Appendix C 

Air Quality Data
Air Quality Data i 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix C 

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Appendix C.1 

Historical Air Quality Data
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Table of Contents 

Quarterly Average PM2.5 Values (μg/m3) for the Hickory and Greensboro-Winston Salem-High 

Point Nonattainment Areas ..............................................................................................................1 

Annual Average PM2.5 Values (μg/m3

) for the Hickory and Greensboro-Winston Salem-High 

Point Nonattainment Areas ..............................................................................................................2 

3-year Current PM2.5 Design Values (μg/m3

) for the Hickory and Greensboro-Winston SalemHigh Point Nonattainment Areas .....................................................................................................3 

Summary table of PM2.5 Values used in for Designations in the Hickory and GreensboroWinston Salem-High Point Nonattainment Areas ...........................................................................4 

The 2004 Average Percent Composition of PM2.5 for North Carolina, as Determined by Data 

from Speciated Trends Network Monitors. .....................................................................................5 

The 2004 Average Percent Composition of PM2.5 for Hickory, North Carolina as Determined by 

Data from the Hickory Speciated Trends Network Monitor ...........................................................6 

The 2004 Average Percent Composition of PM2.5 for Lexington, North Carolina, as Determined 

by Data from the Lexington Speciated Trends Network Monitor ...................................................7 

The 2004 Average Percent Composition of PM2.5 for Greensboro, North Carolina, as Determined 

by Data from the Mendenhall Speciated Trends Network Monitor ................................................8 
Historical Air Quality Data iii 

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Table 1. Quarterly Average PM2.5 Values (μg/m3) for the Hickory and GreensboroWinston Salem-High Point Nonattainment Areas 

Monitoring Site County Quarter 2000 2001 2002 2003 2004 2005 2006 2007 2008

Hickory Catawba 

Q1 16.1 15.3 13.3 12.9 13.1 13.6 13.1 12.5 12.9

Q2 16.6 16.6 14.3 16.1 14.9 16.7 15.5 14.7 13.0

Q3 18.9 18.8 21.1 19.3 19.6 20.6 19.8 20.0 14.8

Q4 18.9 13.2 12.7 11.8 12.4 12.8 12.4 11.0 10.8

Lexington Davidson 

Q1 17.1 14.8 14.9 12.6 13.9 13.2 12.9 13.1 13.2

Q2 17.8 18.6 15.0 16.1 15.7 15.8 14.3 13.5 13.7

Q3 18.4 18.8 19.3 19.1 18.0 20.3 19.8 20.1 16.1

Q4 18.9 13.6 14.3 12.9 13.2 12.3 13.6 11.8 11.6

Mendenhall Guilford 

Q1 11.7 11.6 11.8 11.5 10.6 11.0 10.7

Q2 13.1 13.6 14.4 13.1 13.7 12.8 12.1

Q3 18.3 16.5 16.5 19.3 19.1 17.8 13.9

Q4 11.7 11.7 13.2 12.2 12.9 10.6 9.0 

Quarterly average PM2.5 values are presented in micrograms per meter cubed (μg/m3). 

Bolded values represent quarters whose average values exceed the level of the annual PM2.5 

National Ambient Air Quality Standard (i.e. greater than 15.0 μg/m3). 

The Mendenhall site was not in operation during 2000 or 2001. 

Italics value represents estimated 4th quarter 2006 data at Mendenhall. There was an extended 

loss of monitoring data at the Mendenhall site during the 4th quarter of 2006. The NCDAQ has 

performed an extensive data imputation study to estimate a 4th quarter average concentration 

such that an appropriate annual average concentration and design value could be calculated. 

This study, titled “Mendenhall PM2.5 Data Imputation for 4Q2006” can be found in Appendix 

C.3. 
Historical Air Quality Data 2 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix C.1 

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Table 2. Annual Average PM2.5 Values (μg/m3) for the Hickory and Greensboro-Winston 

Salem-High Point Nonattainment Areas 

Monitoring 

Site County 

Annual Averages 

2000 2001 2002 2003 2004 2005 2006 2007 2008

Hickory Catawba 17.6 16.0 15.4 15.0 15.0 15.9 15.2 14.5 12.8 

Lexington Davidson 18.0 16.5 15.9 15.2 15.2 15.4 15.1 14.6 13.7 

Mendenhall Guilford 13.7 13.3 14.0 14.0 14.1 13.0 11.4 

Annual average PM2.5 values are presented in micrograms per meter cubed (μg/m3). 

Bolded values represent annual average values that exceed the level of the annual PM2.5 

National Ambient Air Quality Standard (i.e. greater than 15.0 μg/m3

). 

The Mendenhall site was not in operation during 2000 or 2001. 

Italics value represents a 2006 annual average that used estimated 4th quarter 2006 data at 

Mendenhall. There was an extended loss of monitoring data at the Mendenhall site during the 

4th quarter of 2006. The NCDAQ has performed an extensive data imputation study to estimate 

a 4th quarter average concentration such that an appropriate annual average concentration and 

design value could be calculated. This study, titled “Mendenhall PM2.5 Data Imputation for 

4Q2006” can be found in Appendix C.3. 
Historical Air Quality Data 3 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix C.1 

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Table 3. 3-year Current PM2.5 Design Values (μg/m3) for the Hickory and GreensboroWinston Salem-High Point Nonattainment Areas 

Monitoring Site County 

Design Values 

2000-

2002 

2001-

2003 

2002-

2004 

2003-

2005 

2004-

2006 

2005-

2007 

2006-

2008 

Hickory Catawba 16.3 15.5 15.1 15.3 15.4 15.2 14.2 

Lexington Davidson 16.8 15.8 15.4 15.2 15.2 15.1 14.5 

Mendenhall Guilford 13.7 13.8 14.0 13.7 12.9 

PM2.5 design values are presented in micrograms per meter cubed (μg/m3). 

Bolded values represent design values that exceed the level of the annual PM2.5 National 

Ambient Air Quality Standard (i.e. greater than 15.0 μg/m3

). 

The Mendenhall site was not in operation during 2000 or 2001. So, the first design value period 

that can be calculated is 2002-2004. 

Italics values represent design values that used estimated 4th quarter 2006 data at Mendenhall. 

There was an extended loss of monitoring data at the Mendenhall site during the 4th quarter of 

2006. The NCDAQ has performed an extensive data imputation study to estimate a 4th quarter 

average concentration such that an appropriate annual average concentration and design value 

could be calculated. This study, titled “Mendenhall PM2.5 Data Imputation for 4Q2006” can be 

found in Appendix C.3. 
Historical Air Quality Data 4 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix C.1 

North Carolina Attainment Demonstration August 21, 2009 

Table 4. Summary table of PM2.5 Values used in for Designations in the Hickory and 

Greensboro-Winston Salem-High Point Nonattainment Areas 

County

FRM 

Monitoring

Site

Year

1st 

Quarter

(Q1)

2nd 

Quarter

(Q2)

3rd 

Quarter

(Q3)

4th 

Quarter

(Q4)

Annual

Average

Design 

Value

2001 15.3 16.6 18.8 13.2 16.0

2002 13.3 14.3 21.1 12.7 15.4

2003 12.9 16.1 19.3 11.8 15.0

2001 14.8 18.6 18.8 13.6 16.5

2002 14.9 15.0 19.3 14.3 15.9

2003 12.6 16.1 19.1 12.9 15.2

2001 12.0 16.7 18.0 12.9 14.9

2002 11.7 13.1 18.3 11.7 13.7

2003 11.6 13.6 16.5 11.7 13.3

Guilford Mendenhall 14.0

Catawba Hickory 15.5

Davidson Lexington 15.8

PM2.5 design values are presented in micrograms per meter cubed (μg/m3). 

Bolded values represent design values that exceed the level of the annual PM2.5 National 

Ambient Air Quality Standard (i.e. greater than 15.0 μg/m3). 

Underline values represent values that incorporate data from the Edgeworth & Bellmeade (37-

081-0009) monitoring site. The Mendenhall site replaced the Edgeworth & Bellmeade site in 4th

quarter 2001. 
Historical Air Quality Data 5 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix C.1 

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North Carolina

PM2.5 Speciatiated Trends Network

2004 Average Precentage Composition

7%

29%

11%

29%

4%

3%

17%

Nitrate

Sulfate

Ammonium

Oraganic Carbon

Elemental Carbon

Crustal Component

Other

Figure 1. The 2004 Average Percent Composition of PM2.5 for North Carolina, as 

Determined by Data from Speciated Trends Network Monitors. 
Historical Air Quality Data 6 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix C.1 

North Carolina Attainment Demonstration August 21, 2009 

Hickory PM2.5 Speciatiated Trends Network Monitor

2004 Average Precentage Composition

7%

30%

11%

30%

5%

2%

15%

Nitrate

Sulfate

Ammonium

Organic carbon

Elemental carbon

Crustal component

Other

Figure 2. The 2004 Average Percent Composition of PM2.5 for Hickory, North Carolina as 

Determined by Data from the Hickory Speciated Trends Network Monitor 
Historical Air Quality Data 7 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix C.1 

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Lexington PM2.5 Speciatiated Trends Network Monitor

2004 Average Precentage Composition

6%

30%

28% 10%

3%

2%

21%

Nitrate

Sulfate

Ammonium

Organic carbon

Elemental carbon

Crustal component

Other

Figure 3. The 2004 Average Percent Composition of PM2.5 for Lexington, North Carolina 

as Determined by Data from the Lexington Speciated Trends Network Monitor 
Historical Air Quality Data 8 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix C.1 

North Carolina Attainment Demonstration August 21, 2009 

Mendenhall PM2.5 Speciatiated Trends Network Monitor

2004 Average Precentage Composition

6%

31%

26% 10%

3%

3%

21%

Nitrate

Sulfate

Ammonium

Organic carbon

Elemental carbon

Crustal component

Other

Figure 4. The 2004 Average Percent Composition of PM2.5 for Greensboro, North Carolina 

as Determined by Data from the Mendenhall Speciated Trends Network Monitor 
Appendix C.2 

Air Quality Data Used For Modeling 
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1. Air Quality Data for 2002 

As part of the Visibility Improvement State and Tribal Association of the Southeast (VISTAS) 

Phase I modeling study, the VISTAS emissions and air quality modeling team of ENVIRON 

International Corporation, Alpine Geophysics (AG) and the University of California at Riverside 

(UCR) compiled ambient monitoring data for both gas and particulate species to be used in the 

VISTAS regional haze model performance evaluation. UCR took the lead in this activity. The 

contractor’s report (found in Attachment C.1) describes the sources of the ambient data and the 

steps taken in the processing and quality assurance (QA) of the data. In addition, the ambient 

data is being processed and formatted in preparation for use in software packages designed for 

the model performance evaluation. 

The goal was to preprocess the ambient data and present plots of ambient data on the project 

website so model performance evaluations when the model simulations were completed. The 

ambient data are available from the following 9 monitoring networks or databases: 

• EPA’s AQS (Environmental Protection Agency’s Air Quality System) database 

• PAMS (Photochemical Assessment Monitoring Stations) 

• IMPROVE (The Interagency Monitoring of Protected Visual Environments) 

• SEARCH (Southeastern Aerosol Research and Characterization) 

• EPA’s STN (Speciation Trends Network) 

• NADP (National Atmospheric Deposition Program) 

• CASTNET (Clean Air Status and Trend Network) 

• PM Supersites 

• NARSTO SOS99 Aircraft data (for the July 1999 episode only) 

• TVA measurement network 

• Georgia Institute of Technology Assessment of Spatial Aerosol Composition in 

Atlanta (ASACA) monitors. 

Note that there is some overlap in the above monitoring networks. For example, some data from 

PAMS (such as ozone and nitrogen oxides) are included in the AQS database, and it appears that 

the STN network may also include speciated particulate matter data from IMPROVE and other 

monitoring networks. Attachment C.1 contains maps of many of the existing and planned (as of 

January 2002) urban and rural fine particulate matter speciation networks. The available data 

from the above monitoring networks for the three episodes of VISTAS regional haze modeling 

(July 9-22, 1999, July 7-28, 2001 and January 1- 21, 2002) was obtained. The attempt was made 

to reconcile data from the various networks and to perform a high level QA of the ambient data. 

The monitoring networks, the available data, and QA efforts are described in their entirety in the 

previously mentioned VISTAS report. 

Additional data used in the air quality modeling include the Total Ozone Mapping Spectrometer 

(TOMS). TOMS data is available for 24-hour average and is obtained from 

http://toms.gsfc.nasa.gov/eptoms/ep.html. The TOMS data is used in the CMAQ radiation 

model (JPROC) to calculate photolysis rates.
Air Quality Data Used For Modeling 2 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix C.2 

North Carolina Attainment Demonstration August 21, 2009 

1.1 Quality Assurance – Overview 

The VISTAS Phase II emissions and air quality modeling team received emissions, 

meteorological and air quality data from other VISTAS contractors or other sources. As a first 

line of QA, a Gatekeeper function was defined to assure the data have been received correctly, 

evaluate the quality of the data, and document the data received. Separate air quality, 

meteorological and emissions Gatekeepers have been identified whose roles are defined below. 

In addition, a Data Management Gatekeeper has been defined who will post data, reports and 

results to the project website and archive all key data generated in the project. 

• Air Quality Data Gatekeeper. Obtain air quality data as appropriate for model input 

development and model performance evaluation and assure quality of all air quality 

data obtained, consistent with approved QA plan. This gatekeeper will also provide 

documentation of evaluation and generate IC/BC inputs for CMAQ for all modeling 

runs. 

• Meteorological Gatekeeper. Obtain meteorological data, as MM5 or MCIP files, as 

appropriate for annual 2002 modeling runs and other episode periods and perform 

data quality checks as approved in QA plan together with appropriate documentation 

of model performance evaluation activities.

1.2 Quality Assurance of Air Quality Data 

In gathering data from the monitoring networks it is assumed that the agency or researcher 

responsible for collecting the data performed quality assurance on the data. However, it is 

possible that the data sets may contain erroneous data (i.e., missing data, zeros during 

calibration, unrealistic values, etc). Due to poor documentation or poor formatting of some data 

sets it is also possible that mistakes may be made in our processing of the data. To guard against 

this possibility, a plan to perform a high level QA by visually inspecting time-series plots and 

scatter plots of the ambient data was developed and is outlined as follows: 

Plots of Time Series:

Time series plots are generated for PAMS species. The plots should be inspected for the 

following: 

• Large "jumps" or "dips" in the concentrations 

• Periodicity of peaks, calibration carryover 

• Unexpected diurnal behavior (i.e., isoprene) 

• Unexpected relationships among species 

• High single-hour concentrations of less abundant species 
Air Quality Data Used For Modeling 3 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix C.2 

North Carolina Attainment Demonstration August 21, 2009 

Scatter Plots:

Scatter plots may be prepared for the following: 

• Total NMOC vs. species group totals, vs. individual species 

• Benzene vs. Toluene, Acetylene, Ethane 

• Scatter plots comparing data for a single species measured with different sampling 

methods 

• Plots of reconstructed mass versus measured mass (to reveals if anything unusual is 

happening with the chemical measurements) 

• Plots of molal particulate ammonium versus the molal sum of sulfate and particulate 

nitrate (as a sanity check on the ion balance in the PM chemical measurements) 

If data is identified that appears flawed it will be flagged and either corrected if the error is in the 

processing step, or removed from the data set.

Further detail on the quality assurance of air quality data modeling data can be found in the 

VISTAS document in Attachment C.1. Additional information on quality assurance procedures 

can be found in the QAPP, as referenced in Appendix G of the SIP. 
Air Quality Data Used For Modeling 4 

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Attachment C.2-1 

Review and Assessment of Available Ambient 

Air Quality Data to Support Modeling and 

Modeling Performance Evaluation for the 

Three VISTAS Phase I Episodes 
Air Quality Data Used For Modeling i 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Attachment C.2-1 

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July 2003 

Draft VISTAS Emissions and 

Air Quality Modeling— 

Phase I Task 3 Report: 

Review and Assessment of Available Ambient 

Air Quality Data to Support Modeling and 

Modeling Performance Evaluation for the 

Three VISTAS Phase I Episodes 

Prepared by: 

ENVIRON International Corporation 

101 Rowland Way 

Novato, California 94945 

Alpine Geophysics LLC 

3479 Reeves Drive 

Ft. Wright, Kentucky 41017 

UC Riverside 

CE-CERT 

1084 Columbia Avenue 

Riverside, California 92507 

UC Davis 

Mechanical and Aeronautical Engineering 

Davis, California 95616 

Revised July 22, 2003 

 101 Rowland Way, Suite 220, Novato, CA 94945 415.899.0700 

Air Quality Data Used For Modeling 

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Attachment C.2-1 

 August 21, 2009
July 2003 

i 

TABLE OF CONTENTS 

Page 

1.0. SUMMARY.......................................................................................1-1 

2.0. EPA’S AIR QUALITY SYSTEM (AQS)...................................................2-1 

3.0. PHOTOCHEMICAL ASSESSMENT MONITORING 

 STATIONS (PAMS) ............................................................................3-1 

4.0. INTERAGENCY MONITORING OF PROTECTED 

 VISUAL ENVIRONMENTS (IMPROVE) ................................................4-1 

5.0. SOUTHEASTERN AEROSOL RESEARCH AND 

 CHARACTERIZATION (SEARCH) .......................................................5-1 

6.0. SPECIATION TRENDS NETWORK (STN)..............................................6-1 

7.0. NATIONAL ATMOSPHERIC DEPOSITION PROGRAM (NADP)................7-1 

8.0. CLEAN AIR STATUS AND TREND NETWORK (CASTNet).......................8-1 

9.0. PM SUPERSITES...............................................................................9-1 

10.0. AIRCRAFT DATA............................................................................ 10-1 

11.0. DATA QUALITY ASSURANCE .......................................................... 11-1 

APPENDICES 

Appendix A: Locations of Speciated PM and IMPROVE Monitors 

Air Quality Data Used For Modeling 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 

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Attachment C.2-1 

 August 21, 2009
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ii 

TABLES 

Table 2-1. Currently available hourly concentration data 

 from AQS for three VISTAS episode selections....................................2-2 

Table 3-1. Species measured in PAMS network. ................................................3-2 

Table 4-1. Species of PM2.5 measured in IMPROVE network.................................4-2 

Table 4-2. Species mappings for IMPROVE species............................................4-4 

Table 4-3. Definitions of CMAQ species names used in Table 4-2...........................4-5 

Table 5-1. PM2.5 species measured in search network. .........................................5-1 

Table 5-2. Coarse PM species measured in search network. ..................................5-2 

Table 6-1. Species of PM2.5 measured in STN network.........................................6-1 

Table 8-1. Species mappings for CASTNET species............................................8-2 

Table 9-1. Locations of PM Supersites. ...........................................................9-1 

FIGURES 

Figure 2-1. Overlap among ambient data collected from 

 several monitoring networks...........................................................2-1 

Figure 2-2. Distribution of average ambient O3 

 concentrations (July 9-22, 1999). .....................................................2-4 

Figure 2-3. Distribution of average ambient PM2.5 

 Concentrations (July 9-22, 1999). ....................................................2-5 

Figure 2-4. Distribution of average ambient NO2 

 concentrations (July 9-22, 1999). .....................................................2-6 

Figure 2-5. Distribution of average ambient CO 

 concentrations (July 9-22, 1999). .....................................................2-7 

Figure 2-6. Distribution of average ambient SO2 

 concentrations (July 9-22, 1999). .....................................................2-8 

Figure 2-7. Distribution of average ambient O3 

 concentrations (July 7-28, 2001). .....................................................2-9 

Figure 2-8. Distribution of average ambient PM2.5 

 concentrations (July 7-28, 2001). ................................................... 2-10 

Figure 2-9. Distribution of average ambient O3 

 concentrations (January 1-21, 2002)................................................ 2-11 

Figure 2-10. Distribution of average ambient PM2.5 

 concentrations (January 1-21, 2002)................................................ 2-12 

Figure 3-1. Distribution of average ambient HCHO concentrations 

 (July 9-22, 1999) from the PAMS network .........................................3-4 

Air Quality Data Used For Modeling 

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Attachment C.2-1 

 August 21, 2009
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iii 

Figure 3-2. Distribution of average ambient acetone concentrations 

 (July 9-22, 1999) from the PAMS network .........................................3-5 

Figure 3-3. Distribution of average ambient HCHO concentrations 

 (January 1-21, 2002) from the PAMS network.....................................3-6 

Figure 3-4. Distribution of average ambient acetone concentrations 

 (January 1-21, 2002) from the PAMS network.....................................3-7 

Figure 5-1. Locations of the SEARCH monitoring sites (Source: Atmospheric 

 Research and Analysis, Inc., www.atmopheric-research.com). .................5-3 

Figure 7-1. Locations of NADP National Trends Network (NTN) monitors 

 (Figure obtained from the EPA NADP website). ..................................7-4 

Figure 7-2. Locations of the AIRMoN wet and dry deposition monitoring 

 sites (Figure obtained from the EPA AIRMoN website)..........................7-5 

Figure 7-3. Locations of the AIRMoN wet deposition monitoring sites 

 (Figure obtained from the EPA AIRMoN website). ...............................7-6 

Figure 8-1. Locations of CASTNet monitoring sites (Figure obtained 

 from EPA CASTNet website). ........................................................8-3 

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G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec1.gst.doc 1-1 

1.0 SUMMARY 

As part of the VISTAS Phase I modeling study, the VISTAS emissions and air quality 

modeling team of ENVIRON International Corporation, Alpine Geophysics (AG) and the 

University of California (UC) are compiling ambient monitoring data for both gas and 

particulate species to be used in the VISTAS regional haze model performance evaluation. 

The University of California at Riverside (UCR) is taking the lead in this activity. This report 

describes the sources of the ambient data and the steps that will be used in the processing and 

quality assurance (QA) of the data. In addition, the ambient data is being processed and 

formatted in preparation for use in software packages designed for the model performance 

evaluation. Our goal is to preprocess the ambient data and present plots of ambient data on the 

project website so that we can rapidly complete model performance evaluations when the 

model simulations are completed. As the processing of ambient data are completed, plots of 

the data are being made available at the project website: 

http://pah.cert.ucr.edu/vistas/ambient.shtml. A large amount of this data is currently available 

at the project website, and we are continuing to develop the website to facilitate the display of 

time series plots. 

The ambient data are available from the following 9 monitoring networks or databases: 

• EPA’s AQS (Air Quality System) database 

• PAMS (Photochemical Assessment Monitoring Stations) 

• IMPROVE (The Interagency Monitoring of Protected Visual Environments) 

• SEARCH (Southeastern Aerosol Research and Characterization) 

• EPA’s STN (Speciation Trends Network) 

• NADP (National Atmospheric Deposition Program) 

• CASTNET (Clean Air Status and Trend Network) 

• PM Supersites 

• NARSTO SOS99 Aircraft data (for the July 1999 episode only) 

• TVA measurement network 

• Georgia Institute of Technology Assessment of Spatial Aerosol Composition in Atlanta 

(ASACA) monitors. 

Note that there is some overlap in the above monitoring networks. For example, some data 

from PAMS (such as O3 and NOx) are included in the AQS database, and it appears that the 

STN network may also include speciated PM data from IMPROVE and other monitoring 

networks. Appendix A shows maps of many of the existing and planned (as of January, 2002) 

urban and rural PM2.5 speciation networks. 

We have obtained most of the available data from the above monitoring networks for the three 

episodes of VISTAS regional haze modeling (July 9-22, 1999, July 7-28, 2001 and January 1-

21, 2002). Although a comprehensive quality assurance and validation effort is beyond the 

scope of this effort, we are attempting to reconcile data from the various networks and to 

perform a high level QA of the ambient data. The monitoring networks, the available data, and 

QA efforts are described below. We expect that during the next two months we will continue 

Air Quality Data Used For Modeling 

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Attachment C.2-1 

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G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec1.gst.doc 1-2 

to gather and process ambient data, and will rely on expertise from other team members and 

VISTAS in identifying ambient data. We have not yet investigated the possibility of systematic 

bias among different analytical methods used in the various networks. However, this is an 

important subject to be addressed because of the possibility that networks employ different 

sampling methods. 

Measurement data from monitors operated by the Tennessee Valley Authority (TVA) and 

Georgia Technology’s ASACA monitors have not yet been acquired. ASACA has been 

operating three Particle Composition Monitors and three TEOMS in Atlanta since early 1999. 

When acquired, these data will be documented on the project website. 

Air Quality Data Used For Modeling 

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Attachment C.2-1 

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G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-1 

2.0 EPA’S AIR QUALITY SYSTEM (AQS) 

The Air Quality System (AQS) database is EPA's repository of “criteria air pollutant”

monitoring data since the 1970s. The criteria air pollutants are: 

• Carbon Monoxide (CO) 

• Nitrogen Dioxide (NO2) 

• Sulfur Dioxide (SO2) 

• Ozone (O3) 

• Particulate Matter (PM10 and PM2.5) 

• Lead (Pb) 

Ambient concentrations of these pollutants from more than 4000 monitoring stations are 

reported to AQS on weekly or monthly basis. While several other monitoring networks (e.g. 

PAMS, IMPROVE, CASTNet, and etc.) owned and operated by different agencies collect 

various air pollutants, only criteria air pollutants are reported to AQS (as shown in Figure 2-

1). 

Figure 2-1. Overlap among ambient data collected from several monitoring networks.

PM25, PM10 

O3, SO2 

PM25, PM10 

O3, NOx, CO, Pb, etc 

EPA PM Sites 

Other monitoring stations 

from state, local agencies 

VOCs O3, NOx 

Sp. PM25, 

Visibility

HNO3, NO3, 

SO4,

Air Quality Data Used For Modeling 

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Although direct access to full AQS raw data is currently not available, several archived data 

files can be downloaded from EPA’s website http://www.epa.gov/ttn/airs/airsaqs/. Table 2-1 

summarizes the data that are currently available and processed for hourly average 

concentration data from AQS for three VISTAS episodes. We have requested the remaining 

AQS data from EPA and will process them upon arrival. Note that the AQS includes O3 data 

from the State/Local/National Air Monitoring Stations (SLAMS). 

Table 2-1. Currently* available hourly concentration data from AQS for three VISTAS episode 

selections. 

Period O3 PM2.5 NO2 CO SO2

July 9-22, 1999 X X X X X 

July 7-28, 2001 X X 

Jan. 1-21, 2002 X X 

* Efforts are underway to acquire the NO2, CO and SO2 measurements for the July 2001 and January 2002 

episodes. 

These data are presented on the VISTAS project website, http://www.cert.ucr.edu/vistas

/ambient.shtml, under the title of “Ambient data for Model Evaluation”, as animated figures 

that show spatial variation of O3, PM2.5, NO2, CO and SO2 concentrations. These data will 

also be made available as time series plots for each monitor site. The spatial distributions for 

average species concentrations within each VISTAS episode are shown in Figures 2-2 through 

2-10. These plots are intended to be illustrative of the results on the project web page, and 

for more detailed evaluation please see the ambient data page: 

http://pah.cert.ucr.edu/vistas/ambient.shtml. 

Note that the web page also includes plots that zoom in on the southeastern US domain. 

July 9-22, 1999 

Hourly O3 concentrations were available at 1113 stations over USA for July 9-22, 1999. 

Figure 2-2 shows the distribution of average O3 concentrations for these stations. 

Hourly PM2.5 concentrations were available at 25 stations over USA for July 9-22, 1999. 

Figure 2-3 shows the distribution of average PM2.5 concentrations for these stations. 

Hourly NO2 concentrations were available at 170 stations over USA for July 9-22, 1999. 

Figure 2-4 shows the distribution of average NO2 concentrations for these stations. 

Hourly CO concentrations were available at 442 stations over USA for July 9-22, 1999. 

Figure 2-5 shows the distribution of average CO concentrations for these stations. 

Hourly SO2 concentrations were available at 563 stations over USA for July 9-22, 1999. 

Figure 2-6 shows the distribution of average SO2 concentrations for these stations. 

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July 7-28, 2001 

Hourly O3 concentrations were available at 1113 stations over USA for July 7-28, 2001. 

Figure 2-7 shows the distribution of average O3 concentrations for these stations. 

Hourly PM2.5 concentrations were available at 148 stations over USA for July 7-28, 2001. 

Figure 2-8 shows the distribution of average PM2.5 concentrations for these stations. 

January 1-21, 2002 

Hourly O3 concentrations were also available at 501 stations over USA for January 1-21, 

2002. Figure 2-9 shows the distribution of average O3 concentrations for these stations. 

Hourly PM2.5 concentrations were available at 180 stations over USA for January 1-21, 2002. 

Figure 2-10 shows the distribution of average PM2.5 concentrations for these stations. 

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Figure 2-2. Distribution of average ambient O3 concentrations (July 9-22, 1999).

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Figure 2-3. Distribution of average ambient PM2.5 concentrations (July 9-22, 1999). 

Air Quality Data Used For Modeling 

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Figure 2-4. Distribution of average ambient NO2 concentrations (July 9-22, 1999). 

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Figure 2-5. Distribution of average ambient CO concentrations (July 9-22, 1999).

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Figure 2-6. Distribution of average ambient SO2 concentrations (July 9-22, 1999).

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Figure 2-7. Distribution of average ambient O3 concentrations (July 7-28, 2001). 

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Figure 2-8. Distribution of average ambient PM2.5 concentrations (July 7-28, 2001). 

Air Quality Data Used For Modeling 

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Figure 2-9. Distribution of average ambient O3 concentrations (January 1-21, 2002).

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Figure 2-10. Distribution of average ambient PM2.5 concentrations (January 1-21, 2002). 

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3.0 PHOTOCHEMICAL ASSESSMENT MONITORING STATIONS (PAMS) 

In response to the 1990 Clean Air Act Amendments, EPA has required more extensive 

monitoring of ozone and its precursors in areas with persistently high ozone levels. 

Photochemical Assessment Monitoring Stations (PAMS) have been established by the States to 

collect and report detailed data for volatile organic compounds, nitrogen oxides, ozone and 

meteorological parameters. The EPA lists five objectives for the PAMS program: 

1. Provide a speciated ambient air database which is both representative and useful for 

ascertaining ambient profiles and distinguishing among various individual VOC. 

2. Provide local, current meteorological and ambient data to serve as initial and boundary 

condition information for photochemical grid models, a method that simulates 

meteorological and physical processes that affect air pollution emissions in the 

atmosphere. 

3. Provide a representative, speciated ambient air database which is characteristic of 

source emission impacts. 

4. Provide ambient data measurements which would allow later preparation of unadjusted 

and adjusted pollutant trends reports. 

5. Provide additional measurements of selected criteria pollutants. 

Because different types of ambient monitoring data are required to characterize regional 

background concentration, emissions sources, and peak pollutant levels, four different types of 

PAMS sites are used: 

• Type I: Upwind and background characterization 

• Type II: Maximum ozone precursor emissions impact 

• Type III: Maximum ozone concentration 

• Type IV: Extreme downwind monitoring 

Hourly average concentrations of O3, NO, NO2, NOx and about 60 species of VOC (volatile 

organic compounds) are measured at each PAMS station. Archived data files can be 

downloaded from the website: http://www.epa.gov/ttn/airs/airsaqs/archived%

20data/archivedaqsdata.htm. Table 3-1 shows the species names, carbon numbers and 

molecule weights for 60 species of VOC measured in PAMS. The PAMS data are currently 

available for VISTAS’ two modeling periods of July 9-22, 1999 and Jan. 1-21, 2002. Since 

some data, such as O3, NO, NO2 and NOx may also be included in the AQS database, we are 

still considering the best approach to treat these data (i.e., present them with the AQS data 

only, or with the PAMS data only, or both). 

For the data from selected PAMS sites, hourly average concentrations are available for 64 

species (Table 2-1) for July 9-22, 1999 and Jan. 1-21, 2002. The animated graphics that show 

five species (TNMOC, THC, HCHO, acetaldehyde and acetone) concentrations have been put 

on the VISTAS project website (http://pah.cert.ucr.edu/vistas/ambient.shtml, under the title of 

“Ambient data for Model Evaluation”. Example plots of average concentrations for two 

important species (HCHO and acetone) are shown in Figures 3-1 through 3-4. 

Air Quality Data Used For Modeling 

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Table 3-1. Species measured in PAMS network. 

Name Description 

Carbon 

number MWt 

NO NITRIC OXIDE 

NO2 NITROGEN DIOXIDE 

NOX OXIDES OF NITROGEN 

PAMHC SUM OF PAMS TARGET COMPOUNDS 

TNMOC TOTAL NMOC 

ETHAN ETHANE AKA-METHYLMETHANE 2 30.07 

ETHYL ETHYLENE AKA-ETHENE 2 62.07 

PROPA PROPANE AKA-DIMETHYLMETHANE 3 44.10 

PRPYL PROPYLENE AKA-PROPENE 4 102.09 

ACETE ACETYLENE AKA-ETHYNE 2 26.04 

NBUTA N-BUTANE AKA-BUTANE 4 58.12 

ISBTA ISOBUTANE AKA-2-METHYLPROPANE 4 58.12 

T2BTE TRANS-2-BUTENE 4 56.11 

C2BTE CIS-2-BUTENE 4 56.11 

NPNTA N-PENTANE AKA-AMYL HYDRIDE 5 72.15 

ISPNA ISOPENTANE AKA-2-METHYLBUTANE 5 72.15 

1PNTE 1-PENTENE AKA-PROPYLETHYLENE 5 70.14 

T2PNE TRANS-2-PENTENE 5 70.14 

C2PNE CIS-2-PENTENE AKA-CIS-B-N-AMYLENE 5 70.14 

3MPNA 

3-METHYLPENTANE AKADIETHYLMETHYLMETHANE 16 226.45 

NHEXA N-HEXANE 6 86.18 

NHEPT N-HEPTANE AKA-DIPROPYLMETHANE 7 100.21 

NOCT N-OCTANE 8 114.23 

NNON N-NONANE AKA-NONYL HYDRIDE 9 128.26 

NDEC N-DECANE 10 142.29 

CYPNA CYCLOPENTANE AKA-PENTAMETHYLENE 5 70.14 

ISPRE ISOPRENE AKA-3-METHYL-1,3-BUTADIENE 5 68.12 

22DMB 2,2-DIMETHYLBUTANE AKA-NEOHEXANE 6 86.18 

2M1PE 

2-METHYL-1-PENTENE AKA-1-METHYL-1-

PROPYLETHYLENE 6 84.16 

24DMP 2,4-DIMETHYLPENTANE 7 100.21 

CYHXA CYCLOHEXANE AKA-HEXAMETHYLENE 6 84.16 

3MHXA 3-METHYLHEXANE 7 100.21 

224TM 2,2,4-TRIMETHYLPENTANE AKA-ISOOCTANE 8 114.23 

234TM 2,3,4-TRIMETHYLPENTANE 8 114.23 

3MHEP 3-METHYLHEPTANE 8 114.23 

MCYHX 

METHYLCYCLOHEXANE AKAHEXAHYDROTOLUENE 7 98.19 

MCPNA METHYLCYCLOPENTANE 6 84.16 

2MHXA 2-METHYLHEXANE AKA-ISOHEPTANE 8 

1BUTE 1-BUTENE AKA-ETHYLETHYLENE 4 56.11 

23DMB 2,3-DIMETHYLBUTANE AKA-DIISOPROPYL 6 86.18 

2MPNA 2-METHYLPENTANE AKA-ISOHEXANE 7 

23DMP 2,3-DIMETHYLPENTANE 7 100.21 

FORM FORMALDEHYDE AKA-OXYMETHYLENE 1 30.03 

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Name Description 

Carbon 

number MWt 

ACETA ACETALDEHYDE AKA ACETIC ALDEHYDE 2 44.05 

ACET ACETONE AKA-DIMETHYLKETONE 3 58.08 

NUNDC N-UNDECANE 11 156.31 

2MHEP 2-METHYLHEPTANE 8 

O3 OZONE 

M/PXY M/P XYLENE 8 106.17 

BZ BENZENE 6 78.11 

TOLU TOLUENE AKA METHYLBENZENE 7 92.14 

EBENZ ETHYLBENZENE AKA-PHENYLETHANE 8 106.17 

OXYL O-XYLENE AKA-1,2-DIMETHYLBENZENE 8 106.17 

135TB 1,3,5-TRIMETHYLBENZENE AKA-MESITYLENE 9 120.20 

124TB 1,2,4-TRIMETHYLBENZENE AKA-PSEUDOCUMENE 9 120.20 

NPBZ N-PROPYLBENZENE AKA-1-PHENYLPROPANE 9 120.20 

ISPBZ ISOPROPYLBENZENE AKA CUMENE 9 120.20 

OETOL O-ETHYLTOLUENE 8 

METOL M-ETHYLTOLUENE 8 

PETOL P-ETHYLTOLUENE (AKA 4-ETHYLTOLUENE) 8 

MDEB M-DIETHYLBENZENE 8 

PDEB P-DIETHYLBENZENE AKA-1,4-DIETHYLBENZENE 6 147.01 

STYR STYRENE AKA ETHENYLBENZENE 8 104.15 

123TB 1,2,3-TRIMETHYLBENZENE 9 120.20 

July 9-22, 1999 

Hourly HCHO concentrations are available at 92 stations over USA for July 9-22, 1999. 

Figure 2-11 shows the distribution of average HCHO concentrations during that period of time 

for these stations. 

Hourly Acetone concentrations are available at 130 stations over USA for July 9-22, 1999. 

Figure 2-12 shows the distribution of average acetone concentrations for these stations. 

Jan. 1-21, 2002 

Hourly HCHO concentrations are available at 57 stations over USA for Jan. 1-21, 2002. 

Figure 2-13 shows the distribution of average HCHO concentrations for these stations. 

Hourly Acetone concentrations are available at 67 stations over USA for Jan. 1-21, 2002. 

Figure 2-14 shows the distribution of average Acetone concentrations for these stations. 

We have recently obtained more speciated VOC data for additional PAMS sites and are 

currently evaluating these data. 

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Figure 3-1. Distribution of average ambient HCHO concentrations (July 9-22, 1999) from the 

PAMS network. 

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Figure 3-2. Distribution of average ambient acetone concentrations (July 9-22, 1999) from the 

PAMS network. 

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Figure 3-3. Distribution of average ambient HCHO concentrations (January 1-21, 2002) from 

the PAMS network. 

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Figure 3-4. Distribution of average ambient acetone concentrations (January 1-21, 2002) from 

the PAMS network. 

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4.0 INTERAGENCY MONITORING OF PROTECTED VISUAL ENVIRONMENTS 

(IMPROVE) 

The Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring 

network consists of air quality data from Class I areas that include national parks and 

wilderness areas where visibility is deemed an important attribute. There are also IMPROVE 

protocol monitoring sites that are not located in Class I areas (see Appendix A for locations). 

This monitoring program is an interagency effort with the U.S. Environmental Protection 

Agency (USEPA) and the U.S. Department of the Interior (USDOI), including the U.S. Forest 

Service, U.S. Fish and Wildlife Service, and the Bureau of Land Management. The 

IMPROVE fine particle network collects PM2.5 and PM10 samples over a twenty four hour 

using IMPROVE samplers. The current network consists of over 160 monitoring sites, mainly 

located in Class I ("Clean Air") areas. Some of the earliest sites have been in operation since 

March 1988, although most sites were installed in the mid 1990’s or later. The PM samples 

are analyzed for PM2.5 mass and its elemental constituents, organics, ions, light absorption and 

PM10 mass. 

The objectives of IMPROVE are: (1) to establish current visibility and aerosol conditions in 

mandatory class I areas; (2) to identify chemical species and emission sources responsible for 

existing man-made visibility impairment; (3) to document long-term trends for assessing 

progress towards the national visibility goal; (4) and to provide regional haze monitoring 

representing all visibility-protected federal class I areas where practical. In 1999 there were 70 

IMPROVE sites including 30 sites in Class I areas and an additional 40 sites using the 

IMPROVE protocol. By 2002 there were approximately 110 IMPROVE sites and 53 

IMPROVE Protocol sites. 

The standard IMPROVE sampler has four sampling modules, listed below, although some 

sites only include the Module A: 

Module A: PM2.5 particles on Teflon are analyzed at UC Davis using the following methods: 

• gravimetric mass for PM2.5

• hybrid integrating plate/sphere method for optical absorption 

• Proton Elastic Scattering Analysis (PESA) for hydrogen 

• Proton Induced X-ray Emission (PIXE) for Na-Mn 

• X-Ray Fluorescence (XRF) for Fe-Pb 

Module B: PM2.5 particles on nylon. A denuder before the nylon filter removes nitric acid 

vapors. These are analyzed by ion chromatography (IC) at Research Triangle Institute for 

nitrate (NO3

-

), chloride (Cl-), sulfate (SO4

2-), and nitrite (NO2-

). 

Module C: PM2.5 particles on quartz. These are analyzed at Desert Research Institute for 

carbon using the Thermal Optical Reflectance (TOR) combustion method. A secondary filter 

at selected sites is used to determine artifacts. These are reported in 8 temperature categories. 

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Module D: PM10 particles on Teflon. All are measured for PM10 mass. Approximately 4% are 

analyzed by the other four methods listed for Module A. 

For selected IMPROVE sites, there also are transmissometer data to directly measure light 

extinction and nephelometer data to provide direct measurements of light scattering. 

The IMPROVE data have passed the “Level 0 and Level 1” quality assurance and control 

procedure conducted by the Crocker Nuclear Laboratory at UC Davis, and are added to the 

online database after a 30 day period to allow the States, Tribes, FLM’s or any other 

organization a chance to review and comment on the accuracy, credibility, and/or 

representativeness of aerosol speciation data collected and the reconciliation of any issues these 

organizations may find. In the past, we have not performed any additional QA of the 

IMPROVE data, and have simply used the data obtained from the Cooperative Institute for 

Research in Atmosphere (CIRA) website: http://vista.cira.colostate.edu/improve. 

A 24 hour averaging period is used for IMPROVE data. Prior to 2000, two 24 hour samples 

were collected twice a week, on Wednesday and Saturday. After 2000, 24 hour samples were 

collected every three days. 

Table 4-1 lists the 41 species measured at IMPROVE sites. The IMPROVE data are available 

for all three VISTAS modeling episodes. The downloaded data have been formatted for 

evaluation software, and time series plots are also available for download on the project 

website. Additional QA of these data can be performed, as discussed in Section 10, using time 

series and scatter plots of these data. 

Table 4-1. Species of PM2.5 measured in IMPROVE network. 

CODE NAME CODE NAME 

AL Aluminum: Fine NH4 Ammonium ion: Fine 

AS Arsenic: Fine NI Nickel: Fine 

BR Bromine: Fine NO3 Nitrate: Fine 

CA Calcium: Fine OC1 Carbon: Fine organic (OC1) 

CHL Chloride: Fine OC2 Carbon: Fine organic (OC2) 

CL Chlorine: Fine OC3 Carbon: Fine organic (OC3) 

CR Chromium: Fine OC4 Carbon: Fine organic (OC4) 

CU Copper: Fine OP Carbon: Fine organic (OP) 

EC1 Carbon: Fine elemental (EC1) P Phosphorus: Fine 

EC2 Carbon: Fine elemental (EC2) PB Lead: Fine 

EC3 Carbon: Fine elemental (EC3) RB Rubidium: Fine 

FE Iron: Fine S Sulfur: Fine 

H Hydrogen: Fine SE Selenium: Fine 

K Potassium: Fine SI Silicon: Fine 

MASS PM2.5: mass SO4 Sulfate: Fine 

MASS PM10: mass SR Strontium: Fine 

MG Magnesium: Fine TI Titanium: Fine 

MN Manganese: Fine V Vanadium: Fine 

MO Molybdenum: Fine ZN Zinc: Fine 

N2 Nitrite: Fine ZR Zirconium: Fine 

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CODE NAME CODE NAME 

NA Sodium: Fine 

For most species, the IMPROVE data cannot be matched directly to the model gas and PM 

species because of lumping schemes in the model chemistry. Therefore, it is necessary to 

convert the ambient data and model species into forms that can be directly compared. Table 4-

2 shows the mapping scheme used for the IMPROVE comparison to the model species. Table 

4-3 list definitions for CMAQ model species. Definitions for compounds in Table 4-2 include 

the following: OC is organic carbon; EC is elemental carbon or soot; CM is coarse mass; 

RCFM is reconstructed fine mass; and Bext_Recon is reconstructed extinction coefficient. 

We note that in previous model evaluation for WRAP the sulfate and nitrate were assumed to 

be full neutralized and that ammonium sulfates and nitrate were represented in CMAQ as 

1.375*(ASO4J + ASO4I) +1.29*(ANO3J + ANO3I), respectively. This was used for 

consistency with the formula used to calculate the model reconstructed extinction coefficient. 

For VISTAS we propose to use the CMAQ model ammonium mass (ANH4J+ANH4J) 

explicitly because this provides a more accurate estimate of modeled fine mass. 

In previous modeling studies we have not had access to direct measurements of ammonium ion 

(NH4

+). VISTAS is funding the Research Triangle Institute (RTI) to measure NH4+ at 10 sites 

beginning in September, 2002. Prior to September, NH4

+ data will be available for only 3 

sites: Great Smokey Mountains (GRSM), Shenandoah (SHEN), Class I areas, and Dolly Sods 

(DOSO). 

Concerns have been raised regarding the accuracy of the IMPROVE HNO3 data, and we have 

found that it tends to be lower than HNO3 data from the CASTNET network. However, we 

do not have a basis for adjusting or rejecting the IMPROVE HNO3 data, and further 

evaluation of HNO3 data from all monitoring networks is required. In September 2003 EPA 

will conduct a field study intercomparison of HNO3 methods using a Chemical Ionization 

Mass Spectrometer as a reference method. This may provide insight into the accuracy of 

HNO3 methods employed at the different monitoring networks, however, it is unlikely that 

results from this study will be available in 2003 to affect the VISTAS Phase I modeling. 

There are concerns that the coarse mass (CM) IMPROVE measurements may also include 

some sulfate, nitrate and other species (e.g., sea salt and organics) that occur in the coarse 

mode. There are two different mechanism by which NO3 can be transferred to the coarse 

mode: formation of coarse mode sodium nitrate or calcium nitrate, and the ammonium nitrate 

distributions that can extend in to the coarse mode. In previous CMAQ performance 

evaluations, all nitrate has been assumed to be in the fine mode. If substantial fractions of 

nitrate are in the coarse mode, CMAQ would be expected to over predict the mass of fine 

nitrate. Speciation of the CM fraction is ongoing at several IMPROVE sites to investigate this 

issue. A coarse mode speciation measurement program was begun in spring, 2002, and it is 

possible that initial results will be available in fall, 2002. 

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We are also investigating the modal distributions of the Aitken and accumulation modes in 

CMAQ. Although these modes are typically assumed to be entirely PM2.5, we have found that 

some of the mass does extend in to the coarse mode. 

Table 4-2. Species mappings for IMPROVE species. Note that CMAQ represents fine PM 

species in two size modes: Aitken nuclei (0.03 to 0.5 μm) and accumulation mode (0.5 to 

2.5 μm) and in CMAQ by represented by J and I, respectively. Compounds listed include coarse 

mass (CM); 

Compound IMPROVE Species CMAQ Mapping 

SO4 SO4 ASO4J + ASO4I 

NO3 NO3 ANO3J + ANO3I 

NH4+ NH4+ ANH4J + ANH4I 

OC 1.4*(OC1+OC2+OC3+OC4+OP) AORGAJ + AORGAI + AORGPAJ + 

AORGPAI + AORGBJ + AORGBI 

EC EC1+EC2+EC3-OP AECJ + AECI 

SOIL 2.2*Al + 2.49*Si + 1.63*Ca + 

2.42*Fe + 1.94*Ti A25I +A25J 

CM MT – FM ACORS + ASEAS + ASOIL 

PM25a FM 

ASO4J + ASO4I + ANO3J + ANO3I + ANH4J 

+ ANH4I + AORGAJ + AORGAI + AORGPAJ 

+ AORGPAI + AORGBJ + AORGBI + AECJ + 

AECI + A25J + A25I 

RCFM 1.375*SO4 + 1.29*NO3 + EC + OC 

+ SOIL Same for PM25 

PM10 MT 

ASO4J + ASO4I + ANO3J + ANO3I + ANH4J 

+ ANH4I + AORGAJ + AORGAI + AORGPAJ 

+ AORGPAI + AORGBJ + AORGBI + AECJ + 

AECI + A25J + A25I + ACORS + ASEAS + 

ASOIL 

Bext_Recon 

(1/Mm) 

10b

 + 3*f(RH)c(1.375*SO4 + 

1.29*NO3) + 4*OC + 10*EC + SOIL

+ 0.6*CM 

10b

 + 3*f(RH)c[1.375*(ASO4J + ASO4I) + 

1.29*(ANO3J + ANO3I)] + 4*1.4*(AORGAJ + 

AORGAI + AORGPAJ + AORGPAI + AORGBJ 

+ AORGBI) + 10*(AECJ + AECI) + 1*(A25J + 

A25I) + 0.6*(ACORS + ASEAS + ASOIL) 

a Measured; b

 Rayleigh scattering correction; c f(RH) site and day specific relative humidity adjustment 

factor. 

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Table 4-3. Definitions of CMAQ species names used in Table 4-2. 

CMAQ Mapping 

ASO4 Aerosol sulfate 

ANO3 Aerosol nitrate 

ANH4 Ammonium ion 

AORGPA Organic aerosols from primary organic emissions 

AORGA, AORGB Secondary organic aerosols from anthropogenic sources 

(aromatics, paraffin) and biogenic source, respectively. 

AEC Elemental Carbon 

A25 Unspecified fine mass including fine crustal material (fine soil)

ASOIL Soil-derived (crustal) coarse materials 

ASEAS Sea salt (only in coarse mode) 

ACORS Unspecified anthropogenic coarse mass 

RCFM Reconstructed fine mass 

MT Mass total 

CM Coarse mass 

Bext_recon Reconstructed extinction coefficient 

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5.0 SOUTHEASTERN AEROSOL RESEARCH AND CHARACTERIZATION 

(SEARCH) 

SEARCH is a monitoring network for Southeastern Aerosol Research and Characterization. 

There are 8 monitoring sites located in four states in the SEARCH network whose locations 

are shown in Figure 5-1. Daily PM2.5 data are measured for 46 species, while daily coarse PM 

data are measured for 18 species in SEARCH network. Archived data file can be downloaded 

from the website (http://www.atmospheric-research.com/public/index.html). Tables 5-1 and 5-

2 show the names for 46 PM2.5 species and 18 coarse PM species. The SEARCH data are 

currently available for two of the VISTAS Phase I modeling episodes (July 9-22, 1999 and 

July 7-28, 2001). 

The frequency of the PM2.5 measurements of SEARCH has varied from 1998 to the present. 

Measurements were made daily at all sites for a little over one year. Subsequently, the 

measurements frequency varies from daily to every third or sixth day depending on the 

specific site of interest. Semi-continuous TEOM PM2.5 mass measurements have been 

available since the onset of the program and are reported as hourly averages. Semi-continuous 

measurements of PM chemical components have been phased in over the years, initially at 

Jefferson Street (Atlanta) and later at other sites. However, the ARS Data Gap report 

indicates that such hourly data have not yet been made generally available on the SEARCH 

web site. We need to determine whether the modeling team has this data or how best to get 

access to this data. 

Table 5-1. PM2.5 species measured in SEARCH network. 

Name Name Name Name 

FRM Mass PM25 Major 

Metal Oxides XRF Pb Al2O3 

FRM SO4 PCM2 SO4 XRF Sb SiO2 

FRM NO3 PCM2 NO3 XRF Se K2O 

FRM NH4 PCM2 NH4 XRF Sn CaO 

PCM1 Mass PCM2 CL XRF Ti TiO2 

PCM1 SO4 PCM3 EC XRF Zn Fe2O3 

PCM1 NO3 PCM3 OC WS Chromium XRF S 

PCM1 Vol NO3 XRF As WS Copper TEOM Mass 

PCM1 Teflon NO3 XRF Ba WS Iron BackupPCM3 EC 

PCM1 NH4 XRF Br WS Manganese BackupPCM3 OC 

PCM1 Vol NH4 XRF Cu WS Nickel 

PCM1 Teflon NH4 XRF Mn WS Vanadium 

Air Quality Data Used For Modeling 

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Table 5-2. Coarse PM species measured in SEARCH network. 

Name Name Name Name 

Coarse Mass Chromium Vanadium TiO2 

Coarse SO4 Copper Al2O3 Fe2O3 

Coarse NO3 Iron SiO2 Coarse S 

Coarse NH4 Manganese K2O 

CoarseMajorMetalOxides Nickel CaO 

For the data from SEARCH network, daily average PM2.5 concentrations for 46 species (Table 

5-1) and daily coarse PM concentration for 18 species (Table 5-2) are available for two 

modeling episodes (July 9-22, 1999 and July 7-28, 2001). 

We are working with the SEARCH sponsors and scientists to obtain data suitable for model 

performance evaluation and obtain data for the January 2002 episode. Locations of the 

SEARCH monitors are shown in Figure 5-1. 

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Figure 5-1. Locations of the SEARCH monitoring sites (Source: Atmospheric Research and 

Analysis, Inc., www.atmopheric-research.com).

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6.0 SPECIATION TRENDS NETWORK (STN) 

EPA’s Speciation Trends Network (STN) includes about 215 monitoring stations nationwide. 

It appears that among these 215 sites may include IMPROVE sites or other data from other 

networks. This, however, needs to be verified. Daily PM2.5 data are measured for 64 species 

in the STN network. Some archived STN data files were obtained from the 

website:http://www.epa.gov/ttn/airs/airsaqs/archived%20data/archivedaqsdata.htm. 

Additional documentation of the STN and descriptions of the data are still needed. Table 6-1 

shows the codes and names for 64 species measured in STN. Locations of the STN monitors, 

as well as other specified PM monitors, are shown in Appendix A. 

Table 6-1. Species of PM2.5 measured in STN network. 

Code Description Code Description 

88101 PM2.5 - Local Conditions 88160 Tin PM2.5 LC 

88102 Antimony PM2.5 LC 88161 Titanium PM2.5 LC 

88103 Arsenic PM2.5 LC 88162 Samarium PM2.5 LC 

88104 Aluminum PM2.5 LC 88163 Scandium PM2.5 LC 

88105 Beryllium PM2.5 LC 88164 Vanadium PM2.5 LC 

88107 Barium PM2.5 LC 88165 Silicon PM2.5 LC 

88109 Bromine PM2.5 LC 88166 Silver PM2.5 LC 

88110 Cadmium PM2.5 LC 88167 Zinc PM2.5 LC 

88111 Calcium PM2.5 LC 88168 Strontium PM2.5 LC 

88112 Chromium PM2.5 LC 88169 Sulfur PM2.5 LC 

88113 Cobalt PM2.5 LC 88170 Tantalum PM2.5 LC 

88114 Copper PM2.5 LC 88172 Terbium PM2.5 LC 

88115 Chlorine PM2.5 LC 88176 Rubidium PM2.5 LC 

88117 Cerium PM2.5 LC 88179 Uranium PM2.5 LC 

88118 Cesium PM2.5 LC 88180 Potassium PM2.5 LC 

88121 Europium PM2.5 LC 88183 Yttrium PM2.5 LC 

88124 Gallium PM2.5 LC 88184 Sodium PM2.5 LC 

88126 Iron PM2.5 Lc 88185 Zirconium PM2.5 LC 

88127 Hafnium PM2.5 LC 88186 Tungsten PM2.5 LC 

88128 Lead PM2.5 LC 88301 Ammonium Ion PM2.5 LC 

88131 Indium PM2.5 LC 88302 Sodium Ion Pm2.5 LC 

88132 Manganese PM2.5 LC 88303 Potassium Ion PM2.5 LC 

88133 Iridium PM2.5 LC 88304 OCX Carbon 

88134 Molybdenum PM2.5 LC 88305 Organic Carbon PM2.5 LC 

88136 Nickel PM2.5 LC 88306 Total Nitrate PM2.5 LC 

88140 Magnesium PM2.5 LC 88307 Elemental Carbon PM2.5 LC 

88142 Mercury PM2.5 LC 88308 Carbonate Carbon PM2.5 LC 

88143 Gold PM2.5 LC 88309 Volatile Nitrate PM2.5 LC 

88146 Lanthanum PM2.5 LC 88310 Non-volatile Nitrate PM2.5 LC 

88147 Niobium PM2.5 LC 88311 OCX2 Carbon 

88152 Phosphorus PM2.5 LC 88312 Total Carbon PM2.5 LC 

88154 Selenium PM2.5 LC 88403 Sulfate PM2.5 LC 

Air Quality Data Used For Modeling 

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G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec7.gst.doc 7-1 

7.0 NATIONAL ATMOSPHERIC DEPOSITION PROGRAM (NADP) 

The National Atmospheric Deposition Program/National Trends Network (NADP/NTN) is 

designed to measure wet deposition. The network is a cooperative effort between State 

Agricultural Experiment Stations, the U.S. Geological Survey, U.S. Department of 

Agriculture, and other governmental and private entities. It includes over 200 sites in the 

continental United States, Alaska, and Puerto Rico, and the Virgin Islands whose locations are 

shown in Figure 7-1. The purpose of the network is to collect data on the chemistry of 

precipitation for monitoring of geographical and temporal long-term trends. The precipitation 

at each station is collected weekly is analyzed for hydrogen (acidity as pH), sulfate, nitrate, 

ammonium, chloride, and base cations (such as calcium, magnesium, potassium and sodium). 

The NADP network includes a quality assurance program, so we expect to use this data 

without any additional QA. 

The major wet deposition network -- the National Atmospheric Deposition Program (NADP) -

- has a large array of about 200 stations. Weekly samples of precipitation are collected at these 

stations and then sent to a single central laboratory for chemical analysis. The weekly 

sampling network has three severe drawbacks: 

Because the NADP program uses a weekly sampling period the data has poor temporal 

resolution and the sample chemistry can be affected by chemical and biological activity. The 

NADP also includes the Atmospheric Integrated Research Monitoring Network (AIRMoN) 

which was designed to study precipitation chemistry trends with greater temporal resolution. 

Precipitation samples are collected daily from a network of nine wet deposition sites and 

analyzed for the same constituents as the NADP/NTN samples. AIRMoN also includes a dry 

deposition network and these are described next. NADP measures weekly deposition of 

compounds in units of mg/l as well as precipitation in units of l. The PM models will output 

deposition in terms of hourly mass flux per grid cell (e.g., 12 km x 12 km). The modeled 

results will be accumulated to weekly deposition fluxes per unit area (e.g., gm/km2

/week). 

Using the NADP precipitation and information on the NADP Sampler, the NADP deposition 

measurements will be converted to the same units as the modeled values. 

AIRMoN Dry Deposition Data 

The AIRMoN program includes both dry and wet deposition components. Figure 7-2 shows 

the locations of monitoring sites for both programs. The dry deposition data is described at 

their web page: http://www.arl.noaa.gov/research/projects/airmon_data.html. The description 

of the AIRMoN dry deposition data from the website is included verbatim here for 

convenience: 

Dry deposition rates are computed in the AIRMoN network by combining estimates of sitespecific and time-evolving deposition velocities with measurements of air concentrations 

obtained using a weekly sampling protocol. The intent has been to mirror the 0900 Tuesday 

sample change standards adopted by the National Atmospheric Deposition Program. At times, 

the AIRMoN dry samples are obtained over substantially different periods, because of 

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operator absence or problems with instrumentation. The data summaries have been arranged 

so that each sequential week is represented, even though some of the initial data represent 

periods longer than a single week. 

The AIRMoN concentration sampler is a three-element filterpack, with a leading teflon filter 

to remove particles, a middle nylon filter to extract nitric acid vapor, and a final doped 

cellulose filter intended primarily to sample sulfur dioxide. An inlet tube is used to impose a 

small amount of heat on the incoming air stream, to protect against liquid formation on the 

filters in periods of high humidities. There is no doubt that this influences the measurement of 

ammonium nitrate. In practice, any temperature change imposed on collected ammonium 

nitrate particles will cause some change in the sample, so that any long-term accumulative 

measurement of related species (such as that reported here) will be susceptible to error 

because of the effects of the diurnal cycle in air temperature. Tests of the AIRMoN sampling 

system indicate that particulate ammonium nitrate deposited on the teflon filter is 

incompletely disassociated with minor consequences on the measurement of nitric acid vapor 

and of sulfur dioxide, but with major influence on the measurement of nitrate on the doped 

cellulose filter. For this reason, measurements of nitrate reported here are considered to be 

unreliable. 

It should also be noted that tests indicate that the values associated with nitric acid vapor are 

underestimates, on the average by 25%. The values listed should be increased accordingly, to 

correct for this error (due to deposition on the walls of the inlet tube). 

Deposition velocities tabulated here are derived using a multi-layer numerical model, driven 

by field observations of selected key variables (such as wind speed, the standard deviation of 

the wind direction, surface wetness, incident solar radiation, temperature, humidity, plant 

species distribution, etc.) It is estimated that these deposition velocities might be in error by 

as much as 30%. 

Weekly average deposition rates are computed as the product of the weekly average 

deposition velocities and the weekly average concentration, thus omitting consideration of a 

correlation term that can be significant when air concentrations display a consistent and 

significant diurnal cycle. 

Regarding the last paragraph above, there are in fact large diurnal variations in several trace 

species of interest, and the errors introduced by this approach should be further investigated. 

Omitting consideration of a correlation term is likely to introduce additional errors greater than 

the 30% error mentioned above. 

As noted on the AIRMoN web page, there is very large uncertainty in dry deposition 

estimates, and it is possible that the data on the website will be modified as more is learned 

about the processes that control dry deposition. 

UCR is currently investigating the availability of the NADP and AIRMoN data. We expect to 

obtain the AIRMoN data from the website, but we still need to determine if other NADP data 

is available. 

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AIRMoN Wet Deposition Data: 

The AIRMoN wet deposition program is described at the web page: 

http://www.arl.noaa.gov/research/projects/airmon_wet.html 

The AIRMoN wet deposition monitoring employs a daily sample collection protocol, thus 

differentiating itself from the weekly operations of the mainstream NADP stations. In practice, 

daily sampling provides a greatly improved quantification of ammonium deposition. At the 

National Atmospheric Deposition Program Technical Committee Meeting in 1994 (October 

24-27) final decisions were made regarding the AIRMoN-wet quality assurance plan; a system 

of flags will be used to alert data users to specific problems. 

UCR is currently downloading data from both wet and dry AIRMoN networks.

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Figure 7-1. Locations of NADP National Trends Network (NTN) monitors (Figure obtained from 

the EPA NADP website).

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Figure 7-2. Locations of the AIRMoN wet and dry deposition monitoring sites (Figure obtained 

from the EPA AIRMoN website).

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Figure 7-3. Locations of the AIRMoN wet deposition monitoring sites (Figure obtained from the 

EPA AIRMoN website).

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G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec8.gst.doc 8-1 

8.0 CLEAN AIR STATUS AND TREND NETWORK (CASTNET) 

CASTNET is designed to measure dry deposition and it is comprised of 123 sites across the 

United States as shown in Figure 8-1. It includes measurements of ambient concentration and 

meteorology and land use which are then used to calculate dry deposition rates. For the 

model performance evaluation we will use the ambient concentration measurements to 

compare with the model. Dry deposition data are measured for 13 species including: TOTAL 

SO4, TOTAL NO3, TOTAL NH4, CA, MG, NA, K, NSO4, NHNO3, WSO2, WNO3, 

TOTAL SO2, and TOTAL NO3. 

Detailed data collection procedures are described at the EPA CASTNET website: 

http://www.epa.gov/castnet. In short, atmospheric concentration data are collected at each 

site with open-faced, 3-stage filter packs. The filter pack contains a Teflon filter for collection 

of particulate species, a nylon filter for nitric acid and a base-impregnated cellulose 

(Whatman) filter for sulfur dioxide. Filter packs are exposed for 1-week intervals (i.e., 

Tuesday to Tuesday) at a flow rate of 1.5 liters per minute (3.0 liters per minute for western 

sites), and sent to the Harding ESE, Gainesville, FL laboratory for chemical analysis. 

All three filters are extracted and analyzed for certain species: 

Teflon filter: SO4

2-, NO3-

, NH4

+

Nylon filter: SO4

2-, NO3-

Cellulose filter (Whatman): SO4

2-, NO3-

The sulfate, nitrate and ammonium in the teflon filter extract are interpreted as particulate 

species (listed above as TSO4, TNO3 and TNH4). The nitrate in the nylon filter extract is 

interpreted as nitric acid. The sum of sulfate in the nylon and cellulose filter extracts is 

interpreted as sulfur dioxide (SO2). Any nitrate detected in the cellulose filter extract is not 

interpreted, since it likely represents a host of oxidized nitrogen species. 

Because aerosol nitrate on the teflon filter can revolatilize, the nitrate on the teflon filter 

represent those particles that have not volatilized during the sampling period. The nitrate on 

the nylon back filter represents nitric acid that was originally in the atmosphere plus any that 

was produced through revolatilization of the nitrate particles on the teflon filter. Thus, 

because of the volatilization losses, the CASTNet particulate nitrate is less than the IMPROVE 

nitrate. Also, because IMPROVE uses a nitrate denuder to remove ambient nitric acid, the 

CASTNet total nitrate (the sum of the teflon and nylon filter nitrates) is greater than the 

IMPROVE nitrate. Details are provided in Appendix G of the 2000 IMPROVE Report and by 

Ames and Malm [Ames R.B and W.C. Malm, Comparison of sulfate and nitrate particle mass 

concentrations measured by IMPROVE and the CDN, Atmospheric Environment, 2001, 905-

916.] Thus, the IMPROVE and CASTNet nitrate values cannot be directly compared. Moreover, 

the CASTNet NO3 and HNO3 data cannot be directly compared to the modeled species. In 

previous applications we have compared the CASTNet data to the model for the sum 

NO3+HNO3 and we propose to use the same approach for the VISTAS model evaluation.

The CASTNet data have been downloaded from: http://www.epa.gov/castnet/data.html. 

The CASTNET data are available for all three modeling episodes. The actual start time and 

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collection period varies among sites, so this makes it difficult to automate the model 

performance evaluation, and additional effort is required to match the sample period with the 

model output period. 

As in the case of the IMPROVE data, measured species can not be compared directly to the 

models species, and Table 8-1 shows the mapping scheme to be used for comparing the 

CASTNET data to model species. 

Table 8-1. Species mappings for CASTNet species. 

Compound CASTNet Species CMAQ Mapping

Gaseous HNO3 NHNO3 (nylon filter) 2176.9*DENSa*HNO3 

Particulate NO3 TNO3 (Teflon filter) ANO3J + ANO3I 

Total HNO3 + NO3

NHNO3 (nylon filter) + TNO3 

(Teflon filter) 

2176.9*DENSa

*HNO3 + 

ANO3J + ANO3I 

Particulate NH4 TNH4 (Teflon filter) ANH4I + ANH4J 

Gaseous SO2 TOTAL_SO2 2211.5*DENS*SO2 

Particulate SO4 TSO4 (Teflon filter) ASO4I + ASO4J 

a Air density obtained from MCIP outputs

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Figure 8-1. Locations of CASTNet monitoring sites (Figure obtained from EPA CASTNet 

website). 

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G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec9.gst.doc 9-1

9.0 PM SUPERSITES 

There are 8 PM Supersites. The locations of these sites are listed in Table 9-1. The data 

includes PM size distribution, ions, metal elements, EC, OC, radical, and gaseous pollutants 

is measured in Supersites. Available data varies with the site. The archived data files can be 

downloaded from the website (ftp://ftp.supersitesdata.umd.edu). 

Eastern Supersites Program (ESP01) 

The ESP01 was initially planned as an effort to coordinate an intensive monitoring in July 

2001 among the three PM Supersites projects (New York, Pittsburgh, and Baltimore). As 

other researchers expressed interest in the program it grew in scope and eventually over 30 

groups were involved in ESP01. The intensive monitoring program was planned for June 30, 

2001 at 0000 hrs to July 29, 2001 at 2400 hrs. The program included an extensive set of gas, 

PM, PM precursor, photochemical, visibility, and meteorological measurements. The study 

also included an extensive network of Radar Profilers (NOAA FSL) which will be useful for 

evaluating transport within and above the boundary layer. EPA plans to centralize all air 

quality and meteorological data in one location within several relational databases to allow for 

easy access to the data and to help ensure that modelers and data analysts will be working with 

uniform databases. Details on data collected at the PM Supersites can be found at 

www.epa.gov/ttn/amtic/supersites.html. We have contacted Paul Solomon and Marc 

Pitchford to determine the schedule for releasing this database. 

Table 9-1. Locations of PM Supersites. 

Location Institution 

Atlanta GA Georgia Institute of Technology 

Baltimore, MD University of Maryland at College Park 

Fresno, CA Desert Research Institute 

Houston, TX University of Texas at Austin 

Los Angeles, CA University of California, Los Angeles 

New York, NY University at Albany, State University of New York 

Pittsburgh, PA Carnegie Mellon University 

St. Louis, MO Washington University 

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10.0 AIRCRAFT DATA 

Of the three episodes selected for Phase I, only during episode 3 (13-21 July 1999) were there 

research aircraft flights performed that provide aloft meteorological and air quality data 

suitable for model performance testing. These data were collected during the Southern 

Oxidants Study (SOS) intensive field program performed principally in Nashville, TN during 

the summer of 1999. An overview of the SOS/Nashville 1999 aircraft data has been submitted 

to VISTAS as Appendix A to the Task 2 report (ENVIRON, 2003) and is not repeated here. 

Aircraft data are available through the NARSTO Archive and are available online at: 

http://eosweb.larc.nasa.gov/project/narsto/table_narsto.html. We have downloaded the 

NOAA WP3 data, but have not yet attempted to process this data for model evaluation. The 

Brookhaven/DOE data does not appear to be accessible through the NARSTO archive, and we 

will contact Brookhaven directly to access this data. 

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11.0 DATA QUALITY ASSURANCE 

In gathering data from the monitoring networks we have assumed that QA was performed by 

the agency or researcher responsible for collecting the data. However, it is possible that the 

data sets may contain erroneous data (i.e., missing data, zeros during calibration, unrealistic 

values, etc). Due to poor documentation or poor formatting of some data sets it is also 

possible that we will make mistakes in our processing of the data. To guard against this 

possibility, we plan to perform a high level QA by visually inspecting time-series plots and 

scatter plots of the ambient data as follows: 

Plots of Time Series 

Time series plots are generated for PAMS species. The plots should be inspected for the 

following: 

• Large "jumps" or "dips" in the concentrations 

• Periodicity of peaks, calibration carryover 

• Unexpected diurnal behavior (i.e., isoprene) 

• Unexpected relationships among species 

• High single-hour concentrations of less abundant species 

Scatter Plots 

Scatter plots may be prepared for the following: 

• Total NMOC vs. species group totals, vs. individual species 

• Benzene vs. Toluene, Acetylene, Ethane 

• Scatter plots comparing data for a single species measured with different sampling 

methods 

• Plots of reconstructed mass versus measured mass (to reveals if anything unusual is 

happening with the chemical measurements) 

• Plots of molal particulate ammonium versus the molal sum of sulfate and particulate 

nitrate (as a sanity check on the ion balance in the PM chemical measurements)

If we identify data that appears flawed it will be flagged and either corrected if the error is in 

the processing step, or removed from the data set. 

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Attachment C.2-1 

 August 21, 2009
Appendix A 

Locations of Speciated PM and IMPROVE Monitors 

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Attachment C.2-1 

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Trends (54)

Supplemental (~215 sites currently known)

Supersites

Daily Sites 

IMPROVE

IMPROVE Protocol Castnet conversion Deploy in 2002 Deploy in 2003

Current/Planned

 Urban & Rural PM2.5Speciation Networks 

S

SS

SS

SS

SS

SS

Air Quality Data Used For Modeling The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 North Carolina Attainment Demonstration

 48 Attachment C.2-1  August 21, 2009
IMPROVE or IMPROVE Protocol sites

IMPROVE Protocol Sites t/b deployed 2002 2003

From Discussions with RPOs and Tribal Agencies

Castnet sites converted to IMPROVE

Supplemental Speciation Sites

Class I Areas

Tribal Lands

Current/Planned IMPROVE, IMPROVE Proto

Rural Supplemental PM2.5 Speciation Sit

Air Quality Data Used For Modeling The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 North Carolina Attainment Demonstration

 49 Attachment C.2-1  August 21, 2009
Air Quality Data Used For Modeling 50 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Attachment C.2-1 

North Carolina Attainment Demonstration August 21, 2009 

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Appendix C.3 

Mendenhall PM2.5 Data Imputation for 

4Q2006 Memorandum 
Mendenhall PM2.5 Data Imputation for 4Q2006 Memorandum i 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix C.3 

North Carolina Attainment Demonstration August 21, 2009 

(This page intentionally left blank) 
PM2.5 Data Imputation 

- 1 - 

Memorandum 

To: Hoke Kimball 

CC: Joelle Burleson, George Bridgers 

From: Wayne L. Cornelius 

Date: 2007-05-09 (revised 2008-12-16 and 2009-04-14) 

Re: Mendenhall PM2.5 Data Imputation for 4Q2006

Contents 

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

Methods..................................................................................................................................................................2

Results ....................................................................................................................................................................2

Discussion ..............................................................................................................................................................4

Recommendations ..................................................................................................................................................4

List of Tables 

Table 1 Regression Analysis using the Lexington, Cherry Grove, Burlington and Hattie Avenue PM2.5 data ....3

Table 2 Regression Analysis using the Lexington and Hopedale PM2.5 data .......................................................3

Table 3 Imputed Raw Data.....................................................................................................................................3

Table 4 Quarterly Summaries.................................................................................................................................4

Table 5 Weighted Annual Means and Design Value..............................................................................................4

List of Figures 

Figure 1 Map of Monitor Locations .......................................................................................................................2

Introduction 

The North Carolina Division of Air Quality (DAQ) is in nonattainment for Davidson County for PM2.5 from 

the monitoring station in the city of Lexington (AQS Site ID: 370570002). DAQ has performed design value 

calculations with the PM2.5 data for 2005-2008 for Lexington. These calculations indicate that the design value 

for this monitor will be in attainment for this time period and thus DAQ will be applying for PM2.5 

nonattainment redesignation. Redesignation requires assessing the PM2.5 data from PM2.5 monitors at 

Mendenhall (Guilford Co., ID 370810013), Lexington (Davidson Co., ID 370570002) and Hattie (Forsyth Co., 

ID 370670022). 

The design value calculations for 2005-2008 for the Mendenhall site are incomplete because no valid PM2.5 

data were collected during the fourth quarter of 2006. This happened because of major complications in having 

to move the site. DAQ moved the site about 100 yards because a 2 story field house that was constructed 

Mendenhall PM2.5 Data Imputation for 4Q2006 Memorandum 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 

North Carolina Attainment Demonstration

 1 

 Appendix C.3 

August 21, 2009
PM2.5 Data Imputation 

immediately adjacent to the monitoring site (unpublished letter to Artra Cooper, 12 December 2006). The 

construction was started without DAQ’s knowledge. When DAQ realized what was happening it was too late to 

stop the project, the new field house was built, and the site no longer met ambient siting criteria. 

Figure 1 Map of Monitor Locations 

Since there were no 4Q2006 data with which to calculate a proper Mendenhall Design value, the DAQ Planning 

and Ambient staff decided to present to EPA Region IV an estimate of what the missing Mendenhall PM2.5 

sample values would have been if they had been properly observed, along with the resulting Design value 

summary statistics. The estimate is based on linear regression using data acquired during the four years, 2002 

through 2005 at surrounding sites including those in the MSA and also Hopedale (Alamance County, ID 

370010002) and Cherry Grove (Caswell Co., ID 370330001). These monitor locations are shown in Figure 1 

(an extraneous PM2.5 monitoring site at Clemmons, southwest Forsyth County, is also shown for reference but 

was not used in the analysis). 

. 

Methods 

The estimation procedure is as follows: 

1. Fit a linear regression to the 2002-2005 PM2.5 data of the regressors to determine equation coefficients 

2. Estimate missing sample values for Mendenhall by substituting the corresponding observed PM2.5 

data in 4Q2006 into the regression equation 

3. Compute quarterly averages for Mendenhall including the imputed 4Q2006 data using actual data 

where available and imputed data where provided by the regression procedure 

4. Compute weighted averages for each year 

5. Compute the completed Design value for Mendenhall derived by averaging the weighted annual means 

Results 

I applied two regression fits to the data, starting with the most inclusive possible model, using Lexington, 

Cherry Grove, Hopedale and Hattie Avenue all as predictors. Estimates from this model are shown in Table 1. 

In this combination, Lexington, Cherry Grove and Hattie Avenue are not significant predictors for Mendenhall. 

- 2 - 

Mendenhall PM2.5 Data Imputation for 4Q2006 Memorandum 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 

North Carolina Attainment Demonstration

 2 

 Appendix C.3 

August 21, 2009
PM2.5 Data Imputation 

Table 1 Regression Analysis using the Lexington, Cherry Grove, Burlington and Hattie Avenue PM2.5 

data 

Call: lm(formula = MH ~ LX + UC + HD + HA, data = MH4q.md3, na.action = na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -3.763 -1.161 -0.3787 0.5814 11.34

Coefficients: 

Regressor Value Std. Error t value Pr(>|t|) 

(Intercept) 0.6105 0.5347 1.1417 0.2565

 LX 0.1615 0.1165 1.3861 0.1690

 UC 0.0791 0.0947 0.8346 0.4061

 HD 0.4782 0.1495 3.1982 0.0019

 HA 0.2466 0.1414 1.7445 0.0844

Residual standard error: 2.111 on 93 degrees of freedom 

Multiple R-Squared: 0.8798 

The second regression removed Cherry Grove and Hattie Avenue from the model. The resulting model had a 

residual standard error of 2.094 and R2

= 0.877. Both Lexington and Hopedale were significant in this 

regression, but the intercept term was not significant, so I fit the model with its intercept forced to zero. This 

model’s estimates are shown in Table 2. The regression equation is shown as equation (1) 

MH = LX + *6322.0*3464.0 HD (Equation 1) 

I fit (1) to the Lexington and Mendenhall data values acquired during 4Q2006. Table 3 shows the regressors for 

the 22 days with valid data for both regressors, and the resulting Mendenhall estimates. The average of the 22 

imputed samples is 12.92. 

Table 2 Regression Analysis using the Lexington and Hopedale PM2.5 data 

Call: lm(formula = MH ~ -1 + LX + HD, data = MH4q.md3, na.action = na.exclude) 

Residuals: 

 Min 1Q Median 3Q Max 

 -4.258 -0.9024 -0.1271 0.915 11.67

Coefficients: 

Regressor Value Std. Error t value Pr(>|t|) 

LX 0.3464 0.0891 3.8873 0.0002

HD 0.6322 0.0957 6.6042 0.0000

Residual standard error: 2.103 on 107 degrees of freedom 

Table 4 shows the quarterly averages for 2004, 2005 and 2006, including the imputed value for 4Q2006 and the 

11 actual values for the remaining quarters. Finally Table 5 shows the 3 annual means and the overall Design 

value result that obtains from them, 14.01. 

Table 3 Imputed Raw Data 

Sampling_Date Mendenhall Hopedale Lexington 

10/02/2006 11.485813 11.1 12.9 

10/05/2006 23.900103 24.6 24.1 

- 3 - 

Mendenhall PM2.5 Data Imputation for 4Q2006 Memorandum 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 

North Carolina Attainment Demonstration

 3 

 Appendix C.3 

August 21, 2009
PM2.5 Data Imputation 

- 4 - 

Sampling_Date Mendenhall Hopedale Lexington 

10/08/2006 6.785218 7.5 5.9 

10/11/2006 13.710670 14.4 13.3 

10/20/2006 11.761229 11.7 12.6 

10/26/2006 11.848665 11.4 13.4 

11/01/2006 16.130242 16.2 17.0 

11/04/2006 12.507779 13.1 12.2 

11/07/2006 10.703944 12.0 9.0 

11/16/2006 4.536147 4.6 4.7 

11/22/2006 3.983631 4.0 4.2 

11/25/2006 16.255683 14.7 20.1 

11/28/2006 18.157377 15.9 23.4 

12/01/2006 4.790717 4.4 5.8 

12/04/2006 9.893171 8.8 12.5 

12/10/2006 16.526730 14.8 20.7 

12/13/2006 25.615500 24.3 29.6 

12/19/2006 18.599245 18.9 19.2 

12/22/2006 8.990912 9.4 8.8 

12/25/2006 6.246493 6.1 6.9 

12/28/2006 14.394680 13.4 17.1 

12/31/2006 17.455206 17.2 19.0

Table 4 Quarterly Summaries 

Period CY2004 CY2005 CY2006 

1Q 11.76 11.45 10.55

2Q 14.40 13.12 13.71

3Q 16.54 19.25 19.07

4Q 13.19 12.21 12.92

 Table 5 Weighted Annual Means and Design Value 

Period CY2004 CY2005 CY2006 2004-2006 D.V. 

Mean 13.97 14.01 14.06 14.01 

Discussion 

I maintain that the estimated Design value presented in Table 5 is an accurate prediction of the result that would 

have been obtained from Mendenhall for 2004-2006, had siting conditions not changed during 4Q2006. The 

imputed average is also the most accurate and appropriate value to use for the 2006-2008 Design value 

calculations at Mendenhall to assist with the redesignation package for the Lexington site. 

Recommendations 

Design value calculations for the Greensboro-Winston-Salem-High Point MSA (or any subsequently redefined 

area that includes Greensboro) for any group of years that includes 2006 should use the imputed 4Q2006 value 

as a surrogate for the missing “actual” 4Q2006 at the Mendenhall site. 

For future consideration, we can apply (1) to data acquired after 2006 from Lexington, Hopedale and 

Mendenhall. We can also repeat the regression fitting exercise using data acquired from the regressor sites in 

2007 and later instead of 2002-2005 Either of these actions can be used to demonstrate how well the moved site 

location “represents” the original location.

Mendenhall PM2.5 Data Imputation for 4Q2006 Memorandum 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 

North Carolina Attainment Demonstration

 4 

 Appendix C.3 

August 21, 2009