Modeled Attainment Test Software User's Manual


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Abt Associates Inc.
4800 Montgomery Lane
Bethesda, MD 20814
www. abtassociates. com
Modeled Attainment
Test Software
User's Manual
Prepared for
Office of Air Quality Planning and
Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC
Brian Timin, Project Manager
May 2007
Prepared by
Abt Associates Inc.

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Table of Contents
Chapter 1 Welcome to MATS, the Modeled Attainment
Test Software	6
1.1	How to Use this Manual	7
1.2	Computer Requirements	8
1.3	Installing MATS	8
1.4	Installing an Updated Version of MATS	10
1.5	Uninstalling MATS,	10
1.6	Contact for Comments and Questions	11
Chapter 2 Terminology & File Types	13
2.1	Common Terms	13
2.1.1	ASR File 		14
2.1.2	BMP File 		14
2.1.3	Class I Area		14
2.1.4	Configuration File		14
2.1.5	CSV File 		14
2.1.6	Deciviews		15
2.1.7	Design Value		15
2.1.8	Domain 		16
2.1.9	Extinction		16
2.1.10	FRM Monitors		16
2.1.11	Gradient Adjustment		16
2.1.12	IMPROVE Monitors		16
2.1.13	Interpolation		16
2.1.14	Inverse Distance Weights		17
2.1.15	Log File 		17
2.1.16	Output Navigator		18
2.1.17	Output File		18
2.1.18	Point Estimate		18
2.1.19	RRF 		18
2.1.20	SANDWICH 		18
2.1.21	Scenario Name		19
2.1.22	SMAT 		19
2.1.23	Spatial Field		20
2.1.24	Spatial Gradient		20
2.1.25	STN Monitors		20
2.1.26	Temporal Adjustment		20
2.1.27	VNA 		20
VNA - Detailed Description	 20
2.2	File Types 	23
Chapter 3 Overview of MATS Components	24
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Contents
3.1	Start 	24
3.1.1	PM Analysis	 26
3.1.2	Ozone Analysis	 26
3.1.3	Visibility Analysis	 30
3.2	Output Navigator	34
3.3	Map View 	38
3.4	Help		39
Chapter 4 PM Analysis: Quick Start Tutorial	41
Chapter 5 PM Analysis: Details	42
Chapter 6 Ozone Analysis: Quick Start Tutorial	43
6.1	Step 1.	Start MATS	43
6.2	Step 2. Desired Output		45
6.3	Step 3. Data Input	45
6.4	Step 4. Filtering and Interpolation	47
6.5	Step 5. RRF & Spatial Gradient	48
6.6	Step 6. Final Check	49
6.7	Step 7. Load & Map Results	52
6.8	Step 8. View & Export Results	60
Chapter 7 Ozone Analysis: Details	66
7.1	Choose Desired Output	66
7.1.1	Scenario Name		67
7.1.2	Point Estimates		69
Baseline Ozone		70
Temporally-Adjust Baseline Ozone		70
7.1.3	Spatial Field		71
Baseline - interpolate monitor data to spatial field		72
Baseline - interpolate gradient-adjusted monitor data to spatial field		72
Forecast - interpolate monitor data to spatial field. Ternporally-adjust ozone
levels 		73
Forecast - interpolate gradient-adjusted monitor data to spatial field.
Temporally-adjust ozone levels		73
7.1.4	Output Variable Description		74
Ozone Monitors - monitor data, temporally adjusted 2015.csv		74
Ozone Monitors - county high monitoring sites, temporally adjusted
2015.csv		75
Spatial Field - interpolated monitor data, temporally adjusted;
gradient-adjusted monitor data, temporally adjusted 2015.csv		76
7.2	Data Input 	77
7.2.1	Monitor Data		78
7.2.2	Model Data		79
EPA Default Model Data		80
7.2.3	Using Model Data		80
Nearby Monitor Calculation - Example 1		82
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7.3	Filtering and Interpolation	83
7.3.1	Choose Ozone Design Values		84
7.3.2	Valid Ozone Monitors		85
Minimum Number Design Values		86
Max Distance from Domain		87
Required Design Values		88
7.3.3	Default Interpolation Method		89
7.4	RRF and Spatial Gradient	91
7.4.1	RRF Setup		92
RRF Calculation - Example 1		93
RRF Calculation - Example 2		95
RRF Calculation - Example 3		98
RRF Calculation - Example 4		100
RRF Calculation - Example 5		101
RRF Calculation Spatial Gradient with Backstop Threshold - Example 6		103
7.4.2	Spatial Gradient Setup		106
Spatial Gradient Calculation - Example 1		106
Spatial Gradient Calculation - Example 2		108
Spatial Gradient Calculation - Example 3		110
7.5	Final Check	112
Chapter 8 Visibility Analysis: Quick Start Tutorial	115
8.1	Step 1. Start MATS	115
8.2	Step 2. Desired Output	117
8.3	Step 3. Data Input	118
8.4	Step 4. Filtering	119
8.5	Step 5. Final_Check	120
8.6	Step 6. Load and Map Results	123
8.7	Step 7. Working with Configuration File	132
Chapter 9 Visibility Analysis: Details	139
9.1	Choose Desired Output	139
9.1.1	Scenario Name		140
9.1.2	Forecast Visibility at Class I Areas		142
Old IMPROVE Equation		144
New IMPROVE Equation		145
Choose Model Grid Cell		147
9.1.3	Output Variable Description		147
Forecasted Visibility Data.csv		148
Forecasted Visibility - all design values.csv		149
Class 1 Area and IMPROVE Monitor Identifiers and Locations.csv		151
Used Model Grid Cells - Base/Future Data.csv		152
9.2	Data Input	152
9.2.1	Monitor Data Input		153
Monitor Data Description (Old Equation)		154
Monitor Data Description (New Equation)		155
Linkage between Monitors & Class I Areas		157
9.2.2	Model Data Input		158
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Contents
Using Model Data for Temporal Adjustment	 160
RRF Calculation - Example with Mean	 162
RRF Calculation - Example with Maximum	 163
9.3	Filtering 	165
9.3.1 Example Valid Visibility Monitors	 166
9.4	Final Check	168
Chapter 10 Output Navigator	171
10.1	Add Output Files to Map	174
10.2	View Files	176
10.2.1	Configuration File	 176
10.2.2	Log File	 178
10.2.3	Output Files	 180
10.3	Extract Files	182
Chapter 11 Map View	186
11.1	Loading Variables	186
11.1.1 Loading with Taskbar	 188
11.2	Plotting a Value	191
11.2.1 Plotting Options	 194
11.3	Zoom Options & Pan View	199
11.4	Standard Layers	201
11.5	Exporting Maps & Data Files	203
11.5.1 Exporting CSV Data File	 206
11.6	Removing Data	207
Chapter 12 Frequently Asked Questions	210
12.1	Error: MATS will not create a folder for extracting files	210
12.2	Where is there a description of output variables?	210
12.3	Why no PM analysis?,	210
Chapter 13 References	211
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Welcome to MATS, the Modeled Attainment Test Software
1 Welcome to MATS, the Modeled Attainment Test
Software
The Modeled Attainment Test Software (MATS) is primarily intended as a tool to
implement the modeled attainment tests for particulate matter (PM2 5) and ozone (03), and
to perform the uniform rate of progress analysis for regional haze (visibility). Detailed
information on the attainment tests can be found in U.S. EPA's modeling guidance,
"Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of the
Air Quality Goals for Ozone, PM2 5, and Regional Haze." The modeling guidance can be
found at http ://www. epa. gov/ttn/scram/guidance sip .htm.
This Chapter provides a brief description of how to use this manual, computer
requirements, steps to install and uninstall MATS, and contact information for comments
and questions:
How to Use this Manual
Computer Requirements
Installing MATS
Uninstalling MATS
Contact for Comments and Questions.
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Welcome to MATS, the Modeled Attainment Test Software
W
Help -
Start | Map View Output Navigator
PM Analysis
Ozone Analysis
Visibility Analysis
Stop
Info
1.1 How to Use this Manual
This manual provides step-by-step instructions on how to use MATS.
New users should start with the Overview of MATS Components chapter, which is very
short, but provides a good overview of the model and how it works. You can then use
tutorial chapters to get started using the model. There are separate tutorials for Particulate
Matter fPM). Ozone, and Visibility. In addition to these relatively simple tutorials, you can
go on to learn more on each subject in the chapters on PM Analysis: Details. Ozone
Analysis: Details, and Visibility Analysis: Details. Use the rest of the manual to answer
any specific questions you may have. There is a chapter on the Output Navigator, which is
the starting point for examining your results. The Map View chapter details how to map
results. Finally, the Frequently Asked Questions chapter reviews and answers some of the
common questions that arise when using MATS.
In sections that provide instructions on navigating the model, the following conventions are
observed: menu items, buttons, and tab and selection box labels are in bold type; prompts
and messages are enclosed in quotation marks; and drop-down menu items, options to click
or check, and items that need to be filled in or selected by the user are italicized. Common
terms are defined in the Terminology and File Types chapter. The Reference section
provides citations for documents relevant to MATS.
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Welcome to MATS, the Modeled Attainment Test Software
1.2	Computer Requirements
MATS requires a Windows platform, and can be used on machines running Windows2000,
as well as more recent versions of Windows. In particular, MATS requires a computer
with:
•	Windows 2000 or greater.
•	512 megabytes of RAM or greater.
•	Intel® or compatible processor, Pentium 166 MHz or higher. 1 GHz processor or greater
recommended for optimum performance.
•	A CD-ROM drive for CD based installation. Alternatively, a high speed internet
connection can be used to download the installer. The installer package can be found at:
•	At least 3 GB free space recommended.
1.3	Installing MATS
Load the installation file (MATS_Setup.exe) onto your hard drive. Double-click the file.
This will initiate the installation process, which takes about five to ten minutes, depending
on the speed of your computer.
i#MATS - InstallShield Wizard
Welcome to the InstallShield Wizard for
MATS
The InstallShield(R) Wizard will allow you to modify, repair, or
remove MATS. To continue, click Next
< Back
Next >
Cancel
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Welcome to MATS, the Modeled Attainment Test Software
Click the Next button. This will bring up the MATS - InstallShield Wizard.
i# MATS - InstallShield Wizard
Ready to Install the Program
The wizard is ready to begin installation.
If you want to review or change any of your installation settings, click Back
the wizard.
Current Settings:
Setup Type:
Typical
Destination Folder:
C:\Program Files\Abt Assoc iates\Jv1ATS\
User Information:
Name: Don
Company: Don
InstallShield
Click Cancel to exit
< Back
Install
Cancel
Click the Install button. After the installation of MATS, a final window will appear to
complete the process.
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Welcome to MATS, the Modeled Attainment Test Software
i$MATS - InstallShield Wizard
InstallShield Wizard Completed
The InstallShield Wizard has successfully installed MATS. Click
Finish to exit the wizard.
Q Launch the program
Finish
Click the Finish button.
Note that some problems have occurred in the past, when trying to install MATS from a
network drive. If this problem occurs, move the MATS_ Setup.exe file to your local hard
drive.
1.4	Installing an Updated Version of MATS
If a previous version of MATS is already installed on your computer, you will need to
uninstall the old version using the Windows Control Panel prior to installing the new
version (see next section). Note that uninstalling M ATS will not delete your MATS output
files.
1.5	Uninstalling MATS
To uninstall MATS, go to Control Panel, Add/Remove Programs and highlight MATS.
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Welcome to MATS, the Modeled Attainment Test Software
&Add or Remove Programs
Change or
Remove
Programs
&
Add New
Programs
&
Add/Remove
Windows
Components

Set Program
Access and
Defaults
Currently installed programs:
~ Show updates Sort by: Name
'i Tunes
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47.74MB

# Java 2 Runtime Environment, SE vl.4.2_03
Size
107.00MB
If MATS
Size 1.622.00MB

Click here for suDDort information.
Used
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To change this program or remove it from your computer, click Change or
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~ McAfee VirusScan



iSJ Microsoft .NET Framework 1.1



1§I Microsoft ,NET Framework 1,1 Hotfix (KB886903)


Microsoft Compression Client Pack 1.0 for Windows XP


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0 MSXML 4.0 SP2 (KB927978)
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Click the Remove button. This will bring up a window asking you to confirm the removal.
Add or Remove Programs

. J Are you sure you want to remove MATS from your computer?
Note that removing the software will not remove the files that you have generated with
MATS. For example, the Output folder will remain with any files (e.g., *.ASR files) that
you have created.
1.6 Contact for Comments and Questions
For comments and questions, please contact Brian Timin at the United States
Environmental Protection Agency.
Address: C339-01, USEPA Mailroom, Research Triangle Park, NC 27711
Email: timin.brian@epa.gov
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Telephone: 919-541-1850
Welcome to MATS, the Modeled Attainment Test Software
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Terminology & File Types
2 Terminology & File Types
The first section of this chapter explains Common Terms used in this user's manual and in
the model, and references, where possible, other sections in this manual to find more
detailed information. The second section describes the File Types used in MATS.
2.1 Common Terms
The following include terms commonly used in MATS:
ASR File
BMP File
Class I Area
Configuration File
CSV File
Deciviews
Design Value
Domain
FRM Monitors
Gradient Adjustment
IMPROVE Monitors
Interpolation
Inverse Distance Weights
Log File
Output Navigator
Output File
Point Estimate
RRF
SANDWICH
Scenario Name
SMAT
Spatial Field
Spatial Gradient
STN Monitors
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Terminology & File Types
Temporal Adjustment
VNA
2.1.1 ASR File
An ASR File contains three types of results from a MATS run: Log File. Configuration File
. and Output Files. The extension .ASR is used after the Scenario Name. The data in an.
ASR file can viewed and extracted using the Output Navigator.
2.1.2 BMP File
BMP is a standard file format for computers running the Windows operating system. The
format was developed by Microsoft for sotoring bitmap files in a device-independent
bitmap (DIB) format that will allow Windows to display the bitmap on any type of display
device. The term "device independent" means that the bitmap specifies pixel color in a
form independent of the method used by a display to represent color.*
* See: http://www.prepressure.com/formats/bmp/fileformat.htm.
2.1.3 Class I Area
A Class I Area is defined by the Clean Air Act to include national parks greater than 6,000
acres, wilderness areas and national memorial parks greater than 5,000 acres, and
international parks that existed as of August 1977.* The Regional Haze rule requires
visibility improvements in 156 specific Class I areas. The MATS visibility analysis will
calculate visibility values for these areas.
* See: http://vista.cira.colostate.edu/views/Web/General/Glossarv.aspx.
2.1.4 Configuration File
A Configuration File stores the choices that you have made when using MATS. A useful
feature of a Configuration File is that it is reusable. You can use an existing Configuration
File, make some minor changes to generate a new set of results, without having to
explicitly set each of the choices you made in the previous Configuration. The section on
the Output Navigator provides additional details on accessing and viewing a Configuration
File.
2.1.5 CSV File
Is a comma separated values (CSV) file (*.csv) which can be read using a text editor, or by
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Terminology & File Types
various spreadsheet and database programs, such as Microsoft Excel.
Note: Detailed formatting in .csv files such as leading zeroes and "" cannot be seen in
Excel. To see formatting of MATS input files, open .csv files with a text editor, such as
WordPad.
r	
9 OZONE_ASIP_input.csv - WordPad


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File Edit View
Insert Format Help




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|DesignValue




A
ID, TYPE,
LAT, LONG, POC, DVYEAR, 03, STATE NAME,
_COUNTY_NAME


"010030010",
,30 . 497770,-67 .SO 1309, 1,2001,-9,
"Alabama"
"Baldwin"


"010030010",
,30 .497778,-87 .881309, 1,2002, 82,
"Alabama"
"Baldwin"


"010030010",
,30 . 497778,-87 .881389, 1,2003, 76,
"Alabama"
"Baldwin"


"010030010",
,30 . 497778,-87 .881389, 1,2004, 76,
"Alabama"
"Baldwin"


"010270001",
,33 .281111,-85 .802222, 1,2001, 84,
"Alabama"
"Clay"


"010270001",
,33 .281111,-85 .802222, 1,2002, 82,
"Alabama"
"Clay"


"010270001",
,33 .281111,-85 .802222, 1,2003, 80,
"Alabama"
"Clay"


"010270001",
,33 .281111,-85 .802222, 1,2004, 76,
"Alabama"
"Clay"


"010510001",
,32 .498333,-86.136667, 1,2001, 79,
"Alabama"
"Elmore"


"010510001",
,32 .498333,-86.136667, 1,2002, 80,
"Alabama"
"Elmore"


"010510001",
,32 .498333,-86.136667, 1,2003, 76,
"Alabama"
"Elmore"


"010510001",
,32 .498333,-86.136667, 1,2004, 74,
"Alabama"
"Elmore"


"010550011",
,33 .9039,-86.0539, 1,2001,-9,"Alabama","Etowah"


"010550011",
,33 .9039,-86.0539, 1,2002,-9,"Alabama","Etowah"


"010550011",
,33 .9039,-86.0539, 1,2003,-9,"Alabama","Etowah"


"010550011",
,33 .9039,-86.0539, 1,2004,75,"Alabama","Etowah"


"010730023",
,33 .553056,-86.815, 1,2001,-9,"Alabama","Jefferson"


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2.1.6	Deciviews
A third measure of visibility is the deciview index, which EPA selected as the standard
metric for tracking progress in EPA's regional haze program, largely because it provides a
linear scale for perceived visual changes over a wide range of conditions. 1 On a particle-
free, pristine day, the deciview index has a value of zero (SVR=391 km). On a relatively
clear day in the Great Smoky Mountains the deciview index might be about 16 (SVR=79
km) and on a relatively hazy day the deciview index might be about 31 (SVR=201 km).
For each 10 percent increase in light-extinction, the deciview index goes up by one. So,
higher deciview values mean worse visibility. Under many scenic conditions, a change of
one deciview is considered to be just perceptible by the average person.
2.1.7	Design Value
The monitored reading used by EPA to determine an area's air quality status; e.g., for
ozone, the 3 year average of the annual fourth highest reading measured at each monitor is
the design value. Ozone design values are calculated in accordance with 40 CFR Part
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Terminology & File Types
50.10, and Appendix I to Part 50. The calculation of annual and 24-hour average PM2 5
design values can be found in 40 CFR Part 50, Appendix N.
2.1.8 Domain
A Domain (or Model Domain) refers to the coverage of an air quality model, or the area of
the country for which there are model values. MATS calculates design values and/or
spatial fields for an area encompassed by the coordinates given within a MATS input file.
2.1.9 Extinction
Light extinction is the sum of the light scattering and light absorption by particles and gases
in the atmosphere, and is measured in inverse megameters (Mm-1), relating how much light
is extinguished per megameter. Higher extinction values mean worse visibility.
2.1.10 FRM Monitors
Federal Reference Method (FRM) monitors used to determine attainment or
nonattainment.The term "FRM" is frequently used to describe the network of PM2 5 mass
monitors.
2.1.11 Gradient Adjustment
A gradient adjustment is used to scale, or adjust, monitor data when using monitor data to
estimate air pollution levels in unmonitored areas. It is calculated as the ratio of the model
value in the unmonitored area to the value in the monitored area. In MATS, gradient
adjustments can be used for PM Analyses and Ozone Analyses.
2.1.12 IMPROVE Monitors
Interagency Monitoring of PROtected Visual Environments (IMPROVE) is a collaborative
monitoring program established in the mid-1980s. IMPROVE objectives are to provide
data needed to assess the impacts of new emission sources, identify existing man-made
visibility impairment, and assess progress toward the national visibility goals that define
protection of the 156 Class I areas.*
* See: http://vista.cira.colostate.edu/views/Web/General/Glossarv.aspx.
2.1.13 Interpolation
Interpolation is the process of estimating the air quality level in an unmonitored area by
using one or more nearby air quality monitors. The technique used in MATS is called
Voronoi Neighbor Averaging (VNA).
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2.1.14	Inverse Distance Weights
The weight given to any particular monitor is inversely proportional to its distance from the
point of interest.
Example, Inverse Distance Weights
Assume there are four monitors (A, B, C, and D) that are a varying distance from a point E.
Assume the distances are 10, 15, 15, and 20 kilometers respectively. The weights will be as
follows:
WeightA= 10/(10+15+15+20)= 10/60 = 0.17
WeightB = 15 / (10+15+15+20) = 0.25
Weightc = 15 / (10+15+15+20) = 0.25
WeightD = 20 / (10+15+15+20) = 0.33
Example, Inverse Distance Squared Weights
Assume there are four monitors (A, B, C, and D) that are a varying distance from a point E.
Assume the distances are 10, 15, 15, and 20 kilometers respectively. The weights will be as
follows:
WeightA= 10/(102+152+152+202)= 100/950 = 0.11
WeightB = 15 / (102+152+152+202) = 0.24
Weightc = 15 / (102+152+152+202) = 0.24
WeightD = 20 / (102+152+152+202) = 0.42
2.1.15	Log File
A Log File provides information on a variety of technical aspects regarding how a results
file (*. ASR) was created. This includes the version of MATS, the date and time the *.ASR
file was created.
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Start Map View Output Navigator | Run Log
Close
»» Start MATS.exe v 1.1.0.4 2007-02-25 22:1 4:00
Starting iteration 0
Loading Default membership file...0.086 s.
Loading wind profiles file...0.026 s.
Loading Ozone monitor data...0.228 s.
WARNING: Base year of modeling changed to agree with Ozone data
Loading Baseline Model Data...87.966 s.
Calculating metricfor Gradients...7.610 s.
Interpolating to spatial fields...13.510 s.
Reading future modeling file: C:\Program Files\Abt Associates\MATS\SampleData\ozone_model_data_2015.csv...91.075 s.
Running future year estimates at monitors...1.040 s.
Spatial interpolations to model cells...53.1 45 s.
Total execution time: 262.823 s.
«« Stop MATS.exe 2007-02-25 22:18:24
2.1.16 Output Navigator
The Output Navigator allows you to load results files that you have previously created.
You can then view these data in maps and in tables, or export the data to text files, which
you can then load into a program such as Excel. Additional details are in the Output
Navigator Chapter.
2.1.17 Output File
An Output File is one of the file types within a *.ASR results file. The types of Output
Files available depend on the type of analysis (PM, Ozone, or Visibility) and the output
choices that you have specified in the Configuration File.
2.1.18 Point Estimate
A calculation within MATS that is performed at (or near) the location of ambient air
monitors. The output files will contain base and/or future year results at each valid
monitoring location.
2.1.19 RRF
The relative response factor is the ratio of the future year modeled concentration predicted
near a monitor (averaged over multiple days) to the base year modeled concentration
predicted near the monitor (averaged over the same days).
2.1.20 SANDWICH
The SANDWICH process is used to adjust STN and IMPROVE monitor data so that it is
consistent with FRM monitor data. SANDWICH stands for Sulfates, Adjusted Nitrates, D
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erived Water. Inferred Carbonaceous mass, and estimated aerosol acidity (H+).*
* For more details, see: Frank, N., 2006: "Retained Nitrate, Hydrated Sulfates, and Carbonaceous
Mass in Federal Reference Method Fine Particulate Matter for Six Eastern U.S. Cities" J. Air Waste
Mange. Assoc., 56, 500-511.
2.1.21 Scenario Name
The name given to a set of results generated by MATS. The Scenario Name is used in
several ways: (1) the results file (*. ASR) uses the Scenario Name; (2) an output folder,
containing results extracted from a *.ASR file, is given the Scenario Name; and (3) the
Output File names begin with the Scenario Name.
The Scenario Name is specified when choosing the desired output, such as in the case of an
ozone analysis.
Desired output
Data Input
Filtering/Interpolation
RRF/Spatial Gradient
Final Check
Choose Desired Output
Scenario Name: |j~
Point Estimates
Forecast
p" Temporally^adjust ozone levels at monitors.
Spatial Field
Baseline
|~ Interpolate monitor data to spatial field
|~ Interpolate gradient-adjusted monitor data to spatial field.
Forecast
|~ Interpolate monitor data to spatial field. Temporally adjust ozone levels.
Interpolate gradient-adjusted monitor data to spatial field. Temporally adjust.
< Back
Next >


Cancel
2.1.22 SMAT
The Speciated Modeled Attainment Test (SMAT) is used to forecast PM2 5 values. The
main steps are as follows:
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Terminology & File Types
• Derive quarterly mean concentrations for each component of PM2 5 by multiplying FRM
PM2 5 by fractional composition of each species;
• Calculate a model-derived relative response factor fRRF) for each species;
• Multiply each RRF times each ambient PM2 5 component (for each quarter) to get the
future concentrations;
• Sum the future quarterly average components; and
• Average the four mean quarterly future PM2 5 concentrations.
2.1.23 Spatial Field
A Spatial Field refers to air pollution estimates made at the center of each grid cell in a
specified modeling domain. For example, MATS might calculate ozone design values for
each grid cell in the modeling domain. Several types of Spatial Fields can be calculated for
ozone and PM. (See the sections for ozone and PM for additional details.)
2.1.24 Spatial Gradient
A Spatial Gradient is the ratio of mean model values at an unmonitored location over the
mean model values at a monitor. Spatial Gradients can be used in the calculation of Spatial
Fields for ozone and PM. (See the sections for ozone and PM for additional details.)
2.1.25 STN Monitors
In meeting the requirements to monitor and gather data on the chemical makeup of fine
particles, EPA established a Speciation Trends Network (STN). These STN monitors were
placed at various national air monitoring stations (NAMS) and State and local air
monitoring stations (SLAMS) across the Nation.
2.1.26 Temporal Adjustment
A temporal adjustment refers to multiplying ambient monitor data with a model derived
relative response factor (RRF) in order to generate an estimated future year concentration.
2.1.27 VNA
Voronoi Neighbor Averaging (VNA) is an algorithm used by MATS to interpolate air
quality monitoring data to an unmonitored location. MATS first identifies the set of
monitors that best "surround" the center of the population grid cell, and then takes an
inverse-distance weighted average of the monitoring values.
2.1.27.1 VNA - Detailed Description
Voronoi Neighbor Averaging (VNA) algorithm uses monitor data directly or in
combination with modeling data. MATS first identifies the set of monitors that best
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"surround" the point of interest.

*
*
*
#
*
*
*
*
# = Center Grid-Cell "E"
*
= Air Pollution Monitor
In particular, MATS identifies the nearest monitors, or "neighbors," by drawing a polygon,
or " Voronoi" cell, around the center of the point of interest. The polygons have the special
property that the boundaries are the same distance from the two closest points.
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#	= Center Grid-Cell "E"
*
= Air Pollution Monitor
MATS chooses those monitors that share a boundary with the center of grid-cell "E."
These are the nearest neighbors, we use these monitors to estimate the air pollution level
for this grid-cell.
15 miles
* 15 miles
20 miles
# = Center Grid-Cell "E"
"k
= Air Pollution Monitor
To estimate the air pollution level in each grid-cell, MATS calculates an inverse-distance
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weighted average of the monitor values. The further the monitor is from the grid cell, the
smaller the weight. In the figure below, the weight for the monitor 10 miles from the center
of grid-cell E is calculated as follows:
l
20 +16 + 14.
The weights for the other monitors are calculated in a similar fashion. MATS then
calculates an inverse-distance weighted average for grid-cell E as follows:
Estimate = 0.35*80 ppb + 0.24*90 ppb+ 0.24*60 ppb + 0.18*100 ppb = 81.2 ppb
A
B
Monitor:
1995 90 ppb ^
15 miles
C
*
D
Monitor: *
1995 80 ppb
10 miles
/E
. # .
F
/
Monitor:
1995 60 ppb
15 miles
G
*
/ "
"k
Monitor:
1995 100 ppb
20 miles
I
*
# = Center Grid-Cell "E"
*
= Air Pollution Monitor
2.2 File Types
The primary results file generated by MATS has a ASR extension, which is specific to
MATS. To view the results you have generated in other programs (e.g., MS Excel), you
can export .CSV files using the Output Navigator.
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3 Overview of MATS Components
Upon starting MATS for the first time, you will see the following main window.

Help -
Start | Map View Output Navigator
PM Analysis
Ozone Analysis
Visibility Analysis
Stop
Info
There are three main tabs: Start, Map View, and Output Navigator. The Start tab allows
vou to calculate Particulate Matter fPM). Ozone and Visibility levels. The Map View tab
allows to map your results. The Output Navigator tab allows you to view your results
either as tables or maps. Finally, the Help menu at the top of the main window provides
explanations and examples of all of the functionality in MATS.
This Chapter gives a brief description of each of these items. All of these topics are
covered in greater detail in subsequent chapters of this manual.
3.1 Start
The Start tab gives you the choice to analyze Particulate Matter fPM). Ozone or Visibility.
To begin, click on one of the three buttons.
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k
Help -
Start | Map View Output Navigator
PM Analysis
Ozone Analysis
Visibility Analysis
Stop
Info
One of the key features of MATS is the Configuration. This is a reusable file that stores the
choices that you have made when using MATS. You can use an existing Configuration
File, make some minor changes to generate a new set of results, without having to
explicitly set each of the choices you made in the previous Configuration.
When you click on one of the analysis buttons, you will be asked whether you want to
create a new Configuration, or whether you want to use an existing Configuration.
Configuration Management








<• Create New Configuration!
Go
C Open Existing Configuration
Cancel




Make your choice and then click Go. MATS will then take you through a series of
windows specifying the options available for each analysis.
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3.1.1 PM Analysis
< To be added. >
3.1.2 Ozone Analysis
MATS can forecast ozone design values at monitor locations — these forecasts are referred
to as Point Estimates. MATS can also use a variety of approaches to calculate design
values for a Spatial Field. The Choose Desired Output window lets you specify the type
of calculation(s) that you would like MATS to perform. These different assumptions are
discussed in the Choose Desired Output section of the Ozone Analysis: Details chapter.
Desired output
Data Input
Filtering/Interpolation
RRF/Spatial Gradient
Final Check
Choose Desired Output
Scenario Name : [Example 03
Point Estimates
Forecast
p" Temporally-adjust ozone levels at monitors.
Spatial Field
Baseline
F Interpolate monitor data to spatial field
p" Interpolate gradient-adjusted monitor data to spatial field.
Forecast
p" Interpolate monitor data to spatial field. Temporally adjust ozone levels,
p" ^Interpolate gradient-adjusted monitor data to spatial field. Temporally adjust.
Next >
Cancel
The Data Input window lets you specify the data files that you want to use. MATS comes
populated with default data sets, but you can use your own data if you choose. The format
for the data is in the Data Input section of the Ozone Analysis: Details chapter.
The Data Input window also lets you choose how to use model data when calculating a
temporal adjustment at a monitor. This is discussed in detail in the Using Model Data
section of the Ozone Analysis: Details chapter.
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0
¦ Desired output
Data Input
Filtering/Interpolation
Data Input
¦ RRF/Spatial Gradient
¦ Final Check
Monitor Data
Ozone Data SarnpleData\OZONE_ASIP_input_97-05.csv — |

Model Data
Baseline File \SampleData\ozone_model_data_2001 .csv — |
Forecast File |\SampleData\ozone_model_data_2015.csv — |
Using Model Data
Temporal adjustment at monitor | 3/3 ' | |tv1aximuin ' |




< Back Next > Cancel
The Filtering and Interpolation window lets you specify the years of data that you want to
use, any restrictions you want to apply when choosing valid monitors (i.e., monitors that
MATS use in its calculations), and options on the interpolation method. This is discussed
in detail in the Filtering and Interpolation section of the Ozone Analysis: Details chapter.
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X
IfiSI Desired output
¦ Data Input
Filtering/Interpolation
Filtering and Interpolation
¦ RRF/Spatial Gradient
Final Check
Choose Ozone Design Values
StartYear 2000-2002 » End Year 2003-2005

Valid Ozone Monitors

Minimum Number of design values 1
Max Distance from Domain [km] 25
Required Design Values None selected -•

Default Interpolation Method

[inverse Distance Weights '
I- check to set a maximum interpolation distance [km]



Next >

Cancel



The RRF and Spatial Gradient window lets you set parameters used in the calculation of
relative response factors fRRF) and spatial gradients. This is discussed in detail in the RRF
and Spatial Gradient section of the Ozone Analysis: Details chapter.
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H5| Desired output
¦	Data Input
¦	Filtering/Interpolation
RRF/Spatial Gradient
EH Final Check
x
RRF and Spatial Gradient
RRF Setup:
Initial threshold value (ppb)
Minimum number of days in baseline at or above threshold
Minimum allowable threshold value (ppb)
Min number of days at or above minimum allowable threshold
| Enable Backstop minimum threshold for spatial fields
Backstop minimum threshold for spatial fields
Spatial Gradient Setup:
Start Value
End Value
85
10
70
< Back
Next >
Cancel
The last step is to verify the inputs to the analysis.
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Desired output
Data Input
Filtering/Interpolation
RRF/Spatial Gradient
Final Check
Final Check
Verify inputs
Press here to verify your selections...
Checking...
Check OK. Press the finish button to continue..
< Back Finish	Cancel
3.1.3 Visibility Analysis
MATS can forecast visibility in Class I Areas - these forecasts are referred to as Point
Estimates. In addition to specifying the Scenario Name, you can choose the version of the
IMPROVE Algorithm that you want to use. You can also choose whether to use model
data at the monitor linked to each Class I Area, or whether to use model data closest to the
Class I Area centroid. These different assumption are discussed in the Desired Output
section of the Visibility Analysis: Details chapter.
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Choose Desired Outpi
Data Input
Filtering
Final Check
Choose Desired Output
Point Estimates
Scenario Name : Example Visibility
Forecast
W Temporally-adjust visibility levels at Class 1 Areas
IMPROVE Algorithm
(• use old version
use new version
• Use model grid cells at monitor
Use model grid cells at Class 1 area centroid
Next >
Cancel
The Data Input window lets you specify the data files that you want to use. MATS comes
populated with default input data, but you can use your own data if you choose. The format
for the data is in the Data Input section of the Visibility Analysis: Details chapter.
The Data Input window also lets you choose how to use model data when calculating a
temporal adjustment at a monitor. This is discussed in detail in the Using Model Data
section of the Visibility Analysis: Details chapter.
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Choose Desired Output
Data Input
Filtering
Final Check
Data Input
Monitor Data
IMPROVE Monitor Data - Old Algorithm |TS\SampleData\visibility_monitor_data.csv — |
IMPROVE Monitor Data- New Algorithm |C:\Program Files\Abt Associates\MATS\Sa ~
Model Data
Baseline File
Forecast File
Using Model Data
Temporal adjustment at monitor
|ATS\SampleData\visibility_model_2001 .csv 3D
|ATS\SampleData\visibility_model_2015. csv 3D
1x1 E Mean I
< Back
Next >
Cancel
The Filtering window lets you specify the years of data that you want to use, and any
restrictions you want to apply when choosing valid monitors (i.e., monitors that MATS use
in its calculations). This is discussed in detail in the Filtering section of the Visibility
Analysis: Details chapter.
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¦	Choose Desired Output
¦ Data Input
Filtering
¦	Final Check
0
Filtering
Choose Visibility Data Years
Start Monitor Year End Monitor Year Base Model Year
12000	1200*1
2001 I
Valid Visibility Monitors
Minimum years required for a valid monitor |3
Maximum Distance from Domain [km] 2000
< Back
Next >
Cancel
The last step is to verify the inputs to the analysis.
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Choose Desired Output
Data Input
Filtering
Final Check
Final Check
Verify inputs
Press here to verify your selections...
Checking...
Check OK. Press the finish button to continue..
< Back
Finish
Cancel
3.2 Output Navigator
The Output Navigator allows you to load results files (i.e., ASR files) that you have
previously created in MATS. You can view these data in maps and in tables, or export the
data to text files that you can then work with in a program such as Excel.
To start, just click on the Output Navigator tab. Then click on the Load button to choose
the file that you want to examine. You can click the Extract All button, and MATS will
create a folder with all of the files that MATS has generated. (A default name for the folder
is the Scenario Name vou have chosen.)
Extracting All..
Enter Directory Name
Cancel
The files generated by MATS are of two types: (1) Configuration and Log files; and (2)
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Output files containing the results of the MATS calculations.
* C:\Program Files\Abt Associates\MATS\output\Example 03

1 File Edit View Favorites Tools
Help


©Back
' v ,- 0 ) Search
|g? Folders
nn~
1 Address Ilea C:\Program Files\Abt Associates\MATS\output\Example 03
v Bgo
Folders

x Name

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® Documents and Settings
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a Q testing
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Q zNew 5-8-07 witt
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A mConfiguration,cfg
^Example 03 - Ozone Monitors — county high monitoring sites, temporally adjusted 2...
^Example 03 - Ozone Monitors — monitor data, temporally adjusted 2015.csv
^Example 03 - Spatial Field — interpolated monitor data, temporally adjusted; gradie...
0Log File.log
3 KB C
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2KB 1
\
<

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Another option is to right-click on a particular file, and then you can choose whether to use
data to Add to Map, View, or Extract.
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i*
MATS
Help
Start Map View | Output Navigator I
Load
Extract All
Name
Configuration/Log Files
Configuration
Log File
Output Files
Highlight file of interest and right-click to view options to Map, View, and Extract the data.
	[Type
Configuration
Run Log
Example 03 - Ozone Monitors - monitor data. temporally adjuste
Example 03 - Ozone Monitors - county high monitoring sites, temf _
Example 03 - Spatial Field - interpolated monitor data. temporally I
Add To Map
Extract
Monitor Network
Monitor Network
ited monitor... Monitor Network
2kb
1 kb
54kb
3588kb
The View option lets you examine the data and then to export it to a CSV file, which you
can then load into another program such as Microsoft Excel.
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Help '
Start Map View Output Navigator | Monitor Network Data |
0030010
0270001
0331002
0510001
0550011
0730023
0731003
nnc
Refresh ~| Select location and press refresh to see data.... |i ShowAlH]
3
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0
33.281111
34.760556
33.9039
33.553056
-87.650556
-86.0539
-86.815
33.485556
-i 1 -i
-86.915
O-J n n f? 11
Select Quantities that must be >= 0
b_o3_dv
HI f_o3_dv
reference eel I
~	rrf
ppb
~	days
Export Export this data to CSV
| Data |
d 0
date
b_o3_dv ' |f_o3_dv ~ |referenc<0|rrf ~ | ppb ~ |days w
010030010
2005
77.7 68.6
95023 0.883
85.0
11.0
010270001
2005
77.7 61A
108051 0.791
71.0
11.0
010331002
2005
71.0 54.2
92063 0.764
71.0
11.0
010510001
2005
75.0 61.8
106043 0.825
70.0
9.00
010550011
2005
73.0 56.5
105056 0.774
73.0
10.0
010730023
2005
75.7 59.2
100052 0.783
81.0
11.0
010731003
2005
78.0 62.4
99052 0.801
81.0
11.0
010731005
2005
79.2 60.5
99050 0.764
80.0
10.0
B
=top
Info
Choosing the Extract option will allow you to immediately export the data to a CSV file.
The default file name for the CSV file is the same one that you see in the Output Navigator
window {e.g., Example 03 - Ozone Monitors — monitor data.csv).
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Extracting. Enter output file name
Save in: £} Example 03
lib
My Recent
Documents
U
Desktop
B
My Documents
•5'
My Computer
^8
My Network
Places
"Z1
IS & EK
File name:	[Example 03 - Ozone Monitors - monitor data, tem
Save as type: | CSV files f.csv)	~ |
Save
Cancel
Choosing the Add to Map option allows you to create a map of your results.
3.3 Map View
The Map View allows you to perform a variety of mapping tasks. You can zoom in to a
particular location; choose particular colors to map your data, export the maps you have
created to BMP files, among other things. These various options are discussed in detail in
the Map View chapter.
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| MATS
Help "
Start Map View Output Navigator

I
V
0 ®

Tutorial 03 - Ozone Monitors —
0
•
b_o3_dv-7 to 74
0
•
b_o3_dv 74 to 79
0
0
b_o3_dv 79 to 83.5
0
•
b_o3_dv 83.5 to 87.7
0
o
b_o3_dv 87.7 to 102.7
3.4 Help
The Help dropdown menu has the User Manual for MATS and version information.
Long: -111227***. Lat: 49.03484
Extent: Min(-11 **H42,2B.212) Max(-52.668,39.487)
Standard Layers T
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Stop
Info
V MATS
Help
User Manual
About MATS
Navigator
PM Analysis
Ozone Analysis
Visibility Analysis
Loading monitor data
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PM Analysis: Quick Start Tutorial
4 PM Analysis: Quick Start Tutorial
< To be added. >
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PM Analysis: Details
5 PM Analysis: Details
< To be added. >
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Ozone Analysis: Quick Start Tutorial
6 Ozone Analysis: Quick Start Tutorial
In this tutorial you will forecast ozone design values at monitors in the Eastern United
States. The steps in this analysis are as follows:
•	Step 1. Start MATS. Start the MATS program and choose to do an Ozone analysis.
•	Step 2. Desired Output. Choose the output to generate. In this example, you will
forecast ozone levels at monitor locations.
•	Step 3. Data Input. Choose the data files for input to MATS.
•	Step 4. Filtering & Interpolation. Choose the particular years of data and monitors to use
in this analysis.
•	Step 5. RRF & Spatial Gradient. Specify how to generate the relative response factors
(RRFs) used in the forecasts.
•	Step 6. Final Check. Verify the choices you have made.
•	Step 7. Load & Map Results. Load your results and prepare maps of your forecasts.
•	Step 8. View & Export Results. Examine the data in a table format and export these
data.
Each step is explained in detail below.
6.1 Step 1. Start MATS
Double-click on the MATS icon on your desktop, and the following window will appear:
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j" MATS
Help -
| Start | Map View Output Navigator


PM Analysis
Ozone Analysis


Visibility Analysis

Loading monitor data		 Stop
Click the Ozone Analysis button on the main MATS window. This will bring up the
Configuration Management window.
Configuration Management








<• Create New Configuration!
Go
C Open Existing Configuration
Cancel




A Configuration allows you to keep track of the choices that you make when using MATS.
For example, after generating results in MATS, you can go back, change one of your
choices, rerun your analysis, and then see the impact of this change without having to enter
in all of your other choices. For this example, we will start with a New Configuration.
Choose Create New Configuration and click the Go button. This will bring up the
Choose Desired Output window.
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6.2 Step 2. Desired Output.
The Choose Desired Output window allows you to choose the output that you would like
to generate. MATS allows you to forecast Point Estimates (at ambient monitors) or to
generate a Spatial Field of either Baseline values or Forecast values.
In the Scenario Name box type " Tutorial 03" - this will be used to keep track of where
your results are stored and the variable names used in your results files. Leave the box
checked next to Temporallv-adiust ozone levels at monitors. MATS will create forecasts
for each monitor in the monitor file.
Desired output
Data Input
Filtering/Interpolation
RRF/Spatial Gradient
Final Check
Choose Desired Output
Scenario Name : |Tutorial 03
Point Estimates
Forecast
p" Temporally-adjust ozone levels at monitors.
Spatial Field
Baseline
Interpolate monitor data to spatial field
Interpolate gradient-adjusted monitor data to spatial field.
Forecast
Interpolate monitor data to spatial field. Temporally adjust ozone levels.
Interpolate gradient-adjusted monitor data to spatial field. Temporally adjust.
< Back
Next >
Cancel
When your window looks like the window above, click Next. This will bring you to the
Data Input window.
6.3 Step 3. Data Input
The Data Input window allows you to choose the monitor data and the model data that you
want to use. As discussed in more detail in the following chapter (see RRF Setup). MATS
calculates the ratio of the base and future year model data to calculate a relative response
factor (RRF). MATS then multiplies the design value from the monitor data with the RRF
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to calculate a future-year design value.
MATS currently comes loaded with ozone design values for the period from 1997-2005
(1997-1999, 1998-2000, 1999-2001, 2000-2002, 2001-2003, 2002-2004, and 2003-2005);
and it comes loaded with example ozone model data for 2001 and 2015. These are the files
needed to calculate the Point Estimates and Spatial Fields listed in the Desired Output
window.
Use the default settings in the Data Input window. The window should look like the
following:
Desired output
Data Input
Filtering/Interpolation
RRF/Spatial Gradient
Final Check
Data Input
Monitor Data
Ozone Data
MATS\SampleData\QZONE_ASIP_input.csv — |
Model Data
Baseline File
Forecast File
Using Model Data
Temporal adjustment at monitor
\SampleData\ozone_model_data_2001 .csv 3D
|\SampleData\ozone_rnodel_data_2015. csv 3D
3/3 Maximum 3]
< Back
Next >
Cancel
Note that MATS gives you the option to use model data in different ways when calculating
forecasts at each monitor. The user can choose to use the model results from the single grid
cell that contains the monitor or select a grid cell array of 3x3, 5x5, or 7x7 model cells
around each monitor. The example model output dataset contained in MATS is at 12km
resolution. Therefore, for this example, a 3x3 grid cell array should be used (see section
3.2 of the modeling guidance). The default for ozone analysis is to choose the maximum
value each day in the array for the calculation. This is described in more detail in the Using
Model Data section of the Ozone Analysis: Details chapter.
When your window looks like the window above, click Next. This will bring you to the
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Filtering and Interpolation window.
6.4 Step 4. Filtering and Interpolation
The Filtering and Interpolation window has several functions. These include identifying
the years of monitor data that you want to use, choosing the particular monitors in these
data that you want in your analysis, and (when calculating spatial fields) specifying the
interpolation method. Use the default settings pictured in the screenshot below.
• Choose Ozone Design Values. Choose the years of design values that you want to use.
The default is to use a 5 year period (3 design values) that is centered about the base
emissions year. The default in MATS assumes an emissions year of 2002. Therefore, the
design value would be based on data from 2000-2002 up through a design value based on
data from 2002-2004. (That is, the Start Year is 2000-2002 and the End Year is
2002-2004.)
• Valid Ozone Monitors. Identify "valid" monitors — that is, those monitors that you want
to include in the analysis. The defaults are that monitors should have at least one valid
design value period; and, are within 25 kilometers of a model grid cell. You can also
specify that a monitor must have a particular design value (e.g., 2000-2002) to be valid,
however the default is to require none in particular.
• Default Interpolation Method. Choose the interpolation method — that is, the method to
combine the design values from different monitors into a single estimated design value.
This option is only used when generating estimates for a Spatial Field. Since we are only
generating Point Estimates, this set of options is not active.
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H5| Desired output
¦ Data Input
Filtering/Interpolation
¦ RRF/Spatial Gradient
EH Final Check
x
Filtering and Interpolation
Choose Ozone Design Values
Start Year
I End Year 2002-2004 I
2000-2002
Valid Ozone Monitors
Minimum Number of design values 1
Max Distance from Domain [km] |25
Required Design Values
None selected
Default Interpolation Method
Inverse Distance Weights
| check to set a maximum interpolation distance [km]
< Back
Next >
When your window looks like the window above, click Next. This will bring you to the
RRF & Spatial Gradient window, where you can set parameters for the calculation of RRFs
and spatial gradients.
6.5 Step 5. RRF & Spatial Gradient
The RRF and Spatial Gradient window has two sets of options.
• The RRF Setup uses threshold values in the model data to identify the days to be used in
the calculation of relative response factors (RRFsY The details of this process are
somewhat involved and are described in detail in the next chapter. (See: the example
calculations.) A brief summary is the following: The default threshold is set to 85 ppb.*
If there are fewer than 10 model days at or above 85 ppb in the baseline scenario, then
MATS will lower the threshold in increments of 1 ppb, until there are at least 10 days at
or above this new, lower threshold. This process is continued, if needed, until a threshold
of 70 ppb is reached. By default, this is the lowest allowable threshold. If there are fewer
than 5 days at or above this threshold of 70 ppb, then the monitor site will be dropped.
• The Spatial Gradient Setup identifies the model values that will be used in the
calculation of a Spatial Field. Since we are only generating Point Estimates, this set of
options is not active.
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Desired output
Data Input
Filtering/Interpolation
RRF/Spatial Gradient
Final Check
RRF and Spatial Gradient
RRF Setup:
Initial threshold value (ppb)
Minimum number of days in baseline at or above threshold
Minimum allowable threshold value (ppb)
Min number of days at or above minimum allowable threshold
| Enable Backstop minimum threshold for spatial fields
Backstop minimum threshold for spatial fields
Spatial Gradient Setup:
Start Value
End Value
85
10
70
< Back
Next >
Cancel
When your window looks like the window above, click Next. This will take you to the
Final Check window, where you can verify the choices that you have made.
* The default values in MATS are consistent with the recommended values in the EPA modeling
guidance (see section 14.1.1).
6.6 Step 6. Final Check
The Final Check window verifies the choices that you have made. For example, it makes
sure that the paths specified to each of the files used in your Configuration are valid.
Click on the Press here to verify selections button.
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RSlDesired output
¦	Data Input
¦	Filtering/Interpolation
¦	RRF/Spatial Gradient
Final Check
0
Final Check
Verify inputs
|i Press here to verify your selections... ^
Checking...
Check OK. Press the finish button to continue..
< Back
Finish	Cancel
If you encounter any errors, go back to the choices you have previously made by clicking
on the appropriate part (e.g., Data Input) of the tree in the left panel, and then make any
changes required.
When your window looks like the window above, click Finish.
A temporary, new Running tab will appear (in addition to the Start, Map View and Output
Navigator tabs).
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MATS
Utilities * Help -
ZEM
Start 1 Map View Output Navigator | Running I
| Close i|
Name
| Last Message
Tutorial 03.asr
Loading Ozone monitor data...0.156 s.
When the calculations are complete, a small window indicating the results are Done will
appear. Click OK.
After clicking OK, the Output Navigator tab will be active. (The Running tab will no
longer be seen.)
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'ZBK
MATS
Utilities T
Start Map View Output Navigator
Load
ight file of interest and right-click to view options to Map, View, ana Extract the data
Name
No file loaded

The next step (click here) shows you how to map your results with the Output Navigator.
For more details on mapping and other aspects of the Output Navigator, there is a separate
chapter on the Output Navigator.
6.7 Step 7. Load & Map Results
After generating your results, the next step is to use the Output Navigator to load and map
them.
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Help
Start Map View
Output Navigator
Highlight file of interest and right-click to view options to Map, View, and Ex
Stop Info
Load
No file loaded
Name
Type
Size
Click on the Load button and choose the Tutorial OS.asr file.
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Open MATS Result file
Look in: £3 output
My Recent
Documents
0
Desktop
I
My Documents
8*
My Computer
«
My Network
Places
.J Tutorial 03.asr
File name:
Files of type:
MATS Result File

IS & EK
-
"3
Open
Cancel
-M
Under Configuration/Log Files, you will see two files:
•	Configuration: keeps track of the assumptions that you have made in your analysis.
•	Los File ', provides information on a variety of technical aspects regarding how a results
file (*.ASR) was created.
Under Output Files you will see:
•	Tutorial 03 - Ozone Monitors - monitor data, temporally adjusted 2015: contains
forecasted values and the monitor data used.
•	Tutorial 03 - Ozone Monitors - county high monitoring sites, temporally adjusted 2015:
contains forecasted values and the monitor data used for the monitor with the highest
levels in the county.
Right-click on the file Tutorial 03 - Ozone Monitors - monitor data temporally adjusted
2015. This gives you three options: Add to Map, View, and Extract. Choose the Add to
Map option.
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V MATS
Help
Start Map View Output Navigator
Load
Extract All
Highlight file of interest and right-click to view options to Map, View, and Ex
Name

Type
Size
Confiauration/Loa Files



Configuration

Configuration
2kb
Log File

Run Log
1 kb




Tutorial 03 - Ozone Monitors - county hig |
Add To Map
.. Monitor Network
54kb

View



Extract


Stop Info
This will bring up the Map View tab.
r	
J: MATS
. nx
Help "
Start Map View Output Navigator
"\j ft 61 ^ Standard Layers ^
|lData Loaded I


0 ° Tutorial 03 - Ozone Monitors —


1 1

Long: -161.685*** Lat: 66.80317
Extent: Min(-16*^59,-2.464) Max(-4.381,4-4.377)
i i


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To view an enlarged map, use the Zoom to an area Task Bar button on the far left.
Choose the Continental US.
/MATS
Help T

Start | Map View Output Navigator
v m O
Full Extent
Continental US
Maryland
New England
Southern California
Texas
Washington DC
Edit Zoom Frames
Add Current View to List
Standard Layers
To more easily view the location of monitors in particular states, uncheck US Counties
using the Standard Layers drop down menu on the far right of the Task Bar. Your
window should look like the following:
Long: -59.4079"*Lat: 44.15288
Extent: Min(-12***180,23.355) Max(-59.084.44.301)
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US Counties
-MM
Start
Map View Output Navigator
j| Stop Info
Long:-111.117"™*. Lat: 50.96736
Extent: Min(-12**H80,23.355) Max(-59.084,44301)
S ® l3
J Data Loaded I
0 ° Tutorial 03-Ozone Monitors -
Standard Layers T
Zoom in further on the Eastern US using the Zoom in button on the Task Bar. This allows
you to view the results more closely. A dashed line surrounds the area that you have
chosen and should look something like the following:
Umats


_ n x
Help
~



Start
Map View
Output Navigator
V

e# [3
Standard Layers "

IliData Loadedj
0
Tutorial 03-Ozone Monitors —
Long: -73.7876"**Lat: 23.01200
Extent: Min(-12-^80,23.355) Max(-59.084.44.301)
Stop Info
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Right click on the " Tutorial 03 - Ozone Monitors -"layer in the panel on the left side of the
window. Choose the Plot Value option.
V MATS
n x
Plot Value
Long: -110.897*** Lat: 50.27563
Extent: Min(-11***79,24558) Maxf-'IS.935,38.841)
Start
Map View Output Navigator
J | Stop Info
v ^ m
J:Data Loaded!
|^| O Tutorial ~	Mnnitnrc —
Remove
Standard Layers
fl—
Export as CSV File
This will bring up Shape Class Breaks window. In the Value drop-down list, choose the
variable "f o3 t/r" — this is forecasted ozone design value for 2015.
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Shape Class Breaks
Ozone Analysis: Quick Start Tutorial
Layer Name: Tutorial 03-Ozone Monitors -monitor data, ternpon
Value:
Date	12005

<• Bins	Unique Values
Class Count: [5	t	Marker Sizing: o ;
Start Color
End Color
«§ Clear Breaks
V Apply	X Close
Click Apply and then click Close. This will bring you back to the Map View window.
Help "
Start Map View Output Navigator



Data Loaded;
@ °	Tutorial 03 - Ozone Monitors -
0 • f_o3_dv-9 to 60.9
0 ~ f_o3_dv 61 to 67.9
0 • f_o3_dv 67.9 to 71.8
0 0 f_o3_dv71.9to 76.4
0 ° f o3 dv 76.4 to 95.9
Examine the other variables:
b o3 civ: baseline ozone design value;
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I
Long:-116.319*"=*. Lat: 45.90497
Extent: Min(-11 79.24.568) Max(-48.935.38.S41)
Standard Layers T

-------
Ozone Analysis: Quick Start Tutorial
rrf relative response factor used to forecast the ozone design value;
ppb \ value of the threshold used;
day: number of days at or above the threshold.
This is just a brief summary of the mapping possibilities available. For more details, there
is a separate chapter on the Map View. The next step is to go to the Output Navigator to
view the data in a table format.
6.8 Step 8. View & Export Results
After mapping your results, click on the Output Navigator tab, so that you can then view
the data in a table. Right-click on the file Tutorial 03 - Ozone Monitors - monitor data
temporally adjusted 2015. This gives you three options: Add to Map, View, and Extract.
Choose the View option.
b" MATS
Help -
Start Map View | Output Navigator I
Load "| | Extract All ~| Highlight file of interest and right-click to view options to Map, View, and Extract the data.
Name
Type
Size
Configuration/Log Files


Configuration
Configuration
2kb
Log File
Run Log
1 kb
Output Files



Monitor Network
80kb
Tutorial 03 - Ozone Monitors - county high n_
Add To Map
Extract
y adjusted 2015
Monitor Network
Loading monitor data
stop
Info
This will bring up a Monitor Network Data tab. The upper left panel allows you to view
the ID and latitude and longitude of the monitors in your data — at the right of this panel
there is a scrollbar with which you can locate any particular monitor of interest.
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Help '
Start Map View Output Navigator | Monitor Network Data |
|; Close i|
Refresh Select location and press refresh to see data....
id
010030010
010270001
010331002
010510001
010550011
010730023
010731003
m n-7-31 nnc
" IVpe
w Mat
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0
33.281111
34.760556
33.9039
33.553056
-87.650556
-86.0539
-86.815
33.485556
-i 1 -i
-86.915 r
q-j rfii-11 L
Select Quantities that must be >= 0
b_o3_dv
EH f_o3_dv
reference eel I
~ rrf
ppb
~ days
Export Export this data to CSV
J Data [
-------
Ozone Analysis: Quick Start Tutorial
MATS
Help T
Start | Map View 1 Output Navigator | Monitor Network Data |
Refresh Select location and press refresh to see data....
0

T Mat
170310032
170310050
170310063
170310064
170310072
1 nmc
T [long "
33
41 .755833
41.7095611
41.877222
-87.545278
-87.568576
-87.634444
41.790833
-87.601667
Q ~7 CCQ C1 1
Select Quantities that must be >= 0
b_o3_dv
EH f_o3_dv
reference eel I
~ rrf
ppb
~ days
Export Export this data to CSV
J Data [

stop
Info
To view all of the data, click on the Show All button.
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E" MATS
Help -
Start Map View Output Navigator | Monitor Network Data I
170230001
170310001
Refresh ~| Select location and press refresh to see data.... |; ShowAlH]
- type
T Mat
T [long "
39.210883
41.672745
33
-87.668416
-87.732457 C
170310032

41.755833
-87.545278
170310050

41.709561
-87.568576
170310063

41.877222
-87.634444
170310064

41.790833
-87.601667
170310072

77.7
68.6
95023
0.883
85.0
11.0
010270001
2005

77.7
61.4
108051
0.791
71.0
11.0
010331002
2005

71.0
54.2
92063
0.764
71.0
11.0
010510001
2005

75.0
61.8
106043
0.825
70.0
9.00
010550011
2005

73.0
56.5
105056
0.774
73.0
10.0
010730023
2005

75.7
59.2
100052
0.783
81.0
11.0
010731003
2005

78.0
62.4
99052
0.801
81.0
11.0
010731005
2005

79.2
60.5
99050
0.764
80.0
10.0
B
3top
Info
To eliminate missing values (denoted by negative numbers in the lower panel), check one
or more boxes in the panel in the upper right of the window. For example, to eliminate any
monitors that do not have a ozone design value forecast, check the forecasted ozone design
value variable "f o3 dv" and then click the Show All button.
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Help '
Start Map View Output Navigator | Monitor Network Data |
id
170230001
170310001
170310032
170310050
170310063
170310064
170310072
1 titji rime
Refresh ~| Select location and press refresh to see data.... |i ShowAlH]
- type
T Mat
T [long "
39.210883
41.672745
ZE
-87.668416
-87.732457 C
41.755833
-87.545278
41.709561
-87.568576
41.877222
-87.634444
41.790833
-87.601667
41.895833
,11 m:-,ii
-87.607595
Q-7 CCQC1 1
Select Quantities that must be >= 0
b_o3_dv
reference eel I
~ rrf
ppb
~ days
Export Export this data to CSV
| Data |
d 0
date
b_o3_dv ' |f_o3_dv ~ |referenc<[^l|rrf w | ppb ~ |days *
010030010
2005
77.7 68.6
95023 0.883
85.0
11.0
010270001
2005
77.7 61.4
108051 0.791
71.0
11.0
010331002
2005
71.0 54.2
92063 0.764
71.0
11.0
010510001
2005
75.0 61.8
106043 0.825
70.0
9.00
010550011
2005
73.0 56.5
105056 0.774
73.0
10.0
010730023
2005
75.7 59.2
100052 0.783
81.0
11.0
010731003
2005
78.0 62.4
99052 0.801
81.0
11.0
010731005
2005
79.2 60.5
99050 0.764
80.0
10.0
B
Stop
Info
Click the Export button and save the file as "No Negative Forecasts." (It is unnecessary to
add an extension. MATS automatically saves the file as a CSV text file and adds a ".csv"
extension to your file name.) View the file in Excel.
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r
E2 Microsoft Excel - No Negative Forecasts.cs\
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For additional details on generating ozone results, see the chapter on Ozone Analysis:
Details. For additional details on viewing data, see the View Data section in chapter on the
Output Navigator
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Ozone Analysis: Details
7 Ozone Analysis: Details
MATS can forecast design values at ozone monitor locations — these forecasts are referred
to as Point Estimates. MATS can also use a variety of approaches to calculate design
values for a Spatial Field. A Spatial Field refers to a set of values comprising calculations
for each grid cell in a modeling domain from Eulerian grid models such as CMAQ and
CAMx.
The set of choices involved in calculating either Point Estimates or a Spatial Field can be
fairly involved, so MATS keeps track of these choices using a Configuration. When you
begin the process of generating ozone estimates, MATS provides an option to start a new
Configuration or to open an existing Configuration.
Configuration Management

<• Create New Configuration!
Go
C Open Existing Configuration
Cancel

Select your option and then click Go.
MATS will then step you through a series of windows with choices for your analysis.
• Choose Desired Output. Choose whether you want Point Estimates, estimates for a
Spatial Field, or both.
• Data Input. Specify the air quality modeling and ambient monitoring data that you want
to use. Specify which model grid cells will be used when calculating RRFs at monitor
locations.
• Filtering Interpolation. Choose the years of monitoring data. Identify valid monitors.
Define the interpolation approach to be used (when calculating a Spatial Field).
• RRF and Spatial Gradient. Specify the daily ozone values that will be used in the
calculation of RRFs and Spatial Gradients.
• Final Check. Verify the selections that you have made
7.1 Choose Desired Output
MATS lets you choose to generate Point Estimates, which refer to forecasts made at fixed
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locations, such as monitors. MATS can also generate Spatial Fields, which refer to air
pollution estimates made at the center of each grid cell in a specified model domain. (For
example if the model domain has 20 columns and 30 rows, then there are 600 grid cells for
which MATS can generate estimates.) The Spatial Field estimates can be baseline
estimates or forecasts, generated with or without a gradient adjustment.
a Desired output
¦ Data Input
¦ Filtering/Interpolation
¦ RRF/Spatial Gradient
B5I Final Check
Choose Desired Output
Scenario Name : [Example 03
Point Estimates
Forecast
p" Temporally-adjust ozone levels at monitors.
Spatial Field
Baseline
p" Interpolate monitor data to spatial field
p" Interpolate gradient-adjusted monitor data to spatial field.
Forecast
p" Interpolate monitor data to spatial field. Temporally adjust ozone levels,
p ^Interpolate gradient-adjusted monitor data to spatial field. Temporally adjust.
Next >
Cancel
7.1.1 Scenario Name
The Scenario Name allows you to uniquely identify each analysis that you conduct. It is
used in several ways.
• Results file name. The results file is given the Scenario Name (e.g., Tutorial 03.asr).
Note that the extension f .ASR) is specifically designated just for MATS and can only be
used by MATS.
• Organize output. In the Output folder, MATS will generate a folder using the Scenario
Name. MATS will use this folder as a default location for files generated with this
Scenario Name.
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» C:\Program Files\Abt Associates\MATS\output |- [[ ~ |[X |
File Edit View Favorites Tools Help
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,, Folders
m-
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ElC:\Program Files\Abt Associates\MATS\output
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Hi Example 03.asr
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Output file names. The output files generated will begin with the Scenario Name.
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MATS
Help T
Start Map View | Output Navigator |
~EH
Highlight file of interest and right-click to view options to Map, View, and Extract the data.
Name
Type
Size
Configuration/Log Files

!••• Configuration
Configuration
2kb
Log File
Run Log
1 kb
Output Files

Example 03 - Ozone Monitors - monitor data, temporally adjusted 2015
Monitor Network
80kb
Example 03 - Ozone Monitors - county high monitoring sites, temporally adjusted 2015
Monitor Network
54kb
Example 03 - Spatial Field - interpolated monitor data, temporally adjusted; gradient-adjusted monitor..
. Monitor Network
3588kb
Stop
Info
7.1.2 Point Estimates
The calculation of Point Estimates, or future-year ozone levels at monitors, has several
steps (the process is laid out in more detail in Sections 3 and 4 of the EPA modeling
guidance). The first step is to calculate the baseline value as a function of up to three design
values. The second step is to use model data to temporal adjust the baseline value.
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Ozone Analysis: Details
Scenario Name : |03 Tutorial
Point Estimates
Forecast
F Tempo rally-adjust ozone levels at monitors.
Spatial Field
Baseline
r Interpolate monitor data to spatial field
Interpolate gradient-adjusted monitor data to spatial field.
Forecast
r Interpolate monitor data to spatial field. Temporally adjust ozone levels,
nterjaolate gradient-adjusted monitor data to spatial field. Temporally adjust.
7.1.2.1 Baseline Ozone
The baseline ozone design value is the simple average of design values, where the average
carries one significant figure to the right of the decimal point. Generally, one should select
design value years that match the modeling data being used. The EPA modeling guidance
recommends using an average of the 3 design values periods which straddle the emissions
base year. For example, if the modeled emissions base year is 2002, then design values
from 2000-2002, 2001-2003, and 2002-2004 would be averaged. An average of design
values is, in effect, a weighted average of annual averages — 2002 is "weighted" three
times, 2001 and 2003 are weighted twice, and 2000 and 2004 are weighted once. This
creates a 5-year weighted average design value which is used to project future air quality
levels.
The default design value years in MATS are the periods 2000-2004. This assumes a model
base year of 2002. If the base year is not 2002, then the start and end design value period
should be adjusted.
7.1.2.2 Temporally-Adjust Baseline Ozone
The first step in temporally adjusting baseline ozone involves identifying the model grid
cells near the monitor site. Next, MATS calculates the average of daily 8-hour average
maximum model values for both the baseline and future-year model runs, and then takes
the ratio of the two to calculate the RRF. Finally, MATS calculates the future-year design
value by multiplying the RRF with the baseline design value measured at the monitor.
The equation for temporally adjusting baseline ozone is as follows:
Monitor, ^nirc = Monitort ¦ RRF.:
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where:
Monitor; &ture = future-year ozone design value at monitor site i, measured in parts per
billion (ppb)
Monitor; = baseline ozone design value at monitor site i, measured in ppb
RRF; = relative response factor at monitor site i. The RRF is the ratio of the future 8-hour
daily maximum concentration predicted near a monitor (averaged over multiple days) to the
baseline 8-hour daily maximum concentration predicted near the monitor (averaged over
the same days).
NOTE: The calculation of the RRF involves a number of assumptions that are specified in
the RRF and Spatial Gradient window.
7.1.3 Spatial Field
A Spatial Field refers to air pollution estimates made at the center of each grid cell in a
specified model domain. For example, MATS might calculate ozone design values for
each grid cell in a modeling domain.
MATS calculates four types of ozone-related Spatial Fields:
• Baseline - interpolate monitor data to spatial field. This is an interpolation of baseline
monitor values at each grid cell. MATS identifies the "neighbor" monitors for each grid
cell and then calculates an inverse-distance-weighted average of the monitor values at
each grid cell.
• Baseline - interpolate gradient-adjusted monitor data to spatial field. This is an
interpolation of model-adiusted baseline monitor values at each grid cell. MATS
identifies the "neighbor" monitors for each grid cell, it adjusts the monitor values to
account for the modeled spatial gradient, and then calculates an inverse-distance-
weighted average of the monitor values.
• Forecast - interpolate monitor data to spatial field. Temporally Adjust. This is an
interpolation of baseline monitor values at each grid cell that are then temporally adjusted
to a future year. MATS calculates the Baseline - interpolate monitor data to spatial
field and multiplies it with a RRF.
• Forecast - interpolate gradient-adjusted monitor data to spatial field. Temporally adjust.
This is an interpolation of model-adjusted baseline monitor values at each grid cell that
are then temporally adjusted to a future year. MATS calculates the Baseline -
interpolate gradient-adjusted monitor data to spatial field and multiplies it with a
RRF.
Details on the calculations are provided in the following sections.
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Scenario Name : [Example 03
Point Estimates
Forecast
p" Temporal ly^adjust ozone levels at monitors.
Spatial Field
Baseline
[~ Interpolate monitor data to spatial field
[~ Interpolate gradient-adjusted monitor data to spatial field.
Forecast
[~ Interpolate monitor data to spatial field. Temporally adjust o:one levels.
F Interpolate gradient-adjusted monitor data to spatial field. Temporally adjust.
7.1.3.1 Baseline - interpolate monitor data to spatial field
To calculate baseline ozone design values for a spatial field. MATS starts with the baseline
ozone levels at the monitor and interpolates them to the centroid of the spatial field. The
basic form of the equation is as follows:
n
GridcellEba,t!^ = V Weight, ¦ Momtoi)
1-1
where:
Gridcell, baseline = baseline ozone concentration at unmonitored site E;
Weight; = inverse distance weight for monitor i;
Monitor; = baseline ozone concentration at monitor i.
7.1.3.2 Baseline - interpolate gradient-adjusted monitor data to spatial field
Using modeling data for gradient scaling is fairly simple: MATS uses the model value for
the grid cell of interest and the model values for the grid cells containing the monitors to be
interpolated to the grid cell of interest. A general form of the equation is as follows:
ji
GridcellE ba.tbM = T WeightMonitor) ¦ Gradient Adjustment jE
i-i
where:
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Gridcell, baseline = baseline ozone concentration at unmonitored site E;
Weight; = inverse distance weight for monitor i;
Monitor; = baseline ozone concentration at monitor i;
Gradient Adjustment; E = gradient adjustment from monitor i to unmonitored site E.
There are a variety of approaches that might be used to calculate the gradient adjustment.
As a default, MATS averages the five highest daily 8-hour values. The equation can then be
rewritten as follows:
, „ " ModelE
GndceUE ^eline = ^ Weight, Monitor
i-l bastion
where:
Model, baseline = baseline scenario, average of five highest daily 8-hour values at site E;
Model; baseline = baseline scenario, average of five highest daily 8-hour values at monitor
site i.
7.1.3.3 Forecast - interpolate monitor data to spatial field. Temporally-adjust ozone levels
To get the forecasted design value for each grid-cell in the spatial field. MATS multiplies
the Baseline - interpolate monitor data to spatial field for each grid-cell with the RRF
calculated for that grid-cell. The equation is as follows:
Gridcell= GridcellEbasMu RRFE
where:
Gridcell, baseline = baseline ozone concentration at unmonitored site E;
Weight; = inverse distance weight for monitor i;
Monitor; = baseline ozone concentration at monitor i.
NOTE: The RRF is calculated using the same approach as for Point Estimates (except
when a backstop threshold minimum is set l"see section 7.4.1.61).
7.1.3.4 Forecast - interpolate gradient-adjusted monitor data to spatial field. Temporally-adjust ozone levels
To get the forecasted design value for each grid-cell in the spatial field with a gradient
adjustment. MATS multiplies the Baseline - interpolate gradient-adjusted monitor data to
spatial field for each grid-cell with the RRF calculated for that grid cell. The equation is as
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follows:
GridcellE = GridcellE iastliM RRFE
where:
Gridcell, baseline = baseline ozone concentration at unmonitored site E;
Weight; = inverse distance weight for monitor i;
Monitor; = baseline ozone concentration at monitor i.
NOTE: The RRF is calculated using the same approach as for Point Estimates (except
when a backstop threshold minimum is set fsee section 7.4.1.61V
7.1.4 Output Variable Description
MATS generates up to three output files:
• Ozone forecasts for all monitors. The name of this file is "Ozone Monitors — monitor
data, temporally adjusted yyyy.csv" with the Scenario Name appended at the beginning
and the forecast year is inserted at the end (e.g., "Example 03 — Ozone Monitors —
county high monitoring sites, temporally adjusted 2015.csv").
• Ozone forecasts for the highest monitor in each county. The name of this file is "Ozone
Monitors — county high monitoring sites, temporally adjusted yyyy.csv" with the Scenario
Name appended at the beginning and the forecast year inserted at the end.
• Spatial field forecasts. The name of this file is "Spatial Field — interpolated monitor data,
temporally adjusted; gradient-adjusted monitor data, temporally adjusted yyyy.csv" with
the Scenario Name appended at the beginning and the forecast year inserted at the end.
The following sub-sections describe the variables in each file.
7.1.4.1 Ozone Monitors — monitor data, temporally adjusted 2015.csv
An example of this output file is as follows (with variable definitions in the table below):
Example 03 - Ozone Monitors - monitor data, temporally
adjusted 2015.csv
Year
Jd _typ lat long date b_o3_ f_o3_D reference rrf ppb days_state_na_county_na
e DV V cell me me
100300 30.4978-87.88142005 77.7 68.6 95023 0.88 85 11 Alabama Baldwin
10 3
102700 33.2811-85.80222005 77.7 61.4 108051 0.79 71 11 Alabama Clay
01 1
103310 34.7606-87.65062005 71 54.2 92063 0.76 71 11 Alabama Colbert
02 4
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105100
01
105500
11
32.4983-86.13672005 75
33.9039-86.05392005 73
61.8 106043 0.82 70 9 Alabama Elmore
5
56.5 105056 0.77 73 10 Alabama Etowah
Variable Description
Jd The ID is a unique name for each monitor in a particular location. The default value
is the AIRS ID. (This is a character variable.)
_type Leave blank
lat Latitude in decimal degrees. Values in the northern hemisphere are positive, and
those in the southern hemisphere are negative,
long Longitude in decimal degrees. Values in the eastern hemisphere are positive, and
those in the western hemisphere (e.g., United States) are negative,
date The date represents the last year of the chosen design value periods (e.g., if a 5
year period is chosen, 2005 represents the 2001-2005 period ).
b_o3_DV Baseline design value
f_o3_DV Forecasted (future year) design value
referencecell Identifier of the closest model grid cell centroid to the monitor,
rrf Relative response factor is the ratio of the future year modeled concentration
predicted near a monitor (averaged over multiple days) to the base year modeled
concentration predicted near the monitor (averaged over the same days).
ppb Threshold value (measured in parts per billion) used in the rrf calculation
days Number of days at or above the threshold value.
_state_name State name. (This is a character variable.)
_county_name County name. (This is a character variable.)
7.1.4.2 Ozone Monitors — county high monitoring sites, temporally adjusted 2015.csv
An example of this output file is as follows (with variable definitions in the table below):
Example 03 - Ozone Monitors - county high monitoring sites,
temporally adjusted 2015.csv
Year
Jd _typ lat long date b_o3_ f_o3_D reference rrf
e DV V cell
30.4978-87.88142005 77.7 68.6 95023 0.88
100300
10
102700
01
103310
02
105100
01
105500
11
107320
06
107900
02
108900
14
33.2811-85.80222005 77.7 61.4 108051 0.79
1
34.7606-87.65062005 71 54.2 92063 0.76
4
32.4983-86.13672005 75 61.8 106043 0.82
5
33.9039-86.05392005 73 56.5 105056 0.77
4
33.3864-86.81672005 82.5 64.2 100051 0.77
9
34.3428-87.33972005 75.2 58.7 95059 0.78
1
34.6908-86.58312005 79 63.4 100063 0.80
3
ppb days_state_na_county_na
me me
85 11 Alabama Baldwin
71 11 Alabama Clay
71 11 Alabama Colbert
70 9 Alabama Elmore
73 10 Alabama Etowah
81 10 Alabama Jefferson
70 5 Alabama Lawrence
70 10 Alabama Madison
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Variable
Description
Jd
The ID is a unique name for each monitor in a particular location. The default value is

the AIRS ID. (This is a character variable.)
Jype
Leave blank
lat
Latitude in decimal degrees. Values in the northern hemisphere are positive, and those

in the southern hemisphere are negative.
long
Longitude in decimal degrees. Values in the eastern hemisphere are positive, and

those in the western hemisphere (e.g., United States) are negative.
date
The date represents the last year of the selected design value periods (e.g., if a 5 year

period is selected, 2005 represents the 2001-2005 period ).
b_o3_DV
Baseline design value
f_o3_DV
Forecasted (future year) design value
referencecel Identifier of the closest model grid cell centroid to the monitor.
I
rrf Relative response factor is the ratio of the future year modeled concentration predicted
near a monitor (averaged over multiple days) to the base year modeled concentration
predicted near the monitor (averaged over the same days).
ppb Threshold value (measured in parts per billion) used in the rrf calculation
days Number of days at or above the threshold value.
_state_nam State name. (This is a character variable.)
e
_county_na County name. (This is a character variable.)
me
7.1.4.3 Spatial Field — interpolated monitor data, temporally adjusted; gradient-adjusted monitor data,
temporally adjusted 2015.csv
An example of this output file is as follows (with variable definitions in the table below):
Example 03 - Spatial Field - interpolated monitor data, temporally adjusted;
gradient-adjusted monitor data, temporally adjusted 2015.csv
Year
id _type
lat long
date
ga_co
CO
o
J
,_l
i_f_o3
i_b_ga i_f_ga_
ppb day referenc
rrf

o3
o3

s
ecell

10000
28.063 -87.49
2005
-13
79.2
-9
-8
-9
70
0
100001
-9
1
8 91

10000
28.169 -87.48
2005
-13
79.2
-9
-8
-9
70
0
100002
-9
2
8 65

10000
28.275 -87.47
2005
-13
79.2
-9
-8
-9
70
0
100003
-9
3
9 38

10000
28.382 -87.46
2005
71.6
79.1
-9
69.9
-9
70
4
100004
-9
4
0 11

10000
28.488 -87.44
2005
72
79.1
70
70.2
62.1
70
5
100005
0.885
5
1 84

10000
28.594 -87.43
2005
72.2
79.2
69.9
70.4
62.1
70
5
100006
0.883
6
3 56

10000
28.700 -87.42
2005
72.3
79.2
69.8
70.4
62
70
5
100007
0.882
7
5 29

10000
28.806 -87.41
2005
72.5
78.9
69.1
70.5
61.8
70
5
100008
0.877
8
8 00

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Variable Description
Jd The ID is a unique name for each monitor in a particular location. The default value is
the column identifier multiplied by 1000 plus the row. (This is a character variable.)
Leave blank
Latitude in decimal degrees of the center of each grid cell. Values in the northern
hemisphere are positive, and those in the southern hemisphere are negative.
Longitude in decimal degrees of the center of each grid cell. Values in the eastern
hemisphere are positive, and those in the western hemisphere (e.g., United States) are
negative.
The date represents the last year of the selected design value periods (e.g., if a 5 year
period is selected, 2005 represents the 2001-2005 period ).
Modeled concentration (ppb) used for gradient adjustment (average of "start value" and
"end value")
Interpolated (to spatial field) baseline concentration (ppb).
Interpolated (to spatial field) future year concentration (ppb).
Interpolated (to spatial field) gradient adjusted baseline concentration (ppb).
Interpolated (to spatial field) gradient adjusted future year concentration (ppb).
Threshold value (measured in parts per billion) used in the rrf calculation
Number of days at or above the threshold value,
referencecellldentifier of the grid cell. (In the case of spatial fields, this is identical to the JD
variable.)
rrf Relative response factor is the ratio of the future year modeled concentration predicted
near a monitor (averaged over multiple days) to the base year modeled concentration
predicted near the monitor (averaged over the same days).
Jype
lat
long
date
ga_conc
b_o3
f_o3
_b_ga_o3
i_f_ga_o3
ppb
days
7.2 Data Input
In the Data Input window, you need to specify the air quality modeling and ambient
monitoring data that you want to use. In addition, you need to specify which model grid
cells will be used when calculating RRFs at monitor locations.
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H5| Desired output
Data Input
¦ Filtering/Interpolation
¦ RRF/Spatial Gradient
EH Final Check
0
Data Input
Monitor Data
Ozone Data
SampleData\OZONE_ASIP_input_97-05.csv 3D
Model Data
Baseline File
Forecast File
Using Model Data
Temporal adjustment at monitor
\SampleData\ozone_model_data_2001 .csv
|\SampleData\ozone_rnodel_data_2015. csv
3/3 E Maximum E
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7.2.1 Monitor Data
Monitor data should be in the form of a simple text file. The first row specifies the
frequency of the data (e.g., day). The second row presents comma-separated variable
names. The third row begins the data values. Below is an example of the monitor data file
format and descriptions of the variables in the file.
Format of Ozone Monitor Data
|Des ignValue

/V
IM
ID, TYPE,LAT
LONG,POC,
DVYEAR,03, STATE NAME,
COUNTY NAME

"010030010",
30 .497770
,-07 . 001389 , 1, 1999 ,-9 ,
"Alabama","Baldwin"

"010030010",
30 .497770
,-87 .001389,1,2000,-9,
"Alabama","Baldwin"

"010030010",
30 .497770
,-87 .881389,1,2001,-9,
"Alabama","Baldwin"

"010030010",
30 .497770
,-87 .881389,1,2002, 82,
"Alabama","Baldwin"

"010030010",
30 .497770
,-87 .881389,1,2003, 76,
"Alabama","Baldwin"

"010030010",
30 .497770
,-87 .881389,1,2004, 76,
"Alabama","Baldwin"

"010030010",
30 .497770
,-87 .881389,1,2005, 77,
"Alabama","Baldwin"

"010270001",
33 .201111
,-85 .002222,1,1999, 00,
"Alabama","Clay"
V
HI
<

¦
Ozone Monitor Data Variable Descriptions
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Variable
JD
_TYPE
LAT
LONG
DATE
03
_STATE_NAME
COUNTY NAME
Description
The ID is a unique name for each monitor in a particular location. The default
value is the AIRS ID. (This is a character variable.)
Leave this blank.
Latitude in decimal degrees. Values in the northern hemisphere are positive,
and those in the southern hemisphere are negative.
Longitude in decimal degrees. Values in the eastern hemisphere are positive,
and those in the western hemisphere (e.g., United States) are negative.
The time period of the monitor observation. As a convention, the date
represents the last year of the three-year design value period (e.g., 2001
represents the 1999-2001 design value).
Observed monitor value. Note that missing values are represented by a minus
nine (-9).
State name. (This is a character variable.)
County name. (This is a character variable.)
NOTE: Character variables have names that begin with an underscore (i.e., and the
character values used can be kept with or without quotes. (If a character variable has an
embedded space, such as might occur with the name of a location, then use quotes.)
7.2.2 Model Data
The model data should be in the form of a simple text file. The first row specifies the
frequency of the data (e.g., day). The second row presents comma-separated variable
names. The third row begins the data values. The ozone model data should be the daily 8-
hour average maximum concentration in each grid cell. Below is an example of the model
data file format and descriptions of the variables in the file.
Format of Ozone Model Data
Day
_ID,_TYPE, LAT , LONG , DATE , 03
1001, "",28 .471949, -99.489582,20150501,45.2324
10 01, "",28 .471949, -99 . 489582,20150502, 42 . 6581
10 01, "",28 .471949, -99.489582,20150503,47 .4534
1001, "",28 .471949, -99.489582,20150504,51.9678
1001,"",28.471949, -99.489582,20150505,53.6575
10 01,"",28.471949, -99.489582,20150506,47.1936
1001, "",28 .471949, -99.489582,20150507,48.3454
10 01,"",2 8.4719 49, -99.489582,20150508,49.5464
10 01, "",28 .471949, - 99 . 489582,20150509,34.3454|
Ozone Model Data Variable Descriptions
Variable Description
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_ID The ID is a unique number for each model grid cell in the air quality model domain. It
is generally based on the column and row identifiers from the air quality modeling
domain. The default convention is to calculate the ID by multiplying the column
identifier by one thousand (1000) and adding the row identifier. (This is a character
variable.)
_TYPE Leave this blank.
LAT Latitude in decimal degrees of the center of each grid cell. Values in the northern
hemisphere are positive, and those in the southern hemisphere are negative.
LONG Longitude in decimal degrees of the center of each grid cell. Values in the eastern
hemisphere are positive, and those in the western hemisphere (e.g., United States) are
negative.
DATE The time of the monitor observation. The day is represented in the yyyymmdd format
03 Modeled ozone concentration (8-hour average daily maximum).
NOTE: Character variables have names that begin with an underscore {i.e., "_"), and the
character values used can be kept with or without quotes. (If a character variable has an
embedded space, such as might occur with the name of a location, then use quotes.)
7.2.2.1 EPA Default Model Data
The example model output dataset in MATS comprises daily 8-hour average maximums
from May-September at 12km resolution. The baseline year for the modeling is 2001 and
the future year is 2015.
7.2.3 Using Model Data
The RRF for a monitor is calculated from "nearby" model grid cells. For purposes of this
calculation, a monitor is assumed to be at the center of the cell in which it is located, and
this cell is at the center of an array of "nearby" cells.
The number of cells considered "nearby" a monitor is a function of the size of the grid cells
used in the modeling. In the example case of a 12 km grid, EPA uses as a default 3x3 array
of grid cells (see section 3.2 of the EPA modeling guidance for more details).
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H5| Desired output
Data Input
¦ Filtering/Interpolation
¦ RRF/Spatial Gradient
MS] Final Check
0
Data Input
Monitor Data
Ozone Data
SampleData\OZONE_ASIP_input_97-05.csv 3D
Model Data
Baseline File
Forecast File
Using Model Data
Temporal adjustment at monitor
\SampleData\ozone_model_data_2001 .csv 3D
|\SampleData\ozone_model_data_2015. csv 3D
1x1
5x5
7x7
3x3
Maximum 3]
< Back
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Cancel
With the array size determined, MATS gives you two options for how you might use the
modeling data.
• Maximum. For each day of modeling data, MATS will identify the highest 8-hour daily
maximum among the grid cells in the chosen array. In the case of a 3x3 array, MATS
will identify the highest daily 8-hour average maximum from among the nine "nearby"
grid cells for each day and for each monitor site.
• Mean. For each day of modeling data, MATS will average the 8-hour daily values for
the grid cells in the chosen array. In the case of a 3x3 array, MATS will average nine
values.
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H5| Desired output
Data Input
¦ Filtering/Interpolation
¦ RRF/Spatial Gradient
EH Final Check
Data Input
Monitor Data
Ozone Data
SampleData\OZONE_ASIP_input_97-05.csv 3D
Model Data
Baseline File
Forecast File
Using Model Data
Temporal adjustment at monitor
\SampleData\ozone_model_data_2001 .csv
|\SampleData\ozone_rnodel_data_2015. csv
3/3 E Maximum ~
Mean
< Back
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The default choice for the ozone analysis in MATS is to use the maximum value among
the array of grid cells, when calculating temporally-adjusted ozone levels at each monitor.
NOTE: For monitors on the border of a modeling domain — where it may not be possible to
have a full set of neighbors — MATS uses the available modeling data.
7.2.3.1 Nearby Monitor Calculation - Example 1
Given:
(1) Four primary days have been simulated using baseline and future emissions.
(2) The horizontal dimensions for each surface grid cell are 12 km x 12 km.
(3) In each of the 9 grid cells "near" a monitor site I, the maximum daily predicted future
concentrations are 87.2, 82.4, 77.5, and 81.1 ppb.
(4) In each of the 9 grid cells "near" a monitor site I, the maximum daily predicted baseline
8-hour daily maximum ozone concentrations are 98.3, 100.2, 91.6, and 90.7 ppb.
Find:
The site-specific relative response factor for monitoring site I, (RRF)I
Solution:
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(1) For each day and for both baseline and future emissions, identify the 8-hour daily
maximum concentration predicted near the monitor. Since the grid cells are 12 km, a 3 x 3
array of cells is considered "nearby" (see Table 3.2).
(2) Compute the mean 8-hour daily maximum concentration for (a) future and (b) baseline
emissions. Using the information from above, (a) (Mean 8-hr daily max.)future = (87.2 +
82.4 + 77.5 + 81.1 )/4 = 82.1 ppb and (b) (Mean 8-hr daily max.)baseline = (98.3 + 100.2 +
91.6 + 90.7)/4 = 95.2 ppb
(3) The relative response factor for site I is
(RRF)I = (mean 8-hr daily max.)future/(mean 8-hr daily max.)baseline = 82.1/95.2 = 0.862
Figure 3.1. Choosing Ozone Predictions To Estimate RRF's
(a) Predictions With Baseline Emissions
Day 1 Day 2 Day 3 Day 4
97.2
95.5
96.2

100.2
98.5
98.1

87.8
90.1
89.9

85.9
87.9
88.9
97.1
95.2
89.1

100.0
99.1
97.3

90.9
91.6
88.7

87.9
90.5
90.7
97.2
98.3
97 fi

99.5
95.4
97 9

88.5
89.4
90.2

86.9
87.3
88.4

98.3 100.2 91.6 90.7
Mean Baseline Ozone Concentration = (98.3 + 100.2 + 91.6 + 90.7) 14 = 95.2 ppb
Day 1
(b) Predictions With Future Emissions
Day 2 Day 3
Day 4
86.1
85.4
86.)
86.2
84.5
84.;
85.8
87.2
86.!
82.2
80.8
81.2

72,1
76.1
75.5

75.4
78.8
79.8
82.4
79.9
80.5

74.6
77.5
74.3
80.8
79.5
80.9
81.4
77.8
80.1

76.9
77.4
75.6
80,4
76.9
81.1
87.2
82.4
77.5
81.1
Mean Future Ozone Concentration = (87.2 + 82.4 + 77.5 + 81.1) / 4 = 82.1 ppb
7.3 Filtering and Interpolation
The Filtering and Interpolation window allows you to choose the years of monitoring
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data that you will use in your analysis. MATS allows you to specify the rules to determine
the monitors that you will use. And in the case of calculating Spatial Fields, it allows you
to define the interpolation method that MATS will use.
Filtering and Interpolation
Desired output
¦ Data Input
Filtering/Interpolation
¦ RRF/Spatial Gradient
Final Check
Choose Ozone Design Values
Start Year
I End Year 12002-200-1 I
2000-2002
Valid Ozone Monitors
Minimum Number of design values 1
Max Distance from Domain [km] |25
Required Design Values
None selected
Default Interpolation Method
|Inverse Distance Weig
| check to set a maximum interpolation distance [km]
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7.3.1 Choose Ozone Design Values
Choosing the Start Year and the End Year defines the range of the ozone design values
that will be used in the calculation of the baseline ozone level. You can vary the number of
design values used in this calculation.
The database that comes with MATS has design values periods from 1997-2005. The
default approach in MATS is to average 3 design value periods. For example, if the
modeling base year is 2002, then you would use the design values from 2000-2002, 2001-
2003, and 2002-2004. The Start Year is set to 2000-2002 and the End Year is set to
2002-2004.
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Choose Ozone Design Values
Start Year
2000-2002 - |
End Year 2002-2004
Valid Ozone Monitors
Minimum Number of design values
Max Distance from Domain [km]
1997-1999
1998-2000
1999-2001

2000-2002

2001-2003
2002-2004
2003-2005

Required Design Values

None selected

Default Interpolation Method
(Inverse Distance Weights
| check to set a maximum interpolation distance [km]
7.3.2 Valid Ozone Monitors
MATS provides three choices for identifying monitors that are "valid" and thus included in
your analysis.
• Minimum Number of Design Values. Specifies the minimum number of design value
periods that need to be included in the calculation of the baseline ozone design value (1,
2, or 3).
• Max Distance from Domain [km]. Specifies how far a monitor may be from a model
grid cell and still be included in calculations that use that grid cell's model values. (This
is relevant for the calculation of RRFs and gradient-adjustments.)
• Required Design Values. Specifies whether a particular design value period needs to be
valid for the calculations to be performed at that monitor.
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Choose Ozone Design Values
Start Year 2000-2002 E End Year 12002-2004 E
Valid Ozone Monitors
Minimum Number of design values 1
Max Distance from Domain [km] 25
Required Design Values None selected
Default Interpolation Method
(inverse Distance Weights
| check to set a maximum interpolation distance [km] 100
7.3.2.1 Minimum Number Design Values
The Minimum Number of Design Values specifies the minimum number of design values
that need to be available in the potential range of design values specified by the Start Year
and End Year. Monitors that do not meet the minimum are excluded from the calculation
of baseline ozone levels.
Recall that the baseline ozone level is an average of one or more design values. The
number of design values available for this calculation will typically be either 1, 2, or 3
design value periods. The default option is to require that one design value be available in
the specified range.
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Choose Ozone Design Values
Start Year 2000-2002 E End Year 12002-2004 E
Valid Ozone Monitors
Minimum Number of design values ]
Max Distance from Domain [km] 25
Required Design Values None selected
Default Interpolation Method
(inverse Distance Weights
| check to set a maximum interpolation distance [km] 100
Example 1: Point Estimates
When calculating ozone levels at monitors, if MATS finds that a monitor has an
insufficient number of valid design values (e.g., one required but none available in the
specified range), then MATS will set the baseline level and forecast values to missing (
denoted by a negative number).
Example 2: Spatial Field
The calculation of the ozone level in each grid cell of a Spatial Field involves multiple
monitors. MATS uses Voronoi Neighbor Averaging to identify "neighboring" monitors
from the available set of valid monitors, and then calculates an inverse-distance weighted
average of the baseline ozone levels from these neighbors. The Minimum Number of
Design Values determines which monitors are "valid" — that is, those monitors that will be
included in the calculation.
7.3.2.2 Max Distance from Domain
The option to specify the Max Distance from Domain allows you to choose how far (in
kilometers) a monitor may be from the center of a model grid cell and still be included in
calculations that use that grid cell's model values.
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Choose Ozone Design Values
Start Year 2000-2002 E End Year 12002-2004 E
Valid Ozone Monitors
Minimum Number of design values |1
Max Distance from Domain [km] |3j
Required Design Values None selected
Default Interpolation Method
11 nve rs e D i stQn ce We i g hts 3
| check to set a maximum interpolation distance [km] 100
Example 1: Point Estimates
When calculating ozone levels at monitors, if MATS finds that a monitor is further than the
specified Max Distance from Domain, then MATS will drop the monitor from the
analysis. For example, if the Max Distance from Domain is set to 25 kilometers and the
model domain includes the continental U.S., then monitors in Alaska, or other locations far
from the model domain are excluded from calculations involving model data. If an
extremely large distance is specified, say 10,000 kilometers, then all monitors would be
included, regardless of the model domain location
Example 2: Spatial Field
The calculation of the ozone level in each grid cell of a Spatial Field can involve multiple
monitors. MATS uses Voronoi Neighbor Averaging to identify "neighboring" monitors
from the available set of valid monitors, and then calculates an inverse-distance weighted
average of the baseline ozone levels from these neighbors. The Max Distance from
Domain determines which monitors are "valid" — that is, those monitors that will be
included in the calculation.
7.3.2.3 Required Design Values
Using the Required Design Value drop-down list, you can specify that a particular design
value must be available at each monitor included in the analysis. If you want to use all
monitors that have a valid design value for 2001-2003, then MATS will only include those
monitors that have this valid design value. The default is to choose None Selected.
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Choose Ozone Design Values
Start Year 2000-2002 End Year 12002-200-^1
Valid Ozone Monitors
Minimum Number of design values [T~
Max Distance from Domain [km] [25*
Required Design Values
None selected
-

r 1997-1999

Default Interpolation Method
r 1998-2000
r 1999-2001
r 2000-2002

| Inverse Distance Weights

| check to set a maximum interpol
r 2001-2003
r 2002-200-1
r 2003-2005

7.3.3 Default Interpolation Method
The Default Interpolation Method panel allows you to choose how you will interpolate.
or combine, the values from different monitors. One approach is to user Inverse Distance
Weights. This means that the weight given to any particular monitor is inversely
proportional to its distance from the point of interest. A second approach is Inverse
Distance Squared Weights, which means that the weights are inversely proportional to the
square of the distance. And the third approach is Equal Weighting of Monitors. The
default approach for ozone is Inverse Distance Weights.
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Choose Ozone Design Values
Start Year
2000-2002
E End Year 12002-2004 ~Z]
Valid Ozone Monitors
Minimum Number of design values |1
Max Distance from Domain [km]
Required Design Values
25
None selected
3]
Default Interpolation Method
| Inverse Distance Weights
Equal Weighting of Monitors
Inverse Distance Weiqhts
Inverse Distance Squared Weights
When interpolating monitor values, MATS allows you to identify the monitors you want to
use based on their distance away from the point of interest (e.g., the center of a grid cell).
The first step in the interpolation process is to identify the monitors that are nearby, or
neighbors, for each point of interest. The next step is to determine the distance from the
nearby monitors to the point of interest.
The default approach is to include all valid monitors (i.e., those that satisfy the three criteria
in the Valid Ozone Monitors panel), regardless of distance. If you want to limit the use of
monitors based on distance, check the box next to check to set a maximum interpolation
distance, and then specify a distance (in kilometers). A distance of one hundred (100)
kilometers means that any monitors further than 100 kilometers can no longer be used in
the interpolation. If a point of interest has no monitors within the specified distance, then
no value is calculated. The default is to leave this box unchecked.
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Choose Ozone Design Values
Start Year £000-2002 End Year |2002-2004
Valid Ozone Monitors
Minimum Number of design values fl
Max Distance from Domain [km] [25
Required Design Values None selected
Default Interpolation Method
| Inverse Distance Weights
p" icheckto set a maximum interpolation distance [km]: |l 00 « -f- »
3]
7.4 RRF and Spatial Gradient
In calculating an ozone RRF or a Spatial Gradient, typically, not all of the model data are
used. In the case of RRFs, daily values falling below specified thresholds can be excluded
from the calculation (e.g.. RRF Calculation - Example 1). In the case of a spatial gradient,
MATS be be setup to follow the same thresholds as used for point estimates or if a valid
result is needed in all grid cells, a Backstop minimum threshold can be used (e.g.. RRF
Calculation Spatial Gradient with Backstop Threshold - Example 6). MATS also averages
a user-specified range of values to calculate gradient adjustments (e.g., Spatial Gradient
Calculation - Example 1).
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0
H5| Desired output
¦ Data Input
¦ Filtering/Interpolation
?RRF/Spatial Gradient
Final Check
RRF and Spatial Gradient
RRF Setup:

Initial threshold value (ppb)
85
Minimum number of days in baseline at or above threshold
10
Minimum allowable threshold value (ppb)
70
Min number of days at or above minimum allowable threshold |~~
5
Enable Backstop minimum threshold for spatial fields

Backstop minimum threshold for spatial fields
60
Spatial Gradient Setup:

Start Value
1
End Value
5
< Back
Next >
Cancel
7.4.1 RRF Setup
The RRF Setup involves four variables that specify the thresholds and the numbers of days
above the thresholds — Initial threshold value; Minimum number of days in baseline at
or above threshold; Minimum allowable threshold value; and Min number of days at
or above minimum allowable threshold.
The first step in calculating the RRF is to determines the number of days at or above the
Initial threshold value. If the number of days is above the Minimum number of days in
baseline at or above threshold, then MATS averages the 8-hour values for those grid cells
with at least this number. For example, MATS performs the following steps:
• In the case of a 3x3 array, MATS identifies the highest daily 8-hour average maximum
from among the nine "nearby" grid cells for each day and for each monitor site. In the
case where there are 90 days of model outputs, MATS generates 90 daily values. NOTE:
MATS does this calculation separately for the baseline and future-year scenarios. As a
result two different grid cells in the baseline and future-year might be used to represent a
given day.
• The default Initial threshold value is set to 85 ppb. The default Minimum number of
days in baseline at or above threshold is set to 10. If there are fewer than 10 days at or
above 85 ppb in the baseline scenario, then MATS lowers the threshold in increments of
1 ppb, until there are at least 10 days at or above this new, lower threshold. This process
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is continued, if needed, until the Minimum allowable threshold value is reached. The
default Minimum allowable threshold value is 70 ppb. MATS calculates the number
of days at or above the Minimum allowable threshold value. If there are fewer than
the Min number of days at or above minimum allowable threshold, then the monitor
site will be dropped. The default Min number of days at or above minimum
allowable threshold is 5.
• Using the threshold established with the baseline scenario, MATS checks the daily 8-
hour maxima calculated for the baseline scenario, and sets to missing any daily value
falling below the threshold. For any day set to missing in the baseline scenario, MATS
also sets the corresponding day in the future-year scenario to missing.
• For each monitor site, MATS averages the non-missing daily values for the baseline and
future-year scenarios, and then calculates the RRF as the ratio of the future-year average
to the baseline average.
You can also set a Backstop minimum threshold for spatial fields. As noted in Example
6 (below), the backstop minimum threshold allows the minimum threshold to be lowered to
a value below the Minimum allowable threshold value until the minimum number of
days is reached. The backstop threshold is only used for grid cells which do not have
enough days to meet the minimum number of days value with the minimum allowable
threshold. The backstop threshold does not change the calculation for grid cells that
already meet the minimum number of days.
7.4.1.1 RRF Calculation - Example 1
Assume the following default values:
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RRF Setup:

Backstop minimum threshold for spatial fields

Spatial Gradient Setup:

Start Value
1
End Value
5
Assume that there are 15 days of data:
Baseline Baseline
day
value
1
103
2
112
3
98
4
97
5
95
6
95
7
94
8
92
9
90
10
85
11
89
12
88
13
85
14
78
15
78
Future
Future
day
value
1
95
2
97
3
94
4
95
5
94
6
93
7
89
8
86
9
80
10
78
11
80
12
81
13
76
14
75
15
74
MATS will sort the values from high to low based on the Baseline values:
Baseline Baseline Future Future
day value day value
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2
112
1
103
3
98
4
97
5
95
6
95
7
94
8
92
9
90
11
89
12
88
10
85
13
85
14
78
15
78
2
97
1
95
3
94
4
95
5
94
6
93
7
89
8
86
9
80
11
80
12
81
10
78
13
76
14
75
15
74
Note that Day 2 has the highest Baseline value. And note that the Future values are not
sorted high to low, and instead the Future days match the Baseline days.
When you compare these sorted data with the Initial threshold value of 85 ppb, note that
there are thirteen (13) Baseline values at or above this threshold. Since there are more
days than the ten (10) specified as the Minimum number of days in baseline at or above
threshold, MATS will use all 13 days.
MATS will take the top 13 days (highlighted in yellow) and then calculate separate
averages for the Control and Baseline values:
Control average = 87.5385 ppb
Baseline average = 94.0769 ppb.
The RRF equals the ratio of the Control to the Baseline:
RRF = 87.5385 / 94.0769 = 0.930
Note that we report the RRF with three digits after the decimal point. The calculation of
the Baseline and Control averages does not involve any rounding or truncation.
7.4.1.2 RRF Calculation - Example 2
Assume the following default values:
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RRF Setup:

Backstop minimum threshold for spatial fields

Spatial Gradient Setup:

Start Value
1
End Value
5
Assume that there are 15 days of data:
Baseline Baseline
day
value
1
100
2
100
3
98
4
97
5
95
6
95
7
90
8
85
9
84
10
83
11
83
12
83
13
79
14
78
15
78
Future
Future
day
value
1
95
2
97
3
94
4
95
5
94
6
93
7
89
8
86
9
80
10
78
11
80
12
81
13
76
14
75
15
74
MATS will sort the data from high to low based on the Baseline values:
Baseline Baseline Future Future
day value day value
1 100 1 95
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2
100
2
97
3
98
3
94
4
97
4
95
5
95
5
94
6
95
6
93
7
90
7
89
8
85
8
86
9
84
9
80
10
83
10
78
11
83
11
80
12
83
12
81
13
79
13
76
14
78
14
75
15
78
15
74
Note that the Baseline values happen to stay in the same order. And note that the Future
values are not sorted high to low, and instead the Future days match the Baseline days.
When you compare these sorted data with the Initial threshold value of 85 ppb, note that
there are only eight (8) Baseline values (highlighted in yellow) at or above this threshold.
Since there are fewer days than the ten (10) specified as the Minimum number of days in
baseline at or above threshold, MATS will then lower the threshold by one ppb to 84 ppb.
There are nine (9) Baseline values at or above this lower threshold — still less than the
value of ten (10) that specified as the Minimum number of days in baseline at or above
threshold. MATS will lower the threshold again by one ppb to 83 ppb. At this point, there
are twelve (12) days at or above this threshold.
MATS will take the top 12 days:
Baseline
Baseline
Future
Future
day
value
day
value
1
100
1
95
2
100
2
97
3
98
3
94
4
97
4
95
5
95
5
94
6
95
6
93
7
90
7
89
8
85
8
86
9
84
9
80
10
83
10
78
11
83
11
80
12
83
12
81
13
79
13
76
14
78
14
75
15
78
15
74
and then calculate separate averages for the Control and Baseline values:
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Control average = 88.5000 ppb
Baseline average = 91.0833 ppb.
The RRF equals the ratio of the Control to the Baseline:
RRF = 88.5000 / 91.0833 = 0.972
Note that we report the RRF with three digits after the decimal point. The calculation of
the Baseline and Control averages does not involve any rounding or truncation.
7.4.1.3 RRF Calculation - Example 3
Assume the following default values:
RRF Setup:

Backstop minimum threshold for spatial fields

Spatial Gradient Setup:

Start Value
1
End Value
5
Assume that there are 15 days of data:
Baseline Baseline Future Future
day
value
day
value
1
84
1
83
2
85
2
84
3
85
3
84
4
82
4
82
5
78
5
78
6
76
6
75
7
70
7
72
8
70
8
62
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9
70
9
70
10
67
10
62
11
64
11
63
12
63
12
60
13
62
13
62
14
62
14
59
15
59
15
57
MATS will sort the data from high to low based on the Baseline values:
Baseline
Baseline
Future
Future
day
value
day
value
2
85
2
84
3
85
3
84
1
84
1
83
4
82
4
82
5
78
5
78
6
76
6
75
7
70
7
72
8
70
8
62
9
70
9
70
10
67
10
62
11
64
11
63
12
63
12
60
13
62
13
62
14
62
14
59
15
59
15
57
When you compare these sorted data with the Initial threshold value of 85 ppb, note that
there are only two (2) Baseline values at or above this threshold. Since there are fewer days
than the ten (10) specified as the Minimum number of days in baseline at or above
threshold, MATS will then lower the threshold by one ppb to 84 ppb. There are three (3)
Baseline values at or above this lower threshold — still less than the value of ten (10) that
specified as the Minimum number of days in baseline at or above threshold. MATS will
lower the threshold again by one ppb, and eventually get to the Minimum allowable
threshold value of 70 ppb. At this point, there are still only nine (9) days at or above this
threshold.
MATS will take the nine days (highlighted in yellow) above the Minimum allowable
threshold value and then calculate separate averages for the Control and Baseline values:
Control average = 76.6667 ppb
Baseline average = 77.7778 ppb.
The RRF equals the ratio of the Control to the Baseline:
RRF = 76.6667 / 77.7778 = 0.986
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Note that we report the RRF with three digits after the decimal point. The calculation
of the Baseline and Control averages does not involve any rounding or truncation.
7.4.1.4 RRF Calculation - Example 4
Assume the following default values:
RRF Setup:

Backstop minimum threshold for spatial fields

Spatial Gradient Setup:

Start Value
1
End Value
5
Assume that there are 15 days of data:
iseline
Baseline
Future
Future
day
value
day
value
1
67
1
65
2
74
2
73
3
70
3
69
4
68
4
66
5
78
5
77
6
66
6
64
7
66
7
63
8
65
8
63
9
63
9
63
10
62
10
60
11
61
11
61
12
60
12
59
13
60
13
57
14
59
14
56
15
57
15
55
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MATS will sort the data sorted from high to low based on the Baseline values:
Baseline Baseline Future
Future
day value day
5 78 5
2 74 2
3 70 3
4 68 4
1 67 1
6 66 6
7 66 7
8 65 8
9 63 9
10 62 10
11 61 11
12 60 12
13 60 13
14 59 14
15 57 15
value
77
73
69
66
65
64
63
63
63
60
61
59
57
56
55
When you compare these sorted data with the Initial threshold value of 85 ppb, note that
there are zero (0) Baseline values at or above this threshold. Since there are fewer days
than the ten (10) specified as the Minimum number of days in baseline at or above
threshold, MATS will lower the threshold by one ppb, and eventually get to the Minimum
allowable threshold value of 70 ppb.
At this point, there are still only three (3) days at or above this threshold — still less than
the Min number of days at or above minimum allowable. As a result, MATS will not
calculate a RRF and will set the future year design value to missing.
Rather than assume the MATS default values, assume the Minimum allowable threshold
value is 60 ppb:
7.4.1.5 RRF Calculation - Example 5
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RRF Setup:

Initial threshold value (ppb)
85
Minimum number of days in baseline at or above threshold
10
Minimum allowable threshold value (ppb)
SO
Min number of days at or above minimum allowable threshold |~~
5
Enable Backstop minimum threshold for spatial fields

Backstop minimum threshold for spatial fields

Spatial Gradient Setup:

Start Value
1
End Value
5
Assume that there are 15 days of data:
Baseline Baseline
day
value
1
67
2
74
3
70
4
68
5
78
6
67
7
66
8
65
9
63
10
62
11
61
12
60
13
60
14
59
15
57
Future
Future
day
value
1
65
2
73
3
69
4
66
5
77
6
64
7
63
8
63
9
63
10
60
11
61
12
59
13
57
14
56
15
55
MATS will sort the data from high to low based on the Baseline values:
Baseline Baseline Future Future
day value day value
5 78 5 77
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2
74
2
73
3
70
3
69
4
68
4
66
1
67
1
65
6
67
6
64
7
66
7
63
8
65
8
63
9
63
9
63
10
62
10
60
11
61
11
61
12
60
12
59
13
60
13
57
14
59
14
56
15
57
15
55
When you compare these sorted data with the Initial threshold value of 85 ppb, note that
there are zero (0) Baseline values at or above this threshold. Since there are fewer days
than the ten (10) specified as the Minimum number of days in baseline at or above
threshold, MATS will lower the threshold by one ppb, and eventually get to the Minimum
allowable threshold value of 62 ppb. At this point, there are ten (10) days at or above this
threshold.
MATS will take the ten days (highlighted in yellow) above the Minimum allowable
threshold value and then calculate separate averages for the Control and Baseline values:
Control average = 66.3000 ppb
Baseline average = 68.0000 ppb.
The RRF equals the ratio of the Control to the Baseline:
RRF = 66.3000 / 68.0000 = 0.975
Note that we report the RRF with three digits after the decimal point. The calculation of
the Baseline and Control averages does not involve any rounding or truncation.
7.4.1.6 RRF Calculation Spatial Gradient with Backstop Threshold - Example 6
The following is an example showing the difference between RRF s calculated for Point
Estimates and RRFs calculated for Spatial Fields. The key difference is that MATS allows
you to choose a Backstop minimum threshold for spatial fields, which applies just to
Spatial Fields. This extra parameter allows you to calculate RRFs for Spatial Fields exactly
as you would for Point Estimates, except in the case when the minimum number of days
threshold cannot be met (MATS would return a -9 value for point estimates).
An example of such a case of where the two RRF calculations differ is when the number of
days at or above the Minimum allowable threshold value is less than the Minimum
number of days at or above minimum allowable threshold. In this case, MATS would
not calculate an RRF for a Point Estimate. However, if the Backstop minimum threshold
for spatial fields is set to some value lower than the Minimum allowable threshold value
, then MATS could potentially calculate an RRF for all or most grid cells in a Spatial Field.
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The backstop minimum threshold allows the minimum threshold to be lowered to a value
below the Minimum allowable threshold value until the minimum number of days is
reached. The backstop threshold is only used for grid cells which do not have enough days
to meet the minimum number of days value with the minimum allowable threshold. The
backstop threshold does not change the calculation for grid cells that already meet the
minimum number of days.
RRF Setup:

Initial threshold value (ppb)
85
Minimum number of days in baseline at or above threshold
10
Minimum allowable threshold value (ppb)
70
Min number of days at or above minimum allowable threshold |~~
5
p" Enable Backstop minimum threshold for spatial fields

Backstop minimum threshold for spatial fields
60
Spatial Gradient Setup:

Start Value
1
End Value
5
Assume that there are 15 days of data for a grid cell:
Baseline
Baseline
Future
Future
day
value
day
value
1
67
1
65
2
74
2
73
3
70
3
69
4
68
4
66
5
78
5
77
6
67
6
64
7
66
7
63
8
65
8
63
9
63
9
63
10
62
11
61
11
61
10
60
12
60
12
59
13
60
13
57
14
59
14
56
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15 57 15 55
MATS will sort the data from high to low based on the Baseline values:
Baseline
Baseline
Future
Future
day
value
day
value
5
78
5
77
2
74
2
73
3
70
3
69
4
68
4
66
1
67
1
65
6
67
6
64
7
66
7
63
8
65
8
63
9
63
9
63
10
62
11
61
11
61
10
60
12
60
12
59
13
60
13
57
14
59
14
56
15
57
15
55
When you compare these sorted data with the Initial threshold value of 85 ppb, note that
there are zero (0) Baseline values at or above this threshold. Since there are fewer days
than the ten (10) specified as the Minimum number of days in baseline at or above
threshold, MATS will lower the threshold by one ppb, and eventually get to the Minimum
allowable threshold value of 70 ppb.
At this point, there are still only three (3) Baseline values (highlighted in yellow) at or
above this lower threshold — still less than the value of ten (10) that specified as the
Minimum number of days in baseline at or above threshold. This fails the test for
calculating an RRF for a Point Estimate. However, there is still a possibility that MATS
can calculate an RRF for a Spatial Field. MATS just needs to find at least five values
MATS will lower the threshold again by one ppb. At a threshold of 68 ppb, there are four
(4) days. MATS will lower the threshold again by one ppb. At a value of 67 ppb, there are
six (6) days at or above the Backstop minimum threshold for spatial fields.
iseline
Baseline
Future
Future
day
value
day
value
5
78
5
77
2
74
2
73
3
70
3
69
4
68
4
66
1
67
1
65
6
67
6
64
7
66
7
63
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8
9
10
11
12
13
14
15
65
63
62
61
60
60
59
57
8
9
11
10
12
13
14
15
63
63
61
60
59
57
56
55
Since MATS is looking for at least five days, MATS will take these six days (highlighted
in yellow) above the Backstop minimum threshold for spatial fields and then calculate
separate averages for the Control and Baseline values:
Control average = 70.0000 ppb
Baseline average = 71.4000 ppb.
The RRF equals the ratio of the Control to the Baseline:
RRF = 70.0000 / 71.4000 = 0.980
Note that we report the RRF with three digits after the decimal point. The calculation of
the Baseline and Control averages does not involve any rounding or truncation.
In using a spatial gradient to estimate ozone levels, MATS estimates ozone levels in
unmonitored locations by using the values of a nearby monitored data scaled by a ratio of
model values. The ratio, or spatial gradient, is a mean of model values at the unmonitored
location over the mean of the model values at a monitor.
Note that several "nearby" monitors (and their associated model values) are used in the
calculation of ozone values at an unmonitored location. MATS uses a process called
Voronoi Neighbor Averaging (VNA) to identify these neighbors, and then takes an inverse
distance-weighted average of these monitors.
MATS sorts the daily 8-hour maximum ozone values from high to low, averages a certain
number of these values (by default the top five), and then uses these averages in the
calculation of the spatial gradient. Note that the highest days for Cell A and Cell E are
determined independently of each other.
If you want to use a different set of days for the gradient adjustment, you can do so with
the Start Value and End Value. MATS assigns an index of value of 1 to the highest daily
8-hour maximum ozone value in each grid cell. The second-highest an index value of 2.
And so on. Using the Start Value and the End Value, you can identify the values that you
want to average by using this index.
7.4.2 Spatial Gradient Setup
7.4.2.1 Spatial Gradient Calculation - Example 1
Assume a Start Value of "1" and an End Value of "5":
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RRF Setup:

Backstop minimum threshold for spatial fields

Spatial Gradient Setup:

Start Value
1
End Value
5
For this example calculation, assume that we have one monitor and we want to use this
monitor to estimate the ozone level at the center of a nearby grid cell. Further assume that
the monitor resides in grid cell "A" and the we want to estimate the ozone level in grid cell
"E".
With the default Start Value equal to one (1) and the default End Value, equal to five (5),
MATS will average the five highest daily 8-hour maximum ozone values. Note, however,
that the highest days for Cell A and Cell E are determined independently of each other.
Assume that there are 15 days of data:
Day
Cell A
Day
Cell E
1
100
1
68
2
100
2
73
3
98
3
74
4
97
4
78
5
95
5
72
6
95
6
69
7
90
7
77
8
85
8
63
9
84
9
65
10
83
10
61
11
83
11
60
12
83
12
62
13
79
13
58
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14 78 14 56
15 78 15 57
MATS will sort the data for cell A from high to low. Independently, MATS will also sort
the data for Cell E from high to low. In this example, Day 1 has the highest value for cell
A, while the highest value for cell E falls on Day 2.
Day
Cell A
1
100
2
100
3
98
4
97
5
95
6
95
7
90
8
85
9
84
10
83
11
83
12
83
13
79
14
78
15
78
Day
Cell
4
78
7
77
3
74
2
73
5
72
6
69
1
68
9
65
8
63
12
62
10
61
11
60
13
58
15
57
14
56
MATS will take the top 5 days (highlighted in yellow) and then calculate separate averages
for cell A and cell E:
cell E average = 74.8000 ppb
cell A average = 98.0000 ppb.
The Spatial Gradient equals the ratio of Cell E to Cell A:
Spatial Gradient = 74.8000 / 98.0000 = 0.763
Note that we report the Spatial Gradient with three digits after the decimal point. The
calculation of the averages does not involve any rounding or truncation.
7.4.2.2 Spatial Gradient Calculation - Example 2
Assume a Start Value of "2" and an End Value of "3":
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RRF Setup:

Initial threshold value (ppb)
85
Minimum number of clays in baseline at or above threshold
10
Minimum allowable threshold value (ppb)
70
Min number of days at or above minimum allowable threshold |~~
5
v Enable Backstop minimum threshold for spatial fields

Backstop minimum threshold for spatial fields
60
Spatial Gradient Setup:

Start Value
2
End Value
3
For this example calculation, assume that we have one monitor and we want to use this
monitor to estimate the ozone level at the center of a nearby grid cell. Further assume that
the monitor resides in grid cell "A" and the we want to estimate the ozone level in grid cell
"E".
With the default Start Value equal to two (2) and the default End Value, equal to three (3),
MATS will average the second and third highest daily 8-hour maximum ozone values.
Assume that there are 15 days of data:
Day
Cell A
Day
Cell E
1
100
1
68
2
100
2
73
3
98
3
74
4
97
4
78
5
95
5
72
6
95
6
69
7
90
7
77
8
85
8
63
9
84
9
65
10
83
10
61
11
83
11
60
12
83
12
62
13
79
13
58
14
78
14
56
15
78
15
57
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MATS will sort the data for cell A from high to low. Independently, MATS will also sort
the data for Cell E from high to low.
Day
Cell A
1
100
2
100
3
98
4
97
5
95
6
95
7
90
8
85
9
84
10
83
11
83
12
83
13
79
14
78
15
78
Day
Cell E
4
78
7
77
3
74
2
73
5
72
6
69
1
68
9
65
8
63
12
62
10
61
11
60
13
58
15
57
14
56
MATS will take the second and third highest days (highlighted in yellow) and then
calculate separate averages for cell A and cell E:
cell E average = 75.5000 ppb
cell A average = 99.0000 ppb.
The Spatial Gradient equals the ratio of Cell E to Cell A:
Spatial Gradient = 75.5000 / 99.0000 = 0.763
Note that we report the Spatial Gradient with three digits after the decimal point. The
calculation of the averages does not involve any rounding or truncation.
7.4.2.3 Spatial Gradient Calculation - Example 3
Assume a Start Value of "4" and an End Value of "4":
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RRF Setup:

Backstop minimum threshold for spatial fields
60
Spatial Gradient Setup:

Start Value
4
End Value
4
For this example calculation, assume that we have one monitor and we want to use this
monitor to estimate the ozone level at the center of a nearby grid cell. Further assume that
the monitor resides in grid cell "A" and the we want to estimate the ozone level in grid cell
"E".
With the default Start Value equal to four (4) and the default End Value, equal to four (4),
MATS will only use the fourth highest daily 8-hour maximum ozone value in each grid
cell.
Assume that there are 15 days of data:
Day
Cell A
Day
Cell E
1
100
1
68
2
100
2
73
3
98
3
74
4
97
4
78
5
95
5
72
6
95
6
69
7
90
7
77
8
85
8
63
9
84
9
65
10
83
10
61
11
83
11
60
12
83
12
62
13
79
13
58
14
78
14
56
15
78
15
57
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MATS will sort the data for cell A from high to low. Independently, MATS will also sort
the data for Cell E from high to low.
Day
Cell A
Day
Cell E
1
100
4
78
2
100
7
77
3
98
3
74
4
97
2
73
5
95
5
72
6
95
6
69
7
90
1
68
8
85
9
65
9
84
8
63
10
83
12
62
11
83
10
61
12
83
11
60
13
79
13
58
14
78
15
57
15
78
14
56
MATS will take the fourth highest day (highlighted in yellow) for cell A and cell E:
cell E = 73 ppb
cell A = 97 ppb.
The Spatial Gradient equals the ratio of Cell E to Cell A:
Spatial Gradient = 73 / 97 = 0.753
Note that we report the Spatial Gradient with three digits after the decimal point.
7.5 Final Check
The Final Check window verifies the selections that you have made.
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RSlDesired output
¦ Data Input
¦ Filtering/Interpolation
¦ RRF/Spatial Gradient
Final Check
0
Final Check
Verify inputs
|i Press here to verify your selections... ^
Checking...
Check OK. Press the finish button to continue..
< Back
Finish
Cancel
Click the button Press here to verify your selections. If there are any errors, MATS will
present a message letting you know. For example, if the path to a model file is invalid —
perhaps you misspelled the file name — you would get the following error:
Verify inputs
Press here to verify your selections...
Checking...
Avalid monitor data file, base and future model files are required.
Base year data file missing.
After making the necessary correction, click the button Press here to verify your
selections. Then click the Finish button.
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- Verify inputs
Press here to verify your selections...
Checking...
Check OK. Press the finish button to continue..
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Visibility Analysis: Quick Start Tutorial
8 Visibility Analysis: Quick Start Tutorial
In this tutorial you will forecast visibility levels at Class I Areas in the United States. The
steps in this analysis are as follows:
• Step 1. Start MATS. Start the MATS program and choose to do a Visibility analysis.
• Step 2. Desired Output. Choose the output to generate. In this example, you will
forecast visibility levels using the new IMPROVE algorithm and model data at the
IMPROVE monitors.
• Step 3. Data Input. Choose the data files for input to MATS.
• Step 4. Filtering. Choose the years of monitor and model data that you want to use, and
then choose the particular monitors in these data that you want to include in the analysis.
• Step 5. Final Check. Verify the choices you have made.
• Step 6. Load & Map Results. Load your results and prepare maps of your forecasts.
• Step 7. Working with Configuration File. Examine the Configuration file that stores the
choices that you made underlying your analysis.
Each step is explained in detail below.
8.1 Step 1. Start MATS
Double-click on the MATS icon on your desktop, and the following window will appear:
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MATS
Help T
| Start | Map View Output Navigator
PM Analysis
Ozone Analysis
Visibility Analysis
*** Stop Info
Click the Visibility Analysis button on the main MATS window. This will bring up the
Configuration Management window.
Configuration Management

<• Create New Configuration!
Go
C Open Existing Configuration
Cancel

A Configuration allows you to keep track of the choices that you make when using MATS.
For example, after generating results in MATS, you can go back, change one of your
choices, rerun your analysis, and then see the impact of this change without having to enter
in all of your other choices. For this example, we will start with a new Configuration.
Choose Create New Configuration and click the Go button. This will bring up the
Choose Desired Output window.
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8.2 Step 2. Desired Output
The Choose Desired Output window allows you to choose the output that you would like to
generate. MATS allows you to calculate future year (forecast) visibility levels at Class I
Areas.
In the Scenario Name box type "Tutorial Visibility" - this will be used to keep track of
where your results are stored and the variable names used in your results files. Leave the
box checked next to Temporally-adjust visibility levels at Class I Areas. MATS will
create forecasts for each Class I Area in your modeling domain.
MATS provides two algorithms for calculating visibility — an "old version" and a "new
version" of the IMPROVE visibility algorithm (see IMPROVE 2006) (The old and new
versions are discussed in Desired Output section of the Visibility Analysis: Details
chapter.) Choose the new version.
A single IMPROVE monitor is associated with each Class I Area. MATS multiplies the
monitor value with a relative response factor (RRFI which is the the modeled future-year
visibility divided the modeled current-year visibility. In calculating the RRF, MATS allows
you to use either the model values in the grid cell at the IMPROVE monitor or to use the
model values in the grid cell at the Class I Area centroid. Choose the default option of using
model values in the grid cell at the monitor. (For additional details see the Desired Output
section of the Visibility Analysis: Details chapter.)
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Choose Desired Outpi
¦ Data Input
¦ Filtering
¦ Final Check
0
Choose Desired Output
Point Estimates
Scenario Name : [Tutorial Visibility
Forecast
p" Temporally-adjust visibility levels at Class 1 Areas
IMPROVE Algorithm
C use old version (• iuse new version
Use model grid cells at monitor
Use model grid cells at Class 1 area centroid
< Back
Next >
Cancel
When your window looks like the window above, click Next. This will bring up the Data
Input window.
8.3 Step 3. Data Input
The Data Input window allows you to choose the monitor data and the model data that you
want to use. As discussed in more detail in the Visibility Analysis: Details chapter, MATS
calculates the ratio of the model data to calculate a relative response factor fRRF) for the
20% best and 20% worst visibility days separately. MATS then multiplies the visibility
level measured at the monitor for the best days with the RRF for the best days to calculate
a future-year estimate for visibility on the best visibility days. MATS performs an
analogous calculation for the worst visibility days.
MATS comes loaded with IMPROVE visibility monitor values from 2000 through 2004. It
also comes loaded with an example model output dataset for visibility for 2001 and 2015.
These are the key ingredients for creating your visibility forecasts.
Use the default settings in the Data Input window. The window should look like the
following:
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0
I Choose Desired Output
Data Input
¦ Filtering
I Final Check
Data Input
Monitor Data
IMPROVE Monitor Data - Old Algorithm |C:\Program Files\Abt Associates\MATS\Sa
IMPROVE Monitor Data- New Algorithm |04-daily IMPROVE-all data-new equation, csv 3D
Model Data
Baseline File
Forecast File
Using Model Data
Temporal adjustment at monitor
|ATS\SampleData\visibility_model_2001 .csv 3D
|ATS\SampleData\visibility_model_2015. csv 3D
1x1 [Mean I
< Back
Next >
Cancel
Note that MATS gives you the option to use model data in different ways when calculating
forecasts at each monitor. The example model datasets are at 36km resolution. Therefore,
the default is to use a lxl array of model cells around each monitor. This is described in
more detail in the Using Model Data section of the Visibility Analysis: Details chapter.
When your window looks like the window above, click Next. This brings up the visibility
Filtering window.
8.4 Step 4. Filtering
The Filtering window has two sets of functions. The first involves identifying the years of
monitor and model data that you want to use. The second involves identifying the
particular monitors in these data that you want to include in the analysis. Use the default
settings pictured in the screenshot below.
Choose Visibility Data Years
• Specify the range of visibility monitor data that you want to use. The default is to use all
of the available data: 2000 through 2004. (That is, Start Monitor Year set to 2000 and
End Monitor Year set to 2004.)
• Choose the Base Model Year. This should match the metorological year that is being
modeled. It should fall within the range specified by the Start Monitor Year and the End
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Monitor Year. The Base Model Year for the example dataset is 2001.
Valid Visibility Monitors
• Identify the monitors that you want to include in the analysis. First, specify the
Minimum years required for a valid monitor. MATS excludes from the analysis any
monitors with fewer than the Minimum years required for a valid monitor. The
default value is 3 years.
• Specify the Maximum Distance from Domain [km]. Monitors that are further than the
Maximum Distance from Domain [km] are excluded from the analysis. The default
value is 25 kilometers (km).
0
¦ Choose Desired Output
¦ Data Input
Filtering
¦ Final Check
Filtering
Choose Visibility Data Years
Start Monitor Year End Monitor Year Base Model Year
3
Valid Visibility Monitors
Minimum years required for a valid monitor
Max Distance from Centroid to Gridcell Center [km] 25
< Back
Next >
Cancel
8.5 Step 5. Final Check
The Final Check window verifies the choices that you have made. For example, it makes
sure that the paths specified to each of the files used in your Configuration are valid.
Click on the Press here to verify selections button.
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¦ Choose Desired Output
¦ Data Input
¦ Filtering
Final Check
0
Final Check
Verify inputs
| Press here to verify your selections...
Checking...
Check OK. Press the finish button to continue..
< Back
Finish
Cancel
If you encounter any errors, go back to the choices you have previously made by clicking
on the appropriate part (e.g., Data Input) of the tree in the left panel, and then make any
changes required.
When your window looks like the window above, click Finish.
A temporary, new Running tab will appear (in addition to the Start, Map View and Output
Navigator tabs).
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r —
*
MATS

Qn x
Help T
| Start Map View Output Navigator
Running
[
Close |

Name Last Message

Tutorial Visibility.asr Starting Visibility Analysis

Note that MATS is very computation-intensive, so if you try to work with other programs
in addition they may run very slowly. When the calculations are complete, a small window
indicating the results are Done will appear. Click OK.

Done

After clicking OK, the Output Navigator tab will be active. (The Running tab will no
longer be seen.)
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Help T
Start Map View
Output Navigator
Highlight file of interest and right-click to view options to Map, View, and Ex
Name
Type
Size
No file loaded
Stop Info
Load
The next step (click here) shows you how to map your results with the Output Navigator.
For more details on mapping and other aspects of the Output Navigator, there is a separate
chapter on the Output Navigator.
8.6 Step 6. Load and Map Results
The Output Navigator tab allows you to look at and export your results in table form or as
a map. To start, make sure that the Output Navigator tab is active by simply clicking on
the tab.
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MATS
Help T
Start M ap Vi ew 0 utp ut N avi gato r
Load
Highlight file of interest and right-click to view options to Map, View, and Ex
Name
No file loaded
Type
Size
Stop Info
Click on the Load button and choose the Tutorial Visibility.asr file.
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Open MATS Result file
Look in: output

IS ri UK
Lib
My Recent
Documents
vS
Desktop
My Documents
%
My Computer
TI>
My Network
Places
File name:
Files of type:
(Tutorial Visibility.asr
MATS Result File
"3
Open
Cancel
-M
Under Configuration/Log Files, you will see two files:
• Configuration: keeps track of the assumptions that you have made in your analysis.
• Los File ', provides information on a variety of technical aspects regarding how a results
file (*.ASR) was created.
Under Output Files you will see:
• Tutorial Visibility - Forecasted Visibility - all design values: baseline and forecasted
visibility levels for the best and worst days for each year of the five year base period.
• Tutorial Visibility - Used Model Grid Cells - Base Data: baseline model values for PM
species for the grid cells and days used in the RRF calculations.
• Tutorial Visibility - Used Model Grid Cells - Future Data: future-year model values for
PM species for the grid cells and days used in the RRF calculations.
• Tutorial Visibility - Forecasted Visibility Data: baseline and forecasted deciview values
for the best and worst days (averaged across up to five years). Also includes
species-specific relative response factors for the best and worst days.
• Tutorial Visibility - Class 1 Area and IMPROVE Monitor Identifiers and Locations:
monitor latitude and longitude.
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V MATS
Help T
Start M ap Vi ew 0 utp ut N avi gato r
Load
Extract All
Highlight file of interest and right-click to view options to Map, View, and Ex
Name
Type
Size
Confiauration/Loq Files

Configuration
Configuration
2kb
Log File
Run Log
Okb
Output Files

. n x
Help T
Start | Map View
Output Navigator
(±4i;=4 fji
v m i~3
Standard Layers T

Data Loaded
@ ° Tutorial Visibility-Forecasted'

S© °°o
o-JSST
Long: -191.690"°*. Lat: 42.23942
Extent: Min(-16*^59,-2.4M) Max(-4.381,44.377)
Stop Info
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To view an enlarged map, use the Zoom to an area Task Bar button on the far left.
Choose the Continental US.
V MATS
. n x
Help T
Start | Map View Output Navigator
':V- +v"x ['"{.i S® ^ Standard Layers -
Full Extent

Continental US
0
Maryland
New England
Southern California
Texas
Washington DC
Edit Zoom Frames
Add Current View to List
I I

Long: -171.571 ***. Lat: 22.95612
Extent: Min(-16*^59,-2.161) Wax(-1.381.11.377)
i i i
I

The map will then zoom to the Continental US.
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V MATS
Help "
Start
Map View Output Navigator
J Data Loaded |
Standard Layers
@ 0 Tutorial Visibility-Forecasted'
To more easily view the location of monitors in particular states, uncheck US Counties
using the Standard Layers drop down menu on the far right of the Task Bar. Your
window should look like the following:
Long:-75.18519, Lat: 23.1
Extent: Min(-122.899,22.3S7)**»ex(-58.824,44616)
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Help
Start
Map View
Output Navigator
Standard Layers
v US States
J Data Loaded j
@ ° Tutorial Visibility-Forecasted'
Class I Areas
'o o
Info
US Counties
Long: -106.57084, Lat: 50.3E***0
Extent: Min(-122.961.23.271 )*"*ax(-59.02M4.373)
MATS
Right click on the "Tutorial Visibility - Forecasted Visibility Data" layer in the panel on the
left side of the window. Choose the Plot Value option.
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V MATS
n x
Help
Start
Map View Output Navigator
V
J Data Loaded j
@ ° Tutorial Visib"'
Standard Layers "
Remove
Export as CSV File
Plot Value
' %
Long:-127.83721, Lat: 40.22***6
Extent: Min(-122.9G1,23.271 )™bx(-59.025.^-1.373)
i
Stop Info
This will bring up Shape Class Breaks window. In the Value drop-down list, choose the
variable "dv be.sl" — this is forecasted visibility design value for the best visibility days in
2015. (Note that the Date box defaults to the baseline year; in this case 2001.)
Shape Class Breaks

Layer Name: Tutorial Visibility - Forecasted Visibility - all design v
Value:
Date
dv_best
3
12000
Bins C Unique Values
Class Count: Marker Sizing: 0
Start Color
End Color
3§ Clear Breaks
V Apply X Close
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Click Apply and then click Close. This will bring you back to the Map View window.
r MATS
~HI®
Long:-119.-18580, Lat-19.17^7
Extent: Min(-1 22.961,23.271)***ax(-59.025,44.373)
Start
Map View Output Navigator
¦•+ - |i^ g|j [3 Standard Layers *
JiData Loaded,
0 0 Tutorial Visibility-Forecasteu11
@ • dv_best-6 to -6
@ • dv_best-6 to -6
0 ° dv_best-6 to 1.96
@ ° dv_best2.1 to 6.33
0 ° dv_best 6.-15 to 15.66
Examine the other variables:
civ worst: forecasted deciview values for 20% worst days;
dv best forecasted deciview values for 20% best days
base best, baseline design values for best days;
base worst: baseline design values for worst days;
rrf b crustal, rrf bno3, rrf b oc, rrf b ee, rrf b cm, and rrf b so4: relative
response factor used to forecast the best visibility days;
rrf it' crustal, rrf it' no3, rrf it' oc, rrf it' ec, rrf it' cm, and rrf it' .so-/: relative
response factor used to forecast the worst visibility days;
This is just a brief summary of the mapping possibilities available. For more details, there
is a separate chapter on the Map View.
8.7 Step 7. Working with Configuration File
Configurations keep track of the choices that you have made in your analysis. There are
two ways that you can access your configuration. First, you can view your configuration
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using the Output Navigator. Right-click on Configuration and choose View.
Help
Start Map View
Highlight file of interest and right-click to view options to Map, View, and Extract the data
Info
Stop
Load
Extract All
Name
ConfiqurationyLoq Files
Log File
Output Files Extract
Tutorial Visiunuy-rurHuasitid Visibility- all design values
Tutorial Visibility - Used Model Grid Cells - Base Data
Tutorial Visibility-Used Model Grid Cells-Future Data
Tutorial Visibility- Forecasted Visibility Data
Tutorial Visibility- Class 1 Area and IMPROVE Monitor Identifiers and Locat... Monitor Network
Vie1
Type
Configuration
Run Log
Monitor Network
Monitor Network
Monitor Network
Monitor Network
85kb
335kb
336kb
23kb
19kb
Size
Okb
This will take you to the Tutorial Visibility configuration that you used to generate your
visibility results.
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Choose Desired Output
Data Input
Filtering
Final Check
Final Check
Verify inputs
Press here to verify your selections..
Checking...
Check OK. Press the finish button to continue..
-------
Visibility Analysis: Quick Start Tutorial
MATS
Help T
| Start | Map View Output Navigator
PM Analysis
Ozone Analysis
Visibility Analysis
*** Stop Info
Click on the Visibility Analysis button. This will bring up the Configuration
Management window.
Configuration Management

<• Create New Configuration!
Go
C Open Existing Configuration
Cancel

Choose Open Existing Configuration and then click the Go button. This will bring up
the Select a Configuration window. Find the Tutorial Visibility.asr file that you
generated.
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Select a Configuration
Look in: £3 output

IS ri UK
My Recent
Documents
0
Desktop
I
My Documents
Si
My Computer
«
My Network
Places
File name:
Files of type:
(Tutorial Visibility.asr
ASRfiles f.asr)
"3
Open
Cancel
-M
Click Open and this will bring you your Tutorial Visibility configuration. Choose the Use
model grid cells at Class 1 area centroid option.
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Choose Desired Outpi
¦ Data Input
¦ Filtering
¦ Final Check
0
Choose Desired Output
Point Estimates
Scenario Name : [Tutorial Visibility
Forecast
p" Temporally-adjust visibility levels at Class 1 Areas
IMPROVE Algorithm
C use old version (• use new version
O Use model grid cells at monitor
® Use model grid ceils at Class i area ceniroidj
< Back
Next >
Cancel
MATS will now calculate RRFs using model data located over the center of each Class 1
area, instead of using model data located over the monitor linked to each Class 1 area.
To reflect this change in your analysis, change the Scenario Name box to Tutorial
Visibility - Model at Class 1.
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E
Choose Desired Out
Data Input
Filtering
Final Check
Choose Desired Output
Point Estimates
Scenario Name : [Tutorial Visibility- Model at Class 1
Forecast
p" Temporally-adjust visibility levels at Class 1 Areas
IMPROVE Algorithm
C use old version (•
use new version
0 Use model grid cells at monitor
® Use model grid cells at Class 1 area centroid
Next > Cancel
Keep all of the other assumptions the same. At the Filtering window, click the Finish
button. MATS will generate a new set of results and save them in a file called: Tutorial
Visibility - Model at Class l.asr. You can then view and map your results in the same way
as with other results files.
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9 Visibility Analysis: Details
MATS can calculate baseline and future-year visibility levels for the best and worst days
for Class I Areas — these estimates are referred to as Point Estimates. as they refer to
particular locations. MATS gives you several options for how to generate these estimates,
and keeps track of the choices you make with a Configuration.
When you begin the process of generating visibility estimates, MATS provides an option to
start a new Configuration or to open an existing Configuration.
r 1
Configuration Management

(• Create New Configuration!
Go
C Open Existing Configuration
Cancel
'

Select your option and then click Go.
MATS will then step you through a series of windows with choices for your analysis.
• Choose Desired Output. Choose whether you want to calculate Point Estimates at
IMPROVE monitors or at Class I Area centroids and whether to use the old or new
version of the IMPROVE visibility equation.
• Data Input. Specify the air modeling and monitoring data that you want to use. Specify
which model grid cells will be used when calculating RRFs at monitor locations.
• Filtering. Choose the years of monitoring data. Identify valid monitors.
MATS comes with a set of default choices and an example set of input files. If desired you
can use these defaults and skip to the Final Check window and click the Finish button to
generate your calculations.
9.1 Choose Desired Output
In the Choose Desired Output window, you specify the Scenario Name that you would
like to use, as well as choices regarding how you would like to calculate future year
(forecast) visibility levels for Class I Areas. As discussed in the section on Forecasting
Visibility, the forecast calculations have a number of steps. At the end of this section,
there is an example of these calculations.
You may use the "old version" or "new version" of the IMPROVE Equation (IMPROVE.
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2006). which MATS uses to translate PM levels (measured in ug/m3) to visibility levels
(measured in extinction or deciviews). You may also choose between using model data at
the monitor or model data at the center of the Class I Area.*
0
Choose Desired Output
Choose Desired Out
¦ Data Input
¦ Filtering
¦ Final Check
Point Estimates
Scenario Name : |Tutorial Visibility
Forecast
W Temporally-adiust visibility levels at Class 1 Areas
IMPROVE Algorithm
use old version
use new vers on
Use model grid cells at monitor
3 Use model grid cells at Class 1 area centroid
< Back
Next>
Cancel
* Monitors assigned to represent a Class I Area are generally close to the Class I Area. However, in
some cases, the distance can be substantial. For example, the YELL2 monitor in Wyoming (44.5653
latitude, -110.4002 longitude) is located more than a degree longitude away from the Red Rocks Lake
Class I Area (44.64 latitude, -111.78 longitude). By default, MATS uses model data at the monitor.
9.1.1 Scenario Name
The Scenario Name allows you to uniquely identify each analysis that you conduct. It is
used in three ways.
(1) Results file name. The results file is given the Scenario Name (e.g., Tutorial
Visibility.asr). Note that the extension (ASR) is specifically designated just for MATS
and can only be used by MATS.
(2) Organize output. In the Output folder, MATS will generate a folder using the
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Scenario Name. MATS will use this folder as a default location for files generated with
this Scenario Name.
* C:\Program Files\Abt Associates\MATS\output
EBB
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(3) Output file names. The output files generated will begin with the Scenario Name.
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MATS
n x
Help T
Start M ap Vi ew 0 utp ut Navigato r
Load
Extract All
Highlight file of interest and right-click to view options to Map, View, and Ex
Name
Type
Size
Confiauration/Loq Files

Configuration
Configuration
2kb
Log File
Run Log
Okb
Output Files

Tutorial Visibility- Forecasted Visibility- all design values
Monitor Network
85kb
Tutorial Visibility- Used Model Grid Cells - Base Data
Monitor Network
335kb
Tutorial Visibility- Used Model Grid Cells - Future Data
Monitor Network
336kb
Tutorial Visibility- Forecasted Visibility Data
Monitor Network
23kb
Tutorial Visibility- Class 1 Area and IMPROVE Monitor Identifiers a.
Monitor Network
19kb
Stop Info
9.1.2 Forecast Visibility at Class I Areas
MATS provides a forecast of visibility in Class I Areas. The approach used has the
following steps:
• Identify best & worst visibility monitor days in Base Model Year. Use monitored
total extinction data from a user-specified year to identify the 20 percent best and 20
percent worst visibility days at each Class I area. At this stage MATS is using extinction
values (measured in inverse megameters). By the end of this series of calculations,
MATS will convert these extinction visibility measures to deciviews.
Note that you specify the particular year, the Base Model Year, from the available
monitoring data in the Filtering window. MATS labels the year of monitoring data as
the Base Model Year, because this particular year of monitor data matches the baseline
model data, specified in the Data Input window.
• Average best & worst baseline model days. Using baseline speciated model data
(specified in the Data Input window), average the 20 percent best visibility days and
then average the 20 percent worst visibility days at each Class I areas (matched with the
ambient data). The model data comes into MATS as speciated values measured in ug/m3.
Average these speciated values. When done there will be two averages (one "best" the
other "worst") for each species and these averages will be ug/m3.
• Average best & worst forecast model days. Calculate these same averages for the
forecast model data (specified in the Data Input window). That is, identify the same 20
percent best and 20 percent worst visibility days and then average the speciated model
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data (measured in ug/m3).* When done there will be two averages (one "best" the other
"worst") for each species and these averages will be in ug/m3.
• Calculate RRFs. Use the speciated (best & worst) averages from the baseline and
forecast model data to calculate two RRFs for each species. That is, there will be one
RRF for the 20 percent best visibility days and another for the 20 percent worst visibility
days at each Class I area. The "best" RRF is simply the ratio of the baseline "best"
average (measured in ug/m3) to the control scenario "best" average (measured in ug/m3).
The "worst" RRF is calculated in an analogous way. An RRF is unitless and there are
two for each species.
• Identify best & worst visibility days in other monitored years. Using monitored total
extinction, identify the 20 percent best visibility days and the 20 percent worst visibility
days from the other available years of monitoring data. The default in MATS is that there
should be at least three valid years and one of those years should be the base modeling
year (the base meteorological year). (Monitor validity is discussed further in the Valid
Visibility Monitors section.)
Note that the 20 percent best days will occur on a different set of days for each year;
similarly, the 20 percent worst days will occur on a different set of days for each year.
• Multiply RRF with speciated monitor data from each year. Multiply the species-
specific "best" RRF (unitless) with the "best" daily speciated monitor values (measured
in ug/m3) from each of the available years. Do analogous calculations for the worst days.
When done, there will the original (baseline) monitor values and an analogous set of
forecast values (equal to the baseline times the RRF).
Note that the RRF is based on best/worst days identified from the Base Model Year.
This same "Base Model Year" RRF is used with all of the valid monitor years. For
example, if the Base Model Year were 2001, then the RRF developed from 2001
modeling data will be applied to all valid data in the five year ambient base period.
• Convert ug/m3 values to daily extinction values and sum to get total extinction. For
each day in each valid monitor year (for both the baseline and forecast), use either the
New IMPROVE equation or the Old IMPROVE equation to convert ug/m3 values to get
daily total extinction (measured in inverse megameters). After this calculation there will
be a set of total extinction values for the best and worst visibility days in each valid year
for both the baseline and the forecast.
• Convert extinction to deciviews. For each valid year in both the baseline and forecast,
convert the best & worst daily averages from extinction (inverse megameters) to
deciviews (unitless). The formula for this conversion is as follows: Deciviews = 10*ln
(extinction/10)
• Average daily best and worst days. For each valid year, average the daily deciview
values from the 20 percent best visibility days and calculate the same average for the 20
percent worst visibility days. There will be up to five "best" averages and "worst" total
visibility measures (measured in deciviews) for both the baseline and the forecast.
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• Calculate final average. Average the valid best/worst yearly visibility measures. When
done there will be one "best" value and one "worst" value, measured in deciviews
(unitless), for both the baseline and forecast.
* The future days are the same as the base year days. The identification of the 20 percent best and
worst is solely based on the base year ambient data.
9.1.2.1 Old IMPROVE Equation
The Old IMPROVE equation is as follows:
bext = 3 *f(RH)* AMMS04
+ 3 *f(RH)* AMMN03
+ 4*OMC
+ 10*EC
+ CRUSTAL
+ 0.6*CM
+ RAYLEIGH.
where:
bext = total extinction (measured in inverse megameters)
FRH = term to account for enhancement of light scattering due to hydroscopic growth of
suflate and nitrate (unitless)
AMM_S04 = ammonium sulfate (ug/m3)
AMM_N03 = ammonium nitrate (ug/m3)
OMC = organic carbon mass (ug/m3) (OC*1.4)
EC = elemental carbon (ug/m3)
CRUSTAL = fine soil (ug/m3)
CM = coarse particulate matter (ug/m3)
RAYLEIGH = Rayleigh scattering. Accounts for natural scattering of light by gases in the
atmosphere. Assumed to equal 10 inverse megameters at all locations.
Example Calculation Old IMPROVE Equation
The first column "bext" presents the calculated value given the following data.
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bext
ID
LAT
LONG
DATE
FRH
CRUSTAL
AMM N03
OMC
EC
CM
AMM S04
71.04
ACAD1
44.3771
-68.261
20000101
3.22
0.22
1.02
2.05
1.12
2.99
3.09
23.70
ACAD1
44.3771
-68.261
20000105
3.22
0.12
0.11
0.38
0.07
0.89
1.01
34.08
ACAD1
44.3771
-68.261
20000108
3.22
0.13
0.24
0.95
0.15
1.69
1.58
37.86
ACAD1
44.3771
-68.261
20000112
3.22
0.14
0.22
0.69
0.19
4.48
1.89
31.26
ACAD1
44.3771
-68.261
20000115
3.22
0.16
0.19
0.72
0.19
2.65
1.33
39.77
ACAD1
44.3771
-68.261
20000119
3.22
0.18
0.60
1.44
0.30
0.95
1.49
42.24
ACAD1
44.3771
-68.261
20000122
3.22
0.46
0.37
0.80
0.16
15.77
1.44
9.1.2.2 New IMPROVE Equation
The New IMPROVE Equation has a number of additional terms, in relation to the Old
IMPROVE equation. In particular, it takes into account the different effects of small and
large sulfate, nitrate, and organic carbon particles. A separate equation defining small and
large is given below.
bext = 2.2* fs(RH) * [ SMALLAMMS 04] + 4.8*f1(RH)*[LARGE_AMM_S04]
+ 2.4* fs(RH) * [ SMALLAMMN O 3 ] + 5.1*f1(RH)*[LARGE_AMM_N03]
+ 2.8 * [ SM ALLOMC ] + 6.1*[LARGE_0MC]
+ 10*EC
+ CRUSTAL
+ 0.6*CM
+ S SRAYLEIGH
+ 1.7* fss(RH) * SE A S ALT
+ 0.33*NO2.
where:
bext = total extinction (measured in inverse megameters)
fs(RH) = term to account for enhancement of light scattering due to hydroscopic growth of
small ammonium nitrate and ammonium sulfate (unitless)
f[(RH) = term to account for enhancement of light scattering due to hydroscopic growth of
large ammonium nitrate and ammonium sulfate (unitless)
SMALL_AMM_S04 = small ammonium sulfate (ug/m3)
LARGE AMM S04 = large ammonium sulfate (ug/m3)
SMALL_AMM_N03 = small ammonium nitrate (ug/m3)
LARGE_AMM_N03 = large ammonium nitrate (ug/m3)
SMALL OMC = small organic carbon mass (ug/m3) (0C*1.8)
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LARGEOMC = large organic carbon mass (ug/m3) (OC*1.8)
EC = elemental carbon (ug/m3)
CRUSTAL = fine soil (ug/m3)
CM = coarse particulate matter (ug/m3)
SSRAYLEIGH = Site-specific Rayleigh scattering (inverse megameters)
fss(RH) = term to account for enhancement of light scattering due to hydroscopic growth of
sea salt (unitless)
SEA_SALT = Sea salt (ug/m3)
N02 = Nitrogen dioxide levels (parts per billion). This term is assumed to be zero.
The apportionment of the total concentration of sulfate compounds into the concentrations of
the small and large size fractions is accomplished using the following equations:
[Large Sulfate] = [Total Sulfate]/20 ug/m3 x [Total Sulfate], for [Total Sulfate] < 20 ug/m3
[Large Sulfate] = [Total Sulfate], for [Total Sulfate] > 20 ug/m3
[Small Sulfate] = [Total Sulfate] - [Large Sulfate]
The same equations are used to apportion total nitrate and total organic mass
concentrations into the small and large size fractions.
Example Calculation New IMPROVE Equation
The first column "bext" presents the calculated value given the following data.
bext
ID
LAT
LONG
DATE
FRH
FSRH
FLRH
FSSRH
SS RAYLEIGH
71.52
ACAD1
44.3771
-68.261
20000101
3.22
3.82
2.75
3.91
12
24.51
ACAD1
44.3771
-68.261
20000105
3.22
3.82
2.75
3.91
12
34.45
ACAD1
44.3771
-68.261
20000108
3.22
3.82
2.75
3.91
12
38.10
ACAD1
44.3771
-68.261
20000112
3.22
3.82
2.75
3.91
12
35.45
ACAD1
44.3771
-68.261
20000115
3.22
3.82
2.75
3.91
12
40.22
ACAD1
44.3771
-68.261
20000119
3.22
3.82
2.75
3.91
12
43.92
ACAD1
44.3771
-68.261
20000122
3.22
3.82
2.75
3.91
12
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SEA SALT
CRUSTAL
AMM N03
OMC
EC
PM10
CM
AMM S04
LARGE OMC
SMALL OMC
0
0.22
1.02
2.63
1.12
11.05
2.99
3.09
0.35
2.29
0
0.12
0.11
0.49
0.07
2.72
0.89
1.01
0.01
0.48
0
0.13
0.24
1.22
0.15
4.94
1.69
1.58
0.07
1.15
0
0.14
0.22
0.89
0.19
7.82
4.48
1.89
0.04
0.85
0.55116
0.16
0.19
0.93
0.19
5.53
2.65
1.33
0.04
0.89
0
0.18
0.60
1.85
0.30
5.03
0.95
1.49
0.17
1.68
0.192906
0.46
0.37
1.02
0.16
19.56
15.77
1.44
0.05
0.97
LARGE AMM S04
SMALL AMM S04
LARGE AMM N03
SMALL AMM N03
0.48
2.61
0.05
0.97
0.05
0.96
0.00
0.11
0.13
1.46
0.00
0.24
0.18
1.71
0.00
0.22
0.09
1.24
0.00
0.19
0.11
1.38
0.02
0.59
0.10
1.34
0.01
0.37
9.1.2.3 Choose Model Grid Cell
The model data are used to calculate an RRF for each species for both the best and worst
visibility days. The RRF is the ratio of future-year modeled visibility levels over baseline
modeled visibility levels. When forecasting visibility, MATS allows you to choose
whether the RRF will be calculated with model data from the grid cell containing the
monitor or the centroid of the Class I Area.
The representative IMPROVE monitor assignments are taken from Appendix A, Table A-2
of "Guidance for Tracking Progress Under the Regional Haze Rule"
http://www.epa.gov/ttn/oarpg/tl/memoranda/rh tpurhr gd.pdf. Monitors assigned to
represent a Class I Area are generally close to the Class I Area. However, in some cases,
this distance can be substantial. For example, the YELL2 monitor in Wyoming (44.5653
latitude, -110.4002 longitude) is located more than a degree longitude away from the Red
Rocks Lake Class I Area (44.64 latitude, -111.78 longitude). By default, MATS uses
model data at the monitor.
9.1.3 Output Variable Description
MATS generates five output files:
• Visibility forecast (average of design values). (Up to) five year average of forecasted and
base-year average visibility. When you have specified the option Use model grid cells at
monitor, name of this file is "Forecasted Visibility Data.csv" with the Scenario Name
appended at the beginning (e.g., "Tutorial Visibility - Forecasted Visibility Data.csv").
When you have specified the option Use model grid cells at Class 1 area centroid,
name of this file changes to "Forecasted Visibility Data for Class 1 Areas.csv" plus the
Scenario Name (e.g., "Example Visibility - Forecasted Visibility Data for Class 1 Areas,
csv").
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• Visibility forecast (all design values). Forecasted and base-year values for individual
years. (The forecast is based on The name of this file is "Forecasted Visibility - all
design values.csv" plus the Scenario Name (e.g., "Tutorial Visibility - Forecasted
Visibility - all design values.csv").
• Class I areas and the monitors. Contains a list of all of the Class I areas and the monitors
assigned to each. The name of this file is "Class 1 Area and IMPROVE Monitor
Identifiers and Locations.csv" plus the Scenario Name (e.g., "Tutorial Visibility - Class 1
Area and IMPROVE Monitor Identifiers and Locations.csv").
• Base-year model data used. The name of this file is: "Used Model Grid Cells - Base
Data.csv" plus the Scenario Name (e.g., "Tutorial Visibility - Used Model Grid Cells -
Base Data.csv"). This file contains the base year model values for PM species for the
grid cells and days used in the RRF calculations.
• Future-year model data used. The format for this file is the same as for the base-year.
The name of this file is: "Used Model Grid Cells - Future Data.csv" plus the Scenario
Name (e.g., "Tutorial Visibility - Used Model Grid Cells - Future Data.csv"). This file
contains the future year model values for PM species for the grid cells and days used in
the RRF calculations.
The following sub-sections describe the variables in each file.
9.1.3.1 Forecasted Visibility Data.csv
An example of this output file is as follows (with variable definitions in the table below).
Note that the output data includes a large number of variables, so in the sample output
below we have divided the variables into two blocks. In a file actually generated by
MATS, these two blocks would be combined.
Note also that the variables output by MATS depend on whether you have specified using
model data at the monitor or model data at the center of the Class I Area. This is detailed in
the description table below.
Tutorial Visibility - Forecasted
Visibility Data.csv
Year
_id _typ date dv_be dv_worbase_be base_wo rrf_b_crus rrf_b_n rrf_b_o rrf_b_e rrf_b_c rrf_b_s
ACAD 6 200 7S96 "l^l 877 2Z89 1.064 0.653 0.963 0.745 1.573 0.939
1
AGTI 200 9.31 22.38 9.58 23.5 1.097 1.076 0.968 0.698 1.28 1.091
1
ALLA 200 4.67 15.18 5.5 17.84 1.02 0.997 0.978 0.573 1.417 0.69
1
ANAC 200 2.14 10.84 2.58 13.41 1.074 1.047 0.978 0.824 1.273 0.943
1
ARCH 200 3.21 9.51 3.75 11.24 1.105 0.878 0.988 0.85 1.179 1.001
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rrf_w_cru rrf_w_n rrf_w_ rrf_w_ rrf_w_ rrf_w_smonitor_grigridcell_lagridcell_lo monitor_l monitor_lo
at ng
44.3770 -68.2610
33.4640 -116.9700
47.4220 -121.4300
45.8600 -114.0000
38.4590 -109.8200
stal
03
oc
ec
cm
04
dcell
t ng
0.996
0.875
0.913
0.632
1.292
0.751
82139
44.3878 -68.1720
1.123
0.87
0.987
0.763
1.293
1.055
44025
33.4216 -117.1566
1.1
0.817
0.964
0.656
1.104
0.916
88026
47.3626 -121.2959
1.077
0.856
0.972
0.92
1.15
0.904
80040
45.8653 -114.0405
1.063
0.927
0.984
0.858
1.128
0.961
56046
38.4896 -109.7266
Variable
Jd
Jype
date
dv_best
dv_worst
base_best
base_worst
rrf_b_crustal
rrf_b_no3
rrf_b_oc
rrf_b_ec
rrf_b_cm
rrf_b_so4
rrf_w_crustal
rrf_w_no3
rrf_w_oc
rrf_w_ec
rrf_w_cm
rrf_w_so4
monitor_gridcell
Description
Site ID
Leave blank
Meteorological modeling year (used to identify the 20% best and worst
days from the ambient data)
Forecasted (future year) best visibility [up to five year average] (in
deciviews)
Forecasted (future year) worst visibility [up to five year average]
Base-year best visibility [up to five year average]
Base-year worst visibility [up to five year average]
Relative response factor (RRF) for crustal matter on the best visibility days
RRF for nitrate on the best visibility days
RRF for organic mass on the best visibility days
RRF for elemental carbon on the best visibility days
RRF for coarse matter (PM10 minus PM2.5) on the best visibility days
RRF for sulfate on the best visibility days
RRF for crustal matter on the worst visibility days
RRF for nitrate on the worst visibility days
RRF for organic mass on the worst visibility days
RRF for elemental carbon on the worst visibility days
RRF for coarse matter (PM10 minus PM2.5) on the worst visibility days
RRF for sulfate on the worst visibility days
Identifier for grid cell closest to monitor. (This variable only appears if you
specified the Use model grid cell at monitor option.)
class_i_g rid ce 11 Identifier for grid cell closest to Class 1 area. (This variable only appears if
you specified the Use model grid cell at Class 1 area centroid option.)
gridcelljat Centroid latitude in decimal degrees of grid cell used in calculation. Values
in the northern hemisphere are positive, and those in the southern
hemisphere are negative,
gridcelljong Centroid longitude in decimal degrees of grid cell used in calculation.
Values in the eastern hemisphere are positive, and those in the western
hemisphere (e.g., United States) are negative,
monitorjat Monitor latitude. (This variable only appears if you specified the Use
model grid cell at monitor option.)
classjjat Class 1 area centroid latitude. (This variable only appears if you specified
the Use model grid cell at Class 1 area centroid option.)
monitorjong Monitor longitude. (This variable only appears if you specified the Use
model grid cell at monitor option.)
classjjong Class 1 area centroid longitude. (This variable only appears if you
specified the Use model grid cell at Class 1 area centroid option.)
9.1.3.2 Forecasted Visibility - all design values.csv
An example of this output file is as follows (with variable definitions in the table below).
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Note that the variables output by MATS depend on whether you have specified using
model data at the monitor or model data at the center of the Class I Area. This is detailed in
the description table below.
Tutorial Visibility - Forecasted Visibility - all
design values.csv
Year
Jd _typ dat dv_bedv_wo base_b base_w monitor_gri gridcellja gridcelljo monitorj monitorjo
e
ACAD
e
200
n
St
7.95
rst
18.82
est
8.89
orst
21.64
dcell
82139
t ng
44.3878 -68.1720
at ng
44.3770 -68.2610
ACAD
U
200
1
200
8.31
19.99
8.87
23.28
82139
44.3878 -68.1720
44.3770 -68.2610
ACAD
8.01
20.84
8.77
23.91
82139
44.3878 -68.1720
44.3770 -68.2610
ACAD
2
200
o
7.98
20.38
8.77
23.65
82139
44.3878 -68.1720
44.3770 -68.2610
ACAD
200
A
7.56
19.03
8.56
21.98
82139
44.3878 -68.1720
44.3770 -68.2610
AGTI
200
n
-10
-10
-10
-10
44025
33.4216 -117.1566
33.4640 -116.9700
AGTI
U
200
1
9.56
21.85
10.17
22.92
44025
33.4216 -117.1566
33.4640 -116.9700
Variable Description
Jd Site ID
_type Leave blank
date Base year of monitoring data
dv_best Forecasted (future year) best visibility (in deciviews)
dv_worst Forecasted (future year) worst visibility
base_best Base-year best visibility
base_worst Base-year worst visibility
monitor_gridce Identifier for grid cell closest to monitor. (This variable only appears if you specified
II the Use model grid cell at monitor option.)
class_i_gridcel Identifier for grid cell closest to Class 1 area. (This variable only appears if you
specified the Use model grid cell at Class 1 area centroid option.)
gridcelljat Centroid latitude in decimal degrees of grid cell used in calculation. Values in the
northern hemisphere are positive, and those in the southern hemisphere are
negative.
gridcelljong Centroid longitude in decimal degrees of grid cell used in calculation. Values in the
eastern hemisphere are positive, and those in the western hemisphere (e.g., United
States) are negative.
monitorjat Monitor latitude. (This variable only appears if you specified the Use model grid cell
at monitor option.)
classjjat Class 1 area centroid latitude. (This variable only appears if you specified the Use
model grid cell at Class 1 area centroid option.)
monitorjong Monitor longitude. (This variable only appears if you specified the Use model grid
cell at monitor option.)
classjjong Class 1 area centroid longitude. (This variable only appears if you specified the Use
model grid cell at Class 1 area centroid option.)
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9.1.3.3 Class 1 Area and IMPROVE Monitor Identifiers and Locations.csv
An example of this output file is as follows (with variable definitions in the table below).
Note that the output data includes a number of variables, so in the sample output below we
have divided the variables into two blocks. In a file actually generated by MATS, these two
blocks would be combined.
Tutorial Visibility - Class 1 Area and IMPROVE Monitor Identifiers and
Locations.csv
Day
_id _type_class_i_name class_i_stat class_i_l class_i_lon class_i_gridc date
e at g
ACAD Acadia NP ME 44.35 -68.24
AGTI Agua Tibia Wilderness CA 33.42 -116.99
ALLA Alpine Lake Wilderness WA 47.55 -121.16
ANAC Anaconda-Pintler Wilderness MT 45.95 -113.5
ANAD Ansel Adams Wlderness CA 37.74 -119.19
(Minarets)
ARCH Arches NP UT 38.73 -109.58
BADL Badlands NP SD 43.81 -102.36
BALD Mount Baldy Wlderness AZ 33.95 -109.54
_monitor_id monitorjat monitorjong monitor_gridcell
ACAD1
AGTI1
SNPA1
SULA1
KAIS1
CANY1
BADL1
BALD1
44.3771
33.4636
47.4220
45.8598
37.2207
38.4587
43.7435
34.0584
-68.2610
-116.9706
-121.4259
-114.0001
-119.1546
-109.8210
-101.9412
-109.4406
82139
44025
88026
80040
-8
56046
70066
-8
ell
-8
-8
-8
-8
-8
-8
-8
-8
2000010
1
2000010
1
2000010
1
2000010
1
2000010
1
2000010
1
2000010
1
2000010
1
Variable
Description
Jd
Class I area site ID
Jype
Leave blank.
_class_i_name
Class 1 area name
class_i_state
State of Class 1 area
class_i_lat
Latitude in decimal degrees of Class 1 area centroid
class_i_long
Longitude in decimal degrees of Class 1 area centroid
class_i_g rid ce 11
Identifier of grid cell closest to the Class 1 area centroid
date
Meteorological modeling year
_monitor_id
IMPROVE site code (either at Class I area or a representative site)
monitorjat
IMPROVE Monitor latitude
monitorjong
IMPROVE Monitor longitude
monitor_gridcell
Identifier of grid cell closest to the monitor
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9.1.3.4 Used Model Grid Cells - Base/Future Data.csv
An example of this output file is as follows (with variable definitions in the table below):
Tutorial Visibility - Used Model Grid Cells - Base
Data.csv
Day
.
17123
_ty pe g rid ce I l_l a gridcelljo
t ng
25.2532 -80.6316
date
2001010
cm
0.34
crustal
1.63
so4
2.21
ec
0.63
no3
0.18
oc
3.69
_visibility_ra
nk
worst
17123
25.2532
-80.6316
2001011
0.15
0.53
1.32
0.36
0.01
2.01
worst
17123
25.2532
-80.6316
0
2001012
0.43
1.54
2.63
0.58
0.02
3.28
worst
17123
25.2532
-80.6316
2
2001012
0.52
2.29
3.45
0.79
0.12
5.07
worst
17123
25.2532
-80.6316
2001012
8
2001020
0.12
0.85
1.88
0.55
0.11
2.60
worst
17123
25.2532
-80.6316
0.07
0.64
2.11
0.67
0.04
3.87
worst
17123
25.2532
-80.6316
0
2001020
6
0.46
2.95
2.88
1.30
0.36
7.06
worst
Variable Description
Jd The ID is a unique name for each monitor in a particular location. The default value
is the column identifier multiplied by 1000 plus the row. (This is a character
variable.)
_type Leave blank
gridcelljat Latitude at the grid cell centroid in decimal degrees. Values in the northern
hemisphere are positive, and those in the southern hemisphere are negative,
gridcelljong Longitude at the grid cell centroid in decimal degrees. Values in the eastern
hemisphere are positive, and those in the western hemisphere (e.g., United States)
are negative.
date Date of daily average model value with YYYYMMDD format (This is a numeric
variable)
cm Coarse PM (ug/m3)
crustal Crustal PM
so4 Sulfate PM
ec Elemental Carbon
no3 Nitrate PM
oc Organic carbon PM
_visibility_rank worst = 20% worst days used in rrf calculation; best = 20% best days used in rrf
calculation
9.2 Data Input
In the Data Input window, you need to specify the monitor data and model data that you
want to use. MATS comes with monitor and model data. Alternatively, you can add your
own, following the monitor and model format described below. In addition, you need to
specify how MATS will evaluate the model data when calculating RRFs.
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Choose Desired Output
Data Input
Filtering
Visibility Analysis: Details
0
Data Input
Monitor Data
IMPROVE Monitor Data- Old Algorithm |TS\SampleData\visibility_monitor_data.csv _
IMPROVE Monitor Data- New Algorithm |04-daily IMPROVE-all data-new equation.csv 33
Model Data
Baseline File
Forecast File
Using Model Data
Temporal adjustment at monitor
|ATS\SampleData\visibility_rnodel_2001 .csv
|ATS\SampleData\visibility_model_£015.csv 33
1x1 i |Mean
< Back
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Cancel
9.2.1 Monitor Data Input
Daily monitor data, with concentration (ug/m3) and visibility (inverse megameters)
measures for each species, are available from the VIEWS website
http://vista.cira.colostate.edu/views/.* As described in the Forecasting Visibility section,
these monitor data are used to: (1) identify the 20 percent best and worst visibility days for
a given year, and (2) calculate the 5-year baseline conditions.
Note that one IMPROVE monitor is associated with each Class I site, and the calculated
visibility for the IMPROVE site is assumed to representative of the Class I site. MATS
comes with a default "linkage" database that provides the cross-walk that MATS uses for
IMPROVE monitors and Class I Areas.
The tables in the next sub-sections present the old equation and new equation variable
names and descriptions downloaded from the VIEWS website and the variable names used
in MATS. The format the data read into MATS is also included.
By default, MATS includes species concentrations (measured in ug/m3) as well as
extinction estimates (measured in inverse megameters) and deciview values. MATS uses
the concentration estimates. In particular, it uses the variables: AMM_S04, AMM_N03,
OMC, EC, CRUSTAL, and CM. The variable GOOD YEAR indicates whether a
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particular monitor should be used for a given year. A value of " 1" means the monitor can
be used, and a value of "0" means that the monitor should be dropped for the year.
The variable GROUP identifies the percentile for the overall visibility level for a particular
day. A value of "90" means that the particular day is among the 20 percent worst days for
the year. A value of "10" means that the particular day is among the 20 percent best days
for that year. (Days with other GROUP values are not needed)
There are a number of extra variables in the ambient data input file that are not directly
used by MATS (such as extinction values). The additional data can be used to QA MATS
output or for additional data analysis.
* The Visibility Information Exchange Web System (VIEWS) is an online exchange of air quality data,
research, and ideas designed to understand the effects of air pollution on visibility and to support the
Regional Haze Rule enacted by the U.S. Environmental Protection Agency (EPA) to reduce regional
haze and improve visibility in national parks and wilderness areas, http://vista.cira.colostate.edu/views/
9.2.1.1 Monitor Data Description (Old Equation)
The monitor data for the old IMPROVE algorithm includes a large number of variables, so
in the sample format below we have only a portion of the variables listed. The table below
has a complete listing of the variables.
Visibility Monitor Data Format (Old Algorithm)
|Day
_ID, _TYPE, LAT, LONG, DATE, FRH, PM25, CRUSTAL, AMM_N03, OMC, EC, PM10, CM, AM
"ACAD1",44.3771,-68.261,20000101,3.22,8.0645,0.2171958,1.017423,2.04764,1.11
"ACAD1","",44.37 71,-68.261,20000105,3.22,1.8308,0.1202 492,0.11119 8,0.3829,0.067
"ACADl","",44.37 71,-68.261,20000108,3.22,3.2 492,0.128 9 628,0.240972,0.95102,0.14
"ACADl","", 44.37 71,-68 .261,20000112,3 .22,3 .3 448, 0.14435 4, 0 .2193, 0.69384, 0 .18 66,
"ACADl","",44.3771,-68.2 61,20000115,3.22,2.8856,0.1553525,0.187308,0.72184,0.19
"ACADl","",44.3771,-68.2 61,20000119,3.22,4.0888,0.1827762,0.604623,1.44088,0.30
"ACADl","",44.3771,-68.2 61,20000122,3.22,3.7937,0.4609729,0.372681,0.79506,0.16
"ACADl","",44.37 71,-68.261,20000126,3.22,7.92 7 4,0.1164544,0.807927,1.19252,0.14 v

Visibility Monitor Data Variables and Descriptions (Old Algorithm)
Variable Description
_ID IMPROVE site code
_TYPE Leave blank
LAT Latitude in decimal degrees. Values in the northern hemisphere are positive,
and those in the southern hemisphere are negative.
LONG Longitude in decimal degrees. Values in the eastern hemisphere are positive,
and those in the western hemisphere (e.g., United States) are negative.
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DATE Date of daily average ambient data with YYYYMMDD format (This is a numeric
variable)
FRH Monthly climatological relative humidity adjustment factor
PM25 Measured PM2.5 mass (ug/m3)
CRUSTAL Crustal mass (2.2 x [Al] + 2.49 x [Si] + 1.63 x [Ca] + 2.42 x [Fe] + 1.94 x [Ti])
AMM_N03 Ammonium nitrate mass (N03*1.29)
OMC Organic carbon mass (OC*1.4)
EC Elemental carbon
PM10 PM10 mass
CM Coarse mass (PM10 minus PM2.5)
AMM_S04 Ammonium sulfate (S*4.125)
E_AMM_S04 Ammonimum sulfate extinction (Mm-1)
E_AMM_N03 Ammonimum nitrate extinction
E_OMC Organic mass extinction
E_EC Elemental carbon extinction
E_CRUSTAL Crustal extinction
E_CM Coarse mass (PM10 minus PM2.5) extinction
TBEXT Total bext (includes 10 Mm-1 for Rayleigh scattering)
DV Deciviews (calculated from Total bext)
GOOD_YEAR Denotes complete data for the year (1= all quarters >75% completeness, 0=
incomplete)
GROUP 90= 20% worst days and 10= 20% best days for each year (if good_year= 1)
POSSIBLE_NDAYSPossible samples in quarter
NDAYS Actual complete samples per quarter
Quarter completeness (1= complete, 0= incomplete)
COMPLETE_QUAR
TER
SF Sulfur concentration (used to calculate ammonium sulfate)
S04F Sulfate concentration (may be used as a backup in case S is missing)
NOTE: Character variables have names that begin with an underscore {i.e., and the
character values used can be kept with or without quotes. (If a character variable has an
embedded space, such as might occur with the name of a location, then use quotes.)
9.2.1.2 Monitor Data Description (New Equation)
The monitor data for the new IMPROVE algorithm includes a large number of variables, so
in the sample format below we have only a portion of the variables listed. The table below
has a complete listing of the variables.
Visibility Monitor Data Format (New Algorithm)
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Day
_ID,_TYPE,LAT,LONG,DATE,FRH,FSRH,FLRH,FSSRH,SS_RAYLEIGH,SEA_SALT,PM25,CRUSTAL, A J
ACAD1,,44.3 771,-68.261,20000101,3.22,3.82,2.75,3.91,12,0,8.0645,0.2171958,1.017
ACAD1,, 44.3 771,-68 .261,20000105,3.22,3 .82,2 .75,3.91,12,0, 1.8308, 0.1202492, 0 .111
ACAD1,,44.3771,-68.261,20000108,3.22,3.82,2.75,3.91,12,0,3.2492,0.1289628,0.240
ACAD1,,44.3771,-68.261,20000112,3.22,3.82,2.75,3.91,12,0,3.3448,0.144354,0.2193
ACAD1,,44.3771,-68.261,20000115,3.22,3.82,2.75,3.91,12,0.55116,2.8856,0.1553525
ACAD1,,44.3771,-68.261,20000119,3.22,3.82,2.75,3.91,12,0,4.0888,0.1827762,0.604
ACAD1,, 44.3771,-68 .261,20000122,3.22,3 .82,2 .75,3.91, 12,0 .192906,3.7937, 0 .460972
ACAD1,,44.3771,-68.261,20000126,3.22,3.82,2.75,3.91,12,2.29752,7.9274,0.1164544 v
< -J" _ 1 >
Visibility Monitor Data Variables and Descriptions (New Algorithm)
Variable
JD
_TYPE
LAT
LONG
DATE
FRH
FSRH
FLRH
FSSRH
SS_RAYLEIGH
PM25
SEA_SALT
CRUSTAL
AMM_N03
OMC
EC
PM10
CM
AMM_S04
LARGE_OMC
SMALL_OMC
LARGE_AMM_S04
SMALL_AMM_S04
LARGE_AMM_N03
SMALL_AMM_N03
E_AMM_S04
E_AMM_N03
E_OMC
E_EC
E_CRUSTAL
E_CM
E_SEA_SALT
TBEXT
Description
IMPROVE site code
Leave blank
Latitude in decimal degrees. Values in the northern hemisphere are positive,
and those in the southern hemisphere are negative.
Longitude in decimal degrees. Values in the eastern hemisphere are positive,
and those in the western hemisphere (e.g., United States) are negative.
Date of daily average ambient data with YYYYMMDD format (This is a numeric
variable)
Monthly climatological relative humidity adjustment factor
Monthly climatological relative humidity adjustment factor - small sulfate and
nitrate particles
Monthly climatological relative humidity adjustment factor - large sulfate and
nitrate particles
Monthly climatological relative humidity adjustment factor - sea salt
Site-specific Rayleigh scattering (Mm-1)
Measured PM2.5 mass (ug/m3)
Sea salt mass
Crustal mass (2.2 x [Al] + 2.49 x [Si] + 1.63 x [Ca] + 2.42 x [Fe] + 1.94 x [Ti])
Ammonium nitrate mass (N03*1.29)
Organic carbon mass (OC*1.8)
Elemental carbon
PM10 mass
Coarse mass (PM10 minus PM2.5)
Ammonium sulfate (S*4.125)
Large organic mass
Small organic mass
Large ammonium sulfate
Small ammonium sulfate
Large ammonium nitrate
Small ammonium nitrate
Ammonimum sulfate extinction (Mm-1)
Ammonimum nitrate extinction
Organic mass extinction
Elemental carbon extinction
Crustal extinction
Coarse mass (PM10 minus PM2.5) extinction
Sea salt extinction
Total bext (includes site specific Rayleigh scattering)
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DV
GOOD YEAR
Deciviews (calculated from Total bext)
Denotes complete data for the year (1= all quarters >75% completeness, 0=
incomplete)
90= 20% worst days and 10= 20% best days for each year (if good_year= 1)
GROUP
POSSIBLE_NDAYS Possible samples in quarter
NDAYS
COMPLETE
TER
SF
S04F
Actual complete samples per quarter
.QUAR Quarter completeness (1= complete, 0= incomplete)
Sulfur concentration (used to calculate ammonium sulfate)
Sulfate concentration (may be used as a backup in case S is missing)
NOTE: Character variables have names that begin with an underscore (i.e., and the
character values used can be kept with or without quotes. (If a character variable has an
embedded space, such as might occur with the name of a location, then use quotes.)
MATS comes with a default database that provides the linkage between IMPROVE
monitors and Class I Areas. The file is called "156-Class I-coordinates-all site names.csv".
The format of the file and the variable descriptions are as follows:
Format for File Linking IMPROVE Monitors and Class I Areas
|_MONITOR_ID,MonLAT,MonLONG,_CLAS S_I_NAME,_ID,_STATE_ID,LAT,LONG
ACAD1,44.3771,-68.2610,"Acadia NP",ACAD,ME,44.35,-68.24
AGTI1,33.4636,-116.9706,"Agua Tibia Wilderness",AGTI,CA,33.42,-116.99
BADL1,43.7 435,-101.9 412,"Badlands NP",BADL,3D,43.81,-102.36
BALD1,34.0584,-109.4406,"Mount Baldy Wilderness",BALD,AS,33.95,-109.54
BAND1,35.7797,-106.2664,"Bandelier NM",BAND,NM,35.79,-106.34
BIBE1,29.3027,-103.1780,"Big Bend NP",BIBE,TX,29.33,-103.31
BLIS1,38.9761,-120.1025,"Desolation Wilderness",DESO,CA,38.90,-120.17
BLISl,38.9761,-120.1025,"Mokelumne Wilderness",MOKE,CA,38.57,-120.06
BOAPl,33.8695,-106.8520,"Bosque del Apache",BOAP,NM,33.79,-106.85
< mi >
Variables and Descriptions for File Linking IMPROVE Monitors and Class I Areas
Var Name Description (variable type)
9.2.1.3 Linkage between Monitors & Class I Areas
MONITORID IMPROVE monitor identification code (text)
MonLONG
MonLAT
IMPROVE monitor latitude (numeric)
IMPROVE monitor longitude (numeric)
CLASSINAM Name of Class I Area (text)
E
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LONG
LAT
ID
STATE ID
Class I Area identification code (text)
State in which Class I Area is located (text)
Class I Area centroid latitude (numeric)
Class I Area centroid longitude (numeric)
NOTE: Character variables have names that begin with an underscore (i.e., "_"), and the
character values used can be kept with or without quotes. (If a character variable has an
embedded space, such as might occur with the name of a location, then use quotes.)
9.2.2 Model Data Input
The model data for the 20 percent best and worst visibility days are used to calculate
relative response factors (RRFs), which provide an estimate of the relative change in
visibility from the baseline conditions to a future year. Recall from forecast steps that the
monitor data are used to identify the best and worst days. Not the model data. MATS will
match the best and worst measured days to the correct modeled days, by date. The model
data input to MATS is in terms of PM species concentrations (measured in ug/m3).
The following exhibits provide an example of the model data format and a description of
the variables. Note that the first line of the data file gives the frequency of the data. In this
case, daily data. The second line gives the variables names. The data begins on the third
line. Each data line represents a daily observation.
Format for Daily PM Model Data
o
Day

A
o
_ID,
_TYPE
, LAT, LONG, DATE, CM, CRUSTAL, S04, EC, N03, 0C

o
1001
"",18
.362337,-121.659843,20150101,0.0001,0.0003,0.0448,0.0037,0,0.0168

o
1001
"",18
.362337,-121.659843,20150102,0.0058,0.0166,0.1202,0.0072,0,0.0325

o
1001
"",18
.362337,-121.659843,20150103,0.0001,0.0001,0.0838,0.0093,0,0.0412

o
1001
"",18
.362337,-121.659843,20150104,0,0,0.2813,0.0118,0,0.0481

o
1001
"",18
.362337,-121.659843,20150105,0,0,0.6533,0.0354,0.0008,0.1588

o
1001
"",18
.362337,-121.659843,20150106,0,0,0.5854,0.0249,0.0005,0.1146

o
1001
"",18
.362337,-121.659843,20150107,0,0.0001,0.8663,0.0228,0.0003,0.1017

o
1001
"",18
.362337,-121.659843,20150108,0.0011,0.0056,0.4761,0.0147,0,0.0658

o
1001
"",18
.362337,-121.659843,20150109,0.0153,0.057,0.3192,0.0157,0,0.0751

o
1001
"",18
.362337,-121.659843,20150110,0.005,0.0217,0.1882,0.0123,0,0.048

o
1001
"",18
.362337,-121.659843,20150111,0,0,0.0995,0.007,0,0.0219

o
1001
"",18
.362337,-121.659843,20150112,0.0018,0.0022,0.1087,0.0072,0,0.0235

o
1001
"",18
.362337,-121.659843,20150113,0.0015,0.0022,0.1335,0.008,0,0.027

o
1001
"",18
.362337,-121.659843,20150114,0.0002,0.0006,0.1797,0.008,0,0.0256

o
1001
"",18
.362337,-121.659843,20150115,0.0025,0.0061,0.1131,0.0069,0,0.0267

o
1001
"",18
.362337,-121.659843,20150116,0.0189,0.0407,0.2609,0.016,0,0.0658

o
1001
"",18
.362337,-121.659843,20150117,0.0011,0.0021,0.2342,0.0157,0,0.0532
V
<

>
Visibility Model Data Variable Descriptions
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Variable Description
_ID The ID is a unique number for each model grid cell in the air quality model
domain. It is generally based on the column and row identifiers from the air
quality modeling domain. The default convention is to calculate the ID by
multiplying the column identifier by one thousand (1000) and adding the row
identifier. (This is a character variable.)
_TYPE Leave blank
LAT Latitude in decimal degrees of the center of each grid cell. Values in the
northern hemisphere are positive, and those in the southern hemisphere are
negative.
LONG Longitude in decimal degrees of the center of each grid cell. Values in the
eastern hemisphere are positive, and those in the western hemisphere (e.g.,
United States) are negative.
DATE Date of daily average model value with YYYYMMDD format (This is a numeric
variable)
CM Coarse PM (ug/m3)
CRUSTAL Crustal PM
S04 Sulfate PM
EC Elemental carbon
N03 Nitrate PM
OC Organic mass PM
NOTE: Character variables have names that begin with an underscore (i.e., and the
character values used can be kept with or without quotes. (If a character variable has an
embedded space, such as might occur with the name of a location, then use quotes.)
Finally, note that the species variables used by MATS do not exactly correspond to the
speciated monitor data input available from the VIEWS website. The following exhibit
presents the correspondence used by MATS.
Monitor Data
Variable Name (from Views) New Variable Name (used within
MATS)
"Soii CRUSTAL
Amm_N03 AMM_N03
OMC OMC
LAC EC
CM CM
Amm S04 AMM S04
Regardless of the species names used by the air quality model, the model output variables
should be changed to the MATS variable names when creating MATS input files.
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9.2.2.1 Using Model Data for Temporal Adjustment
Relative response factors (RRFs) are calculated for each species: sulfate, nitrate, EC, OMC,
Crustal, and Coarse Matter (CM), by taking the ratio of the average of the 20 percent best
(or worst) days in the future to the average of the 20 percent best (or worst) days in the
baseline. For example, when calculating the sulfate RRF for the 20 percent best days,
MATS does the following calculation:
1
— • 2^ Sulfcitef
future, j,i
RRK
Sulfate j j n
-¦HSu1fat^aseUmU
where:
j = Class I area
i = day identifier
n = number of 20 percent best visibility days
Sulfate = modeled sulfate concentration (in ug/m3) on best visibility days.
When identifying the model data for this calculation, MATS first determines whether you
want to use model values located at the monitor or at the centroid of the Class I Area. You
choose the desired location (monitor or cell centroid) in the Choose Desired Output
window. In addition, you need to specify how many cells around the desired location you
want to use in the calculation (1x1 matrix, 3x3 matrix, etc), and whether you want to use
the maximum or the mean of the model cells.
In the case of a 3x3 matrix with Mean specified, MATS identifies the speciated model
values (measured in ug/m3) from among the nine "nearby" grid cells for each day for each
Class I Area. In the typical case, where there are 365 days of model outputs, MATS will
generate 365 daily values. MATS will do this calculation separately for each species for
both the baseline and future-year scenarios. The Guidance Document recommends using
the Mean of model values when calculating the RRF. Next there is a recommended
example of how MATS calculates the RRF using the Mean for a 3x3 matrix. (An example
with the Maximum is also provided, however this is not the recommended approach for
visibility calculations.)
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0
I Choose Desired Output
Data Input
¦ Filtering
I Final Check
Data Input
Monitor Data
IMPROVE Monitor Data- Old Algorithm |TS\SampleData\visibility_rnonitor_data.csv
IMPROVE Monitor Data - New Algorithm |04-daily IMPROVE-all data-new equation.csv —]
Model Data
Baseline File
Forecast File
Using Model Data
Temporal adjustment at monitor
|ATS\SampleData\visibility_rnodel_20Cl1 .csv 3D
|ATS\SampleData\visibility_model_2015. csv 3D
1x1 -
1x1
3x3

5x5

--J
X
^1

< Back
Next >
Cancel
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0
I Choose Desired Output
Data Input
¦ Filtering
I Final Check
Data Input
Monitor Data
IMPROVE Monitor Data- Old Algorithm |TS\SampleData\visibility_rnonitor_data.csv
IMPROVE Monitor Data - New Algorithm |04-daily IMPROVE-all data-new equation.csv —]
Model Data
Baseline File
Forecast File
Using Model Data
Temporal adjustment at monitor
|ATS\SampleData\visibility_model_20Cl1 .csv 3D
|ATS\SampleData\visibility_model_2015. csv 3D
1x1 T [Mean
Maximum
< Back
Next >
Cancel
9.2.2.1.1 RRF Calculation - Example with Mean
The Guidance Document recommends using the Mean of the model values. In the case of
a 3x3 matrix with Mean specified, MATS averages the speciated model values (measured
in ug/m3) from the nine "nearby" grid cells for each day for each Class I Area.
Assume there are eight best visibility days with the following modeled sulfate values in the
baseline. MATS would average the values for each day.
Best Cell 1 Cell 2 Cell 3 Cell 4 Cell 5 Cell 6 Cell 7 Cell 8 Cell 9 Mean
Days
1 1.795 1.812 1.299 1.609 1.612 1.250 1.692 0.570 1.347 1.443
2 0.164 1.556 1.205 0.270 1.940 1.065 1.156 1.620 1.786 1.196
3 1.709 0.273 1.304 1.213 1.177 1.104 0.368 1.817 1.377 1.149
4 1.119 1.322 1.778 1.154 1.503 1.511 1.251 1.939 0.474 1.339
5 1.910 1.648 1.012 1.635 1.912 1.587 1.508 1.723 1.611 1.616
6 1.490 1.204 1.997 0.989 1.832 0.064 1.469 1.634 1.470 1.350
7 1.136 1.886 1.131 1.282 1.957 1.047 1.335 0.045 1.279 1.233
8 1.304 1.217 1.738 1.243 1.370 1.802 1.374 1.736 1.196 1.442
Assume there are eight best visibility days with the following modeled sulfate values in the
forecast. Again, MATS would average the values for each day.
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Best Cell 1 Cell 2 Cell 3 Cell 4 Cell 5 Cell 6 Cell 7 Cell 8 Cell 9 Max
Days
1 1.789 1.715 1.209 1.560 1.562 1.224 1.492 0.489 1.148 1.354
2 0.137 1.512 1.162 0.181 1.939 1.022 1.113 1.541 1.593 1.133
3 1.695 0.208 1.254 1.198 1.133 1.102 0.200 1.244 1.235 1.030
4 1.090 1.251 1.627 1.126 1.470 1.468 1.120 1.877 0.325 1.262
5 1.815 1.552 0.974 1.549 1.594 1.546 1.407 1.707 1.591 1.526
6 1.327 1.167 1.880 0.957 1.756 0.000 1.318 1.590 1.392 1.265
7 0.989 1.805 1.028 1.212 1.820 1.010 1.183 0.042 1.236 1.147
8 1.127 1.183 1.673 1.238 1.291 1.753 1.220 1.717 1.004 1.356
The average across the daily means for the baseline is 1.346 ug/m3. The average of the
forecast cells is 1.259. The sulfate RRF would then be calculated as: RRF = 1.259 / 1.346
= 0.935.
A similar process occurs for the other species. The end result is 12 RRFs — two for each of
the six species (i.e., sulfate, nitrate, elemental carbon, organic carbon, crustal, and
ammonium).
9.2.2.1.2 RRF Calculation - Example with Maximum
The Modeling Guidance Document recommends using the Mean of the model values for
visibility calculations. The example below, shows the calculations that would be involved
if thq Maximum were chosen. In the case of a 3x3 matrix with Maximum specified, MATS
identifies identify the highest speciated model values (measured in ug/m3) from among the
nine "nearby" grid cells for each day for each Class I Area.
Assume there are eight best visibility days with the following modeled sulfate values in the
baseline:
Best Days
1
2
3
4
5
6
7
8
Cell 1
1.795
0.164
1.709
1.119
1.910
1.490
1.136
1.304
Cell 2
1.812
1.556
0.273
1.322
1.648
1.204
1.886
1.217
Cell 3
1.299
1.205
1.304
1.778
1.012
1.997
1.131
1.738
Cell 4
1.609
0.270
1.213
1.154
1.635
0.989
1.282
1.243
Cell 5
1.612
1.940
1.177
1.503
1.912
1.832
1.957
1.370
Cell 6
1.250
1.065
1.104
1.511
1.587
0.064
1.047
1.802
Cell 7
1.692
1.156
0.368
1.251
1.508
1.469
1.335
1.374
Cell 8
0.570
1.620
1.817
1.939
1.723
1.634
0.045
1.736
Cell 9
1.347
1.786
1.377
0.474
1.611
1.470
1.279
1.196
MATS would choose the cells highlighted in orange:
Best Days
1
2
3
4
Cell 1
1.795
0.164
1.709
1.119
Cell 2
1.812
1.556
0.273
1.322
Cell 3
1.299
1.205
1.304
1.778
Cell 4
1.609
0.270
1.213
1.154
Cell 5
1.612
1.940
1.177
1.503
Cell 6
1.250
1.065
1.104
1.511
Cell 7
1.692
1.156
0.368
1.251
Cell 8
0.570
1.620
1.817
1.939
Cell 9
1.347
1.786
1.377
0.474
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5
1.910
6
1.490
7
1.136
8
1.304
1.648 1.012
1.204 1.997
1.886 1.131
1.217 1.738
1.635 1.912
0.989 1.832
1.282 1.957
1.243 1.370
1.587 1.508
0.064 1.469
1.047 1.335
1.802 1.374
1.723 1.611
1.634 1.470
0.045 1.279
1.736 1.196
Assume there are eight best visibility days with the following modeled sulfate values in the
forecast:
Best Days
1
2
3
4
5
6
7
8
Cell 1
1.789
0.137
1.695
1.090
1.815
1.327
0.989
1.127
Cell 2
1.715
1.512
0.208
1.251
1.552
1.167
1.805
1.183
Cell 3
1.209
1.162
1.254
1.627
0.974
1.880
1.028
1.673
Cell 4
1.560
0.181
1.198
1.126
1.549
0.957
1.212
1.238
Cell 5
1.562
1.939
1.133
1.470
1.894
1.756
1.820
1.291
Cell 6
1.224
1.022
1.102
1.468
1.546
0.000
1.010
1.753
Cell 7
1.492
1.113
0.200
1.120
1.407
1.318
1.183
1.220
Cell 8
0.489
1.541
1.744
1.877
1.707
1.590
0.042
1.717
Cell 9
1.148
1.593
1.235
0.325
1.591
1.392
1.236
1.004
MATS would choose the cells highlighted in orange. Note that the cells chosen for the
forecast can differ from the cells chosen for the baseline.
Best Days
Cell 1
Cell 2
1
1.789
1.715
2
0.137
1.512
3
1.695
0.208
4
1.090
1.251
5
1.815
1.552
6
1.327
1.167
7
0.989
1.805
8
1.127
1.183
Cell 3 Cell 4 Cell 5
1.209 1.560 1.562
1.162 0.181 1.939
1.254 1.198 1.133
1.627 1.126 1.470
0.974 1.549 1.594
1.880 0.957 1.756
1.028 1.212 1.820
1.673 1.238 1.291
Cell 6 Cell 7 Cell 8 Cell 9
1.224 1.492 0.489 1.148
1.022 1.113 1.541 1.593
1.102 0.200 1.244 1.235
1.468 1.120 1.877 0.325
1.546 1.407 1.707 1.591
0.000 1.318 1.590 1.392
1.010 1.183 0.042 1.236
1.753 1.220 1.717 1.004
The average of the best sulfate days chosen from the baseline cells is 1.897 ug/m3. The
average of the forecast cells is 1.821. The sulfate RRF would then be calculated as: RRF =
1.821 / 1.897 = 0.960.
A similar (independent) process occurs for the other species. The particular cells chosen
for sulfate may be quite different from the cells chosen for, say, nitrate. Continuing with
the example you might have the following pattern for baseline:
Best Days Cell 1
1 0.071
2 0.561
3 0.005
Cell 2 Cell 3
0.133 0.620
0.891 0.105
0.384 0.604
Cell 4 Cell 5
0.788 0.808
0.689 0.695
0.077 0.545
Cell 6 Cell 7
0.964 0.763
0.302 0.523
0.046 0.177
Cell 8 Cell 9
0.938 0.158
0.209 0.485
0.664 0.821
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4
0.926
0.651
0.111
0.334
0.887
0.548
0.447
0.547
0.730
5
0.630
0.995
0.769
0.888
0.379
0.121
0.779
0.130
0.558
6
0.700
0.761
0.993
0.556
0.659
0.877
0.761
0.474
0.821
7
0.074
0.189
0.619
0.987
0.279
0.757
0.470
0.189
0.701
8
0.848
0.100
0.964
0.535
0.566
0.315
0.440
0.011
0.852
And the following pattern for the forecast:

Best Days Cell 1
Cell 2
Cell 3
Cell 4
Cell 5
Cell 6
Cell 7
Cell 8
Cell 9
1
0.068
0.122
0.556
0.711
0.766
0.594
0.653
0.877
0.152
2
0.514
0.515
0.092
0.615
0.687
0.283
0.460
0.198
0.479
3
0.004
0.380
0.513
0.076
0.504
0.044
0.175
0.591
0.801
4
0.769
0.581
0.099
0.327
0.852
0.537
0.416
0.482
0.719
5
0.563
0.940
0.673
0.859
0.374
0.113
0.685
0.123
0.505
6
0.610
0.694
0.910
0.514
0.621
0.828
0.649
0.438
0.794
7
0.072
0.179
0.597
0.954
0.244
0.701
0.428
0.176
0.695
8
0.837
0.090
0.940
0.522
0.500
0.295
0.434
0.011
0.809
The end result is 12 RRFs — two for each of the six species {i.e., sulfate, nitrate, elemental
carbon, organic carbon, crustal, and ammonium).
9.3 Filtering
MATS loads in the monitor data that you have specified in Data Input window, and then in
the Filtering window MATS presents the available years of monitor data for your analysis.
You specify a range of years with the Start Monitor Year and End Monitor Year
drop-down menus.
Using the Base Model Year drop-down menu, you can also specify the year that you want
to use to determine the "best" and "worst" monitor days. The Base Model Year needs to
fall within the range specified by the Start Monitor Year and End Monitor Year. Once
you have specified the Base Model Year, MATS will then identify and save for each
monitor the particular dates during this year that registered the best and worst visibility
days. These dates are then used to identify the model values used in the calculation of
RRFs for the temporal adjustment, as seen in the Example in the Model Data Input section.
Given a particular range of years that you have chosen, you can also specify the criteria for
a monitor to be included in the analysis. With the Minimum years required for a valid
monitor box, you specify the minimum number of years of data that a monitor must have (
e.g., three years).
You can also specify the Maximum Distance from Domain. That is, you can choose the
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maximum distance that a monitor (or Class I Area centroid) can be from the nearest model
grid cell centroid. For example, if the Maximum Distance from Domain is 25, and a
monitor is more than 25 kilometers from the nearest model grid cell centroid, then a
forecast is not generated for this particular monitor. (More detailed examples regarding
distance and monitor validity are available here.)

Filtering
m
Choose Visibility Data Years
Start Monitor Year End Monitor Year Base Model Year
12000 3D l20CM 3] I2001 1]
Valid Visibility Monitors
Minimum years required for a valid monitor
Max Distance from Centroid to Gridcell Center [km] £5
< Back
Next >
Cancel
¦ Choose Desired Output
¦ Data Input
Filtering
¦ Final Check
9.3.1 Example Valid Visibility Monitors
Using the Maximum Distance from Domain, you can choose the maximum distance that
a monitor (or Class I Area centroid) can be from the nearest model grid cell centroid.
Whether you calculate the distance from a monitor or a Class I Area centroid depends on
whether you have specified Use model grid cells at monitor or Use model grid cells at
Class I area centroid.
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Point Estimates
Scenario Name: |
Forecast
p" Temporally-adjust visibility levels at Class 1 Areas
IMPROVE Algorithm
use old version C use new version
® Use model grid cells at monitor
Use model grid cells at Class 1 area centroid
Example 1
Assume you have chosen Use model grid cells at monitor. If you have set the Maximum
Distance from Domain to 25, and a monitor is more than 25 kilometers from the nearest
model grid cell centroid, then a forecast is not generated for this particular monitor. And by
extension, a forecast is not generated for the Class I Areas that are associated with this
particular monitor.
Recall from the section on the Linkage between Monitors and Class I Areas that more than
one Class I Area may be linked to a monitor. Highlighted in yellow below are some
examples of monitors associated with more than Class I Area. For example, if the CHIR1
monitor is more than 25 kilometers from the nearest model grid cell centroid, then no
forecasts would be generated for the three Class I Areas associated with this monitor (i.e.,
Chiricahua NM, Chiricahua Wilderness, and Galiuro Wilderness).
MONITOR MonLAT MonLONG CLASS I NAME
ID

ACAD1
44.377
-68.261
AGTI1
33.464
-116.971
BADL1
43.744
-101.941
BALD1
34.058
-109.441
BAND1
35.780
-106.266
BIBE1
29.303
-103.178
BLIS1
38.976
-120.103
BLIS1
38.976
-120.103
BOAP1
33.870
-106.852
BOWA1
47.947
-91.496
Acadia NP
Agua Tibia Wilderness
Badlands NP
Mount Baldy Wilderness
Bandelier NM
Big Bend NP
Desolation Wlderness
Mokelumne Wlderness
Bosque del Apache
Boundary Waters Canoe Area
ID
STATE
LAT LONG

ACAD
ME
44.35 -68.24
AGTI
CA
33.42 -116.99
BADL
SD
43.81 -102.36
BALD
AZ
33.95 -109.54
BAND
NM
35.79 -106.34
BIBE
TX
29.33 -103.31
DESO
CA
38.9 -120.17
MOKE
CA
38.57 -120.06
BOAP
NM
33.79 -106.85
BOWA
MN
48.06 -91.43
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BRCA1
37.618
-112.174
Bryce Canyon NP
BRCA
UT
37.57 -112.17
BRET1
29.119
-89.207
Breton
BRET
LA
29.87 -88.82
BRID1
42.975
-109.758
Bridger Wilderness
BRID
WY
42.99 -109.49
BRID1
42.975
-109.758
Fitzpatrick Wilderness
FITZ
WY
43.24 -109.6
BRIG1
39.465
-74.449
Brigantine
BRIG
NJ
39.49 -74.39
CABI1
47.955
-115.671
Cabinet Mountains Wlderness
CABI
MT
48.18 -115.68
CACR1
34.454
-94.143
Caney Creek Wlderness
CACR
AR
34.41 -94.08
CANY1
38.459
-109.821
Arches NP
ARCH
UT
38.73 -109.58
CANY1
38.459
-109.821
Canyonlands NP
CANY
UT
38.23 -109.91
CAPI1
38.302
-111.293
Capitol Reef NP
CAPI
UT
38.06 -111.15
CHAS1
28.748
-82.555
Chassahowitzka
CHAS
FL
28.69 -82.66
CHIR1
32.009
-109.389
Chiricahua NM
CHIR
AZ
32.01 -109.34
CHIR1
32.009
-109.389
Chiricahua Wilderness
CHIW
AZ
31.86 -109.28
CHIR1
32.009
-109.389
Galiuro Wlderness
GAL I
AZ
32.6 -110.39
COHU1
34.785
-84.627
Cohutta Wilderness
COHU
GA
34.93 -84.57
CRLA1
42.896
-122.136
Crater Lake NP
CRLA
OR
42.92 -122.13
CRLA1
42.896
-122.136
Diamond Peak Wlderness
DIPE
OR
43.53 -122.1
CRLA1
42.896
-122.136
Gearhart Mountain Wlderness
GEMO
OR
42.51 -120.86
CRLA1
42.896
-122.136
Mountain Lakes Wilderness
MOLA
OR
42.33 -122.11
CRM01
43.461
-113.555
Craters of the Moon NM
CRMO
ID
43.39 -113.54
Example 2
Assume you have chosen Use model grid cells at Class I area centroid. If you have set
the Maximum Distance from Domain to 25, and a Class I Area is more than 25
kilometers from the nearest model grid cell centroid, then a forecast is not generated for this
particular Class I Area (e.g., Chiricahua, NM).
As noted above, the CHIR1 monitor is linked to two other Class I Areas. If these two other
areas are within 25 kilometers of a model grid cell centroid, then the monitor values from
CHIR1 would be used in the forecast for these two areas (along with the model values
associated with the centroid of each area).
9.4 Final Check
The Final Check window verifies the selections that you have made.
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¦ Choose Desired Output
¦ Data Input
¦ Filtering
Final Check
Final Check
Verify inputs
Press here to verify your selections
< Back
Finish
Cancel
Click the button Press here to verify your selections. If there are any errors, MATS will
present a message letting you know. For example, if the path to a model file is invalid —
perhaps you misspelled the file name — you would get the following error:
- Verify inputs
[i Press hereto verify your selections... j
Checking...
Model baseline data missing.
Check OK. Press the finish button to continue..
After making the necessary correction, click the button Press here to verify your
selections. Then click the Finish button.
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- Verify inputs
Press here to verify your selections...
Checking...
Check OK. Press the finish button to continue..
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Output Navigator
10 Output Navigator
The Output Navigator allows you to load results files (i.e., *.ASR files) that you have
previously created in MATS. You can view these data in maps and in tables, or export the
data to text files that you can then work with in a program such as Excel.
To start, just click on the Output Navigator tab.
Help T
Start Map View
Output Navigator
Highlight file of interest and right-click to view options to Map, View, and Ex
Name
Type
Size
No file loaded
Stop Info
Load
MATS
n x
Click on the Load button to view your file of interest. This will bring up the Open MATS
Results file window. Choose a results file (with the . ASR extension) This will bring you
back to the Output Navigator and display the available files.
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Output Navigator
V MATS
Help T
Start M ap Vi ew 0 utp ut N avi gato r
Load
Extract All
Highlight file of interest and right-click to view options to Map, View, and Ex
Name
Type
Size
Confiauration/Loq Files

Configuration
Configuration
2kb
Log File
Run Log
Okb
Output Files

Stop Info
For the Configuration File and Log File you will see two options: View and Extract.
MATS
Help "
Start Map View 0utput Navigator
Load
Extract All
Highlight file of interest and right-click to view options to Map, View, and Ex
Name
Type
Size
Confiquration/Loq Files
H Conf if¦
Configuration
2kb
Log
Run Log
Okb
Output Fxtract

1 1
Long: -185.462*** Lat: 56.32710
Extent: Min(-16*^59,-2.464) Max(-4.381,44.377)
i i i

To map your results, click on the Output Navigator tab. Load the ASR file that you want
to view and then right-click on the particular Output File that you want to map. This will
give you three choices, Add to Map, View, and Extract. * Choose the Add To Map option.
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Output Navigator
Help
Output Navigator
Highlight file of interest and right-click to view options to Map, View, and Ex
Stop Info
Load
Extract All
Name
Configuration/Loo Files
Configuration
Log File
Output Files
Tutorial Visibility - Forecasted Visibility - all design values
Tutorial Visibility-Used Model Grid Cells-Base Data
Tutorial Visibility - Used Model Grid Cells - Future Data
jTutorial Visibility - Forecast^
Tutorial Visibility - Class 1 A
Add To Map
View
Extract
or Identifiers a... Monitor Network
Type
Configuration
Run Log
Monitor Network
Monitor Network
Monitor Network
Monitor Network
85kb
335kb
336kb
19kb
Size
This will bring you back to the Map View tab.
r
V MATS
BUI
Help "
Start Map View Output Navigator
^ V S3 [3
Standard Layers "
JjData Loaded j

W ® Tutorial Visibility-Forecasted'

°°o (P
\°fl
*'.i t
o
I I i

Long:-176.762""". Lat: 66.30396
Extent: Min(-1e***59,-2.464) Max(-4.381,44.377)
1 1 1

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Output Navigator
Details on how to generate a variety of maps are in the Map View chapter.
* Note that if you right-click on the Configuration File or Log File vou will only see two options: View
and Extract. The Add To Map option is only relevant to the Output Files, as the Configuration and
Log files do not have a geographic component.
10.2 View Files
To view a file of interest, right-click on it, and then choose View. There are three basic
types of files available: Configuration. Log, and Output files.
Help T
Start Map View
Output Navigator
Highlight file of interest and right-click to view options to Map, View, and Ex
Stop Info
Load
Extract All
MATS
Configuration/Log Files
Configuration
Log File
Output Files
Tutorial Visibility- Forecasted Visibility- all design values
Tutorial Visibility- Used Model Grid Cells - Base Data
Tutorial Visibility - Used Model Grid Cells - Future Data
jTutorial Visibility - Fq
Tutorial Visibility- CI
Add To Map
View
Extract
- Monitor Identifiers a... Monitor Network
Configuration
Run Log
Monitor Network
Monitor Network
Monitor Network
Monitor Network
85kb
335kb
336kb
19kb
2kb
Okb
Name
Type
Size
10.2.1 Configuration File
A Configuration File stores the choices that you have made when using MATS. A useful
feature of a Configuration File is that it is reusable. You can use an existing Configuration
File, make some minor changes to generate a new set of results, without having to
explicitly set each of the choices you made in the previous Configuration.
To view a Configuration file from the Output Navigator, right-click on the file.
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Output Navigator
S> MATS
Help -
~HIE]
Start Map View Output Navigator
Load
Extract All
Highlight file of interest and right-click to view options to Map, View, and Ex
Name
Configuration/Log Files
Log Filf'
Output Fi
Extract
Tutor
al Visibil
Tutor
al Visibil
Tutor
al Visibil
Tutor
al Visibil
Tutor
al Visibil
Tutorial Visibility- Class 1 Area and IMPROVE Monitor Identifiers a... Monitor Network
Type
Size

Configuration
2kb
Run Log
Okb
Monitor Network
85kb
Monitor Network
335kb
Monitor Network
336kb
Monitor Network
23kb
Monitor Network
19kb
stop IBa
This will bring up the options that you chose when generating your results.
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Output Navigator
Choose Desired Outpi
¦ Data Input
¦ Filtering
¦ Final Check
Choose Desired Output
Point Estimates
Scenario Name
Forecast
Temporally-adiust visibility levels at Class 1 Areas
IMPROVE Algorithm
use old version
use new VHtoiut
Use model grid cells at monitor
Use model grid cells at Class 1 area centroid
< Back
Next>
Cancel
10.2.2 Log File
A Log File provides information on a variety of technical aspects regarding how a results
file (*. ASR) was created. This includes the version of MATS, the date and time the *.ASR
file was created.
To view a Log file from the Output Navigator, right-click on the file.
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Output Navigator
a" MATS
Help '
Ifr fl
ZIP®
Start Map View Output Navigator
Load
Extract All
Highlight file of interest and right-click to view options to Map, View, and Ex
Name

Type
Size
Confiauration/Loa Files

Configuration

Configuration
2kb

Run Log
Okb
Output Fi mSm

Tutorial Extract
sted Visibility-all design values
Monitor Network
85kb
Tutorial Visibility/ - Used Model Grid Cells - Base Data
Monitor Network
335kb
Tutorial Visibility-Used Model Grid Cells-Future Data
Monitor Network
336kb
Tutorial Visibility - Forecasted Visibility Data
Monitor Network
23kb
Tutorial Visibility-Class 1 Area and IMPROVE Monitor Identifiers a..
. Monitor Network
10kb
Stop Info
A separate Run Log tab will appear.
V MATS
Help
~OS
Start Map View Output Navigator Run Log
Close
»» Start MATS.exe v 1.1.0.105 2007-05-17 18:10:45
Jcfc/c/c/ctc/cJc/c/cJcJc/cjcJc/c/c/cJcJc/ctc/cJc/ctcAcJc/cJcJcJcJctctc/ctcJcJc/cJctcJcJeJcJcJcJcJctcJc/cJc/cfc/cJcJcAcJcJctcJctcJc/cJcJcJc/cJcfcJcAzJcteJcfcJc/c
Starting iteration 0
Run visibility analysis...364.628 s.
Total execution time: 364.634 s.
Jc/ctc/ctc/c/c/ctcfctctctcJc/c/c/cfcJcJcJc/c/c/cJc/cJctcfctc/ctc/cJctcfc/c/ctcJcJctcJc/ctctctcJc/c/cfc/cJc/c/cJcJctcJc/cJc/cJcJctctcfc/cJctcJc/c/cA
<«< Stop MATS.exe
2007-05-1718:16:49
Stop Info
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Output Navigator
Click the Close button when you have finished viewing it. (The Run Log tab will
disappear.)
10.2.3 Output Files
An Output file is one of the file types within a *.ASR results file. The types of Output Files
available depend on the type of analysis (PM. Ozone, or Visibility) and the output choices
that you have specified in the Configuration File.
To view an Output file Output Navigator, right-click on a file.
V MATS
Help T
Start Map View 0utput Navigator
Load
Extract All
Highlight file of interest and right-click to view options to Map, View, and Ex
Name
Configuration/Log Files
Configuration
Log File
Output Files
Tutorial Visibility - Forecasted Visibility - all design values
Tutorial Visibility- Used Model Grid Cells - Base Data
Tutorial Visibility- Used Model Grid Cells - Future Data
Tutorial Visibility- Forecasted Visibility Data
Type
Configuration
Run Log
Monitor Network
Monitor Network
Monitor Network
Monitor Network
Tutorial Visibility-( Add To Map
Extract
VE Monitor Identifiers a... Monitor Network
Size
2kb
Okb
85kb
335kb
336kb
19kb
Stop Info
This will bring up a separate tab with the data available for viewing.
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Output Navigator
f MATS MdllX
Help T
Start Map View Output Navigator | Monitor Network Data
Close
Refresh Select location and press refresh to see data...
Show All
id 0|type 0
lat
long
ACAD

44.35
-68.24'
AGTI

33.42
-116.93
ALLA

47.55
-121.16
ANAC

45.95
-113.5
ARCH

38.73
-109.58 i
BADL

43.81
-102.36
BAND

35.73
-106.34
~ IDC

on do
mo 01
Select Quantities that must be >= 0
|L_I dv_best
D dv_worst
base_best
~ base_worst
P rrf_b_crustal
~ rrf_b_n03
rrf_b_oc
rrf_b_ec
rrf_b_cm
~ rrf_b_s ~ 4
rrf_w_crustal
Q rrf_w_n03
rrf w oc
0
Export Export this data to CSV
J Data [

Stop Info
The upper left panel has the ID and latitude and longitude for each point in the dataset.
id 0
type
lat
long T
A.
010030010

30.497778
-87.881389

010270001

33.281111
-85.802222

010331002

34.760556
-87.650556

010510001

32.498333
-86.136667

01055001 1

33.9039
-86.0539

010732006

33.386389
-86.816667

010790002

34.342778
-87.339722

mnonnnia

a cnnon
oc cooncc

Clicking the Show All button will cause the data to appear in the lower panel
| Data |
lid 0
date
b_o3_dv ~ I
f_o3_dv ~
|referenc< T |
rrf
ppb • | days
121171002
2005
77 5
64.1
150014
0.828
70.0 9.00
121275002
2005
69.7
57.8
151019
0.830
71.0 10.0
121290001
2005
74.6
-9.00
125023
-9.00
70.0 4.00
130210012
2005
86.7
72.1
126049
0.832
80.0 11.0
130510021
2005
68.5
58.4
146045
0.853
78.0 10.0
130570001
2005
78 0
60 5
11606?
n 776
77 nl 100
~
~
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Output Navigator
The upper right panel allows you to select choose output with values greater than zero.
After choosing one or more variables, click the Show All button.
Note that missing values have a value of "-9", so this allows you to eliminate missing data.
In the example below, the values for ID "121290001" are removed because the value for
"f_°3_dv" is missing.
MATS MnJ[X
Help *
Start Map View Output Navigator | Monitor Network Data
Close
Refresh Select location and press refresh to see data...
Show All
id Hltype 0
lat Hl'ong 0
010030010

30.497778
-87.881389
010270001

33.281111
-85.802222
010331002

34.760556
-87.650556
010510001

32.498333
-86.136667
010550011

33.9039
-86.0539
010732006

33.386389
-86.816667
010790002

34.342778
-87.339722
ni nonnm a

oa ennon
oc coincc
Select Quantities that must be >= 0
b o3 dv
' e
reference eel I
~ rrf
~ ppb
D days
Export Export this data to CSV
| Data |
id 0
| date ~
b_o3_dv 7
|f_o3_dv ~ | reference T |
rrf H|ppb 0| days 0|
121171002
[2005
77.5
64.1
150014
0.828
70.0 9.00
121275002
2005
69.7
57.8
151019
0.830
tTTo| 10.0
130210012
2005
86.7
72.1
126049
0.832
80.0 11.0
130510021
[2005
68.5
58.4
146045
0.853
78.0 10.0
130570001
2005
78.0
60.5
116062
0.776
77.0 10.0
nnRqnnn?
?nn^
77 R
Rn r
1
n 7Fi?
I 7Gn\ 11 n
E
Stop
10.3 Extract Files
Extracting files allows you to export files from MATS and view them in another program.
This is most relevant to the Output files, which you may want to view and manipulate in a
database program such as Excel. MATS will generate CSV files. These are easily
viewable in a number of programs.
From Output Navigator load the results f .ASR) file of interest.
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Output Navigator
Help
Start Map View
Output Navigator
Highlight file of interest and right-click to view options to Map, View, and Ex
Stop Info
Load
Extract All
If
IZBIEJ
Configuration/Log Files
Configuration
Log File
Output Files
Tutorial 03 - Ozone Monitors - monitor data, temporally adjusted 2... Monitor Network
Tutorial 03 - Ozone Monitors - county high monitoring sites, tempo... Monitor Network
Name
Type
Configuration
Run Log
Size
One quick method to extract all of the data in the results file is to click the Extract All
button. An Extracting All screen will appear with a suggested name for the folder storing
the results. MATS will use the Scenario Name as the default folder name. If desired you
can rename the folder to whatever you desire.
Extracting All...

X
Enter Directory Name:
luSSISEI
Cancel
Click OK, and then MATS will export all of the files to this folder.
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Output Navigator
* C:\Program FilesYAbt Associates\MATS\output\Tutorial 03
r«n fx |
File Edit View Favorites Tools
Help

*i
^ Back * ' 0 yP Search
!;; Folders
US'
Address Q C:\Program Files\Abt Associates\MATS\output\Tutorial 03
Folders
b£| MATS
lei configs
data
£3 help
maps
a- CD output
£3 Tutorial 03
Tutorial Visibility
l£D sampledata
a c) work
Name
BlConfiguration.cfg
13 Log F
S Tutor
^1 Tutor
le.log
al 03 - Ozone Monitors — county high.,
al 03 - Ozone Monitors — monitor dat..
H Go
Type
CFG File
2KB TextDocun
51KB Microsoft E;
75 KB Microsoft E:
Size
3 KB
An alternative is to extract individual files. Right click on the file of interest, and choose
the Extract option.
ff MATS
Help '
Start Map View Output Navigator
Load
Extract All
Highlight file of interest and right-click to view options to Map, View, and Ex
Name
Type
Size
Configuration/Log Files
Configuration Configuration
Log File Run Log
Output Files
Example 03 - Ozone Monitors - monitor data, temporally adjusted ... Monitor Network
Example 03 - Ozone Monitors - county high monitoring sites, temp... Monitor Network
2kb
1 kb
80kb
54kb
Example 03 - Spatial Field - interpolated monitor data, tempo
Add To Map
View
Extract
Stop Info
This will bring up the Extracting. Enter output file name window. By default, MATS
will generate a folder with the Scenario Name (e.g., Example 03) and export a file with the
same name used internally by MATS. You can change both the folder and the exported file
name if desired.
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Output Navigator
Extracting. Enter output file name
Save in: |Q Example 03 Z1 a & ek
My Recent
Documents
0
Desktop
c>
My Documents
51
My Computer
*3
My Network
Places
File name:
Example 03 - Spatial Field - interpolated monitor

Save

Save as type:
| CSV files f.csv)

Cancel

_
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Map View
11 Map View
The Map View tab allows you to further explore your results. Initially, it is empty, with
just a blank map of the United States.
r
V MATS
- [~ X
Help T
Start M ap Vi ew 0 utp ut Navigato r
¦I!t V © [3 Standard Layers T
|:Data Loaded j

i i

Long: -185.462*** Lat: 56.32710
Extent: Min(-16*^59,-2.464) Max(-4.381,44.377)
i i
I I I

This section discusses how to:
• Load data onto the map;
• Choosing colors to represent the data (referred to as "plotting" in MATS);
• Zoom in and out on your map;
• Add and remove outlines for states, counties and Class 1 areas (these outlines are referred
to as " Standard Layers");
• Exporting maps and CSV files;
• Removing data from a map.
11.1 Loading Variables
There are two ways to bring data into a map. First, you may load data into a map with the
Output Navigator. Alternatively, you can load data directly from the map view taskbar
(see next sub-section).
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Map View
To map your results, click on the Output Navigator tab. Load the ASR file that you want
to view and then right-click on the particular Output File that you want to map. Choose
the Add to Map option.
V MATS
n x
Help -
Start Map View Output Navigator
Load
Extract All
Highlight file of interest and right-click to view options to Map, View, and Ex
Name

Type
Size
Confiauration/Loa Files

Configuration

Configuration
2kb
Log File

Run Log
Okb
OutDut Files

Monitor Network
23kb
Tutorial Visibility - Class 1 A
Add To Map
or Identifiers a..
Monitor Network
19kb
View
Extract

Stop Info
This will bring you back to the Map View tab.
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Map View
r
^MATS
- [~ X
Help T
Start Map View Output Navigator
@ ^ Standard Layers T
| Di it i Loaded j

@ ° Tutorial Visibility-Forecasted'

1 B -••f
^80™ t
o
1 1 1

Long: -199.517*** Lat: 35.68098
Extent: Min(-16*^59,-2.^6^) Max(-4.381,44.377)
i i i

Usually the next step is to plot your data. This is discussed next.
11.1.1 Loading with Taskbar
You can load data for mapping directly from the Map View tab, once you have exported
your results file (as discussed in the Extract Files sub-section of the Output Navigator
section). To start, click on the MapView tab.
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Map View
r
V MATS
- n x
Help T
Start Map View! Output Navigator
¦¦+V l.int) V S® [?> Standard Layers T
| Data Loaded

¦-4®

1 1
Long: -1 77.877*** Lat: 66.7-1268
Extent: Min(-16*^59,-2.464) Max(-A.381,44377)
i i
Xofc*

Click on the Open a monitor network file button:
This will bring you to the Open Monitor Network window. Browse to the folder with the
data file that you want to load.
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Map View
Open Monitor Network
lib
My Recent
Documents
Desktop
o
My Documents
Si
My Computer
My Network
Places
Look in: LJ Tutorial Visibility

a & ek
^Tutorial Visibility - Class 1 Area and IMPROVE Monitor Identifiers and Locations.c<
Tutorial Visibility - Forecasted Visibility - all design values.csv
^Tutorial Visibility - Forecasted Visibility Data.csv
^Tutorial Visibility - Used Model Grid Cells - Base Data.csv
^Tutorial Visibility - Used Model Grid Cells - Future Data.csv
File name: (Tutorial Visibility- Forecasted Visibility- all design
Files oftype: (Monitor Network 3
Open
Cancel
3$
Click the Open button after selecting your file (or just double-click on the file you want to
load) and this will take you to back to the Map View tab.
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Map View
V MATS
. nfx
Help "
Start i Map View i Output Navigator
+j \" v i'"l K" 61 ^ Standard Layers T
| Data Loaded

@ ® Tutorial Visibility-Forecasted'
A!'4 f
9 8o
I I I

-
Long: -159.764'*** Lat: 73.74217
Extent: Min(-16*^59,-2.464) Max(-4.381,44.377)
i i

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Map View
Right click on the text in the left panel and choose Plot Value.
y
V MATS

1
¦ »
_ n x
Help
-

1
Start
Map View
Output Navigator

v m [3
Standard Layers T

Data Loaded
@ ® Tut---
Remove
Export as CSV File
Plot Value
(&z
** - f
©
Long: -199.517"". Lat: 35.68098
Extent: Win(-16^59,-2.464) Max(-4.381,44.377)
Stop Info
This will bring up the Shape Class Breaks window.
Shape Class Breaks
Layer Name: Tutorial Visibility-Forecasted Visibility Data
Value:
Date fioQI
<• Bins Unique Values
Class Count: [5 t Marker Sizing: 0 I
Start Color
End Color
Clear Breaks
X Close
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Map View
Here you can choose the variable (or "Value") that you want to display and how it will be
seen. Scroll through the drop-down Value menu and choose d\> best. This is forecasted
visibility (measured in deciviews) on the days with the best visibility. (Note that a
description of all results variables generated by MATS are in separate "Output Variable
Description" sections for PM, Ozone, and Visibility.)
Shape Class Breaks
Layer Name: Tutorial Visibility- Forecasted Visibility Data
Value:
Date
Class Count:
dv best
dv_worst
base_best
base_worst
rrf_b_crustal
rrf_b_n03
rrf_b_oc
rrf b ec
Start Color
End Color
Clear Breaks
V Apply
X Close
There are a variety of display options that you can choose. These options are discussed in
detail in the Plotting Options section. After choosing your display options, then click the
Apply button. View the map in the Map View tab. (You can move the Shape Class
Breaks window, if it is obscuring the map.)
If you want to change your display options, go back to the Shape Class Breaks window,
make the changes, and click Apply again. You may do this as many times as needed.
When you are satisfied with the map, click the Close button in the Shape Class Breaks
window. This will bring you back to the Map View tab.
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Map View
r
i'MATS
. nx
Help T
Start M ap Vi ew 0 utp ut Navigato r
Vx',"!) V ® |3
Standard Layers T
| Di it i Loaded j

@ ° Tutorial Visibility-Foreoasteu
0 • dv_best 0.-17 to 2.17
0 • dv_best 2.21 to 3.15
0 0 dv_best 3.21 to 4.95
0 • dv_best 5.06 to 7.96

1 1 1

0 ° dv_best 8.44 to 14.65

Long: -199.517*** Lat: 35.68098
Extent: Min(-16*^59,-2.464) Max(-4.381,44.377)
i i
1 1

11.2.1 Plotting Options
MATS gives you a number of plotting options with the Shape Class Breaks window. These
are demonstrated with the results file "Tutorial Visibility - Forecasted Visibility - all design
values.csv" generated after completing the visibility tutorial. The same concepts hold for
other results files.
With the Date drop-down menu you can specify a particular year (assuming the data have
multiple years).
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Map View
Shape Class Breaks
Layer Name: Tutorial Visibility- Forecasted Visibility- all design v
Value: |base_worst 3
Date
Class Count:
2000
~
12000
2001
2002

2003

2004

Start Color
End Color
Clear Breaks
V Apply
X Close
With the Class Count option, you can specify into how many groups you want to divide
your data. The default is to use 5 bins. For most purposes this is a reasonable number.
Shape Class Breaks
Layer Name: Tutorial Visibility - Forecasted Visibility-all design v
Value: |base_worst
Date [2001

IBinsi C Unigue Values
Class Count: f5 Marker Sizing:
Start Color
End Color
Clear Breaks
V' Apply X Close
If you choose Unique Values option, you will have a separate bin or group for each unique
value in your data. This can lead to hundreds of bins. Generally, this is not an option that
you would want to choose. Note that if you choose this option, the Class Count and
Marker Sizing (discussed next) will be inoperative.
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Map View
Shape Class Breaks
Layer Name: Tutorial Visibility- Forecasted Visibility- all design v
Value:
Date
base worst
12001
Bins
"3
-
(•
Class Count: p
Start Color
I
Unique Values;
Marker Sizing:
End Color
Clear Breaks
V Apply X Close
The Marker Sizing allows you to vary the size of the marker on your map based on the
data values. The default is a Marker Sizing value of "0", which keeps the marker on your
map all the same size. A value of" 1" and higher gives the larger values progressively
larger markers on the map.
Shape Class Breaks
Layer Name: Tutorial Visibility - Forecasted Visibility-all design v
Value:
Date
12001
<• Bins
Class Count: 5
Start Color
|base_worst
3
C Unique Values
T] Marker Sizing: in
End Color
Clear Breaks
V' Apply X Close
The Start Color option allows you to set the color of the markers for the lowest values.
The End Color option allows you to set the color of the markers for the highest values.
MATS uses a mix of these two colors for intermediate values. The default colors are blue
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Map View
and yellow for the start and end.
If you want to change the Start Color, click on the blue square. This will bring up the
Color window. The simplest option is to click on the color you prefer from the pre-
defined Basic colors panel (in the upper half of the Color window), and then click OK.
(You can also double-click on desired color.)
Color
Custom colors:
Define Custom Colors >>
OK
Cancel

If for some reason, you do not see the color you want to use in the Basic colors panel, you
can click the Define Custom Colors button. Click in the large multi-color square to
identify the color you want and then adjust the hue with the slider bar on the far right. You
can save the color you generate by clicking the Add to Custom Colors button.
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Map View
Color
? X
Basic colors:

¦
Custom colors:
Define Custom Colors »
Green:
Color|Solid Lum
OK
Cancel
Add to Custom Colors
When satisfied, click OK. This will bring you back to the Shape Class Breaks window.
Shape Class Breaks
Layer Name: Tutorial Visibility- Forecasted Visibility- all design v
Value: |base_worst Z!
Date
12001
<• Bins
~ZI
Class Count: 1
C Unique Values
^ Marker Sizing:
Start Color
End Color
Clear Breaks
V Apply X Close
You can change the End Color through a similar process.
To test how your colors look, click the Apply button. If you do not like what you see, click
the Clear Breaks button. When you are finally satisfied with the look of your map, click
the Close button.
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Map View
11.3 Zoom Options & Pan View
In addition to the plotting options available in the Shape Class Breaks window, there are
various options on the task bar that you can choose to adjust the map. There are standard
Zoom in + and Zoom out ~ . options, as well as a Pan option 1 that lets you manually
move the map.
In addition, there is a Zoom to an area drop-down menu + '. This lets you zoom to
pre-specified regions, or "zoom frames", such as the continental US.
'< MATS
-

Continental US

Maryland
New England
Southern California
Texas
Washington DC
Edit Zoom Frames
Add Current View to List
i

Long: -47.1053au°"'Lat: 63.05882
Extent: Min(-16*^59,-2.464) Max(-4.381,44.377)
i i
I Stop Info
Choosing the Continental US zooms in so that you just see the continental US.
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Map View
V MATS
Help "
3EIIK
Start
Map View Output Navigator
V "Vv
Nt
Standard Layers T
JiDataLoaded
@ • Tutorial Visibility-Forecasted
0 • dv_best 0.-17 to 2.17
0 • dv_best 2.21 to 3.15
0 0 dv_best 3.21 to 495
0 ° dv_best 5.06 to 7.96
0 ° dv best 8.4-1 to 14.65
Long: -194.012"™". Lat: 24.54296
Extent: Min(-16*^59,-2.464) Max(-4.381.44.377)
Stop Info
If desired, you can change the "Zoom Frames" to whatever you are currently viewing.
Choose Add Current View to List from the list of options in the drop-down menu.
% MATS
B ' te. 1 b
- n x
Help T
Start iMap View! Output Navigator
''"i1 V S5 ^ Standard Layers T
Full Extent

All
Continental US
Maryland
New England
Southern California
Texas
Washington DC
Edit Zoom Frames

°
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Map View
This will bring up the Add Frame window. Type in whatever name you want to use for
this "zoom frame" and this will be available whenever you use MATS.
r
Add frame..

I Enter area name

|aii|

OK
Cancel

11.4 Standard Layers
The Standard Layers drop-down menu allows you to add and remove state, county, and
Class 1 area borders. By default, MATS displays the state and county borders. These can
often provide useful context to maps, however, at times they can obscure the markers
somewhat — this is most often a problem with the county boundaries.
To eliminate a layer, open the Standard Layers drop-down menu and click on the active
layer that you want to remove.
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V MATS
n x
Help T
Start j M ap Vi ew I Q utp ut N avi g ato r
(%(-

J Data
Loaded
0 ° Tutorial Visibility-Forecasteus
@ • base_worst-6to-6
M • base_worst-6 to -6
@ ° base_worst-6 to 11.61
@ ° base_worst 11.61 to 18.98
@ ° base worst 19.05 to 32.1
Info
Stop
Lonq: -107.30196, Lat: 53.06408 *"*
Extent: Min(-122.319,20.1 60) Mav^6.705,-16.997)
This will bring up a map view with the layer removed.
Standard Layers
* US States
US Counties
Class I Areas
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V MATS
MPfX
Start
Map View; Output Navigator
J Data Loaded
0 ° Tutorial Visibility-Forecaster1
0 • base_worst-6to-6
© • base_worst-6 to-6
0 0 base_worst-6 to 11.61
0 0 base_worst 11.61 to 18.98
0 ° base_worst 19.05 to 32.1
Standard Layers '
Info
¦top
Long:-116.03933, Lat: 53.87312 *A*
Extent: Min(-122.319,20.160) Ma>~*l6.705,46.997)
To add a layer back, choose the layer you want to add from the Standard Layers drop-
down menu.
11.5 Exporting Maps & Data Files
MATS allows you to export maps and data from the Map View tab. To export a BMP file.
click on the Export current map view to an image file option Ml. (The next sub-section
discusses exporting the underlying data.)
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Map View
VMATS

- nx
Help
*

Start
Map View!
Output Navigator

Standard Layers "

0 ° Tutorial Visibility-Forecasteu1
@ • dv_worst-6 to -6
@ • dv_worst-6 to -6
0 O dv_worst-6 to 9.64
0 Q dv_worst9.64to 17
0 dv_worst 17.09 to 27.7
This will up a window where you can name your image. Browse to whatever folder in
which you want to store your image.
Long:-126.5011 6. Lat: 45.92114***
Extent: Min(-125.726,44.322) May*-! 8.026,50.208)
o
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Map View
Save image to...
My Recent
Documents
u
Desktop
I
My Documents
My Computer
«
My Network
Places
Save in: Tutorial Visibility

IS ri UK
File name:
Save as type:
BMP
"3
Save
Cancel

Your BMP file can be easily viewed in a variety of software applications. Note however,
that this is just an image, and you will not be able to work with it in a GIS program the way
you might work with a .SHP file.
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Map View
0 Washington State.bmp - Windows Picture and Fax Vie... [-~|[Dl[X"
11.5.1 Exporting CSV Data File
If desired, you can export a CSV file with the data used to generate your map. Just right
click on variable of interest in the left panel, and choose the Export as CSV File option.
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Map View
V MATS
n x
Standard Layers
Data Loaded
@ ° Tutorial V
Remove
@ • dv_wor-
@ © dv_woi
@ Q dv_wc
Export as CSV File
Plot Value
@ Q dv_worst9.64to 17
dv worst 17.09 to 27.7
Start
Map View! Output Navigator
Long: -124.56182, Lat: 48.77052
Extent: Min(-125.726,44.322) May-" 1 8.026,50.208)
i ~
Note that this exports the same data that the Output Navigator would export. Choose
whichever approach is easier.
11.6 Removing Data
You can have multiple data files in a map. If you decide to remove a datafile, right click on
the variable that you want choose the Remove option.
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Map View
Remove
Plot Value
V MATS
3ES[x
Start | Map View; 0utput Navigator
•v
J Data Loaded

Standard Layers T
0 ©
@ •
0 •
0 ®
0 °
0 °
0 o
0
0
0
0
0
Tutorial Visibility-Used Model
so4-6 to -6
so4-6 to -6
so4 -6 to 0.2062
so4 0.2273 to 0.8009
so4 0.807 to -1.2145
Tutorial Visibility - ForecastSa
• dv_worst-6to
O dv_worst-6to
O dv_worst-6to
Q dv_worst9.64to 17
dv_worst 17.09 to 27.7
Long: -1 27.5261 1, Lat: 48.03775 ***
Extent: Min(-125.728,4-1.322) Mav"»18.02E.50.208)
This will bring back the map without the undesired data.
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Map View
Help '
Start
Map View! Output Navigator
+ -+ - m
J Data Loaded
Standard Layers "
0 ° Tutorial Visibility-Used Mociel
0
•
so4 -6 to -6
@
•
soA -6 to -6
0
O
so4-6 to 0.2062
0
©
so4 0.2273 to 0.8009
0
o
soA 0.807 to 4.21 ^5
Long:-125.92055, Lat: 48.50506 *A*
Extent: Min(-125.726,44.322) Ma>***18.026,50.208)
' r
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Frequently Asked Questions
12 Frequently Asked Questions
This section answers questions that have arisen when running MATS.
12.1 Error: MATS will not create a folder for extracting files
If an output folder already exists MATS will return an error if you click the Extract All
button. This occurs even if the pre-existing folder is empty.
Error
Unable to create directory
This error can be avoided by using a different folder name or removing the pre-existing
folder.
12.2 Where is there a description of output variables?
Descriptions of the output variables are in the separate "Details" sections for PM, Ozone.
and Visibility.
12.3 Why no PM analysis?
This feature is under development.
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References
13 References
Frank, Neil, (2006): "Retained Nitrate, Hydrated Sulfates, and Carbonaceous Mass in
Federal Reference Method Fine Particulate Matter for Six Eastern U.S. Cities" JAWMA.
Vol 56: 500-511.
IMPROVE, (2006), "Revised IMPROVE Algorithm for Estimating Light Extinction from
Particle Speciation Data", January 2006,
http://vista.cira.colostate.edu/improve/Publications/GrayLit/gray_literature.htm
U.S. EPA (2006), "Procedures for Estimating Future PM2.5 Values for the PM NAAQS
Final Rule by Application of the Speciated Modeled Attainment Test (SMAT)"
http://www.epa.gov/scram001/guidance sip.htm.
U.S. EPA (2007), "Guidance on the Use of Models and Other Analyses for Demonstrating
Attainment of Air Quality Goals for Ozone, PM2.5 and Regional Haze", April 2007.
EPA-454/B-07-002, http://www.epa.gov/scram001/guidance sip.htm.
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