Modeled Attainment

Test Software

April 2014

Prepared for

Office of Air Quality Planning and

Standards

U.S. Environmental Protection Agency 

Research Triangle Park, NC

Brian Timin, Project Manager

Prepared by

Abt Associates Inc.

User's Manual

Abt Associates Inc.

4550 Montgomery Avenue

Bethesda, MD 20814

www.abtassociates.com
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Table of Contents

Chapter 1 Welcome to MATS, the Modeled Attainment

Test Software 9

1.1 How to Use this Manual 10

1.2 Computer Requirements 11

1.3 Installing MATS 11

1.4 Installing an Updated Version of MATS 14

1.5 Uninstalling MATS 14

1.6 Contact for Comments and Questions 15

Chapter 2 Terminology & File Types 16

2.1 Common Terms 16

2.1.1 ASR File 17

2.1.2 BMP File 17

2.1.3 Class I Area 17

2.1.4 Configuration File 17

2.1.5 CSV File 18

2.1.6 Deciviews 18

2.1.7 Design Value 18

2.1.8 Domain 19

2.1.9 Extinction 19

2.1.10 FRM Monitors 19

2.1.11 Gradient Adjustment 19

2.1.12 IMPROVE Monitors 19

2.1.13 Interpolation 19

2.1.14 Inverse Distance Weights 20

2.1.15 Log File 21

2.1.16 Output Navigator 22

2.1.17 Output File 22

2.1.18 Point Estimate 22

2.1.19 RRF 22

2.1.20 SANDWICH 22

2.1.21 Scenario Name 22

2.1.22 SMAT 23

2.1.23 Spatial Field 23

2.1.24 Spatial Gradient 24

2.1.25 STN Monitors 24

2.1.26 Temporal Adjustment 24

2.1.27 VNA 24

VNA - Detailed Description 24

2.2 File Types 28
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Chapter 3 Overview of MATS Components 29

3.1 Start 29

3.1.1 Annual PM Analysis 30

3.1.2 Daily PM Analysis 36

3.1.3 Ozone Analysis 43

3.1.4 Visibility Analysis 48

3.2 Output Navigator 52

3.3 Map View 54

3.4 Help 54

Chapter 4 Annual PM Analysis: Quick Start Tutorial 56

4.1 Step 1. Start MATS 56

4.2 Step 2. Output Choice 57

4.3 Step 3. Output Choice - Advanced 59

4.4 Step 4. Data Input 60

4.5 Step 5. Species Fractions Calculation Options 61

4.6 Step 6. Species Fractions Calculation Options - Advanced 63

4.7 Step 7. PM2.5 Calculation Options 64

4.8 Step 8. Model Data Options 65

4.9 Step 9. Final Check 66

4.10 Step 10. Map Output 70

4.11 Step 11. View Output 78

Chapter 5 Annual PM Analysis: Details 82

5.1 Output Choice 83

5.1.1 Scenario Name 84

5.1.2 Standard Analysis 86

Step 1: Baseline Quartelry Average PM2.5 Calculation 86

Step 2: Baseline Quarterly Average Species Calculation 87

Step 3: Forecasted Quarterly Average Species Calculation 94

Step 4: Forecasted Design Value Calculation 96

Output Description 97

5.1.3 Quarterly Model Data 98

5.1.4 Species Fractions 100

Species Fractions Calculation 100

Output Description 101

5.2 Output Choice - Advanced 102

5.2.1 Spatial Field Estimates 104

Gradient-Adjustment- ("Fused fields") 104
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106

Output Description - Interpolate FRM & Speciation Monitor Data to Spatial

Field

108

Output Description - Interpolate Gradient-Adjusted FRM & Speciation

Monitor Data to Spatial Field

5.2.2 Miscellaneous Output 109

Quarterly Average Files 109

Output Description - High County Sites 113

Species Fractions Spatial Field 113

Output Description - Quarterly Average Speciated Monitors 115

Design Value Periods 116

Neighbor Files 116

5.3 Data Input 118

5.3.1 Species Data Input 120

5.3.2 PM2.5 Monitor Data Input 122

Unofficial Daily PM2.5 Monitor Data Input 122

Official Quarterly PM2.5 Monitor Data Input 124

5.3.3 Model Data Input 125

5.4 Species Fractions Calculation Options 126

5.4.1 Monitor Data Years 127

5.4.2 Delete Specifed Data Values 128

5.4.3 Minimum Data Requirements 129

5.5 Species Fractions Calculation Options - Advanced 131

5.5.1 Interpolation Options for Species Fractions Calculation 131

5.5.2 Miscellaneous Options 133

5.5.3 Internal Precision of the Calculations 133

5.6 PM2.5 Calculation Options 134

5.6.1 PM2.5 Monitor Data Years 135

5.6.2 Design Values 135

Completion Code Use 136

5.6.3 Valid FRM Monitors 137

5.6.4 NH4 Future Calculation 137

5.7 Model Data Options 137

5.8 Final Check 138

Chapter 6 Daily PM Analysis: Quick Start Tutorial 140

6.1 Step 1. Start MATS 140

6.2 Step 2. Output Choice 141

6.3 Step 3. Output Choice - Advanced 143

6.4 Step 4. Data Input 143

6.5 Step 5. Species Fractions Calculation Options 144

6.6 Step 6. Species Fractions Calculation Options - Advanced 146

6.7 Step 7. PM2.5 Calculation Options 148

6.8 Step 8. Model Data Options 148

6.9 Step 9. Final Check 149

6.10 Step 10. Map Output 153

6.11 Step 11. View Output 159
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Chapter 7 Daily PM Analysis: Details 162

7.1 Output Choice 162

7.1.1 Scenario Name 164

7.1.2 Standard Analysis 165

Step 1: Baseline Top 32 Ranked PM2.5 Calculation 166

Step 2: Baseline Top 32 Ranked Species Calculation 166

Step 3: Calculate Relative Response Factors 174

Step 4: Forecasted Peak Species Calculation 174

Step 5: Forecasted Design Value Calculation 177

Output Description 180

7.1.3 Quarterly Peak Model Data 181

7.1.4 Species Fractions 182

Species Fractions Calculation 183

Output Description 183

7.2 Output Choice - Advanced 184

7.2.1 Miscellaneous Outputs 185

Daily Files 186

Output Description - High County Sites 189

Design Value Periods 190

Output Description - Quarterly Average Speciated Monitors 190

Neighbor Files 191

7.3 Data Input 192

7.3.1 Species Data Input 194

7.3.2 PM2.5 Monitor Data Input 196

Unofficial Daily PM2.5 Monitor Data Input 196

Official Daily PM2.5 Monitor Data Input 198

7.3.3 Model Data Input 199

7.4 Species Fractions Calculation Options 201

7.4.1 Monitor Data Years 202

7.4.2 Delete Specifed Data Values 203

7.4.3 Minimum Data Requirements 203

7.5 Species Fractions Calculation Options - Advanced 205

7.5.1 Using Monitor Data to Calculate Species Fractions 206

7.5.2 Interpolation Options for Species Fractions Calculation 209

7.5.3 Miscellaneous Options 210

7.5.4 Internal Precision of the Calculations 211

7.6 PM2.5 Calculation Options 211

7.6.1 PM2.5 Monitor Data Years 212

7.6.2 Valid FRM Monitors 212

7.6.3 NH4 Future Calculation 213

7.7 Model Data Options 213

7.8 Final Check 214

Chapter 8 Ozone Analysis: Quick Start Tutorial 216

8.1 Step 1. Start MATS 216
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8.2 Step 3. Data Input 217

8.3 Step 2. Output Choice 219

8.4 Step 4. Filtering and Interpolation 220

8.5 Step 5. RRF & Spatial Gradient 221

8.6 Step 6. Final Check 222

8.7 Step 7. Load & Map Output 224

8.8 Step 8. View & Export Output 231

Chapter 9 Ozone Analysis: Details 236

9.1 Choose Desired Output 236

9.1.1 Scenario Name 237

9.1.2 Point Estimates 239

Baseline Ozone 239

Temporally-Adjust Baseline Ozone 239

9.1.3 Spatial Field 240

Baseline - interpolate monitor data to spatial field 241

Baseline - interpolate gradient-adjusted monitor data to spatial field 242

242

Forecast - interpolate monitor data to spatial field. Temporally-adjust ozone

levels

243

Forecast - interpolate gradient-adjusted monitor data to spatial field.

Temporally-adjust ozone levels

9.1.4 Design Value Periods 243

9.1.5 Ozone Output Variable Description 244

Ozone Monitors -- monitor data, temporally adjusted 2015.csv 244

245

Ozone Monitors -- county high monitoring sites, temporally adjusted

2015.csv

245

Spatial Field -- interpolated monitor data, temporally adjusted;

gradient-adjusted monitor data, temporally adjusted 2015.csv

9.2 Data Input 246

9.2.1 Monitor Data 247

9.2.2 Model Data 248

EPA Default Model Data 249

9.2.3 Using Model Data 249

Nearby Monitor Calculation - Example 1 251

9.3 Filtering and Interpolation 253

9.3.1 Choose Ozone Design Values 253

9.3.2 Valid Ozone Monitors 254

Minimum Number Design Values 254

Required Design Values 255

9.3.3 Default Interpolation Method 255

9.4 RRF and Spatial Gradient 256

9.4.1 RRF Setup 257

RRF Calculation - Example 1 259

RRF Calculation - Example 2 260

RRF Calculation - Example 3 263

RRF Calculation - Example 4 266

RRF Calculation - Example 5 267

RRF Calculation Spatial Gradient with Backstop Threshold - Example 6 269

9.4.2 Spatial Gradient Setup 272
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Spatial Gradient Calculation - Example 1 273

Spatial Gradient Calculation - Example 2 275

Spatial Gradient Calculation - Example 3 277

9.5 Final Check 279

9.5.1 Running MATS in Batch Mode 280

Chapter 10 Visibility Analysis: Quick Start Tutorial 281

10.1 Step 1. Start MATS 281

10.2 Step 2. Output Choice 282

10.3 Step 3. Data Input 284

10.4 Step 4. Filtering 285

10.5 Step 5. Final_Check 286

10.6 Step 6. Load and Map Results 289

10.7 Step 7. Working with Configuration File 298

Chapter 11 Visibility Analysis: Details 304

11.1 Choose Desired Output 304

11.1.1 Scenario Name 305

11.1.2 Forecast Visibility at Class I Areas 307

Old IMPROVE Equation 309

New IMPROVE Equation 310

Choose Model Grid Cell 312

11.1.3 Visibility Output Variable Description 312

Forecasted Visibility Data.csv 313

Forecasted Visibility - all design values.csv 314

Class 1 Area and IMPROVE Monitor Identifiers and Locations.csv 315

Used Model Grid Cells - Base/Future Data.csv 316

11.2 Data Input 316

11.2.1 Monitor Data Input 317

Monitor Data Description (Old Equation) 318

Monitor Data Description (New Equation) 319

Linkage between Monitors & Class I Areas 321

11.2.2 Model Data Input 322

Using Model Data for Temporal Adjustment 324

11.3 Filtering 329

11.3.1 Example Valid Visibility Monitors 330

11.4 Final Check 332

11.4.1 Running MATS in Batch Mode 334

Chapter 12 Output Navigator 335

12.1 Add Output Files to Map 338

12.2 View Files 340
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12.2.1 Configuration File 340

12.2.2 Log File 342

12.2.3 Output Files 343

12.3 Extract Files 347

Chapter 13 Map View 350

13.1 Loading Variables 350

13.1.1 Loading with Taskbar 352

13.2 Plotting a Value 355

13.2.1 Plotting Options 358

13.3 Zoom Options & Pan View 362

13.4 Standard Layers 364

13.5 Exporting Maps & Data Files 365

13.5.1 Exporting CSV Data File 367

Chapter 14 Frequently Asked Questions 369

14.1 Where is there a description of output variables? 369

14.2 Removing Data 369

Chapter 15 References 371
Welcome to MATS, the Modeled Attainment Test Software

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1 Welcome to MATS, the Modeled Attainment

Test Software

1

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 (O3), 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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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 Annual

and Daily Particulate Matter (PM), Ozone, and Visibility. In addition to these relatively

simple tutorials, you can go on to learn more on each subject in the chapters on Annual PM

Analysis: Details, Daily 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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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.
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Click the Next button. This will bring up the MATS - InstallShield Wizard.
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Click the Install button. After the installation of MATS, a final window will appear to

complete the process.
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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 MATS 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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Click the Remove button. This will bring up a window asking you to confirm the removal.

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

Telephone: 919-541-1850.
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22 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
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Spatial Gradient

STN Monitors

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 storing 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://views.cira.colostate.edu/web/Glossary.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.
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2.1.5 CSV File

A comma separated values (CSV) file (*.csv) can be read using a text editor, or by 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.

2.1.6 Deciviews

The deciview index is a measure of visibility. EPA selected the deciview index 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. On a

particle-free, pristine day, the deciview index has a value of zero (Slant Visual Range

(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 design value is the monitored reading used by EPA to determine an area's air quality
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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 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 (daily or annual) 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://views.cira.colostate.edu/web/Glossary.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

Inverse distance weights is a weighting scheme where 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:

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:
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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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2.1.16 Output Navigator

The Output Navigator in MATS 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 point estimate is 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 IMPROVEmonitor data so that it is

consistent with FRM monitor data. SANDWICH stands for Sulfates, Adjusted Nitrates, D

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 Scenario Name is 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.
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2.1.22 SMAT

The Speciated Modeled Attainment Test (SMAT) is used to forecast PM2.5 values. The

main steps are as follows: · 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 (RRF) 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
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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

“surround” the point of interest, and then takes an inverse-distance weighted average of the

monitoring values.
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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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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.
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To estimate the air pollution level in each grid-cell, MATS calculates an inverse-distance

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:

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 
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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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33 Overview of MATS Components

Upon starting MATS for the first time, you will see the following main window.

There are three main tabs: Start, Map View, and Output Navigator. The Start tab allows

you to calculate Annual and Daily PM, 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 (PM), Ozone or Visibility. To begin, click on one of the three buttons.

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.
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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.

Make your choice and then click Go. MATS will then take you through a series of

windows specifying the options available for each analysis.

3.1.1 Annual PM Analysis

With the Standard Analysis, MATS can forecast annual PM2.5 design values at monitor 

locations. MATS can also calculate quarterly model data files and a species fractions file. 

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 Output

Choice section of the Annual PM Analysis: Details chapter.

In the Output Choice Advanced window, MATS lets you choose from among two main

options: Spatial Field Estimates and Miscellaneous Output that is generally used for

quality assurance (QA). Within each of these two main options there are a number of
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choices. Details regarding these choices are in the Output Choice - Advanced section of

the Annual PM Analysis: Details chapter.

In the Data Input window, you specify the MATS input files that are used in each

scenario. There are three main types of files which must be specified. These include

ambient PM2.5 species data, ambient total PM2.5 data (FRM and IMPROVE), and gridded

model output data (e.g. CMAQ or CAMx data). There is specific terminology that is used

on the Data Input page. "Official" data refers to PM2.5 FRM data that can be used to

determine official design values for compliance purposes (comparison to the NAAQS). 

Other datasets which may not have rigid regulatory significance are sometimes referred to

as "unofficial" data. The format for the data is in the Data Input section of the Annual PM

Analysis: Details chapter.
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The Species Fractions Calculation Options has two main sections. One involving

speciated monitor data (e.g., STN and IMPROVE monitors) and the other total PM2.5

monitor data (FRM and IMPROVE). For each type of data you can specify the years of

interest, whether you want to delete certain data, and the minimum amount of data for a

monitor to be considered "valid" (and thus included in the calculations). Details on these

options are in theSpecies Fractions Calculation Options section of the Annual PM

Analysis: Details chapter.
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The Species Fractions Calculation Options - Advanced screen allows you to make

relatively advanced choices for your analysis. Generally speaking, the default options

settings are consistent with the EPA modeling guidance document (note: the start and end

years should always be set to match the relevant base modeling year). One set of options

allows you to specify the interpolation weighting that you want to use and whether the

interpolation involves a maximum distance or not. The second set of options involves

choices regarding ammonium, blank mass, and organic carbon. Details on these options

are in the Species Fractions Calculation Options - Advanced section of the Annual PM

Analysis: Details chapter.
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The PM2.5 Calculation Options window allows you to specify the particular years of

monitor data that you want to use from the input file you specified in the Data Input

section. You can specify whether to use "official" or "custom" design values and whether

monitors should have a minimum number of design values or a design value for a

particular year. You can also specify how to calculate future NH4 levels. Details on these

options are in the PM2.5 Calculation Options section of the Annual PM Analysis: Details

chapter.
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You also can use the Model Data Options to specify how to use the model data. This is

described in the Model Data Options section of the Annual PM Analysis: Details chapter.

The last step is to verify the inputs to the analysis.
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3.1.2 Daily PM Analysis

With the Standard Analysis, MATS can forecast daily PM2.5 design values at monitor 

locations. MATS can also calculate quarterly model data files and a species fractions file. 

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 Output

Choice section of the Daily PM Analysis: Details chapter.
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In the Output Choice Advanced window, MATS lets you choose from among a variety

of options that are generally used for quality assurance (QA). Details regarding these

choices are in the Output Choice - Advanced section of the Daily PM Analysis: Details

chapter.
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In the Data Input window, you specify the MATS input files that are used in each

scenario. There are three main types of files which must be specified. These include

ambient PM2.5 species data, ambient total PM2.5 data (FRM and IMPROVE), and gridded

model output data (e.g. CMAQ or CAMx data). There is specific terminology that is used

on the Data Input page. "Official" data refers to PM2.5 FRM data that can be used to

determine official design values for compliance purposes (comparison to the NAAQS). 

Other datasets which may not have rigid regulatory significance are sometimes referred to

as "unofficial" data. The format for the data is in the Data Input section of the Daily PM

Analysis: Details chapter.
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The Species Fractions Calculation Options has two main sections. One involving

speciated monitor data (e.g., STN and IMPROVE monitors) and the other total PM2.5

monitor data (FRM and IMPROVE). For each type of data you can specify the years of

interest, whether you want to delete certain data, and the minimum amount of data for a

monitor to be considered "valid" (and thus included in the calculations). Details on these

options are in theSpecies Fractions Calculation Options section of the Daily PM Analysis:

Details chapter.
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The Species Fractions Calculation Options - Advanced screen allows you to make

relatively advanced choices for your analysis. Generally speaking, the default options

settings are consistent with the EPA modeling guidance document (note: the start and end

years should always be set to match the relevant base modeling year). A first set of options

provides different options for choosing peak monitor days. A second set of options allows

you to specify the interpolation weighting that you want to use and whether the

interpolation involves a maximum distance or not. The third set of options involves

choices regarding ammonium, blank mass, and organic carbon. Details on these options

are in the Species Fractions Calculation Options - Advanced section of the Daily PM

Analysis: Details chapter.
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The PM2.5 Calculation Options window allows you to specify the particular years of

monitor data that you want to use from the input file you specified in the Data Input

section. You can specify whether monitors should have a minimum number of design

values or a design value for a particular year. You can also specify how to calculate future

NH4 levels. Details on these options are in the PM2.5 Calculation Options section of the

Daily PM Analysis: Details chapter.
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You also can use the Model Data Options to specify how to use the model data. This is

described in the Model Data Options section of the Daily PM Analysis: Details chapter.

The last step is to verify the inputs to the analysis.
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3.1.3 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.
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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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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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The RRF and Spatial Gradient window lets you set parameters used in the calculation of

relative response factors (RRF) 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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The last step is to verify the inputs to the analysis.
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3.1.4 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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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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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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The last step is to verify the inputs to the analysis.
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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 you have chosen.)

The files generated by MATS are of two types: (1) Configuration and Log files; and (2)

Output files containing the results of the MATS calculations.

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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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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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 O3 - Ozone Monitors -- monitor data.csv). Finally, 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.

3.4 Help

The Help dropdown menu has the User Manual for MATS and version information.
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44 Annual PM Analysis: Quick Start Tutorial

In this tutorial you will forecast annual PM2.5 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 Annual PM

analysis. · Step 2. Output Choice. Choose the output to generate. In this example, you will do two

things: forecast annual PM2.5 levels at monitor locations and output a species fractions

file (which you can subsequently reuse, as discussed here). · Step 3. Output Choice - Advanced. With these advanced options, you can generate

spatial fields and a variety of files useful for quality assurance. Simply review these

options and then uncheck them all. (If you are interested, these options are all described

here.) · Step 4. Data Input. Choose the particular years of data and monitors to use in this

analysis. · Step 5. Species Fractions Calculation Options. Specify how to generate the relative

response factors (RRFs) used in the forecasts. · Step 6. Species Fractions Calculation Options - Advanced. This window allows you to

make relatively advanced choices for your analysis, such as choosing different ways to

interpolate the monitor data. · Step 7. PM2.5 Calculation Options. Among other things you can specify the particular

years of monitor data that you want to use. · Step 8. Model Data Options. Choose how to use the model data, such as determining

the maximum distance the model data can be from a monitor. · Step 9. Final Check. Verify the choices you have made. · Step 10. Map Output. Prepare maps of your forecasts. · Step 11. View & Export Output. Examine the data in a table format.

Each step is explained below. Additional details are provided in the section Annual PM

Analysis: Details. 4.1 Step 1. Start MATS

Double-click on the MATS icon on your desktop, and the following window will appear:
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Click the Annual PM Analysis button on the main MATS window. This will bring up

the Configuration Management window.

 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.

4.2 Step 2. Output Choice

The Choose Desired Output window allows you to choose the output that you would like
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to generate. MATS allows you to conduct a Standard Analysis (i.e., forecast Point

Estimates at ambient monitors), output quarterly model data, and output a species fractions

file. · In the Scenario Name box type “Tutorial Annual PM” – this will be used to keep track

of where your results are stored and the variable names used in your results files. · Standard Analysis. Leave the box checked next to "Interpolate monitor data to FRM

monitor sites. Temporally-adjust." MATS will create forecasts for each monitor in the

monitor file. (Additional details are in the Standard Analysis section.) · Quarterly Model Data. Uncheck these options. (If checked, MATS generates quarterly

model files that MATS generates from daily data that you have provided. This is useful

if you want to reuse model files -- the quarterly files are much smaller and MATS will

run faster if it can skip the step of creating quarterly data from the daily. These files are 

described here.) · Species Fraction. Check the box next to Output species fractions file. This will

generate a reusable file described here. · Actions on run completion. Check the box next to Automatically extract all selected

output files. Upon completing its calculations, MATS will extract the results into a

folder with the name of your scenario.
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When your window looks like the window above, click Next. This will bring you to the

Output Choice - Advanced window.

4.3 Step 3. Output Choice - Advanced

With the advanced options in the Output Choice - Advanced window, you can generate

spatial fields and a variety of files useful for quality assurance. Simply review these

options and then uncheck them all. (If you are interested, these options are all described

here.)
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When your window looks like the window above, click Next. This will bring you to the

Data Input window.

4.4 Step 4. Data Input

The Data Input window allows you to choose the species and PM2.5 monitor data and the

model data that you want to use. As discussed in more detail in the following chapter (see 

Standard Analysis), MATS calculates the ratio of the base and future year model data to

calculate a relative response factor (RRF) for each PM species. MATS uses the PM2.5

monitor data and interpolated species monitor data to estimate species values at each FRM

site, multiplies the species values from the monitor data with the species-specific RRFs,

and then estimates a future-year design value. (Additional details on Data Input are

available here.)

Use the default settings in the Data Input window. The window should look like the

following:
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When your window looks like the window above, click Next. This will bring you to the

Species Fractions Calculation Options window.

4.5 Step 5. Species Fractions Calculation Options

The Species Fractions Calculation Options window has several functions related to the

IMPROVE-STN (species) monitor data and the (unofficial) PM2.5 monitor data. These

functions include identifying the years of monitor data that you want to use, deleting any

specific data values, and choosing the minimum data requirements of monitors you want in

your analysis. · Monitor Data Years. Choose the years of monitor data that you want to use. The

default is to use the three-year period 2006-2008. (That is, for both IMPROVE-STN and

PM2.5 monitor data, the Start Year is 2006 and the End Year is 2008.) The default

period is based on a modeling year of 2007. The start and end years should be changed to

applicable time periods, depending on the base modeling year. · Delete Specified Data Values. The default is to delete the observations specified by

EPA. As described in the Data Input section, valid data are given a value of "0" and

observations that should be deleted are given a value of "1" to "10". (Leave unchecked

the option for the user to flag data.) · Minimum Data Requirements. There are three sets of minimum data requirements:

1. Minimum number of valid days per valid quarter. This is the minimum number

of site-days per valid quarter. The default is 11 days, which corresponds to >

75% completeness for monitors on a 1 in 6 day schedule. This is a minimum

number of samples that is routinely used in calculations of quarterly average
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concentrations.

2. Minimum number of valid quarters required for valid season. This number of

years of data (within the start year and end year specified) for which we have

valid quarters for a given season. The default value is 1 year. If the value is set

= 2, then there will need to be 2 years of valid data from quarter1 in order for

quarter one to be considered complete (and the same for the other 3 quarters).

3. Minimum number of valid seasons required for valid monitor. This is the

number of valid seasons that are needed in order for a particular monitor's data

to be considered valid. The default is 1 for IMPROVE-STN monitor data and

the range is 1-4. For example, if the value is = 1, then a monitor's data will be

used in the species fractions calculations if it has at least one valid season. If

the value = 4, then the site must have all 4 seasons of valid data to be used. The

default for PM2.5 depends on whether the data are used in point calculations

(default = 4) or spatial field calculations (default = 1).

Use the default settings pictured in the screenshot below. (All of these options are

described in detail here.)
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When your window looks like the window above, click Next. This will bring you to the

Species Fractions Calculation Options window.

4.6 Step 6. Species Fractions Calculation Options -

Advanced

The Species Fractions Calculation Options - Advanced screen allows you to make

relatively advanced choices for your analysis. Generally speaking, the default options

settings are consistent with the EPA modeling guidance document. One set of options

allows you to specify the interpolation weighting that you want to use and whether the

interpolation involves a maximum distance or not. The second set of options involves

choices regarding ammonium, blank mass, and organic carbon. 
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Use the default settings pictured in the screenshot below. (All of these options are

described in detail here.)

When your window looks like the window above, click Next. This will bring you to the

PM2.5 Calculation Options window.

4.7 Step 7. PM2.5 Calculation Options

The PM2.5 Calculation Options window allows you to specify the particular years of

monitor data that you want to use from the input file you specified in Step 4 (Data Input).

Keep the default settings: · PM2.5 Monitor Data Years. Start Year = 2005 and End Year = 2009. · Official vs. Custom Values. Specify "official" design values, which is the recommended

default setting. · Valid FRM Monitors. Keep the minimum number of design values equal to the default

value of 1, and do not specify any particular design values for inclusion in the

calculations. · NH4 Future Calculation. You can also specify how you want to forecast NH4 values.

Use the default approach, which is to use baseline DON values.
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Use the default settings pictured in the screenshot below. (All of these options are

described in detail here.)

When your window looks like the window above, click Next. This will bring you to the

Model Data Options window.

4.8 Step 8. Model Data Options

The Model Data Options section allows you to specify the Temporal Adjustment at

Monitor. This option specifies how many model grid cells to use in the calculation of

RRFs for point estimates and for spatial estimates. Use the default option: 3x3 set of grid

cells. Note that for PM analyses, MATS calculates mean concentrations across the grid

cell array (as compared to maximum concentrations used for ozone analyses).

Use the default settings pictured in the screenshot below. (All of these options are

described further here.)
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When your window looks like the window above, click Next. This will bring you to the

Final Check window.

4.9 Step 9. 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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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 either Save Scenario & Run or

Save Scenario. Save Scenario & Run will cause MATS to immediately run the scenario.

A temporary, new Running tab will appear (in addition to the Start, Map View and

Output Navigator tabs).
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When the calculations are complete, a small window indicating the results are Done will

appear. Click OK.

After clicking OK, MATS will open a folder with the results files already exported. 
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Output Navigator tab will also be active. (The Running tab will no longer be seen.)

MATS will automatically load the output files associated with the .asr configuration that

just finished running.
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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. 4.10 Step 10. Map Output

After generating your results, Output Navigator can be used to load and/or map them. If a

run just finished, the output files will already be loaded into output navigator.

If files from a previous run need to be loaded then click on the Load button and choose

the Tutorial Annual PM.asr file.
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Under Configuration/Log Files, you will see two files: · Configuration: keeps track of the assumptions that you have made in your analysis. · Log File: provides information on a variety of technical aspects regarding how a results

file (*.ASR) was created.

Under Output Files you will see: · Tutorial Annual PM Quarterly Avg Spec Frac Point: contains species fractions and

interpolated species values. (Note that this is a reusable file that you can load into

MATS.) · Tutorial Annual PM Annual PM25 Point: contains base & future PM2.5, species values,

and RRFs. (Note that the annual RRFs and annual species values are not used anywhere

in the calculation of design values, and here just for information.)

Right-click on the file Tutorial Annual PM Annual PM25 Point. This gives you three

options: Add to Map, View, and Extract. Choose the Add to Map option. 
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This will bring up the Map View tab.
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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. 
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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:
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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:
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Right click on the "Tutorial Annual PM Annual PM25 Point" layer in the panel on the left

side of the window. Choose the Plot Value option.
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This will bring up Shape Class Breaks window. In the Value drop-down list, choose the

variable "f_pm25_ann_dv" -- this is forecasted PM2.5 design value.

Click Apply and then click Close. This will bring you back to the Map View window.
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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.

4.11 Step 11. View Output

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 Annual PM Annual PM25 Point. This

gives you three options: Add to Map, View, and Extract. Choose the View option. 
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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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To view the data for a particular monitor -- in this example, monitor ID = "010491003" --

highlight this monitor. MATS will then display the values for this monitor in the bottom

panel.
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To view all of the data again, click on the Show All button. 

For additional details on generating annual PM results, see the chapter on Annual PM

Analysis: Details. For additional details on viewing data, see the View Data section in

chapter on the Output Navigator.
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55 Annual PM Analysis: Details

MATS can forecast annual design values at PM2.5 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 PM2.5 estimates, MATS provides an option to start a new

Configuration or to open an existing Configuration. 

Select your option and then click Go. MATS will then step you through a series of windows with choices for your analysis.

· Output Choice. Choose whether you want to run the Standard Analysis, and whether to

output a species fractions file and/or quarterly model data. · Output Choice - Advanced. This option provides miscellaneous Point Estimate output

files, as well as baseline and forecast year Spatial Fields and gradient-adjusted output. · Data Input. Load species monitoring data or a species fractions file. Load PM2.5

ambient monitoring data. Finally, load the modeling data that you want to use. · Species Fractions Calculation Options. Choose the years of daily STN-IMPROVE and

FRM monitoring data. Identify valid monitors. Delete specified values. · Species Fractions Calculation Options - Advanced. Choose interpolation options for

PM2.5 and species monitoring data. Choose assumptions for the ammonium calculation,

default blank mass, and organic carbon. · PM2.5 Calculation Options - FRM Monitor Data. Choose the years of quarterly FRM

monitoring data. Choose whether to use official design values or custom design values. 

Identify valid monitors. Choose the approach for calculating future NH4. · Model Data Options. Specify the maximum distance of monitors from modeling

domain. Specify which model grid cells will be used when calculating RRFs at monitor

locations. · Final Check. Verify the selections that you have made.
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5.1 Output Choice

In the Output Choice window, MATS lets you specify the name of your Scenario, and

then to choose up to three options: Standard Analysis, which refer to forecasts made at

FRM PM2.5 monitor locations; Quarterly Model Data, which allows you to create

quarterly averages from daily model output data (output data from grid models such as

CMAQ and CAMx), and then subsequently reuse this file; and Species Fractions, which

outputs a reusable species fractions file.

By checking the box next to Automatically extract all selected output files, MATS will

create a separate folder with your chosen Scenario Name in the MATS "Output" folder,

and then export .CSV files with the results of your analysis. Alternatively, you can export

the results from the Output Navigator, but checking this box is a little easier.

Standard Analysis. The Standard Analysis refers to the calculation of future year PM2.5

design values at FRM monitor locations. This is the main part of the modeled attainment

test for PM2.5. There are several calculations involved in this analysis. MATS will

interpolate PM2.5 species data, calculate species concentrations at each FRM site and

project design values to a future year using gridded model data. Most MATS users will

run this analysis and it is therefore checked by default. 

Quarterly Model Data. MATS requires two types of data input: ambient monitor data

and gridded model output data. For the annual PM2.5 calculations, MATS will accept

either MATS formatted daily average gridded model files or quarterly average files. If

daily average model files are used as inputs, MATS will calculate quarterly averages from

the daily averages and optionally output the quarterly average concentrations into text files
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(CSV files). The quarterly average text files can then be re-used in subsequent MATS

analyses. Quarterly average input files are smaller and run faster than daily average files. 

There are two options to output quarterly average model concentration CSV files: · Check the “Output quarterly average model data file” box to create quarterly average

CSV for all grid cells in the modeling domain. MATS will create one baseline year file

and one future year file. This will create relatively large files, but they will still be ~90

times smaller than daily average files (assuming a full year of model data). · The second option is to check the “Output used quarterly average model data file”. This

option will only output the grid cells that are subsequently used in the particular MATS

configuration. For example, if MATS calculates future year design values at 20 FRM

sites using a 1 X 1 grid array, then MATS will output base and future model values for

only 20 grid cells (assuming each monitor is in a unique grid cell). The advantage of

these files is that they are extremely small. But if subsequent MATS runs use a different

set of monitors or grid arrays, then the files may not contain all of the necessary data to

complete the analysis. This option is recommended as a QA tool to examine the grid

cells and the model concentrations that MATS is using in the analysis.

Species Fraction. Checking the “Output species fraction file” box will create an output

file containing the calculated PM2.5 species fractions at each FRM site used by MATS. 

This species fraction file can be re-used in MATS as an input file. The species fraction file

can be useful for several reasons. One, using a species fraction file saves time because

MATS won’t have to interpolate species data and calculate fractions each time it is run.

Two, it can provide consistency between MATS runs by ensuring that the same species

fractions are used each time. And for the same reason, the species fraction file can be used

interchangeably between different users to ensure that multiple groups are using the same

species fractions (if that is a goal). And finally, the fractions file can serve as a template

for creating a custom species fractions file using whatever data and techniques (e.g.

alternative interpolation techniques) are desired by any particular user.

5.1.1 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., Example Annual

PM.asr). Note that the extension (.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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· Output file names. The output files generated will begin with the Scenario Name.
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5.1.2 Standard Analysis

Future-year PM2.5 design values are calculated at each FRM monitoring site through a

series of calculations: 

Step 1. Baseline quarterly average PM2.5 calculation at each FRM monitor site;

Step 2. Baseline quarterly average species calculation using the quarterly weightedaverage baseline PM2.5 concentration and species fractions at each FRM monitor site;

Step 3. Forecasted quarterly average species calculation using relative response factors

(RRFs) at each FRM monitor site;

Step 4. Forecasted design value calculation at each FRM monitor site. 

In this section, we go into some detail describing these steps. However, you should note

that MATS gives you a number of options affecting the exact steps that MATS follows,

such as determining which years of monitoring data to use and to choosing which monitors

to include in the calculations. These options are detailed in the PM2.5 Calculation Options

section. The output from the Standard Analysis is described here. 5.1.2.1 Step 1: Baseline Quartelry Average PM2.5 Calculation

The first step in the Standard Analysis is to calculate baseline PM2.5 levels using the

("official") quarterly average PM2.5 file (described in the Data Input section). MATS uses

these quarterly values to calculate 3-year averages of consecutive years of data for each

quarter, and then to average these averages to get a single PM2.5 estimate for each quarter.

Example Calculation Baseline PM2.5 Concentration

Starting with the following quarterly values:

Quarter 2005 2006 2007 2008 2009

Q1 11.8188 10.7107 9.7133 10.6966 9.9839

Q2 13.2400 11.1333 9.3138 15.1655 9.6680

Q3 18.7960 11.0700 12.6652 12.0667 14.4964

Q4 14.2579 9.4032 9.7903 10.7414 11.2778

MATS calculates 3-year averages of consecutive years of data for each quarter:

Quarter 2005-2007 2006-2008 2007-2009

Q1 10.7476 10.3735 10.1313

Q2 11.2290 11.8709 11.3824

Q3 14.1771 11.9340 13.0761

Q4 11.1505 9.9783 10.6032

MATS averages the 3-year averages to get a single estimate for each quarter:
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Quarter Avg

Q1 10.4175

Q2 11.4941

Q3 13.0624

Q4 10.5773

5.1.2.2 Step 2: Baseline Quarterly Average Species Calculation

Since the FRM monitors do not have speciated data (and the majority of FRM sites are not

co-located with a speciation monitor), MATS uses speciated PM2.5 monitor data from

other monitoring networks, such as STN and IMPROVE, to estimate the PM2.5

attributable to the following species: sulfate (SO4), nitrate (NO3), elemental carbon (EC),

organic carbon (OC), crustal matter, particle bound water (PBW), ammonium (NH4), and,

data permitting, salt.

Note that the process of calculating species fractions is involved and discussed in detail in

the Species Fractions Calculations & Output section of this user manual. Nevertheless, the

use of species fractions to calculate species concentrations is straightforward. The

weighted quarterly species average is calculated by multiplying weighted quarterly average

FRM baseline values (minus the assumed blank mass, specified by the user) with species

fractions that have been estimated for each FRM monitor. The calculation is as follows:

Note that MATS calculates species fractions from speciated monitors for a limited number

of years. As a result, rather than have species fractions specifically calculated for each

quarter and each year, MATS uses a single set of species fractions to calculate the

weighted quarterly average species concentrations. The species data should be

"representative" of the species fractions that occur during the 5 year FRM monitoring

period selected in MATS.

Example Calculation Baseline Species Concentrations

MATS multiplies the (non-blank) baseline quarterly PM2.5 values:

FRM PM2.5 Blank Mass Non-Blank Mass

10.4175 0.5 9.9175

11.4941 0.5 10.9941
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13.0624 0.5 12.5624

10.5773 0.5 10.0773

with the species fractions calculated for this particular site:

Quarter SO4 NO3 OCMMB EC PBW NH4 Crustal (Salt)

Q1 0.2382 0.1108 0.3346 0.0201 0.1049 0.1209 0.0705 -- Q2 0.2637 0.0727 0.3178 0.0545 0.1095 0.1273 0.0545 -- Q3 0.1432 0.0557 0.5621 0.0238 0.0959 0.0955 0.0238 -- Q4 0.2580 0.1389 0.2756 0.0396 0.1094 0.1190 0.0595 -- The product is the estimated baseline species concentrations by quarter.

Quarter SO4 NO3 OCMMB EC PBW NH4 Crustal

Q1 2.3623 1.0989 3.3184 0.1993 1.0403 1.1990 0.6992

Q2 2.8991 0.7993 3.4939 0.5992 1.2039 1.3996 0.5992

Q3 1.7989 0.6997 7.0613 0.2990 1.2047 1.1997 0.2990

Q4 2.5999 1.3997 2.7773 0.3991 1.1025 1.1992 0.5996

Baseline Calculation - General

MATS does several calculations to generate the species concentrations used to calculate

species fractions. In general, calculation of the concentrations of SO4, NO3, EC, crustal,

and salt are straightforward. The concentrations are derived directly from the ambient data

file or an interpolation of that data. However, the calculation of ammonium, particle

bound water (PBW) and organic carbon (OC) are more complicated and calculated

internally in MATS (as discussed in the following sections).

MATS uses speciated monitor data to estimate individual species fractions at FRM sites

using the “SANDWICH” process (Frank, 2006) (SANDWICH stands for Sulfates,

Adjusted Nitrates, Derived Water, Inferred Carbonaceous mass, and estimated aerosol

acidity [H+]). The data input to the PM calculations in MATS includes quarterly FRM

monitor data and speciated monitor data from STN and IMPROVE sites that has been

partially adjusted to match the anomalies in FRM data (e.g., nitrate volatilization).

The default species input data file contains aerosol nitrate data (NO3r) that has been

adjusted to account for volatilization. Additional SANDWICH adjustments are made

within MATS. These include calculation of particle bound water (PBW) and organic

carbon by mass balance (OCMmb). 

When there is more than one year of speciated data, MATS will create quarterly average

species levels for each year at each monitor, and then average the seasonal values across

the available years to get a single estimate for each species for each quarter at each

monitor. (See the section on Species Fractions Calculation Options for additional details

on how multiple years of speciated monitor data are combined.)
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Note that MATS can calculate quarterly species concentrations at FRM monitor sites or a

spatial field. MATS will allow you to reuse the species fractions file for point estimates or

spatial fields. And in addition to calculating species fractions with monitor data -- on the 

Output Choice - Advanced window, you can choose gradient-adjusted species fractions,

which are based on monitor plus model data.

Interpolation

About 75 percent of FRM monitors are not co-located with an STN monitor, so the

estimation of the quarterly averages of the individual species at those FRM sites depends

on the interpolated quarterly averages from speciated monitors (e.g., STN). Individual

species are interpolated to the latitude and longitude associated with each FRM monitor. 

(For FRM monitors that are co-located with an STN monitor, MATS simply uses the

species values from the co-located STN monitor.) You can find details on the

interpolation process and different options for interpolation at the section on Interpolation

Options for Species Fractions Calculation. Calculations After Interpolation

After the interpolation of quarterly averages for retained NO3 (NO3r), SO4, OCM, crustal,

EC, and DON, a few additional steps are necessary to generate speciated quarterly averages

at each FRM monitor site. These include calculating retained NH4 (NH4r), PBW, blank

mass, and organic carbon mass (OCMMB), the latter of which is calculated through a mass

balance approach.

Summary of Calculations After Interpolation of STN & IMPROVE Speciated Monitor Data

Calculation Description

Calculate Retained Ammonium

(NH4r)

Calculate ammonium associated with retained nitrate (NO3r) and

SO4. MATS calculates NH4r using DON, SO4, and NO3r.

(Alternatively MATS can use directed measured ammonium).

Calculate Particle Bound Water

(PBW)

Calculate amount of water associated with ammonium sulfate and

ammonium nitrate, which are hygroscopic.

Estimate Blank Mass Account for contamination in FRM monitors.

Calculate Organic Carbon Mass

(OCMMB)

Calculate organic carbon mass with a mass balance approach.

Calculate Species Fractions Divide species estimates for SO4, NO3r, OCMMB, EC, crustal

material, NH4r, and PBW by the non-blank PM2.5 mass. (The

inclusion of salt is optional and is not included in the default MATS

data.)

The following sections describe the calculations of ammonium, PBW, and organic carbon

(by difference).

Retained Ammonium Calculation

MATS calculates retained ammonium two different ways. The default approach is to use

interpolated degree of neutralization of sulfate (DON) values from the speciated monitors.

The alternative approach is to use interpolated NH4 values from speciated monitors (e.g.,
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STN). In the Species Fractions Calculations Options - Advanced section, you have the

option to choose the approach that you prefer to use. The two approaches are described

here.

Default approach using measured pre-calculated DON, SO4, and retained NO3

(NO3r):

Because of uncertainties in NH4 speciation measurements, by default MATS calculates

ammonium values using the degree of sulfate neutralization (DON). MATS uses precalculated daily DON values that are included in the species data input file ("Species-forfractions-xxxx.csv"). The values for DON are calculated from the amount of ammonium

associated with sulfate (NH4SO4) as follows:

And the estimated NH4SO4 is calculated as follows: 

where 0.29 is the molar ratio of NH4 to NO3 and NH4measured and NO3retained reflect the

amounts of NH4 and NO3 retained on the FRM filter. The amount of NH4SO4 is not

allowed to exceed the fully neutralized amount of 0.375 multiplied by the estimated sulfate

ion concentration.

MATS then calculates ammonium using interpolated monitor values of DON, SO4, and

NO3r as follows:

Alternative Approach Using Measured Ammonium. The alternative approach is to use

interpolated NH4 values from STN monitors. This approach has several steps. 

First, MATS calculates “adjusted” NH4:

where the PctEvap factor refers to the percentage of ammonium associated with the

volatilized nitrate that is also lost. (As discussed in the Species Fractions Calculation

Options - Advanced section, this factor is adjustable from 0 to 100 percent.) The default

assumption is that no ammonium is volatilized (0 percent).

Second, MATS calculates NH4 associated with SO4:

Third, MATS calculates DON:
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Finally, using the same equation as in the default approach, MATS calculates NH4r by

substituting the calculated DON for the interpolated (measured) DON value:

Particle Bound Water Calculation

Because ammoniated sulfate and ammonium nitrate are hygroscopic, the retained sulfate

and nitrate mass will include water. Particle bound water (PBW) is estimated using the

Aerosol Inorganic Model (AIM) (Clegg et al, 1998). For computational convenience, a

polynomial regression equation was fit to the calculated water mass from AIM and the

three input values that fed into AIM (sulfate, nitrate and ammonium). AIM was run with

typical FRM filter equilibration conditions of 35% RH and 22 deg C (295 deg K).

MATS calculates particle-bound water (PBW) using levels of SO4, NO3r, and NH4r as

follows. (Note that this is the same equation that MATS uses to calculate future-year

PBW, the difference being the future-year PBW uses future-year values of SO4, NO3r, and

NH4r, and here MATS uses base-year values.)

The calculation uses one of two equations, depending on the acidity of the ammoniated

sulfate (represented by DON). S, N, and A in the equations are the relative fraction of

SO4, NO3r, and NH4r respectively. 

S = SO4 / (SO4 + NO3r + NH4r);

N = NO3r / (SO4 + NO3r + NH4r);

A = NH4r / (SO4 + NO3r + NH4r);

 if DON le 0.225 then

 PBW = {595.556 

 - 1440.584*S

 - 1126.488*N

 + 283.907*(S**1.5)

 - 13.384*(N**1.5)

 - 1486.711*(A**1.5)

 + 764.229*(S**2)

 + 1501.999*(N*S)

 + 451.873*(N**2)
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 - 185.183*(S**2.5)

 - 375.984*(S**1.5)*N

 - 16.895*(S**3)

 - 65.814*(N**1.5)*S

 + 96.825*(N**2.5)

 + 83.037*(N**1.5)*(S**1.5)

 - 4.419*(N**3)

 + 1720.818*(A**1.5)*S

 + 1220.383*(A**1.5)*N

 - 311.496*(A**1.5)*(S**1.5)

 + 148.771*(A**1.5)*(N**1.5)

 + 1151.648*(A**3)} * (SO4+NO3r+NH4);

ELSE

 PBW = {202048.975 

 - 391494.647 *S

 - 390912.147 *N

 + 442.435 *(S**1.5)

 - 155.335 *(N**1.5)

 - 293406.827 *(A**1.5)

 + 189277.519 *(S**2)

 + 377992.610 *N*S

 + 188636.790 *(N**2)

 - 447.123 *(S**2.5)

 - 507.157 *(S**1.5)*N

 - 12.794 *(S**3)

 + 146.221 *(N**1.5)*S

 + 217.197 *(N**2.5)

 + 29.981 *(N**1.5)*(S**1.5)
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 - 18.649 *(N**3)

 + 216266.951 *(A**1.5)*S

 + 215419.876 *(A**1.5)*N

 - 621.843 *(A**1.5)*(S**1.5)

 + 239.132 *(A**1.5)*(N**1.5)

 + 95413.122 *(A**3)} * (SO4+NO3r+NH4) .

Organic Carbon Mass Calculation

Measured organic carbon mass is not directly used in the calculation of species fractions in

MATS because of (1) many uncertainties in estimating carbonaceous mass from carbon

measurements (Turpin & Lim, 2001; Chow et al, 2004) (2) differences in carbon

measurement protocol between urban and rural monitoring locations, (3) a relatively

“bumpy” surface of urban carbon concentrations as derived from urban and rural organic

carbon measurements and (4) lack of carbon measurements at all FRM locations. The

MATS approach estimates carbon by mass balance comparing precisely measured FRM

PM2.5 mass (EPA, 2003) with the sum of its non-carbon components.

Total carbonaceous mass contains both elemental carbon (EC) and organic carbon mass

(OCM). We measure EC from the interpolated STN and IMPROVE monitors, while we

calculate OCM using a mass balance approach -- and refer to it as OCMMB. To calculate

OCMMB, we subtract the other estimated retained species (including EC) from the PM2.5

level measured at the FRM site as follows:

The value for OCMMB could be very small, or even be calculated as negative (if the sum

of the species enclosed in the curly brackets exceeded the FRM PM2.5 monitor value). To

ensure that the OCMMB does not get too small, an OCMMB "mass balance

floor" (default) value is set to 1.0 times the interpolated value of blank-adjusted organic

carbon (OCb). It is also possible that the value of the floor by itself could exceed the FRM

total PM2.5 value. In this case, MATS imposes a (user-adjustable) "ceiling," such that

OCMMB does not exceed a percentage of the total non-blank mass. The default ceiling

value is set to 0.8 or 80% of PM2.5 mass. (You can modify the floor and ceiling

assumptions in the Species Fractions Calculation Options - Advanced window.)

To account for these possibilities, MATS uses the following series of equations to

calculate OCMMB:
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where the "Floor" variable has a default value in MATS of "1.0" and the "Ceiling" variable

has a default value in MATS of "0.8".

There are at least two things to note with this approach. (1) When the final OCMMB

value is equal to the "floor," then the sum of the species will exceed the PM2.5 value at the

FRM monitor. To ensure that the sum of the species just equals the FRM PM2.5 value,

MATS reduces all of the species (except OCMMB) by the same percentage until the sum

of the species just equals the FRM PM2.5 value. (2) When the final OCMMB value is

equal to the "ceiling," then the sum of the species will be less than the PM2.5 value at the

FRM monitor. In that case, MATS increases all of the species by the same percentage

until the sum of the species just equals the FRM PM2.5 value

Blank Mass Assumption

The field blank typically has a value of between 0.3 and 0.5 ug/m3, which appears to result

from contamination of the FRM filter. For calculating retained PM2.5, MATS uses a

default blank mass value of 0.5 ug/m3. If desired, you can change the default blank mass

value at the Species Fractions Calculations Options - Advanced window.

5.1.2.3 Step 3: Forecasted Quarterly Average Species Calculation

To calculate forecasted quarterly species averages for each of the four quarters in a given

year, MATS uses weighted average quarterly species concentrations and both baseline and

forecasted (e.g., 2020) air quality modeling. Because this calculation involves modeling

data from two different years, this is referred to as a "temporal adjustment."

The forecasted weighted quarterly average for each species is calculated by multiplying the

baseline weighted quarterly average for each monitor species with the ratio of the modeling

data. This process gives forecasted weighted quarterly averages of six species: SO4, NO3,

OCM, EC, crustal material, and NH4. The form of the equation is as follows:
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In other words, baseline species concentrations are assumed to change in the same

proportion as the model data in the same location from the baseline to the forecasted. 

These proportions, called relative response factors (RRF), are simply the ratio of the

modeled forecasted species to the modeled baseline species. 

MATS calculates RRFs for each quarter for each of six species: SO4, NO3, OCM, EC,

crustal material, and (optionally) NH4. (Additional information on the calculation of the

RRFs can be found here.) The calculation of forecasted weighted quarterly average species

concentrations can be rewritten as:

Additional calculations are needed to estimate future-year quarterly averages of NH4 and

particle-bound water (PBW), which is calculated using forecasted levels of NH4, NO3, and

SO4. (Details on the PBW calculation can be found here.)

Recall that the default base year NH4 calculation is as follows:

MATS can calculate the future year NH4 concentration using modeled NH4 RRFs, or by

using the the base year DON value combined with future year SO4 and NO3 values

(default approach) as follows:

The option for choosing which approach to use for calculating future NH4 is given in the 

PM2.5 Calculation Options window. Finally, note that PBW is calculated after future year

NH4, using the previously identified water equation and future year concentrations of

NH4, SO4, and NO3. 

Example Calculation Forecasted Species Concentrations
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MATS multiplies the baseline species concentrations:

Quarter SO4 NO3 OCMMB EC PBW NH4 Crustal

Q1 2.3623 1.0989 3.3184 0.1993 1.0403 1.1990 0.6992

Q2 2.8991 0.7993 3.4939 0.5992 1.2039 1.3996 0.5992

Q3 1.7989 0.6997 7.0613 0.2990 1.2047 1.1997 0.2990

Q4 2.5999 1.3997 2.7773 0.3991 1.1025 1.1992 0.5996

with the RRFs for each quarter and species:

Quarter SO4 NO3 OCMMB EC PBW NH4 Crustal

Q1 0.9737 0.9873 0.9636 0.9872 -- 0.9634 0.9808

Q2 0.9898 0.9991 0.9979 0.9917 -- 0.9697 0.9891

Q3 0.9784 0.9775 0.9875 0.9944 -- 0.9702 0.9759

Q4 0.9700 0.9800 0.9761 0.9843 -- 0.9790 0.9818

The product is the forecasted species concentrations by quarter.

Quarter SO4 NO3 OCMMB EC PBW NH4 Crustal

Q1 2.3003 1.0849 3.1975 0.1968 -- 1.1552 0.6858

Q2 2.8695 0.7985 3.4865 0.5942 -- 1.3571 0.5927

Q3 1.7600 0.6840 6.9728 0.2973 -- 1.1640 0.2918

Q4 2.5219 1.3717 2.7109 0.3928 -- 1.1741 0.5887

5.1.2.4 Step 4: Forecasted Design Value Calculation

The forecasted weighted quarterly averages for the species are then added together to get

the estimated forecasted quarterly average PM2.5 level:

MATS averages the four quarterly averages these forecasted quarterly average PM2.5

levels to calculate forecasted design values:

The output file containing the results of this Standard Analysis are described here. Example Calculation Forecasted Design Values

Using hypothetical data from the previous example calculation plus PBW values and
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assumed blank mass, MATS sums the species to get quarterly PM2.5 values.

Quarter SO4 NO3 OCMMB EC PBW NH4 Crustal Blank

Mass

PM2.5

Q1 2.3003 1.0849 3.1975 0.1968 0.5000 1.1552 0.6858 0.5000 9.6205

Q2 2.8695 0.7985 3.4865 0.5942 0.4000 1.3571 0.5927 0.5000 10.5985

Q3 1.7600 0.6840 6.9728 0.2973 0.6000 1.1640 0.2918 0.5000 12.2699

Q4 2.5219 1.3717 2.7109 0.3928 0.7000 1.1741 0.5887 0.5000 9.9601

MATS then averages the four quarters to get a forecasted design value:

Quarter PM2.5

Q1 9.6205

Q2 10.5985

Q3 12.2699

Q4 9.9601

Forecasted

Design Value

10.61

5.1.2.5 Output Description

The output file is named "Annual PM25 Point.csv" with the Scenario Name appended at

the beginning and the forecast year is inserted at the end (e.g., "Example PM -- Annual

PM25 Point 2020.csv"). The table below describes the variables in the output file for the

Standard Analysis.

Note: The RRF variables in this file are not the actual RRFs used to calculate future year

PM2.5 and PM2.5 species. They are the resultant annual average RRFs calculated by

dividing the future annual average concentrations (in this file) by the base year annual

average concentrations (in this file). The actual RRFs are calculated on a quarterly

average basis and are contained in the quarterly average output files. There are no

quarterly average RRFs for water or NH4 (if DON, NO3, and SO4 are used to calculate

NH4).

Output file name: "Scenario Name + Annual PM25 Point"

Variable Description

_id 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 FRM data

_STATE_NAME State name. (This is a character variable.)

_COUNTY_NAM

E

County name. (This is a character variable.)

monitor_lat Latitude at the monitor site in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are negative.
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monitor_long Longitude at the monitor site in decimal degrees. Values in the eastern

hemisphere are positive, and those in the western hemisphere (e.g., United

States) are negative.

monitor_gridcell Identifier of grid cell closest to the monitor

b_pm25_ann_DVBase year 5 year weighted average PM2.5 annual design value

f_pm25_ann_DV Future year 5 year weighted average PM2.5 annual design value

b_blank_mass Base year blank mass concentration (ug/m3)

b_crustal_mass Base year crustal mass concentration (ug/m3)

b_EC_mass Base year elemental carbon mass concentration (ug/m3)

b_NH4_mass Base year ammonium mass concentration (ug/m3)

b_Ocmb_mass Base year organic carbon mass (by difference) concentration (ug/m3)

b_SO4_mass Base year sulfate ion mass concentration (ug/m3)

b_NO3_mass Base year nitrate ion mass concentration (ug/m3)

b_water_mass Base year water mass concentration (ug/m3)

b_salt_mass Base year salt mass concentration (ug/m3) 

f_blank_mass Future year blank mass concentration (ug/m3)

f_crustal_mass Future year crustal mass concentration (ug/m3)

f_EC_mass Future year elemental carbon mass concentration (ug/m3)

f_NH4_mass Future year ammonium mass concentration (ug/m3)

f_Ocmb_mass Future year organic carbon mass (by difference) concentration (ug/m3)

f_SO4_mass Future year sulfate ion mass concentration (ug/m3)

f_NO3_mass Future year nitrate ion mass concentration (ug/m3)

f_water_mass Future year water mass concentration (ug/m3)

f_salt_mass Future year salt mass concentration (ug/m3) 

rrf_crustal Resultant annual relative response factor- Crustal Mass

rrf_ec Resultant annual response factor- Elemental Carbon Mass

rrf_nh4 Resultant annual relative response factor- Ammonium Mass. 

rrf_oc Resultant annual relative response factor- Organic Carbon Mass

rrf_so4 Resultant annual relative response factor- Sulfate Mass

rrf_no3 Resultant annual relative response factor- Nitrate Mass

rrf_water_mass Resultant annual relative response factor- Water Mass

rrf_salt Resultant annual relative response factor- Salt Mass (set equal to 1 if modeled

salt is not used)

5.1.3 Quarterly Model Data

The Quarterly Model Data option gives you the option of creating a small, reusable file

with quarterly values from a much larger file with daily values. Since annual PM2.5

MATS works with quarterly values there is no loss of precision. To save time, it is

possible to use daily values only for an initial run with MATS and check this Quarterly
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Model Data option. Then for subsequent runs (that use the same modeled data), use the

quarterly file that MATS generates. However, this will only work for subsequent MATS

runs that use exactly the same base and future year photochemical model data (such as

sensitivity runs that test the various ambient data settings in MATS). 

Alternatively, you can generate a baseline and future quarterly average model file outside

of MATS (using a program such as SAS, STATA, etc), and then load these quarterly files

into MATS, bypassing the use of any daily model data in MATS. The format of the

quarterly file is described below.

MATS generates two files: · Base-year file: "Baseline Quarterly Avg Model Data.csv" with the Scenario Name (e.g.,

Example PM Annual) appended at the beginning (e.g., "Example PM Annual -- Baseline

Quarterly Avg Model Data.csv"). · Future year file (e.g., "Example PM -- Future Quarterly Avg Model Data.csv." The

format of the two files is the same. 

The table below describes the variables in the Quarterly Model Data file.

Output file name: "Scenario Name + Baseline/Future Quarterly Avg Model Data"

Variable Description

_id The ID is a unique identifier for each model grid cell. The default value is the

column identifier multiplied by 1000 plus the row. (This is a character variable.)

_type

lat Latitude at the grid cell centroid in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are negative.

long 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 Year and Quarter (01= 1st quarter, 04= 2nd quarter, etc.)

crustal Crustal PM

nh4 Ammonium PM

so4 Sulfate PM

ec Elemental Carbon

no3 Nitrate PM

oc Organic carbon PM

pm25 PM2.5 mass (only used to gradient adjust PM2.5 for gradient adjusted spatial

fields)

cm Coarse PM (ug/m3) (only used for visbility calculations)

MATS also produces a set of "used" quarterly average model day files (base and future). 

These have the same format as the quarterly average model data files, but only contain data
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for the model grid cells that were used in the MATS point calculations. These files can

also be re-used and are also useful for QA purposes. 

The table below describes the variables in the Used Quarterly Model Data file:

Output file name: "Scenario Name + Used Baseline/Future Quarterly Avg Model

Data"

Variable Description

_id The ID is a unique identifier for each model grid cell. The default value is the

column identifier multiplied by 1000 plus the row. (This is a character variable.)

_type

lat Latitude at the grid cell centroid in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are negative.

long 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 Year and Quarter (01= 1st quarter, 04= 2nd quarter, etc.)

crustal Crustal PM

nh4 Ammonium PM

so4 Sulfate PM

ec Elemental Carbon

no3 Nitrate PM

oc Organic carbon PM

pm25 PM2.5 mass (only used to gradient adjust PM2.5 for gradient adjusted spatial

fields)

cm Coarse PM (ug/m3) (only used for visbility calculations)

5.1.4 Species Fractions

Species fractions are simply the fraction of quarterly average PM2.5 at a given monitor

attributable to seven (and potentially eight) species: nitrate (NO3), sulfate (SO4), organic

carbon (OC), crustal, elemental carbon (EC), ammonium (NH4), and particle-bound water

(PBW). (And pending data availability, an eighth species, salt, can be included as well; the

default MATS species files include salt data. But salt is an optional species on the model

files. If base and future year modeled salt data is supplied, a salt RRF will be calculated. 

If there is no salt data in the model files, then the salt RRF will be set to 1.)

When you check the Species Fractions option, you get a reusable file with species fractions

for each FRM monitor. Making the file reusable allows you to generate consistent results

and, perhaps most importantly, allows the same file to be used by different MATS users.

5.1.4.1 Species Fractions Calculation

After calculating the ambient level of SO4, NO3, OCMMB, EC, PBW, NH4, and crustal,

MATS then divides these ambient levels by the non-blank mass of PM2.5. To get non-
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blank PM2.5, MATS subtracts the blank mass from the FRM PM2.5 value. MATS then

divides each of the species (except blank mass) by non-blank PM2.5.

Example Calculation of Species Fractions

The table below gives an example of theses calculations. The fraction is calculated by

dividing mass (ug/m3) by the Non-blank Mass. Note that salt is optional.

Units FRM

PM2.5

Blank

Mass

Non-Bla

nk Mass

SO4 NO3 OCMM

B

EC PBW NH4 Crusta

l

(Salt)

Concentrati

on

10.9175 0.5 10.4175 2.41601.1555 3.4847 0.2101 1.1555 1.2605 0.7353 -- Fraction 0.23190.1109 0.3345 0.0202 0.1109 0.1210 0.0706 -- 5.1.4.2 Output Description

The output file is named "Quarterly Avg Spec Frac Point.csv" with the Scenario Name

appended at the beginning (e.g., "Example PM -- Quarterly Avg Spec Frac Point.csv").

The table below describes the variables in the file.

The interpolated variables (starting with i_xxx) are created when MATS is run, but are not

needed when re-using a fractions file. They are also not needed when running with a user

generated fractions file.

 Output file name: "Scenario Name + Quarterly Avg Spec Frac Point"

Variable Description

_id 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.)

_STATE_NAME State name. (This is a character variable.)

_COUNTY_NAME County name. (This is a character variable.)

monitor_lat Latitude at the monitor site in decimal degrees. Values in the

northern hemisphere are positive, and those in the southern

hemisphere are negative.

monitor_long Longitude at the monitor site in decimal degrees. Values in the

eastern hemisphere are positive, and those in the western

hemisphere (e.g., United States) are negative.

quarter Quarter

pm25_mass_frac PM2.5 mass used to calculate species fractions (calculated from

the "PM2.5 for fractions" file) 

fcr Crustal fraction of PM2.5 mass

fec Elemental carbon fraction of PM2.5 mass

fnh4 Ammonium fraction of PM2.5 mass

focm Organic carbon fraction of PM2.5 mass

fso4 Sulfate ion fraction of PM2.5 mass

fno3 Nitrate ion fraction of PM2.5 mass

fwater Water fraction of PM2.5 mass

fsalt Salt fraction of PM2.5 mass
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blank_mass Blank mass

don Degree of neutralization of sulfate used to calculate ammonium

mass (0.000 - 0.375)

i_so4 Interpolated sulfate ion

i_no3r Interpolated retained nitrate ion

i_ocb Interpolated blank-adjusted organic carbon

i_ec Interpolated elemental carbon

i_crustal Interpolated crustal

i_don Interpolated degree of neutralization of sulfate (DON).

i_nh4 Interpolated ammonium

i_no3 Interpolated nitrate ion (only used to calculate volatilized

ammonium; if option is selected)

i_salt Interpolated salt

Notes:

 i_ocb is only used to to calculate the OCMmb "floor".

i_nh4 is not used if DON is used to calculate the ammonium concentration (and fraction).

i_no3 is only used to calculate the "volatilized ammonium", if the option is selected (not

used by default).

5.2 Output Choice - Advanced

In the Output Choice Advanced window, MATS lets you choose from among two main

options: Spatial Field Estimates and Miscellaneous Output that is generally used for

quality assurance (QA). Within each of these two main options there are a number of

choices.
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Spatial Field Estimates. This option gives you PM2.5 forecasts for each grid cell in

the modeling domain (e.g., CMAQ 36 km). · The Interpolate FRM & speciation monitor data to spatial field. Temporally-adjust

option calculates interpolated spatial fields that are temporally adjusted. This option

creates gridded spatial fields of future year PM2.5 data. To create PM2.5 spatial fields,

MATS interpolates both speciation data and PM2.5 data (FRM and IMPROVE). · The Interpolate gradient-adjusted FRM & speciation monitor data to spatial field.

Temporally-adjust option calculates interpolated spatial fields that are temporally

adjusted and gradient adjusted. Check this option to create gridded spatial fields of

gradient adjusted future year PM2.5 data. To create PM2.5 spatial fields, MATS

interpolates both gradient adjusted speciation data and PM2.5 data (FRM and

IMPROVE). This option creates the recommended spatial field for the “Unmonitored

Area Analysis” from the PM2.5 modeling guidance.

Miscellaneous Output. This provides a variety of output files that can be used to QA

the your calculations. · Quarterly average files. The MATS default is to output the annual average results for

all analyses (point, spatial fields). Checking any one of these boxes will also output the

more detailed quarterly average calculations (the quarterly average calculations are the

basis of all of the MATS annual PM2.5 calculations). · High county sites. The MATS default is to output the point results for all FRM sites.

Checking this box will also create a file which contains only the single highest monitor

in each County (based on the highest future year value). This dataset is a subset of the

all sites file.
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· Species fractions spatial field. This option is the same as the species fraction file

created from the standard analysis except it outputs the species fractions file created

from a spatial field. The file will contain species fraction data for each quarter for each

grid cell. · Quarterly average speciated monitors. This file contains the raw quarterly average

speciated data that MATS uses to do interpolations (to calculate species fractions). This

data is derived from the “species for fractions” input file. · Design Value Periods. This file contains standard MATS output for each design period

within the period covered by the analysis. · Neighbor files. The neighbor files contain the “nearest neighbor” data for the VNA

interpolation scheme. The data includes the distance to neighbor monitors and weights

used to do the interpolations. There is information for each FRM monitor (for point

analyses) or each grid cell (for spatial fields) for each quarter and for each species.

5.2.1 Spatial Field Estimates

With the Spatial Field Estimates option you can estimate PM2.5 for each grid cell in a

spatial field by interpolating both FRM monitor data and speciated monitor data to the

grid cell centroid (identified by latitude and longitude). A key difference between Spatial

Field Estimates and Standard Analysis is that the Spatial Field Estimates requires the

interpolation of FRM monitor data. MATS interpolates the "unofficial" daily PM2.5

monitor data from FRM and IMPROVE sites to each grid cell centroid and then averages

the values for each quarter from the different PM2.5 sites. (Recall, as discussed in the 

Baseline PM2.5 Calculation section, the Standard Analysis uses "official" PM2.5 monitor

data.)

The basic form of the spatial calculation is as follows:

GridcellE, baseline = estimated quarterly average baseline species concentration at grid cell E;

Weighti = inverse distance weight for monitor i;

Monitori = quarterly average baseline species concentration at monitor i.

Note that MATS lets you choose whether to use air quality modeling to scale the 

interpolation of speciated monitor values to grid cell centroids (spatial fields). This use of

modeling data is referred to as a gradient adjustment, and is discussed in the next section. 5.2.1.1 Gradient-Adjustment- ("Fused fields")

Using modeling data for a gradient adjustment is fairly simple. MATS calculates species

fractions using monitor data in basically the same way as in the species fractions
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calculation for the Standard Analysis, however, MATS includes model data to help inform

the calculation. In particular, MATS uses model values for the grid cell of interest and the

model values for the grid cells containing the speciated/PM2.5 monitors to be interpolated

to cell of interest. (Note that "unofficial" PM2.5 values are used in the interpolation, and

they are treated in essentially the same way as monitor data for particular species, such as

SO4.)

A general form of the equation for the interpolated species values is as follows:

where:

SpeciesE, baseline = estimated baseline quarterly average species/PM2.5

concentration at cell E;

Weighti = inverse distance weight for monitor i;

Monitori = baseline quarterly average species/PM2.5 concentration at

monitor i;

Gradient Adjustmenti, E = gradient adjustment from monitor i to cell E.

This interpolation calculation can be rewritten somewhat more precisely as:

where:

ModelE, baseline = modeled quarterly average baseline species/PM2.5 concentration at cell E;

Modeli, baseline = modeled quarterly average baseline species/PM2.5 concentration at

monitor site i.

MATS uses this gradient approach for PM2.5 and for most species -- SO4, NO3r, EC,

OCb, Crustal, and NH4. However, for DON (degree of neutralization), MATS does not

use a gradient adjustment and simply interpolates the estimated DON values as is from

each monitor. Options regarding the use of model data are discussed in more detail in the

section on Model Data Options. Once MATS has interpolated quarterly species values at the cell of interest, calculations

proceed in exactly the same way as described for species fractions and forecasted PM2.5

values for the Standard Analysis.
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Example Calculation for Interpolated Gradient-Adjusted Baseline Species

Concentrations

For each quarter, MATS identifies the speciated monitors that are nearby ("neighbors") to a

given cell of interest "E", and determines the weight that should be given to each monitor

in the interpolation process. Monitors that are nearby get a greater weight.

Speciated Monitor Values

Monitor

ID

Quarter SO4 NO3r EC OCb Crustal NH4 DON Interpolation

Wght

A Q1 2.30 1.10 3.32 0.20 1.10 1.20 0.21 0.2

B Q1 2.90 0.80 3.49 0.60 1.20 1.40 0.19 0.4

C Q1 1.80 0.70 7.06 0.30 1.20 1.20 0.18 0.15

D Q1 2.60 1.40 2.78 0.40 1.10 1.20 0.32 0.25

MATS then identifies the speciated model values at each of the speciated monitors (A-D)

and for the given cell of interest (E):

Model Values at Speciated Monitor Sites A-D

Monitor ID Quarter SO4 NO3 EC OC Crustal NH4 DON

A Q1 1.60 1.03 2.99 0.15 0.75 0.86 -- B Q1 2.54 0.70 3.20 0.47 1.07 1.04 -- C Q1 1.66 0.59 5.91 0.23 1.13 0.81 -- D Q1 1.93 1.15 2.13 0.27 1.00 1.12 -- Model Values at Cell E

Monitor ID Quarter SO4 NO3 EC OC Crustal NH4 DON

E Q1 2.10 0.98 3.10 0.15 1.00 1.30 -- MATS then calculates the speciated values at cell E using the monitor and model data (and

interpolation weights) using the equation above:

Interpolated Speciated Monitor Values

Monitor ID Quarter SO4 NO3r EC OCb Crustal NH4 DON

A Q1 3.02 1.04 3.44 0.20 1.46 1.82 0.21

B Q1 2.40 1.12 3.38 0.19 1.13 1.75 0.19

C Q1 2.27 1.16 3.71 0.19 1.06 1.93 0.18

D Q1 2.83 1.19 4.05 0.22 1.10 1.39 0.32

Wght. Avg. Q1 2.61 1.13 3.61 0.20 1.18 1.70 0.23

Note that MATS does a similar calculation (not shown) when interpolating "unofficial"

PM2.5 values to the given cell of interest, denoted "E".

5.2.1.2 Output Description - Interpolate FRM & Speciation Monitor Data to Spatial

Field

The output file is named "Annual PM25 Spatial Field" with the Scenario Name appended
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at the beginning and the forecast year is inserted at the end (e.g., "Example PM -- Annual

PM25 Spatial Field 2020.csv"). The table below describes the variables in the output file.

Output file name: "Scenario Name + Annual PM25 Spatial Field"

Variable Description

_id The ID is a unique identifier for each model grid cell. The default value is the

column identifier multiplied by 1000 plus the row. (This is a character

variable.)

gridcell_lat Latitude at the grid cell centroid in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are negative.

gridcell_long 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.

b_pm25_ann_DV Base year interpolated PM2.5 from unofficial data file 

f_pm25_ann_DV Future year interpolated PM2.5 

b_blank_mass Base year blank mass concentration (ug/m3)

b_crustal_mass Base year crustal mass concentration (ug/m3)

b_EC_mass Base year elemental carbon mass concentration (ug/m3)

b_NH4_mass Base year ammonium mass concentration (ug/m3)

b_Ocmb_mass Base year organic carbon mass (by difference) concentration (ug/m3)

b_SO4_mass Base year sulfate ion mass concentration (ug/m3)

b_NO3_mass Base year nitrate ion mass concentration (ug/m3)

b_water_mass Base year water mass concentration (ug/m3)

b_salt_mass Base year salt mass concentration (ug/m3)

f_blank_mass Future year blank mass concentration (ug/m3)

f_crustal_mass Future year crustal mass concentration (ug/m3)

f_EC_mass Future year elemental carbon mass concentration (ug/m3)

f_NH4_mass Future year ammonium mass concentration (ug/m3)

f_Ocmb_mass Future year organic carbon mass (by difference) concentration (ug/m3)

f_SO4_mass Future year sulfate ion mass concentration (ug/m3)

f_NO3_mass Future year nitrate ion mass concentration (ug/m3)

f_water_mass Future year water mass concentration (ug/m3)

f_salt_mass Future year salt mass concentration (ug/m3)

rrf_crustal Resultant annual relative response factor- Crustal Mass

rrf_ec Resultant annual response factor- Elemental Carbon Mass

rrf_nh4 Resultant annual relative response factor- Ammonium Mass. 

rrf_oc Resultant annual relative response factor- Organic Carbon Mass

rrf_so4 Resultant annual relative response factor- Sulfate Mass

rrf_no3 Resultant annual relative response factor- Nitrate Mass

rrf_water_mass Resultant annual relative response factor- Water Mass

rrf_salt Resultant annual relative response factor- Salt Mass
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5.2.1.3 Output Description - Interpolate Gradient-Adjusted FRM & Speciation

Monitor Data to Spatial Field

The output file is named "Grad Adj Annual PM25 Spatial Field" with the Scenario Name

appended at the beginning and the forecast year is inserted at the end (e.g., "Example PM

-- Grad Adj Annual PM25 Spatial Field 2020.csv"). The table below describes the

variables in the output file.

 Output file name: "Scenario Name + Grad Adj Annual PM25 Spatial Field"

Variable Description

_id The ID is a unique identifier for each model grid cell. The default value is the

column identifier multiplied by 1000 plus the row. (This is a character

variable.)

gridcell_lat Latitude at the grid cell centroid in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are negative.

gridcell_long 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.

b_pm25_ann_DV_ga Base year gradient adjusted interpolated PM2.5 from unofficial data file 

f_pm25_ann_DV_ga Future year gradient adjusted interpolated PM2.5 

b_blank_mass_ga Base year blank mass concentration (ug/m3)

b_crustal_mass_ga Base year crustal mass concentration (ug/m3)

b_EC_mass_ga Base year elemental carbon mass concentration (ug/m3)

b_NH4_mass_ga Base year ammonium mass concentration (ug/m3)

b_Ocmb_mass_ga Base year organic carbon mass (by difference) concentration (ug/m3)

b_SO4_mass_ga Base year sulfate ion mass concentration (ug/m3)

b_NO3_mass_ga Base year nitrate ion mass concentration (ug/m3)

b_water_mass_ga Base year water mass concentration (ug/m3)

b_salt_mass_ga Base year salt mass concentration (ug/m3)

f_blank_mass_ga Future year blank mass concentration (ug/m3)

f_crustal_mass_ga Future year crustal mass concentration (ug/m3)

f_EC_mass_ga Future year elemental carbon mass concentration (ug/m3)

f_NH4_mass_ga Future year ammonium mass concentration (ug/m3)

f_Ocmb_mass_ga Future year organic carbon mass (by difference) concentration (ug/m3)

f_SO4_mass_ga Future year sulfate ion mass concentration (ug/m3)

f_NO3_mass_ga Future year nitrate ion mass concentration (ug/m3)

f_water_mass_ga Future year water mass concentration (ug/m3)

f_salt_mass_ga Future year salt mass concentration (ug/m3)

rrf_crustal Resultant annual relative response factor- Crustal Mass

rrf_ec Resultant annual response factor- Elemental Carbon Mass

rrf_nh4 Resultant annual relative response factor- Ammonium Mass. 

rrf_oc Resultant annual relative response factor- Organic Carbon Mass
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rrf_so4 Resultant annual relative response factor- Sulfate Mass

rrf_no3 Resultant annual relative response factor- Nitrate Mass

rrf_water_mass Resultant annual relative response factor- Water Mass

rrf_salt Resultant annual relative response factor- Salt Mass

5.2.2 Miscellaneous Output

MATS gives you ability to generate a number of specialized files for quality assurance,

identify the monitor in each county with the highest forecast, and other reasons. These

files include: · Quarterly Average Files. Contain baseline PM2.5 and species concentrations, RRFs,

forecasted PM2.5 and species concentrations. Available for point estimates and spatial

fields, as well as gradient-adjusted spatial fields. These files are important because all of

the basic PM2.5 calculations in MATS occur on a quarterly average basis. The "true"

RRFs and PM2.5 and species concentrations are found in the quarterly average files. · High County Sites. Monitor chosen in county based on highest future-year value.

Contains baseline PM2.5 and species concentrations, RRFs, forecasted PM2.5 and

species. File only available for the Standard Analysis (point) results. · Species Fractions for Spatial Fields. Contains quarterly species fractions and

interpolated species values for spatial fields. · Quarterly Average Speciated Monitors. Contains baseline quarterly average monitored

species concentrations at STN and IMPROVE sites. This is the subset of species data

that MATS uses for each particular scenario (based on the MATS inputs and

configuration settings). · Design Value Periods. This file contains standard MATS output for each design period

within the period covered by the analysis. · Neighbor Files. Contains neighbor identifiers for interpolation to both FRM sites or to

spatial fields. 

5.2.2.1 Quarterly Average Files

The Quarterly Average Files provide intermediate calculations performed by MATS. In

particular, these files have the weighted quarterly average baseline and future values for

PM2.5 and its constituent species. In addition it gives the speciated RRFs. There are

potentially three Quarterly Average Files: one for Point Estimates and two for Spatial Field

Estimates (with and without gradient adjustment).

Output Description - Quarterly Average Point

The output file is named "Quarterly PM25 Point" with the Scenario Name appended at

the beginning (e.g., "Example Annual PM -- Quarterly PM25 Point.csv"). The table
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below describes the variables in the output file.

Output file name: "Scenario Name + Quarterly PM25 Point + Year"

Variable Description

_id 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 FRM data

_state_name State name. (This is a character variable.)

_county_name County name. (This is a character variable.)

monitor_lat Latitude at the monitor site in decimal degrees. Values in the

northern hemisphere are positive, and those in the southern

hemisphere are negative.

monitor_long Longitude at the monitor site in decimal degrees. Values in the

eastern hemisphere are positive, and those in the western

hemisphere (e.g., United States) are negative.

monitor_gridcell Identifier of grid cell closest to the monitor

quarter Quarter

b_pm25_ann_q_DV Base year 5 year weighted average PM2.5 (quarter) design

value 

f_pm25_ann_q_DV Future year 5 year weighted average PM2.5 (quarter) design

value

b_blank_mass_q Base year blank mass concentration (ug/m3)

b_crustal_mass_q Base year crustal mass concentration (ug/m3)

b_EC_mass_q Base year elemental carbon mass concentration (ug/m3)

b_NH4_mass_q Base year ammonium mass concentration (ug/m3)

b_Ocmb_mass_q Base year organic carbon mass (by difference) concentration

(ug/m3)

b_SO4_mass_q Base year sulfate ion mass concentration (ug/m3)

b_NO3_mass_q Base year nitrate ion mass concentration (ug/m3)

b_water_mass_q Base year water mass concentration (ug/m3)

f_blank_mass_q Future year blank mass concentration (ug/m3)

f_crustal_mass_q Future year crustal mass concentration (ug/m3)

f_EC_mass_q Future year elemental carbon mass concentration (ug/m3)

f_NH4_mass_q Future year ammonium mass concentration (ug/m3)

f_Ocmb_mass_q Future year organic carbon mass (by difference) concentration

(ug/m3)

f_SO4_mass_q Future year sulfate ion mass concentration (ug/m3)

f_NO3_mass_q Future year nitrate ion mass concentration (ug/m3)

f_water_mass_q Future year water mass concentration (ug/m3)

rrf_crustal Relative response factor- Crustal Mass

rrf_ec Relative response factor- Elemental Carbon Mass

rrf_nh4 Relative response factor- Ammonium Mass. (Only calculated if

ammonium in model data and measured ammonium ["NH4"]

used in calculations.)

rrf_oc Relative response factor- Organic Carbon Mass

rrf_so4 Relative response factor- Sulfate Mass

rrf_no3 Relative response factor- Nitrate Mass
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Output Description - Quarterly Average Spatial Field

The output file is named "Quarterly PM25 Spatial Field" with the Scenario Name

appended at the beginning (e.g., "Example Annual PM -- Quarterly PM25 Spatial Field. csv"). The table below describes the variables in the output file.

Output file name: "Scenario Name + Quarterly PM25 Spatial Field + Year"

Variable Description

_id The ID is a unique identifier for each model grid cell. The default

value is the column identifier multiplied by 1000 plus the row. 

(This is a character variable.)

gridcell_lat Latitude at the grid cell centroid in decimal degrees. Values in the

northern hemisphere are positive, and those in the southern

hemisphere are negative.

gridcell_long 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.

quarter Quarter

b_pm25_ann_q_DV Base year 5 year weighted average PM2.5 (quarter) design value 

f_pm25_ann_q_DV Future year 5 year weighted average PM2.5 (quarter) design

value

b_blank_mass_q Base year blank mass concentration (ug/m3)

b_crustal_mass_q Base year crustal mass concentration (ug/m3)

b_EC_mass_q Base year elemental carbon mass concentration (ug/m3)

b_NH4_mass_q Base year ammonium mass concentration (ug/m3)

b_Ocmb_mass_q Base year organic carbon mass (by difference) concentration

(ug/m3)

b_SO4_mass_q Base year sulfate ion mass concentration (ug/m3)

b_NO3_mass_q Base year nitrate ion mass concentration (ug/m3)

b_water_mass_q Base year water mass concentration (ug/m3)

f_blank_mass_q Future year blank mass concentration (ug/m3)

f_crustal_mass_q Future year crustal mass concentration (ug/m3)

f_EC_mass_q Future year elemental carbon mass concentration (ug/m3)

f_NH4_mass_q Future year ammonium mass concentration (ug/m3)

f_Ocmb_mass_q Future year organic carbon mass (by difference) concentration

(ug/m3)

f_SO4_mass_q Future year sulfate ion mass concentration (ug/m3)

f_NO3_mass_q Future year nitrate ion mass concentration (ug/m3)

f_water_mass_q Future year water mass concentration (ug/m3)

rrf_crustal Relative response factor- Crustal Mass

rrf_ec Relative response factor- Elemental Carbon Mass

rrf_nh4 Relative response factor- Ammonium Mass. (Only calculated if

ammonium in model data and measured ammonium ["NH4"]

used in calculations.)

rrf_oc Relative response factor- Organic Carbon Mass

rrf_so4 Relative response factor- Sulfate Mass

rrf_no3 Relative response factor- Nitrate Mass
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Output Description - Quarterly Average Spatial Field - Gradient Adjusted

The output file is named "Grad Adj Quarterly PM25 Spatial Field" with the Scenario

Name appended at the beginning (e.g., "Example Annual PM -- Grad Adj Quarterly PM25

Spatial Field.csv"). The table below describes the variables in the output file.

Output file name: "Scenario Name + Grad Adj Quarterly PM25 Spatial Field + Year"

Variable Description

_id The ID is a unique identifier for each model grid cell. The default

value is the column identifier multiplied by 1000 plus the row. 

(This is a character variable.)

gridcell_lat Latitude at the grid cell centroid in decimal degrees. Values in the

northern hemisphere are positive, and those in the southern

hemisphere are negative.

gridcell_long 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.

quarter Quarter

b_pm25_ann_q_DV_ga Base year 5 year weighted average PM2.5 (quarter) design value 

f_pm25_ann_q_DV_ga Future year 5 year weighted average PM2.5 (quarter) design

value

b_blank_mass_q_ga Base year blank mass concentration (ug/m3)

b_crustal_mass_q_ga Base year crustal mass concentration (ug/m3)

b_EC_mass_q_ga Base year elemental carbon mass concentration (ug/m3)

b_NH4_mass_q_ga Base year ammonium mass concentration (ug/m3)

b_Ocmb_mass_q_ga Base year organic carbon mass (by difference) concentration

(ug/m3)

b_SO4_mass_q_ga Base year sulfate ion mass concentration (ug/m3)

b_NO3_mass_q_ga Base year nitrate ion mass concentration (ug/m3)

b_water_mass_q_ga Base year water mass concentration (ug/m3)

f_blank_mass_q_ga Future year blank mass concentration (ug/m3)

f_crustal_mass_q_ga Future year crustal mass concentration (ug/m3)

f_EC_mass_q_ga Future year elemental carbon mass concentration (ug/m3)

f_NH4_mass_q_ga Future year ammonium mass concentration (ug/m3)

f_Ocmb_mass_q_ga Future year organic carbon mass (by difference) concentration

(ug/m3)

f_SO4_mass_q_ga Future year sulfate ion mass concentration (ug/m3)

f_NO3_mass_q_ga Future year nitrate ion mass concentration (ug/m3)

f_water_mass_q_ga Future year water mass concentration (ug/m3)

rrf_crustal_ga Relative response factor- Crustal Mass

rrf_ec_ga Relative response factor- Elemental Carbon Mass

rrf_nh4_ga Relative response factor- Ammonium Mass. (Only calculated if

ammonium in model data and measured ammonium ["NH4"] used

in calculations.)

rrf_oc_ga Relative response factor- Organic Carbon Mass

rrf_so4_ga Relative response factor- Sulfate Mass

rrf_no3_ga Relative response factor- Nitrate Mass
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5.2.2.2 Output Description - High County Sites

In this file, MATS reports the monitor with the highest forecasted PM2.5 design value in

each county. The name of this file is "High County Sites" with the Scenario Name

appended at the beginning (e.g., "Example Annual PM -- High County Sites.csv"). The

table below describes the variables in the output file.

Output file name: "Scenario Name + High County Sites + Year"

Variable Description

_id 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 FRM data

_state_name State name. (This is a character variable.)

_county_name County name. (This is a character variable.)

monitor_lat Latitude at the monitor site in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are

negative.

monitor_long Longitude at the monitor site in decimal degrees. Values in the eastern

hemisphere are positive, and those in the western hemisphere (e.g.,

United States) are negative.

monitor_gridcell Identifier of grid cell closest to the monitor

b_pm25_ann_DV Base year 5 year weighted average PM2.5 annual design value

f_pm25_ann_DV Future year 5 year weighted average PM2.5 annual design value

b_blank_mass Base year blank mass concentration (ug/m3)

b_crustal_mass Base year crustal mass concentration (ug/m3)

b_EC_mass Base year elemental carbon mass concentration (ug/m3)

b_NH4_mass Base year ammonium mass concentration (ug/m3)

b_Ocmb_mass Base year organic carbon mass (by difference) concentration (ug/m3)

b_SO4_mass Base year sulfate ion mass concentration (ug/m3)

b_NO3_mass Base year nitrate ion mass concentration (ug/m3)

b_water_mass Base year water mass concentration (ug/m3)

f_blank_mass Future year blank mass concentration (ug/m3)

f_crustal_mass Future year crustal mass concentration (ug/m3)

f_EC_mass Future year elemental carbon mass concentration (ug/m3)

f_NH4_mass Future year ammonium mass concentration (ug/m3)

f_Ocmb_mass Future year organic carbon mass (by difference) concentration (ug/m3)

f_SO4_mass Future year sulfate ion mass concentration (ug/m3)

f_NO3_mass Future year nitrate ion mass concentration (ug/m3)

f_water_mass Future year water mass concentration (ug/m3)

5.2.2.3 Species Fractions Spatial Field

Contains quarterly species fractions and interpolated species values (with or without

gradient-adjustment) for spatial fields. (Note that you cannot currently load this file into
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MATS, and instead this file is currently only for information. A future version of MATS

may allow loading in species fractions for spatial fields.)

Output Description - Species Fractions Spatial Field

In this file, MATS reports the quarterly species fractions. The name of this file is "

Quarterly Avg Spec Frac Spatial Field" with the Scenario Name appended at the

beginning (e.g., "Example Annual PM -- Quarterly Avg Spec Frac Spatial Field.csv"). The

table below describes the variables in the output file.

Output file name: "Scenario Name + Quarterly Avg Spec Frac Spatial Field"

Variable Description

_ID The ID is a unique identifier for each model grid cell. The

default value is the column identifier multiplied by 1000

plus the row. (This is a character variable.)

gridcell_lat Latitude at the grid cell centroid in decimal degrees. Values

in the northern hemisphere are positive, and those in the

southern hemisphere are negative.

gridcell_long 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.

quarter Quarter

PM25_mass_frac PM2.5 mass used to calculate species fractions (calculated

from the "PM2.5 for fractions" file

fcr Crustal fraction of PM2.5 mass

fec Elemental carbon fraction of PM2.5 mass

fnh4 Ammonium fraction of PM2.5 mass

focm Organic carbon fraction of PM2.5 mass

fso4 Sulfate ion fraction of PM2.5 mass

fno3 Nitrate ion fraction of PM2.5 mass

fwater Water fraction of PM2.5 mass

fsalt Salt fraction of PM2.5 mass

blank_mass Blank mass

don Degree of neutralization of sulfate used to calculate

ammonium mass (0.000 - 0.375)

i_SO4 Interpolated sulfate ion

i_NO3R Interpolated nitrate ion

i_OCB Interpolated blank-adjusted organic carbon

i_EC Interpolated elemental carbon

i_CRUSTAL Interpolated crustal

i_DON Interpolated degree of neutralization of sulfate (DON).

i_SALT Interpolated salt

i_NH4 Interpolated ammonium

Output Description - Gradient-Adjusted Species Fractions Spatial Field

In this file, MATS reports the gradient adjusted quarterly species fractions. The name of
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this file is "Grad Adj Quarterly Avg Spec Frac Spatial Field" with the Scenario Name

appended at the beginning (e.g., "Example Annual PM -- Grad Adj Quarterly Avg Spec

Frac Spatial Field.csv"). The table below describes the variables in the output file.

Output file name: "Scenario Name + Grad Adj Quarterly Avg Spec Frac Spatial

Field"

Variable Description

_ID The ID is a unique identifier for each model grid cell. The

default value is the column identifier multiplied by 1000 plus

the row. (This is a character variable.)

gridcell_lat Latitude at the grid cell centroid in decimal degrees. Values

in the northern hemisphere are positive, and those in the

southern hemisphere are negative.

gridcell_long 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.

quarter Quarter

PM25_mass_frac_ga PM2.5 mass used to calculate species fractions (calculated

from the "PM2.5 for fractions" file)

fcr_ga Crustal fraction of PM2.5 mass

fec_ga Elemental carbon fraction of PM2.5 mass

fnh4_ga Ammonium fraction of PM2.5 mass

focm_ga Organic carbon fraction of PM2.5 mass

fso4_ga Sulfate ion fraction of PM2.5 mass

fno3_ga Nitrate ion fraction of PM2.5 mass

fwater_ga Water fraction of PM2.5 mass

fsalt_ga Salt fraction of PM2.5 mass

blank_mass_ga Blank mass

don_ga Degree of neutralization of sulfate used to calculate

ammonium mass (0.000 - 0.375)

i_SO4_ga Interpolated sulfate ion

i_NO3R_ga Interpolated nitrate ion

i_OCB_ga Interpolated blank-adjusted organic carbon

i_EC_ga Interpolated elemental carbon

i_CRUSTAL_ga Interpolated crustal

i_DON Interpolated degree of neutralization of sulfate (DON). 

Note that DON is not gradient-adjusted.

i_SALT_ga Interpolated salt

i_NH4_ga Interpolated ammonium

5.2.2.4 Output Description - Quarterly Average Speciated Monitors

In this file, MATS reports the quarterly averages at the speciated monitors. The name of

this file is "Quarterly Avg Speciated Monitors" with the Scenario Name appended at the

beginning (e.g., "Example Annual PM -- Quarterly Avg Speciated Monitors.csv"). The

table below describes the variables in the output file.

Output file name: "Scenario Name + Quarterly Avg Speciated Monitors"
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Variable Description

_id IMPROVE/STN Site Code

_type

_state_name State name. (This is a character variable.)

_county_name County name. (This is a character variable.)

monitor_lat Latitude at the monitor site in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are

negative.

monitor_long Longitude at the monitor site in decimal degrees. Values in the eastern

hemisphere are positive, and those in the western hemisphere (e.g., United

States) are negative.

monitor_gridcell Identifier of grid cell closest to the monitor

quarter Quarter

b_crustal_mass Base year crustal mass concentration (ug/m3)

b_EC_mass Base year elemental carbon mass concentration (ug/m3)

b_NH4_mass Base year ammonium mass concentration (ug/m3)

b_Ocmb_mass Base year organic carbon mass (by difference) concentration (ug/m3)

b_SO4_mass Base year sulfate ion mass concentration (ug/m3)

b_NO3_mass Base year nitrate ion mass concentration (ug/m3)

b_water_mass Base year water mass concentration (ug/m3)

5.2.2.5 Design Value Periods

Normally, MATS will output one set of files covering the entire analysis period specified

by the user. The outputs represent the averages of the values for each 3-year design value

period. If the "Output design value periods" option is checked, MATS will produce

discrete outputs for each design value period. The output files will be the same as a

standard analysis, but with "Period 1", "Period 2", etc., attached at the end of the name.

Please note, however, that checking this option will substantially increase the MATS run

time, by up to four times.

5.2.2.6 Neighbor Files

MATS calculates the nearby monitors or "neighbors" separately for each species when

interpolating to points, and similarly for interpolating to spatial fields, which also involves

interpolation of PM monitors. · Neighbors for Interpolating Species to Points. · Neighbors for Interpolating PM2.5 & Species to Spatial Fields. Output Description - Neighbors for Interpolating Species to Points

In this file, MATS reports the neighbors involved in interpolating species values (except

NH4 and DON) to the FRM monitor sites. The name of this file is "Neighbor File Point" with the Scenario Name appended at the beginning (e.g., "Example Annual PM --
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Neighbor File Point.csv"). The table below describes the variables in the output file.

Output file name: "Scenario Name + Neighbor File Point"

Variable Description

_id 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.)

_state_name State name. (This is a character variable.)

_county_name County name. (This is a character variable.)

monitor_lat Latitude at the monitor site in decimal degrees. Values in the

northern hemisphere are positive, and those in the southern

hemisphere are negative.

monitor_long Longitude at the monitor site in decimal degrees. Values in the

eastern hemisphere are positive, and those in the western

hemisphere (e.g., United States) are negative.

monitor_gridcell Identifier of grid cell closest to the monitor

quarter Quarter

_neighbor IMPROVE/STN Site Code. (This is a character variable.)

neighbor_gridcell Identifier of grid cell closest to the neighbor

distance Distance in kilometers from FRM monitor site and

IMPROVE/STN/SPECTRE neighbor.

weightdistance Inverse-distance weight

weightdistancesquared Inverse-distance-squared weight

pollutant Pollutant (e.g., SO4). Note interpolation approach can vary by

pollutant

Output Description - Neighbors for Interpoatling PM & Species to Spatial Field

In this file, MATS reports the neighbors involved in interpolating NH4 and DON to the

Spatial Field. The name of this file is "Neighbor File Spatial Field - PM" with the

Scenario Name appended at the beginning (e.g., "Example Annual PM -- Neighbor File

Spatial Field - PM.csv"). The table below describes the variables in the output file.

Output file name: "Scenario Name + Neighbor File Spatial Field"

Variable Description

_id The ID is a unique identifier for each model grid cell. The default

value is the column identifier multiplied by 1000 plus the row. (This

is a character variable.)

gridcell_lat Latitude at the grid cell centroid in decimal degrees. Values in the

northern hemisphere are positive, and those in the southern

hemisphere are negative.

gridcell_long 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.

quarter Quarter

_neighbor IMPROVE/STN Site Code. (This is a character variable.)

neighbor_gridcell Identifier of grid cell closest to the neighbor

distance Distance in kilometers from FRM monitor site and

IMPROVE/STN/SPECTRE neighbor.
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weight Inverse-distance weight

Interpolation method Inverse-distance-squared weight

pollutant Pollutant (e.g., SO4). Note interpolation approach can vary by

pollutant

5.3 Data Input

In the Data Input window, you specify the MATS input files that are used in each scenario.

 There are three main types of files which must be specified. These include ambient

PM2.5 species data, ambient total PM2.5 data (FRM and IMPROVE), and gridded model

output data (e.g. CMAQ or CAMx data). 

There is specific terminology that is used on the Data Input page. "Official" data refers to

PM2.5 FRM data that can be used to determine official design values for compliance

purposes (comparison to the NAAQS). Other datasets which may not have rigid regulatory

significance are sometimes referred to as "unofficial" data. The individual input file

choices are explained below.

Species Data. MATS needs ambient PM2.5 species data to calculate species

concentrations at FRM monitoring sites and spatial fields. Users have a choice of

supplying a “Species Monitor Data File” or a “Species Fractions File”. · Species Monitor Data File. The default is to provide a species monitor data file.

MATS is populated with daily average species data from STN and IMPROVE sites

across the country. However, users can also provide their own ambient data file. 
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MATS uses the daily average species data to calculate species fractions at each FRM

monitor (or at each grid cell, in the case of spatial fields). The species fraction data is

combined with the “unofficial daily average PM2.5 data” to calculate species

concentrations. The default MATS species data file contains all available data. 

However, there is a data flag to indicate site days that are recommended to be removed

from the species fractions calculations. This is not necessarily the same data flags that

have been identified by State agencies. We have incorporated flagging routines that

remove data that are considered to be outliers and/or incomplete data. (A description

of the flags is provided in the section on Species Fractions Calculation Options.) · Species Fraction File. Alternatively, the user can choose to use a pre-calculated

species fractions file which contains quarterly species information for the FRM

monitors of interest. MATS can also re-use a species fractions file (either "point" or

"spatial fields") that has previously been generated by MATS. To re-use a previously

created fractions file, simply supply the correct path to the file. When re-using spatial

fields species fractions files, either "spatial field" or gradient adjusted spatial fields"

must be selected from the drop-down box. MATS cannot use both types of spatial

fields species fractions files at the same time . (The calculation of species fractions is

discussed here.)

PM2.5 Monitor Data. MATS uses both "official" and "unofficial" data in its

calculations. · Unofficial Daily Average PM2.5 Data File. The “unofficial daily average PM2.5” file

contains the PM2.5 data that is needed to calculate species fractions. It is used in

combination with the “species monitor data file” from above. The unofficial daily

average PM2.5 file is not needed if the user supplies a pre-calculated species fractions

file. 

Similar to the species monitor data file from above, the “unofficial” PM2.5 data file

contains a data flag to indicate site days that are recommended to be removed from the

species fractions calculations. The flagged data is matched between the species file and

the PM2.5 file so that the same site days are removed. However, the PM2.5 data file

contains additional data (sites that don’t contain speciation measurements) and therefore

has additional flagged site days. These are not the same data flags that have been

identified by State agencies. We have incorporated flagging routines that remove data

that are considered to be outliers and/or incomplete data. (A description of the flags is

provided in the section on Species Fractions Calculation Options.) The user is free to

unflag existing data or add flags as necessary and appropriate. 

· Official Quarterly Average FRM Data File. The “official quarterly average file”

contains all of the “official” quarterly average FRM data that has been used to

calculate PM2.5 design values. It is used to calculate design values and 5 year

weighted average design values as part of the attainment test. 
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The default data file in MATS was created by the Air Quality Analysis Group within

OAQPS. In most cases, the data should not be altered, however in some cases (e.g.

sensitivity analyses) there may be a need to add or remove data.

Model Data. The “model data” refers to gridded model output from models such as

CMAQ or CAMx. The user can choose either daily model data input or quarterly model

data input (which is just a quarterly average of the daily model data). Either will work

for annual PM2.5. The default setting is daily average data. Recall that MATS can

generate quarterly average model data (which can then be re-used in subsequent MATS

runs). 

Model data must be selected for all MATS runs. The size of the modeling grid defines

the outputs for point estimates and for spatial fields. For point estimates, MATS will

output the results for all specified monitors within the domain. For spatial fields, MATS

will create spatial fields that match the size of the gridded model domain.

Note that you need to specify both a Baseline File and a Forecast File. The baseline file

should be consistent with the historical monitor data that you use, and the forecast year

is the future-year of interest.

5.3.1 Species Data Input

The species data may be in form of monitor data (specified below) that MATS then uses to

calculate species fractions, or it may be in the form of species fractions directly (specified

here).

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 Speciated Monitor Data
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Speciated Monitor Data Variable Descriptions

Variable Description

_id The ID is a unique name for each monitor in a particular location. (This is a

character variable.)

LAT Latitude at the monitor site in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are negative.

LONG Longitude at the monitor site in decimal degrees. Values in the eastern

hemisphere are positive, and those in the western hemisphere (e.g., United

States) are negative.

_TYPE STN or IMPROVE network data

DATE Date of daily average ambient data with YYYYMMDD format (This is a

numeric variable)

SO4 Measured sulfate ion 

NO3R Estimated nitrate retained on FRM filter, using measured nitrate, hourly T and

RH

NH4 Measured NH4+ ion

OCB OC blank adjusted, STN uses constant blank value per sampler type,

IMPROVE uses backup filter methodology

EC Measured EC

CRUSTAL Using IMPROVE algorithm, Crustal, aka "Fine Soil" = 2.2 × [Al] + 2.49 × [Si] +

1.63 × [Ca] + 2.42 × [Fe] + 1.94 × [Ti]

SALT Estimated salt using Cl (Salt=1.8*Cl), where Cl is elemental chloride

DON Degree of neutralization of SO4 (0-0.375)

H2O_AIM Calculated water using AIM and measured SO4, adjusted NO3 and measured

NH4
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OC Measured OC

NO3 Measured nitrate ion

SO4_3S Sulfate value derived from S, as per IMPROVE protocol. i.e. SO4_3S = 3*S. 

CRUSTAL_ALT Alternative Crustal calculation using measured Si, Fe, Ca, Ti (modified formula

without Al) = 3.73 × [Si] + 1.63 × [Ca] + 2.42 × [Fe] + 1.94 × [Ti]

FRM_MASS FRM mass

MEASURED_FM STN or IMPROVE sampler measured fine mass (Teflon filter)

RCFM Reconstructed Fine Mass using IMPROVE protocol, RCFM = [Amm_Sulfate] +

[Amm_Nitrate] + [OCM] + [EC] + [Fine Soil]

Al Measured Al

Ca Measured Ca

Fe Measured Fe

Ti Measured Ti

Si Measured Si

EPA_FLAG Flag to indicate data that EPA recommends to be removed from the species

fractions calculations. 0 = valid data, 1 or greater = data that has been flagged

and should be removed

USER_FLAG Flag to indicate additional data that the user wants to remove from the species

fractions calculations. 0 = valid data, 1 or greater = data that has been flagged

and should be removed

Note: 

Some variables are supplied for QA purposes only and are either not used by MATS or are

calculated internally by MATS. For example, "OC" is not used by MATS (OCb is used)

and H20_AIM is calculated internally. 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.)

5.3.2 PM2.5 Monitor Data Input

MATS uses the "unofficial" daily PM2.5 file for the calculation of all species fractions

files (both point and spatial field) and in the calculation of PM2.5 levels in spatial fields. 

MATS uses the "official" quarterly PM2.5 file for point estimates.

5.3.2.1 Unofficial Daily PM2.5 Monitor Data Input

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.
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Format of Unofficial PM2.5 Monitor Data

Unofficial PM2.5 Monitor Data Variable Descriptions

Variable Description

_ID The ID is a unique name for each monitor in a particular location. (This is

a character variable.)

_TYPE FRM, IMPROVE, or other "special" data -- may include dummy sites. 

(This is a character variable.)

LAT Latitude at the monitor site in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are

negative.

LONG Longitude at the monitor site 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 ambient data with YYYYMMDD format (This is a

numeric variable)

PM25 Measured PM2.5 mass (ug/m3)

EPA_FLAG Flag to indicate data that EPA recommends to be removed from the

species fractions calculations. 0 = valid data, 1 = data that has been

flagged and should be removed

USER_FLAG Flag to indicate additional data that the user wants to remove from the

species fractions calculations. 0 = valid data, 1 or greater = data that has

been flagged and should be removed.

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
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space, such as might occur with the name of a location, then use quotes.)

5.3.2.2 Official Quarterly PM2.5 Monitor Data Input

Monitor data should be in the form of a simple text file. The first row specifies the

frequency of the data (e.g., quarter). 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 Official Quarterly PM2.5 Monitor Data

Official Quarterly PM2.5 Monitor Data Variable Descriptions

Variable Description

_ID 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 at the monitor site in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are

negative.

LONG Longitude at the monitor site 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 quarterly average ambient data with YYYYMMDD format. The

date represents the first day of each quarter. (This is a numeric variable)

 PM25 PM2.5 mass

 NDAYS Number of complete days in each quarter

 _SUBSTITUTION_CODEIndicates whether the design value period was determined to be

complete by using substitution procedures.

 COMPLETION_CODE Official design value completion codes (1, 2, 3, 4, or 5). Codes are valid

for the end year of each 3 year design value period.

 _STATE_NAME State name. (This is a character variable.)
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 _COUNTY_NAME 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.)

5.3.3 Model Data Input

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. Below is an example of the model data file

format and descriptions of the variables in the file. Note that there is both a base year and

a future year model file. The format for both is the same.

Note that you can load in either daily model data or quarterly model data.

Format of Model Data
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Model Data Variable Descriptions

Variable Description

_ID 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

LAT Latitude at the grid cell centroid in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are negative.

LONG 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 mass (ug/m3)

CRUSTAL Crustal PM2.5 mass

NH4 Ammonium mass

SO4 Sulfate PM

EC Elemental carbon

NO3 Nitrate PM

OC Organic mass PM

PM25 PM2.5 mass

SALT Salt

Note: · The "PM25" mass variable is used to calculate PM2.5 model "gradients" for gradient

adjusted spatial fields. It is up to the user to provide a modeled PM2.5 mass

concentration using an appropriate definition of modeled PM2.5 mass. · 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.)

5.4 Species Fractions Calculation Options

The Species Fractions Calculation Options has two main sections. One involving

speciated monitor data (e.g., STN and IMPROVE monitors) and the other total PM2.5

monitor data (FRM and IMPROVE). For each type of data you can specify the years of

interest, whether you want to delete certain data, and the minimum amount of data for a

monitor to be considered "valid" (and thus included in the calculations). In the next

sections, we describe each of these three options in more detail. Note that these options

apply more or less in the same way for both speciated monitor data and total PM2.5

monitor data. Also note that these options are no longer relevant if you have loaded a 

species fractions file. · IMPROVE-STN Monitor Data. The speciation data from STN and IMPROVE

monitors are interpolated by MATS in order to provide species data for any point in a

modeling domain. The interpolated species data is used to calculate species fractions at
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FRM monitors (point estimates) and/or species fractions at all grid cells (spatial fields). 

Note that you do not need to have values for all species for a monitor to be considered

valid, as each species is considered individually. However, the "EPA_Flag" variable in

the default "species for fractions" file has been set so that all monitor days that do not

have complete species data are not used in the calculations (flag = 1). If the user wants

to use the incomplete species data, the flag can changed to "0". · PM2.5 Monitor Data. The total PM2.5 data from FRM are used by MATS to calculate

species fractions for point estimates (in conjunction with the interpolated speciation data

from STN and IMPROVE monitors). The interpolated species data is used to calculate

species fractions at FRM monitors (point estimates) and/or species fractions at all grid

cells (spatial fields).

5.4.1 Monitor Data Years

Using the Start Year and End Year drop-down menu options, you can choose more than

one year of speciated data for the calculation of species fractions. The default approach in
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MATS is to use three years of data. 

MATS handles multiple years of data by calculating averages for each species by quarter

and year. MATS then averages the quarterly values across the years (e.g., average quarter

1 values of SO4 across two years to get a single "quarter 1" estimate). After completing

this step, MATS will have four quarterly estimates for each species at each speciated

monitor. These quarterly values are then ready to be interpolated to FRM sites or to grid

cell centroids in spatial fields.

5.4.2 Delete Specifed Data Values

The default is to delete the observations specified by EPA. (That is, these observations are

excluded from a particular analysis, while they of course remain in the database.) As

described in the Data Input section, valid data are given a value of "0" and observations

that are deleted are given a value of "1" to "10", as follows: 

0. Data is OK to use

1. Important species is missing (e.g. no3, so4, oc, etc)

2. Constructed mass < 30% total mass

3. Constructed mass > 2 times total mass

4. Fire event

5. total mass < so4

6. total mass < crustal

7. OC is outside QA criteria

8. Soil is outside QA criteria

9. Both OC and soil are outside QA criteria

10. Regional concurrence on exceptional event 

There is also an option for the user to flag data, using the same convention of "0" for valid

data and "1" to "10" for data marked for deletion. If both the EPA-specified and Userspecified flags are checked, then MATS deletes any observations that are marked for

deletion by either the EPA or the user. This makes it easy for the user to flag additional

data for removal from the calculations (without deleting the actual record from the ambient

data file).
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5.4.3 Minimum Data Requirements

There are three sets of minimum data requirements: · Minimum number of valid days per valid quarter. This is the minimum number of

site-days per valid quarter. The default is 11 days, which corresponds to > 75%

completeness for monitors on a 1 in 6 day schedule. This is a minimum number of

samples that is routinely used in calculations of quarterly average concentrations. · Minimum number of valid quarters required for valid season. This is the number of

years of data (within the start year and end year specified) for which we have valid

quarters for a given season. The default value is 1 year. If the value is set = 2, then there

will need to be 2 years of valid data from quarter1 in order for quarter one to be

considered complete (and the same for the other 3 quarters). · Minimum number of valid seasons required for valid monitor. This is the number of

valid seasons that are needed in order for a particular monitor's data to be considered

valid. The default is 1 for IMPROVE-STN monitor data and the range is 1-4. For

example, if the value is = 1, then a monitor's data will be used in the species fractions

calculations if it has at least one valid season. If the value = 4, then the site must have all

4 seasons of valid data to be used. The default for PM2.5 depends on whether the data

are used in point calculations (default = 4) or spatial field calculations (default = 1).

Example 1: Minimum Days = 11, Minimum Years = 1, Minimum Seasons = 1

Consider the default assumptions and the following data from three monitors:
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With the default assumptions, MATS would then use the highlighted observations and

calculate averages for each quarter at each monitor in the following way:

Note that MATS would not calculate an average in quarter 2 for Monitor 1.

Example 2: Minimum Days = 11, Minimum Years = 2, Minimum Seasons = 1

Assuming the following data from three monitors:

Assuming that a minimum of two years of data for each season, MATS would then use the

highlighted observations highlighted and calculate averages for each quarter at each

monitor in the following way:
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Note that the requirement of 2 years, reduces to a single quarter the seasonal averages

calculated for Monitors 1 and 3. If you had further required that a monitor needed, say, 4

seasons, then MATS would have only calculated averages for Monitor 2.

5.5 Species Fractions Calculation Options - Advanced

The Species Fractions Calculation Options - Advanced screen allows you to make

relatively advanced choices for your analysis. Generally speaking, the default options

settings are consistent with the EPA modeling guidance document. One set of options

allows you to specify the interpolation weighting that you want to use and whether the

interpolation involves a maximum distance or not. The second set of options involves

choices regarding ammonium, blank mass, and organic carbon.

5.5.1 Interpolation Options for Species Fractions Calculation

The Interpolation Options panel allows you to choose how you will interpolate, or
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combine, the values from different monitors. One approach is to use 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 PM is 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 (in

kilometers) 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 Species Fractions Calculation Options panel), regardless of distance. If you

want to limit the use of monitors based on distance, type in the distance you want to use

(e.g., 100) next to the pollutant of interest. 

 You can also change the number using the arrows. The double arrow on the right

increases the number in units of 100:

and the double arrow on the left decreases the number in units of 100. The upper arrow

increases the number in single digits:
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and the lower arrow reduces the number in single digits.

Note that 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.

5.5.2 Miscellaneous Options

The Miscellaneous Options panel lets you make choices regarding: · Ammonium. This allows you to specify whether MATS uses degree of neutralization

(DON) values to calculate ammonium (NH4) or whether it uses measured ammonium in

conjunction with an assumption about the percentage of NH4 that evaporates. The

default option is to use DON values. If you want to use measured ammonium, you need

to click the button and choose a NH4 percentage evaporating (e.g., 50). The default is

"0", which assumes that no ammonium evaporates from the FRM filters. The

calculations underlying the default and alternative ammonium calculations are discussed

in detail in the section on species fractions calculations. · Default Blank Mass. The Default Blank Mass option simply allows you to set default

blank mass to the desired level. The default is 0.5. You can type desired value, or use

the arrows to increase or decrease the value. · Organic Carbon. This allows you to set the "floor" and the "ceiling" for the mass

balance calculation for organic carbon. The calculations involved are discussed in detail

in the section on species fractions calculations.

5.5.3 Internal Precision of the Calculations

All calculations in MATS are carried out with single precision. In addition, most output

files by default generate outputs only up to 3 digits after the decimal. Therefore, the base
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year DV (b_pm25_ann_q_DV) may differ slightly from the sum of the component species

of the quarterly PM2.5 MATS output files. In particular, the water calculation requires

additional precision in the species fractions. It is therefore recommended to have at least

seven decimal digits in the species fractions. This may be accomplished by increasing

precision of the species and species fractions calculations to 7 (or more) significant digits

by modifying the MATS.ini file as follows: set species_calc_precision=7 and

species_fraction_precision=7. Please note that the future year species always add up to the

future year DV. However, increases in species fractions precision may result in very small

changes in future DV due to the dependence of the future concentrations on the base year

concentrations.

5.6 PM2.5 Calculation Options

The PM2.5 Calculation Options window allows you to specify the particular years of

monitor data that you want to use from the input file you specified in the Data Input

section. You can also specify the following: · Official vs. Custom Values. You can specify whether to use "official" design values,

which are generally recommended, or use "custom" design values. If you choose to use

custom design values, then you specify the minimum number of days of observations

each valid quarter and the minimum number of valid quarters. · Valid FRM Monitors. You can also specify the minimum number of design values (the

default is 1) and whether you want to make sure that particular design values have to be

used in the calculations. · NH4 Future Calculation. You can also specify how you want to forecast NH4 values.

The default approach is to use baseline DON values, and the alternative is to use baseline

NH4 and a RRF value for NH4.
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5.6.1 PM2.5 Monitor Data Years

Using the Start Year and End Year drop-down menu options, you can choose more than

one year of official PM monitor data for the calculation of future PM2.5 values. The

default approach in MATS is to use five years of data.

MATS handles multiple years of data as described in the Baseline PM2.5 Calculation

section.

5.6.2 Design Values

MATS gives you option to choose "official" design values, which are generally

recommended, or to choose "custom" design values. If you choose to use custom design

values, then you need to specify the minimum number of days of observations each valid

quarter and the minimum number of valid quarters.
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· Minimum days for valid quarter. This is the minimum number of site-days per valid

quarter. The default is 11 days, which corresponds to > 75% completeness for monitors

on a 1 in 6 day schedule. This is a minimum number of samples that is routinely used in

calculations of quarterly average concentrations. · Minimum valid quarters in design value period. This is the number of quarters for which

we have data within three consecutive design value periods. The default value is 12

quarters. If the value is set = 11, then there will need to be at least 11 valid quarters (i.e.,

two years must have 4 valid quarters and one year must have at least 3 valid quarters.)

5.6.2.1 Completion Code Use

The Completion Code is a variable in the official quarterly PM2.5 monitor datafile that

MATS uses to identify valid data. As noted in the section on Official Quarterly PM2.5

Monitor Data Input, the completion code has values of: 1, 2, 3, 4, or 5; and the codes are

valid for the end year of each 3 year design value period. MATS uses the Completion

Code variable somewhat differently when using official design values and when using

custom design values. · When using official data, MATS will only use completion codes: 1 and 2. · When using custom data, MATS will potentially use completion codes: 1, 2, 3, and 4 (if

the user specified completion criteria are met). 

The following is an explanation of the official EPA completion codes:

Code "1"- complete data and violates the NAAQS

Code "2"- complete data that is below the NAAQS

Code "3"- incomplete data and violates the NAAQS

Code "4" - incomplete data that is below the NAAQS

Code "5" - data that is not comparable to the NAAQS and should not be used. For

example, FRM data collected at a micro-scale site cannot be compared to to the annual

PM2.5 NAAQS.
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5.6.3 Valid FRM Monitors

By default, MATS assumes that there only needs to be one design value for a monitor to be

considered valid. In addition, MATS assumes that no particular design value is required,

so different monitors with different years of data could be used. For example, if you

specify the start year and end year as 2005 and 2009 (giving potential design values of

2005-2007, 2006-2008, and 2007-2009), then one monitor could have data for, say,

2005-2007 and another monitor data for 2006-2008, and both monitors would be used.

5.6.4 NH4 Future Calculation

As described in the section on Forecasted Species Calculation, MATS can forecast NH4

using two different approaches. The default approach is to use base year DON values.

5.7 Model Data Options

The Model Data Options section allows you to specify the Temporal Adjustment at

Monitor. This option specifies how many model grid cells to use in the calculation of

RRFs for point estimates and for spatial estimates. Using the drop-down menu, you can

choose 1x1, 3x3, 5,x5, and 7x7. (The default for a 12 kilometer by 12 kilometer grid is to

use a 3x3 set of grid cells. The choice of grid size is discussed in the following EPA

guidance: http://www.epa.gov/scram001/guidance/guide/final-03-pm-rh-guidance.pdf) For

PM analyses, MATS calculates mean concentrations across the grid cell array (as

compared to maximum concentrations used for ozone analyses).
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5.8 Final Check

The Final Check window verifies the selections that you have made. 
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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:

After making the necessary correction, click the button Press here to verify your

selections. Then click the Finish button.
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66 Daily PM Analysis: Quick Start Tutorial

In this tutorial you will forecast daily peak PM2.5 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 a Daily PM analysis. · Step 2. Output Choice. Choose the output to generate. In this example, you will do two

things: forecast daily peak PM2.5 levels at monitor locations and output a species

fractions file (which you can subsequently reuse, as discussed in the Output Choice

section of the Daily PM Analysis: Details chapter). · Step 3. Output Choice - Advanced. With these advanced options, you can generate a

variety of files useful for quality assurance. Simply review these options and then

uncheck them all. (If you are interested, these options are described in the Output

Choice - Advanced section of the Daily PM Analysis: Details chapter.) · Step 4. Data Input. Choose the particular years of data and monitors to use in this

analysis. · Step 5. Species Fractions Calculation Options. Specify how to generate the relative

response factors (RRFs) used in the forecasts. · Step 6. Species Fractions Calculation Options - Advanced. This window allows you to

make relatively advanced choices for your analysis, such as choosing different ways to

interpolate the monitor data. · Step 7. PM2.5 Calculation Options. Among other things you can specify the particular

years of monitor data that you want to use. · Step 8. Model Data Options. Choose how to use the model data, such as determining

the maximum distance the model data can be from a monitor and identifying peak model

values. · Step 9. Final Check. Verify the choices you have made. · Step 10. Map Output. Prepare maps of your forecasts. · Step 11. View & Export Output. Examine the data in a table format.

Each step is explained below. Additional details are provided in the section Daily PM

Analysis: Details. 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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Click the Daily PM Analysis button on the main MATS window. This will bring up the

Configuration Management window. 

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.

6.2 Step 2. Output Choice

The Choose Desired Output window allows you to choose the output that you would like
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to generate. MATS allows you to conduct a Standard Analysis (i.e., forecast Point

Estimates at ambient monitors), output quarterly model data, and output a species fractions

file. · In the Scenario Name box type “Tutorial Daily PM” – this will be used to keep track of

where your results are stored and the variable names used in your results files. · Standard Analysis. Leave the box checked next to "Interpolate monitor data to FRM

monitor sites. Temporally-adjust." MATS will create forecasts for each monitor in the

monitor file. · Quarterly Peak Model Data. Check this option. MATS generates quarterly model files

that MATS generates from daily data that you have provided. This is useful if you want

to reuse model files -- the quarterly files are much smaller and MATS will run faster if it

can skip the step of creating quarterly data from the daily. · Species Fraction. Check the box next to "Output species fractions" file. · Actions on run completion. Check the box next to "Automatically extract all selected

output files". Upon completing its calculations, MATS will extract the results into a

folder with the name of your scenario.

Additional details on each of these options are Output Choice section of the Daily PM

Analysis - Advanced chapter.
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When your window looks like the window above, click Next. This will bring you to the

Output Choice - Advanced window.

6.3 Step 3. Output Choice - Advanced

With the advanced options in the Output Choice - Advanced window, you can generate a

variety of files useful for quality assurance. Simply review these options and then uncheck

them all. (If you are interested, these options are all described in the Output Choice -

Advanced section of the Daily PM Analysis: Details.)

When your window looks like the window above, click Next. This will bring you to the

Data Input window.

6.4 Step 4. Data Input

The Data Input window allows you to choose the species and the PM2.5 monitor data and

model data that you want to use. As discussed in more detail in the following chapter (see 

Standard Analysis), MATS calculates the ratio of the base and future year model data to

calculate a relative response factor (RRF) for each PM species. MATS uses the PM2.5

monitor data and interpolated species monitor data to estimate species values at each FRM

site, multiplies the species values from the monitor data with the species-specific RRFs,

and then estimates a future-year design value. (Additional details on Data Input are

available here.)
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Use the default settings in the Data Input window. The window should look like the

following:

When your window looks like the window above, click Next. This will bring you to the

Species Fractions Calculation Options window.

6.5 Step 5. Species Fractions Calculation Options

The Species Fractions Calculation Options window has several functions related to the

IMPROVE-STN (species) monitor data and the (unofficial) PM2.5 monitor data. These

functions include identifying the years of monitor data that you want to use, deleting any

specific data values, and choosing the minimum data requirements of monitors you want in

your analysis. · Monitor Data Years. Choose the years of monitor data that you want to use. The

default is to use the three-year period 2006-2008. (That is, for both IMPROVE-STN and

PM2.5 monitor data, the Start Year is 2006 and the End Year is 2008.) The default

period is based on a modeling year of 2007. The start and end years should be changed

to applicable time periods, depending on the base modeling year. · Delete Specified Data Values. The default is to delete the observations specified by

EPA. As described in the Data Input section, valid data are given a value of "0" and

observations that should be deleted are given a value of "1" to "10". (Leave unchecked

the option for the user to flag data.)
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· Minimum Data Requirements. There are three sets of minimum data requirements:

1. Minimum number of valid days per quarter. This is the minimum number of

site-days per valid quarter. The default is 11 days, which corresponds to > 75%

completeness for monitors on a 1 in 6 day schedule. This is a minimum number

of samples that is routinely used in calculations of quarterly average

concentrations.

2. Minimum number of valid quarters per valid year. This is the minimum

required number of valid quarters for a valid year. The default for

IMPROVE-STN monitor data is 1 quarter, and the default for PM2.5 monitor

data is 4 quarters. If the value is set = 4, then all 4 quarters must be valid in

order for a given year to be considered complete.

3. Minimum number of valid years required for valid monitor. This is the number

of valid years that are needed in order for a particular monitor's data to be

considered valid. The default is 1 for both IMPROVE-STN and PM2.5 monitor

data. For example, if the value is = 1, then a monitor's data will be used in the

species fractions calculations if it has at least one valid year. If the value = 3,

then the monitor must have 3 years of valid data to be used.

Use the default settings pictured in the screenshot below. (All of these options are

described in detail here.)
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When your window looks like the window above, click Next. This will bring you to the

Species Fractions Calculation Options - Advanced window.

6.6 Step 6. Species Fractions Calculation Options -

Advanced

The Species Fractions Calculation Options - Advanced screen allows you to make

relatively advanced choices for your analysis. Generally speaking, the default options

settings are consistent with the EPA modeling guidance document. The first set of options

lets you specify which monitor data you want to use to characterize peak values (e.g., top

10 percent of daily monitor days). The second set of options allows you to specify the

interpolation weighting that you want to use and whether the interpolation involves a

maximum distance or not. The third set of options involves choices regarding ammonium,
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blank mass, and organic carbon. 

Use the default settings pictured in the screenshot below. (All of these options are

described in the Species Fractions Calculation Options - Advanced section in the Daily PM

Analysis: Details chapter.)

When your window looks like the window above, click Next. This will bring you to the

PM2.5 Calculation Options window.
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6.7 Step 7. PM2.5 Calculation Options

The PM2.5 Calculation Options window allows you to specify the particular years of

monitor data that you want to use from the input file you specified in Step 4 (Data Input).

Keep the default settings: · PM2.5 Monitor Data Years. Start Year = 2005 and End Year = 2009. · Valid FRM Monitors. Keep the minimum number of design values equal to the default

value of 1, and do not specify any particular design values for inclusion in the

calculations. · NH4 Future Calculation. You can also specify how you want to forecast NH4 values.

Use the default approach, which is to use baseline DON values.

Use the default settings pictured in the screenshot below. (All of these options are

described in detail here.)

When your window looks like the window above, click Next. This will bring you to the

Model Data Options window.

6.8 Step 8. Model Data Options

The Model Data Options section allows you to specify three items: · Temporal Adjustment at Monitor. This option specifies how many model grid cells to

use in the calculation of RRFs for point and spatial estimates. Use the default option for

both: 1x1 set of grid cells. Note that for PM analyses, MATS calculates mean
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concentrations across the grid cell array. (Compare this with the maximum

concentrations used for ozone analyses, however, in the case of a 1x1 set of grid cells the

mean and maximum are the same.) · Advanced Options: RRF Model Values Used. This option lets you specify which

monitor data you want to use to characterize peak values (e.g., top 10 percent of daily

model days).

Use the default settings pictured in the screenshot below. (All of these options are

described further here.)

When your window looks like the window above, click Next. This will bring you to the

Final Check window.

6.9 Step 9. 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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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 either Save Scenario & Run or

Save Scenario. Save Scenario & Run will cause MATS to immediately run the scenario.

A temporary, new Running tab will appear (in addition to the Start, Map View and

Output Navigator tabs).
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When the calculations are complete, a small window indicating the results are Done will

appear. Click OK.

After clicking OK, MATS will open a folder with the results files already exported. 
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Output Navigator tab will also be active. (The Running tab will no longer be seen.)

MATS will automatically load the output files associated with the .asr configuration that

just finished running. 
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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. 6.10 Step 10. Map Output

After generating your results, Output Navigator can be used to load and/or map them. If a

run just finished, the output files will already be loaded into output navigator. (If files from

a previous run need to be loaded then click on the Load button and choose the Tutorial

Daily PM.asr file.)

Under Configuration/Log Files, you will see two files: · Configuration: keeps track of the assumptions that you have made in your analysis. · Log File: provides information on a variety of technical aspects regarding how a results

file (*.ASR) was created.

Under Output Files you will see four files with model data and one with forecasted design

values: · Tutorial Daily PM Baseline Quarterly Peak Model Data: contains baseline peak

quarterly species and PM2.5 values for all grid-cells in the modeling domain. · Tutorial Daily PM Future Quarterly Peak Model Data: contains future peak quarterly

species and PM2.5 values for all grid-cells in the modeling domain. · Tutorial Daily PM Used Baseline Quarterly Peak Model Data Point: contains only

baseline peak quarterly species and PM2.5 values that were used in the analysis. · Tutorial Daily PM Used Future Quarterly Peak Model Data Point: contains only

baseline peak quarterly species and PM2.5 values that were used in the analysis. · Tutorial Daily PM Daily PM25 Point: contains peak base & future PM2.5, species

values, and RRFs. (Note that the annual RRFs and annual species values are not used

anywhere in the calculation of design values, and are here just for information.)

Right-click on the file Tutorial Daily PM Daily PM25 Point. This gives you three

options: Add to Map, View, and Extract. Choose the Add to Map option. 
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This will bring up the Map View tab.

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

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:
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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:
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Right click on the "Tutorial Annual PM Annual PM25 Point" layer in the panel on the left

side of the window. Choose the Plot Value option.
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This will bring up Shape Class Breaks window. In the Value drop-down list, choose the

variable "f_pm25_ann_dv" - this is forecasted PM2.5 design value. Set the Marker

Sizing to 1 - this will give larger values a somewhat larger marker on the map.

Click Apply and then click Close. This will bring you back to the Map View window.

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
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view the data in a table format.

6.11 Step 11. View Output

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 Daily PM Daily PM25 Point. This

gives you three options: Add to Map, View, and Extract. Choose the View option. 

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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To view the data for a particular monitor -- in this example, monitor ID = "010491003" --

highlight this monitor. MATS will then display the values for this monitor in the bottom

panel.
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To view all of the data again, click on the Show All button. 

For additional details on generating annual PM results, see the chapter on Daily PM

Analysis: Details. For additional details on viewing data, see the View Data section in

chapter on the Output Navigator.
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77 Daily PM Analysis: Details

MATS can forecast daily design values at PM2.5 monitor locations -- these forecasts are

referred to as Point Estimates. The set of choices involved in calculating Point Estimates

can be fairly involved, so MATS keeps track of these choices using a Configuration. When you begin the process of generating PM2.5 estimates, MATS provides an option to

start a new Configuration or to open an existing Configuration.

Select your option and then click Go. MATS will then step you through a series of windows with choices for your analysis.

· Output Choice. Choose whether you want to run the Standard Analysis, and whether to

output a species fractions file and/or quarterly model data. · Output Choice - Advanced. This option provides miscellaneous Point Estimate output

files that are mainly used for QA. · Data Input. Load species monitoring data or a species fractions file. Load PM2.5

ambient monitoring data. And, load the modeling data that you want to use. · Species Fractions Calculation Options. Choose the years of daily STN-IMPROVE and

FRM monitoring data. Identify valid monitors. Delete specified values. · Species Fractions Calculation Options - Advanced. Choose method to identify peak

monitor values. Choose interpolation options for PM2.5 and species monitoring data. 

Choose assumptions for the ammonium calculation, default blank mass, and organic

carbon. · PM2.5 Calculation Options - FRM Monitor Data. Choose the years of quarterly FRM

monitoring data. Identify valid monitors. Choose the approach for calculating future

NH4. · Model Data Options. Specify the maximum distance of monitors from modeling

domain. Choose method to identify peak model values. Specify which model grid cells

will be used when calculating RRFs at monitor locations. · Final Check. Verify the selections that you have made.

7.1 Output Choice

In the Output Choice window, MATS lets you specify the name of your Scenario, and
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then to choose up to three file options: Standard Analysis, which refers to forecasts made

at FRM PM2.5 monitor locations; Quarterly Model Data, which allows you to create

quarterly "peak" averages from daily model output data (output data from grid models

such as CMAQ and CAMx), and then subsequently reuse this file; and Species Fractions, which outputs a reusable species fractions file.

By checking the box next to Automatically extract all selected output files, MATS will

create a separate folder with your chosen Scenario Name in the MATS "Output" folder,

and then export .CSV files with the results of your analysis. Alternatively, you can export

the results from the Output Navigator, but checking this box is a little easier.

Standard Analysis. The Standard Analysis refers to the calculation of future year PM2.5

design values at FRM monitor locations. This is the main part of the modeled attainment

test for PM2.5. There are several calculations involved in this analysis. MATS will

interpolate PM2.5 species data, calculate species concentrations at each FRM site and

project design values to a future year using gridded model data. Most MATS users will

run this analysis and it is therefore checked by default. 

Quarterly Peak Model Data. MATS requires two types of data input: ambient monitor

data and gridded model output data. For the daily PM2.5 calculations, MATS will accept

either MATS formatted daily average gridded model files or quarterly peak average files. 

If daily average model files are used as inputs, MATS will calculate quarterly peak

averages from the daily averages and optionally output the quarterly peak average

concentrations into text files (CSV files). The quarterly average text files can then be

re-used in subsequent MATS analyses. Quarterly average input files are smaller and run
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faster than daily average files. Choosing this option will have MATS generate two types

of quarterly peak average model concentration CSV files: · All Data. MATS outputs quarterly peak data for all grid cells in the modeling domain.

MATS will create one baseline year file and one future year file. This will create

relatively large files, but they will still be much smaller than daily average files. · Used Data. MATS outputs quarterly peak data for the grid cells that are subsequently

used in the particular MATS configuration. For example, if MATS calculates future

year design values at 20 FRM sites using a 1 X 1 grid array, then MATS will output base

and future model values for only 20 grid cells (assuming each monitor is in a unique grid

cell). The advantage of these files is that they are extremely small. But if subsequent

MATS runs use a different set of monitors or grid arrays, then the files may not contain

all of the necessary data to complete the analysis. This option is recommended as a QA

tool to examine the grid cells and the model concentrations that MATS is using in the

analysis.

Species Fraction. Checking the “Output species fraction file” box will create an output

file containing the calculated PM2.5 species fractions at each FRM site used by MATS. 

This species fraction file can be re-used in MATS as an input file. The species fraction file

can be useful for several reasons. One, using a species fraction file saves time because

MATS won’t have to interpolate species data and calculate fractions each time it is run.

Two, it can provide consistency between MATS runs by ensuring that the same species

fractions are used each time. And for the same reason, the species fraction file can be used

interchangeably between different users to ensure that multiple groups are using the same

species fractions (if that is a goal). And finally, the fractions file can serve as a template

for creating a custom species fractions file using whatever data and techniques (e.g.

alternative interpolation techniques) are desired by any particular user.

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., Example Daily

PM.asr). Note that the extension (.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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· Output file names. The output files generated will begin with the Scenario Name.

7.1.2 Standard Analysis

Future-year PM2.5 design values are projected at each FRM monitoring site through a

series of calculations: 
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Step 1. Baseline Top 32 Ranked PM2.5 Calculation. MATS uses "official" daily PM2.5

data to select the eight highest daily PM2.5 values for each quarter for each year.

Step 2. Baseline Top 32 Ranked Species Calculation. MATS calculates species fractions

using "unofficial" PM2.5 monitor data and speciated monitor data. It then multiplies the

species fractions with the baseline PM2.5 concentrations selected in Step 1.

Step 3. Calculate Relative Response Factors (RRFs). RRFs are calculated for each quarter

and each species by dividing the future year quarterly peak modeled concentrations by the

base year quarterly peak modeled concentrations.

Step 4. Forecasted Peak Species Calculation. Multiplying RRFs with baseline ambient

species values, MATS calculates forecasted peak species values at each FRM monitor site.

The species values are aggregated to obtain PM2.5 forecasted values.

Step 5. Forecasted Design Value Calculation. Using the forecasted values, MATS selects

the 98th percentile PM2.5 for each year at each FRM monitor site. The design values are

calculated as the averages of the 98th percentile values over three years. 

In this section, we go into some detail describing these steps. However, you should note

that MATS gives you a number of options affecting the exact steps that MATS follows,

such as determining which years of monitoring data to use and to choosing which monitors

to include in the calculations. These options are detailed in the PM2.5 Calculation Options

section. The output from the Standard Analysis is described here. 7.1.2.1 Step 1: Baseline Top 32 Ranked PM2.5 Calculation

The first step in projecting the daily design value is to identify the eight highest daily

PM2.5 concentrations in each quarter over the entire year. This results in 32 data points

for each year (2005-2009 by default) for each FRM site. For each of these values, future

values will be forecast using the process outlined in the next two sections. Please note that

each of the 32 data points records a variable called "Rank 98". This variable indicates the

position of the value representative of the 98th percentile with regards to PM if the 32 data

points were sorted in descending order on their PM2.5 values. This variable value is

identical for all data points in a given year and is used to determine the forecasted "98th

Percentile" value later in the computation. The Rank 98 value is based on the actual rank

of the 98th percentile value in the ambient FRM data for each site for each year. It is based

on the number of samples per year.

7.1.2.2 Step 2: Baseline Top 32 Ranked Species Calculation

Since the FRM monitors do not have speciated data (and the majority of FRM sites are not

co-located with a speciation monitor), MATS uses speciated PM2.5 monitor data from

other monitoring networks, such as STN and IMPROVE, to estimate the PM2.5

attributable to the following species: sulfate (SO4), nitrate (NO3), elemental carbon (EC),

organic carbon (OC), crustal matter, particle bound water (PBW), ammonium (NH4), and,

data permitting, salt.
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To estimate these species, MATS uses the “SANDWICH” process (Frank, 2006) 

(SANDWICH stands for Sulfates, Adjusted Nitrates, Derived Water, Inferred

Carbonaceous mass, and estimated aerosol acidity [H+]). The default species input data

file contains aerosol nitrate data (NO3r) that has been adjusted to account for

volatilization. Additional SANDWICH adjustments are made within MATS. These

include calculation of particle bound water (PBW) and organic carbon by mass balance

(OCMmb). 

Determine Valid Days and Calculate Peak Quarterly Averages for Speciated Monitor

Data

A first step in calculating baseline peak species values is deleting invalid days of data. 

MATS deletes days from the speciated monitor data that are missing “MEASURED_FM”

values. Days that have a “EPA FLAG” value greater than “0” are also deleted. MATS

then uses the Minimum Data Requirements specified on the Species Fractions Calculations

Options tab, to delete data that are incomplete for a particular species. In the default case,

there should be 11 days each quarter, at least 1 valid quarter per valid year, and at least 1

valid year per valid monitor. In the default case, only the specification of a minimum

number of values per quarter has any effect on the data. If a particular quarter in a given

year for a given species does not have 11 non-missing species values, then data for that

species is dropped for that particular quarter at that monitor.

The number of peak days is then calculated. In the default case, the top 10% of days =

round (0.10*non-missing days in quarter). The speciated monitor data is then sorted on

“MEASURED_FM” at each monitor, for each year and quarter, and the peak days are

chosen. (Options for choosing peak days are described in the Species Fractions

Calculation Options - Advanced section.)

MATS then averages the peak daily values in each year-quarter, and then averages the

values across years. For example, if for 2002-2004 we have SO4 averages of peak values

in quarter 1 of 8.2 ug/m3(for 2002), 6.2 ug/m3 (for 2003), and 3.6 ug/m3 (for 2004), then

the average across the years would equal 6.0 (=[8.2+6.2+3.6]/3).

Interpolate Quarterly Species Values to FRM Monitors

About 75 percent of FRM monitors are not co-located with an STN monitor, so the

estimation of the quarterly peaks of the individual species at these FRM sites depends on

the interpolated quarterly peaks from speciated monitors (e.g., IMPROVE). Individual

species are interpolated to the latitude and longitude associated with each FRM monitor. 

(For FRM monitors that are co-located with an STN monitor, MATS simply uses the

species values from the co-located STN monitor.) 

For the speciated monitors that are not co-located, MATS uses the Voronoi Neighbor

Averaging (VNA) procedure to interpolate the peak quarterly species values to FRM

monitors. The VNA procedure identifies the nearby ("neighbor") speciated monitors for

each FRM site, and then assigns a weight to each speciated monitor that gets used to

generate an initial, weighted-average for each species at each FRM site. Generally

speaking, speciated monitors that are relatively far away get a smaller weight. (You can
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find details on the interpolation process and different options for interpolation at the

section on Interpolation Options for Species Fractions Calculation.)

After Interpolation to FRM Monitors: SANDWICH and Species Fractions

Calculations

After the interpolation of quarterly values for retained NO3 (NO3r), SO4, OCb, crustal,

EC, and DON, a few additional steps are necessary to generate speciated values at each

FRM monitor site. These include calculating retained NH4 (NH4r), PBW, blank mass,

and organic carbon mass (OCMMB), the latter of which is calculated through a mass

balance approach. (The following sections describe the calculations of ammonium, PBW, and organic carbon [by difference], as well as the blank mass assumption.)

After generating quarterly values at each FRM monitor site, MATS can then calculate the

species fractions. (The species fractions calculations are straightforward and are described

further in the Species Fractions section.)

Summary of Calculations After Interpolation of STN & IMPROVE Speciated Monitor Data

Calculation Description

Calculate Retained Ammonium

(NH4r)

Calculate ammonium associated with retained nitrate (NO3r) and

SO4. MATS calculates NH4r using DON, SO4, and NO3r.

(Alternatively MATS can use directly measured ammonium).

Calculate Particle Bound Water

(PBW)

Calculate amount of water associated with ammonium sulfate and

ammonium nitrate, which are hygroscopic.

Estimate Blank Mass Account for contamination on FRM monitor filters.

Calculate Organic Carbon Mass

(OCMMB)

Calculate organic carbon mass with a mass balance approach.

Calculate Species Fractions Divide species estimates for SO4, NO3r, OCMMB, EC, crustal

material, NH4r, and PBW by the non-blank PM2.5 mass. (The

inclusion of salt is optional.)

Using Species Fractions to Estimate Top 32 Ranked Species Values

The use of species fractions to calculate species concentrations is straightforward. The

weighted species average is calculated by multiplying the top 32 ranked FRM baseline

values (calculated in Step 1, minus the assumed blank mass, specified by the user) with

species fractions for the corresponding quarter that have been estimated for each FRM

monitor. The calculation is as follows:
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Note that MATS calculates species fractions from speciated monitors for a limited number

of years. As a result, rather than have species fractions specifically calculated for each

quarter and each year, MATS uses a single set of quarter-specific species fractions to

calculate the weighted peak average species concentrations. The species data should be

"representative" of the species fractions that occur during the 5-year FRM monitoring

period selected in MATS.

Example Calculation Baseline Species Concentrations

MATS multiplies the (non-blank) top 32 ranked baselinePM2.5 values. Below is an

example showing the calculation for four data points in the same year. 

Day FRM PM2.5 Blank Mass Non-Blank Mass

2/10 10.4175 0.5 9.9175

5/5 11.4941 0.5 10.9941

8/7 13.0624 0.5 12.5624

11/4 10.5773 0.5 10.0773

with the species fractions calculated for this particular site:

Quarter SO4 NO3 OCMMB EC PBW NH4 Crustal (Salt)

Q1 0.2382 0.1108 0.3346 0.0201 0.1049 0.1209 0.0705 -- Q2 0.2637 0.0727 0.3178 0.0545 0.1095 0.1273 0.0545 -- Q3 0.1432 0.0557 0.5621 0.0238 0.0959 0.0955 0.0238 -- Q4 0.2580 0.1389 0.2756 0.0396 0.1094 0.1190 0.0595 -- The product is the estimated baseline species concentrations. 

Day SO4 NO3 OCMMB EC PBW NH4 Crustal

2/10 2.3623 1.0989 3.3184 0.1993 1.0403 1.1990 0.6992

5/5 2.8991 0.7993 3.4939 0.5992 1.2039 1.3996 0.5992

8/7 1.7989 0.6997 7.0613 0.2990 1.2047 1.1997 0.2990

11/4 2.5999 1.3997 2.7773 0.3991 1.1025 1.1992 0.5996

Retained Ammonium Calculation

MATS calculates retained ammonium two different ways. The default approach is to use

interpolated degree of neutralization of sulfate (DON) values from the speciated monitors.

The alternative approach is to use interpolated NH4 values from speciated monitors (e.g.,

STN). In the Species Fractions Calculations Options - Advanced section, you have the

option to choose the approach that you prefer to use. The two approaches are described

here.

Default approach using measured pre-calculated DON, SO4, and retained NO3

(NO3r):
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Because of uncertainties in NH4 speciation measurements, by default MATS calculates

ammonium values using the degree of sulfate neutralization (DON). MATS uses precalculated daily DON values that are included in the species data input file ("Species-forfractions-xxxx.csv"). The values for DON are calculated from the amount of ammonium

associated with sulfate (NH4SO4) as follows:

And the estimated NH4SO4 is calculated as follows: 

where 0.29 is the molar ratio of NH4 to NO3 and NH4measured and NO3retained reflect the

amounts of NH4 and NO3 retained on the FRM filter. The amount of NH4SO4 is not

allowed to exceed the fully neutralized amount of 0.375 multiplied by the estimated sulfate

ion concentration.

MATS then calculates ammonium using interpolated monitor values of DON, SO4, and

NO3r as follows:

Alternative Approach Using Measured Ammonium. The alternative approach is to use

interpolated NH4 values from STN monitors. This approach has several steps. 

First, MATS calculates “adjusted” NH4:

where the PctEvap factor refers to the percentage of ammonium associated with the

volatilized nitrate that is also lost. (As discussed in the Species Fractions Calculation

Options - Advanced section, this factor is adjustable from 0 to 100 percent.) The default

assumption is that no ammonium is volatilized (0 percent).

Second, MATS calculates NH4 associated with SO4:

Third, MATS calculates DON:

Finally, using the same equation as in the default approach, MATS calculates NH4r by

substituting the calculated DON for the interpolated (measured) DON value:
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Particle Bound Water Calculation

Because ammoniated sulfate and ammonium nitrate are hygroscopic, the retained sulfate

and nitrate mass will include water. Particle bound water (PBW) is estimated using the

Aerosol Inorganic Model (AIM) (Clegg et al, 1998). For computational convenience, a

polynomial regression equation was fit to the calculated water mass from AIM and the

three input values that fed into AIM (sulfate, nitrate and ammonium). AIM was run with

typical FRM filter equilibration conditions of 35% RH and 22 deg C (295 deg K).

MATS calculates particle-bound water (PBW) using levels of SO4, NO3r, and NH4r as

follows. (Note that this is the same equation that MATS uses to calculate future-year

PBW, the difference being the future-year PBW uses future-year values of SO4, NO3r, and

NH4r, and here MATS uses base-year values.)

The calculation uses one of two equations, depending on the acidity of the ammoniated

sulfate (represented by DON). S, N, and A in the equations are the relative fraction of

SO4, NO3r, and NH4r respectively. 

S = SO4 / (SO4 + NO3r + NH4r);

N = NO3r / (SO4 + NO3r + NH4r);

A = NH4r / (SO4 + NO3r + NH4r);

 if DON le 0.225 then

 PBW = {595.556 

 - 1440.584*S

 - 1126.488*N

 + 283.907*(S**1.5)

 - 13.384*(N**1.5)

 - 1486.711*(A**1.5)

 + 764.229*(S**2)

 + 1501.999*(N*S)

 + 451.873*(N**2)

 - 185.183*(S**2.5)

 - 375.984*(S**1.5)*N

 - 16.895*(S**3)
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 - 65.814*(N**1.5)*S

 + 96.825*(N**2.5)

 + 83.037*(N**1.5)*(S**1.5)

 - 4.419*(N**3)

 + 1720.818*(A**1.5)*S

 + 1220.383*(A**1.5)*N

 - 311.496*(A**1.5)*(S**1.5)

 + 148.771*(A**1.5)*(N**1.5)

 + 1151.648*(A**3)} * (SO4+NO3r+NH4);

ELSE

 PBW = {202048.975 

 - 391494.647 *S

 - 390912.147 *N

 + 442.435 *(S**1.5)

 - 155.335 *(N**1.5)

 - 293406.827 *(A**1.5)

 + 189277.519 *(S**2)

 + 377992.610 *N*S

 + 188636.790 *(N**2)

 - 447.123 *(S**2.5)

 - 507.157 *(S**1.5)*N

 - 12.794 *(S**3)

 + 146.221 *(N**1.5)*S

 + 217.197 *(N**2.5)

 + 29.981 *(N**1.5)*(S**1.5)

 - 18.649 *(N**3)

 + 216266.951 *(A**1.5)*S

 + 215419.876 *(A**1.5)*N
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 - 621.843 *(A**1.5)*(S**1.5)

 + 239.132 *(A**1.5)*(N**1.5)

 + 95413.122 *(A**3)} * (SO4+NO3r+NH4) .

Organic Carbon Mass Calculation

Measured organic carbon mass is not directly used in the calculation of species fractions in

MATS because of (1) many uncertainties in estimating carbonaceous mass from carbon

measurements (Turpin & Lim, 2001; Chow et al, 2004) (2) differences in carbon

measurement protocol between urban and rural monitoring locations, (3) a relatively

“bumpy” surface of urban carbon concentrations as derived from urban and rural organic

carbon measurements and (4) lack of carbon measurements at all FRM locations. The

MATS approach estimates carbon by mass balance comparing precisely measured FRM

PM2.5 mass (EPA, 2003) with the sum of its non-carbon components.

Total carbonaceous mass contains both elemental carbon (EC) and organic carbon mass

(OCM). We measure EC from the interpolated STN and IMPROVE monitors, while we

calculate OCM using a mass balance approach -- and refer to it as OCMMB. To calculate

OCMMB, we subtract the other estimated retained species (including EC) from the PM2.5

level measured at the FRM site as follows:

The value for OCMMB could be very small, or even be calculated as negative (if the sum

of the species enclosed in the curly brackets exceeded the FRM PM2.5 monitor value). To

ensure that the OCMMB does not get too small, an OCMMB "mass balance

floor" (default) value is set to 1.0 times the interpolated value of blank-adjusted organic

carbon (OCb). It is also possible that the value of the floor by itself could exceed the FRM

total PM2.5 value. In this case, MATS imposes a (user-adjustable) "ceiling," such that

OCMMB does not exceed a percentage of the total non-blank mass. The default ceiling

value is set to 0.8 or 80% of PM2.5 mass. (You can modify the floor and ceiling

assumptions in the Species Fractions Calculation Options - Advanced window.)

To account for these possibilities, MATS uses the following series of equations to

calculate OCMMB:

where the "Floor" variable has a default value in MATS of "1.0" and the "Ceiling" variable
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has a default value in MATS of "0.8".

There are at least two things to note with this approach. (1) When the final OCMMB

value is equal to the "floor," then the sum of the species will exceed the PM2.5 value at the

FRM monitor. To ensure that the sum of the species just equals the FRM PM2.5 value,

MATS reduces all of the species (except OCMMB) by the same percentage until the sum

of the species just equals the FRM PM2.5 value. (2) When the final OCMMB value is

equal to the "ceiling," then the sum of the species will be less than the PM2.5 value at the

FRM monitor. In that case, MATS increases all of the species by the same percentage

until the sum of the species just equals the FRM PM2.5 value

Blank Mass Assumption

The field blank typically has a value of between 0.3 and 0.5 ug/m3, which appears to result

from contamination of the FRM filter. For calculating retained PM2.5, MATS uses a

default blank mass value of 0.5 ug/m3. If desired, you can change the default blank mass

value at the Species Fractions Calculations Options - Advanced window.

7.1.2.3 Step 3: Calculate Relative Response Factors

Relative response factors (RRFs) are calculated as the ratio of the modeled forecasted

species to the modeled baseline species. In other words, baseline species concentrations are

assumed to change in the same proportion as the model data in the same location from the

baseline to the forecasted. 

where:

RRFi,Q = relative response factor for species "i" in quarter Q

Modeli,futureQ = Modeled forecasted species “i” peak in quarter Q

Modeli,baseQ = Modeled baseline species “i” peak in quarter Q

MATS calculates RRFs for each quarter for each of six species: SO4, NO3, OCM, EC,

crustal material, and (optionally) NH4. (Additional information on the calculation of the

RRFs can be found in the Model Data Options section.) 

7.1.2.4 Step 4: Forecasted Peak Species Calculation

To calculate forecasted peak species values, MATS uses each valid baseline peak species

value and the RRFs calculated from baseline and forecasted (e.g., 2020) air quality

modeling. If five years of species estimates are available and if all quarters are valid, then

this calculation gives 160 estimates (32*5) for each species. Because this calculation

involves modeling data from two different years (a base year and a future year), this is
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referred to as a "temporal adjustment."

The forecasted weighted peak for each species is calculated by multiplying the baseline

quarterly peak values for each monitor species with the quarterly RRF for that species. 

This process gives 32 peak values per year of six species: SO4, NO3, OCM, EC, crustal

material, and NH4. The form of the equation for each species is as follows (Please note

that these computations are repeated 32 times, once for each set of peak species values!):

where:

Speciesi,futureY,D = Estimated forecasted species “i” peak in year Y and top 32 ranked day D 

Speciesi,baseY,D = Monitored baseline species “i” peak in year Y and top 32 ranked day D

RRFi,Q = relative response factor for species "i" in quarter Q

Additional calculations are needed to estimate future-year peak values of NH4 and

particle-bound water (PBW), which is calculated using forecasted levels of NH4, NO3, and

SO4. (Details on the PBW calculation can be found here.)

Recall that the default base year NH4 calculation is as follows:

MATS can calculate the future year NH4 concentration using modeled NH4 RRFs, or by

using the the base year DON value combined with future year SO4 and NO3 values

(default approach) as follows:

The option for choosing which approach to use for calculating future NH4 is given in the 

PM2.5 Calculation Options window. Finally, note that PBW is calculated after future year

NH4, using the previously identified water equation and future year concentrations of

NH4, SO4, and NO3. 

Example Calculation Forecasted Species Concentrations Using RRFs

MATS multiplies the baseline species concentrations for SO4, NO3, OCMMB, EC, and

Crustal. Below is an example showing the calculation for one year of data. Note,

however, that if all five years and all quarters of baseline quarterly peak PM2.5 (calculated

in Step 1) are valid, then there will be 32 sets of species values for each of five years.

Quarter SO4 NO3 OCMMB EC PBW NH4 Crustal

1 2.3623 1.0989 3.3184 0.1993 1.0403 1.199 0.6992
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Quarter SO4 NO3 OCMMB EC PBW NH4 Crustal

1 2.253966 1.059887 3.052326 0.192227 0.957682 1.14892 0.650875

1 2.153474 0.960213 3.008953 0.18716 0.941016 1.127472 0.629825

1 2.018054 0.92684 2.948333 0.180317 0.920214 1.072046 0.581908

1 1.883425 0.836795 2.883676 0.179808 0.849054 1.069356 0.564332

1 1.719184 0.818943 2.600576 0.165809 0.831113 0.982041 0.540465

1 1.704355 0.737319 2.477937 0.156328 0.757506 0.929672 0.515625

1 1.624759 0.696826 2.345071 0.14772 0.690914 0.849403 0.477717

2 2.8991 0.7993 3.4939 0.5992 1.2039 1.3996 0.5992

2 2.863181 0.728432 3.301485 0.557751 1.197191 1.329165 0.587084

2 2.847375 0.721621 3.054537 0.525272 1.183466 1.250154 0.568748

2 2.779412 0.720248 2.918015 0.501874 1.075874 1.187874 0.53307

2 2.523706 0.689026 2.759803 0.457018 0.97586 1.106502 0.507829

2 2.302765 0.666994 2.613514 0.446125 0.967615 1.098137 0.489328

2 2.264156 0.621376 2.519566 0.430883 0.891127 1.020688 0.460858

2 2.126643 0.613058 2.271892 0.40016 0.883748 0.946814 0.451779

3 1.7989 0.6997 7.0613 0.299 1.2047 1.1997 0.299

3 1.779318 0.69014 6.817854 0.29281 1.112251 1.192717 0.276236

3 1.625583 0.652832 6.644656 0.274995 1.004904 1.11704 0.269622

3 1.499236 0.605743 6.146527 0.262567 0.923129 1.012394 0.257635

3 1.416128 0.569719 5.763759 0.241669 0.841649 0.935202 0.251172

3 1.343383 0.526069 5.496105 0.238273 0.834924 0.934347 0.246456

3 1.283216 0.509961 5.42566 0.238036 0.8062 0.897558 0.222655

3 1.250273 0.479969 5.275127 0.22308 0.758142 0.832071 0.214994

4 2.5999 1.3997 2.7773 0.3991 1.1025 1.1992 0.5996

4 2.472886 1.380473 2.650604 0.385612 1.073826 1.113676 0.585284

4 2.374537 1.26463 2.471683 0.35995 1.027229 1.103675 0.546542

4 2.145454 1.13918 2.416613 0.354706 0.97877 1.096208 0.519894

4 1.993059 1.12821 2.205443 0.353203 0.954289 1.034843 0.494129

4 1.889724 1.05256 2.003872 0.32705 0.860628 1.016929 0.450157

4 1.71124 0.96074 1.822678 0.308817 0.823144 0.984487 0.410878

4 1.683092 0.957319 1.821362 0.288313 0.79719 0.973683 0.394142

with the RRFs for each quarter and species:

Quarter SO4 NO3 OCMMB EC PBW NH4 Crustal

1 0.9737 0.9873 0.9636 0.9872 -- -- 0.9808

2 0.9898 0.9991 0.9979 0.9917 -- -- 0.9891

3 0.9784 0.9775 0.9875 0.9944 -- -- 0.9759

4 0.97 0.98 0.9761 0.9843 -- -- 0.9818

The product is the forecasted species concentrations by day (typically 8 days per quarter).

Quarter SO4 NO3 OCMMB EC PBW NH4 Crustal

1 2.300172 1.084944 3.19761 0.196749 -- -- 0.685775

1 2.194687 1.046427 2.941222 0.189766 -- -- 0.638378
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Quarter SO4 NO3 OCMMB EC PBW NH4 Crustal

1 2.096837 0.948018 2.899427 0.184765 -- -- 0.617732

1 1.964979 0.915069 2.841014 0.178009 -- -- 0.570735

1 1.833891 0.826168 2.77871 0.177506 -- -- 0.553497

1 1.67397 0.808542 2.505915 0.163687 -- -- 0.530088

1 1.65953 0.727955 2.38774 0.154327 -- -- 0.505725

1 1.582028 0.687977 2.25971 0.145829 -- -- 0.468544

2 2.869529 0.798581 3.486563 0.594227 -- -- 0.592669

2 2.833976 0.727777 3.294552 0.553121 -- -- 0.580685

2 2.818332 0.720972 3.048122 0.520912 -- -- 0.562549

2 2.751062 0.719599 2.911887 0.497709 -- -- 0.52726

2 2.497964 0.688406 2.754007 0.453224 -- -- 0.502294

2 2.279277 0.666394 2.608026 0.442422 -- -- 0.483994

2 2.241062 0.620817 2.514275 0.427307 -- -- 0.455835

2 2.104951 0.612506 2.267121 0.396839 -- -- 0.446855

3 1.760044 0.683957 6.973034 0.297326 -- -- 0.291794

3 1.740885 0.674612 6.732631 0.291171 -- -- 0.269579

3 1.59047 0.638144 6.561598 0.273455 -- -- 0.263124

3 1.466852 0.592114 6.069696 0.261096 -- -- 0.251426

3 1.38554 0.556901 5.691712 0.240316 -- -- 0.245119

3 1.314366 0.514233 5.427404 0.236939 -- -- 0.240516

3 1.255499 0.498487 5.357839 0.236703 -- -- 0.217289

3 1.223267 0.46917 5.209188 0.221831 -- -- 0.209812

4 2.521903 1.371706 2.710923 0.392834 -- -- 0.588687

4 2.3987 1.352864 2.587255 0.379558 -- -- 0.574631

4 2.303301 1.239337 2.41261 0.354298 -- -- 0.536595

4 2.08109 1.116396 2.358856 0.349137 -- -- 0.510432

4 1.933267 1.105646 2.152733 0.347657 -- -- 0.485136

4 1.833033 1.031509 1.955979 0.321916 -- -- 0.441964

4 1.659903 0.941525 1.779116 0.303969 -- -- 0.4034

4 1.632599 0.938173 1.777832 0.283786 -- -- 0.386968

As discussed earlier, PBW and the (default) NH4 are calculated based on other species and

not directly from RRFs.

7.1.2.5 Step 5: Forecasted Design Value Calculation

The forecasted top 32 ranked values for all species are then added together to get

forecasted top 32 ranked PM2.5 values for each year of monitor data (in rare cases there

may be less than 32 forecasted values due to missing data):
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This procedure is repeated for each of the years of monitoring data (e.g., 2005-2009). The

forecasted values are then sorted high to low. The value at the position indicated by the

Rank 98 variable in each year (as in 7.1.2.1, Step 1) is then taken to be the representative

final forecasted design value for the year ( PM 2.5, Y – it will be referred to as the

estimated 98th percentile value going forward).

PM 2.5, Y = PM 2.5, Y, Rank 98

The forecasted 98th percentile values for each of the 5 years are averaged over 3 year

intervals (e.g., 2005-2007, 2006-2008, 2007-2009):

The design value is then the average of the three peak design values, creating 5-year

weighted averages for each monitor:

The output file containing the results of this Standard Analysis are described here. Example Calculation Forecasted Design Values

Using hypothetical data from the previous example calculation plus PBW values and

assumed blank mass, MATS sums the species to get peak PM2.5 values for each year. The

following table shows the calculations for one year (2005), with the selected 98th

percentile value in bold italics. In reality, there would be four additional years of data,

each with the same format as the table below.

Forecasted Species Concentrations

Day Year Quarter SO4 NO3 OCMM

B

EC PBW NH4 Crustal Blank

Mass

Salt

Mass

Rank 98 PM2.5

20050727 2005 3 8.4849 0 11.4993 1.1421 2.6976 2.5964 1.9296 0.5 0.045 4 28.8949

20050624 2005 2 5.6601 0 13.2829 1.0199 2.0787 1.9244 1.5417 0.5 0.0716 4 26.0793

20050805 2005 3 6.5993 0 8.9439 0.8883 2.0981 2.0194 1.5008 0.5 0.035 4 22.5848

20050913 2005 3 6.3731 0 8.6373 0.8578 2.0262 1.9502 1.4493 0.5 0.0338 4 21.8277

20051019 2005 4 4.6897 0.2708 9.2791 1.4062 1.6759 1.809 1.6536 0.5 0.1708 4 21.4551

20051001 2005 4 4.4206 0.2552 8.7467 1.3255 1.5797 1.7052 1.5587 0.5 0.161 4 20.2526
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Forecasted Species Concentrations

Day Year Quarter SO4 NO3 OCMM

B

EC PBW NH4 Crustal Blank

Mass

Salt

Mass

Rank 98 PM2.5

20050826 2005 3 5.6943 0 7.7173 0.7664 1.8104 1.7425 1.295 0.5 0.0302 4 19.5561

20050910 2005 3 4.9778 0 6.7463 0.67 1.5826 1.5232 1.132 0.5 0.0264 4 17.1583

20050621 2005 2 3.6838 0 8.645 0.6638 1.3529 1.2525 1.0034 0.5 0.0466 4 17.148

20051031 2005 4 3.6902 0.2131 7.3016 1.1065 1.3187 1.4235 1.3012 0.5 0.1344 4 16.9892

20050122 2005 1 2.6556 0.0487 9.4565 1.2007 0.9542 0.9967 1.0406 0.5 0.074 4 16.927

20050820 2005 3 4.8647 0 6.5929 0.6548 1.5466 1.4886 1.1063 0.5 0.0258 4 16.7797

20050326 2005 1 2.5838 0.0473 9.201 1.1683 0.9284 0.9697 1.0124 0.5 0.072 4 16.4829

20050630 2005 2 3.4783 0 8.1627 0.6267 1.2774 1.1826 0.9474 0.5 0.044 4 16.2191

20050519 2005 2 3.415 0 8.0143 0.6153 1.2542 1.1611 0.9302 0.5 0.0432 4 15.9333

20050904 2005 3 4.563 0 6.1841 0.6142 1.4507 1.3963 1.0377 0.5 0.0242 4 15.7702

20050507 2005 2 3.3676 0 7.9029 0.6068 1.2368 1.145 0.9173 0.5 0.0426 4 15.719

20050724 2005 3 4.3744 0 5.9285 0.5888 1.3908 1.3386 0.9948 0.5 0.0232 4 15.1391

20050528 2005 2 3.1304 0 7.3464 0.5641 1.1497 1.0643 0.8527 0.5 0.0396 4 14.6472

20050522 2005 2 2.9723 0 6.9754 0.5356 1.0916 1.0106 0.8096 0.5 0.0376 4 13.9327

20051010 2005 4 2.9791 0.172 5.8945 0.8933 1.0646 1.1492 1.0504 0.5 0.1085 4 13.8116

20050119 2005 1 2.1101 0.0387 7.5141 0.9541 0.7582 0.792 0.8268 0.5 0.0588 4 13.5528

20050510 2005 2 2.8775 0 6.7528 0.5185 1.0568 0.9783 0.7838 0.5 0.0364 4 13.5041

20050110 2005 1 2.024 0.0371 7.2074 0.9151 0.7273 0.7596 0.7931 0.5 0.0564 4 13.02

20051109 2005 4 2.71 0.1565 5.3621 0.8126 0.9684 1.0454 0.9556 0.5 0.0987 4 12.6093

20051004 2005 4 2.6908 0.1554 5.3241 0.8068 0.9616 1.038 0.9488 0.5 0.098 4 12.5235

20050227 2005 1 1.8948 0.0347 6.7474 0.8567 0.6808 0.7111 0.7425 0.5 0.0528 4 12.2208

20051224 2005 4 2.5947 0.1498 5.1339 0.778 0.9272 1.0009 0.9149 0.5 0.0945 4 12.0939

20051013 2005 4 2.537 0.1465 5.0198 0.7607 0.9066 0.9786 0.8946 0.5 0.0924 4 11.8362

20050221 2005 1 1.7513 0.0321 6.2362 0.7918 0.6293 0.6573 0.6862 0.5 0.0488 4 11.333

20050125 2005 1 1.6795 0.0308 5.9806 0.7594 0.6035 0.6303 0.6581 0.5 0.0468 4 10.889

20050101 2005 1 1.579 0.0289 5.6228 0.7139 0.5674 0.5926 0.6187 0.5 0.044 4 10.2673

Then MATS selects the 98th percentile concentration in each year based on the variable

"Rank 98" (e.g., 21.8277 for year 2005, which corresponds to the row in bold italics in the

table above):

Day Year Annual Peak

20050913 2005 21.8277

20060818 2006 19.4299

20070807 2007 19.6192

20080530 2008 15.2901

20090305 2009 14.7958

These annual peaks are then averaged for each three-year period, and then MATS averages

these three-year averages to get the forecasted peak design value:
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Year Annual Peak

2005-2007 20.29226667

2006-2008 18.11306667

2007-2009 16.56836667

Forecasted

Design

Value 18.32456667

7.1.2.6 Output Description

The output file is named "Daily PM25 Point.csv" with the Scenario Name appended at the

beginning (e.g., "Example Daily PM Daily PM25 Point.csv"). The table below describes

the variables in the output file for the Standard Analysis. Note the following: The RRF variables in this file are not the actual RRFs used to

calculate future peak PM2.5 and PM2.5 species. They are the effective RRFs calculated by

dividing the future peak concentrations (in this file) by the base year peak concentrations

(in this file). The actual RRFs are calculated on a quarterly basis and are contained in the

quarterly peak output files. Even though there are no quarterly RRFs for water or NH4 (if

DON, NO3, and SO4 are used to calculate NH4), the resultant RRFs are included in the

quarterly peak output files, based on the future year water and NH4 concentrations divided

by the base year water and NH4 concentrations.

Output file name: "Scenario Name + Daily PM25 Point"

Variable Description

_id 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 FRM data

_STATE_NAME State name. (This is a character variable.)

_COUNTY_NAM

E

County name. (This is a character variable.)

monitor_lat Latitude at the monitor site in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are negative.

monitor_long Longitude at the monitor site in decimal degrees. Values in the eastern

hemisphere are positive, and those in the western hemisphere (e.g., United

States) are negative.

monitor_gridcell Identifier of grid cell closest to the monitor

b_pm25_d_DV Base year 5 year weighted average PM2.5 24-hr average (daily) design value

f_pm25_d_DV Future year 5 year weighted average PM2.5 24-hr average (daily) design value

b_blank_mass Base year blank mass concentration (ug/m3)

b_crustal_mass Base year crustal mass concentration (ug/m3)

b_EC_mass Base year elemental carbon mass concentration (ug/m3)
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b_NH4_mass Base year ammonium mass concentration (ug/m3)

b_Ocmb_mass Base year organic carbon mass (by difference) concentration (ug/m3)

b_SO4_mass Base year sulfate ion mass concentration (ug/m3)

b_NO3_mass Base year nitrate ion mass concentration (ug/m3)

b_water_mass Base year water mass concentration (ug/m3)

b_salt_mass Base year salt mass concentration (ug/m3) 

f_blank_mass Future year blank mass concentration (ug/m3)

f_crustal_mass Future year crustal mass concentration (ug/m3)

f_EC_mass Future year elemental carbon mass concentration (ug/m3)

f_NH4_mass Future year ammonium mass concentration (ug/m3)

f_Ocmb_mass Future year organic carbon mass (by difference) concentration (ug/m3)

f_SO4_mass Future year sulfate ion mass concentration (ug/m3)

f_NO3_mass Future year nitrate ion mass concentration (ug/m3)

f_water_mass Future year water mass concentration (ug/m3)

f_salt_mass Future year salt mass concentration (ug/m3) 

rrf_crustal Resultant annual relative response factor- Crustal Mass

rrf_ec Resultant annual response factor- Elemental Carbon Mass

rrf_nh4 Resultant annual relative response factor- Ammonium Mass. 

rrf_oc Resultant annual relative response factor- Organic Carbon Mass

rrf_so4 Resultant annual relative response factor- Sulfate Mass

rrf_no3 Resultant annual relative response factor- Nitrate Mass

rrf_water_mass Resultant annual relative response factor- Water Mass

rrf_salt Resultant annual relative response factor- Salt Mass (set equal to 1 if modeled

salt is not used)

7.1.3 Quarterly Peak Model Data

The Quarterly Peak Model Data option gives you the option of creating a small, reusable

file with quarterly peak values from a much larger file with daily values. Since daily

PM2.5 MATS works with quarterly peak values there is no loss of precision. To save

time, it is possible to use daily values only for an initial run with MATS and check this 

Quarterly Peak Model Data option. Then for subsequent runs (that use the same

modeled data), use the quarterly file that MATS generates. However, this will only work

for subsequent MATS runs that use exactly the same base and future year photochemical

model data (such as sensitivity runs that test the various ambient data settings in MATS). 

Alternatively, you can generate a baseline and future quarterly average model file outside

of MATS (using a program such as SAS, STATA, etc), and then load these quarterly files

into MATS, bypassing the use of any daily model data in MATS. The format of the

quarterly file is described below.

MATS generates baseline and future model files for the complete model domain:
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· Base-year file: "Baseline Quarterly Peak Model Data.csv" with the Scenario Name (e.g.,

Example Daily PM) appended at the beginning (e.g., "Example Daily PM Baseline

Quarterly Peak Model Data.csv"). · Future year file: "Future Quarterly Peak Model Data.csv."

MATS also produces a set of "used" quarterly peak model files (base and future). These

have the same format as the complete quarterly peak model data files, but only contain data

for the model grid cells that were used in the MATS point calculations. These files can

also be re-used and are also useful for QA purposes. · Base-year file: "Used Baseline Quarterly Peak Model Data Point.csv." · Future year file: "Used Future Quarterly Peak Model Data Point.csv."

The format of all four of these model files is the same. The table below describes the

variables in the Quarterly Peak Model Data file.

Output file name: "Scenario Name + (Used) Baseline/Future Quarterly Peak Model

Data (Point)"

Variable Description

_id The ID is a unique identifier for each model grid cell. The default value is the

column identifier multiplied by 1000 plus the row. (This is a character variable.)

_type

lat Latitude at the grid cell centroid in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are negative.

long 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 Year and Quarter (01= 1st quarter, 04= 2nd quarter, etc.)

crustal Crustal PM

nh4 Ammonium PM

so4 Sulfate PM

ec Elemental Carbon

no3 Nitrate PM

oc Organic carbon PM

pm25 PM2.5 mass (only used to gradient adjust PM2.5 for gradient adjusted spatial

fields)

cm Coarse PM (ug/m3) (only used for visbility calculations)

7.1.4 Species Fractions

Species fractions are simply the fraction of quarterly average PM2.5 at a given monitor

attributable to seven (and potentially eight) species: nitrate (NO3), sulfate (SO4), organic

carbon (OC), crustal, elemental carbon (EC), ammonium (NH4), and particle-bound water

(PBW). (And pending data availability, an eighth species, salt, can be included as well, the
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default MATS species files include salt data. But salt is an optional species on the model

files. If base and future year modeled salt data is supplied, a salt RRF will be calculated. 

If there is no salt data in the model files, then the salt RRF will be set to 1.)

When you check the Species Fractions option, you get a reusable file with species

fractions for each FRM monitor. Making the file reusable allows you to generate

consistent results and, perhaps most importantly, allows the same file to be used by

different MATS users.

7.1.4.1 Species Fractions Calculation

After calculating the ambient level of SO4, NO3, OCMMB, EC, PBW, NH4, and crustal

(as described in Step 2 of the Standard Analysis), MATS then divides these ambient levels

by the non-blank mass of PM2.5. To get non-blank PM2.5, MATS subtracts the blank

mass from the FRM PM2.5 value. MATS then divides each of the species (except blank

mass) by non-blank PM2.5.

Example Calculation of Species Fractions

The table below gives an example of theses calculations. The fraction is calculated by

dividing mass (ug/m3) by the Non-blank Mass. Note that salt is optional.

Units FRM

PM2.5

Blank

Mass

Non-Bla

nk Mass

SO4 NO3 OCMM

B

EC PBW NH4 Crusta

l

(Salt)

Concentrati

on

10.9175 0.5 10.4175 2.41601.1555 3.4847 0.2101 1.1555 1.2605 0.7353 -- Fraction 0.23190.1109 0.3345 0.0202 0.1109 0.1210 0.0706 -- 7.1.4.2 Output Description

The output file is named "Quarterly Peak Spec Frac Point.csv" with the Scenario Name

appended at the beginning (e.g., "Example Daily PM Quarterly Peak Spec Frac Point.csv

"). The table below describes the variables in the file.

The interpolated variables (starting with i_xxx) are created when MATS is run, but are not

needed when re-using a fractions file. They are also not needed when running with a user

generated fractions file.

Output file name: "Scenario Name + Quarterly Peak Spec Frac Point"

Variable Description

_id 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.)

_STATE_NAME State name. (This is a character variable.)

_COUNTY_NAME County name. (This is a character variable.)

monitor_lat Latitude at the monitor site in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are

negative.
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monitor_long Longitude at the monitor site in decimal degrees. Values in the eastern

hemisphere are positive, and those in the western hemisphere (e.g.,

United States) are negative.

monitor_gridcell Identifier of grid cell closest to the monitor

quarter Quarter

pm25_mass_frac PM2.5 mass used to calculate species fractions

fcr Crustal fraction of PM2.5 mass

fec Elemental carbon fraction of PM2.5 mass

fnh4 Ammonium fraction of PM2.5 mass

focm Organic carbon fraction of PM2.5 mass

fso4 Sulfate ion fraction of PM2.5 mass

fno3 Nitrate ion fraction of PM2.5 mass

fwater Water fraction of PM2.5 mass

fsalt Salt fraction of PM2.5 mass

blank_mass Blank mass

don Degree of neutralization of sulfate used to calculate ammonium mass

(0.000 - 0.375)

i_so4 Interpolated sulfate ion

i_no3r Interpolated retained nitrate ion

i_ocb Interpolated blank-adjusted organic carbon

i_ec Interpolated elemental carbon

i_crustal Interpolated crustal

i_don Interpolated degree of neutralization of sulfate (DON).

i_nh4 Interpolated ammonium

i_no3 Interpolated nitrate ion (only used to calculate volatilized ammonium; if

option is selected)

i_salt Interpolated salt

Note: · i_ocb is only used to to calculate the OCMmb "floor". · i_nh4 is not used if DON is used to calculate the ammonium concentration (and

fraction). · i_no3 is only used to calculate the "volatilized ammonium", if the option is selected (not

used by default).

7.2 Output Choice - Advanced

In the Output Choice Advanced window, MATS lets you choose various files grouped

under the heading Miscellaneous Outputs. These files are generally used for QA and are

described below.
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7.2.1 Miscellaneous Outputs

MATS gives you ability to generate a number of specialized files for quality assurance,

identify the monitor in each county with the highest forecast, and other reasons. These

files include: · Daily files. The MATS default is to output the 98th percentile design values, which are

based on a weighted average of five years of data. However, checking this box will

output more detailed calculations (the peak calculations are the basis of all of the design

value calculations). There are two daily files. The first contains data for years, quarters

and days, and contains baseline PM2.5 and species concentrations, RRFs, forecasted

PM2.5 and species concentrations. The "true" RRFs and PM2.5 and species

concentrations are found in this file. A second file has the 98th percentile PM2.5 value

in the baseline and future for each year at each monitor. Both of these daily files are

important because all of the basic PM2.5 calculations in MATS occur on a daily basis. · High county sites. The MATS default is to output the point results for all FRM sites.

Checking this box will also create a file which contains only the single highest monitor

in each county (based on the highest future-year value). This dataset is a subset of the

all sites file.
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· Quarterly average speciated monitors. This file contains the raw quarterly peak

speciated data at STN and IMPROVE sites that MATS uses to do interpolations (to

calculate species fractions). This is the subset of species data that MATS uses for each

particular scenario (based on the MATS inputs and configuration settings). · Design value periods. This file contains standard MATS output for each individual

design value period within the period covered by the analysis. · Neighbor files. The neighbor files contain the “nearest neighbor” data for the VNA

interpolation scheme. The data includes the distance to neighbor monitors and weights

used to do the interpolations. There is information for each FRM monitor for each

quarter and for each species.

7.2.1.1 Daily Files

The Daily Files provide intermediate calculations performed by MATS. There are two

files generated by checking this option: · "Daily All Years All Days PM25 Point". This file has baseline and future values for

PM2.5 Top 32 ranked values and constituent species. In addition it gives the speciated

RRFs. · "Daily All Years High Days PM25 Point". This file identifies the 98th percentile

PM2.5 value in the baseline and future for each year at each monitor. Note that the

baseline and future quarters and days may differ.

When generating these files, MATS appends the Scenario Name at the beginning (e.g., "

Example Daily PM Daily All Years All Days PM25 Point.csv"). The variables in each file

are described below.

Output file name: "Scenario Name + Daily All Years All Days PM25 Point"

Variable Description

_id 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 FRM data

_STATE_NAME State name. (This is a character variable.)

_COUNTY_NAME County name. (This is a character variable.)

monitor_lat Latitude at the monitor site in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are

negative.

monitor_long Longitude at the monitor site in decimal degrees. Values in the eastern

hemisphere are positive, and those in the western hemisphere (e.g.,

United States) are negative.

monitor_gridcell Identifier of grid cell closest to the monitor

day Day (yyyymmdd)

year Year (up to 5 years)
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quarter Quarter

b_pm25_d_conc Base year top PM2.5 concentrations 

f_pm25_d_conc Future year top PM2.5 concentrations

b_blank_mass Base year blank mass concentration (ug/m3)

b_crustal_mass Base year crustal mass concentration (ug/m3)

b_EC_mass Base year elemental carbon mass concentration (ug/m3)

b_NH4_mass Base year ammonium mass concentration (ug/m3)

b_Ocmb_mass Base year organic carbon mass (by difference) concentration (ug/m3)

b_SO4_mass Base year sulfate ion mass concentration (ug/m3)

b_NO3_mass Base year nitrate ion mass concentration (ug/m3)

b_water_mass Base year water mass concentration (ug/m3)

b_salt_mass Base year salt mass concentration (ug/m3)

f_blank_mass Future year blank mass concentration (ug/m3)

f_crustal_mass Future year crustal mass concentration (ug/m3)

f_EC_mass Future year elemental carbon mass concentration (ug/m3)

f_NH4_mass Future year ammonium mass concentration (ug/m3)

f_Ocmb_mass Future year organic carbon mass (by difference) concentration (ug/m3)

f_SO4_mass Future year sulfate ion mass concentration (ug/m3)

f_NO3_mass Future year nitrate ion mass concentration (ug/m3)

f_water_mass Future year water mass concentration (ug/m3)

f_salt_mass Future year salt mass concentration (ug/m3)

rrf_crustal_q Relative response factor- Crusatal Mass

rrf_ec_q Relative response factor- Elemental Carbon Mass

rrf_nh4_q Relative response factor- Ammonium Mass. (Not used if future year

ammonium is calculated using base year DON values) 

rrf_oc_q Relative response factor- Organic Carbon Mass

rrf_so4_q Relative response factor- Sulfate Mass

rrf_no3_q Relative response factor- Nitrate Mass

rrf_water_q Relative response factor- Water Mass

rrf_salt_q Relative response factor- Salt Mass

Output file name: "Scenario Name + Daily All Years High Days PM25 Point"

Variable Description

_id 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 FRM data

_STATE_NAME State name. (This is a character variable.)

_COUNTY_NAME County name. (This is a character variable.)
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monitor_lat Latitude at the monitor site in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are

negative.

monitor_long Longitude at the monitor site in decimal degrees. Values in the eastern

hemisphere are positive, and those in the western hemisphere (e.g.,

United States) are negative.

monitor_gridcell Identifier of grid cell closest to the monitor

98th_percent_rank

year Year (up to 5 years)

date_b Base date

b_high_quarter Base high quarter number

b_pm25_d_conc Base year peak (98th percentile) PM2.5 concentration

date_f Future date

f_high_quarter Future high quarter number

f_pm25_d_conc Future year peak (98th percentile) PM2.5 concentration

f_blank_mass Future year blank mass concentration for high days (ug/m3)

f_crustal_mass Future year crustal mass concentration for high days (ug/m3)

f_EC_mass Future year elemental carbon mass concentration for high days (ug/m3)

f_NH4_mass Future year ammonium mass concentration for high days (ug/m3)

f_Ocmb_mass Future year organic carbon mass (by difference) concentration for high

days (ug/m3)

f_SO4_mass Future year sulfate ion mass concentration for high days (ug/m3)

f_NO3_mass Future year nitrate ion mass concentration for high days (ug/m3)

f_water_mass Future year water mass concentration for high days (ug/m3)

f_salt_mass Future year salt mass concentration for high days (ug/m3)

rrf_crustal_q Relative response factor for high days - Crusatal Mass

rrf_ec_q Relative response factor for high days - Elemental Carbon Mass

rrf_nh4_q Relative response factor for high days - Ammonium Mass. (Not used if

future year ammonium is calculated using base year DON values) 

rrf_oc_q Relative response factor for high days - Organic Carbon Mass

rrf_so4_q Relative response factor for high days - Sulfate Mass

rrf_no3_q Relative response factor for high days - Nitrate Mass

rrf_water_q Relative response factor for high days - Water Mass

rrf_salt_q Relative response factor for high days - Salt Mass
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7.2.1.2 Output Description - High County Sites

In this file, MATS reports the monitor with the highest forecasted peak PM2.5 design

value in each county. The name of this file is "High County Sites" with the Scenario

Name appended at the beginning (e.g., "Example Daily PM -- High County Sites.csv").

The table below describes the variables in the output file.

 "Scenario Name + High County Sites"

Variable Description

_id 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 FRM data

_STATE_NAME State name. (This is a character variable.)

_COUNTY_NAMECounty name. (This is a character variable.)

monitor_lat Latitude at the monitor site in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are negative.

monitor_long Longitude at the monitor site in decimal degrees. Values in the eastern

hemisphere are positive, and those in the western hemisphere (e.g., United

States) are negative.

monitor_gridcell Identifier of grid cell closest to the monitor

b_pm25_d_DV Base year 5 year weighted average PM2.5 24-hr average (daily) design value

f_pm25_d_DV Future year 5 year weighted average PM2.5 24-hr average (daily) design value

b_blank_mass Base year blank mass concentration (ug/m3)

b_crustal_mass Base year crustal mass concentration (ug/m3)

b_EC_mass Base year elemental carbon mass concentration (ug/m3)

b_NH4_mass Base year ammonium mass concentration (ug/m3)

b_Ocmb_mass Base year organic carbon mass (by difference) concentration (ug/m3)

b_SO4_mass Base year sulfate ion mass concentration (ug/m3)

b_NO3_mass Base year nitrate ion mass concentration (ug/m3)

b_water_mass Base year water mass concentration (ug/m3)

b_salt_mass Base year salt mass concentration (ug/m3) 

f_blank_mass Future year blank mass concentration (ug/m3)

f_crustal_mass Future year crustal mass concentration (ug/m3)

f_EC_mass Future year elemental carbon mass concentration (ug/m3)

f_NH4_mass Future year ammonium mass concentration (ug/m3)

f_Ocmb_mass Future year organic carbon mass (by difference) concentration (ug/m3)

f_SO4_mass Future year sulfate ion mass concentration (ug/m3)

f_NO3_mass Future year nitrate ion mass concentration (ug/m3)

f_water_mass Future year water mass concentration (ug/m3)

f_salt_mass Future year salt mass concentration (ug/m3) 

rrf_crustal Resultant annual relative response factor- Crustal Mass
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rrf_ec Resultant annual response factor- Elemental Carbon Mass

rrf_nh4 Resultant annual relative response factor- Ammonium Mass. 

rrf_oc Resultant annual relative response factor- Organic Carbon Mass

rrf_so4 Resultant annual relative response factor- Sulfate Mass

rrf_no3 Resultant annual relative response factor- Nitrate Mass

rrf_water_mass Resultant annual relative response factor- Water Mass

rrf_salt Resultant annual relative response factor- Salt Mass (set equal to 1 if modeled

salt is not used)

7.2.1.3 Design Value Periods

Normally, MATS will output one set of files covering the entire analysis period specified

by the user. The outputs represent the averages of the values for each 3-year design value

period. If the "Output design value periods" option is checked, MATS will produce

discrete outputs for each design value period. The output files will be the same as a

standard analysis, but with "Period 1", "Period 2", etc., attached at the end of the name.

Please note, however, that checking this option will substantially increase the MATS run

time, by up to four times.

7.2.1.4 Output Description - Quarterly Average Speciated Monitors

In this file, MATS reports the quarterly peak values at the speciated monitors. The

speciated data is split into two files: · "Quarterly Peak Speciated Monitors". This has crustal, EC, OC, SO4, NO3, retained

NO3, and salt quarterly peak speciated values. · "Quarterly Peak NH4_DON Monitors". This has NH4 and DON quarterly peak

speciated values.

MATS generates these files with the Scenario Name appended at the beginning (e.g., "

Example Daily PM Quarterly Peak Speciated Monitors.csv"). The tables below describe

the variables in each file.

 Output file name: "Scenario Name + Quarterly Peak Speciated Monitors"

Variable Description

_id IMPROVE/STN Site Code

_type Monitor type (e.g., STN)

monitor_lat Latitude at the monitor site in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are

negative.

monitor_long Longitude at the monitor site in decimal degrees. Values in the eastern

hemisphere are positive, and those in the western hemisphere (e.g., United

States) are negative.

monitor_gridcell Identifier of grid cell closest to the monitor

quarter Quarter
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b_crustal_mass Base year crustal mass concentration (ug/m3)

b_EC_mass Base year elemental carbon mass concentration (ug/m3)

b_OCB_mass Base year organic carbon mass blank adjusted (ug/m3)

b_SO4_mass Base year sulfate ion mass concentration (ug/m3)

b_NO3_mass Base year nitrate ion mass concentration (ug/m3)

b_no3r_mass Base year retained nitrate ion mass concentration (ug/m3)

b_salt_mass Base year salt mass concentration (ug/m3)

 Output file name: "Scenario Name + Quarterly Peak NH4 & DON Monitors"

Variable Description

_id IMPROVE/STN Site Code

_type Monitor type (e.g., STN)

monitor_lat Latitude at the monitor site in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are

negative.

monitor_long Longitude at the monitor site in decimal degrees. Values in the eastern

hemisphere are positive, and those in the western hemisphere (e.g., United

States) are negative.

monitor_gridcell Identifier of grid cell closest to the monitor

quarter Quarter

b_NH4_mass Base year ammonium mass concentration (ug/m3)

b_DON Base year degree of neutralization (DON)

7.2.1.5 Neighbor Files

MATS calculates the nearby monitors or "neighbors" separately for each species when

interpolating to FRM monitors ("points"). In this file, MATS reports the neighbors

involved in interpolating species values to the FRM monitor sites. 

The name of this file is "Neighbor File Point" with the Scenario Name appended at the

beginning (e.g., "Example Daily PM Neighbor File Point.csv"). The table below describes

the variables in the output file.

Output file name: "Scenario Name + Neighbor File Point"

Variable Description

_id 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.)

_state_name State name. (This is a character variable.)

_county_name County name. (This is a character variable.)

monitor_lat Latitude at the monitor site in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are

negative.

monitor_long Longitude at the monitor site 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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monitor_gridcell Identifier of grid cell closest to the monitor

quarter Quarter

_neighbor IMPROVE/STN Site Code. (This is a character variable.)

neighbor_gridcell Identifier of grid cell closest to the neighbor

distance Distance in kilometers from FRM monitor site and

IMPROVE/STN/SPECTRE neighbor.

weightdistance Inverse-distance weight

weightdistancesquared Inverse-distance-squared weight

pollutant Pollutant (e.g., SO4). Note interpolation approach can vary by pollutant

7.3 Data Input

In the Data Input window, you specify the MATS input files that are used in each scenario.

 There are three main types of files which must be specified. These include ambient

PM2.5 species data (STN and IMPROVE), ambient total PM2.5 data (FRM and

IMPROVE), and gridded model output data (e.g. CMAQ or CAMx data). 

There is specific terminology that is used on the Data Input page. "Official" data refers to

PM2.5 FRM data that can be used to determine official design values for compliance

purposes (comparison to the NAAQS). Other datasets which may not have rigid regulatory

significance are sometimes referred to as "unofficial" data. The individual input file

choices are explained below.

Species Data. MATS needs ambient PM2.5 species data to calculate species
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concentrations at FRM monitoring sites. Users have a choice of supplying a “Species

Monitor Data File” or a “Species Fractions File”. · Species Monitor Data File. The default is to provide a species monitor data file.

MATS is populated with daily average species data from STN and IMPROVE sites

across the country. However, users can also provide their own ambient data file. 

MATS uses the daily average species data to calculate species fractions at each FRM

monitor. The species fraction data is combined with the “unofficial daily average

PM2.5 data” to calculate species concentrations. The default MATS species data file

contains all available data. However, there is a data flag to indicate site days that are

recommended to be removed from the species fractions calculations. This is not

necessarily the same data flags that have been identified by State agencies. MATS has

incorporated flagging routines that remove data that are considered to be outliers

and/or incomplete data. (A description of the flags is provided in the section on 

Species Fractions Calculation Options.) · Species Fraction File. Alternatively, the user can choose to use a pre-calculated

species fractions file which contains quarterly species information for the FRM

monitors of interest. MATS can also re-use a species fractions file that has previously

been generated by MATS. To re-use a previously created fractions file, simply supply

the correct path to the file. (The calculation of species fractions is discussed here.)

PM2.5 Monitor Data. MATS uses both "official" and "unofficial" data in its

calculations. · Unofficial Daily Average PM2.5 Data File. The “unofficial daily average PM2.5” file

contains the PM2.5 data that is needed to calculate species fractions. It is used in

combination with the “species monitor data file” from above. The unofficial daily

average PM2.5 file is not needed if the user supplies a pre-calculated species fractions

file. 

Similar to the species monitor data file from above, the “unofficial” PM2.5 data file

contains a data flag to indicate site days that are recommended to be removed from the

species fractions calculations. The flagged data is matched between the species file and

the PM2.5 file so that the same site days are removed. However, the PM2.5 data file

contains additional data (sites that don’t contain speciation measurements) and therefore

has additional flagged site days. These are not the same data flags that have been

identified by State agencies. MATS has incorporated flagging routines that remove data

that are considered to be outliers and/or incomplete data. (A description of the flags is

provided in the section on Species Fractions Calculation Options.) The user is free to

unflag existing data or add flags as necessary and appropriate. · Official Daily Average FRM Data File. The “official daily average file” contains all

of the “official” daily FRM data that has been used to calculate daily PM2.5 design

values. It is used to calculate design values and 5 year weighted-average design values

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The default data file in MATS was created by the Air Quality Analysis Group within

OAQPS. In most cases, the data should not be altered, however in some cases (e.g.

sensitivity analyses) there may be a need to add or remove data.

Model Data. The “model data” refers to gridded model output from models such as

CMAQ or CAMx. The user can choose either daily model data input or quarterly model

data input (which would be quarterly peak values calculated from the daily model data).

Either will work. The default setting is daily average data. Recall that MATS can

generate quarterly peak model data (which can then be re-used in subsequent MATS

runs). 

Model data must be selected for all MATS runs. The size of the modeling grid defines

the outputs for point estimates. For point estimates, MATS will output the results for all

specified monitors within the domain.

Note that you need to specify both a Baseline File and a Forecast File. The baseline file

should be consistent with the historical monitor data that you use, and the forecast year

is the future-year of interest.

7.3.1 Species Data Input

The species data may be in form of monitor data (specified below) that MATS then uses to

calculate species fractions, or it may be in the form of species fractions directly (specified

here).

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.
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Format of Speciated Monitor Data

Speciated Monitor Data Variable Descriptions

Variable Description

_id The ID is a unique name for each monitor in a particular location. (This is a

character variable.)

LAT Latitude at the monitor site in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are negative.

LONG Longitude at the monitor site in decimal degrees. Values in the eastern

hemisphere are positive, and those in the western hemisphere (e.g., United

States) are negative.

_TYPE STN or IMPROVE network data

DATE Date of daily average ambient data with YYYYMMDD format (This is a

numeric variable)

SO4 Measured sulfate ion 

NO3R Estimated nitrate retained on FRM filter, using measured nitrate, hourly T and

RH

NH4 Measured NH4+ ion

OCB OC blank adjusted, STN uses constant blank value per sampler type,

IMPROVE uses backup filter methodology

EC Measured EC

CRUSTAL Using IMPROVE algorithm, Crustal, aka "Fine Soil" = 2.2 × [Al] + 2.49 × [Si] +

1.63 × [Ca] + 2.42 × [Fe] + 1.94 × [Ti]

SALT Estimated salt using Cl (Salt=1.8*Cl), where Cl is elemental chloride

DON Degree of neutralization of SO4 (0-0.375)
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H2O_AIM Calculated water using AIM and measured SO4, adjusted NO3 and measured

NH4

OC Measured OC

NO3 Measured nitrate ion

SO4_3S Sulfate value derived from S, as per IMPROVE protocol. i.e. SO4_3S = 3*S. 

CRUSTAL_ALT Alternative Crustal calculation using measured Si, Fe, Ca, Ti (modified formula

without Al) = 3.73 × [Si] + 1.63 × [Ca] + 2.42 × [Fe] + 1.94 × [Ti]

FRM_MASS FRM mass

MEASURED_FM STN or IMPROVE sampler measured fine mass (Teflon filter)

RCFM Reconstructed Fine Mass using IMPROVE protocol, RCFM = [Amm_Sulfate] +

[Amm_Nitrate] + [OCM] + [EC] + [Fine Soil]

Al Measured Al

Ca Measured Ca

Fe Measured Fe

Ti Measured Ti

Si Measured Si

EPA_FLAG Flag to indicate data that EPA recommends to be removed from the species

fractions calculations. 0 = valid data, 1 or greater = data that has been flagged

and should be removed

USER_FLAG Flag to indicate additional data that the user wants to remove from the species

fractions calculations. 0 = valid data, 1 or greater = data that has been flagged

and should be removed

Note: Some variables are supplied for QA purposes only and are either not used by MATS

or are calculated internally by MATS. For example, "OC" is not used by MATS (OCb is

used) and H20_AIM is calculated internally. 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.)

7.3.2 PM2.5 Monitor Data Input

MATS uses the "unofficial" daily PM2.5 file for the calculation of species fractions files. 

MATS uses the "official" daily PM2.5 file for point estimates.

7.3.2.1 Unofficial Daily PM2.5 Monitor Data Input

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.
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Format of Unofficial Daily PM2.5 Monitor Data

Unofficial Daily PM2.5 Monitor Data Variable Descriptions

Variable Description

_ID The ID is a unique name for each monitor in a particular location. (This is

a character variable.)

_TYPE FRM or IMPROVE data. (This is a character variable.)

LAT Latitude at the monitor site in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are

negative.

LONG Longitude at the monitor site 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 ambient data with YYYYMMDD format (This is a

numeric variable)

PM25 Measured PM2.5 mass (ug/m3)

EPA_FLAG Flag to indicate data that EPA recommends to be removed from the

species fractions calculations. 0 = valid data, 1 = data that has been

flagged and should be removed

USER_FLAG Flag to indicate additional data that the user wants to remove from the

species fractions calculations. 0 = valid data, 1 or greater = data that has

been flagged and should be removed.

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.)
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7.3.2.2 Official Daily PM2.5 Monitor Data Input

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 Official Daily PM2.5 Monitor Data

Official Daily PM2.5 Monitor Data Variable Descriptions

Variable Description

_ID 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 FRM data

MONTIOR_LAT Latitude at the monitor site in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are negative.

MONITOR_LONG Longitude at the monitor site 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 ambient data with YYYYMMDD format (This is a

numeric variable)

 PM25 Measured PM2.5 mass (ug/m3)

 98_PERCENTILE Official 98th percentile values for each site for each year are identified by a

flag = "1"
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 EPA_FLAG Official flags to indicate data that has been removed from design value

calculations. 0 = valid data, 1 = data that has been flagged and removed 

USER_FLAG Flag to indicate additional data that the user wants to remove from design

value calculations. 0 = valid data, 1 = data that has been flagged and should

be removed.

COMPLETION_COD

E

Official design value completion codes (1, 2, 3, 4, 5, and -9). Codes are valid

for the end year of each 3 year design value period.

 _STATE_NAME State name. (This is a character variable.)

 _COUNTY_NAME County name. (This is a character variable.)

RANK_98 Rank order of the official 98th percentile measured value for the year (single

value for each site year).

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.)

7.3.3 Model Data Input

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. Below is an example of the model data file

format and descriptions of the variables in the file. Note that there is both a base year and

a future year model file. The format for both is the same.

Note that you can load in either daily model data or quarterly model data.
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Format of Model Data

Model Data Variable Descriptions

Variable Description

_ID The ID is a unique value for each model grid cell. The default value is the

column identifier multiplied by 1000 plus the row. (This is a character variable.)

_TYPE Leave blank

LAT Latitude at the grid cell centroid in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are negative.

LONG 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)

Crustal Crustal PM2.5 mass

NH4 Ammonium mass

SO4 Sulfate PM

EC Elemental carbon

NO3 Nitrate PM

OC Organic mass PM

PM25 PM2.5 mass

CM Coarse PM mass (ug/m3)

SALT Salt (Optional)

Note: 
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· The "PM25" mass variable is used to sort the model data to determine the "peak"

modeled PM2.5 days for the RRF calculations (based on the high base year PM2.5

concentrations). It is up to the user to provide a modeled PM2.5 mass concentration

using an appropriate definition of modeled PM2.5 mass. · 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.)

7.4 Species Fractions Calculation Options

The Species Fractions Calculation Options has two main sections. One involving

speciated monitor data (e.g., STN and IMPROVE monitors) and the other total PM2.5

monitor data (FRM and IMPROVE). For each type of data you can specify the years of

interest, whether you want to delete certain data, and the minimum amount of data for a

monitor to be considered "valid" (and thus included in the calculations). In the next

sections, we describe each of these three options in more detail. Note that these options

apply more or less in the same way for both speciated monitor data and total PM2.5

monitor data. Also note that these options are no longer relevant if you have loaded a 

species fractions file. · IMPROVE-STN Monitor Data. The speciation data from STN and IMPROVE

monitors are interpolated by MATS in order to provide species data for any point in a

modeling domain. The interpolated species data is used to calculate species fractions at

FRM monitors. Note that you do not need to have values for all species for a monitor to

be considered valid, as each species is considered individually. However, the

"EPA_Flag" variable in the default "species for fractions" file has been set so that all

monitor days that do not have complete species data are not used in the calculations (flag

= 1). If the user wants to use the incomplete species data, the flag can changed to "0". · PM2.5 Monitor Data. The total PM2.5 data from FRM are used by MATS to calculate

species fractions (in conjunction with the interpolated speciation data from STN and

IMPROVE monitors).
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7.4.1 Monitor Data Years

Using the Start Year and End Year drop-down menu options, you can choose more than

one year of speciated data for the calculation of species fractions. The default approach in

MATS is to use three years of data. 
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MATS handles multiple years of data by calculating averages for each species by quarter

and year. MATS then averages the quarterly values across the years (e.g., average quarter

1 values of SO4 across two years to get a single "quarter 1" estimate). For example, if for

2006-2008 we have SO4 averages of peak values in quarter 1 of 8.2 ug/m3 (for 2006), 6.2

ug/m3 (for 2007), and 3.6 ug/m3 (for 2008), then the average across the years would equal

6.0 (=[8.2+6.2+3.6]/3).

After completing this step, MATS will have four quarterly estimates for each species at

each speciated monitor. These quarterly values are then ready to be interpolated to FRM

sites.

7.4.2 Delete Specifed Data Values

The default is to delete the observations specified by EPA. (That is, these observations are

excluded from a particular analysis, while they of course remain in the database.) As

described in the Data Input section, valid data are given a value of "0" and observations

that are deleted are given a value of "1" to "10", as follows: 

0. Data is OK to use

1. Important species is missing (e.g. no3, so4, oc, etc)

2. Constructed mass < 30% total mass

3. Constructed mass > 2 times total mass

4. Fire event

5. total mass < so4

6. total mass < crustal

7. OC is outside QA criteria

8. Soil is outside QA criteria

9. Both OC and soil are outside QA criteria

10. Regional concurrence on exceptional event 

There is also an option for the user to flag data, using the same convention of "0" for valid

data and "1" to "10" for data marked for deletion. If both the EPA-specified and Userspecified flags are checked, then MATS deletes any observations that are marked for

deletion by either the EPA or the user. This makes it easy for the user to flag additional

data for removal from the calculations (without deleting the actual record from the ambient

data file).

7.4.3 Minimum Data Requirements

There are three sets of minimum data requirements:
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· Minimum number of valid days per quarter. This is the minimum number of

site-days per valid quarter. The default is 11 days, which corresponds to > 75%

completeness for monitors on a 1 in 6 day schedule. This is a minimum number of

samples that is routinely used in calculations of quarterly average concentrations. · Minimum number of valid quarters per valid year. This is the number of valid

quarters of data required for a given year. The default value is 1 quarter. If the value is

set = 2, then there will need to be 2 quarters of valid data for a given year to be valid. · Minimum number of valid years required for valid monitor. This is the number of

valid years that are needed in order for a particular monitor's data to be considered valid.

The default is 1 for IMPROVE-STN monitor data and the range is 1-4.

Example 1: Minimum Days = 11, Minimum Quarters = 1, Minimum Years = 1

Consider the default assumptions and the following number of observations from three

monitors:

Monitor 1 Monitor 2 Monitor 3

Year Quarter # Obs. # Obs. # Obs.

2006 1 8 11 11

2 6 13 8

3 11 4 12

4 5 12 8

2007 1 11 10 9

2 9 11 12

3 12 13 10

4 6 12 9

2008 1 6 15 12

2 7 11 10

3 12 12 10

4 13 9 12

With the default 11 minimum days required, MATS would then consider the highlighted

quarters (above) as valid. MATS would then use all of the highlighted quarters as all of the

following years are valid.

Monitor 1 Monitor 2 Monitor 3

Year Avg (ug/m3) Avg (ug/m3) Avg (ug/m3)

2006 v v v

2007 v v v

2008 v v v
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Example 2: Minimum Days = 11, Minimum Quarters = 2, Minimum Years = 2

Consider the data from three monitors in the previous examples:

Minimum Days = 11, Minimum Quarters = 2, Minimum Years = 2

Monitor 1 Monitor 2 Monitor 3

Year Quarter # Obs. # Obs. # Obs.

2006 1 8 11 11

2 6 13 8

3 11 4 12

4 5 12 8

2007 1 11 10 9

2 9 11 12

3 12 13 10

4 6 12 9

2008 1 6 15 12

2 7 11 10

3 12 12 10

4 13 9 12

With the default 11 minimum days required, MATS would then consider the highlighted

quarters (above) as valid. MATS would then use only a subset of the highlighted quarters,

as not all of the years are valid. Monitor 1 would not be used at all, and data from Monitor

3 would only be used from 2006 and 2008.

Monitor 1 Monitor 2 Monitor 3

Year Avg (ug/m3) Avg (ug/m3) Avg (ug/m3)

2006 -- v v

2007 -- v -- 2008 -- v v

7.5 Species Fractions Calculation Options - Advanced

The Species Fractions Calculation Options - Advanced screen allows you to make

relatively advanced choices for your analysis. Generally speaking, the default options

settings are consistent with the EPA modeling guidance document. The first set of options

lets you specify which monitor data you want to use. The second set of options allows you

to specify the interpolation weighting that you want to use and whether the interpolation

involves a maximum distance or not. The third set of options involves choices regarding

ammonium, blank mass, and organic carbon. 
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7.5.1 Using Monitor Data to Calculate Species Fractions

The Using Monitor Data to Calculate Species Fractions panel allows you to choose how

you will choose IMPROVE and STN speciated monitor data and ("unofficial") PM2.5

monitor data to calculate quarterly peak values. There are three options: · Use Top X Percent of Daily Monitor Days. MATS chooses the top "X" percent of

days per quarter that you specify and averages them. · Use All Daily Monitor Values Greater than Fixed Amount (ug/m3

). MATS chooses

all monitor values greater than or equal to a fixed amount that you specify and averages

them. Note that you also need to specify a minimum number of days greater than or
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equal to your specified amount. If there are an insufficient number of days then MATS

drops the data for that particular quarter. · Use Top X Number of Daily Monitor Days. MATS chooses the top "X" number of

days per quarter that you specify and averages them.

These three options work in essentially the same way for IMPROVE and STN speciated

monitor data and for ("unofficial") PM2.5 monitor data. Below are some examples of how

these options work.

Note that the IMPROVE and STN speciated monitor data uses the "measured_FM"

variable to identify peak days. Note also that it is possible that the option used to identify

peak days (e.g., Use Top X Percent of Daily Monitor Days) does not give a unique set of

days because a number of days may have the same value (see Example 1, below).

Example 1: Use Top 10 Percent of Daily Monitor Days

Assume the default of using the top 10 percent of days. Let's say there are 27 days in the

first quarter (January-March) at a particular monitor for a given year, and 24, 25, and 32

valid days of data in quarters 2,3, and 4. MATS will then calculate the number of peak

days in each quarter as follows:

Year 2006, Q1: round(0.10*27) = round(2.7) = 3 

Year 2006, Q2: round(0.10*24) = round(2.4) = 2

Year 2006, Q3: round(0.10*25) = round(2.5) = 3

Year 2006, Q4: round(0.10*32) = round(3.2) = 3.

In the sample data for quarter 1 (below), there are not three unique peak days because the
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third and fourth highest days (highlighted in yellow) have the same value. In this case,

MATS uses the top 4 days.

Sample Data for Quarter 1 - Highlighted Top 10 Percent

Day measured_FM

1 13.9

2 13.8

3 13.6

4 13.6

5 13.0

6 12.6

7 12.6

8 12.4

9 12.1

10 11.7

11 11.4

12 10.4

13 10.2

14 10.1

15 9.6

16 9.5

17 9.4

18 9.0

19 8.9

20 8.4

21 8.2

22 8.2

23 7.6

24 7.4

25 7.3

26 6.6

27 6.6

Example 2: Use All Daily Monitor Values Greater than 13 ug/m3

With the same sample data and with the choice to use monitor values greater than 13 ug/m3

, MATS will select the top five days. Note that if you had selected values greater than 14

ug/m3, MATS would not have any data and would have dropped this quarter.

Sample Data for Quarter 1 - Highlighted Greater than or Equal to 13 ug/m3

Day measured_FM

1 13.9

2 13.8

3 13.6

4 13.6

5 13.0

6 12.6

7 12.6

8 12.4

9 12.1

10 11.7

11 11.4

12 10.4
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13 10.2

14 10.1

15 9.6

16 9.5

17 9.4

18 9.0

19 8.9

20 8.4

21 8.2

22 8.2

23 7.6

24 7.4

25 7.3

26 6.6

27 6.6

Example 3: Use Top 3 Daily Monitor Days

With the same sample data and with the choice to use the top three monitor days, MATS

would select the top four days, because the third and fourth days have the same value. This

is the same result as in Example 1 (above). 

7.5.2 Interpolation Options for Species Fractions Calculation

The Interpolation Options panel allows you to choose how you will interpolate, or

combine, the values from different monitors. One approach is to use 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 PM is 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 (in

kilometers) 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 Species Fractions Calculation Options panel), regardless of distance. If you
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want to limit the use of monitors based on distance, type in the distance you want to use

(e.g., 100) next to the pollutant of interest. 

 You can also change the number using the arrows. The double arrow on the right

increases the number in units of 100:

and the double arrow on the left decreases the number in units of 100. The upper arrow

increases the number in single digits:

and the lower arrow reduces the number in single digits.

Note that 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.

7.5.3 Miscellaneous Options

The Miscellaneous Options panel lets you make choices regarding: · Ammonium. This allows you to specify whether MATS uses degree of neutralization

(DON) values to calculate ammonium (NH4) or whether it uses measured ammonium in

conjunction with an assumption about the percentage of NH4 that evaporates. The

default option is to use DON values. If you want to use measured ammonium, you need

to click the button and choose a NH4 percentage evaporating (e.g., 50). The default is

"0", which assumes that no ammonium evaporates from the FRM filters. The

calculations underlying the default and alternative ammonium calculations are discussed

in detail in the section on species fractions calculations. · Default Blank Mass. The Default Blank Mass option simply allows you to set default

blank mass to the desired level. The default is 0.5. You can type desired value, or use

the arrows to increase or decrease the value. · Organic Carbon. This allows you to set the "floor" and the "ceiling" for the mass

balance calculation for organic carbon. The calculations involved are discussed in detail
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in the section on species fractions calculations.

7.5.4 Internal Precision of the Calculations

All calculations in MATS are carried out with single precision. In addition, most output

files by default generate outputs only up to 3 digits after the decimal. Therefore, the base

year DV (b_pm25_d_q_DV) may differ slightly from the sum of the component species of

the quarterly PM2.5 MATS output files. In particular, the water calculation requires

additional precision in the species fractions. It is therefore recommended to have at least

seven decimal digits in the species fractions. This may be accomplished by increasing

precision of the species and species fractions calculations to 7 (or more) significant digits

by modifying the MATS.ini file as follows: set species_calc_precision=7 and

species_fraction_precision=7. Please note that the future year species always add up to the

future year DV. However, increases in species fractions precision may result in very small

changes in future DV due to the dependence of the future concentrations on the base year

concentrations.

7.6 PM2.5 Calculation Options

The PM2.5 Calculation Options window allows you to specify the particular years of

monitor data that you want to use from the input file you specified in the Data Input

section. You can also specify the following: · Valid FRM Monitors. You can specify the minimum number of design values (the

default is 1) and whether you want to make sure that particular design values have to be

used in the calculations. · NH4 Future Calculation. You can also specify how you want to forecast NH4 values.

The default approach is to use baseline DON values, and the alternative is to use baseline

NH4 and a RRF value for NH4. (These calculations are described in detail here.)
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7.6.1 PM2.5 Monitor Data Years

Using the Start Year and End Year drop-down menu options, you can choose more than

one year of official PM monitor data for the calculation of future PM2.5 values. The

default approach in MATS is to use five years of data.

7.6.2 Valid FRM Monitors

By default, MATS assumes that there only needs to be one design value for a monitor to be

considered valid. In addition, MATS assumes that no particular design value is required,

so different monitors with different years of data could be used. For example, if you

specify the start year and end year as 2005 and 2009 (giving potential design values of

2005-2007, 2006-2008, and 2007-2009), then one monitor could have data for, say,

2005-2007 and another monitor data for 2006-2008, and both monitors would be used.
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7.6.3 NH4 Future Calculation

As described in the section on Forecasted Species Calculation, MATS can forecast NH4

using two different approaches. The default approach is to use base year DON values.

7.7 Model Data Options

The Model Data Options section allows you to specify: · Temporal Adjustment at Monitor. This option specifies how many model grid cells to

use in the calculation of RRFs for point estimates and for spatial estimates. Using the

drop-down menu, you can choose 1x1, 3x3, 5,x5, and 7x7. (The default for a 12

kilometer by 12 kilometer grid is to use a 1x1 set of grid cells. The choice of grid size is

discussed in the following EPA guidance: 

http://www.epa.gov/scram001/guidance/guide/final-03-pm-rh-guidance.pdf) For PM

analyses, MATS calculates mean concentrations across the grid cell array (as compared

to maximum concentrations used for ozone analyses). · Advanced Options: RRF Model Values Used. This option allows you to choose three

different ways to calculate quarterly peak modeled values: (1) the top X percent of daily

model days, (2) all daily model values greater than or equal to a specified amount, and

(3) the top X number of model days. (These three options are described in detail in 

Species Fractions Calculation Options - Advanced section.) The peak modeled days are

determined by sorting the PM2.5 variable in the baseline model data input file.
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7.8 Final Check

The Final Check window verifies the selections that you have made. 
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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:

After making the necessary correction, click the button Press here to verify your

selections. Then click the Finish button.
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88 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. Output Choice. 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 Output. Load your output and prepare maps of your forecasts. · Step 8. View & Export Output. Examine the data in a table format and export these

data.

Each step is explained below. Additional details are provided in the section Ozone

Analysis: Details. 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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Click the Ozone Analysis button on the main MATS window. This will bring up the

Configuration Management window. 

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.

8.2 Step 3. Data Input

The Data Input window allows you to choose the monitor data and the model data that
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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 to calculate a future-year design value. 

MATS currently comes loaded with ozone design values for the period from 1998-2009

(1998-2000, 1999-2001, 2000-2002, 2001-2003, 2002-2004, 2003-2005, 2004-2006,

2005-2007, 2006-2008, 2007-2009); 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:

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

Filtering and Interpolation window.
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8.3 Step 2. Output Choice

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 O3” – 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 ozone levels at monitors. MATS will create forecasts

for each monitor in the monitor file. 

Check the box next to Automatically extract all selected output files. MATS will create a

separate folder called "Tutorial O3" in the MATS "Output" folder, and then export .CSV

files with the results of your analysis. Alternatively, you can export the results from the 

Output Navigator, but checking this box is a little easier.

When your window looks like the window above, click Next. This will bring you to the

Data Input window.
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8.4 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 2007. Therefore,

the design value would be based on data from 2005-2007 up through a design value

based on data from 2007-2009. (That is, the Start Year is 2005-2007 and the End Year

is 2007-2009.) · 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., 2005-2007) 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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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. 8.5 Step 5. RRF & Spatial Gradient

The RRF and Spatial Gradient window has two sets of options. · The RRF Setup determines the days to be used in the calculation of relative response

factors (RRFs). By default, MATS will select the top 10 highest days for the calculation.

Each monitor must meet the minimum allowable threshold value and the minimum

number of days at or above minimum allowable threshold value specified in this screen,

or it is dropped from the calculation. The default values are 60 and 5, respectively. · 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.

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.
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* The default values in MATS are consistent with the recommended values in the EPA modeling

guidance (see section 14.1.1).

8.6 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.

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 either Save Scenario & Run or

Save Scenario. Save Scenario & Run will cause MATS to immediately run the scenario.

A temporary, new Running tab will appear (in addition to the Start, Map View and

Output Navigator tabs).
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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.) MATS will automatically load the output files associated with the .asr

configuration that just finished running. 
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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. 8.7 Step 7. Load & Map Output

After generating your results, Output Navigator can be used to load and/or map them. If a

run just finished, the output files will already be loaded into output navigator.

If files from a previous run need to be loaded then click on the Load button and choose

the Tutorial O3.asr file.
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Under Configuration/Log Files, you will see two files: · Configuration: keeps track of the assumptions that you have made in your analysis. · Log File: provides information on a variety of technical aspects regarding how a results

file (*.ASR) was created.

Under Output Files you will see: · Tutorial O3 - Ozone Monitors - monitor data, temporally adjusted 2015: contains

forecasted values and the monitor data used. · Tutorial O3 - 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 O3 - 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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This will bring up the Map View tab.
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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. 
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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:

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:
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Right click on the "Tutorial O3 - Ozone Monitors -"layer in the panel on the left side of the

window. Choose the Plot Value option.
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This will bring up Shape Class Breaks window. In the Value drop-down list, choose the

variable "f_o3_dv" -- this is forecasted ozone design value for 2015.

Click Apply and then click Close. This will bring you back to the Map View window.

Examine the other variables: 

b_o3_dv: baseline ozone design value;
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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.

8.8 Step 8. View & Export Output

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 O3 - Ozone Monitors - monitor data

temporally adjusted 2015. This gives you three options: Add to Map, View, and Extract. Choose the View option. 

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. The lower

left panel allows you to view the other variables in the data.
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The default option is to show all of the data in the lower left panel. If, however, you want

to just view the data for a particular monitor -- in this example, monitor ID = "010331002"

-- use the scrollbar (if needed) and then highlight this monitor. MATS will then display

the values for this monitor in the bottom panel.
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To view all of the data again, click on the Show All button.

To eliminate missing values (denoted by negative numbers in the lower panel), check one
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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". MATS will automatically drop these values. (Note that the

monitor that we previously highlighted [monitor ID = "010331002] has now dropped out

of the display.)

Click the Export button and save the file as "O3 Tutorial 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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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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99 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. 

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.

9.1 Choose Desired Output

MATS lets you choose to generate Point Estimates, which refer to forecasts made at fixed

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.
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By checking the box next to Automatically extract all selected output files, MATS will

create a separate folder with your chosen Scenario Name in the MATS "Output" folder,

and then export .CSV files with the results of your analysis. Alternatively, you can export

the results from the Output Navigator, but checking this box is a little easier.

9.1.1 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., Example O3.asr). 

Note that the extension (.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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· Output file names. The output files generated will begin with the Scenario Name.
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9.1.2 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.

9.1.2.1 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 2007, then design values

from 2005-2007, 2006-2008, and 2007-2009 would be averaged. An average of design

values is, in effect, a weighted average of annual averages -- 2007 is “weighted” three

times, 2006 and 2008 are weighted twice, and 2005 and 2009 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 2005-2009. This assumes a model

base year of 2007. If the base year is not 2007, then the start and end design value period

should be adjusted. 

9.1.2.2 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:

where:

Monitori, future = future-year ozone design value at monitor site i, measured in parts per

billion (ppb)
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Monitori = baseline ozone design value at monitor site i, measured in ppb

RRFi = 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.

9.1.3 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-adjusted 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-distanceweighted 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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9.1.3.1 Baseline - interpolate 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:

where:

GridcellE, baseline = baseline ozone concentration at unmonitored site E;

Weighti = inverse distance weight for monitor i;

Monitori = baseline ozone concentration at monitor i;

Gradient Adjustmenti, 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:

where:

ModelE, baseline = baseline scenario, average of five highest daily 8-hour values at site E;

Modeli, baseline = baseline scenario, average of five highest daily 8-hour values at monitor
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site i.

9.1.3.2 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:

where:

GridcellE, baseline = baseline ozone concentration at unmonitored site E;

Weighti = inverse distance weight for monitor i;

Monitori = baseline ozone concentration at monitor i;

Gradient Adjustmenti, 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:

where:

ModelE, baseline = baseline scenario, average of five highest daily 8-hour values at site E;

Modeli, baseline = baseline scenario, average of five highest daily 8-hour values at monitor

site i.

9.1.3.3 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:
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where:

GridcellE, baseline = baseline ozone concentration at unmonitored site E;

Weighti = inverse distance weight for monitor i;

Monitori = 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 [see section 9.4.1.6]).

9.1.3.4 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

follows:

where:

GridcellE, baseline = baseline ozone concentration at unmonitored site E;

Weighti = inverse distance weight for monitor i;

Monitori = 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 [see section 9.4.1.6]).

9.1.4 Design Value Periods

Normally, MATS will output one set of files covering the entire analysis period specified

by the user. The outputs represent the averages of the values for each 3-year design value

period. If the "Output design value periods" option is checked, MATS will produce

discrete outputs for each design value period. The output files will be the same as a

standard analysis, but with "Period 1", "Period 2", etc., attached at the end of the name.

Please note, however, that checking this option will substantially increase the MATS run

time, by up to four times.
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The maximum design values are based on the highest future year value from the three

design value periods. These values can be output by checking the "Output design value

periods maxima" option. The output file will be the same as a standard analysis, but with

 “maximum forecast” inserted into the file name.

9.1.5 Ozone 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 O3 -- 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.

9.1.5.1 Ozone Monitors -- monitor data, temporally adjusted 2015.csv

The table below describes the variables in the output file.

Variable Description

_id 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, 2009 represents the 2005-2009 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
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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.)

9.1.5.2 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):

Variable Description

_id 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 selected design value periods (e.g., if a 5 year

period is selected, 2009 represents the 2005-2009 period ).

b_o3_DV Baseline design value

f_o3_DV Forecasted (future year) design value

referencecel

l

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_nam

e

State name. (This is a character variable.)

_county_na

me

County name. (This is a character variable.)

9.1.5.3 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):

Variable Description

_id 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

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.
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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 date represents the last year of the selected design value periods (e.g., if a 5 year

period is selected, 2009 represents the 2005-2009 period ).

ga_conc Modeled concentration (ppb) used for gradient adjustment (average of "start value"

and "end value")

i_b_o3 Interpolated (to spatial field) baseline concentration (ppb).

i_f_o3 Interpolated (to spatial field) future year concentration (ppb).

i_b_ga_o3 Interpolated (to spatial field) gradient adjusted baseline concentration (ppb).

i_f_ga_o3 Interpolated (to spatial field) gradient adjusted future year concentration (ppb).

ppb Threshold value (measured in parts per billion) used in the rrf calculation

days Number of days at or above the threshold value.

referencecellIdentifier of the grid cell. (In the case of spatial fields, this is identical to the _ID

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).

9.2 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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9.2.1 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

Ozone Monitor Data Variable Descriptions

Variable Description
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_ID 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 this 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 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).

O3 Observed monitor value. Note that missing values are represented by a minus

nine (-9).

_STATE_NAME State name. (This is a character variable.)

_COUNTY_NAME 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.)

9.2.2 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

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 

O3 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.)

9.2.2.1 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.

9.2.3 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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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. · Maximum-paired in space. For each day of modeling data, MATS will identify the grid

cell with the highest 8-hour daily maximum in the chosen array in the baseline file, and

calculate the RRF using the same grid cell in the control file.
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The default choice for the ozone analysis in MATS is to use the maximum-paired in

space option 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.

9.2.3.1 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
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Solution:

(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.”

(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.
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9.3 Filtering and Interpolation

The Filtering and Interpolation window allows you to choose the years of monitoring

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.

9.3.1 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 (1998-2000)-(2007-

2009). The default approach in MATS is to average 3 design value periods. For example,

if the modeling base year is 2007, then you would use the design values from 2005-2007,

2006-2008, and 2007-2009. The Start Year is set to 2005-2007 and the End Year is set

to 2007-2009.
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9.3.2 Valid Ozone Monitors

MATS provides two 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). · Required Design Values. Specifies whether a particular design value period needs to

be valid for the calculations to be performed at that monitor.

9.3.2.1 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.

Example 1: Point Estimates
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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.

9.3.2.2 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.

9.3.3 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 use 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.

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
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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 two 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.

9.4 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). You can also specify

a sub-range of days included in the model data files and base and future year model days

can be paired by high concentration instead of by date. MATS also averages a userspecified range of values to calculate gradient adjustments (e.g., Spatial Gradient

Calculation - Example 1).
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9.4.1 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 or select directly the top number of days based on value (Top X

option in GUI). 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 can do this calculation separately for the baseline and future-year

scenarios. As a result – if you chose an option other than “Maximum – paired in space” -

two different grid cells in the baseline and future-year might be used to represent a given

day. 
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· MATS updated approach is as follows: MATS allows the user to specify the Top X days

to be used in computing the RRF. MATS will then select the specified number of days

based on their ranking by value and date. If there are fewer than the Min number of days

at or above minimum allowable threshold, then the monitor site will be dropped.

Otherwise the selected days will be used in computing the RRF. This new approach

replaces the legacy approach that used the following algorithm: 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 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.

Another option is to specify a Subrange of days to use in the model RRF calculation. The

start day and end day of the subrange are specified based on a count of the number of days

from the first day in the file. For example, if the model files contained data for June 1stAugust 30th, a subrange start and end day of "31" and "61" respectively would specify the

July 1st-July 31st period.

There is also an option to pair days based on high concentration instead of date. This

may be useful for model runs where the future year meteorology is different than the base

year (such as climate modeling). MATS will pair the future year highest concentrations

with the highest concentrations in the base year file, regardless of date. The number of

days in the RRF will continue to be based on the threshold variables. For example, if 11

days are greater than the selected threshold in the base year, then the RRF will be

calculated based on the 11 highest base and future year concentration days.
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9.4.1.1 RRF Calculation - Example 1

Assume the following default values:

Assume that there are 15 days of data:

Baseline day Baseline

value

Future day Future value

1 103 1 95

2 112 2 97

3 98 3 94

4 97 4 95

5 95 5 94

6 95 6 93

7 94 7 89

8 92 8 86

9 90 9 80

10 85 10 78

11 89 11 80

12 88 12 81

13 85 13 76

14 78 14 75

15 78 15 74
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MATS will sort the values from high to low based on the Baseline values:

Baseline day Baseline

value

Future day Future value

2 112 2 97

1 103 1 95

3 98 3 94

4 97 4 95

5 95 5 94

6 95 6 93

7 94 7 89

8 92 8 86

9 90 9 80

11 89 11 80

12 88 12 81

10 85 10 78

13 85 13 76

14 78 14 75

15 78 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.

MATS will take the top 10 days (highlighted in yellow) and then calculate separate

averages for the Control and Baseline values:

Control average = 90.3 ppb

Baseline average = 96.5 ppb.

The RRF equals the ratio of the Control to the Baseline:

RRF = 90.3 / 96.5 = 0.936

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.

9.4.1.2 RRF Calculation - Example 2

In this example, MATS uses a threshold value instead of the top X number of days.

Assume the following values:
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Assume that there are 15 days of data:

Baseline day Baseline

value

Future day Future 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
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MATS will sort the data from high to low based on the Baseline values:

Baseline day Baseline

value

Future day Future 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

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 day Baseline

value

Future day Future 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
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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:

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.

9.4.1.3 RRF Calculation - Example 3

In this example, MATS uses a threshold value instead of the top X number of days. 

Assume the following values:
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Assume that there are 15 days of data: 

Baseline day Baseline

value

Future day Future 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

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
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MATS will sort the data from high to low based on the Baseline values:

Baseline day Baseline

value

Future day Future 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

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.
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9.4.1.4 RRF Calculation - Example 4

In this example, MATS uses a threshold value instead of the top X number of days. 

Assume the following values:

Assume that there are 15 days of data:

Baseline day Baseline

value

Future day Future 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
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13 60 13 57

14 59 14 56

15 57 15 55

MATS will sort the data sorted from high to low based on the Baseline values:

Baseline day Baseline

value

Future day Future value

5 78 5 77

2 74 2 73

3 70 3 69

4 68 4 66

1 67 1 65

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

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.

9.4.1.5 RRF Calculation - Example 5

In this example, MATS uses a threshold value instead of the top X number of days.

Assume the following values:
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Assume that there are 15 days of data:

Baseline day Baseline

value

Future day Future 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 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 from high to low based on the Baseline values:

Baseline day Baseline

value

Future day Future 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 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.

9.4.1.6 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
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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. 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.

Assume that there are 15 days of data for a grid cell:

Baseline day Baseline

value

Future day Future value

1 67 1 65

2 74 2 73
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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

15 57 15 55

MATS will sort the data from high to low based on the Baseline values:

Baseline day Baseline

value

Future day Future 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 
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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. Baseline day Baseline

value

Future day Future 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

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.

9.4.2 Spatial Gradient Setup

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
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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.

9.4.2.1 Spatial Gradient Calculation - Example 1

Assume a Start Value of "1" and an End Value of "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
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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

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 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
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14 78 15 57

15 78 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.

9.4.2.2 Spatial Gradient Calculation - Example 2

Assume a Start Value of "2" and an End Value of "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
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"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

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 second and third highest days (highlighted in yellow) and then
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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.

9.4.2.3 Spatial Gradient Calculation - Example 3

Assume a Start Value of "4" and an End Value of "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.
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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

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.
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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.

9.5 Final Check

The Final Check window verifies the selections that you have made. 

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:
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After making the necessary correction, click the button Press here to verify your

selections. .

When your window looks like the window above, click either Save Scenario & Run or

Save Scenario. Save Scenario & Run will cause MATS to immediately run the scenario.

9.5.1 Running MATS in Batch Mode

The Save Scenario button will save the scenario as a configuration file (.cfg file). The

"*.cfg" file will be saved in the .\MATS\output directory. Several .cfg files can be created

with the MATS interface and run later in batch mode. To do this, edit the default batch file

located in the .\MATS directory. The file "batchmats.bat" should be edited with a text

editor to point to the name and location of the .cfg files that will be run in batch mode.

After editing the batchmats.bat file, simply run the .bat file. MATS will start and run in

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1010 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. Output Choice. 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. Additional details are provided in the section 

Visibility Analysis: Details. 10.1 Step 1. Start MATS

Double-click on the MATS icon on your desktop, and the following window will appear:
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Click the Visibility Analysis button on the main MATS window. This will bring up the

Configuration Management window.

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.

10.2 Step 2. Output Choice

The Choose Desired Output window allows you to choose the output that you would like
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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 (RRF), 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.) 

Check the box next to Automatically extract all selected output files. MATS will create a

separate folder called "Tutorial Visibility" in the MATS "Output" folder, and then export

.CSV files with the results of your analysis. Alternatively, you can export the results from

the Output Navigator, but checking this box is a little easier.
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When your window looks like the window above, click Next. This will bring up the Data

Input window.

10.3 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 (RRF)

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 2010.

It also comes loaded with an example model output dataset for visibility for 2002 and

2020. 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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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 3x3 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.

10.4 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: 2005 through 2009. (That is, Start Monitor Year set to 2005 and

End Monitor Year set to 2009.) · 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 2007. 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.

10.5 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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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 either Save Scenario & Run or

Save Scenario. Save Scenario & Run will cause MATS to immediately run the scenario. 

A temporary, new Running tab will appear (in addition to the Start, Map View and

Output Navigator tabs). 
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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.

After clicking OK, the Output Navigator tab will be active. (The Running tab will no

longer be seen.). MATS will automatically load the output files associated with the .asr

configuration that just finished running. 
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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. 10.6 Step 6. Load and Map Results

After generating your results, Output Navigator can be used to load and/or map them. If a

run just finished, the output files will already be loaded into output navigator.

If files from a previous run need to be loaded then click on the Load button and choose

the Tutorial Visibility.asr file.
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Click on the Load button and choose the Tutorial Visibility.asr file.
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Under Configuration/Log Files, you will see two files: · Configuration: keeps track of the assumptions that you have made in your analysis. · Log 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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Right-click on the file Tutorial Visibility - Forecasted Visibility Data. This gives you three

options: Add to Map, View, and Extract. Choose the Add to Map option. 
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This will bring up the Map View.
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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. 
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The map will then zoom to the Continental US.

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:
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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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This will bring up Shape Class Breaks window. In the Value drop-down list, choose the

variable "dv_best" -- 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 2002.)

Click Apply and then click Close. This will bring you back to the Map View window.

Examine the other variables: 
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dv_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_b_no3, rrf_b_oc, rrf_b_ec, rrf_b_cm, and rrf_b_so4: relative

response factor used to forecast the best visibility days;

rrf_w_crustal, rrf_w_no3, rrf_w_oc, rrf_w_ec, rrf_w_cm, and rrf_w_so4: 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. 10.7 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

using the Output Navigator. Right-click on Configuration and choose View.
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This will take you to the Tutorial Visibility configuration that you used to generate your

visibility results. 

A second way to access your Tutorial Visibility configuration is to go back to the Start

window.
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Click on the Visibility Analysis button. This will bring up the Configuration

Management window. 

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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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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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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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 1.asr. You can then view and map your results in the same way

as with other results files.
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1111 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. 

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.

11.1 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,

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
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the monitor or model data at the center of the Class I Area.*

By checking the box next to Automatically extract all selected output files, MATS will

create a separate folder with your chosen Scenario Name in the MATS "Output" folder,

and then export .CSV files with the results of your analysis. Alternatively, you can export

the results from the Output Navigator, but checking this box is a little easier.

* 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.

11.1.1 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

Scenario Name. MATS will use this folder as a default location for files generated with
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this Scenario Name.

(3) Output file names. The output files generated will begin with the Scenario Name.
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11.1.2 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.
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· 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

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 speciesspecific “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
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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. · 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.

11.1.2.1 Old IMPROVE Equation

The Old IMPROVE equation is as follows:

bext = 3*f(RH)*AMM_SO4 

+ 3*f(RH)*AMM_NO3 

+ 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_SO4 = ammonium sulfate (ug/m3)

AMM_NO3 = 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.
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Example Calculation Old IMPROVE Equation

The first column "bext" presents the calculated value given the following data.

11.1.2.2 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)*[SMALL_AMM_SO4] + 4.8*fl(RH)*[LARGE_AMM_SO4] 

+ 2.4*fs(RH)*[SMALL_AMM_NO3] + 5.1*fl(RH)*[LARGE_AMM_NO3]

+ 2.8*[SMALL_OMC] + 6.1*[LARGE_OMC]

+ 10*EC

+ CRUSTAL

+ 0.6*CM

+ SS_RAYLEIGH

+ 1.7*fss(RH)*SEA_SALT

+ 0.33*NO2.

where:

bext = total extinction (measured in inverse megameters)

f

s(RH) = term to account for enhancement of light scattering due to hydroscopic growth of

small ammonium nitrate and ammonium sulfate (unitless)

f

l(RH) = term to account for enhancement of light scattering due to hydroscopic growth of

large ammonium nitrate and ammonium sulfate (unitless)

SMALL_AMM_SO4 = small ammonium sulfate (ug/m3)
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LARGE_AMM_SO4 = large ammonium sulfate (ug/m3)

SMALL_AMM_NO3 = small ammonium nitrate (ug/m3)

LARGE_AMM_NO3 = large ammonium nitrate (ug/m3)

SMALL_OMC = small organic carbon mass (ug/m3) (OC*1.8)

LARGE_OMC = 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)

SS_RAYLEIGH = 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)

NO2 = 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] 3 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.
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11.1.2.3 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/t1/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.

11.1.3 Visibility Output Variable Description

MATS generates five output files:
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· 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"). · 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.

11.1.3.1 Forecasted Visibility Data.csv

The table below describes the variables in the output file. 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.

Variable Description

_id Site ID

_type Leave blank

date Meteorological modeling year (used to identify the 20% best and worst

days from the ambient data)
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dv_best Forecasted (future year) best visibility [up to five year average] (in

deciviews)

dv_worst Forecasted (future year) worst visibility [up to five year average]

base_best Base-year best visibility [up to five year average]

base_worst Base-year worst visibility [up to five year average]

rrf_b_crustal Relative response factor (RRF) for crustal matter on the best visibility

days

rrf_b_no3 RRF for nitrate on the best visibility days

rrf_b_oc RRF for organic mass on the best visibility days

rrf_b_ec RRF for elemental carbon on the best visibility days

rrf_b_cm RRF for coarse matter (PM10 minus PM2.5) on the best visibility days

rrf_b_so4 RRF for sulfate on the best visibility days

rrf_w_crustal RRF for crustal matter on the worst visibility days

rrf_w_no3 RRF for nitrate on the worst visibility days

rrf_w_oc RRF for organic mass on the worst visibility days

rrf_w_ec RRF for elemental carbon on the worst visibility days

rrf_w_cm RRF for coarse matter (PM10 minus PM2.5) on the worst visibility days

rrf_w_so4 RRF for sulfate on the worst visibility days

monitor_gridcell Identifier for grid cell closest to monitor. (This variable only appears if you

specified the Use model grid cell at monitor option.)

class_i_gridcell 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.)

gridcell_lat 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.

gridcell_long 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.

monitor_lat Monitor latitude. (This variable only appears if you specified the Use

model grid cell at monitor option.)

class_i_lat Class 1 area centroid latitude. (This variable only appears if you specified

the Use model grid cell at Class 1 area centroid option.)

monitor_long Monitor longitude. (This variable only appears if you specified the Use

model grid cell at monitor option.)

class_i_long Class 1 area centroid longitude. (This variable only appears if you

specified the Use model grid cell at Class 1 area centroid option.)

11.1.3.2 Forecasted Visibility - all design values.csv

The table below describes the variables in the output file. 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.

Variable Description

_id Site ID

_type Leave blank
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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

ll

Identifier for grid cell closest to monitor. (This variable only appears if you specified

the Use model grid cell at monitor option.)

class_i_gridcel

l

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.)

gridcell_lat 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.

gridcell_long 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.

monitor_lat Monitor latitude. (This variable only appears if you specified the Use model grid

cell at monitor option.)

class_i_lat Class 1 area centroid latitude. (This variable only appears if you specified the Use

model grid cell at Class 1 area centroid option.)

monitor_long Monitor longitude. (This variable only appears if you specified the Use model grid

cell at monitor option.)

class_i_long Class 1 area centroid longitude. (This variable only appears if you specified the Use

model grid cell at Class 1 area centroid option.)

11.1.3.3 Class 1 Area and IMPROVE Monitor Identifiers and Locations.csv

The table below describes the variables in the output file. 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.

Variable Description

_id Class I area site ID

_type 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_gridcell 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)

monitor_lat IMPROVE Monitor latitude

monitor_long IMPROVE Monitor longitude

monitor_gridcell Identifier of grid cell closest to the monitor
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11.1.3.4 Used Model Grid Cells - Base/Future Data.csv

The table below describes the variables in the output file. 

Variable Description

_id 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

gridcell_lat Latitude at the grid cell centroid in decimal degrees. Values in the northern

hemisphere are positive, and those in the southern hemisphere are negative.

gridcell_long 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

11.2 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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11.2.1 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_SO4, AMM_NO3,

OMC, EC, CRUSTAL, and CM. The variable GOOD_YEAR indicates whether a

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. 
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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/

11.2.1.1 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. The variables in bold are required variables on the

input file. The additional variables are provided for QA purposes and are optional.

Visibility Monitor Data Format (Old Algorithm)

Visibility Monitor Data Variables and Descriptions (Old Algorithm) [Variables in

bold are required]

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.

 DATE Date of daily average ambient data with YYYYMMDD format (This is a
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numeric variable)

 FRH Monthly climatological relative humidity adjustment factor

 PM25 Measured PM2.5 mass (ug/m3)

 CRUSTAL Crustal mass ( 2.2 × [Al] + 2.49 × [Si] + 1.63 × [Ca] + 2.42 × [Fe] + 1.94 × [Ti])

 AMM_NO3 Ammonium nitrate mass (NO3*1.29)

 OMC Organic carbon mass (OC*1.4)

 EC Elemental carbon

 PM10 PM10 mass

 CM Coarse mass (PM10 minus PM2.5)

 AMM_SO4 Ammonium sulfate (S*4.125)

 E_AMM_SO4 Ammonimum sulfate extinction (Mm-1)

 E_AMM_NO3 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

COMPLETE_QUAR

TER

Quarter completeness (1= complete, 0= incomplete)

 SF Sulfur concentration (used to calculate ammonium sulfate)

 SO4F 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.)

11.2.1.2 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. The variables in bold are required variables

on the input file. The additional variables are provided for QA purposes and are optional. 

Note that sea salt mass is optional, but is used in the visibility calculations if provided.

Visibility Monitor Data Format (New Algorithm)
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Visibility Monitor Data Variables and Descriptions (New Algorithm) [Variables in

bold are required]

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.

DATE Date of daily average ambient data with YYYYMMDD format (This is a

numeric variable)

FRH Monthly climatological relative humidity adjustment factor

FSRH Monthly climatological relative humidity adjustment factor - small sulfate and

nitrate particles

FLRH Monthly climatological relative humidity adjustment factor - large sulfate and

nitrate particles

FSSRH Monthly climatological relative humidity adjustment factor - sea salt

SS_RAYLEIGH Site-specific Rayleigh scattering (Mm-1)

PM25 Measured PM2.5 mass (ug/m3)

SEA_SALT Sea salt mass

CRUSTAL Crustal mass ( 2.2 × [Al] + 2.49 × [Si] + 1.63 × [Ca] + 2.42 × [Fe] + 1.94 × [Ti])

AMM_NO3 Ammonium nitrate mass (NO3*1.29)

OMC Organic carbon mass (OC*1.8)

EC Elemental carbon

PM10 PM10 mass

CM Coarse mass (PM10 minus PM2.5)

AMM_SO4 Ammonium sulfate (S*4.125)

LARGE_OMC Large organic mass

SMALL_OMC Small organic mass

LARGE_AMM_SO4 Large ammonium sulfate

SMALL_AMM_SO4 Small ammonium sulfate

LARGE_AMM_NO3 Large ammonium nitrate
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SMALL_AMM_NO3 Small ammonium nitrate

E_AMM_SO4 Ammonimum sulfate extinction (Mm-1)

E_AMM_NO3 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

E_SEA_SALT Sea salt extinction

TBEXT Total bext (includes site specific 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_NDAYS Possible samples in quarter

NDAYS Actual complete samples per quarter

COMPLETE_QUAR

TER

Quarter completeness (1= complete, 0= incomplete)

SF Sulfur concentration (used to calculate ammonium sulfate)

SO4F 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.)

11.2.1.3 Linkage between Monitors & Class I Areas

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
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Variables and Descriptions for File Linking IMPROVE Monitors and Class I Areas

Var Name Description (variable type)

_MONITOR_ID IMPROVE monitor identification code (text)

MonLAT IMPROVE monitor latitude (numeric)

MonLONG IMPROVE monitor longitude (numeric)

_CLASS_I_NAME Name of Class I Area (text)

_ID Class I Area identification code (text)

_STATE_ID State in which Class I Area is located (text)

LAT Class I Area centroid latitude (numeric)

LONG 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.)

11.2.2 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
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Visibility Model Data Variable Descriptions

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

 SO4 Sulfate PM

 EC Elemental carbon

 NO3 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
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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)

Soil CRUSTAL

Amm_NO3 AMM_NO3

OMC OMC

LAC EC

CM CM

Amm_SO4 AMM_SO4

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.

11.2.2.1 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:

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
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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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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 Days Cell 1 Cell 2 Cell 3 Cell 4 Cell 5 Cell 6 Cell 7 Cell 8 Cell 9 Mean

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.

Best Days Cell 1 Cell 2 Cell 3 Cell 4 Cell 5 Cell 6 Cell 7 Cell 8 Cell 9 Max

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
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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).

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 the 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 Cell 1 Cell 2 Cell 3 Cell 4 Cell 5 Cell 6 Cell 7 Cell 8 Cell 9

1 1.795 1.812 1.299 1.609 1.612 1.250 1.692 0.570 1.347

2 0.164 1.556 1.205 0.270 1.940 1.065 1.156 1.620 1.786

3 1.709 0.273 1.304 1.213 1.177 1.104 0.368 1.817 1.377

4 1.119 1.322 1.778 1.154 1.503 1.511 1.251 1.939 0.474

5 1.910 1.648 1.012 1.635 1.912 1.587 1.508 1.723 1.611

6 1.490 1.204 1.997 0.989 1.832 0.064 1.469 1.634 1.470

7 1.136 1.886 1.131 1.282 1.957 1.047 1.335 0.045 1.279

8 1.304 1.217 1.738 1.243 1.370 1.802 1.374 1.736 1.196

MATS would choose the cells highlighted in orange:

Best Days Cell 1 Cell 2 Cell 3 Cell 4 Cell 5 Cell 6 Cell 7 Cell 8 Cell 9

1 1.795 1.812 1.299 1.609 1.612 1.250 1.692 0.570 1.347

2 0.164 1.556 1.205 0.270 1.940 1.065 1.156 1.620 1.786

3 1.709 0.273 1.304 1.213 1.177 1.104 0.368 1.817 1.377

4 1.119 1.322 1.778 1.154 1.503 1.511 1.251 1.939 0.474

5 1.910 1.648 1.012 1.635 1.912 1.587 1.508 1.723 1.611

6 1.490 1.204 1.997 0.989 1.832 0.064 1.469 1.634 1.470

7 1.136 1.886 1.131 1.282 1.957 1.047 1.335 0.045 1.279

8 1.304 1.217 1.738 1.243 1.370 1.802 1.374 1.736 1.196
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Assume there are eight best visibility days with the following modeled sulfate values in

the forecast:

Best Days Cell 1 Cell 2 Cell 3 Cell 4 Cell 5 Cell 6 Cell 7 Cell 8 Cell 9

1 1.789 1.715 1.209 1.560 1.562 1.224 1.492 0.489 1.148

2 0.137 1.512 1.162 0.181 1.939 1.022 1.113 1.541 1.593

3 1.695 0.208 1.254 1.198 1.133 1.102 0.200 1.744 1.235

4 1.090 1.251 1.627 1.126 1.470 1.468 1.120 1.877 0.325

5 1.815 1.552 0.974 1.549 1.894 1.546 1.407 1.707 1.591

6 1.327 1.167 1.880 0.957 1.756 0.000 1.318 1.590 1.392

7 0.989 1.805 1.028 1.212 1.820 1.010 1.183 0.042 1.236

8 1.127 1.183 1.673 1.238 1.291 1.753 1.220 1.717 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 Cell 3 Cell 4 Cell 5 Cell 6 Cell 7 Cell 8 Cell 9

1 1.789 1.715 1.209 1.560 1.562 1.224 1.492 0.489 1.148

2 0.137 1.512 1.162 0.181 1.939 1.022 1.113 1.541 1.593

3 1.695 0.208 1.254 1.198 1.133 1.102 0.200 1.244 1.235

4 1.090 1.251 1.627 1.126 1.470 1.468 1.120 1.877 0.325

5 1.815 1.552 0.974 1.549 1.594 1.546 1.407 1.707 1.591

6 1.327 1.167 1.880 0.957 1.756 0.000 1.318 1.590 1.392

7 0.989 1.805 1.028 1.212 1.820 1.010 1.183 0.042 1.236

8 1.127 1.183 1.673 1.238 1.291 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 Cell 2 Cell 3 Cell 4 Cell 5 Cell 6 Cell 7 Cell 8 Cell 9

1 0.071 0.133 0.620 0.788 0.808 0.964 0.763 0.938 0.158

2 0.561 0.891 0.105 0.689 0.695 0.302 0.523 0.209 0.485

3 0.005 0.384 0.604 0.077 0.545 0.046 0.177 0.664 0.821

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:
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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).

11.3 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). 
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11.3.1 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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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

_ID

MonLAT MonLONG_CLASS_I_NAME _ID _STATE_

ID

LAT LONG

ACAD1 44.377 -68.261 Acadia NP ACAD ME 44.35 -68.24

AGTI1 33.464 -116.971 Agua Tibia Wilderness AGTI CA 33.42 -116.99

BADL1 43.744 -101.941 Badlands NP BADL SD 43.81 -102.36

BALD1 34.058 -109.441 Mount Baldy Wilderness BALD AZ 33.95 -109.54

BAND1 35.780 -106.266 Bandelier NM BAND NM 35.79 -106.34

BIBE1 29.303 -103.178 Big Bend NP BIBE TX 29.33 -103.31
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BLIS1 38.976 -120.103 Desolation Wilderness DESO CA 38.9 -120.17

BLIS1 38.976 -120.103 Mokelumne Wilderness MOKE CA 38.57 -120.06

BOAP1 33.870 -106.852 Bosque del Apache BOAP NM 33.79 -106.85

BOWA1 47.947 -91.496 Boundary Waters Canoe Area BOWA MN 48.06 -91.43

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 Wilderness CABI MT 48.18 -115.68

CACR1 34.454 -94.143 Caney Creek Wilderness 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 Wilderness GALI 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 Wilderness DIPE OR 43.53 -122.1

CRLA1 42.896 -122.136 Gearhart Mountain Wilderness GEMO OR 42.51 -120.86

CRLA1 42.896 -122.136 Mountain Lakes Wilderness MOLA OR 42.33 -122.11

CRMO1 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). 

11.4 Final Check

The Final Check window verifies the selections that you have made. 
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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:

After making the necessary correction, click the button Press here to verify your

selections. 
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When your window looks like the window above, click either Save Scenario & Run or

Save Scenario. Save Scenario & Run will cause MATS to immediately run the scenario. 

11.4.1 Running MATS in Batch Mode

The Save Scenario button will save the scenario as a configuration file (.cfg file). The

"*.cfg" file will be saved in the .\MATS\output directory. Several .cfg files can be created

with the MATS interface and run later in batch mode. To do this, edit the default batch file

located in the .\MATS directory. The file "batchmats.bat" should be edited with a text

editor to point to the name and location of the .cfg files that will be run in batch mode.

After editing the batchmats.bat file, simply run the .bat file. MATS will start and run in

the background. 
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1212 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.

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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The files listed fall into two categories: Configuration/Log Files and Output Files. The

Configuration File stores the assumptions used in generating your results file. The Log

File stores information regarding the version of MATS used to create the results file and

the date and time of its creation.

To examine a file, right-click on the file that you want to view. For Output Files, this will

give you three choices, Add to Map, View, and Extract.
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For the Configuration File and Log File you will see two options: View and Extract.
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12.1 Add Output Files to Map

The Map View tab is initially empty, starting with just a blank map of the United States.

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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This will bring you back to the Map View tab.

Details on how to generate a variety of maps are in the Map View chapter.
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* Note that if you right-click on the Configuration File or Log File you 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. 

12.2 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.

12.2.1 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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This will bring up the options that you chose when generating your results.
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12.2.2 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.

A separate Run Log tab will appear.
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Click the Close button when you have finished viewing it. (The Run Log tab will

disappear.)

12.2.3 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.
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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. The lower

left panel allows you to view the other variables in the data.
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The default option is to show all of the data in the lower left panel. If, however, you want

to just view the data for a particular monitor -- in this example, monitor ID = "010331002"

-- use the scrollbar (if needed) and then highlight this monitor. MATS will then display

the values for this monitor in the bottom panel.
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To view all of the data again, click on the Show All button.

To eliminate missing values (denoted by negative numbers in the lower panel), check one
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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". MATS will automatically drop these values. (Note that the

monitor that we previously highlighted [monitor ID = "010331002] has now dropped out

of the display.)

If you want to save the data, you can click the Export button and save the file. (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.) You can then view the file in Excel.

12.3 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 (as opposed to the Configuration/Log 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.

The simplest way to extract files is to check the box "Automatically Extract All Selected

Output Files" on the initial window of the Configuration File (the same window where

you give the Scenario Name). Alternatively, you can use the Output Navigator.From

Output Navigator load the results (.ASR) file of interest. 
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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.

Click OK, and then MATS will export all of the files to this folder.
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An alternative is to extract individual files. Right click on the file of interest, and choose

the Extract option.

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 O3) 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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1313 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.

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.

13.1 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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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.

This will bring you back to the Map View tab.
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Usually the next step is to plot your data. This is discussed next. 13.1.1 Loading with Taskbar

You can load data for mapping directly from the MapView 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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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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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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13.2 Plotting a Value

To plot a value you must first load one or more variables into the MapView. Right click

on the text in the left panel and choose Plot Value.
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This will bring up the Shape Class Breaks window.

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 dv_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 described in the "Details"

sections for Annual PM, Daily PM, Ozone, and Visibility.)
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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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13.2.1 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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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.

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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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.

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

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 predefined Basic colors panel (in the upper half of the Color window), and then click OK.
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(You can also double-click on desired color.)

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. 

When satisfied, click OK. This will bring you back to the Shape Class Breaks window.
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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.

13.3 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 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.
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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.
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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.

13.4 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. This will bring up a map view with the layer removed.

To add a layer back, choose the layer you want to add from the Standard Layers dropdown menu.
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13.5 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 . (The next subsection discusses exporting the underlying data.)

This will up a window where you can name your image. Browse to whatever folder in

which you want to store your image.
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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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13.5.1 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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Note that this exports the same data that the Output Navigator would export. Choose

whichever approach is easier.
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1414 Frequently Asked Questions

This section answers questions that have arisen when running MATS.

14.1 Where is there a description of output variables?

Descriptions of the output variables are in the separate "Details" sections for Annual PM, Daily PM, Ozone, and Visibility. 14.2 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.

This will bring back the map without the undesired data.
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References

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1515 References

Chow, J.C., J.G. Watson, L.W.A. Chen, W.P. Arnott, and H. Hoosmuller (2004).

"Equivalence of Elemental Carbon by Thermal/Optical Reflectance and Transmittance

with Different Temperature Protocols", Environmental Science and Technology, 38(16):

4414-4422. 

Clegg, S.L., P. Brimblecombe, and A.S. Wexler (1998). "Aerosol Inorganics Model: A

Thermodynamic Model of the System H+-NH4+-SO42--NO3--H2O) at Tropospheric

Temperatures." J. Phys. Chem. 102A: 2137:2154.

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.

Turpin, B.J. and H-J Lim (2001). "Species Contributions to PM2.5 Mass Concentrations:

Revisiting Common Assumptions for Estimating Organic Mass", Aerosol Science and

Technology, 35: 602-610.

U.S. EPA (2003). "Federal Reference Method Quality Assurance Report."

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.