i



## I. **Ta ble 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 Emiss ions
................................ .......... 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 Anal ys es ................................ ................................ ......... 37 VI .C. Ammonium Sulfate Analysis
................................ ................................ ........ 47 VI .C.1. 20% Most Impaired Ammonium Sulfate Day s ................................ ......... 47 VI .C.2. Regional Ammonium Sulfate Trends
................................ ....................... 49 VI .D. Coarse Mass An al ys is
................................ ................................ ................... 51 VI .D.1. Worst 20% Coarse Mass Day s ................................ .............................. 52 VI .D.2. Large Point Source Locations
................................ ............................... 53 VI .E. Method Comparison Summary
................................ ................................ ..... 56 VII. Reasonable Progress Goals
................................ ................................ ................... 59 VIII. Conclusions
................................ ................................ ................................ ........... 64ii



## II. **Ta ble of**  **Ta bles**  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 Tabl e 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 Day s at Arizona Class Iarea IMPROVE Sites for the Progress Period (2005
- 2009).
................................ ................................ ................................ ................................ 35 Table 11: Relative Contribution of Pollutants to Visibility Conditions on the 20% Least Impaired Day s at Arizona Class Iarea 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 IIMPROVE 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 Day s for Arizona Class IIMPROVE 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 Iarea IMPROVE Site for the 20% Worst Ammonium Sulfate Day s. ................................ ........ 48 Table 16: 2000
- 2010 Coarse Matter Visibility Extinction (mM - 1 ) Trends and Baseline vs. Progress Period Comparisons at each Arizona Class Iarea 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 Day s and the 20% Worst Day s ............................. 59 Table 18: Arizona Class I Area Reasonable Progress Goals Comparison to Progress Period Visibility

for the 20% Worst Day s. '2018 Projected Visibility' was extrapolated based on the rate of Visibility change betwe en 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 - ye ar 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 Day s. '2018 Project ed 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.
...................... 63iii

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

for the 20% Best Day s. '2018 Projected Visibility' was extrapolated based on the rate of Visibility change between the Baseline and Progress Period Visibilities.
................................ ................................ ................................ ........................ 64iv



## III. **Ta ble of Figur**  **es** 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 C ategory
............................... 17 Figure 4: 2002 and 2008 Volatile Organic Compound Emissions by Source Category
. 18 Figure 5: 2002 and 2008 Primary O rganic 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 20 02 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 day s 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 Iarea IMPROVE Sites.
................................ ................................ ................................ ............... 37 Figure 15: Average Extinction for Baseline and Progress Period Extinction for Worst (Most Impaired) Day s 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) Day s Measured at Arizona Class Iarea 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 (L east Impaired) Day s Measured at Arizona Class Iarea 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 Day s. .............................. 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

day s. ................................ ................................ ........................... 50 Figure 21: 10
- ye ar annual average ammonium sulfate extinction trends for 20% worst day s at CIA IMPROVE sites in the WRAP region.
................................ .......................... 50 Figure 22: 10
- ye ar annual average ammonium sulfate extinction trends for all measured day s at CIA IMPROVE sites in the WRAP region.
................................ .......................... 51 Figure 23: Average Annual Coarse Mass Extinction (mM
- 1) at each Arizona Class Iarea IMPROVE Site for the 20% Worst Coarse Mass Day s. ................................ ................... 53v

Figure 24: Locations of Arizona Class Iareas, Class I area 50 km buffers, Class Iarea IMPROVE monitors, and L arge Point Source Emitters of PM10 (>100 tons/year). IMPROVE site values correspond to Visibility Extinction (mM
- 1) of Coarse Mass averaged over th e 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 monit or in 2002.
................................ ................................ ................................ ................ 58 Figure 28: Monthly

Average Aerosol Extinction measured by the CHIR1 IMPROVE monitor in 2008.
................................ ................................ ................................ ................ 581



## IV. **Intr oduction**  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. O n December 21, 2012 the EPA published within the Federal Registeri a partial disapproval of Arizona’s Reg ional Haze State Implementation Plan. This document was created to address certain deficiencies identified by the EPA, specifically

4 0 CFR § 51.308(d)(4)(v) which requires to submission of the most recently available Emissions Inventory in a Regional Haze SIP. F urthermore, this document presents an alternative methodology , which has been used by the EPA in previous studies, for the anal ys is 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 Inventor**  **ies** The 2002 Arizona Emission Inventory (EI) was originall y constructed to fulfill the requirement of 40 CFR § 51.308 (d)(4)(v) ii , which states a baseline ye ar emission inventory must be contained within a State’s implementation plan for regional haze
. O n December 21, 2012 EPA published within the Federal Registeriii a partial disapproval of Arizona’s Regi onal 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 ye ar of which data are available. In order to fulfill this requirement, the Arizona Department of Environmental Quality ( ADEQ) is presenting a 2008 emission inventory c alculated 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 ( SO 2 ) , Nitric Oxide (NO x) , Ammonia (NH 3 ) , 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 briefl y here, followed b y 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 p oint locations, ty picall y because they

are regulated and their locations are available in regulatory

r eports. Point sources can be further subdivided into electric generating unit (EGU) sources and non - EGU sources, particularl y in criteria inventories in which EGUs are a primary s ource of NO X and SO 2 . Examples of non - EGU point s ources 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 non
road 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 c leaning fac ilities, may b e treated either as stationary

area sources or as point sources.

 On

-Road Mobile Sources: Vehicular sources that travel on roa dway s. Emissions are estimated as the product of emiss ions factors and activity data, vehicle miles traveled (VMT )
. E xamples 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 ty pes that either move under their o wn 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 ty pes of activities from engine sources for drill rigs and compressor engines, to sources such as condensate tanks and fugitive gas emissions. The variety o f emissions ty

pes for sources specific to oil and gas activity c an, in some cas es, 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 f luxes modeled from b iogenic land use data , which characterize s the ty pes of vegetation that exist in particular areas. Emissions are generally d erived 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 h ave a variety of sources that could include anthropogenic sources, natural sources, and natural sources that may be influenced by

anthropogenic activity
. F or emissions summary p urposes, dust is classified here as fugitive dust and windblown dust. F ugitive dust inclu des 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 h ave a mix of natural and anthropogenic influences. Natural s ources include wildland fires, while anthropogenic sources can include agricultural and prescribed fires.


### V.A. **2002 Arizona Em ission Inventory**  **Methodology** The 2002 Arizona Emission Inventory c alculations and results are described in detail in the Arizona Regi onal Haze SIP iv

. P lease refer to Chapter 8 of this document for detailed calculation methodology

of the 2002 Arizona Emission Inventory
. The 2002 Arizona Emission Inventory s erved as the baseline inventory for the 2018 emission inventory

e stimation. The methodology b y which 2018 emissions were estimated from the 2002 emission inventory c an 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 Em ission Inventory**  **Methodology** The creation of the 2008 State of Arizona Emission Inventory w as a collection of efforts between WRAP, ARS, and ADEQ. This inventory r epresents 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 w ere 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 metho dology

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, m any o f the calculated source category emissions methodologies and input data changed between the 2002 and 2008 emission inventory p reparations in order to enhance the accuracy o f estimate d statewide emissions
. F or 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 EIw as 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 EIp resented 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 Em**  **iss ion**  **Inventory Methodology**  **Com parisons**  Table 1 presents the input data and methodologies utilized to calculate each source category for the 2002 and 2008 EI s. This table illustrates how some source category methodologies, input data, or modeling resolutio ns changed dramaticall y, 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 Pl an02d)**  **2008 Emission Inventory**  **(WRAP WestJump08 and DEASCO**  **3 )**  **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 hourl y 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 pres ented here represent a 5
- ye ar 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 availabl e 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 separatel y.6

**Inventory Sector**  **2002 Baseline Inventory (WRAP Pl**  **an02d) 2008**  **Emission Inventory (WRAP WestJump08**  **and DEASCO 3**  **) 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 s t ate 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 considerabl y 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 cate gories 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&G7

**Inventory Sector**  **2002 Baseline Inventory (WRAP Pl**  **an02d) 2008**  **Emission Inventory (WRAP WestJump08**  **and DEASCO 3**  **) Comments** 

 Heaters  Pneumatic Devices  Condensate and oil tanks  Dehydrators  Com pletion Venting More details are available here: http://vista.cira.col ostate.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) P hase 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
-blee d) and specific information on control device efficiency, among other im provements 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 de termine 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 high
quality 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, wer e used. 4) Phase III used more refined methodologies to estimate emissions,8

**Inventory Sector**  **2002 Baseline Inventory (WRAP Pl**  **an02d) 2008**  **Emission Inventory (WRAP WestJump08**  **and DEASCO 3**  **) Comments** 

Phase III also asked states and operators for gas composition data by basin that greatl y increased the information available about VOC emissions rates. On
- Road Mobile EPA MOBILE 6 model applied by ENVIRON contractor using NM IMVMT 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 MOVES2010 a 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 contribute d to some differences in emissions reported, but other disparities are due to a combinati on 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 county
level population and activity data.9

**Inventory Sector**  **2002 Baseline Inventory (WRAP Pl**  **an02d) 2008**  **Emission Inventory (WRAP WestJump08**  **and DEASCO 3**  **) 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 D ust

– data obtained from the Federal Highway Administration and State and local datasets. Vegetative scavenging factors were applied pre
processing at the county l evel 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 post
processing at the higher resolution grid cell level , compared to 2002 data. More details are available here: http://wrapair2.org/WestJ ump AQMS.aspx Note that fugitive dust and road dust categories were available separately in WRAP P lan02d inventories, but are combined for s ummary purposes here. For the 2008 inventory , vegetative scavenging factors were applied to the combined sources; thus these source categories are not easil y separated.10

**Inventory Sector**  **2002 Baseline Inventory (WRAP Pl**  **an02d) 2008**  **Emission Inventory (WRAP WestJump08**  **and DEASCO 3**  **) Comments**  Windblow n Dust Generated using WRAP Windblown Dust Model

and 2002 MM5 meteorology , at 36km grid cell resolution. Vegetative scavenging factors applied pre
processing at the county l evel More details are available here: http://vista.cira.colostate.edu/TSS/Results/Emissio ns.aspx Generated using WRAP Windblown Dust Model and 2008 WRF meteorology , at 4km and 12km grid cell resolution for the WRAP region
. Vegetative scavenging factors applied post
processing 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 ac tual 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, whic h 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 res olution
. 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. Ex amples of biogenic emissions input factors that may affect differences between the BEIS3.12 and MEGAN2.10 model outputs include:

 Different meteorological years and mo dels11

**Inventory Sector**  **2002 Baseline Inventory (WRAP Pl**  **an02d) 2008**  **Emission Inventory (WRAP WestJump08**  **and DEASCO 3**  **) Comments**  (2002 MM5 vs. 2008 WRF).  Higher tem

poral and spatial variability of land cover and other environmental input factors.  Im proved emissions factors based on better sources of

d ata (e.g., satellites and field studies). A model comparison study b etween 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 Fire s 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 separat e 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 o f 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 w ildfires.12

**Inventory Sector**  **2002 Baseline Inventory (WRAP Pl**  **an02d) 2008**  **Emission Inventory (WRAP WestJump08**  **and DEASCO 3**  **) Comments**  More details are available here: http://www.wrapfets.org/d easco3.cfm13 

### V.D. **2002 and 2008 Em issions**  The 2008 Arizona statewide EI was originally c reated by

WRAP and ARS to fulfill the requirement s of 40 CFR § 51.308(g)(4) which states individual States must address the change in the emissions of pollutants which cont ribute to visibility

i mpairment every

5 ye ars for all sources and activities. ADEQ is currentl y 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 p rovide s 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 sh ould not be made for many o f the source sectors due to these emission changes likely resulting from model resolution, methodology , and input data enhancements
. D espite this concern, g eneral 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 a s compared to the 2002 inventory , with the largest difference reported in point sources.

 For nitrogen dioxide, all source cate gories except area sources exhibited lower emissions in the 2008 inventory a s compared to the 2002 inventory , with the largest difference reported for mobile sources.

 Ammonia emissions remained relativel y similar between inventories, with 2008 area sources showing slightl y higher estimated emissions than 2002 and 2008 on - road mobile sources showing lower estimated emissions
.

 2008 EIv olatile organic compound emissions were much lower than 2002 in biogenic sector, due to enhancements in biogenic inventory methodology
.

 Primary

o rganic aerosol emissions from fire were higher in 2002 than 2008
. Note that current ye ar inventories represent only s napshots of fire emissions for the ye ar 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 EIa s 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 EIw as also s lightly 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 Ari zona Emissions**  Table 2 and Figure 1 present 2002 and 2008 estimated SO 2 emissions by source category
. Tables 3 and Figure 2 present data for NO x , and subsequent Tables and F igures (Tables 4 through 9 and Figures 3 through 8 ) present NH 3 , VOCs , POA , EC , Fines and CM emissions for the ye ars of 2002 and 2008. S ource 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 EI s.15

**Table 2**  **: Arizona Sulfur Dioxide Emissions by Source Category Source Category**  **Sulfur Dioxide Emissions (tons/year)**  **2002 (Plan02**  **d )**  **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 f

ire 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**T o** **n s** **/ Y** **e a** **r 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 Categor** **y Nitrogen Oxides**  **Emissions (tons/year) 2002**  **(Plan02 d**  **) 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 Nat ural**  **44,881 18,769**  **- 26,112 (** **- 58%)**  **All Sources Total Emissions**  **368,339 290,344**  **- 77,995 (** **- 21%)**  *Natural f

ire 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**T o** **n s** **/ Y** **e a** **r 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**  **(Plan02 d**  **) 2008** 

**(WestJump2008) Differe**  **nce**

**(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 f

ire 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**T o** **n s** **/ Y** **e a** **r 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 Emiss**  **ions by Source Category Source Category**  **Volatile Organic Compound Emissions (tons/year)**  **2002 (Plan02**  **d )**  **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 5 4,589
- 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 f

ire totals for the 2008 inventory include both anthropogenic and natural sources. Update d 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**T o** **n s** **/ Y** **e a** **r Vo**  **latile 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 Em** **issions by Source Category Source Category**  **Primary Organic Aerosol Emissions (tons/year)**  **2002 (Plan02**  **d )**  **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 f

ire 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**T o** **n s** **/ Y** **e a** **r 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**  **(Plan02 d**  **) 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 Anthropogen ic**  **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 f

ire 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**T o** **n s** **/ Y** **e a** **r 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 To tal Fire Natural Fire Anthro Fire Point **Figure 6**  **: 2002 and 2008**  **Elemental Carbon Emissions by Source Category** 21

**Table 8**  **: Arizona Fine Part iculate Matter**  **Em issions by Source Category**  **Source Category Fine Particulate Matter**  **Emissions (tons/year) 2002**  **(Plan02 d**  **) 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 f

ire 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**T o** **n s** **/ Y** **e a** **r 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 Em** **issions by Source Category**22

**Table 9**  **: Arizona Coarse Pa**  **rticulate Matter Emissions by Source Category Source Category**  **Coarse Particulat e Matter**  **Em issions (tons/year)**  **2002 (Plan02**  **d )**  **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 F ugitive 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 17,5**  **36 (26%) All Sources**  **Total Emissions 158,099**  **239,983 81,884 (52%)**  *Natural f

ire 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**T o** **n s** **/ Y** **e a** **r Coarse P**  **articula te**  **Matter Emissions by State Arizona**  Windblown Dust Fugitive/Road Dust Off-Road Mobile On-Road Mobile Off-Shore WRAP Area O&G Area Biogenics To tal 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 publicl y available records of State and County

estimated populations for each year dating to 1980 v . For the ye ars of 2002 and 2008, ADOA estimates Arizona state - wide populations to be 5,470,720 and 6,629,455 respectivel y. This is an increase in state - wide population of 21.2%. An increase in population this significant will undoubtedly l ead to pollutant emission changes for a number of source categories; however, the extreme degree to which certain pollutants change for gi ven source categories indicate that population increases are not solely responsible for emission changes between the 200 2 and 2008 EI s. B elow is a list of methodology , input data, and model resolution changes which are believed to significantl y contribute to emission differences between the 2002 and 2008 EI s. 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 S ource SOx and NOx emissions between the 2002 and 2008 EI s . This review indicated that the se changes are due to a mixture methodological chang es and data completion issue s . Therefore, ADEQ believes a more accurate indicator of NO x and SO x changes are the IMPROVE data. 2. Biogenic emission differences for NO x and VOCs are p rimaril y due to methodology , source data, and modeling resolution enhancements between 2002 and 2008. 3. Ammonia emission differences for On
- road Mobile are p rimarily 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 v ehic le NH 3 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 p rimarily d ue to the switch between MOBILE6 and M OVES (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 fairl y closel y to one another. ADE Q internal review revealed that many , if not most, sources within the State of Arizona were not reporting PM 2.5 prior to 2006 which likel y explains the drastic change in Fines emissions between the 2002 and 2008 EIs. 7. Area source Fines emission differences are partially d ue 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. wo rkforce.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.pdf24

8. Fugitive and Road Dust Fines and CM emission differences are p rimarily due to NEIc h anges. C alculation methodology c hanges resulted in an overall increase in Paved Road Dust emission of 128% for the 2008 NEI. 9. Windblown D ust Fines and CM emission differences are p rimarily 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 origin ation or calculation methodologies for emi ssion estimations by sector. S ince 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 c ompiled emission inventories. In this section ADEQ present s WRAP and ARS produced figures of differences in 2002 and 2008 emission inventories by s tate for 3 different pollutants to determine if the previously m entioned 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 Secti on V.D.2. The most evident trend in this f igure 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 d ue 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
- 2008 vii , it is unlikely that this small decrease in VMT would be seen in every s tate. 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 l ikely d ue 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 b ased, rather than characteristic of the entire western US. Coarse Mass emissions are due to phy sical disturbance of an area of land by anthropogenic activities (e.g. construction, driving on unpaved roadway s, etc), natural activities (e.g. animal movement or burrowing), or a mixture of the natural and anthropogenic activ ities (e.g. wind suspension of dust from a cleared area). While the activity which creates the emissions may c hange, 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.pdf25

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

-300,000 -200,000 -100,000 0 100,000 200,000 300,000 400,000 500,000 600,000AZCACOIDMTNVNMNDORSDUTWAWY**T o** **n s** **/ Y** **e a** **r Diff** **erence 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 Ar ea 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 vegetati on 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 southwest ern 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 l arge role in how Windblown Dust emissions differed between the 2002 and 2008 EI s. All five southwestern US States exhibit similar emission differences for Windblown Dust and Fugitive and Road Dust. Southwestern US State Windblown Dust emissions are l ikely t o 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 f rom 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 v 1.5 was used for th is 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 80,000 100,000AZCACOIDMTNVNMNDORSDUTWAWY**T o** **n s** **/ Y** **e a** **r Diff** **erence 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 tren ds 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 d ue to calculation methodology

c hanges. 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 EI s are problematic due to source data and methodology c hanges. Therefore, ADEQ has determined that IM PROVE monitoring data are more appropriate surrogate s for assessing visibility c hange 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 s tandards viii

. 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 Com** 

**pleteness Requirem ents**  The follo wing information was gathered directly f rom ARS and describes IMPROVE data completeness for the State of Arizona. F urthermore, it outlines the steps and methods utilized to gap fill missing data sets. P rogress for the Regional Haze Rule ( RHR) is determin ed using 5
- ye ar average visibility conditions. EPA’s guidance for tracking Regional Haze p rogress ix includes data completeness requirements designed to ensure that calculated averages include enough data to sufficientl y represent each daily , annual and 5
- ye ar period. The guidance specifies that the 2000
- 2004 baseline period and each subsequent 5
- ye ar 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 ye ars must contain at least 75% of all possible daily samples

viii Enviro nmental 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 ye ars must not contain more than 10 consecutive missing daily samples

 The 5
- ye ar baseline and each 5
- ye ar progress period averages must contain at least 3 complete ye ars 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 sit e. The SIAN1 site, the Sierra Ancha Wilderness Area , did not meet RHR data completeness criteria for the ye ars 2006, 2007 and 2008, which interrupted the requirement for 3 complete ye ars required for a 5
- year average. Substitution methodology

w as consistent with methodology previously a pplied to the 2000
- 2004 baseline period for seven WRAP sites
. The data substitution methods include estimating missing species from other on - site measurements and appropriatel y scaling data collected at a nearby s ite 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 ye ars 2006, 2007 and 2008. Years deemed complete were not changed, although there may

h ave been missing samples during those ye ars. 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 a ssociated 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 s ubtracting 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 quarterl y to account for seasonal variations. Because the carbon data substitution methods were not sufficient to complete the required ye ars, a second method was applied that involved scaling data from the closest neighboring IMPROVE site, TON T1. This site had previously b een 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 Ariz ona, as a donor site for TONT1. Species specific correlations between SIAN1 and TONT1 of mass data collected during the 2005
- 200929

period were calculated quarterl y, and applied to adjust TONT1 data to apply t o missing SIAN1 day s. Figure 12 presents bar charts showing dail y SIAN1 extinction data, including substituted data, for the 2005
- 2009 progress period ye ars. Original RHR data in blue and substituted data by species in the standard IMPROVE colors. Substituted day s are also indicated by a black bar under

neath the day , and the red line indicates the threshold above which day s are counted in the 20% worst day s for that ye ar. Note that some of the m issing extinction day s had partial data available and onl y individual species missing in a given sample were substituted. Figure 13 presents similar bar charts indicating speciation of all data, with day s in which all or part of the day w as substituted indicated by a b lack bar underneath the day
. Note that very f ew of the substituted day s were counted among the 20% worst day s 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 a**  **nd Progress Period Visibility** This section summarizes IM PROVE 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

r equirements for periodic progres s ( CFR 51.308(g)(3)(iii))
. Furthermore, a more robust 10 year trend analy sis is presented to determine how an alternate methods of visibility trend anal ys i s may

a ffect the conclusions drawn from IMPROVE monitoring data anal ys is. 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 day s continues to improve at each Federal CIA, and that visibility o n 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. Inaddition to presenting the results of EPA standardized 20% worst and 20% best day s comparisons (RH R method) , ADEQ submits an alternative method of assessing visibility

c hanges at State of Arizona IMPROVE monitoring sites between the ye ars of 2000 and 2009. This method utilizes Theil statistics (Theil method) to calculate an annual trend for the 10 ye ar period of interest and is an EPA accepted method for annual pollutant trend analysis x

. 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 detai led information is provided in the monitoring and data summaries sub
- sections.

 For RHR method anal ysis of the 20% best day s, the 5
- ye ar 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 day s, the 5
- ye ar average deciview metric decreased at most sites, but increased at the GRCA2 and IKBA1 sites. The The il method showed similar results, except no statistically significant increasing trends. GRCA2 and PEFO1 showed no change when anal yz ed using the Theil method
.

- All sites experienced visibility e xtinction decreases of ammonium nitrate using the RHR method
. The Thiel method showed no significant increasing trends and four s ites with significant decreasing trends. C entral 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 Emiss ions Trends Report, 2003.33 - RHR method analysis showed that ammonium sulfate increased at most sites, though 20% worst day s for ammonium sulfate showed no increases using the Theil method. O bservations regarding ammonium sulfate were as follows:

 Ammonium sulfate RHR method increases were primaril y due to higher than average ammonium sulfate measure d in 2005
.

 When selecting the 20% worst day s 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 e xtincti on from particulate organic mass at all sites except GRCA2 and IKBA1. Theil method analysis showed statisticall y significant particulate organic mass decreases at four Arizona sites and no statistically s ignificant increasing trends at any of the other si tes.

- An overall visibility d eciview increase at the GRCA2 site was seen when anal yz ing 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

c omponents associated with wildfire emissions are given below. These results indicate GRCA2 visibility de ciview changes were at least partially

due t o the 2009 wildfires :

 E lemental carbon showed a fairl y large increase in visibility e xtinction using the RHR method; however, annual average elemental carbon measurements did not show increasing trends using the Theil method
.

 GRCA2 showed an increase in v isibility e xtinction 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 anal ys is for the GRCA2 site where the 2009 ye ar 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 decreas e 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 - Th e overall visibility d eciview increase at IKBA 1 was affected by high measurements in 2005
. P OM and ammonium sulfate are the primary c ontributing visibility

c omponents to the overall increasing deciview trend . These two visibility components are discussed below:

 Particulate organic mass showed a larg e increase visibility extinction using the RHR method, but did not show an increasing trend using the Theil method. Particulate matter increases were strongl y controlled b y 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 anal ys is for the IKBA1 site where the 2005 ye ar 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) Vi sibility**  This section addresses the regulatory question , **w hat**  **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
- ye ar averages be calculated over successive 5
- year p eriods, i.e. 2000
- 2004, 2005
- 2009, 2010
- 2014, etc. (EPA 2003). Current visibility c onditions are represented here as the most recent successive 5
- ye ar average period available, or the 2005
- 2009 period average, although the most recent IMPROVE monitoring data currentl y 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 day s for each of th e Federal CIA IMPROVE monitors in Arizona. Figure 1 4 presents 5
- ye ar average extinction for the current progress period for both 20% most impaired and 20% least impaired day s. Specific observations for the current visibility c onditions on the 20% most imp aired day s are as follows:35  The largest contributor s to aerosol extinction on the 20% most impaired day s at Arizona sites were particulate organic mass, ammonium sulfate and coarse mass.

 The highest aerosol extinction (15.2 dv) was measured at the SYCA1 si te, where particulate organic mass was the largest contributor to aerosol extinction, followed by

c oarse mass. The lowest aerosol extinction (11.8 dv) was measured at the BALD1 site. Specific observations for the current visibility c onditions on the 20% le ast impaired day s are as follows:

 Rayleigh, or the background visibility i mpairment 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 day s at the Arizona sites ranged between 2.2 deciview (GRCA2) and 8
. 0 deciview (SAWE 1).

 For all Arizona sites except SIAN1 and SAWE1 , ammonium sulfate was the largest contributor to aerosol extinction for the 20% least impaired day s.

 At the SIAN1 site, particulate organic mass was the largest contributor to aerosol extinction for the best day s , followed by ammonium sulfate. At the SAWE1 site, coarse mass was the largest contributor, followed by ammonium sulfate.

**Table 10**  **: Relative C ontribution 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** * **Ammo nium**  **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 V** **isibility Conditions on the 20% Least**  **Im paired 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) 2 3% (2) 12% (4) 6% (6) 16% (3) 1% (7) *Highest contribution per site is highlighted in bold.3711.8dv2.912.24.4122.213.45.1134.613.66.714.98135.315.25.113.85.7

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 x** **t i** **n c** **t i** **o n** *******( M** **m -** **1 )**  

### First Progress Period (2005 **- 2009)**  **Av erage**  **Extinction, 20% W** **orst (W) and Best (B) Day s** 
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**  **Im paired)**  **and Best**  **(Least Im** **paired) Days**  **Mea sured**  **at Arizona**  **Class I area IMPROVE Sites.** 



### VI.B.2. **Visibility Trend Analyse s**  This section addresses the regulatory question, **w hat**  **is the**  **difference between**  **current visibility**  **conditions for**  **the most**  **impaired and**  **least impaired**  **days and**  **baseline visibility**  **con ditions**  **(40 CFR**  **51.308 (g)(3)(ii))?**  Baseline visibility c onditions are the basis against which improvements in worst day v isibility, and lack of degradation for the best day visibility , are judged. Included here are comparisons between the 5
- ye ar average b aseline 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 anal ys is for visibility trend anal ys is for the 2000
- 2009 period
. A lternative methodology i s presented to better understand how anamalous ye ars may

h ave affected visibility

c hanges as measured at Arizona Class I Areas. The Theil method was chosen to characterize visibility trends, as this method has been generall y ac cepted by

E PA in previous trend analy ses, most notably in previously prepared EPA National Air Quality and Emissions Trends Reports xi

. While the RHR method is the important metric for RHR regulatory p urposes, 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
- ye ar visibility

trends were anal yz ed for the State or Arizona in order to better understand how anamalous ye ars may h ave affected visibility

c hanges a s measured at Arizona Class I Areas. The Theil method was chosen to characterize visibility trends, as this method has been generall y accepted by EPA in previous trend anal ys es, most notably in previously

prepared EPA National Air Quality and Emissions Trends Reports xii

. EPA described the statistical method as follows: “The Theil test is a nonparametric statistical test that can be used instead of regression
based methods for discerning a monotonic trend. It examines whether the concentration from year to ye ar tends to increase or decrease consistently , making it a test of monotonicity
. This test is not concerned with the magnitude of the ye ar - to - ye ar differences. The null hy

pothesis 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 hy pothesis 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. S econd, this test wa s 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 ye ars 2000
- 2009, with a trend defined as the slope derived using Thiel method
. Trend statistics are useful in anal yz ing changes in air quality data, because these statistics can show the overall tendency o f measurements over long periods of time to increase or decrease, while minimizing the effects of the ye ar - to - ye ar fluctuations which are common in air quality data. The significance of the trend is represented using p - values calculated using Mann
Kendall trend statistics. Determining a significance level helps to distinguish random variability

in data fro m a tendency t o increase or decrease over time, where lower p values indicate higher confidence levels in the computed slopes. In some cases, trends may s how decreasing tendencies where the difference between the 5
- ye ar averages do not. Inthese cases, th e 5
- year average is the important metric for RHR regulatory purposes, but trend statistics may b e 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 ibid39 

### VI.B.2.ii. **2000 -**  **2009 Visibility Trend Analy s**  **es Results**  This section presents vis ibility p rogress 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 day s by use of the RHR method , and Table 13 presents similar data for the least impaired day s. Averages that increased are depicted in red text and averages that decreased in blue. Figure 15 presents the 5
- ye ar average extinction for the baseline and current progress period averages for 20% most impaired day s and Figure 16 presents the differences in averages by

c omponent, with increases represented above the zero line and decreases below the zero line. Figures 1 7 and 1 8 present similar plots for the 20% least impaired day s using the RHR method
. 10 ye ar trends for individual visibility e xtinction components using the Theil method are presented in Table 1 4
. Only averages with p - value statistics les s than 0.15 (85% confiden ce level) are presented in T able 1 4 , with increasing slopes in red and decreasing slopes in blue. The regional haze regulations refer specificall y to changes in extinction for the 20% most impaired and least impaired day s, but tren d statistics are also presented in Table 1 4 for an average of all sampled days. Selection of the most impaired and least impaired day s can vary

seasonall y from ye ar to ye ar , so in some cases the annual average of all sampled day s may

better represent actual aerosol component trends over time. Some general observations regarding changes in visibility i mpairment at sites in Arizona are as follows: For the 20% most impaired day s, the RHR method exhibited deciview metric increase s between the 2000
- 2004 and 20 05
- 2009 periods at the GRCA2 and IKBA1 sites and decrease s at all other Arizona sites. Theil method anal ys is showed no sites with significantl y increasing deciview metric trends between 2000
- 2009 and significantl y 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 ex cept 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 and40 TONT1 sites. In contrast, no statistically s ignificant 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 anal ys is of ammonium nitrate extinction showed decreases at all Arizona sites for the 20% most impaired day s. Anal ys is of all measured day s showed no increasing trends, and decreasing trends on the order of 0.1 Mm - 1 /year at the IKBA1, SAGU1, SAWE1, SIAN1 and TONT1 sit es.

 RHR method anal ys is of coarse mass revealed increasing extinction values at BALD1, SAGU1, SYCA1, and TONT1. H owever, onl y the BALD1 site showed a statistically

s ignificant increasing trend for coarse mass for all measured day s on the order of approxim ately 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 day s (BALD1 and PEFO1) while SYCA1 showed a significantly decreasing trend.

 In creases in deciview at the GRCA2 site using the RHR method were mostly d ue 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 l arge events between June and August of 2009. These increases were partial ly o ffset by

d ecreases in ammonium nitrate and coarse mass. This site did not show significantly increasing ammonium sulfate trends using the Theil method.

 In creases in deciview at the IKBA1 site were mostly d ue to increased ammonium sulfate and particul ate organic mass measurements. Higher progress period measurements at IKBA1 were influenced by l arge events in July 2 005. These increases were partially o ffset by d ecreases in ammonium nitrate and soil. This site did not show significantly increasing amm onium sulfate trends using the Theil method. For the 20% least impaired days, the RHR method exhibited decreasing deciview metric s at all sites except GRCA2, where the measured deciview average remained the same . Notable differences for individual component averages on the 20% least impaired day s 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 anal ys is 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 -**  **2 004**  **Baseline Period**  **2005 -** 

**2009 Progress**  **Period Change** 

**in dv*** **Amm .** 

**Sulfate Amm**  **. Nitrate**  **POM EC**  **Soil CM**  **Sea Salt**  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 re d indicate increases in extinction, values in blue indicate decreases.

 sites experienced statistically s ignificant decreases in ammonium sulfate trends.

 Ammonium nitrate decreased at all but the GRCA2 site using the RHR method and 4 of the decreasing sit es 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 Pe**  **riod (2005 -**  **2009) on the 20% Least I**  **mpaired 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**  **Sea Salt**  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.4311.8dv11.813.412.211.71213.313.413.21314.813.616.214.913.71315.315.214.213.8

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 x** **t i** **n c** **t i** **o n** *******( M** **m -** **1 )**  

### Baseline (B) and First Progress (P) Period **Av erage**  **Extinction, 20% W** **orst Day s** 
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** **i n** **E x** **t i** **n c** **t i** **o 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 of deciview (dv) is shown above resepective bars. *Change in total extinction in units deciview (dv) is shown resepective **Figure 16**  **: Difference**  **b etween**  **Average Extinction**  **for Current**  **Progress Period**  **(2005 -**  **2009) and**  **Baseline Period**  **(2000 -**  **2004) for**  **the Worst**  **(Most Im** **pai red)**  **Days Measured at Arizona Class I area IMPROVE Sites.** 443dv2.94.94.42.22.25.45.154.66.96.78.686.25.35.65.16.55.7

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 x** **t i** **n c** **t i** **o n** *******( M** **m -** **1 )**  

### Baseline (B) and First Progress (P) Period **Av erage**  **Extinction, 20% B**  **est Day s** 
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=-0.1-0.50-0.3-0.4-0.2-0.6-0.9-0.5-0.8

-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** **i n** **E x** **t i** **n c** **t i** **o 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 b**  **etween 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 S**  **ignificant 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**  **Element al** 

**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 Day s -- **- 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 Day s **- 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 Day s -- -- 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 Al l Day s -- -- **- 0.1** 
-- 0.0
-- -- 0.0 PEFO1 20% Best **- 0.1** 
-- 0.0 **- 0.1** 
-- -- -- 0.0

20% Worst
-- -- -- -- -- **0.1**
-- 0.0 All Day s **- 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 Day s **- 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 Day s **- 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
-- -- 0.046 **Site Group**  **Annual Trend*** **(Mm -**  **1 /year** **) Site**  **Total (dv)**  **Ammonium**

**Sulfate Ammonium** 

**Nitrate Particulate** 

**Organic Mass**  **Element al** 

**Carbon Soil**  **Coarse**

**Mass Sea**  **Salt** 20% Worst **- 0.2** 
-- -- -- -- -- -- 0.0 All Day s **- 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 Day s **- 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 Day s **- 0.1** 
-- **- 0.1**  **- 0.2**  **- 0.1** 
-- -- 0.0 *(
-- ) Indicates statistically insignificant trend (<85% confidence level).47 

### VI **.C. Amm**  **onium Sulfate Analysis**  Several anal ys es 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 day s (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.). F urthermore, ADEQ has shown that ammonium sulfate has decreased at all IM PROVE monitors for the most recent five ye ar progress period for all site for the 20% most impaired day s and for all sites except two for the 20% least impaired day s.



### VI.C.1. **20% Most Impaired Ammonium Sulfate Days**
Inthi s section ADEQ presents an alternate anal ys is performed by ARS in which the 20% most impaired ammonium sulfate day s were isolated, averaged annually and then averaged for the baseline and progress periods. This analy sis was performed in order to better un derstand how the worst 20% visibility day s for a particular pollutant change between the baseline and progress periods rather than examining the 20% worst visibility day s for all combined pollutants. The combination anal ys is required by the RHR can cause seasonal shifts in the day s 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 impaire d day s for the period of 2000
- 2009. F igure 19 presents the annual average of the 20% most impaired day s. E xtinction decreases between 2000 and 2004. The ye ars of 2005 and 2007 show exceptionally high ammonium sulfate averages, followed by c onsecutive de creasing years between 2007 and 2009. When baseline and progress period extinction48 

### Worst 20% Ammonium Sulfat **e Days** 

2 4 6 8 10 12 14

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009**A v** **e r** **a g** **e A** **n n** **u a** **l E** **x t** **i n** **c t** **i o** **n (** **M m** **- 1** **)** BA LD1 CHIR1 GRCA2 IK BA 1 PEFO1 SAGU 1 SAWE1 SIAN1 SY CA 1 TONT1

**Figure 19**  **: Average**  **Annual Ammonium**  **Sulfate Extinction**  **(mM -**  **1 )**  **at each**  **Arizona Class I area IM**  **PROVE Site for the 20% Worst Ammonium Sulfate Day s**  **. 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**  **a t each**  **Arizona Class I area IM PROVE 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 IM PROVE sites show increasing ammonium sulfate extinctions except SAWE1. H owever, when 2000
- 2009 worst 20% ammonium sulfate day s trends are analyzed, no sites show increasing trends. B ALD1, CHIR1, PEFO1, SAGU1, and SAWE1 all exhibit statistically s ignificant decreasing ammonium sulfate extinction trends. The extreme differences are strongl y 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 illustrat e the effect of the 2005 year,49

ADEQ presents the results of an anal ys is in Table 15 where the RHR method is altered to include the ye ar 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 day s. 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 anal yz e regional trends in pollut ants in order to better understand which phenomena are more representative of State issues and which extend beyond State boundaries to surrounding areas. This ty pe of analysis allows for a better understanding of which emission increases are locall y based in origin and which may b e more representative of a regional trend and thus may be due to some uncontrollable external factor (e.g. NO x emissions originating from a point source located within another State or Country , PM emission increases which are seen in regional trends and thus may b e related to environmental factors, etc.). In this section we anal yz e regional maps of IM PROVE 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 day s between the baseline (2000
- 2004) and progress (2005
- 2009) periods for all IMPROVE monito rs in the western United States. F igure 20 shows fairl y ubiquitous and substantial increases in ammonium sulfate across the State of Arizona, State of New Mexico, western Texas, and south
- central Colorado. T he ammonium sulfate increases seen in this figure are obviously r egional in extent; however, it is difficult to determine an origination point. These increases could be due to a singular or combination50

**Figure 20**  **: Magnitude**  **of visibility**  **component extinction**  **s that**  **have in**  **creased 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 C IA 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 e xtinction increased regionall y 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 day s (Figure 21) and all day s (Figure 22) found that there was either: 1) no statistically significant trends at IM PROVE monitors within the four corners region (i.e. Arizona, Utah, Colorado, and New Mexico) or 2) the 10 ye ar annual av erage ammonium sulfate extinction trends at these IMPROVE monitors exhibited statistically s ignificant decreases. S imilar to what was previousl y reported for the State of Arizona, regional Theil method trends disagree with the RHR method of a 5 ye ar avera ge comparison of the 20% most impaired day s between baseline and progress periods. F urthermore, this agreement between State of Arizona and south
- western US regional trends may indicate that 2005 and 2007 were outlier ye ars for ammonium sulfate extinction within the entire 4 corners region and the RHR method does not reflect more recent visibility e xtinction improvements for this aerosol.



### VI **.D. Coarse Mass Analysis**  Coarse Particulate matter is generally recognized as having origination sources which are locall y based. In this section, Coarse Mass (CM) is analy zed more closely

in order to52

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 re sults of an alternate approach in selecting the 20% most impaired day analy sis, similar to the trend performed for ammonium sulfate in Section VI .C. F urthermore, a qualitative anal ys is of IM PROVE site location in relation to major PM 10 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 anal ys es have been presented in this document to examine how Coarse Mass extinction has changed within the 2000
- 2009 period at IM PROVE monitoring sites within the State of Arizona. When ex amining the State as a whole, coarse mass has shown no discernable spatial trends at the IM PROVE sites between the baseline period and progress period average s for the 20% most impaired day s (Section VI .B.2.). Furthermore, Theil statistic trends for the 20% most impaired day s onl y resulted in the BALD1 site exhibiting the onl y statisticall y significant trend between 2000
- 2009, where an increase has been noted (Section VI.B.4.ii.). In this section ADEQ presents an alternate anal ys is performed b y ARS in which the 20% most impaired coarse mass day s were isolated, averaged annuall y and then averaged for the baseline and progress periods. This anal ys is was perfo rmed in order to better understand how the worst 20% visibility day s 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 anal ys is required b y 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 anal ys is is also performed for each monitor on the annually averaged 20% most impaired day s for the period of 2000
- 2010. This trend anal ys is was extended past prior anal yses (2000
- 2009) to include 2010 since this year was shown to be fairl y unique in how coarse mass responded across Arizona. As shown in Figure 2 3 , coarse mass is generally low in the ye ars 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 day s. When comparing the baseline period to the progress period for the 20% worst coarse mass visibility

day s ( Table 16 ) all monitors except BALD1, PEFO1, and TONT1 recorded decreased extinction. Furthermore, the CHIR1, SAWI 1, SAGU1, and SIAN1 monitors exhibited drastic decreases in CM extinction for the 20% most impaired CM day s. Theil statistics over the 11 year period showed decreasing trends at all sites except 2; however, onl y CHIR1 and SIAN1 showed statistically significant decreases, while BALD1 and PEFO exhibited statistically significant increasing trend for CM on the 20% most impaired CM day s.53 

### AZ Worst CM Days

0 10 20 30 40 50 60 70

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010**A v** **e r** **a g** **e C** **M E** **x t** **i n** **c t** **i o** **n (** **M m** **- 1** **)** BALD1 CHI R1 GRCA2 IK BA1 PE FO 1 SAGU1 SAWE1 SI AN 1 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**  **: 2 000**  **- 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 anal ys es of coarse mass extinction between 2000 and 2009/2010 have shown fairl y mi xed results; however, there is evidence which suggests coarse mass emissions originate from areas close to the individual IMPROVE monitors. Table 1 6 presents baseline vs. progress period differences which generall y 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 centrall y located within the state within relatively close proximity ; however, these three show a54

respective small decrease, large decrease, and small in crease in CM extinction for the 20% most impaired CM days. Similarl y, 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 PM 10 emitting sources within the State against the location of individual IMPROVE monitors to determine if there is an evident trend. This anal ys is 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 a**  **rea 50**  **km buffers,**  **Class I**  **area IMPROVE**  **monitors, and**  **Large Point**  **Source Em** **itters of**  **PM10 (>100**  **tons/year). I**  **MPROVE site**  **values correspond**  **to Visibility**  **Extinction (mM**  **- 1)**  **of Coarse Mass averaged over t**  **he 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 day s. This map indicates that the 20% most impaired days may be impacted by

local PM 10 sources at some monitors, while other sites show little to no effect on CM extinction from large PM 10 sources. TONT1, SAWE1, and SAGU1 are all monitors which show relativel y high CM extinction values over the progress period and are located relatively close to several large PM 10 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 relativel y s mall PM 10 emitter in comparison with other large sources on the map. Also, PEFO1 and BALD1 are located near very

large PM 10 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 PM 10 point sources and IMPROVE monitor CM extinctions for the 20% most impaired days in the State of Arizona. A finer scale EI around e ach monitor site may be needed in order to better understand individual site trends for CM extinction.



### VI **.E. Method Com** 

**parison Summ**  **ary** The basic premise of the RHR is to ensure that visibility on the 20% worst day s continues to improve, and that visibility o n the 20% best day s does not get worse, as measured in units of deciview s (dv) calculated using data measured at IMPROVE monitoring sites. Progress is measured in discreet 5
- ye ar average increments , beginning with the 2000
- 20 04 baseline average, and proceeding with each subsequent 5
- ye ar 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 d eciviews into 5 ye ar averages can result in anomalous ye ars, experiencing extreme events , which can identify longterm trends in visibility change which due not truly e xist , specificall y for the 20% worst day c omparisons. As an example, this is evident in data presented in this document for the Stat e 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 day s resulting in overall elev ated visibility progress period deciview averages for the site. In contrast, when the Theil statistical method was utilized over the 10 year period to analy

ze extinction trends for overall deciviews at the site and extinction trends for individual visibil ity i mpairment components, no significant increases were seen. Inthis case, anomalous ye ars for individual visibility c omponent extinctions, due to extreme events, presented visibility d egradation using the RHR method, while a more standardized statistic al trend method (the Theil method) showed no significantly increasing visibility

d egradation trends. This is merel y one case where ADEQ, WRAP, and ARS have shown that outlier data can significantl y 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, t o determine the 5
- ye ar average of the 20% best and worst day s, the highest and lowest 20% of day s for each complete ye ar are first selec ted and averaged on an annual basis, with a 5
- ye ar average calculated from these annual averages. The selection of the 20% best and worst days may b e significantly

influenced by

l arge episodic events, and as such, may

n ot represent the same time period fro m ye ar to ye ar. This selection of day s may a ffect the averages for individual species in way s that are independent from actual increases or decreases from one 5
- ye ar period to the next. V isibility i mpairment is the result of the cumulative effect of several different particle pollutant ty

pes. 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 b e favored during warmer summer months. Other pollutants, such as particulate organic mass, may b e impacted by large and variable episodic events such as wildland fires. Variable occurrence of large episodic events may

c ause high extinction measurements that wi ll drive selection of 20% worst day s to coincide with the episodic events, effectivel y introducing the possibility that the worst day s occur at different times each year. As an illustration of events driving the selection of the worst day s, consider daily average aerosol extinction calculated from IMPROVE data at the CHIR1 site in Arizona. Figures 2 5 and 2 6 present daily

a erosol extinction measurements for 2002 and 2008 at CHIR1, with the 20% worst days represented by an orange box with an “x” below the day. F or 2002, large wildfire events in June and July c ontributed to high POM measurements, resulting in more of the worst day s selected during this period. In 2008, more of the worst day s were selected in August and October. As an illustration of the seas onal patterns of individual compounds, consider the monthly averages of aerosol extinction calculated from IM PROVE data at the CHIR1 site. Figure 2 7 presents monthly

average aerosol pollution for CHIR1 measured during 2002, and Figure 2 8 presents monthly averages in 2008. For both years, plots show that ammonium sulfate is highest between July and September. The month ly p lots also show the higher POM that coincided with wildfire events in 2002, which affected the selection of more of the worst day s between May and July

in 2002, and more worst day s in August and October in 2008. The seasonal patterns of ammonium sulfate mean that even if annual ammonium sulfate stayed the same, worst day s in May and July

w ill have higher ammonium sulfate than worst day s that occur between August and October. For this case, Table 1 7 presents the annual averages of ammonium sulfate for both the 20% worst day s and all measured day s. For these years, the annual average of ammonium sulfate extinction decreases, while the 20% wors t day average actuall y increased.58

**Figure 25**  **: Daily Aerosol Extinction measured by the Chiricahua CHIR1**  **IMPROVE monitor during 2002.**

**Figure 26**  **: Daily Aerosol Extinction measured by the Chir** **icahua 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 ye ars of 2000
- 2009. These methods are slight alterations in how the analyzed data is chosen (i.e. choosing the 20% most/least impaired day s particular to a certain pollutant rather than for the entire suite of po llutants) and how the variability

inevitably seen with pollution data can be anal yz ed to account for the effects of large individual events which may s kew overall pollution trends (i.e. the utilization of the Theil statistical trend analysis). ADEQ contes ts that these anal ys es represent logical methods which are comparable to and in some way s improve upon the standardized methodology r equired by

the RHR. F urthermore, these methods are not drasticall y different from previous EPA methodology , and in the cas e of Theil statistical comparison, actually

r epresent previously

utilized EPA methodology for similar comparisons. 

## VII. **Reasonable Pr ogr**  **ess 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 progress xiv

. In this section ADEQ present s Arizona’s progress towards reaching the previously p resented 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 accuratel y measure visibility changes within a region
. However, ADEQ also provides analysis within this section relating trends seen at the IM PROVE monitors to those noted within the emission inventories where appropriate. F inally , ADEQ compares State
- wide extinction trends for individual visibility i mpairment 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 day s. An alternate analysis of RP is also included which illustrates the effect that one single ye ar has on the original results. F urthermore, an additional analy sis is provided which show how specific fire events can have a large impact on the baseline versus progress period comparis on. Table s 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 day s , respectivel y . The Table s further present 2018 projected visibility b ased on the visibility rate of change between the baseline period and progress period s . The 2018 projected visibility w as calculated for each IMPROVE monitoring site using the following equation: 

##        



  5

* 16 PP BP BP PV 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 ye ar of the baseline period (2002), and that the average progress period visibility i s the visibility

f or the midpoint ye ar of the progress period (2007). The 2018 projected visibility

v alues can be utilized in two way s: 1) comparison with ADEQs previously

c alculated RPGs, or 2) comparison with ADEQs previously c alculated URPs. This method is a rather simplistic method but is believed to be more representative of actual progress compared to utilization of a highl y uncertain EI. Table 18 presents the projected visibility f or each IM PROVE site as compared to the ADEQs RPGs and URPs for the 20% most impaired day s. S ix monitoring locations (sh own in blue) are expected to surpass ADEQ’s RPGs for 2018. F urthermore, CHIR1, SAWE1, SAGU1, and SIAN1 are projected to surpass the URP s calculated for these sites for 2018. While BALD1 and SYCA1 are expected to experience visibility improvements by 2 018 , these improvements are not expected to meet the RPGs calculated by ADEQ. Two sites are projected to experience visibility d egradatio n by

2 018 when compared to 2002; these sites are GRCA2 and IKBA1. V isibility d egradation at GRCA2 and IKBA1 are most accuratel y explained through large, singular wildfire events which skew RHR method results for the 20% most impaired day s. Within this document ADEQ has shown evidence of the effect individual61

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. T able 19 presents an analy sis where two ye ars ’ (2003 and 2009) visibility e xtinction 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 ye ar of 2003, total extinction for the GRCA2 monitor was recalculated using the 10 ye ar average extinction values for EC (2.7 mM - 1 ) and POM (10.7 mM - 1 ). Thi s was repeated for the ye ar of 2009. B oth ye ars, 2003 and 2009, experienced l arge 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 RH R method. The table shows that without substitution, total visibility extinction increases by

0.5 mM - 1 using the RHR method, while EC and POM extinction normalization for the ye ar of 2003 cause d this degradation to increase to 2.9 mM - 1 , and 2009 extinctio n normalization caused the trend to reverse with total visibility i mprovement on the order of 2.3 mM - 1 at GRCA2. This exercise expresses the degree to which one large event can skew visibility trends for the 20% most impaired days. While individual event s can cause skewed results, the same can be said for individual ye ars. In previous sections of this document ADEQ has discussed the exceptionality o f the ye ar of 2005 when compared to other ye ar s’ visibility . While ADEQ had data for the more recent visib ility

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 anal yz ing visibility trends. The ye ar of 2005 was an exceptionally high ye ar for visibi lity

e xtinction throughout the State. In Table 20, ADEQ performed an altered version of the RHR method where the baseline period was shifted to include the ye ar 2005 (i.e. 2000
- 2005) and the progress period was shifted to include the most recentl y availab le 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 effe ct of the ye ar 2005. Inthis comparison, all IM PROVE monitor sites are not just meeting Arizona’s previously s et RPG values, but are also exceeding URPs by 2 018. Again, this analysis shows the limitations of the RHR methodology as one ye ar near the mid
- point has a strong influence on the overall trends
. Table 21 presents the projected visibility f or each IM PROVE site as compared to the ADEQs RPGs for the 20% least impaired days. N one of the sites are projected to exp erience visibility degradation on th e 20% least impaired day s. F urthermore, all sites except GRCA2 are projected to surpass 2018 RPGs for the 20% least impaired day s.62

**Table 18**  **: Arizona Class I Area Reasonable Progress Goals Comparison to Progress Period Visibi** **lity 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 Vi**  **sibility' was ext rapolated based on the rate of v** **isibility 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. Adjus**  **ted 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 includ ed 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 b**  **ased 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 visibi** **lity** 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 de ficiency f or 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 EIin some Source Categories for a variety o f pollutants. Where this inventory is not reliably c omparable to the 2002 State of Arizona EI , ADEQ has provided an overview of the methodology , input data, and model resolution enhancements which have changed betwe en the 2002 and 2008 inventory preparations
. ADEQ has als o included a review of IMPROVE monitor data between the ye ars of 2000 and 2009. This review presented standardized 20% best and worst visibility day comparisons between the baseline and progr ess periods as well as Theil statistical trend anal ys is as an alternative approach for understanding 10 ye ar trends. V isibility a erosol extinction indicates that ammonium nitrate, organic mass, and elemental carbon extinctions are improving within almost all Arizona Class 1 areas. F ine 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

h igh years (2005 and 2007) for ammonium sulfate extinction revealed increasing ammo nium sulfate visibility e xtinction between the baseline and progress periods; however, decreasing trends in ammonium sulfate in previous and more recent ye ars resulted in Theil statistics which either showed no statistically s ignificant visibility extincti on increases or statistically s ignificant visibility

d ecreases 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 s ubmitted RPGs and URPs for 2018. Th ese data indicated that if the current pace of visibility c hange was continued, no site would experience an increase in visibility reduction for the 20% least impaired visibility day s in 2018. S ix monitoring locations are expected to surpass ADEQ’s previousl y submitted RPGs for 2018 for the 20% most impaired days . Furthermore, f our sites, CHIR1, SAWE1, SAGU1, and SIAN1 are projected to surpass the previously acc epted URP s for the 20% most i mpaired day s in 2018. Only two sites are projected to experience visibility degradation for the 20% most impaired day s in 2018 when compared to 2002, these sites are GRCA2 and IKBA1.