output parameters used as meteorological inputs to AERMOD include wind speed, wind 

direction, temperature, Convective Boundary Height (m), Mechanical Boundary Height 
-82- 

(m), and Monin-Obukhov Length (m). A summary of the values1 produced for 

December 5-7 for each monitoring site is presented in Tables 5-11 and 5-12. 

Table 5-11 

Meteorological Data File Used for December 2006 Modeling at Durango Complex 

Date/Hour* 

Wind Direction 

(Degrees) 

Wind Speed 

(m/sec) Temperature (K)

Convective 

Boundary Layer 

Height (m) 

Mechanical 

Boundary Layer 

Height (m) 

Monin-Obukhov 

Length (m) 

06120501 141 1.4 281 -999 44 7.2 

06120502 252 0.61 280.2 -999 21 5.2 

06120503 317 0.77 279.4 -999 29 6.5 

06120504 329 0.58 278.8 -999 19 4.9 

06120505 335 0.51 278.1 -999 16 4.3 

06120506 41 0.28 277.6 -999 7 2.4 

06120507 333 0.47 277.5 -999 14 3.9 

06120508 358 0.56 279.6 -999 18 4.7 

06120509 34 0.28 287 -999 7 4.8 

06120510 85 1.04 291.3 234 213 -15.3 

06120511 71 1.29 292.4 286 296 -14.9 

06120512 108 3.25 294.5 345 544 -40.1 

06120513 94 2.8 295.9 410 462 -27.2 

06120514 73 1.46 296.8 469 346 -16.8 

06120515 56 0.83 297.6 512 192 -6.9 

06120516 286 0.76 297.5 529 147 -10.2 

06120517 259 0.56 296.4 -999 37 6.4 

06120518 277 0.89 291.5 -999 37 7.8 

06120519 276 0.35 288.3 -999 9 3 

06120520 181 0.76 286.5 -999 18 4 

06120521 231 0.31 285 -999 8 2.6 

06120522 298 0.56 283.9 -999 18 4.8 

06120523 144 0.99 283.1 -999 26 5.1 

06120524 197 0.67 281.9 -999 15 3.5 

06120601 205 0.93 280.9 -999 24 4.8 

06120602 67 0.92 280.3 -999 38 7.8 

06120603 153 1.29 279.7 -999 39 6.6 

06120604 272 0.71 278.6 -999 26 6 

06120605 7 0.71 278.1 -999 26 6 

06120606 90 0.55 278 -999 -999 -99999 

06120607 312 0.75 277.9 -999 28 6.5 

06120608 290 0.5 279 -999 15 4.3 

 

1

 The Date/Hour format includes two digits each for year, month, day and hour – YYMMDDHH. -999 

indicates missing data. For the convective boundary height is mainly driven by solar energy and that is 

why it is missing during night time and early morning. -99999 also indicates missing data for MoninObukhov Length. 

 
-83- 

Table 5-11 

Meteorological Data File Used for December 2006 Modeling at Durango Complex 

Date/Hour* 

Wind Direction 

(Degrees) 

Wind Speed 

(m/sec) Temperature (K)

Convective 

Boundary Layer 

Height (m) 

Mechanical 

Boundary Layer 

Height (m) 

Monin-Obukhov 

Length (m) 

06120609 348 0.58 283.6 -999 19 16.8 

06120610 140 0.45 289.6 74 66 -1.3 

06120611 170 0.78 292.3 156 123 -2.3 

06120612 254 0.61 294.2 260 155 -3 

06120613 321 0.47 294.9 366 122 -1.9 

06120614 238 0.65 296.4 480 161 -3.7 

06120615 250 1.21 297.2 564 271 -14.4 

06120616 266 1.29 296.8 596 251 -30.6 

06120617 304 1.53 296 -999 158 58.9 

06120618 293 0.61 291.3 -999 40 5.4 

06120619 226 0.49 288.6 -999 15 4.2 

06120620 114 0.62 287 -999 13 3.2 

06120621 132 0.96 286.1 -999 25 5 

06120622 88 0.38 284.9 -999 10 3.2 

06120623 61 0.4 283.5 -999 11 3.4 

06120624 306 0.8 282.3 -999 31 6.8 

06120701 171 0.72 281 -999 16 3.7 

06120702 27 0.53 280.9 -999 17 4.5 

06120703 193 0.46 280.3 -999 8 2.4 

06120704 81 0.61 279.8 -999 21 5.1 

06120705 163 0.5 279.5 -999 9 2.6 

06120706 353 0.67 279.5 -999 24 5.7 

06120707 16 0.5 279.4 -999 15 4.2 

06120708 344 0.28 280.6 -999 7 2.4 

06120709 185 0.37 286.9 -999 6 3.6 

06120710 283 0.59 292.3 61 123 -5.2 

06120711 76 1.94 295.6 130 452 -34.4 

06120712 71 3.98 298.6 213 1085 -156.9 

06120713 70 3.86 299.6 452 1049 -137.8 

06120714 74 3.46 300.2 723 909 -113.5 

06120715 83 3 300.6 854 743 -102.9 

06120716 68 3.14 300 902 737 -241.7 

06120717 66 2.89 298.9 -999 545 225.3 

06120718 70 2.29 296 -999 276 46.5 

06120719 77 2.26 293.8 -999 250 43 

06120720 88 3.24 293.5 -999 600 138.6 

06120721 84 3.34 292.8 -999 636 149.4 

06120722 82 4.84 293.3 -999 1231 404.3 

06120723 80 4.26 292.3 -999 993 267 

06120724 91 3.96 291.8 -999 537 108.4 
-84- 

Table 5-12 

Meteorological Data File used for December, 2006 Modeling at West 43rd

Date/Hour* 

Wind Direction 

(Degrees) 

Wind Speed 

(m/sec) Temperature (K)

Convective 

Boundary Layer 

Height (m) 

Mechanical 

Boundary Layer 

Height (m) 

Monin-Obukhov 

Length (m) 

06120501 151 0.68 279.1 -999 20 4.7 

06120502 282 0.78 278.9 -999 18 4 

06120503 -999 -999 278 -999 -999 -99999 

06120504 304 0.66 277.7 -999 14 3.4 

06120505 243 0.58 276.7 -999 12 3 

06120506 245 0.28 276.3 -999 4 1.4 

06120507 316 0.64 277 -999 14 3.3 

06120508 338 0.63 278.3 -999 13 3.2 

06120509 304 1.12 282.2 -999 32 10.7 

06120510 295 0.58 286.8 216 80 -2.3 

06120511 93 2.13 290 283 421 -30.6 

06120512 101 3.14 291.9 341 673 -62.5 

06120513 97 2.98 293.1 404 638 -52.8 

06120514 92 2.09 293.9 461 428 -25.7 

06120515 85 1.12 294.6 502 179 -5.4 

06120516 79 0.61 295 519 80 -3.1 

06120517 212 0.64 294.5 -999 17 4.4 

06120518 263 1.39 291.6 -999 44 7.5 

06120519 219 0.68 288.2 -999 15 3.6 

06120520 185 1.18 285.7 -999 34 6.1 

06120521 109 0.67 284.1 -999 20 4.7 

06120522 280 0.48 282.6 -999 9 2.5 

06120523 163 0.85 281.3 -999 28 5.9 

06120524 226 1.11 280.6 -999 31 5.7 

06120601 203 1.36 279.5 -999 42 7 

06120602 182 1.44 278.3 -999 46 7.4 

06120603 173 1.34 277.9 -999 55 9.2 

06120604 239 1.1 277.1 -999 31 5.6 

06120605 9 1.13 276.5 -999 32 5.8 

06120606 114 0.92 276.7 -999 -999 -99999 

06120607 240 1.32 276.1 -999 40 6.9 

06120608 280 0.85 275.9 -999 21 4.5 

06120609 302 0.41 279.2 -999 7 6.2 

06120610 183 0.51 283.9 71 73 -1.6 

06120611 192 0.89 287.1 149 138 -3 

06120612 3 0.74 289.8 249 121 -1.9 

06120613 109 0.63 291.6 353 134 -2.4 

06120614 284 0.55 293.7 462 91 -1.2 

06120615 253 1.51 295.4 545 223 -9.9 

06120616 255 1.28 295.4 577 163 -13.3 
-85- 

Table 5-12 

Meteorological Data File used for December, 2006 Modeling at West 43rd

Date/Hour* 

Wind Direction 

(Degrees) 

Wind Speed 

(m/sec) Temperature (K)

Convective 

Boundary Layer 

Height (m) 

Mechanical 

Boundary Layer 

Height (m) 

Monin-Obukhov 

Length (m) 

06120617 297 1.78 294.6 -999 114 36.5 

06120618 271 1.31 291.6 -999 41 7 

06120619 145 0.85 288 -999 28 5.9 

06120620 95 1.27 285.8 -999 51 8.8 

06120621 107 1.76 284.8 -999 83 12.2 

06120622 77 1.09 283.3 -999 31 5.6 

06120623 145 0.81 282 -999 26 5.6 

06120624 212 0.85 280.8 -999 21 4.4 

06120701 178 1.01 279.8 -999 36 7 

06120702 50 0.68 279.3 -999 15 3.5 

06120703 194 0.56 278.3 -999 11 2.9 

06120704 100 0.79 278 -999 25 5.4 

06120705 169 0.58 277.4 -999 16 4 

06120706 8 0.53 277.4 -999 10 2.7 

06120707 78 0.89 277.5 -999 22 4.6 

06120708 211 0.39 278.4 -999 7 2 

06120709 248 0.46 280.9 -999 8 4.1 

06120710 274 1.04 285.7 58 138 -7.2 

06120711 269 1.37 289.8 122 206 -7.5 

06120712 65 3.37 295.1 203 566 -43.9 

06120713 72 6.1 297.5 415 1210 -186.8 

06120714 80 4.69 298.1 681 868 -102 

06120715 75 3.72 298.4 824 627 -74.1 

06120716 60 4.87 298.5 872 841 -328.1 

06120717 62 4.44 297.7 -999 663 286.6 

06120718 71 3.01 294.4 -999 306 50.2 

06120719 74 3.69 291.9 -999 437 90.3 

06120720 83 4.54 291.6 -999 644 151.4 

06120721 87 3.58 291 -999 418 82.9 

06120722 93 4.06 290.7 -999 727 177.2 

06120723 90 4.02 290.6 -999 714 173 

06120724 115 2.42 289.6 -999 281 36.5 

Wind speed, wind direction, and temperature all play important roles in air dispersion. 

Additional parameters produced by AERMET that also influence dispersion include 

convective boundary height and mechanical boundary height, which are used to 

determine planetary boundary layer (PBL) height. PBL height is a term to describe the 

elevation level up to which vertical mixing of ground-based emissions takes place. A 

low PBL height indicates weak dispersion and the potential for pollutant concentrations 

to remain high. AERMET estimates the height of the PBL during convective conditions 
-86- 

as the maximum of the estimated (or measured if available) convective boundary layer 

height (zic) and the estimated (or measured if available) mechanical mixing height (zim). 

In early morning hours, or overnight when the atmosphere is stable, there is little or no 

convective mixing, and so the height of the PBL would be determined mainly by 

mechanical mixing. Therefore, AERMET sets the height of the boundary layer to the 

mechanical mixing height. In the daytime convection tends to take over, and so the 

height of the PBL is determined primarily by convective mixing. Therefore, AERMET 

assumes the height of the boundary layer is the greater of the mechanical and convective 

mixing heights. The Monin-Obukhov length (L) is used as the stability parameter, and is 

computed by the AERMET meteorological preprocessor. It is negative during the day 

when surface heating results in an unstable atmosphere and positive at night when the 

surface cools (stable atmosphere). Values near zero during the day time (negative) 

indicate very stable conditions while values near zero during the night time (positive) 

indicate very unstable conditions. 

It should be noted that hour “06120606” data at both sites and hour “06120503” data at 

West 43rd are missing in the AERMET data. The model simulated impacts for these 

hours by averaging the preceding and subsequent hour estimates. 

Similar to December 2005 design days, low wind speeds with significant inversion are 

evident in December 5, 6, and the first half of December 7, 2006. The inversions 

recorded on the December 2005 design days, however, were more severe than those in 

2006. 

5.4 Model Performance

AERMOD was configured with the meteorological inputs and emission inventories 

described above and used to estimate each source’s contribution to hourly concentrations 

on each of the episode days for each of the monitoring sites. It should be noted that no 

estimates of background were included in this analysis. Subsequent to the completion of 

this study, background values were estimated using monitoring data available in 

December 2006 (because earlier data was determined to be invalid). Those values were 

included in the air quality modeling prepared for the Five Percent Plan TSD. 

The hourly source specific concentrations were contrasted with the hourly and daily 

concentrations recorded at the Durango Complex and West 43rd Avenue monitors to 

assess model performance. Figures 5-6 through 5-11 provide a summary of how well 

model predictions compare with measured concentrations on an hourly basis. The figures 

display each source category’s contribution to the hourly predicted concentration. Listed 

below are a brief set of comments on each of the figures. Note the scale is not uniform 

across all of the figures. 
-87- 

Figure 5-6 

Comparison of Diurnal Distribution of Measured Concentrations 

and AERMOD Predicted Source Concentrations for 

Durango Complex (December 5, 2006) 

Time

 0:00 4:00 8:00 12:00 16:00 20:00 0:00

PM10 (ug/m3

)

0

100

200

300

400

500

600

700

800

900

1000

1100

0

100

200

300

400

500

600

700

800

900

1000

1100

Traffic

Trackout

Industrial Area Sources

Industrial Point Sources

Vacant Lots 

Unpaved Parking Lots

Local Truck Yard

Agriculture Operations

Construction Activities

PM10 observation

Figure 5-7 

Comparison of Diurnal Distribution of Measured Concentrations 

and AERMOD Predicted Source Concentrations for 

West 43rd Avenue (December 5, 2006) 

 0:00 4:00 8:00 12:00 16:00 20:00 0:00

PM10 (ug/m3

)

0

100

200

300

400

500

600

700

800

900

0

100

200

300

400

500

600

700

800

900

Time
-88- 

Figure 5-8 

Comparison of Diurnal Distribution of Measured Concentrations 

and AERMOD Predicted Source Concentrations for 

Durango Complex (December 6, 2006) 

Time

0:00

4:00

8:00

12:00

16:00

20:00

0:00

PM10 (ug/m3

)

0

100

200

300

400

500

600

700

0

100

200

300

400

500

600

700

Traffic

Trackout

Industrial Area Sources

Industrial Point Sources

Vacant Lots

Unpaved Parking Lots

Local Truck Yard

Agriculture Operations

Construction Activities

PM10 Observations

Figure 5-9 

Comparison of Diurnal Distribution of Measured Concentrations 

and AERMOD Predicted Source Concentrations for 

West 43rd Avenue (December 6, 2006) 

Time

 0:00 4:00 8:00 12:00 16:00 20:00 0:00

PM10 (ug/m3

)

0

100

200

300

400

500

600

700

800

900

0

100

200

300

400

500

600

700

800

900
-89- 

Figure 5-10 

Comparison of Diurnal Distribution of Measured Concentrations 

and AERMOD Predicted Source Concentrations for 

Durango Complex (December 7, 2006) 

Time

 0:00 4:00 8:00 12:00 16:00 20:00 0:00

PM10 (ug/m3

)

0

100

200

300

400

500

600

700

0

100

200

300

400

500

600

700

Traffic 

Trackout

Industrial Area Sources

Industrial Point Sources

Vacant Lots

Unpaved Parking Lots

Local Truck Yard

Agriculture Operations

Construction Activities

Windblown Soil

PM10 Observations

Figure 5-11 

Comparison of Diurnal Distribution of Measured Concentrations 

and AERMOD Predicted Source Concentrations for 

West 43rd Avenue (December 7, 2006) 

Time

 0:00 4:00 8:00 12:00 16:00 20:00 0:00

PM10 (ug/m3

)

0

100

200

300

400

500

600

700

800

900

0

100

200

300

400

500

600

700

800

900
-90- 

• Durango Complex (December 5, 2006) – Figure 5-6 shows that while predicted 

values exceeded the morning and evening peaks, they tracked the diurnal profile 

of the day. Predicted concentrations from road sources alone (i.e., traffic and 

trackout) exceed the early portion of the morning peak. As the wind shifts to the 

northeast and brings in emissions from the truck yard at 9 am, the three sources 

(traffic, trackout and truck yard) produce a significant overprediction of the 

measured concentration suggesting an over estimate of the emissions from one or 

more of those sources. The overprediction of the evening concentrations is more 

difficult to diagnose since the predicted concentrations reflect the rise in 

anthropogenic activity that starts about 4 pm when a larger number of sources is 

shown to be impacting the monitor. The measured concentrations, however, do 

not reflect the increased activity until later in the evening. This is surprising since 

the mixing height data, displayed in Figure 5-1, have dropped significantly by 4 

pm and wind speeds after 2 pm remain are uniformly low (i.e., below a 

meter/second). One other point of note is that industrial sources are not shown to 

have any significant impact on this day. 

 

• West 43rd Avenue (December 5, 2006) – Again, Figure 5-7 shows that the 

predicted values generally tracked the diurnal profile of the day. However, unlike 

the Durango Complex, industrial area source emissions are shown to be a 

significant contributor, particularly during the early morning hours. Since these 

estimates are based on activity profiles obtained from the large operators for this 

date, there is confidence that this finding is correct even though the impact is 

overpredicted. The predicted drop in traffic and trackout related emissions after 8 

am, which is based on actual count data, however, does not match the elevated 

concentrations observed in the monitoring data. As shown in Figure 5-2 the 

decline in the morning concentrations lags the increase in the ceiling and the 

cause of this relationship is not clear. 

• Durango Complex (December 6, 2006) – Figure 5-8 shows a strong correlation 

between predicted and measured concentrations particularly during the morning 

hours. These results suggest that insights from the field study incorporated into 

the inventory development and modeling analysis provide a significant gain in 

insight into the causes of exceedances recorded in the Salt River. 

• West 43rd Avenue (December 6, 2006) – In contrast to Figure 5-8, Figure 5-9 

shows that the improved representation of source specific activities seen at the 

Durango Complex was not replicated at the West 43rd Avenue monitor on the 

same date. While the predictions track the early morning increase in 

concentrations, the subsequent decline in anthropogenic activity shown in the 

predicted concentrations significantly underestimates the measured concentrations 

after 8 am. Figure 5-2 shows that despite a steady rise in the mixing height after 8 

am and increase in dispersion potential the concentrations still continue to rise 

before falling in later hours. The mixture of sources represented in the evening 

and nighttime hours significantly underestimate the concentrations recorded. 
-91- 

• Durango Complex (December 7, 2006) – A familiar pattern of underpredicting 

early and late morning concentrations (i.e., after 8 am) despite a rise in mixing 

height is apparent in Figure 5-10. Also, the predicted values completely miss the 

elevated concentrations caused by the onset of higher winds starting at noon (i.e., 

roughly 4 m/sec or 9 mph). Clearly, the estimate of windblown emissions fails to 

account for the impacts recorded at the monitor. This finding indicates that the 

method used to represent these sources needs to be reexamined. 

• West 43rd Avenue (December 7, 2006) – Figure 5-11 shows reasonable agreement 

between predicted and measured concentrations during the morning hours. 

Unlike the source distribution for the Durango Complex, industrial impacts are 

more evident at the West 43rd Ave. monitor during the morning hours. Unlike the 

Durango Complex, the increase in midday concentrations associated with elevated 

winds at West 43rd Ave is relatively modest despite the influence of higher winds 

(i.e., those exceeding 13 mph). The result is relatively good agreement between 

predicted and measured concentrations for the remainder of the day. It should be 

noted that the elevated winds recorded at mid day came from the northeast and the 

mix of sources impacting the monitors during these hours is significantly different 

from those the southwest that impacted both monitors under the high wind design 

day addressed in the Five Percent Plan. 

In addition to the day-specific descriptions provided above, a comparison between 

predicted and measured concentrations is presented below in Table 5-13. When 

reviewing these comparisons, it is important to remember that background values have 

not been included. As can be seen the predicted values account for between 58 to 91 

percent of the measured values. While the inclusion of background would lead to an 

overprediction of the December 5th concentration, it would substantially diminish the 

margin between the predicted and measured concentrations on December 6th and 7th. 

Another metric that provides insight into how well the predicted values track the 

measured values comes from a correlation coefficient (i.e., R2

) of the hourly relation 

between predicted and measured concentrations. The results show values ranging 

between 0.27 and 0.66. 

Table 5-13 

Comparison of Predicted and Monitored Concentrations 

(averaged for both sites) 

December 5, 2006 December 6, 2006 December 7, 2006 

μg/m3 % of 

Monitored μg/m3 % of Monitored μg/m3 % of Monitored

Predicted 132.6 91.3 110.9 67.6 97.7 58.3 

Monitored 145.3 - 164.0 - 167.5 - 
-92- 

These findings represent a significant improvement relative to the Salt River TSD where 

predicted concentrations on low wind days only accounted for 14 percent of the measured 

concentrations and the R2

 was 0.03. 

5.5 Modeled Impacts of Nearby Sources

To better evaluate the modeled impacts of nearby sources at each monitoring site, a set of 

modeling outputs were prepared showing impacts from sources within 0.5 and 1.0 mile 

from both the Durango Complex and West 43rd Avenue monitoring sites (essentially 

replicating portions of the runs displayed in Figures 5-6 through 5-11). Comparisons 

between these results and the figures in the report are cumbersome as the source category 

contributions are not as distinct in the subsequent runs as in the initial runs. Therefore, a 

tabular summary of the hourly and overall daily comparisons is presented below for each 

monitor in Tables 5-14 and 5-15. 

Table 5-14 provides a summary of the comparisons for the Durango Complex monitor. It 

shows that overall sources located within a half mile of the monitor accounted for more 

than 50% of the predicted concentrations on two of the three days modeled. Sources 

located within a mile of the monitor accounted for 70+% on two of the three days 

modeled. Clearly these results support the conclusion that nearby sources dominate the 

monitored concentrations. The results, however, are far from consistent as the share of 

predicted concentrations on December 6, 2006, from sources located within a half mile 

were 42% and exceeded 50% only when the distance from the monitor was increased to a 

mile. A review of the hourly values across all of the days shows they are highly variable 

and obviously sensitive to the direction of the wind, the location of individual sources, 

and the emissions density (i.e., g/sec-m2

) of those sources. One of the reasons it is 

difficult to see a pattern in the hourly values is that the Durango Complex is surrounded 

by a dense network of anthropogenic activity to the east, west, and north. The only area 

where the density of emission sources diminishes is to the south, southwest (e.g., where 

there is a relatively undisturbed vacant lot, traffic on Lower Buckeye, and the landfill). 

Thus, when winds come from the southwest the share of the predicted concentrations 

from nearby sources falls off. 

The picture that emerges for the West 43rd Avenue monitoring site is somewhat different 

as the local share of predicted concentrations is lower. Table 5-15 shows that sources 

located within a half mile of the monitor accounted for between 37% - 41% of the 

predicted concentrations from all sources across all of the days modeled. When the 

distance from the monitor is increased to a mile, the local share increased to between 

47% - 59%. While these statistics are not as compelling as those from the Durango 

Complex, they still indicate that a dominant share of predicted concentrations comes 

from nearby sources. A review of a satellite image for the area surrounding the West 43rd

Avenue monitor shows the distribution of anthropogenic activity is markedly different 

and less dense than the area surrounding the Durango Complex. The river bed is located 

directly to the north and east and again comes into play roughly a third of a mile to the 
-93- 

west. Anthropogenic activity within the river bed has been severely restricted through 

the implementation of control measures. Thus, on low wind days, air must travel a longer 

distance before the effects of large sources can impact the monitor. 

 

Table 5-14 

Share of Predicted Concentrations from Sources Located 

Within Selected Distances from the Durango Complex Monitor 

Hour December 5, 2006 December 6, 2006 December 7, 2006 

0.5 mile 1.0 mile 0.5 mile 1.0 mile 0.5 mile 1.0 mile 

1 78.7% 83.8% 20.6% 27.8% 10.1% 12.8% 

2 10.8% 15.6% 62.0% 76.6% 60.1% 81.5% 

3 47.4% 70.5% 42.8% 48.2% 35.6% 45.3% 

4 59.8% 80.5% 8.9% 39.9% 54.3% 75.3% 

5 56.3% 78.0% 37.3% 60.9% 37.6% 42.1% 

6 64.2% 77.0% 56.3% 72.3% 43.7% 63.9% 

7 60.8% 76.8% 62.2% 75.9% 59.0% 69.9% 

8 43.5% 67.4% 44.9% 63.9% 45.6% 66.2% 

9 79.7% 87.5% 45.5% 58.6% 53.0% 62.9% 

10 75.7% 80.5% 20.7% 28.1% 36.8% 37.1% 

11 88.2% 89.8% 23.4% 45.3% 95.1% 96.4% 

12 85.5% 88.8% 14.6% 21.5% 96.3% 97.4% 

13 85.2% 88.1% 44.3% 59.5% 97.6% 98.3% 

14 94.0% 95.1% 31.5% 36.8% 97.2% 98.1% 

15 93.7% 95.2% 43.7% 50.1% 94.3% 96.2% 

16 55.4% 69.7% 55.2% 56.0% 97.1% 98.9% 

17 39.1% 47.0% 53.9% 63.6% 78.6% 85.8% 

18 26.5% 59.4% 45.3% 66.2% 68.4% 79.5% 

19 8.7% 50.8% 35.4% 48.7% 56.2% 73.5% 

20 43.9% 52.7% 73.2% 79.7% 41.1% 67.2% 

21 42.9% 61.2% 71.5% 76.9% 64.8% 78.5% 

22 43.5% 60.5% 10.8% 48.1% 80.9% 87.8% 

23 67.7% 75.8% 46.7% 65.7% 82.3% 88.5% 

24 18.7% 22.3% 50.2% 64.6% 48.6% 70.1% 

Total 57.1% 69.8% 41.7% 55.6% 63.9% 73.9% 
-94- 

Table 5-15 

Share of Predicted Concentrations from Sources Located 

Within Selected Distances from the West 43rd Avenue Monitor 

Hour December 5, 2006 December 6, 2006 December 7, 2006 

0.5 mile 1.0 mile 0.5 mile 1.0 mile 0.5 mile 1.0 mile 

1 6.1% 7.1% 52.8% 70.3% 75.5% 83.7% 

2 23.9% 26.3% 46.1% 68.0% 41.0% 60.8% 

3 51.8% 66.1% 44.6% 53.9% 51.2% 67.0% 

4 23.8% 31.2% 15.9% 39.4% 51.9% 68.9% 

5 20.9% 34.0% 19.1% 39.1% 49.3% 63.2% 

6 20.9% 39.7% 48.0% 63.4% 37.0% 53.6% 

7 26.7% 44.8% 50.3% 64.0% 47.8% 61.8% 

8 27.4% 34.0% 54.1% 67.6% 78.4% 89.5% 

9 29.1% 40.4% 37.2% 51.5% 28.8% 45.3% 

10 26.3% 30.5% 48.8% 61.4% 23.1% 28.5% 

11 64.2% 80.3% 60.7% 66.9% 25.5% 33.3% 

12 79.3% 88.4% 21.9% 31.9% 32.3% 51.4% 

13 76.3% 88.8% 42.9% 52.5% 38.4% 61.1% 

14 61.2% 78.3% 45.9% 57.3% 62.0% 84.2% 

15 52.9% 69.2% 67.6% 82.6% 62.9% 84.0% 

16 50.1% 59.9% 76.5% 80.5% 23.6% 48.6% 

17 42.8% 60.8% 17.5% 28.5% 10.7% 34.5% 

18 27.3% 35.0% 28.0% 40.7% 27.4% 77.7% 

19 42.4% 54.4% 45.7% 57.2% 25.7% 68.1% 

20 69.5% 78.5% 60.6% 83.7% 21.6% 56.8% 

21 20.1% 23.6% 25.0% 32.1% 31.3% 69.8% 

22 16.4% 19.7% 45.6% 80.3% 7.7% 54.1% 

23 12.2% 14.3% 15.6% 21.9% 20.8% 61.6% 

24 18.7% 21.1% 23.5% 35.1% 7.1% 10.2% 

Total 37.1% 46.9% 41.4% 55.4% 36.7% 59.1% 

5.6 Transport Considerations

Several lines of investigation were used to assess the contribution of PM-10 transport into 

the Salt River area during the field study period. One of these investigations included the 

use of a mobile monitoring platform to measure ambient PM-10 concentrations along 

transects that were parallel to and outside the modeling domain boundaries. Other 

investigations included surface level and upper air back trajectory analyses, deposition 

analyses, LIDAR measurements and interstation hourly PM-10 comparisons. 
-95- 

Back trajectory analyses using surface-level meteorological data revealed that air parcels 

meander within the modeling domain during stagnant wind regimes when the highest 

PM-10 concentrations were measured in the Salt River area in December 2006. Figure 3-

6 shows a typical 7-hour meander pattern of the air parcel that arrived at the West 43rd

Avenue monitoring site at 9:00 a.m. on December 6, 2006, when the hourly PM-10 

concentration was recorded as 371.5 μg/m3

. During the nocturnal hours represented by 

this back trajectory, the average mixing height was computed by AERMET to be 35 

meters. The settling time of 6.6 micron diameter particles (the average size of particles 

collected in dustfall jars at the W. 43rd Avenue site) from a release height of 35 meters 

was calculated to be 2.8 hours, using the deposition velocity estimation methodology 

described in Chapter 3.3. This means that the vast majority of particles contributing to 

the 9:00 a.m. concentration at the monitor were entrained into the air from sources within 

the modeling domain. 

With regard to upper air parcels, SODAR measurements showed that at 50 meters above 

the ground winds quickly transported material mixed aloft during the day out of the 

modeling domain. Similar results were seen from forward trajectory analysis of radar 

data measuring higher altitude winds. Thus, locally emitted particulate mixed into the 

deeper surface layer during the day cannot be a significant source of deposition within the 

modeling domain. Additional insight into this issue could be provided by the LIDAR 

measurements. The results of that effort, however, have not become available for review. 

Another source of insight into transport was gained from a review of hourly 

measurements of PM-2.5 to PM-10 on days with stagnant conditions. Figure 5-12 

provides a plot of the diurnal distribution of that ratio and changes in mixing height from 

data collected at the Durango Complex monitor. It shows the ratio remains relatively 

stable throughout the day. Given the changes in anthropogenic activity throughout the 

day, this is somewhat of a surprise but consistent with the results of the APS 

measurements which showed little variation in particle size distribution throughout the 

modeling domain. The ratio does, however, exhibit an upward rise in the late morning 

and again late at night. The cause of the midday rise is the result of many offsetting 

factors, including a rise in mixing height, residence time in the atmosphere, secondary 

formation, deposition rate, etc. It is difficult to see a pattern of transport among these 

factors. The cause of elevated nighttime patterns is a little more straightforward. 

Anthropogenic activity was extremely low relative to the rest of the day, so the amount of 

new particulate being emitted was limited. The mixing height remained relatively stable 

so its impact on deposition was also limited. Monitoring data (both displayed and not 

displayed in Figure 5-12) show that nighttime concentrations remained very stable 

despite the absence of anthropogenic activity and suggest an undefined source of 

particulate which could be other unidentified local sources and transport. Another insight 

is that the ratio of PM-2.5/PM-10 continued to rise, which could be the result of increased 

deposition of heavier particles as the night continued or a combination of that 

phenomenon and transport of lighter particles into the modeling domain (which is likely 

since they will remain aloft for longer periods of time to survive the longer transport 

distances indicated by the upper air data). Additional measurement and analysis would 

be required to confirm these effects. 
-96- 

Figure 5-12 

Average PM-2.5/PM-10 Ratio at Durango Complexa

a. Average PM-2.5/PM-10 ratio was calculated at Durango Complex during two low wind speed, high 

PM-10 episodes with total of 5 days: Dec. 5th, 2006 (PM-10 24 hour average: 115 μg/m3); Dec. 6th, 

2006 (PM-10 24 hour average: 170 μg/m3

); Dec. 13th, 2006 (PM-10 24 hour average: 136 μg/m3); 

Dec. 14th, 2006 (PM-10 24 hour average: 148 μg/m3

); Dec. 15th, 2006 (PM-10 24 hour average: 139 

μg/m3

). 

Finally, transport of material from outside of the modeling domain was addressed in the 

in the Five Percent Plan documentation. Separate background estimates were prepared 

and incorporated into the analysis of both low and high wind events.1

 A summary of the 

background share of total predicted normalized concentrations (Tables V-5-17 through 

V-5-19 from the Five Percent Plan TSD) is presented below in Table 5-16. It shows that 

the share of background ranged between 18% - 36% for low wind days and 43% - 55% 

on high wind days. While these estimates are significantly lower than those included in 

the 2005 ADEQ analysis, which ranged between 72% - 77%, they show that transport 

accounts for a significant, but not dominant, share of the concentrations impacting the 

Salt River monitors. 

Transport is a significant factor on high wind days. Although the Five Percent Plan 

documentation did analyze this contribution for the high wind day of February 15, 2006, 

no similar high wind event occurred during the November-December 2006 field study. 

The modeling of high wind events is also problematical. As shown in Figures 5-8 and 

5-9 of the Five Percent Plan documentation, AERMOD consistently underpredicted the 

sharp rise in afternoon concentrations caused by the onset of high winds. The reason for 

 

1

 Estimates of background were prepared based on an analysis of concentrations recorded at monitors 

located outside of the modeling domain, transit time to Salt River monitors, and settling velocity rates. 

Since this calculation captures the contribution of transport and not carryover from one hour to the next, the 

term background may be misleading. 
-97- 

Table 5-16 

Share of Predicted Design Day Contributions From Transport 

For Salt River Monitors 

Date Durango 

Complex 

West 43rd

Avenue 

Bethune 

Elementary 

School 

Low Wind Days 

12/12/05 20% 18% 21% 

12/13/05 25% 36% 24% 

High Wind Day 

02/15/06 55% 43% - 

the shortfall is thought to be “the failure of the wind-dependent emission factor algorithm 

in AERMOD to duplicate the initial hour spike in windblown emissions and the depletion 

of surface particles available for entrainment in subsequent hours even when average 

hourly wind velocities increase.” While the same problem is thought to be one cause of a 

failure of AERMOD to duplicate the somewhat elevated concentrations recorded at the 

W. 43rd Avenue monitor at 6:00 p.m. on December 7, 2006, a contributing factor is that 

wind speeds on that date were considerably lower and less consistent than those typically 

associated with windblown dust generation. Because the wind speeds on that date were 

also inconsistent between the two monitoring sites, different phenomena are thought to be 

responsible for the underprediction of concentrations recorded on that date. 

The highest winds on December 7, 2006, were recorded at the West 43rd Avenue monitor. 

Table 5-17 presents a comparison of the winds and concentrations recorded at that 

monitor during the February 15, 2006 high wind design day and December 7, 2006. To 

simplify the comparison only the highest wind hours are presented. As can be seen, the 

winds recorded on December 7th were lighter and more variable than those recorded on 

the high wind design day, which had sustained speeds above 14 mph for a four-hour 

period starting at 4:00 p.m. Similarly, the concentrations recorded on December 7th were 

considerably lower than on February 15th. Clearly, the higher wind speeds on December 

7th were not the cause of the exceedance of the ambient PM-10 standard; the high 

concentrations recorded under stagnation conditions in the morning were the cause of the 

exceedance. The underprediction of the peaks recorded at 1:00 p.m. and 7:00 p.m. are 

the result of lowered anthropogenic activity and relatively low and variable “high” wind 

speeds that did not produce significant windblown dust emission estimates. 

The wind speeds recorded on December 7, 2006 at the Durango Complex monitor were 

considerably lower than at the West 43rd Avenue monitor. The 8.9 mph and 8.6 mph 

wind speeds recorded at noon and 1:00 p.m. on December 7th cannot be responsible for 

the sharp rise in concentrations (i.e., on the order of 500 μg/m3

) recorded during 

subsequent hours at the Durango Complex. The height of the peak also contradicts the 
-98- 

Table 5-17 

Comparison of Afternoon Wind Speeds and Concentrations 

Recorded During High Wind Events at the 

West 43rd Avenue Monitor on February 15, 2006 and December 7, 2006 

Hour February 15, 2006 December 7, 2006 

mph μg/m3 Mph μg/m3

Noon 3.2 51.5 7.5 111.2 

1 pm 2.0 46.6 13.6 135.9 

2 pm 3.5 66.5 10.5 66.0 

3 pm 9.6 225.8 8.3 28.1 

4 pm 15.5 829.4 10.9 23.0 

5 pm 14.6 591.0 9.9 36.7 

6 pm 15.1 274.3 6.7 54.7 

7 pm 14.4 346.2 8.3 134.0 

8 pm 11.0 97.5 10.2 53.1 

“event.” Sierra contacted MCAQD to determine whether any Rule 316 violations were 

recorded on this date. A review of the violations recorded in the Salt River area found 

none were issued on this date. The lack of violation on this date, however, does not mean 

that an emissions event did not occur—it just means that one was not recorded. 

This result is consistent with the observation that top-down inventories cannot be relied 

upon to assess source-specific emissions on design days. Access to day-specific activity 

data is critical to understanding source-specific emissions. Since regularly collected 

activity data may not always capture information on the critical emissions source(s), 

investigations of unusual activity (e.g., accidents, construction, violations, etc.) should be 

undertaken immediately after an exceedance is recorded (i.e., while insight is still fresh in 

people’s minds even if the activity data were not recorded). Insights gained from this 

effort will aid in tracking progress towards attainment and in subsequent modeling 

investigations. 

### 
-99- 

6. CONCLUSIONS 

Improving the understanding of PM-10 source-receptor relationships in the Salt River 

area, which is dominated by fugitive dust sources, has been a challenge. When the vast 

majority of particulates are produced by sources of fugitive dust, the tools (e.g., chemical 

and elemental speciation approaches) that can be applied to this problem are significantly 

limited. In light of these limitations, this study has treated ambient PM-10 as a gaseous 

pollutant and has focused on development of an accurate emissions inventory and 

dispersion modeling to identify the sources of PM-10 emissions. 

At each step in the investigation and analyses, new insights were assimilated into the 

design of the subsequent analytical task. The success of this effort is demonstrated by the 

improved model performance relative to the Salt River TSD, where predicted 

concentrations for low wind days represented 14 percent of the monitored values and the 

R2

 was 0.03. In this study predicted concentrations accounted for 58 to 91 percent of the 

monitored value and the R2

 ranged between 0.27 and 0.66 depending on the day 

examined. 

Presented below is a summary of the insights gained in this process. 

Source Strength

• Paved road emissions, especially in areas of heavy soil silt loadings, are the 

largest contributor to ambient PM-10 in the Salt River area. 

• Day-specific analysis of source emissions is critical to understanding the 

relationship of source contributions to monitored concentrations and determining 

the effectiveness of control measures in demonstrating attainment. 

Low Wind, Stagnant Days

• Highest PM-10 impacts occur during early morning after emissions-generating 

activities commence operation and before inversion heights rise as a result of 

solar groundheating. 

• Emissions from nearby sources (i.e., within 1 mile) dominate PM-10 

concentrations recorded at the monitor. 
-100- 

• The site-specific location of emission sources—especially area sources such as 

paved roads, unpaved parking lots, etc.—is critical to the correlation of modeled 

PM-10 impacts to monitored impacts and, thus, the determination of sourcereceptor relationships in areas dominated by fugitive dust sources. 

• Modeled estimates consistently underpredicted measured concentrations during 

late night and early morning hours when anthropogenic activity was low and after 

the morning peak in anthropogenic activity when the mixing height was 

increasing and emission levels were dropping. While the causes of this shortfall 

are not well understood, they are thought to include underestimates of emissions 

associated with “events” (e.g., accidents that divert traffic flows, localized 

construction activity, failure of emission control systems, etc.) particularly during 

nighttime hours. 

Higher Wind Conditions

• Elevated wind speeds (those above stagnant conditions but below the threshold 

required for windblown emissions) generally disperse and reduce concentrations 

carrying over from stagnant wind periods. This effect is seen in the late afternoon 

and nighttime concentrations recorded on December 7, 2006, at both monitors. 

The above findings were used to model the attainment of the 24-hour PM-10 standard in 

the Five Percent Plan. The study findings also provided valuable insights that aided 

selection of measures in the Plan that will reduce PM-10 emissions during stagnant and 

windy conditions. The implementation of the committed control measures in the Plan is 

expected to achieve attainment of the federal standard in the Salt River area, and 

throughout the PM-10 nonattainment area, by December 31, 2010. 

6.1 Recommendations for Future Studies

In light of the above findings, it is recommended that both meteorological and 

concentration measurements in the Salt River area be recorded over shorter time intervals 

(e.g. 5-minute averages). This information is critical to assessing air movement, 

dispersion and source significance under the low wind stagnant conditions that occur in 

late November and early December. In addition, it recommended that the following 

studies be considered: 

• Conduct a saturation monitoring study under low wind stagnant conditions. Data 

is needed on concentrations and particle size distribution recorded at multiple 

locations within the Salt River for extended periods of time to support a better 

understanding of dispersion and source significance. 

• Evaluate the ability of “puff” models (e.g., CALPUFF) to better represent 

dispersion within the Salt River. Puff models have the ability to account for 
-101- 

carryover (i.e., previous hour’s contributions) and the effects of meandering wind 

currents. 

• Identify and collect data on the activity of critical emission sources during periods 

of high PM-10 concentrations. Attention should be focused on records used to 

prepare emission inventories (e.g., travel activity, operating hours, permits, etc.), 

as well as records of unusual activity that provide insight into unexpected source 

significance (e.g., accidents, notices of violation, interviews, etc.). This 

information should be assembled and used to assess source significance for each 

day the standard is exceeded. 

• Collect additional silt measurements in the Salt River area during periods of high 

PM-10 concentrations. Data are needed for roads with and without visible 

trackout. The data should be combined with previous measurements to improve 

confidence in estimates of control program benefits and to track progress towards 

attainment. 

###