i

I. Table of Contents

I. Table of Contents......................................................................................................... i

II. Table of Tables ........................................................................................................... ii

III. Table of Figures..................................................................................................... iv

IV. Introduction............................................................................................................. 1

V. 2002 and 2008 Emission Inventories.......................................................................... 1

V.A. 2002 Arizona Emission Inventory Methodology................................................ 3

V.B. 2008 Arizona Emission Inventory Methodology................................................ 3

V.C. 2002 & 2008 Emission Inventory Methodology Comparisons .......................... 4

V.D. 2002 and 2008 Emissions................................................................................. 13

V.D.1. Estimated 2002 and 2008 Arizona Emissions .......................................... 14

V.D.2. Summary of Major Methodological Changes........................................... 23

V.D.3. Regional Inventory Trends for Emissions................................................ 24

VI. IMPROVE Monitoring Data................................................................................. 27

VI.A. Data Completeness Requirements ................................................................ 27

VI.A.1. SIAN1 data substitution methodology.................................................. 28

VI.B. Baseline and Progress Period Visibility........................................................ 32

VI.B.1. Progress Period (2005-2009) Visibility .................................................... 34

VI.B.2. Visibility Trend Analyses......................................................................... 37

VI.C. Ammonium Sulfate Analysis........................................................................ 47

VI.C.1. 20% Most Impaired Ammonium Sulfate Days......................................... 47

VI.C.2. Regional Ammonium Sulfate Trends ....................................................... 49

VI.D. Coarse Mass Analysis................................................................................... 51

VI.D.1. Worst 20% Coarse Mass Days.............................................................. 52

VI.D.2. Large Point Source Locations............................................................... 53

VI.E. Method Comparison Summary..................................................................... 56

VII. Reasonable Progress Goals................................................................................... 59

VIII. Conclusions........................................................................................................... 64
ii

II.Table of Tables

Table 1: Emission Inventory Methodology Comparison for 2002 and 2008. ................... 5

Table 2: Arizona Sulfur Dioxide Emissions by Source Category ................................... 15

Table 3: Arizona Nitrogen Oxide Emissions by Source Category .................................. 16

Table 4: Arizona Ammonia Emissions by Source Category ........................................... 17

Table 5: Arizona Volatile Organic Compound Emissions by Source Category.............. 18

Table 6: Arizona Primary Organic Aerosol Emissions by Source Category................... 19

Table 7: Arizona Elemental Carbon Emissions by Source Category .............................. 20

Table 8: Arizona Fine Particulate Matter Emissions by Source Category ...................... 21

Table 9: Arizona Coarse Particulate Matter Emissions by Source Category .................. 22

Table 10: Relative Contribution of Pollutants to Visibility Conditions on the 20% Most 

Impaired Days at Arizona Class I area IMPROVE Sites for the Progress Period (2005-

2009). ................................................................................................................................ 35

Table 11: Relative Contribution of Pollutants to Visibility Conditions on the 20% Least 

Impaired Days at Arizona Class I area IMPROVE Sites for the Progress Period (2005-

2009). ................................................................................................................................ 36

Table 12: Difference in Aerosol Extinction by Component between the Baseline Period 

(2000-2004) and the Progress Period (2005-2009) on the 20% Most Impaired Days for 

Arizona Class I IMPROVE Sites...................................................................................... 41

Table 13: Difference in Aerosol Extinction by Component between the Baseline Period 

(2000-2004) and the Progress Period (2005-2009) on the 20% Least Impaired Days for 

Arizona Class I IMPROVE Sites...................................................................................... 42

Table 14: Statistically Significant 2000-2009 Annual Average Trends for Aerosol 

Extinction by Component for Arizona Class I area IMPROVE Sites. ............................. 45

Table 15: 2000-2009 Ammonium Sulfate Visibility Extinction (mM-1) Trends, Baseline 

(2000-2004) vs. Progress (2005-2009) Period Comparisons, and Altered Baseline (2000-

2005) vs. Altered Progress (2005-2009) Period Comparisons at each Arizona Class I area 

IMPROVE Site for the 20% Worst Ammonium Sulfate Days......................................... 48

Table 16: 2000-2010 Coarse Matter Visibility Extinction (mM-1) Trends and Baseline vs. 

Progress Period Comparisons at each Arizona Class I area IMPROVE Site for the 20% 

Worst Coarse Matter Days................................................................................................ 53

Table 17: CHIR1 IMPROVE Site comparison of Baseline and Progress Period 

Ammonium Sulfate Averages for All Days and the 20% Worst Days............................. 59

Table 18: Arizona Class I Area Reasonable Progress Goals Comparison to Progress 

Period Visibility for the 20% Worst Days. '2018 Projected Visibility' was extrapolated 

based on the rate of Visibility change between the Baseline and Progress Period 

Visibilities......................................................................................................................... 62

Table 19: Alternative method for the 20% Most Impaired Days at GRCA2. EC and 

POM visibility extinctions are replaced by ten-year average for 2003 and 2009............. 62

Table 20: Arizona Class I Area Reasonable Progress Goals Adjusted Comparison to the 

Altered Progress Period Visibility (2006-2010) for the 20% Worst Days. '2018 Projected 

Visibility' was extrapolated based on the rate of visibility change between the Baseline 

and Progress Period Visibilities. In this case the Baseline period was altered to the years 

2000-2005 and the Progress Period was adjusted to the years 2006-2010....................... 63
iii

Table 21: Arizona Class I Area Reasonable Progress Goals Comparison to Progress 

Period Visibility for the 20% Best Days. '2018 Projected Visibility' was extrapolated 

based on the rate of Visibility change between the Baseline and Progress Period 

Visibilities......................................................................................................................... 64
iv

III. Table of Figures

Figure 1: 2002 and 2008 Sulfur Dioxide Emissions by Source Category ....................... 15

Figure 2: 2002 and 2008 Nitrogen Oxide Emissions by Source Category...................... 16

Figure 3: 2002 and 2008 Ammonia Emissions by Source Category............................... 17

Figure 4: 2002 and 2008 Volatile Organic Compound Emissions by Source Category . 18

Figure 5: 2002 and 2008 Primary Organic Aerosol Emissions by Source Category....... 19

Figure 6: 2002 and 2008 Elemental Carbon Emissions by Source Category.................. 20

Figure 7: 2002 and 2008 Fine Particulate Matter Emissions by Source Category .......... 21

Figure 8: 2002 and 2008 Coarse Particulate Matter Emissions by Source Category ...... 22

Figure 9: Regional differences in VOC emissions between 2002 and 2008 Emission 

Inventories......................................................................................................................... 25

Figure 10: Regional differences in Coarse Mass emissions between 2002 and 2008 

Emission Inventories......................................................................................................... 25

Figure 11: Regional differences in Fine Mass emissions between 2002 and 2008 

Emission Inventories......................................................................................................... 26

Figure 12: IMPROVE SIAN1 data collected during the 2005-2009 progress period, 

where original SIAN1 RHR data are depicted in dark blue, and substituted data are 

depicted with separate colors by species. ......................................................................... 30

Figure 13: IMPROVE SIAN1 data collected during the 2005-2009 progress period, 

where substituted days are depicted with a black bar beneath the data. ........................... 31

Figure 14: Average Extinction for Current Progress Period (2005-2009) for the Worst 

(Most Impaired) and Best (Least Impaired) Days Measured at Arizona Class I area 

IMPROVE Sites................................................................................................................ 37

Figure 15: Average Extinction for Baseline and Progress Period Extinction for Worst 

(Most Impaired) Days Measured at Arizona Class I area IMPROVE Sites..................... 43

Figure 16: Difference between Average Extinction for Current Progress Period (2005-

2009) and Baseline Period (2000-2004) for the Worst (Most Impaired) Days Measured at 

Arizona Class I area IMPROVE Sites. ............................................................................. 43

Figure 17: Average Extinction for Baseline and Progress Period Extinction for Best 

(Least Impaired) Days Measured at Arizona Class I area IMPROVE Sites..................... 44

Figure 18: Difference between Average Extinction for Current Progress Period (2005-

2009) and Baseline Period (2000-2004) for the Best (Least Impaired) Days Measured at 

Arizona Class I area IMPROVE Sites. ............................................................................. 44

Figure 19: Average Annual Ammonium Sulfate Extinction (mM-1) at each Arizona Class 

I area IMPROVE Site for the 20% Worst Ammonium Sulfate Days............................... 48

Figure 20: Magnitude of visibility component extinctions that have increased between 

the baseline average (2000-2004) and the first progress period average (2005-2009) for 

the 20% worst visibility days............................................................................................ 50

Figure 21: 10-year annual average ammonium sulfate extinction trends for 20% worst 

days at CIA IMPROVE sites in the WRAP region........................................................... 50

Figure 22: 10-year annual average ammonium sulfate extinction trends for all measured 

days at CIA IMPROVE sites in the WRAP region........................................................... 51

Figure 23: Average Annual Coarse Mass Extinction (mM-1) at each Arizona Class I area 

IMPROVE Site for the 20% Worst Coarse Mass Days.................................................... 53
v

Figure 24: Locations of Arizona Class I areas, Class I area 50 km buffers, Class I area 

IMPROVE monitors, and Large Point Source Emitters of PM10 (>100 tons/year). 

IMPROVE site values correspond to Visibility Extinction (mM-1) of Coarse Mass 

averaged over the progress period (2005-2009). .............................................................. 55

Figure 25: Daily Aerosol Extinction measured by the Chiricahua CHIR1 IMPROVE 

monitor during 2002. ........................................................................................................ 58

Figure 26: Daily Aerosol Extinction measured by the Chiricahua CHIR1 IMPROVE 

monitor during 2008. ........................................................................................................ 58

Figure 27: Monthly Average Aerosol Extinction measured by the CHIR1 IMPROVE 

monitor in 2002................................................................................................................. 58

Figure 28: Monthly Average Aerosol Extinction measured by the CHIR1 IMPROVE 

monitor in 2008................................................................................................................. 58
1

IV. Introduction

In 2011, the State of Arizona submitted a Regional Haze State Implementation Plan (SIP) 

to the Environmental Protection Agency (EPA) to fulfill the requirements of 40 CFR § 

51.308. On December 21, 2012 the EPA published within the Federal Registeria partial 

disapproval of Arizona’s Regional Haze State Implementation Plan. This document was 

created to address certain deficiencies identified by the EPA, specifically 40 CFR § 

51.308(d)(4)(v) which requires to submission of the most recently available Emissions 

Inventory in a Regional Haze SIP. Furthermore, this document presents an alternative 

methodology, which has been used by the EPA in previous studies, for the analysis of 

visibility trends and provides supplemental information to address visibility trends within 

the State of Arizona between the years of 2000 and 2009 and how these trends compare 

to Reasonable Progress Goals and Uniform Rates of Progress.

V. 2002 and 2008 Emission Inventories

The 2002 Arizona Emission Inventory (EI) was originally constructed to fulfill the 

requirement of 40 CFR § 51.308(d)(4)(v)ii, which states a baseline year emission 

inventory must be contained within a State’s implementation plan for regional haze. On 

December 21, 2012 EPA published within the Federal Registeriii a partial disapproval of 

Arizona’s Regional Haze State Implementation Plan. One disapproved provision was 

based on 40 CFR § 51.308(d)(4)(v) which further requires a submission of a statewide 

emission inventory for the most recent year of which data are available. In order to fulfill 

this requirement, the Arizona Department of Environmental Quality (ADEQ) is 

presenting a 2008 emission inventory calculated and compiled by the Western Regional 

Air Partnership (WRAP) and Air Resource Specialists, Inc (ARS).

The 2002 and 2008 emission inventories calculate emissions of the regional haze 

contributing pollutants: Sulfur Dioxide (SO2), Nitric Oxide (NOx), Ammonia (NH3), 

Volatile Organic Compounds (VOCs), Primary Organic Aerosols (POA), Elemental 

Carbon (EC), Fine Particulate Matter (Fine), and Coarse Particulate Matter (CM). For 

the purpose of consistency, these pollutants were reported for all major source categories. 

Where possible these source categories were further partitioned into anthropogenic and 

natural sources. Source categories for both anthropogenic and natural sources are listed 

and described briefly here, followed by information related to the inventories.

 

i Environmental Protection Agency (EPA). Federal Register Volume 77, No. 246. Dec. 21, 2012.

ii Environmental Protection Agency (EPA). Code of Federal Regulations Title 40 Volume 2 Section 51. 

2011.

iii Environmental Protection Agency (EPA). Federal Register Volume 77, No. 246. Dec. 21, 2012.
2

 Point Sources: These are sources that are identified by point locations, 

typically because they are regulated and their locations are available in 

regulatory reports. Point sources can be further subdivided into electric 

generating unit (EGU) sources and non-EGU sources, particularly in criteria 

inventories in which EGUs are a primary source of NOX and SO2. Examples 

of non-EGU point sources include chemical manufacturers and furniture 

refinishers.

 Area Sources: Sources that are treated as being spread over a spatial extent 

(usually a county or air district) and that are not movable (as compared to nonroad mobile and on-road mobile sources). Because it is not possible to collect 

the emissions at each point of emission, they are estimated over larger regions. 

Examples of stationary area sources are residential heating and architectural 

coatings. Numerous sources, such as dry cleaning facilities, may be treated 

either as stationary area sources or as point sources.

 On-Road Mobile Sources: Vehicular sources that travel on roadways. 

Emissions are estimated as the product of emissions factors and activity data, 

vehicle miles traveled (VMT). Examples of on-road mobile sources include 

light-duty gasoline vehicles and heavy-duty diesel vehicles.

 Off-Road Mobile Sources: Off-road mobile sources are vehicles and engines 

that encompass a wide variety of equipment types that either move under their 

own power or are capable of being moved from site to site. Examples include 

agricultural equipment such as tractors or combines, locomotives and oil field 

equipment such as mechanical drilling engines. 

 Oil and Gas Sources: Oil and gas sources consist of a number of different 

types of activities from engine sources for drill rigs and compressor engines, 

to sources such as condensate tanks and fugitive gas emissions. The variety of 

emissions types for sources specific to oil and gas activity can, in some cases, 

overlap with mobile, area or point sources, but these can also be extracted and 

treated separately.

 Biogenic Emissions: Biogenic emissions are based on the activity fluxes 

modeled from biogenic land use data, which characterizes the types of 

vegetation that exist in particular areas. Emissions are generally derived using 

modeled estimates of biogenic gas-phase pollutants from land use 

information, emissions factors for different plant species, and meteorology 

data.

 Dust: Dust emissions may have a variety of sources that could include 

anthropogenic sources, natural sources, and natural sources that may be 

influenced by anthropogenic activity. For emissions summary purposes, dust 

is classified here as fugitive dust and windblown dust. Fugitive dust includes 

sources such as road dust, agricultural operations, construction and mining 

operations and windblown dust from vacant lands. 
3

 Fire: Fire sources are difficult to predict and control, and may have a mix of 

natural and anthropogenic influences. Natural sources include wildland fires, 

while anthropogenic sources can include agricultural and prescribed fires.

V.A. 2002 Arizona Emission Inventory Methodology

The 2002 Arizona Emission Inventory calculations and results are described in detail in 

the Arizona Regional Haze SIP

iv

. Please refer to Chapter 8 of this document for detailed 

calculation methodology of the 2002 Arizona Emission Inventory.

The 2002 Arizona Emission Inventory served as the baseline inventory for the 2018 

emission inventory estimation. The methodology by which 2018 emissions were 

estimated from the 2002 emission inventory can be found in detail in the Arizona 

Regional Haze SIP and an overview of these methodologies are shown in Table 1. 

V.B. 2008 Arizona Emission Inventory Methodology

The creation of the 2008 State of Arizona Emission Inventory was a collection of efforts 

between WRAP, ARS, and ADEQ. This inventory represents the most complete and 

recent calculation of pollutant emissions from all identified sources currently available at 

the time of this document’s preparation. Portions of this inventory were derived from the 

2008 EPA National Emissions Inventory, while other portions were calculated using a 

variety of emission models and techniques. Source data and emission estimation 

methodology are discussed in the following section and compared to those methods 

utilized during the creation of the 2002 EI.

The 2002 emission inventory reported emissions based on the most readily available and 

accurate source data and methods at the time of preparation; however, many of the 

calculated source category emissions methodologies and input data changed between the 

2002 and 2008 emission inventory preparations in order to enhance the accuracy of

estimated statewide emissions. For this reason, many of the source category emission 

differences between the 2002 and 2008 inventories presented in this document should be 

viewed as a mixture of methodology, input data, and actual emissions changes.

Furthermore, since the Arizona 2018 EI was estimated through the adjustment of the 

2002 baseline Arizona emission inventory, emissions from these two EIs are more readily 

comparable than emissions from the 2008 EI presented here due to the aforementioned 

methodology and source data differences.

 

iv “Arizona State Implementation Plan: Regional Haze Under Section 308 of the Federal Regional Haze 

Rule” Arizona Department of Environmental Quality (ADEQ) Air Quality Division, 2011.
4

V.C. 2002 & 2008 Emission Inventory Methodology 

Comparisons

Table 1 presents the input data and methodologies utilized to calculate each source 

category for the 2002 and 2008 EIs. This table illustrates how some source category

methodologies, input data, or modeling resolutions changed dramatically, which in some 

cases is reflected in estimated emissions between the two inventories. 
5

Table 1: Emission Inventory Methodology Comparison for 2002 and 2008.

Inventory 

Sector

2002 Baseline Inventory

(WRAP Plan02d)

2008 Emission Inventory

(WRAP WestJump08 

and DEASCO3)

Comments

Point 

Sources

The WRAP generated point source inventories for 

both actual reported 2002 (Base02b) EGU and all 

other point source data, and for a 2000-2004 

average of EGU point sources (Plan02d). 

Inventories were generated using hourly EPA 

CAMD CEM data for EGUs. Other point sources 

for both Base02b and Plan02d were developed in 

consultation with states by the ERG contractor.

Plan02d emissions are used here because they are 

consistent with what was reported as baseline 

conditions for most WRAP region SIPs.

More details are available here:

http://vista.cira.colostate.edu/TSS/Results/Emissio

ns.aspx

and 

http://www.wrapair.org/forums/ssjf/pivot.html

2008 inventories were 

generated using hourly

EPA CAMD CEM data 

for EGUs. Other point 

sources are from the 2008 

NEI v2. 

More details are available 

here:

http://wrapair2.org/WestJ

umpAQMS.aspx

Baseline conditions presented here 

represent a 5-year average for EGUs, 

while progress period conditions are 

represented with 2008 data.

Area 

Sources

Plan02d emissions inventories were developed in 

consultation with Arizona RPOs by the ERG 

contractor.

More details are available here:

http://vista.cira.colostate.edu/TSS/Results/Emissio

ns.aspx

Arizona reported area 

sources from the 2008 

NEI v2.

More details are available 

here:

http://wrapair2.org/WestJ

umpAQMS.aspx

Note that area oil and gas sources are 

reported separately.
6

Inventory 

Sector

2002 Baseline Inventory

(WRAP Plan02d)

2008 Emission Inventory

(WRAP WestJump08 

and DEASCO3)

Comments

Point Oil 

and Gas

State reported point oil and gas sources for 2002 

are included here in the point source inventory

totals. 

More details are available here:

http://www.wrapair.org/forums/ssjf/pivot.html

Different basins are 

comprised of a 

combination of state 

reported point oil and gas 

from the 2008 NEI v2 for 

some areas and updated 

WRAP Phase III 

inventories for other 

areas. 

These emissions were 

developed separately in 

some cases, but are

included in the point 

source inventory totals

(see above). 

More details are available 

here:

http://wrapair2.org/WestJ

umpAQMS.aspx

This industry has expanded and 

evolved considerably since 2002. 

Area Oil 

and Gas

Developed using WRAP Phase II emissions 

methodologies. Emissions process estimated 

included:

 Drill Rigs

 Wellhead Compressor Engines

 CBM Pump Engines

Developed using WRAP 

Phase III emissions 

methodologies using 2008 

production data. The 

following additional 

categories were included 

in addition to those listed 

Note that many more source 

categories were counted in 2008 than 

in 2002.

Other differences between 2002 

Phase II O&G emissions vs. Phase 

III/WestJumpAQMS 2008 O&G 
7

Inventory 

Sector

2002 Baseline Inventory

(WRAP Plan02d)

2008 Emission Inventory

(WRAP WestJump08 

and DEASCO3)

Comments

 Heaters

 Pneumatic Devices

 Condensate and oil tanks

 Dehydrators

 Completion Venting

More details are available here:

http://vista.cira.colostate.edu/TSS/Results/Emissio

ns.aspx

and 

http://www.wrapair.org/forums/ogwg/documents/2

007-10_Phase_II_O&G_Final)Report(v10-

07%20rev.s).pdf

for 2002:

 Lateral compressor engines

 Workover rigs

 Salt-water disposal engines

 Artificial lift engines 

 Vapor recovery units (VRUs)

 Miscellaneous or exempt 

engines

 Flaring

 Fugitive emissions

 Well blowdowns

 Truck loading

 Amine units (and gas 

removal)

 Water tanks

More details are available 

here:

http://wrapair2.org/WestJ

umpAQMS.aspx

emissions:.

1)Phase III 2008 estimates included new

and/or revised estimation methodologies 

for each of the equipment types and 

processes included, e.g., the surveys 

provided counts by device type (low-bleed 

vs. high-bleed) and specific information on 

control device efficiency, among other 

improvements to activity data. Phase II 

did not have that information available, 

since no surveys were made in Phase II.

2)Phase III used detailed surveys of 

operators in each basin to determine 

activities, practices, and counts of small 

“area-source” equipment not typically 

permitted by the state. WestJumpAQMS 

then carried forward these survey data and 

adjusted emissions to 2008 based on 

production data and any controls added 

after Phase III.

3)Phase III/WestJumpAQMS used the highquality and complete IHS commercial 

database of O&G production data by well 

by basin. This was not used in Phase II, 

instead the state O&G Commission 

databases, which have been improved quite 

a bit over time, were used.

4) Phase III used more refined 

methodologies to estimate emissions, 
8

Inventory 

Sector

2002 Baseline Inventory

(WRAP Plan02d)

2008 Emission Inventory

(WRAP WestJump08 

and DEASCO3)

Comments

Phase III also asked states and 

operators for gas composition data by

basin that greatly increased the 

information available about VOC 

emissions rates.

On-Road 

Mobile

EPA MOBILE6 model applied by ENVIRON 

contractor using NMIM VMT defaults for all 

counties except Maricopa, for which local inputs 

were provided.

More information is available in the Emissions 

Method section of the WRAP TSS documentation:

http://vista.cira.colostate.edu/TSS/Results/Emissio

ns.aspx

and 

http://www.wrapair.org/forums/ef/UMSI/0606_W

RAP_Mobile_Source_EI_Final_Report.pdf

EPA MOVES2010a

model in inventory mode

utilizing national default 

data for each county and 

MET4MOVES for 

meteorological data.

More details are available 

here:

http://wrapair2.org/WestJ

umpAQMS.aspx

Differences in models contributed to 

some differences in emissions 

reported, but other disparities are due 

to a combination of VMT changes

and new controls on vehicles.

Off-Road 

Mobile

EPA draft NONROAD2004 model version data by

ENVIRON with inputs from Arizona for vehicle 

population allocation and county level locomotive 

emissions.

More details are available here:

http://vista.cira.colostate.edu/TSS/Results/Emissio

ns.aspx

and

http://www.wrapair.org/forums/ef/UMSI/0606_W

State reported off-road 

mobile sources for 2008 

(NEI08v2).

More details are available 

here:

http://wrapair2.org/WestJ

umpAQMS.aspx

The off-road models include both 

emission factors and default countylevel population and activity data.
9

Inventory 

Sector

2002 Baseline Inventory

(WRAP Plan02d)

2008 Emission Inventory

(WRAP WestJump08 

and DEASCO3)

Comments

RAP_Mobile_Source_EI_Final_Report.pdf

Fugitive 

Dust and 

Road Dust

Emission Methodology based on AP-42 guidance 

and CARB procedures for the following source 

categories:

 Agricultural Operations – County specific 

cropland acreage provided by the State.

 Construction Operations – data obtained 

from the US Census Bureau and the 

Department of Commerce.

 Road Dust – data obtained from the Federal 

Highway Administration and State and 

local datasets.

Vegetative scavenging factors were applied preprocessing at the county level

More details are available here:

http://vista.cira.colostate.edu/TSS/Results/Emissio

ns.aspx

Extracted from state 

reported area sources for 

2008 (NEI08v2).

Vegetative scavenging 

factors were applied postprocessing at the higher 

resolution grid cell level, 

compared to 2002 data.

More details are available 

here:

http://wrapair2.org/WestJ

umpAQMS.aspx

Note that fugitive dust and road dust 

categories were available separately

in WRAP Plan02d inventories, but 

are combined for summary purposes 

here. For the 2008 inventory, 

vegetative scavenging factors were 

applied to the combined sources; thus 

these source categories are not easily 

separated.
10

Inventory 

Sector

2002 Baseline Inventory

(WRAP Plan02d)

2008 Emission Inventory

(WRAP WestJump08 

and DEASCO3)

Comments

Windblow

n Dust

Generated using WRAP Windblown Dust Model 

and 2002 MM5 meteorology, at 36km grid cell 

resolution.

Vegetative scavenging factors applied preprocessing at the county level

More details are available here:

http://vista.cira.colostate.edu/TSS/Results/Emissio

ns.aspx

Generated using WRAP 

Windblown Dust Model 

and 2008WRF

meteorology, at 4km and 

12km grid cell resolution 

for the WRAP region.

Vegetative scavenging 

factors applied postprocessing at the grid cell 

level.

More details are available 

here:

http://wrapair2.org/WestJ

umpAQMS.aspx

Difference between 2002 and 2008 

meteorology introduce factors that 

make judgment of progress for this 

category difficult.

1)MM5 vs. WRF met models – different 

actual meteorology in each year and 

increased grid cell resolution in 2008.

2)Higher resolution of grid cells leads to 

higher average wind speeds in individual 

cells, which leads to increased windblown 

dust emissions aggregated at the county 

level.

3)MM5 Layer 1 36 meter height winds vs. 

WRF average winds across lowest 3 layers 

spanning ~40 meter height.

Error in 2002 WBD model 

application treating rainfall in cm as 

inches.

Biogenic BEIS3.12 with BELD3 landuse and 2002 MM5 

meteorology data, at 36km grid cell resolution.

More details are available here:

http://vista.cira.colostate.edu/TSS/Results/Emissio

ns.aspx

MEGAN2.10 with 2008 

WRF meteorology data, at 

4 and 12 km grid cell 

resolution

More details are available 

here:

http://www.wrapair2.org/e

missions.aspx#

Comparisons of biogenic inventories 

between baseline and progress years 

will show large differences due to 

methodology, and not actual changes 

in emissions. Examples of biogenic 

emissions input factors that may

affect differences between the 

BEIS3.12 and MEGAN2.10 model 

outputs include:

 Different meteorological years and models 
11

Inventory 

Sector

2002 Baseline Inventory

(WRAP Plan02d)

2008 Emission Inventory

(WRAP WestJump08 

and DEASCO3)

Comments

(2002 MM5 vs. 2008 WRF).

 Higher temporal and spatial variability of 

land cover and other environmental input 

factors.

 Improved emissions factors based on better 

sources of data (e.g., satellites and field 

studies).

A model comparison study between 

BEIS3.12 and MEGAN2.10 was 

performed by WRAP and can be 

found at: 

http://wrapair2.org/pdf/Memo_9_Bi

ogenics_May9_2012_Final.pdf

Fires WRAP Phase III fire inventory

More details are available here:

http://vista.cira.colostate.edu/TSS/Results/Emissio

ns.aspx

Current summaries use

interim WESTJUMP08 

fire data currently based 

on satellite fire data for 

2008. This inventory does 

not separate 

anthropogenic from 

natural fire.

DEASCO3 fire 

summaries will include 

separate reporting of 

anthropogenic and natural 

fires.

Baseline conditions are represented 

with a 5-year average of fire activity

at the same locations and occurrence 

dates as actually occurred in 2002, 

while progress period conditions are 

represented with actual 2008 data.

Comparisons between these 

inventories are complicated by the 

variable and sporadic nature of 

wildfires.
12

Inventory 

Sector

2002 Baseline Inventory

(WRAP Plan02d)

2008 Emission Inventory

(WRAP WestJump08 

and DEASCO3)

Comments

More details are available 

here:

http://www.wrapfets.org/d

easco3.cfm
13

V.D. 2002 and 2008 Emissions

The 2008 Arizona statewide EI was originally created by WRAP and ARS to fulfill the 

requirements of 40 CFR § 51.308(g)(4) which states individual States must address the 

change in the emissions of pollutants which contribute to visibility impairment every 5

years for all sources and activities. ADEQ is currently submitting WRAP and ARS’ 

version of the 2008 EI to fulfill the requirement of 40 CFR § 51.308(d)(4)(v). While the 

WRAP and ARS 2008 emission inventory provides the most consistent inventory

available in relation to the baseline 2002 State of Arizona EI, Table 1 illustrates the 

differences which occurred during the calculation of these inventories. Inferences related 

to emission changes between 2002 and 2008 should not be made for many of the source 

sectors due to these emission changes likely resulting from model resolution, 

methodology, and input data enhancements. Despite this concern, general observations 

regarding emission differences between the two inventories are listed below.

 For sulfur dioxide, all categories except area sources exhibited lower 

emissions in the 2008 inventory as compared to the 2002 inventory, with the 

largest difference reported in point sources.

 For nitrogen dioxide, all source categories except area sources exhibited lower 

emissions in the 2008 inventory as compared to the 2002 inventory, with the 

largest difference reported for mobile sources.

 Ammonia emissions remained relatively similar between inventories, with 

2008 area sources showing slightly higher estimated emissions than 2002 and 

2008 on-road mobile sources showing lower estimated emissions.

 2008 EI volatile organic compound emissions were much lower than 2002 in 

biogenic sector, due to enhancements in biogenic inventory methodology.

 Primary organic aerosol emissions from fire were higher in 2002 than 2008. 

Note that current year inventories represent only snapshots of fire emissions 

for the year 2008.

 Elemental carbon showed large decreases in fire emissions, but on-road 

mobile emissions were higher in the 2008 inventory than the 2002 inventory.

 Fine particulate matter (crustal) and coarse mass were much larger for

windblown, fugitive and road dust sectors of the 2008 EI as compared to the 

2002 EI. The increase in windblown dust is thought to be due in part to 

enhancements in dust inventory methodology. The 2008 EI was also slightly

higher for area and point sources for the crustal components of fine particulate 

matter as compared to the 2002 EI.
14

V.D.1.Estimated 2002 and 2008 Arizona Emissions

Table 2 and Figure 1 present 2002 and 2008 estimated SO2 emissions by source category. 

Tables 3 and Figure 2 present data for NOx, and subsequent Tables and Figures (Tables 4

through 9 and Figures 3 through 8) present NH3, VOCs, POA, EC, Fines and CM 

emissions for the years of 2002 and 2008. Source categories are qualified (highlighted) 

in Tables where methodology, input data, or modeling resolution enhancements are 

believed to significantly affect emission differences between the 2002 and 2008 EIs.
15

Table 2: Arizona Sulfur Dioxide Emissions by Source Category

Source Category

Sulfur Dioxide Emissions (tons/year)

2002

(Plan02d)

2008

(WestJump2008)

Difference

(Percent Change)

Anthropogenic Sources

Point 94,716 79,015 -15,700

Area 2,677 3,678 1,001

On-Road Mobile 2,715 812 -1,904

Off-Road Mobile 4,223 673 -3,550

Area Oil and Gas 0 0 0

Fugitive and Road 

Dust 0 0 0

Anthropogenic Fire*

Total Anthropogenic 104,330 84,177 -20,153 (-19%)

Natural Sources

Natural Fire* 4,559 607 -3,952

Biogenic 0 0 0

Wind Blown Dust 0 0 0

Total Natural 4,559 607 -3,952 (-87%)

All Sources

Total Emissions 108,890 84,784 -24,105 (-22%)

*Natural fire totals for the 2008 inventory include both anthropogenic and natural 

sources. Updated data distinguishing these sources are expected.

-40,000

-20,000

0

20,000

40,000

60,000

80,000

100,000

120,000

2002 plan02d 2008 WestJump Difference

Tons/Year

Sulfur Dioxide Emissions by State

Arizona

Windblown Dust

Fugitive/Road Dust

Off-Road Mobile

On-Road Mobile

Off-Shore

WRAP Area O&G

Area

Biogenics

Total Fire

Natural Fire

Anthro Fire

Point

Figure 1: 2002 and 2008 Sulfur Dioxide Emissions by Source Category
16

Table 3: Arizona Nitrogen Oxide Emissions by Source Category

Source Category

Nitrogen Oxides Emissions (tons/year)

2002

(Plan02d)

2008

(WestJump2008)

Difference

(Percent Change)

Anthropogenic Sources

Point 69,968 60,759 -9,209

Area 9,049 39,403 30,354

On-Road Mobile 178,009 137,555 -40,453

Off-Road Mobile 66,414 33,857 -32,557

Area Oil and Gas 17 0 -17

Fugitive and Road 

Dust 0 0 0

Anthropogenic Fire*

Total Anthropogenic 323,458 271,575 -51,882 (-16%)

Natural Sources

Natural Fire* 17,218 3,513 -13,704

Biogenic 27,664 15,256 -12,408

Wind Blown Dust 0 0 0

Total Natural 44,881 18,769 -26,112 (-58%)

All Sources

Total Emissions 368,339 290,344 -77,995 (-21%)

*Natural fire totals for the 2008 inventory include both anthropogenic and natural 

sources. Updated data distinguishing these sources are expected.

-200,000

-100,000

0

100,000

200,000

300,000

400,000

2002 plan02d 2008 WestJump Difference

Tons/Year

Nitrogen Oxides Emissions by State

Arizona

Windblown Dust

Fugitive/Road Dust

Off-Road Mobile

On-Road Mobile

Off-Shore

WRAP Area O&G

Area

Biogenics

Total Fire

Natural Fire

Anthro Fire

Point

Figure 2: 2002 and 2008 Nitrogen Oxide Emissions by Source Category
17

Table 4: Arizona Ammonia Emissions by Source Category

Source Category

Ammonia Emissions (tons/year)

2002

(Plan02d)

2008

(WestJump2008)

Difference

(Percent Change)

Anthropogenic Sources

Point 531 971 440

Area 32,713 34,878 2,165

On-Road Mobile 5,035 2,377 -2,658

Off-Road Mobile 48 40 -8

Area Oil and Gas 0 0 0

Fugitive and Road 

Dust 0 0 0

Anthropogenic Fire*

Total Anthropogenic 38,326 38,265 -61 (0%)

Natural Sources

Natural Fire* 3,878 0 -3,878

Biogenic 0 0 0

Wind Blown Dust 0 0 0

Total Natural 3,878 0 -3,878 (-100%)

All Sources

Total Emissions 42,203 38,265 -3,939 (-9%)

*Natural fire totals for the 2008 inventory include both anthropogenic and natural 

sources. Updated data distinguishing these sources are expected.

-10,000

0

10,000

20,000

30,000

40,000

50,000

2002 plan02d 2008 WestJump Difference

Tons/Year

Ammonia Emissions by State

Arizona

Windblown Dust

Fugitive/Road Dust

Off-Road Mobile

On-Road Mobile

Off-Shore

WRAP Area O&G

Area

Biogenics

Total Fire

Natural Fire

Anthro Fire

Point

Figure 3: 2002 and 2008 Ammonia Emissions by Source Category
18

Table 5: Arizona Volatile Organic Compound Emissions by Source Category

Source Category

Volatile Organic Compound Emissions (tons/year)

2002

(Plan02d)

2008

(WestJump2008)

Difference

(Percent Change)

Anthropogenic Sources

Point 5,464 3,489 -1,975

Area 102,918 100,256 -2,661

On-Road Mobile 110,424 54,589 -55,834

Off-Road Mobile 56,901 42,297 -14,604

Area Oil and Gas 46 12 -34

Fugitive and Road 

Dust 0 0 0

Anthropogenic Fire*

Total Anthropogenic 275,753 200,644 -75,109 (-27%)

Natural Sources

Natural Fire* 37,232 4,989 -32,243

Biogenic 1,576,698 686,255 -890,443

Wind Blown Dust 0 0 0

Total Natural 1,613,930 691,243 -922,686 (-57%)

All Sources

Total Emissions 1,889,682 891,887 -997,795 (-53%)

*Natural fire totals for the 2008 inventory include both anthropogenic and natural 

sources. Updated data distinguishing these sources are expected.

-1,500,000

-1,000,000

-500,000

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

2002 plan02d 2008 WestJump Difference

Tons/Year

Volatile Organic Compounds Emissions by State

Arizona

Windblown Dust

Fugitive/Road Dust

Off-Road Mobile

On-Road Mobile

Off-Shore

WRAP Area O&G

Area

Biogenics

Total Fire

Natural Fire

Anthro Fire

Point

Figure 4: 2002 and 2008 Volatile Organic Compound Emissions by Source 

Category
19

Table 6: Arizona Primary Organic Aerosol Emissions by Source Category

Source Category

Primary Organic Aerosol Emissions (tons/year)

2002

(Plan02d)

2008

(WestJump2008)

Difference

(Percent Change)

Anthropogenic Sources

Point 276 410 134

Area 4,728 6,445 1,718

On-Road Mobile 1,583 2,666 1,083

Off-Road Mobile 2,006 1,383 -624

Area Oil and Gas 0 0 0

Fugitive and Road 

Dust 535 1,393 858

Anthropogenic Fire*

Total Anthropogenic 9,128 12,298 3,169 (35%)

Natural Sources

Natural Fire* 48,625 5,669 -42,957

Biogenic 0 0 0

Wind Blown Dust 0 0 0

Total Natural 48,625 5,669 -42,957 (-88%)

All Sources

Total Emissions 57,754 17,966 -39,787 (-69%)

*Natural fire totals for the 2008 inventory include both anthropogenic and natural 

sources. Updated data distinguishing these sources are expected.

-60,000

-40,000

-20,000

0

20,000

40,000

60,000

80,000

2002 plan02d 2008 WestJump Difference

Tons/Year

Primary Organic Aerosol Emissions by State

Arizona

Windblown Dust

Fugitive/Road Dust

Off-Road Mobile

On-Road Mobile

Off-Shore

WRAP Area O&G

Area

Biogenics

Total Fire

Natural Fire

Anthro Fire

Point

Figure 5: 2002 and 2008 Primary Organic Aerosol Emissions by Source Category
20

Table 7: Arizona Elemental Carbon Emissions by Source Category

Source Category

Elemental Carbon Emissions (tons/year)

2002

(Plan02d)

2008

(WestJump2008)

Difference

(Percent Change)

Anthropogenic Sources

Point 26 283 257

Area 449 1,337 889

On-Road Mobile 1,761 5,559 3,798

Off-Road Mobile 2,752 1,813 -940

Area Oil and Gas 0 0 0

Fugitive and Road 

Dust 39 47 8

Anthropogenic Fire*

Total Anthropogenic 5,027 9,039 4,012 (80%)

Natural Sources

Natural Fire* 9,719 412 -9,307

Biogenic 0 0 0

Wind Blown Dust 0 0 0

Total Natural 9,719 412 -9,307 (-96%)

All Sources

Total Emissions 14,745 9,450 -5,295 (-36%)

*Natural fire totals for the 2008 inventory include both anthropogenic and natural 

sources. Updated data distinguishing these sources are expected.

-15,000

-10,000

-5,000

0

5,000

10,000

15,000

20,000

2002 plan02d 2008 WestJump Difference

Tons/Year

Elemental Carbon Emissions by State

Arizona

Windblown Dust

Fugitive/Road Dust

Off-Road Mobile

On-Road Mobile

Off-Shore

WRAP Area O&G

Area

Biogenics

Total Fire

Natural Fire

Anthro Fire

Point

Figure 6: 2002 and 2008 Elemental Carbon Emissions by Source Category
21

Table 8: Arizona Fine Particulate Matter Emissions by Source Category

Source Category

Fine Particulate Matter Emissions (tons/year)

2002

(Plan02d)

2008

(WestJump2008)

Difference

(Percent Change)

Anthropogenic Sources

Point 632 4,434 3,801

Area 4,223 7,906 3,684

On-Road Mobile 1,080 511 -569

Off-Road Mobile 0 97 97

Area Oil and Gas 0 0 0

Fugitive and Road 

Dust 10,072 24,592 14,520

Anthropogenic Fire*

Total Anthropogenic 16,007 37,540 21,533 (>100%)

Natural Sources

Natural Fire* 3,945 1,938 -2,006

Biogenic 0 0 0

Wind Blown Dust 6,422 9,307 2,885

Total Natural 10,367 11,246 879 (8%)

All Sources

Total Emissions 26,373 48,785 22,412 (85%)

*Natural fire totals for the 2008 inventory include both anthropogenic and natural 

sources. Updated data distinguishing these sources are expected.

-10,000

0

10,000

20,000

30,000

40,000

50,000

60,000

2002 plan02d 2008 WestJump Difference

Tons/Year

Fine Particulate Matter Emissions by State

Arizona

Windblown Dust

Fugitive/Road Dust

Off-Road Mobile

On-Road Mobile

Off-Shore

WRAP Area O&G

Area

Biogenics

Total Fire

Natural Fire

Anthro Fire

Point

Figure 7: 2002 and 2008 Fine Particulate Matter Emissions by Source Category
22

Table 9: Arizona Coarse Particulate Matter Emissions by Source Category

Source Category

Coarse Particulate Matter Emissions (tons/year)

2002

(Plan02d)

2008

(WestJump2008)

Difference

(Percent Change)

Anthropogenic Sources

Point 8,473 5,260 -3,214

Area 1,384 2,389 1,005

On-Road Mobile 1,004 5,597 4,593

Off-Road Mobile 0 162 162

Area Oil and Gas 0 0 0

Fugitive and Road 

Dust 79,316 141,117 61,801

Anthropogenic Fire*

Total Anthropogenic 90,178 154,525 64,348 (71%)

Natural Sources

Natural Fire* 10,125 1,692 -8,433

Biogenic 0 0 0

Wind Blown Dust 57,796 83,765 25,969

Total Natural 67,921 85,457 17,536 (26%)

All Sources

Total Emissions 158,099 239,983 81,884 (52%)

*Natural fire totals for the 2008 inventory include both anthropogenic and natural 

sources. Updated data distinguishing these sources are expected.

-50,000

0

50,000

100,000

150,000

200,000

250,000

300,000

2002 plan02d 2008 WestJump Difference

Tons/Year

Coarse Particulate Matter Emissions by State

Arizona

Windblown Dust

Fugitive/Road Dust

Off-Road Mobile

On-Road Mobile

Off-Shore

WRAP Area O&G

Area

Biogenics

Total Fire

Natural Fire

Anthro Fire

Point

Figure 8: 2002 and 2008 Coarse Particulate Matter Emissions by Source Category
23

V.D.2.Summary of Major Methodological Changes

The Arizona Department of Administration (ADOA) provides publicly available records 

of State and County estimated populations for each year dating to 1980v. For the years of 

2002 and 2008, ADOA estimates Arizona state-wide populations to be 5,470,720 and 

6,629,455 respectively. This is an increase in state-wide population of 21.2%. An 

increase in population this significant will undoubtedly lead to pollutant emission 

changes for a number of source categories; however, the extreme degree to which certain 

pollutants change for given source categories indicate that population increases are not 

solely responsible for emission changes between the 2002 and 2008 EIs. Below is a list 

of methodology, input data, and model resolution changes which are believed to 

significantly contribute to emission differences between the 2002 and 2008 EIs. This list 

describes the possible changes which could affect all qualified data from Tables 2-9.

1. ADEQ has reviewed emission estimates to understand the drastic changes in Area 

Source SOx and NOx emissions between the 2002 and 2008 EIs. This review 

indicated that these changes are due to a mixture methodological changes and data 

completion issues. Therefore, ADEQ believes a more accurate indicator of NOx

and SOx changes are the IMPROVE data.

2. Biogenic emission differences for NOx and VOCs are primarily due to

methodology, source data, and modeling resolution enhancements between 2002 

and 2008.

3. Ammonia emission differences for On-road Mobile are primarily due to a switch 

from the MOBILE6 model to the MOVES model. The 2008 EPA NEI Technical 

Support Document (TSD)vi reported a 54% decrease in highway vehicle NH3 for 

2008.

4. VOC emission differences for On-road Mobile are primarily due to a switch from 

the MOBILE6 model to the MOVES model.

5. On-road Elemental Carbon (EC) and Coarse Particulate Matter (CM) emission 

differences are primarily due to the switch between MOBILE6 and MOVES 

(which estimates higher PM emissions).

6. Reported Point Source Fines emissions exhibit a dramatic increase between 2002 

and 2008, while CM decreases between 2002 and 2008. In theory, these two 

pollutants should track fairly closely to one another. ADEQ internal review 

revealed that many, if not most, sources within the State of Arizona were not 

reporting PM2.5 prior to 2006 which likely explains the drastic change in Fines 

emissions between the 2002 and 2008 EIs.

7. Area source Fines emission differences are partially due to NEI changes. 

Calculation methodology changes resulted in an overall increase in Agricultural 

Tilling and Livestock emissions of 67% for the 2008 NEI.

 

v Arizona Department of Administration (ADOA). 

http://www.workforce.az.gov/pubs/demography/Estimates1980_2009With2000CensusWithNotes.xls

vi EPA. 2012. 2008 National Emissions Inventory v. 2 Technical Support Document. 

http://www.epa.gov/ttn/chief/net/2008neiv2/2008_neiv2_tsd_draft.pdf
24

8. Fugitive and Road Dust Fines and CM emission differences are primarily due to 

NEI changes. Calculation methodology changes resulted in an overall increase in 

Paved Road Dust emission of 128% for the 2008 NEI.

9. Windblown Dust Fines and CM emission differences are primarily due to the 

WRAP Windblown Dust (WBD) Model enhancing meteorological inputs and 

model resolution between the 2002 and 2008 emissions calculations.

V.D.3.Regional Inventory Trends for Emissions

Most of the emission difference qualifying statements ADEQ presents in Section V.D.2. 

are attributable to changes in input data origination or calculation methodologies for 

emission estimations by sector. Since WRAP and ARS created statewide emission 

inventories for all of the western US using similar methodology, it is reasonable to 

believe that these qualifying statements would hold true for all of the compiled emission 

inventories. In this section ADEQ presents WRAP and ARS produced figures of 

differences in 2002 and 2008 emission inventories by state for 3 different pollutants to 

determine if the previously mentioned qualifiers hold true. These graphs split State 

emissions into emitting source categories to identify trends for each of the calculated or 

reported sources.

VOC emissions by State are presented in Figure 9. This figure easily illustrates qualifiers 

#2 and #4 from Section V.D.2. The most evident trend in this figure is the drastic 

decrease in Biogenic emissions for each State. These decreases are extreme and 

ubiquitous throughout the region. This trend supports qualifier #2, that Biogenic VOC 

emission differences are primarily due to enhancements in calculation methodology. In 

addition, On-road Mobile emissions show reasonably large decreases for each State. 

While EPA reports a decrease of national VMT by 0.8% for 2005-2008vii, it is unlikely

that this small decrease in VMT would be seen in every state. The state of Arizona 

showed a 21.2% population increase between 2002 and 2008, which would likely result 

in a substantial VMT increase. Therefore, it is reasonable to believe that qualifier #4, that 

VOC decreases are likely due to a switch from the MOBILE6 model to the MOVES 

model, is true.

Figure 10 presents State emissions for CM for the western US. The trends within this 

Figure are more regionally based, rather than characteristic of the entire western US. 

Coarse Mass emissions are due to physical disturbance of an area of land by

anthropogenic activities (e.g. construction, driving on unpaved roadways, etc), natural 

activities (e.g. animal movement or burrowing), or a mixture of the natural and 

anthropogenic activities (e.g. wind suspension of dust from a cleared area). While the 

activity which creates the emissions may change, the magnitude of emissions created are 

going to be primarily dependant on the local environment. Meteorology, soil

 

vii EPA. 2012. 2008 National Emissions Inventory v. 2 Technical Support Document. 

http://www.epa.gov/ttn/chief/net/2008neiv2/2008_neiv2_tsd_draft.pdf
25

Figure 9: Regional differences in VOC emissions between 2002 and 2008 Emission 

Inventories

-300,000

-200,000

-100,000

0

100,000

200,000

300,000

400,000

500,000

600,000

AZ

CA

CO

ID

MT

NV

NM

ND

OR

SD

UT

WA

WY

Tons/Year

Difference in 

Coarse Mass Emissions by State

Between 2002 and 2008

Windblown Dust

Fugitive/Road Dust

Off-Road Mobile

On-Road Mobile

Off-Shore

WRAP Area O&G

Area

Biogenics

Total Fire

Natural Fire

Anthro Fire

Point

Figure 10: Regional differences in Coarse Mass emissions between 2002 and 2008 

Emission Inventories
26

characteristics, and vegetation coverage are going to play a large role in the magnitude of 

emissions produced from a certain area. Therefore, when examining Figure 10, it is 

important to group the States which have a similar local environment. Arizona, Southern 

California, Nevada, New Mexico, and Utah comprise the southwestern US which is

characterized by its arid nature, in turn leading to sparse vegetation coverage. When 

examining Figure 10 for these five states, it is evident that local environmental factors 

play a large role in how Windblown Dust emissions differed between the 2002 and 2008 

EIs. All five southwestern US States exhibit similar emission differences for Windblown 

Dust and Fugitive and Road Dust. Southwestern US State Windblown Dust emissions

are likely to be more affected by WRAP WBD model resolution increases and the 

decreased precipitation, as reported in Table 1, than surrounding States due to higher 

local wind speeds increasing dust suspension into the atmosphere from dry, unvegetated 

soils. ADEQ believes that Road Dust is primarily responsible for the emission changes 

seen in the Fugitive and Road Dust category. The 2008 NEI reports that road dust 

emissions increased by 128% over the previous NEI for the US. Since the 2008 NEI v1.5 

was used for this source category, it is believed that this is the reason for the difference 

between 2002 and 2008 emissions for the combined categories of Fugitive and Road 

Dust. Furthermore, the aridity of the southwestern US would likely result in road dust 

-40,000

-20,000

0

20,000

40,000

60,000

80,000

100,000

AZ

CA

CO

ID

MT

NV

NM

ND

OR

SD

UT

WA

WY

Tons/Year

Difference in 

Fine Mass Emissions by State

Between 2002 and 2008

Windblown Dust

Fugitive/Road Dust

Off-Road Mobile

On-Road Mobile

Off-Shore

WRAP Area O&G

Area

Biogenics

Total Fire

Natural Fire

Anthro Fire

Point

Figure 11: Regional differences in Fine Mass emissions between 2002 and 2008 

Emission Inventories
27

calculation disparities being maximized in this region, when compared to other regions of 

the US. Fines (Figure 11) show similar regional trends for Windblown Dust and Fugitive 

and Road Dust for the arid southwestern US as was reported for CM, further supporting 

the theory that particulate matter emission differences are at least partially due to 

calculation methodology changes. While not proving qualifying statement #7, the lack of 

a regional trend for Fines originating from Point sources provides credence to this point.

VI. IMPROVE Monitoring Data

As discussed in Section 3, comparisons between the 2002 and 2008 State of Arizona EIs 

are problematic due to source data and methodology changes. Therefore, ADEQ has 

determined that IMPROVE monitoring data are more appropriate surrogates for assessing 

visibility change due to emission increases or decreases within the State of Arizona. 

Comparisons between the baseline (2000-2004) and progress (2005-2009) periods are 

presented in this document in order to address EPA’s assessment within the Federal 

Register that the State of Arizona Reasonable Progress Goals (RPGs) are not acceptable 

for reaching the 2064 natural visibility standardsviii. In this section we present 

IMPROVE data comparisons between the baseline and progress periods, which is 

discussed in terms of how this progress compares to RPGs in Section VII.

VI.A. Data Completeness Requirements

The following information was gathered directly from ARS and describes IMPROVE 

data completeness for the State of Arizona. Furthermore, it outlines the steps and 

methods utilized to gap fill missing data sets.

Progress for the Regional Haze Rule (RHR) is determined using 5-year average visibility 

conditions. EPA’s guidance for tracking Regional Haze progressix includes data 

completeness requirements designed to ensure that calculated averages include enough 

data to sufficiently represent each daily, annual and 5-year period. The guidance specifies 

that the 2000-2004 baseline period and each subsequent 5-year average progress period

meet the following conditions:

 Individual samples must contain all species required for the calculation of 

light extinction (amm. sulfate, amm. nitrate, POM, EC, soil, coarse mass, and 

sea salt)

 Calendar seasons must contain at least 50% of all possible daily samples

 Calendar years must contain at least 75% of all possible daily samples

 

viii Environmental Protection Agency (EPA). Federal Register Volume 77, No. 246. Dec. 21, 2012.

ix EPA. 2003. Guidance for Tracking Progress Under the Regional Haze Rule. 
28

 Calendar years must not contain more than 10 consecutive missing daily 

samples

 The 5-year baseline and each 5-year progress period averages must contain at

least 3 complete years of data

RHR guidance specifies that if a 5-year period has less than three complete years of data, 

then estimates should be prepared through consultation with EPA/OAQPS. For the state 

of Arizona, the 2005-2009 progress period did not have complete data available for one 

site. The SIAN1 site, the Sierra Ancha Wilderness Area, did not meet RHR data 

completeness criteria for the years 2006, 2007 and 2008, which interrupted the 

requirement for 3 complete years required for a 5-year average. Substitution 

methodology was consistent with methodology previously applied to the 2000-2004 

baseline period for seven WRAP sites.

The data substitution methods include estimating missing species from other on-site 

measurements and appropriately scaling data collected at a nearby site which 

demonstrated favorable long-term comparisons. Only years deemed incomplete under 

RHR guidance were candidates for additional data substitutions, which included for the 

SIAN1 the years 2006, 2007 and 2008. Years deemed complete were not changed, 

although there may have been missing samples during those years. Substitution 

methodology is described in detail below.

VI.A.1. SIAN1 data substitution methodology

The first substitution method applied uses organic hydrogen as a surrogate for organic 

carbon (OC) and elemental carbon (EC), which are collected on the IMPROVE C 

module. Hydrogen (H) is measured on the A module filter, and is assumed to be 

primarily associated with organic carbon and inorganic compounds such as ammonium 

sulfate. Therefore, OC can be estimated using the historical comparison between 

estimated organic H and OC. Organic H is estimated by subtracting the portion of H that 

is assumed to be associated with the inorganic compounds from the total H (Org_H = H –

0.25*S). Linear regression statistics were used to correlate all organic H and OC mass 

collected at the SIAN1 site during the 2005-2009 period, and regression statistics were 

applied to organic H to estimate OC on days where organic H was available, but OC was 

not. OC and EC correlations for the period were then used to calculate EC from OC. 

Regression statistics for these substitutions were calculated and applied quarterly to 

account for seasonal variations.

Because the carbon data substitution methods were not sufficient to complete the required 

years, a second method was applied that involved scaling data from the closest 

neighboring IMPROVE site, TONT1. This site had previously been determined to have 

favorable long-term comparisons and similar regional characteristics for substitutions 

performed on the 2000-2004 baseline period, when the SIAN1 site was selected, in 

consultation with the state of Arizona, as a donor site for TONT1. Species specific 

correlations between SIAN1 and TONT1 of mass data collected during the 2005-2009 
29

period were calculated quarterly, and applied to adjust TONT1 data to apply to missing 

SIAN1 days.

Figure 12 presents bar charts showing daily SIAN1 extinction data, including substituted 

data, for the 2005-2009 progress period years. Original RHR data in blue and substituted 

data by species in the standard IMPROVE colors. Substituted days are also indicated by a

black bar underneath the day, and the red line indicates the threshold above which days 

are counted in the 20% worst days for that year. Note that some of the missing extinction 

days had partial data available and only individual species missing in a given sample 

were substituted. Figure 13 presents similar bar charts indicating speciation of all data, 

with days in which all or part of the day was substituted indicated by a black bar 

underneath the day. Note that very few of the substituted days were counted among the 

20% worst days for the substituted years. All summaries for the SIAN1 site in this 

progress report support document include these substituted data, and substituted data and 

detailed methodology information will also be made available on the WRAP TSS website 

(http://vista.cira.colostate.edu/tss/).
30

Figure 12: IMPROVE SIAN1 data collected during the 2005-2009 progress period, 

where original SIAN1 RHR data are depicted in dark blue, and substituted data are 

depicted with separate colors by species.
31

Figure 13: IMPROVE SIAN1 data collected during the 2005-2009 progress period, 

where substituted days are depicted with a black bar beneath the data.
32

VI.B. Baseline and Progress Period Visibility

This section summarizes IMPROVE monitoring and emission data comparing the 2000-

2004 baseline period to the current 2005-2009 progress period for the state of Arizona, in 

line with regulatory requirements for periodic progress (CFR 51.308(g)(3)(iii)).

Furthermore, a more robust 10 year trend analysis is presented to determine how an 

alternate methods of visibility trend analysis may affect the conclusions drawn from 

IMPROVE monitoring data analysis.

Arizona has 12 mandatory Federal Class I areas with associated IMPROVE monitors. 

The basic premise of the RHR is to ensure that visibility on the 20% worst days continues 

to improve at each Federal CIA, and that visibility on the 20% best days does not get 

worse, as measured in units of deciviews (dv) calculated from data collected at 

representative Interagency Monitoring of Protected Visual Environments (IMPROVE) 

monitoring sites. In addition to presenting the results of EPA standardized 20% worst and 

20% best days comparisons (RHR method), ADEQ submits an alternative method of 

assessing visibility changes at State of Arizona IMPROVE monitoring sites between the 

years of 2000 and 2009. This method utilizes Theil statistics (Theil method) to calculate 

an annual trend for the 10 year period of interest and is an EPA accepted method for 

annual pollutant trend analysisx. More description of Theil method is given in Section 

VI.B.4.

Some of the major implications from monitoring and summaries presented in this section 

are listed below, and more detailed information is provided in the monitoring and data 

summaries sub-sections.

 For RHR method analysis of the 20% best days, the 5-year average 

deciview metric decreased at all Arizona sites, except GRCA2 which saw no 

change. The Theil method showed the same results.

 For RHR method analysis of the 20% worst days, the 5-year average 

deciview metric decreased at most sites, but increased at the GRCA2 and 

IKBA1 sites. The Theil method showed similar results, except no statistically 

significant increasing trends. GRCA2 and PEFO1 showed no change when 

analyzed using the Theil method.

- All sites experienced visibility extinction decreases of ammonium 

nitrate using the RHR method. The Thiel method showed no significant 

increasing trends and four sites with significant decreasing trends. Central 

and northern Arizona sites showed statistically significant decreasing 

annual average trends in ammonium nitrate using the Theil method.

 

x EPA. Trends in Monitored Concentrations of Carbon Monoxide. National Air Quality and Emissions 

Trends Report, 2003. 
33

- RHR method analysis showed that ammonium sulfate increased at 

most sites, though 20% worst days for ammonium sulfate showed no 

increases using the Theil method. Observations regarding ammonium 

sulfate were as follows:

 Ammonium sulfate RHR method increases were primarily 

due to higher than average ammonium sulfate measured in 2005.

 When selecting the 20% worst days for ammonium sulfate 

alone, no sites showed increases when using the Theil method, and 

five sites showed statistically significant decreasing trend.

- RHR method analysis exhibited decreases in visibility extinction 

from particulate organic mass at all sites except GRCA2 and IKBA1. 

Theil method analysis showed statistically significant particulate organic 

mass decreases at four Arizona sites and no statistically significant 

increasing trends at any of the other sites.

- An overall visibility deciview increase at the GRCA2 site was seen 

when analyzing the data using the RHR method. Two visibility

components contributing to this increase were particulate organic mass 

and elemental carbon. In June 2009, the GRCA2 was in close proximity

to 3 simultaneous, lightening induced wildfires. Observations regarding 

two visibility components associated with wildfire emissions are given 

below. These results indicate GRCA2 visibility deciview changes were at 

least partially due to the 2009 wildfires:

 Elemental carbon showed a fairly large increase in 

visibility extinction using the RHR method; however, annual 

average elemental carbon measurements did not show increasing 

trends using the Theil method. 

 GRCA2 showed an increase in visibility extinction using 

the RHR method for particulate organic matter; however, annual 

average elemental carbon measurements did not show increasing 

trends using the Theil method.

 ADEQ performed a separate analysis for the GRCA2 site 

where the 2009 year was removed from the progress period in an 

attempt to exclude the extreme effect of the above mentioned fires. 

The average baseline period (2000-2004) was then compared to the 

average altered progress period (2005-2008) total visibility (dv). A 

decrease from 11.7 dv for the baseline period to 11.5 dv for the 

altered progress period was seen. This is further evidence that the 

2009 fires played an important role in the increases seen using the 

RHR method.
34

- The overall visibility deciview increase at IKBA1 was affected by

high measurements in 2005. POM and ammonium sulfate are the 

primary contributing visibility components to the overall increasing 

deciview trend. These two visibility components are discussed below:

 Particulate organic mass showed a large increase visibility

extinction using the RHR method, but did not show an increasing 

trend using the Theil method. Particulate matter increases were 

strongly controlled by a large wildfire in July of 2005.

 Ammonium sulfate showed a large increase in visibility

extinction using the RHR method, but did not show an increasing 

trend using the Theil method. This large increase in ammonium 

sulfate using the RHR method was a regional trend (discussed 

later).

 ADEQ performed a separate analysis for the IKBA1 site

where the 2005 year was removed from the progress period in an 

attempt to exclude the extreme effect of the above mentioned fire. 

The average baseline period (2000-2004) was then compared to the 

average altered progress period (2006-2009) total visibility (dv). A 

decrease from 13.3 dv for the baseline period to 13.1 dv for the 

altered progress period was seen. This is further evidence that the 

2005 fire played an important role in the increases seen using the 

RHR method.

VI.B.1. Progress Period (2005-2009) Visibility

This section addresses the regulatory question, what are the current visibility conditions 

for the most impaired and least impaired days (40 CFR 51.308 (g)(3)(i))? RHR 

guidance specifies that 5-year averages be calculated over successive 5-year periods, i.e. 

2000-2004, 2005-2009, 2010-2014, etc. (EPA 2003). Current visibility conditions are 

represented here as the most recent successive 5-year average period available, or the 

2005-2009 period average, although the most recent IMPROVE monitoring data 

currently available includes 2010.

Tables 10 and 11 present the calculated deciview values for each site, along with the 

percent contribution to extinction from each aerosol component for the 20% most 

impaired and 20% least impaired days for each of the Federal CIA IMPROVE monitors 

in Arizona. Figure 14 presents 5-year average extinction for the current progress period 

for both 20% most impaired and 20% least impaired days.

Specific observations for the current visibility conditions on the 20% most impaired days 

are as follows:
35

 The largest contributors to aerosol extinction on the 20% most impaired

days at Arizona sites were particulate organic mass, ammonium sulfate and 

coarse mass.

 The highest aerosol extinction (15.2 dv) was measured at the SYCA1 site, 

where particulate organic mass was the largest contributor to aerosol 

extinction, followed by coarse mass. The lowest aerosol extinction (11.8 dv) 

was measured at the BALD1 site.

Specific observations for the current visibility conditions on the 20% least impaired days 

are as follows:

 Rayleigh, or the background visibility impairment due to atmospheric 

gases in clean air, was the largest contributor to light extinction at all sites for 

the 20% least impaired days. Average extinction for the least impaired 

visibility days at the Arizona sites ranged between 2.2 deciview (GRCA2) and 

8.0 deciview (SAWE1).

 For all Arizona sites except SIAN1 and SAWE1, ammonium sulfate was 

the largest contributor to aerosol extinction for the 20% least impaired days.

 At the SIAN1 site, particulate organic mass was the largest contributor to 

aerosol extinction for the best days, followed by ammonium sulfate. At the 

SAWE1 site, coarse mass was the largest contributor, followed by ammonium 

sulfate.

Table 10: Relative Contribution of Pollutants to Visibility Conditions on the 20% 

Most Impaired Days at Arizona Class I area IMPROVE Sites for the Progress 

Period (2005-2009).

Site Deciviews 

(dv)

Percent Contribution by Component

(% of Mm-1) and Rank*

Ammonium 

Sulfate

Ammonium 

Nitrate

Particulate 

Organic 

Mass

Elemental 

Carbon Soil Coarse Mass

Sea 

Salt

BALD1 11.8 25% (2) 4% (6) 42% (1) 8% (4) 6% 

(5)

16% 

(3)

0% 

(7)

CHIR1 12.2 36% (1) 5% (5) 16% (3) 5% (6) 10% 

(4)

27% 

(2)

1% 

(7)

GRCA2 12.0 22% (2) 7% (5) 41% (1) 11% (4) 6% 

(6)

12% 

(3)

0% 

(7)

IKBA1 13.4 26% (2) 8% (5) 29% (1) 8% (6) 8% 

(4)

21% 

(3)

1% 

(7)

PEFO1 13.0 23% (2) 5% (6) 31% (1) 11% (4) 8% 

(5)

21% 

(3)

1% 

(7)
36

SAGU1 13.6 25% (2) 9% (5) 18% (3) 8% (6) 11% 

(4)

28% 

(1)

1% 

(7)

SAWE1 14.9 21% (2) 11% (5) 16% (3) 8% (6) 13% 

(4)

31% 

(1)

1% 

(7)

SIAN1 13.0 25% (2) 6% (6) 33% (1) 9% (4) 8% 

(5)

19% 

(3)

1% 

(7)

SYCA1 15.2 15% (4) 4% (6) 29% (1) 9% (5) 15% 

(3)

28% 

(2)

0% 

(7)

TONT1 13.8 28% (1) 8% (5) 21% (3) 7% (6) 9% 

(4)

26% 

(2)

1% 

(7)

*Highest contribution per site is highlighted in bold.

Table 11: Relative Contribution of Pollutants to Visibility Conditions on the 20% 

Least Impaired Days at Arizona Class I area IMPROVE Sites for the Progress 

Period (2005-2009).

Site Deciviews 

(dv)

Percent Contribution by Component

(% of Mm-1) and Rank*

Ammonium 

Sulfate

Ammonium 

Nitrate

Particulate 

Organic 

Mass

Elemental 

Carbon Soil Coarse Mass

Sea 

Salt

BALD1 2.9 35% (1) 7% (5) 26% (2) 13% (4) 5% 

(6)

13% 

(3)

1% 

(7)

CHIR1 4.4 38% (1) 7% (5) 17% (3) 10% (4) 6% 

(6)

21% 

(2)

1% 

(7)

GRCA2 2.2 45% (1) 13% (4) 15% (2) 9% (5) 4% 

(6)

14% 

(3)

1% 

(7)

IKBA1 5.1 29% (1) 10% (5) 28% (2) 12% (4) 5% 

(6)

14% 

(3)

1% 

(7)

PEFO1 4.6 31% (1) 9% (5) 21% (2) 19% (3) 6% 

(6)

14% 

(4)

0% 

(7)

SAGU1 6.7 28% (1) 8% (6) 20% (3) 12% (4) 8% 

(5)

21% 

(2)

2% 

(7)

SAWE1 8.0 24% (2) 8% (6) 18% (3) 11% (4) 10% 

(5) 26% (1) 2% (7)

SIAN1 5.3 27% (2) 7% (5) 32% (1) 17% (3) 5% 

(6)

13% 

(4)

1% 

(7)

SYCA1 5.1 27% (1) 10% (5) 23% (2) 17% (3) 7% 

(6)

15% 

(4)

1% 

(7)

TONT1 5.7 33% (1) 9% (5) 23% (2) 12% (4) 6% 

(6)

16% 

(3)

1% 

(7)

*Highest contribution per site is highlighted in bold.
37

11.8dv

2.9

12.2

4.4

12

2.2

13.4

5.1

13

4.6

13.6

6.7

14.9

8

13

5.3

15.2

5.1

13.8

5.7

0

10

20

30

40

50

60

W B W B W B W B W B W B W B W B W B W B

BALD1 CHIR1 GRCA2 IKBA1 PEFO1 SAGU1 SAWE1 SIAN1 SYCA1 TONT1

E

xtin

ctio

n* (M

m-1

)

First Progress Period (2005-2009)

Average Extinction, 20% Worst (W) and Best (B) Days

Sea Salt

Soil

Coarse Mass

Elemental Carbon

Particulate Organic Mass

Ammonium Nitrate

Ammonium Sulfate

Rayleigh

*Total extinction in units of deciview (dv) is shown above resepective bars.

Figure 14: Average Extinction for Current Progress Period (2005-2009) for the 

Worst (Most Impaired) and Best (Least Impaired) Days Measured at Arizona Class 

I area IMPROVE Sites.

VI.B.2. Visibility Trend Analyses

This section addresses the regulatory question, what is the difference between current 

visibility conditions for the most impaired and least impaired days and baseline 

visibility conditions (40 CFR 51.308 (g)(3)(ii))? Baseline visibility conditions are the 

basis against which improvements in worst day visibility, and lack of degradation for the 

best day visibility, are judged. Included here are comparisons between the 5-year average 

baseline conditions (2000-2004) and the current progress period extinction (2005-2009). 

ADEQ refers to this method as the RHR method within this document.

ADEQ further presents an alternative analysis for visibility trend analysis for the 2000-

2009 period. Alternative methodology is presented to better understand how anamalous 

years may have affected visibility changes as measured at Arizona Class I Areas. The 

Theil method was chosen to characterize visibility trends, as this method has been 

generally accepted by EPA in previous trend analyses, most notably in previously 

prepared EPA National Air Quality and Emissions Trends Reportsxi. While the RHR 

method is the important metric for RHR regulatory purposes, trend statistics (e.g. the 

Theil Method) may be of value to understand and address visibility impairment issues for 

planning purposes.

 

xi EPA. Trends in Monitored Concentrations of Carbon Monoxide. National Air Quality and Emissions 

Trends Report, 2003.
38

VI.B.2.i. Theil Trend

Ten-year visibility trends were analyzed for the State or Arizona in order to better 

understand how anamalous years may have affected visibility changes as measured at 

Arizona Class I Areas. The Theil method was chosen to characterize visibility trends, as 

this method has been generally accepted by EPA in previous trend analyses, most notably

in previously prepared EPA National Air Quality and Emissions Trends Reportsxii. 

EPA described the statistical method as follows:

“The Theil test is a nonparametric statistical test that can be used instead of regressionbased methods for discerning a monotonic trend. It examines whether the concentration 

from year to year tends to increase or decrease consistently, making it a test of 

monotonicity. This test is not concerned with the magnitude of the year-to-year 

differences. The null hypothesis is that there is no monotonic trend in the data. 

The first step in the test is to examine all possible [n(n-1)/2] pairs of data points from a 

given monitor, where n = 8, 9, 10, or 11. Next, a count is taken of all the pairs that show 

an increasing or decreasing trend. The null hypothesis will be rejected and the test results 

will indicate a significant monotonic increasing (or decreasing) trend if this count of the 

data point pairs is greater than (or less than) a certain critical value. A large positive value 

indicates a positive trend, and a large negative value indicates a negative trend. 

The Theil test was applied for two reasons. First, it is appropriate when the errors from a 

linear regression are not normally, or close to normally, distributed. The data here may

not meet the normality assumption. Second, this test was recommended to EPA for 

determining whether an area has a significant trend. Therefore, this test is used in EPA’s 

annual Trends Reports.”xiii

Annual trends reported here were calculated by ARS for the years 2000-2009, with a 

trend defined as the slope derived using Thiel method. Trend statistics are useful in 

analyzing changes in air quality data, because these statistics can show the overall 

tendency of measurements over long periods of time to increase or decrease, while 

minimizing the effects of the year-to-year fluctuations which are common in air quality 

data. The significance of the trend is represented using p-values calculated using MannKendall trend statistics. Determining a significance level helps to distinguish random 

variability in data from a tendency to increase or decrease over time, where lower pvalues indicate higher confidence levels in the computed slopes. In some cases, trends 

may show decreasing tendencies where the difference between the 5-year averages do 

not. In these cases, the 5-year average is the important metric for RHR regulatory 

purposes, but trend statistics may be of value to understand and address visibility

impairment issues for planning purposes.

 

xii EPA. Trends in Monitored Concentrations of Carbon Monoxide. National Air Quality and Emissions 

Trends Report, 2003.

xiii ibid 
39

VI.B.2.ii. 2000-2009 Visibility Trend Analyses 

Results

This section presents visibility progress between 2000-2009 through the use of the two 

previously discussed methodologies: 1) the RHR method and 2) the Theil method.

Table 12 presents the differences between the 2000-2004 baseline period average and the 

2005-2009 progress period average for each site in Arizona for the 20% most impaired 

days by use of the RHR method, and Table 13 presents similar data for the least impaired 

days. Averages that increased are depicted in red text and averages that decreased in blue. 

Figure 15 presents the 5-year average extinction for the baseline and current progress 

period averages for 20% most impaired days and Figure 16 presents the differences in 

averages by component, with increases represented above the zero line and decreases 

below the zero line. Figures 17 and 18 present similar plots for the 20% least impaired 

days using the RHR method.

10 year trends for individual visibility extinction components using the Theil method are 

presented in Table 14. Only averages with p-value statistics less than 0.15 (85% 

confidence level) are presented in Table 14, with increasing slopes in red and decreasing 

slopes in blue. The regional haze regulations refer specifically to changes in extinction 

for the 20% most impaired and least impaired days, but trend statistics are also presented 

in Table 14 for an average of all sampled days. Selection of the most impaired and least 

impaired days can vary seasonally from year to year, so in some cases the annual average 

of all sampled days may better represent actual aerosol component trends over time.

Some general observations regarding changes in visibility impairment at sites in Arizona 

are as follows:

For the 20% most impaired days, the RHR method exhibited deciview metric increases

between the 2000-2004 and 2005-2009 periods at the GRCA2 and IKBA1 sites and 

decreases at all other Arizona sites. Theil method analysis showed no sites with 

significantly increasing deciview metric trends between 2000-2009 and significantly 

decreasing deciview metric trends at all sites except BALD1, GRCA2, IKBA1, PEFO1, 

and SYCA1. Notable differences for individual components extinctions (mM-1) on the 

20% most impaired days were as follows:

 All sites except GRCA2 and IKBA1 measured decreases in particulate 

organic mass using the RHR method. No sites showed significantly 

increasing trends using the Theil method and 4 sites showed significantly

decreasing trends.

 The RHR method analysis of ammonium sulfate showed increased 

extinction at all Arizona sites except SAGU1 and SAWE1, with the largest 

increases in ammonium sulfate were measured at the CHIR1, IKBA1 and 
40

TONT1 sites. In contrast, no statistically significant increasing annual trends 

in ammonium sulfate were measured using the Theil method. Decreasing 

annual ammonium sulfate trends on the order of about 0.1 Mm-1/year were 

measured at the BALD1, CHIR1, SAGU1 and SAWE1 sites. Anomalously 

high ammonium sulfate occurred in 2005 at most Arizona sites, which 

influenced the increases noted using the RHR method.

 RHR method analysis of ammonium nitrate extinction showed decreases 

at all Arizona sites for the 20% most impaired days. Analysis of all measured 

days showed no increasing trends, and decreasing trends on the order of 0.1 

Mm-1/year at the IKBA1, SAGU1, SAWE1, SIAN1 and TONT1 sites.

 RHR method analysis of coarse mass revealed increasing extinction values 

at BALD1, SAGU1, SYCA1, and TONT1. However, only the BALD1 site 

showed a statistically significant increasing trend for coarse mass for all 

measured days on the order of approximately 0.1 Mm-1/year. 

 Soil progress and baseline average differences decreased for five sites 

using the RHR method while measuring highest at the PEFO1, BALD1, and 

TONT1 sites for the 20% most impaired days. Theil method analysis showed 

increasing trends at only 2 Arizona sites for the 20% most impaired days 

(BALD1 and PEFO1) while SYCA1 showed a significantly decreasing trend.

 Increases in deciview at the GRCA2 site using the RHR method were 

mostly due to increases in ammonium sulfate and elemental carbon and the 

lack of a decreasing particulate organic mass extinction which occurred at 

most other Arizona Class 1 Areas. Higher progress period measurements at 

GRCA2 were influenced by large events between June and August of 2009. 

These increases were partially offset by decreases in ammonium nitrate and 

coarse mass. This site did not show significantly increasing ammonium 

sulfate trends using the Theil method. 

 Increases in deciview at the IKBA1 site were mostly due to increased 

ammonium sulfate and particulate organic mass measurements. Higher 

progress period measurements at IKBA1 were influenced by large events in 

July 2005. These increases were partially offset by decreases in ammonium 

nitrate and soil. This site did not show significantly increasing ammonium 

sulfate trends using the Theil method.

For the 20% least impaired days, the RHR method exhibited decreasing deciview metrics

at all sites except GRCA2, where the measured deciview average remained the same. 

Notable differences for individual component averages on the 20% least impaired days 

were as follows:

 The largest decreases were due to particulate organic mass, which 

decreased at all sites except IKBA1 using the RHR method. Theil method 

analysis revealed significantly decreasing trends at 7 of the sites.
41

 Ammonium sulfate decreased at most sites, but increased slightly at the 

GRCA2, SAGU1 and SYCA1 sites using the RHR method. Theil 

methodology revealed no statistically significant increasing site trends and 3 

Table 12: Difference in Aerosol Extinction by Component between the Baseline 

Period (2000-2004) and the Progress Period (2005-2009) on the 20% Most Impaired 

Days for Arizona Class I IMPROVE Sites.

Site

Deciview (dv) Change in Extinction by Component (Mm-1)*

2000-

2004

Baseline

Period

2005-

2009

Progress

Period

Change 

in dv*

Amm.

Sulfate

Amm.

Nitrate POM EC Soil CM SeaSalt

BALD1 11.8 11.8 0.0 +0.3 -0.1 -2.1 -0.7 +0.4 +1.3 +0.1

CHIR1 13.4 12.2 -1.2 +1.0 -0.1 -3.2 -0.5 -0.3 -1.9 +0.2

GRCA2 11.7 12.0 +0.3 +0.5 -0.4 +0.1 +0.5 +0.1 -0.3 0.0

IKBA1 13.3 13.4 +0.1 +1.0 -1.2 +0.7 0.0 -0.3 0.0 +0.1

PEFO1 13.2 13.0 -0.2 +0.5 -0.3 -1.4 +0.5 +0.6 -1.0 +0.1

SAGU1 14.8 13.6 -1.2 -0.1 -3.2 -4.1 -0.9 -0.1 +1.2 +0.2

SAWE1 16.2 14.9 -1.3 -0.7 -2.3 -1.9 -0.5 -1.4 -2.2 +0.2

SIAN1 13.7 13.0 -0.7 +0.7 -0.3 -2.5 +0.1 +0.1 -0.6 +0.2

SYCA1 15.3 15.2 -0.1 +0.7 -0.7 -0.5 +0.4 -1.0 +1.4 0.0

TONT1 14.2 13.8 -0.4 +1.3 -0.5 -3.5 -0.6 +0.4 +0.5 +0.2

*Change is calculated as progress period average minus baseline period average. Values in red indicate 

increases in extinction, values in blue indicate decreases.

 sites experienced statistically significant decreases in ammonium sulfate 

trends.

 Ammonium nitrate decreased at all but the GRCA2 site using the RHR 

method and 4 of the decreasing sites were found to be statistically 

significant using the Theil method.
42

Table 13: Difference in Aerosol Extinction by Component between the Baseline 

Period (2000-2004) and the Progress Period (2005-2009) on the 20% Least Impaired 

Days for Arizona Class I IMPROVE Sites.

Site

Deciview (dv) Change in Extinction by Component (Mm-1)*

2000-

2004

Baseline

Period

2005-

2009

Progress

Period

Change 

in dv*

Amm.

Sulfate

Amm.

Nitrate POM EC Soil CM SeaSalt

BALD1 3.0 2.9 -0.1 -0.1 -0.1 -0.1 0.0 0.0 +0.1 0.0

CHIR1 4.9 4.4 -0.5 -0.2 -0.1 -0.5 -0.1 0.0 0.0 0.0

GRCA2 2.2 2.2 0.0 +0.1 0.0 -0.1 0.0 0.0 0.0 0.0

IKBA1 5.4 5.1 -0.3 -0.3 -0.2 +0.1 0.0 -0.1 -0.1 +0.1

PEFO1 5.0 4.6 -0.4 -0.1 -0.2 -0.4 0.0 +0.1 0.0 0.0

SAGU1 6.9 6.7 -0.2 +0.1 -0.2 -0.2 -0.1 -0.3 +0.3 +0.1

SAWE1 8.6 8.0 -0.6 -0.2 -0.1 -0.5 -0.4 -0.3 +0.2 +0.2

SIAN1 6.2 5.3 -0.9 -0.3 -0.4 -0.7 -0.1 0.0 0.0 0.0

SYCA1 5.6 5.1 -0.5 +0.1 -0.1 -0.6 -0.2 -0.1 +0.1 0.0

TONT1 6.5 5.7 -0.8 -0.2 -0.2 -0.5 -0.2 -0.1 -0.2 +0.1

*Change is calculated as progress period average minus baseline period average. Values in red indicate 

increases in extinction, values in blue indicate decreases.
43

11.8dv

11.8

13.4

12.2

11.7

12

13.3

13.4

13.2

13

14.8

13.6

16.2

14.9

13.7

13

15.3

15.2

14.2

13.8

0

10

20

30

40

50

60

70

B P B P B P B P B P B P B P B P B P B P

BALD1 CHIR1 GRCA2 IKBA1 PEFO1 SAGU1 SAWE1 SIAN1 SYCA1 TONT1

E

xtin

ctio

n* (M

m-1

)

Baseline (B) and First Progress (P) Period

Average Extinction, 20% Worst Days

Sea Salt

Soil

Coarse Mass

Elemental Carbon

Particulate Organic Mass

Ammonium Nitrate

Ammonium Sulfate

Rayleigh

*Total extinction in units of deciview (dv) is shown above resepective bars.

Figure 15: Average Extinction for Baseline and Progress Period Extinction for 

Worst (Most Impaired) Days Measured at Arizona Class I area IMPROVE Sites.

Δdv = 0

-1.2

+0.3

+0.1

-0.2

-1.2

-1.3

-0.7

-0.1-0.4

-10

-8

-6

-4

-2

0

2

4

6

BALD1 CHIR1 GRCA2 IKBA1 PEFO1 SAGU1 SAWE1 SIAN1 SYCA1 TONT1

C

h

a

n

g

e in

E

xtin

ctio

n* (M

m-1

)

First Progress Period - Baseline Period

Change in Extinction, 20% Worst Days

Sea Salt

Soil

Coarse Mass

Elemental Carbon

Particulate Organic Mass

Ammonium Nitrate

Ammonium Sulfate

*Change in total extinction in units *Change in total extinction in units of deciview deciview (dv) is shown (dv) is shown above resepective resepective bars.

Figure 16: Difference between Average Extinction for Current Progress Period 

(2005-2009) and Baseline Period (2000-2004) for the Worst (Most Impaired) Days 

Measured at Arizona Class I area IMPROVE Sites.
44

3dv

2.9

4.9

4.4

2.2

2.2

5.4

5.1

5

4.6

6.9

6.7

8.6

8

6.2

5.3

5.6

5.1

6.5

5.7

0

5

10

15

20

25

30

B P B P B P B P B P B P B P B P B P B P

BALD1 CHIR1 GRCA2 IKBA1 PEFO1 SAGU1 SAWE1 SIAN1 SYCA1 TONT1

E

xtin

ctio

n* (M

m-1

)

Baseline (B) and First Progress (P) Period

Average Extinction, 20% Best Days

Sea Salt

Soil

Coarse Mass

Elemental Carbon

Particulate Organic Mass

Ammonium Nitrate

Ammonium Sulfate

Rayleigh

*Total extinction in units of deciview (dv) is shown above resepective bars.

Figure 17: Average Extinction for Baseline and Progress Period Extinction for Best 

(Least Impaired) Days Measured at Arizona Class I area IMPROVE Sites.

Δdv = -0.1

-0.5

0

-0.3

-0.4

-0.2

-0.6

-0.9

-0.5

-0.8

-2

-1.5

-1

-0.5

0

0.5

1

BALD1 CHIR1 GRCA2 IKBA1 PEFO1 SAGU1 SAWE1 SIAN1 SYCA1 TONT1

C

h

a

n

g

e in

E

xtin

ctio

n (M

m-1

)

First Progress Period - Baseline Period

Change in Extinction, 20% Best Days

Sea Salt

Soil

Coarse Mass

Elemental Carbon

Particulate Organic Mass

Ammonium Nitrate

Ammonium Sulfate

*Change in total extinction in units of deciview (dv) is shown above resepective bars.

Figure 18: Difference between Average Extinction for Current Progress Period 

(2005-2009) and Baseline Period (2000-2004) for the Best (Least Impaired) Days 

Measured at Arizona Class I area IMPROVE Sites.
45

Table 14: Statistically Significant 2000-2009 Annual Average Trends for Aerosol Extinction by Component for Arizona Class 

I area IMPROVE Sites.

Site Group

Annual Trend* (Mm-1/year)

Site 

Total

(dv)

Ammonium 

Sulfate

Ammonium 

Nitrate

Particulate 

Organic 

Mass

Elemental 

Carbon Soil Coarse Mass

Sea 

Salt

BALD1

20% Best -- -- 0.0 -- 0.0 -- 0.0 0.0

20% Worst -- -0.2 -- -- -- 0.1 0.3 0.0

All Days -- -0.1 0.0 -- -- -- 0.1 0.0

CHIR1

20% Best -0.1 0.0 0.0 -0.1 0.0 -- 0.0 0.0

20% Worst -0.3 -- -- -0.7 -0.1 -- -- 0.0

All Days -0.2 -0.1 0.0 -0.2 -0.1 -- -0.1 0.0

GRCA2

20% Best -- -- -- -- 0.0 -- -- 0.0

20% Worst -- -- -0.1 -- -- -- -- --

All Days -- -- 0.0 -- -- -- -- --

IKBA1

20% Best -0.2 -0.1 -0.1 0.0 0.0 0.0 -- 0.0

20% Worst -- -- -- -- 0.0 -- -- 0.0

All Days -- -- -0.1 -- 0.0 -- -- 0.0

PEFO1

20% Best -0.1 -- 0.0 -0.1 -- -- -- 0.0

20% Worst -- -- -- -- -- 0.1 -- 0.0

All Days -0.1 -- 0.0 -- -- 0.0 0.1 0.0

SAGU1

20% Best -0.2 -- -0.1 -0.1 -- -- -- --

20% Worst -0.3 -0.4 -0.5 -0.6 -0.3 -- -- 0.1

All Days -0.2 -0.1 -0.1 -0.2 -0.1 -- -- 0.0

SAWE1

20% Best -0.2 0.0 0.0 -0.1 -0.1 -0.1 -- 0.0

20% Worst -0.3 -0.3 -0.6 -0.5 -- -- -- 0.0

All Days -0.2 -0.1 -0.1 -0.3 -0.1 -- -- 0.0

SIAN1 20% Best -0.2 -0.1 -0.1 -0.1 0.0 -- -- 0.0
46

Site Group

Annual Trend* (Mm-1/year)

Site 

Total

(dv)

Ammonium 

Sulfate

Ammonium 

Nitrate

Particulate 

Organic 

Mass

Elemental 

Carbon Soil Coarse Mass

Sea 

Salt

20% Worst -0.2 -- -- -- -- -- -- 0.0

All Days -0.2 -- -0.1 -0.4 -0.1 -- -- 0.0

SYCA1

20% Best -0.1 -- -- -0.1 -- -- -- 0.0

20% Worst -- -- -- -- 0.1 -0.3 -- --

All Days -0.1 -- 0.0 -- -- -0.1 -- --

TONT1

20% Best -0.2 -0.1 -0.1 -0.1 -0.1 -- -0.1 0.0

20% Worst -0.2 -- -0.1 -0.8 -0.2 -- -- 0.1

All Days -0.1 -- -0.1 -0.2 -0.1 -- -- 0.0

*(--) Indicates statistically insignificant trend (<85% confidence level). 
47

VI.C. Ammonium Sulfate Analysis

Several analyses have been presented in this document to examine how Ammonium 

sulfate extinction has changed within the 2000-2009 period at IMPROVE monitoring 

sites within the State of Arizona. When examining the State as a whole, ammonium 

sulfate has shown increases at the IMPROVE sites between the baseline period and 

progress period averages for the 20% most impaired days (Section VI.B.2.), while 

showing no significant trends or decreasing Theil statistic trends for the 20% most 

impaired, least impaired, and all days (Section VI.B.4.ii.). Furthermore, ADEQ has 

shown that ammonium sulfate has decreased at all IMPROVE monitors for the most 

recent five year progress period for all site for the 20% most impaired days and for all 

sites except two for the 20% least impaired days.

VI.C.1. 20% Most Impaired Ammonium Sulfate Days

In this section ADEQ presents an alternate analysis performed by ARS in which the 20% 

most impaired ammonium sulfate days were isolated, averaged annually and then 

averaged for the baseline and progress periods. This analysis was performed in order to 

better understand how the worst 20% visibility days for a particular pollutant change 

between the baseline and progress periods rather than examining the 20% worst visibility

days for all combined pollutants. The combination analysis required by the RHR can 

cause seasonal shifts in the days chosen within the baseline and progress periods which in 

turn can miss seasonal highs for certain pollutant classes. A Theil statistics trend analysis 

is also performed for each monitor on the annually averaged 20% most impaired days for 

the period of 2000-2009. Figure 19 presents the annual average of the 20% most 

impaired days. Extinction decreases between 2000 and 2004. The years of 2005 and 

2007 show exceptionally high ammonium sulfate averages, followed by consecutive 

decreasing years between 2007 and 2009. When baseline and progress period extinction 
48

Worst 20% Ammonium Sulfate Days

2

4

6

8

10

12

14

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Average Annual Extinction (M

m-1)

BALD1 CHIR1 GRCA2 IKBA1 PEFO1 SAGU1 SAWE1 SIAN1 SYCA1 TONT1

Figure 19: Average Annual Ammonium Sulfate Extinction (mM-1) at each Arizona 

Class I area IMPROVE Site for the 20% Worst Ammonium Sulfate Days.

Table 15: 2000-2009 Ammonium Sulfate Visibility Extinction (mM-1) Trends,

Baseline (2000-2004) vs. Progress (2005-2009) Period Comparisons, and Altered 

Baseline (2000-2005) vs. Altered Progress (2005-2009) Period Comparisons at each 

Arizona Class I area IMPROVE Site for the 20% Worst Ammonium Sulfate Days.

SiteCode Slope p-value

Baseline 

(2000-

2004)

Period 1 

(2005-

2009) Difference

Altered 

Baseline 

(2000-

2005)

Altered 

Period 1 

(2006-

2009) Difference

BALD1 -0.18 0.08 7.52 7.84 0.32 8.15 7.13 -1.02

CHIR1 -0.15 0.14 10.33 10.51 0.18 10.55 10.22 -0.32

GRCA2 -0.05 0.24 6.39 7.12 0.73 6.87 6.70 -0.17

IKBA1 -0.09 0.36 8.16 8.73 0.57 8.48 8.47 -0.02

PEFO1 -0.15 0.03 8.16 8.31 0.15 8.49 7.86 -0.64

SAGU1 -0.29 0.13 9.54 9.58 0.05 10.26 8.87 -1.39

SAWE1 -0.33 0.09 10.05 10.00 -0.05 10.58 9.45 -1.13

SIAN1 -0.07 0.30 7.81 8.71 0.90 8.45 8.14 -0.31

SYCA1 -0.04 0.43 7.30 8.24 0.94 7.99 7.62 -0.37

TONT1 0.00 0.50 8.75 10.18 1.43 9.46 9.65 0.19

averages are compared, all IMPROVE sites show increasing ammonium sulfate 

extinctions except SAWE1. However, when 2000-2009 worst 20% ammonium sulfate 

days trends are analyzed, no sites show increasing trends. BALD1, CHIR1, PEFO1, 

SAGU1, and SAWE1 all exhibit statistically significant decreasing ammonium sulfate 

extinction trends. The extreme differences are strongly influenced by ammonium sulfate 

concentrations measured in 2005. Since this year is more like a midpoint, the data have a 

more neutral effect using the Theil method. To illustrate the effect of the 2005 year, 
49

ADEQ presents the results of an analysis in Table 15 where the RHR method is altered to 

include the year 2005 in the baseline period rather than the progress period. This altered 

RHR method resulted in reduced ammonium sulfate extinction values between the altered 

progress period (2006-2009) when compared to the altered baseline period (2000-2005) 

for all sites except TONT1 for the 20% most impaired ammonium sulfate days. This 

illustrates the strong effect that one outlier year can have in the RHR methodology.

VI.C.2. Regional Ammonium Sulfate Trends

While the State of Arizona SIP only addresses the pollutant emissions and progress goals 

for areas within Arizona’s State boundary, it is important to analyze regional trends in 

pollutants in order to better understand which phenomena are more representative of 

State issues and which extend beyond State boundaries to surrounding areas. This type 

of analysis allows for a better understanding of which emission increases are locally 

based in origin and which may be more representative of a regional trend and thus may

be due to some uncontrollable external factor (e.g. NOx emissions originating from a 

point source located within another State or Country, PM emission increases which are 

seen in regional trends and thus may be related to environmental factors, etc.). In this 

section we analyze regional maps of IMPROVE monitor aerosol extinction changes 

between baseline and progress periods in order to determine if previously identified State 

of Arizona ammonium sulfate trends may be regional phenomena.

Figure 20 shows only those aerosol extinction values which have increased for the 20% 

most impaired days between the baseline (2000-2004) and progress (2005-2009) periods 

for all IMPROVE monitors in the western United States. Figure 20 shows fairly 

ubiquitous and substantial increases in ammonium sulfate across the State of Arizona, 

State of New Mexico, western Texas, and south-central Colorado. The ammonium 

sulfate increases seen in this figure are obviously regional in extent; however, it is 

difficult to determine an origination point. These increases could be due to a singular or 

combination 
50

Figure 20: Magnitude of visibility component extinctions that have increased 

between the baseline average (2000-2004) and the first progress period average 

(2005-2009) for the 20% worst visibility days.

Figure 21: 10-year annual average ammonium sulfate extinction trends for 20% 

worst days at CIA IMPROVE sites in the WRAP region.
51

Figure 22: 10-year annual average ammonium sulfate extinction trends for all 

measured days at CIA IMPROVE sites in the WRAP region.

of point or area sources within the above mentioned States and/or Mexico. 

While average changes in ammonium sulfate visibility extinction increased regionally 

between baseline and progress periods, Theil method statistical analysis of ammonium 

sulfate extinction trends within the south-western US for both the 20% most impaired 

days (Figure 21) and all days (Figure 22) found that there was either: 1) no statistically 

significant trends at IMPROVE monitors within the four corners region (i.e. Arizona, 

Utah, Colorado, and New Mexico) or 2) the 10 year annual average ammonium sulfate 

extinction trends at these IMPROVE monitors exhibited statistically significant 

decreases. Similar to what was previously reported for the State of Arizona, regional 

Theil method trends disagree with the RHR method of a 5 year average comparison of 

the 20% most impaired days between baseline and progress periods. Furthermore, this 

agreement between State of Arizona and south-western US regional trends may indicate 

that 2005 and 2007 were outlier years for ammonium sulfate extinction within the entire 

4 corners region and the RHR method does not reflect more recent visibility extinction 

improvements for this aerosol.

VI.D. Coarse Mass Analysis

Coarse Particulate matter is generally recognized as having origination sources which are 

locally based. In this section, Coarse Mass (CM) is analyzed more closely in order to 
52

gain more insight into trends for this particular pollutant seen between the baseline and 

progress period for all IMPROVE sites around the State. This section presents the results 

of an alternate approach in selecting the 20% most impaired day analysis, similar to the 

trend performed for ammonium sulfate in Section VI.C. Furthermore, a qualitative 

analysis of IMPROVE site location in relation to major PM10 emitting point sources is 

performed in order to determine if an evident pattern exists between point source location 

and IMPROVE monitor location.

VI.D.1. Worst 20% Coarse Mass Days

Several analyses have been presented in this document to examine how Coarse Mass

extinction has changed within the 2000-2009 period at IMPROVE monitoring sites 

within the State of Arizona. When examining the State as a whole, coarse mass has 

shown no discernable spatial trends at the IMPROVE sites between the baseline period 

and progress period averages for the 20% most impaired days (Section VI.B.2.). 

Furthermore, Theil statistic trends for the 20% most impaired days only resulted in the 

BALD1 site exhibiting the only statistically significant trend between 2000-2009, where 

an increase has been noted (Section VI.B.4.ii.). 

In this section ADEQ presents an alternate analysis performed by ARS in which the 20% 

most impaired coarse mass days were isolated, averaged annually and then averaged for 

the baseline and progress periods. This analysis was performed in order to better 

understand how the worst 20% visibility days for a particular pollutant change between 

the baseline and progress periods rather than examining the 20% worst visibility days for 

all combined pollutants. The combination analysis required by the RHR can cause 

seasonal shifts in the days chosen within the baseline and progress periods which in turn 

can miss seasonal highs for certain pollutant classes. A Theil statistics trend analysis is 

also performed for each monitor on the annually averaged 20% most impaired days for 

the period of 2000-2010. This trend analysis was extended past prior analyses (2000-

2009) to include 2010 since this year was shown to be fairly unique in how coarse mass 

responded across Arizona. As shown in Figure 23, coarse mass is generally low in the 

years of 2008 and 2009, but in the year 2010 coarse mass extinction dramatically

increases at some sites while continuing to decrease at others on the 20% most impaired 

days. When comparing the baseline period to the progress period for the 20% worst 

coarse mass visibility days (Table 16) all monitors except BALD1, PEFO1, and TONT1 

recorded decreased extinction. Furthermore, the CHIR1, SAWI1, SAGU1, and SIAN1 

monitors exhibited drastic decreases in CM extinction for the 20% most impaired CM 

days. Theil statistics over the 11 year period showed decreasing trends at all sites except 

2; however, only CHIR1 and SIAN1 showed statistically significant decreases, while 

BALD1 and PEFO exhibited statistically significant increasing trend for CM on the 20% 

most impaired CM days.
53

AZ Worst CM Days

0

10

20

30

40

50

60

70

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Average CM Extinction (M

m-1)

BALD1 CHIR1 GRCA2 IKBA1 PEFO1 SAGU1 SAWE1 SIAN1 SYCA1 TONT1

Figure 23: Average Annual Coarse Mass Extinction (mM-1) at each Arizona Class I 

area IMPROVE Site for the 20% Worst Coarse Mass Days.

Table 16: 2000-2010 Coarse Matter Visibility Extinction (mM-1) Trends and 

Baseline vs. Progress Period Comparisons at each Arizona Class I area IMPROVE 

Site for the 20% Worst Coarse Matter Days.

SiteCode

11-year trend

(2000-2010) p-value

Baseline

(2000-2004)

Period 1

(2005-2009)

Period

Difference

BALD1 0.29 0.15 11.66 13.12 1.46

CHIR1 -2.73 0.02 33.92 25.47 -8.45

GRCA2 -0.38 0.24 11.38 9.04 -2.34

IKBA1 -1.10 0.19 23.27 21.24 -2.03

PEFO1 1.12 0.06 17.42 19.80 2.38

SAGU1 -1.26 0.38 25.88 18.83 -7.05

SAWE1 -0.56 0.31 40.47 20.49 -19.98

SIAN1 -1.56 0.01 22.97 12.76 -10.21

SYCA1 -1.04 0.24 26.36 24.77 -1.59

TONT1 -0.32 0.50 21.08 24.47 3.38

VI.D.2. Large Point Source Locations

Previous analyses of coarse mass extinction between 2000 and 2009/2010 have shown 

fairly mixed results; however, there is evidence which suggests coarse mass emissions 

originate from areas close to the individual IMPROVE monitors. Table 16 presents 

baseline vs. progress period differences which generally show decreasing trends across 

the state, but it is difficult to discern regional trends from monitors within close proximity

of one another. IKBA1, SIAN1, and TONT1 are three monitors which are centrally 

located within the state within relatively close proximity; however, these three show a 
54

respective small decrease, large decrease, and small increase in CM extinction for the 

20% most impaired CM days. Similarly, SAWE1 and SAGU1 are the two monitors of 

closest proximity within the State, but these two monitors show drastically different CM 

extinction baseline period averages. Therefore, ADEQ qualitatively examined the 

location of all NEI reported major PM10 emitting sources within the State against the 

location of individual IMPROVE monitors to determine if there is an evident trend. This

analysis could prove to answer questions regarding whether locally driven point source 

emissions are related to disparities in regional coarse matter trends discussed above.
55

Figure 24: Locations of Arizona Class I areas, Class I area 50 km buffers, Class I 

area IMPROVE monitors, and Large Point Source Emitters of PM10 (>100 

tons/year). IMPROVE site values correspond to Visibility Extinction (mM-1) of

Coarse Mass averaged over the progress period (2005-2009).
56

Figure 24 presents the location and the 2008 annual emissions for each point source in 

Arizona which emitted >100 tons. The figure also presents the progress period (2005-

2009) average extinction (mM-1) for CM for the 20% most impaired days. This map 

indicates that the 20% most impaired days may be impacted by local PM10 sources at 

some monitors, while other sites show little to no effect on CM extinction from large 

PM10 sources. TONT1, SAWE1, and SAGU1 are all monitors which show relatively 

high CM extinction values over the progress period and are located relatively close to 

several large PM10 emitting point sources. However, SYCA1 is a monitor which 

recorded high extinction values for the 20% most impaired days over the progress period 

and it is only located near one large source and this is actually a relatively small PM10

emitter in comparison with other large sources on the map. Also, PEFO1 and BALD1 

are located near very large PM10 emitting sources, yet have some of the lowest CM 

extinction values for the 20% most impaired days recorded over the progress period. 

Overall, it is difficult to discern a visual relationship between large PM10 point sources 

and IMPROVE monitor CM extinctions for the 20% most impaired days in the State of 

Arizona. A finer scale EI around each monitor site may be needed in order to better 

understand individual site trends for CM extinction.

VI.E. Method Comparison Summary

The basic premise of the RHR is to ensure that visibility on the 20% worst days continues 

to improve, and that visibility on the 20% best days does not get worse, as measured in 

units of deciviews (dv) calculated using data measured at IMPROVE monitoring sites. 

Progress is measured in discreet 5-year average increments, beginning with the 2000-

2004 baseline average, and proceeding with each subsequent 5-year average (e.g. 2005-

2009, 2010-2014, etc.). Some of the more subtle, but important, considerations for RHR 

calculations using IMPROVE data measurements are described below.

The discretization of visibility deciviews into 5 year averages can result in anomalous 

years, experiencing extreme events, which can identify longterm trends in visibility

change which due not truly exist, specifically for the 20% worst day comparisons. As an 

example, this is evident in data presented in this document for the State of Arizona 

GRCA2 site. This site experienced high 2005 and 2007 ammonium sulfate extinction 

values (a regional trend) and 2009 fire induced elevations in elemental carbon for the 

20% most impaired days resulting in overall elevated visibility progress period deciview 

averages for the site. In contrast, when the Theil statistical method was utilized over the 

10 year period to analyze extinction trends for overall deciviews at the site and extinction 

trends for individual visibility impairment components, no significant increases were 

seen. In this case, anomalous years for individual visibility component extinctions, due 

to extreme events, presented visibility degradation using the RHR method, while a more 

standardized statistical trend method (the Theil method) showed no significantly

increasing visibility degradation trends. This is merely one case where ADEQ, WRAP, 

and ARS have shown that outlier data can significantly alter the data progress 

interpretations when using the RHR method when compared to other standardized 

statistical trends which better account for anomalous years.
57

Furthermore, to determine the 5-year average of the 20% best and worst days, the highest 

and lowest 20% of days for each complete year are first selected and averaged on an 

annual basis, with a 5-year average calculated from these annual averages. The selection 

of the 20% best and worst days may be significantly influenced by large episodic events, 

and as such, may not represent the same time period from year to year. This selection of 

days may affect the averages for individual species in ways that are independent from 

actual increases or decreases from one 5-year period to the next.

Visibility impairment is the result of the cumulative effect of several different particle 

pollutant types. Many of these pollutants have consistent seasonal patterns. For example, 

ammonium nitrate is temperature sensitive, with formation often favored during colder 

winter months, while ammonium sulfate formation may be favored during warmer 

summer months. Other pollutants, such as particulate organic mass, may be impacted by

large and variable episodic events such as wildland fires. Variable occurrence of large 

episodic events may cause high extinction measurements that will drive selection of 20% 

worst days to coincide with the episodic events, effectively introducing the possibility

that the worst days occur at different times each year.

As an illustration of events driving the selection of the worst days, consider daily average 

aerosol extinction calculated from IMPROVE data at the CHIR1 site in Arizona. Figures 

25 and 26 present daily aerosol extinction measurements for 2002 and 2008 at CHIR1, 

with the 20% worst days represented by an orange box with an “x” below the day. For 

2002, large wildfire events in June and July contributed to high POM measurements, 

resulting in more of the worst days selected during this period. In 2008, more of the 

worst days were selected in August and October.

As an illustration of the seasonal patterns of individual compounds, consider the monthly

averages of aerosol extinction calculated from IMPROVE data at the CHIR1 site. Figure 

27 presents monthly average aerosol pollution for CHIR1 measured during 2002, and 

Figure 28 presents monthly averages in 2008. For both years, plots show that ammonium 

sulfate is highest between July and September. The monthly plots also show the higher 

POM that coincided with wildfire events in 2002, which affected the selection of more of 

the worst days between May and July in 2002, and more worst days in August and 

October in 2008. The seasonal patterns of ammonium sulfate mean that even if annual 

ammonium sulfate stayed the same, worst days in May and July will have higher 

ammonium sulfate than worst days that occur between August and October.

For this case, Table 17 presents the annual averages of ammonium sulfate for both the 

20% worst days and all measured days. For these years, the annual average of ammonium 

sulfate extinction decreases, while the 20% worst day average actually increased.
58

Figure 25: Daily Aerosol Extinction measured by the Chiricahua CHIR1 

IMPROVE monitor during 2002.

Figure 26: Daily Aerosol Extinction measured by the Chiricahua CHIR1 

IMPROVE monitor during 2008.

Figure 27: Monthly Average Aerosol Extinction measured by the CHIR1 

IMPROVE monitor in 2002.

Figure 28: Monthly Average Aerosol Extinction measured by the CHIR1 

IMPROVE monitor in 2008.
59

Table 17: CHIR1 IMPROVE Site comparison of Baseline and Progress Period 

Ammonium Sulfate Averages for All Days and the 20% Worst Days

Year

All Days

Amm. Sulfate

Average (Mm-1)

20% Worst Days

Amm. Sulfate

Average (Mm-1)

2002 5.3 7.8

2008 4.9 9.0

Difference -0.4 Mm-1+2.2 Mm-1

Within this document ADEQ, WRAP, and ARS have presented several different methods 

of analyzing IMPROVE data in order to best understand the trends which are occurring at 

each of the IMPROVE sites between the years of 2000-2009. These methods are slight 

alterations in how the analyzed data is chosen (i.e. choosing the 20% most/least impaired 

days particular to a certain pollutant rather than for the entire suite of pollutants) and how 

the variability inevitably seen with pollution data can be analyzed to account for the 

effects of large individual events which may skew overall pollution trends (i.e. the 

utilization of the Theil statistical trend analysis). ADEQ contests that these analyses 

represent logical methods which are comparable to and in some ways improve upon the 

standardized methodology required by the RHR. Furthermore, these methods are not 

drastically different from previous EPA methodology, and in the case of Theil statistical 

comparison, actually represent previously utilized EPA methodology for similar 

comparisons.

VII. Reasonable Progress Goals

EPA proposed to disapprove Arizona’s Reasonable Progress Goals (RPGs) for 2018 

based on the reasoning that they did not feel ADEQ demonstrated that the goals 

constituted reasonable progressxiv. In this section ADEQ presents Arizona’s progress 

towards reaching the previously presented RPGs and Uniform Rates of Progress (URPs) 

as interpreted through IMPROVE monitor data. ADEQ chose to present IMPROVE data 

trends, as opposed to surrogate measures such as Emission Inventory trends, as 

monitoring data most accurately measure visibility changes within a region. However, 

ADEQ also provides analysis within this section relating trends seen at the IMPROVE 

monitors to those noted within the emission inventories where appropriate. Finally, 

ADEQ compares State-wide extinction trends for individual visibility impairment 

components to regional trends.

 

xiv Environmental Protection Agency (EPA). Federal Register Volume 77, No. 246. Dec. 21, 2012.
60

This section compares the rate of progress between the baseline and progress periods 

towards the goal of natural visibility at each of the Arizona IMPROVE monitors and how 

that rate compares to RPGs and URPs for the 20% most impaired and least impaired 

days. An alternate analysis of RP is also included which illustrates the effect that one 

single year has on the original results. Furthermore, an additional analysis is provided 

which show how specific fire events can have a large impact on the baseline versus 

progress period comparison.

Tables 18 and 19 present the baseline visibility, progress period visibility, URP visibility

for 2018 (not included in Table 19), and the 2018 RPGs for each of the IMPROVE 

monitor sites for the 20% most impaired days and the 20% least impaired days,

respectively. The Tables further present 2018 projected visibility based on the visibility 

rate of change between the baseline period and progress periods. The 2018 projected 

visibility was calculated for each IMPROVE monitoring site using the following 

equation:

 











 

 

5

16 *

BP PP PV BP

where:

PV = 2018 projected visibility (dV)

BP = Average baseline period visibility (dV)

PP = Average progress period visibility (dV)

This equation assumes a linear rate of progress between the baseline and progress period 

that can be extrapolated to 2018, that the average baseline period visibility is the visibility

for the midpoint year of the baseline period (2002), and that the average progress period 

visibility is the visibility for the midpoint year of the progress period (2007). The 2018 

projected visibility values can be utilized in two ways: 1) comparison with ADEQs 

previously calculated RPGs, or 2) comparison with ADEQs previously calculated URPs. 

This method is a rather simplistic method but is believed to be more representative of 

actual progress compared to utilization of a highly uncertain EI.

Table 18 presents the projected visibility for each IMPROVE site as compared to the 

ADEQs RPGs and URPs for the 20% most impaired days. Six monitoring locations 

(shown in blue) are expected to surpass ADEQ’s RPGs for 2018. Furthermore, CHIR1, 

SAWE1, SAGU1, and SIAN1 are projected to surpass the URPs calculated for these sites

for 2018. While BALD1 and SYCA1 are expected to experience visibility improvements 

by 2018, these improvements are not expected to meet the RPGs calculated by ADEQ. 

Two sites are projected to experience visibility degradation by 2018 when compared to 

2002; these sites are GRCA2 and IKBA1.

Visibility degradation at GRCA2 and IKBA1 are most accurately explained through 

large, singular wildfire events which skew RHR method results for the 20% most 

impaired days. Within this document ADEQ has shown evidence of the effect individual 
61

events at an IMPROVE monitoring location can have in misrepresenting visibility trends 

when using the RHR method. Table 19 supplements previously overviewed data to show 

this issue more clearly. Table 19 presents an analysis where two years’ (2003 and 2009) 

visibility extinction data are adjusted for EC and POM to 10 year averages in order to 

reduce the effects of wildfires located near an IMPROVE monitor. In the year of 2003, 

total extinction for the GRCA2 monitor was recalculated using the 10 year average 

extinction values for EC (2.7 mM-1) and POM (10.7 mM-1). This was repeated for the 

year of 2009. Both years, 2003 and 2009, experienced large wildfire events near the 

GRCA2 monitor and this substitution method was utilized in an attempt to reduce the 

effects of these wildfire events on the overall trends of the RHR method. The table 

shows that without substitution, total visibility extinction increases by 0.5 mM-1using the 

RHR method, while EC and POM extinction normalization for the year of 2003 caused

this degradation to increase to 2.9 mM-1, and 2009 extinction normalization caused the 

trend to reverse with total visibility improvement on the order of 2.3 mM-1at GRCA2. 

This exercise expresses the degree to which one large event can skew visibility trends for 

the 20% most impaired days.

While individual events can cause skewed results, the same can be said for individual 

years. In previous sections of this document ADEQ has discussed the exceptionality of 

the year of 2005 when compared to other years’ visibility. While ADEQ had data for the 

more recent visibility year of 2010 available for use in tracking trends at Class I Areas, 

RHR method requires the use of the immediate 5 year period following the baseline 

period when analyzing visibility trends. The year of 2005 was an exceptionally high year 

for visibility extinction throughout the State. In Table 20, ADEQ performed an altered 

version of the RHR method where the baseline period was shifted to include the year 

2005 (i.e. 2000-2005) and the progress period was shifted to include the most recently 

available IMPROVE monitoring data for the progress period (2006-2010). This table 

presents the data in a format similar to Table 18 in order to see how these updated trends 

track to the year 2018 for comparison with RPGs and URPs. It is evident, the strong 

effect of the year 2005. In this comparison, all IMPROVE monitor sites are not just 

meeting Arizona’s previously set RPG values, but are also exceeding URPs by 2018. 

Again, this analysis shows the limitations of the RHR methodology as one year near the 

mid-point has a strong influence on the overall trends.

Table 21 presents the projected visibility for each IMPROVE site as compared to the 

ADEQs RPGs for the 20% least impaired days. None of the sites are projected to 

experience visibility degradation on the 20% least impaired days. Furthermore, all sites 

except GRCA2 are projected to surpass 2018 RPGs for the 20% least impaired days. 
62

Table 18: Arizona Class I Area Reasonable Progress Goals Comparison to Progress 

Period Visibility for the 20% Worst Days. '2018 Projected Visibility' was 

extrapolated based on the rate of Visibility change between the Baseline and 

Progress Period Visibilities.

Reasonable Progress Goals for 20% Worst Days for Arizona Class I Areas

Arizona Class 

I Area Site ID

Baseline 

(dV)

Progress 

(dV)

URP 

based 

2018 

visibility

2018 

RPG 

(dV)

2018 

Projected 

visibility

Chiricahua NM, 

Chiricahua W, 

Galiuro W CHIR1 13.4 12.2 12.0 13.4 9.6

Grand Canyon 

NP GRCA2 11.7 12.0 10.6 11.1 12.7

Mazatzal W, 

Pine Mountain 

W IKBA1 13.4 13.4 11.8 12.8 13.4

Mount Baldy W BALD1 11.9 11.8 10.5 11.5 11.6

Petrified NP PEFO1 13.2 13.0 11.6 12.9 12.6

Saguaro NP -

West Unit SAWE1 16.2 14.9 13.9 16.0 12.0

Saguaro NP -

East Unit SAGU1 14.8 13.6 12.9 14.8 11.0

Sierra Ancha W SIAN1 13.7 13.0 12.0 13.2 11.5

Superstition W TONT1 14.2 13.8 12.4 13.9 12.9

Sycamore 

Canyon W SYCA1 15.3 15.2 13.3 15.0 15.1

Table 19: Alternative method for the 20% Most Impaired Days at GRCA2. EC and 

POM visibility extinctions are replaced by ten-year average for 2003 and 2009.

Alternative RHR Method Analysis for the 20% Worst Days at GRCA2

Total Extinction (Mm-1)

Year 

Adjusted Baseline

Adjusted 

Baseline Progress

Adjusted 

Progress

Standard 

Change

Adjusted 

Change

2003 34.6 32.2 35.1 -- 0.5 2.9

2009 34.6 -- 35.1 32.3 0.5 -2.3
63

Table 20: Arizona Class I Area Reasonable Progress Goals Adjusted Comparison 

to the Altered Progress Period Visibility (2006-2010) for the 20% Worst Days. '2018 

Projected Visibility' was extrapolated based on the rate of visibility change between 

the Baseline and Progress Period Visibilities. In this case the Baseline period was 

altered to the years 2000-2005 and the Progress Period was adjusted to the years 

2006-2010.

Adjusted Reasonable Progress Goals for 20% Worst Days for Arizona Class I 

Areas 

Arizona 

Class I Area Site ID

2000-2005 

Baseline 

(dV)

2006-2010 

Progress 

(dV)

URP based 

2018 

visibility

2018 

RPG 

(dV)

2018 

Projected 

visibility

Chiricahua 

NM, 

Chiricahua W, 

Galiuro W CHIR1 13.3 11.8 12.0 13.4 8.5

Grand Canyon 

NP GRCA2 11.8 11.4 10.6 11.1 10.5

Mazatzal W, 

Pine 

Mountain W IKBA1 13.6 12.6 11.8 12.8 10.4

Mount Baldy 

W BALD1 11.9 11.1 10.5 11.5 9.3

Petrified NP PEFO1 13.3 12.5 11.6 12.9 10.7

Saguaro NP -

West Unit*

SAWE

1 16.0 14.8 13.9 16.0 12.2

Saguaro NP -

East Unit SAGU1 14.7 13.3 12.9 14.8 10.2

Sierra Ancha 

W SIAN1 13.9 12.3 12.0 13.2 8.8

Superstition 

W TONT1 14.2 13.3 12.4 13.9 11.3

Sycamore 

Canyon W SYCA1 15.5 14.7 13.3 15.0 12.9

*2010 data was not included for this unit do to uncertainty of data's accuracy.
64

Table 21: Arizona Class I Area Reasonable Progress Goals Comparison to Progress 

Period Visibility for the 20% Best Days. '2018 Projected Visibility' was 

extrapolated based on the rate of Visibility change between the Baseline and 

Progress Period Visibilities.

Reasonable Progress Goals for 20% Best Days for Arizona Class I Areas

Arizona Class I Area Site ID

Baseline 

(dV)

Progress 

(dV)

2018 

RPG 

(dV)

2018 

Projected 

visibility

Chiricahua NM, 

Chiricahua W, 

Galiuro W CHIR1 4.9 4.4 4.9 3.3

Grand Canyon NP GRCA2 2.2 2.2 2.1 2.2

Mazatzal W, Pine 

Mountain W IKBA1 5.4 5.1 5.2 4.4

Mount Baldy W BALD1 3.0 2.9 2.9 2.7

Petrified NP PEFO1 5.0 4.6 4.7 3.7

Saguaro NP - West 

Unit SAWE1 8.6 8.0 8.3 6.7

Saguaro NP - East 

Unit SAGU1 6.9 6.7 7.0 6.3

Sierra Ancha W SIAN1 6.2 5.3 5.9 3.3

Superstition W TONT1 6.5 5.7 6.2 3.9

Sycamore Canyon W SYCA1 5.6 5.1 5.5 4.0

VIII.Conclusions

This document fills the required EPA Regional Haze SIP deficiency for the State of 

Arizona regarding the submission of a complete and recent emission inventory. In this 

document ADEQ presents a 2008 Emission Inventory which is comparable to the 2002 

EI in some Source Categories for a variety of pollutants. Where this inventory is not 

reliably comparable to the 2002 State of Arizona EI, ADEQ has provided an overview of 

the methodology, input data, and model resolution enhancements which have changed

between the 2002 and 2008 inventory preparations.

ADEQ has also included a review of IMPROVE monitor data between the years of 2000 

and 2009. This review presented standardized 20% best and worst visibility day

comparisons between the baseline and progress periods as well as Theil statistical trend 

analysis as an alternative approach for understanding 10 year trends. Visibility aerosol 

extinction indicates that ammonium nitrate, organic mass, and elemental carbon 

extinctions are improving within almost all Arizona Class 1 areas. Fine Soil and Coarse 

Mass extinction values seem dependant of the local environment surrounding the Class 1 

Areas and show no discernable increasing or decreasing spatial trends across the State. 
65

Anomalously high years (2005 and 2007) for ammonium sulfate extinction revealed 

increasing ammonium sulfate visibility extinction between the baseline and progress 

periods; however, decreasing trends in ammonium sulfate in previous and more recent 

years resulted in Theil statistics which either showed no statistically significant visibility

extinction increases or statistically significant visibility decreases across the State. 

Furthermore, similar trends for ammonium sulfate were noted for the four corners region.

Finally, ADEQ compared overall visibility trends at each of the IMPROVE monitor 

locations against previously submitted RPGs and URPs for 2018. These data indicated 

that if the current pace of visibility change was continued, no site would experience an 

increase in visibility reduction for the 20% least impaired visibility days in 2018. Six 

monitoring locations are expected to surpass ADEQ’s previously submitted RPGs for 

2018 for the 20% most impaired days. Furthermore, four sites, CHIR1, SAWE1, 

SAGU1, and SIAN1 are projected to surpass the previously accepted URPs for the 20% 

most impaired days in 2018. Only two sites are projected to experience visibility

degradation for the 20% most impaired days in 2018 when compared to 2002, these sites 

are GRCA2 and IKBA1.