# Appendix C Air Quality DataAir Quality Data i The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix C North Carolina Attainment Demonstration August 21, 2009 

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# Appendix C.1 Historical Air Quality DataHistorical Air Quality Data i The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix C.1 North Carolina Attainment Demonstration August 21, 2009 

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## Table of Contents

Quarterly Average PM 2.5 

### Values ( μ g/m 3 

### ) for the Hickory and Greensboro-Winston Salem-High Point Nonattainment Areas ..................................................................................................... .........1

Annual Average PM 2.5 

### Values ( μ g/m 3 

### ) for the Hickory and Greensboro-Winston Salem-High Point Nonattainment Areas ..................................................................................................... .........2

3-year Current PM 2.5 

### Design Values ( μ g/m 3 

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

Summary table of PM 2.5 

### Values used in for Designati ons in the Hickory and GreensboroWinston Salem-High Point Nonattainment Areas ...........................................................................4

The 2004 Average Percent Composition of PM 2.5 

### for North Carolina, as Determined by Data from Speciated Trends Network Monitors. .....................................................................................5

The 2004 Average Percent Composition of PM 2.5 

### for Hickory, North Carolina as Determined by Data from the Hickory Speciat ed Trends Network Monitor ...........................................................6

The 2004 Average Percent Composition of PM 2.5 

### for Lexington, North Carolina, as Determined by Data from the Lexington Speciated Trends Network Monitor ...................................................7

The 2004 Average Percent Composition of PM 2.5 

### for Greensboro, North Carolina, as Determined by Data from the Mendenhall Spec iated Trends Network Monitor ................................................8Historical Air Quality Data iii The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix C.1 North Carolina Attainment Demonstration August 21, 2009 

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### Table 1. Quarterly Average PM 2.5 

### Values ( μ g/m 3 

### ) 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 PM 2.5 

### values are presented in micrograms per meter cubed ( μ g/m 3 

### ).

Bolded values represent quart ers whose average values excee d the level of the annual PM2.5 National Ambient Air Quality Standard (i.e. greater than 15.0 μ g/m 3 

### ).

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

Italics value represents estimated 4 th 

### quarter 2006 data at Mende nhall. 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 concen tration and design value could be calculated. This study, titled “Mendenhall PM2.5 Data Im putation 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 North Carolina Attainment Demonstration August 21, 2009



### Table 2. Annual Average PM 2.5 

### Values ( μ g/m 3 

### ) 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 PM 2.5 

### values are presented in micrograms per meter cubed ( μ g/m 3 

### ).

Bolded values represent annual average values that exceed the le vel of the annual PM2.5 National Ambient Air Quality Standard (i.e. greater than 15.0 μ g/m 3 

### ).

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

Italics value represents a 2006 annual av erage that used estimated 4 th 

### quarter 2006 data at Mendenhall. There was an extended loss of moni toring 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 concentra tion 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 North Carolina Attainment Demonstration August 21, 2009 

### Table 3. 3-year Current PM 2.5 

### Design Values ( μ g/m 3 

### ) 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



### PM 2.5 

### design values are presented in micrograms per meter cubed ( μ g/m 3 

### ).

Bolded values represent desi gn values that exceed the leve l of the annual PM2.5 National Ambient Air Quality Standard (i.e. greater than 15.0 μ g/m 3 

### ).

The Mendenhall site was not in operation duri ng 2000 or 2001. So, the first design value period that can be calculated is 2002-2004.

Italics values represent design valu es that used estimated 4 th 

### quarter 2006 data at Mendenhall. There was an extended loss of monitoring data at the Mendenhall site du ring 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 “Mende nhall 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 PM 2.5 

### Values used in for Design ations 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



### PM 2.5 

### design values are presented in micrograms per meter cubed ( μ g/m 3 

### ).

Bolded values represent desi gn values that exceed the leve l of the annual PM2.5 National Ambient Air Quality Standard (i.e. greater than 15.0 μ g/m 3 

### ).

Underline values represent values that incor porate data from the Edgeworth & Bellmeade (37- 081-0009) monitoring site. The Me ndenhall site replaced the Edgeworth & Bellmeade site in 4 th 

### quarter 2001.Historical Air Quality Data 5 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix C.1 North Carolina Attainment Demonstration August 21, 2009 

### 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 PM 2.5 

### for North Carolina, as Determined by Data from Speciat ed 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 PM 2.5 

### for Hickory, North Carolina as Determined by Data from the Hickory Speciated Trends Network MonitorHistorical Air Quality Data 7 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix C.1 North Carolina Attainment Demonstration August 21, 2009 Lexington PM2.5 Speciatiated Trends Network Monitor 2004 Average Precentage Composition

6%

30%

10% 28% 3% 2% 21%

Nitrate

Sulfate

Ammonium

Organic carbon

Elemental carbon

Crustal component

Other



### Figure 3. The 2004 Average Percent Composition of PM 2.5 

### for Lexington, North Carolina as Determined by Data from the Lexi ngton Speciated Trends Network MonitorHistorical 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%

10% 26% 3% 3% 21%

Nitrate

Sulfate

Ammonium

Organic carbon

Elemental carbon

Crustal component

Other



### Figure 4. The 2004 Average Percent Composition of PM 2.5 

### for Greensboro, North Carolina as Determined by Data from the Mende nhall Speciated Trends Network Monitor

# Appendix C.2 Air Quality Data Used For ModelingAir Quality Data Used For Modeling i The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix C.2 North Carolina Attainment Demonstration August 21, 2009 

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## 1. Air Quality Data for 2002

As part of the Visibility Improvement State a nd 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 evaluati on. UCR took the lead in this activity. The contractor’s report (found in Attach ment C.1) describes the sources of the ambient data and the steps taken in the processing and quality assuranc e (QA) of the data. In addition, the ambient data is being processed and formatted in prepar ation for use in software packages designed for the model performance evaluation.

The goal was to preprocess the ambient data and present plots of ambien t data on the project website so model performance evaluations when the model simulations were completed. The ambient data are available from the follo wing 9 monitoring networks or databases:

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

• PAMS (Photochemical Asse ssment Monitoring Stations)

• IMPROVE (The Interagency Monitoring of Protected Visu al Environments)

• SEARCH (Southeastern Aerosol Re search and Characterization)

• EPA’s STN (Speciati on 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 Assessmen t 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 par ticulate 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 th ree 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 mode ling include the Total Ozone Mapping Spectrometer (TOMS). TOMS data is available fo r 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 qua lity 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 docume nt the data received. Separate air quality, meteorological and emissions Gatekeepers have b een identified whose roles are defined below. In addition, a Data Management Gatekeeper ha s been defined who will post data, reports and results to the project website and archiv e all key data generated in the project.

• Air Quality Data Gatekeeper . Obtain air quality data as appropriate for model input development and model performance evaluati on and assure quality of all air quality data obtained, consistent with approved QA pl an. This gatekeeper will also provide documentation of evaluation and generate IC/BC inputs for CMAQ for all modeling

runs.

• 

### Meteorological Gatekeeper . Obtain meteorologi cal 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 e rroneous 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 mist akes may be made in our processing of the data. To guard against this possibility, a plan to pe rform 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 sp ecies. The plots should be inspected for the following:

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

• Periodicity of peaks, calibration carryover

• Unexpected diurnal beha vior (i.e., isoprene)

• Unexpected relationships among species

• High single-hour concentrations of less abundant speciesAir 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 to tals, 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 bala nce 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 assu rance of air quality data modeling data can be found in the VISTAS document in Attachment C.1. Additiona l 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 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix C.2 North Carolina Attainment Demonstration August 21, 2009 

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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 EpisodesAir Quality Data Used For Modeling i 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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### 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 415.899.0700July 2003

i TABLE OF CONTENTS

Page

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

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

3.0. PHOTOCHEMICAL ASSESSMENT MONITORING STATIONS (PAMS) ............................................................................3-1

4.0. INTERAGENCY MONITORING OF PROTECTED VISUAL ENVIRONMEN TS (IMPROVE) ................................................4-1

5.0. SOUTHEASTERN AEROSOL RESEARCH AND CHARACTERIZATIO N (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 TR END NETWORK ( CASTNet).......................8-1

9.0. PM SUPE RSITES...............................................................................9-1

10.0. AIRCRAF T DATA............................................................................ 10-1

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

APPENDICES

Appendix A: Locations of Speciated PM and IMPROVE MonitorsJuly 2003

ii TABLES

Table 2-1. Currently available hourly concentration data from AQS for three VIST AS episode selections....................................2-2

Table 3-1. Species measur ed in PAMS network. ................................................3-2

Table 4-1. Species of PM 2.5 measured in IMPR OVE network.................................4-2 Table 4-2. Species mappings for IMPROVE species............................................4-4 Table 4-3. Definitions of CMAQ speci es names used in Table 4-2...........................4-5

Table 5-1. PM 2.5 species measured in search network. .........................................5-1 Table 5-2. Coarse PM species me asured in search network. ..................................5-2

Table 6-1. Species of PM 2.5 measured in ST N network.........................................6-1

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

Table 9-1. Locations of PM Supe rsites. ...........................................................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 PM 2.5 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 PM 2.5 concentrations (July 7-28, 2001). ................................................... 2-10 Figure 2-9. Distribution of average ambient O3 concentrations (J anuary 1-21, 2002)................................................ 2-11 Figure 2-10. Distribution of average ambient PM 2.5 concentrations (J anuary 1-21, 2002)................................................ 2-12

Figure 3-1. Distribution of average ambient HCHO concentrations (July 9-22, 1999) from the PAMS network .........................................3-4July 2003

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) fr om the PAMS network.....................................3-6 Figure 3-4. Distribution of average ambient acetone concentrations (January 1-21, 2002) fr om 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 AIRM oN 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). ........................................................8-3July 2003

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 st udy, the VISTAS emissions and air quality modeling team of ENVIRON In ternational Corporation, Alpine Geophysics (AG) and the University of California (UC) are compil ing 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 th e steps that will be used in the processing and quality assurance (QA) of the data. In additi on, 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 comp lete model performance evaluations when the model simulations are completed. As the proce ssing 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 de velop the website to facilitate the display of time series plots.

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

• EPA’s AQS (Air Quality System) database

• PAMS (Photochemical Asse ssment Monitoring Stations)

• IMPROVE (The Interagency Monitoring of Protected Visual Environments)

• SEARCH (Southeastern Aerosol Research and Characterization)

• EPA’s STN (Speciation Trends Network)

• NADP (National Atmosphe ric Deposition Program)

• CASTNET (Clean Air Status and Trend Network)

• PM Supersites

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

• TVA measurement network

• Georgia Institute of Technology Assessment of Spatial Aerosol Com position in Atlanta (ASACA) monitors.

Note that there is some overlap in the above monitoring networks. Fo r 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 th e existing and planned (as of January, 2002) urban and rural PM 2.5 speciation networks.

We have obtained most of the available data fr om the above monitoring networks for the three episodes of VISTAS regional haze modeling (Jul y 9-22, 1999, July 7-28, 2001 and January 1- 21, 2002). Although a comprehensive quality assu rance and validation effort is beyond the scope of this effort, we are attempting to rec oncile 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 continueJuly 2003

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 da ta. We have not yet investigated the possibility of systematic bias among different analytical me thods used in the various networks. However, this is an important subject to be addre ssed 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 no t yet been acquired. ASACA has been operating three Particle Composi tion Monitors and three TEOMS in Atlanta since early 1999. When acquired, these data will be documented on the project website.July 2003

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 (PM 10 and PM 2.5 )

• 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 O3, NOx VOCs

Sp. PM25, Visibilit y

HNO3, NO3, SO4 ,July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-2 Although direct access to full AQS raw data is curre ntly 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 av ailable 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 a rrival. Note that the AQS includes O3 data from the State/Local/National Ai r Monitoring Stations (SLAMS).

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

.

Period O 3 PM 2.5 NO 2 CO SO 2 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 NO 2 , CO and SO 2 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 O 3 , PM 2.5 , NO 2 , CO and SO 2 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 O 3 concentrations were available at 1113 stations over USA for July 9-22, 1999. Figure 2-2 shows the distribution of average O 3 concentrations for these stations.

Hourly PM 2.5 concentrations were available at 25 stations over USA for July 9-22, 1999. Figure 2-3 shows the distribution of average PM 2.5 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 averag e 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 averag e 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 averag e SO2 concentrations for these stations.July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-3 July 7-28, 2001

Hourly O 3 concentrations were available at 1113 stations over USA for July 7-28, 2001. Figure 2-7 shows the distribution of average O 3 concentrations for these stations. Hourly PM 2.5 concentrations were available at 1 48 stations over USA for July 7-28, 2001. Figure 2-8 shows the distribution of average PM 2.5 concentrations for these stations.

January 1-21, 2002

Hourly O 3 concentrations were also available at 501 stations over USA for January 1-21, 2002. Figure 2-9 shows the distribution of average O 3 concentrations for these stations.

Hourly PM 2.5 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.July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-4 Figure 2-2. Distribution of average ambient O3 concentrations (July 9-22, 1999).July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-5 Figure 2-3 . Distribution of average ambient PM 2.5 concentrations (July 9-22, 1999)

.July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-6 Figure 2-4. Distribution of average ambient NO2 concentrations (July 9-22, 1999).July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-7 Figure 2-5. Distribution of average ambient CO concentrations (July 9-22, 1999).July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-8 Figure 2-6. Distribution of average ambient SO2 concentrations (July 9-22, 1999).July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-9 Figure 2-7. Distribution of average ambient O3 concentrations (July 7-28, 2001).July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-10 Figure 2-8. Distribution of average ambient PM2.5 concentrations (July 7-28, 2001).July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-11 Figure 2-9. Distribution of average ambient O3 concentrations (January 1-21, 2002).July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-12 Figure 2-10. Distribution of average ambient PM2.5 concentrations (January 1-21, 2002).July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec3.gst.doc 3-1 3.0 PHOTOCHEMICAL ASSESSMENT MONITORING STATIONS (PAMS)

In response to the 1990 Clean Air Act Ame ndments, EPA has required more extensive monitoring of ozone and its precursors in ar eas with persistently high ozone levels. Photochemical Assessment Monitoring Stations (P AMS) 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 disti nguishing among various individual VOC. 2. Provide local, current meteorological and ambient data to serve as initial and boundary condition information for photochemical gr id models, a meth od that simulates meteorological and physical processes that affect air pollution emissions in the atmosphere. 3. Provide a representative, speciated ambien t air database which is characteristic of source emission impacts. 4. Provide ambient data meas urements which would allow la ter preparation of unadjusted and adjusted pollutant trends reports. 5. Provide additional me asurements of selected criteria pollutants.

Because different types of ambient monitoring data are required to characterize regional background concentration, emissions sources, and peak pollutant le vels, 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 O 3 , 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 th ese 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 Ja n. 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”. Exampl e plots of average concentrations for two important species (HCHO and acetone) are shown in Figures 3-1 through 3-4.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec3.gst.doc 3-2 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-NO NYL 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 30.03July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec3.gst.doc 3-3 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 distributio n of average HCHO concentratio ns 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.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec3.gst.doc 3-4 Figure 3-1. Distribution of average ambient HCHO concentrations (July 9-22, 1999) from the PAMS network

.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec3.gst.doc 3-5 Figure 3-2. Distribution of average ambient acetone concentrations (July 9-22, 1999) from the PAMS network

.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec3.gst.doc 3-6 Figure 3-3. Distribution of average ambient HCHO concentrations (January 1-21, 2002) from the PAMS network.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec3.gst.doc 3-7 Figure 3-4. Distribution of average ambient acetone concentrations (January 1-21, 2002) from the PAMS network

.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec4.gst.doc 4-1

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 Cl ass 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 PM 2.5 and PM 10 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 PM 2.5 mass and its elemental constituents, organics, ions, light absorption and PM 10 mass.

The objectives of IMPROVE are: (1) to establis h current visibility and aerosol conditions in mandatory class I areas; (2) to identify chemi cal 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 cl ass 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 a pproximately 110 IMPROVE sites and 53 IMPROVE Protocol sites.

The standard IMPROVE sampler has four samp ling modules, listed below, although some sites only include the Module A:

Module A: PM 2.5 particles on Teflon are analyzed at UC Davis using the following methods:

• gravimetric mass for PM 2.5

• 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: PM 2.5 particles on nylon. A denuder before the nylon filter removes nitric acid vapors. These are analyzed by ion chromatogr aphy (IC) at Research Triangle Institute for nitrate (NO 3 - ), chloride (Cl

- ), sulfate (SO 4 2- ), and nitrite (NO 2 - ).

Module C: PM 2.5 particles on quartz. These are analyzed at Desert Research Institute for carbon using the Thermal Optical Reflectance (T OR) combustion method. A secondary filter at selected sites is used to determine artifacts . These are reported in 8 temperature categories.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec4.gst.doc 4-2 Module D: PM 10 particles on Teflon. All are measured for PM 10 mass. Approximately 4% are analyzed by the other four methods listed for Module A.

For selected IMPROVE sites, there also are tr ansmissometer 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 Labor atory 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 coll ected and the reconciliation of any issues these organizations may find. In the past, we ha ve 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.col ostate.edu/improve

.
A 24 hour averaging period is used for IMPR OVE data. Prior to 2000, two 24 hour samples were collected twice a week, on Wednesday an d Saturday. After 2000, 24 hour samples were collected every three days.

Table 4-1 lists the 41 species measured at IM PROVE 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 ar e also available for download on the project website. Additional QA of these data can be perfor med, as discussed in Section 10, using time series and scatter plots of these data.

Table 4-1. Species of PM 2.5 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 PM 10 : 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: FineJuly 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec4.gst.doc 4-3 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 sche mes 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. Defi nitions 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_R econ is reconstructed extinction coefficient.

We note that in previous model evaluation for WR AP the sulfate and nitrate were assumed to be full neutralized and that ammonium sulfat es and nitrate were represented in CMAQ as 1.375*(ASO4J + ASO4I) +1.29*(ANO3J + ANO3 I), respectively. This was used for consistency with the formula used to calculate the model reconstructed extinction coefficient. For VISTAS we propose to use the C MAQ 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 (NH 4 + ). VISTAS is funding the Research Triangle Institute (RTI) to measure NH4 + at 10 sites beginning in September, 2002. Prior to September, NH 4 + data will be available for only 3 sites: Great Smokey Mountains (GRSM), Shenandoah (SHEN), Cl ass I areas, and Dolly Sods (DOSO).

Concerns have been raised regarding the accu racy of the IMPROVE HNO3 data, and we have found that it tends to be lower than HNO3 da ta 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 ne tworks 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 moni toring 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 (C M) 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 wh ich NO3 can be transferred to the coarse mode: formation of coarse mode sodium nitrate or calcium nitrate, a nd 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 s ubstantial 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 site s to investigate this issue. A coarse mode speciation measuremen t program was begun in spring, 2002, and it is possible that initial results wi ll be available in fall, 2002.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec4.gst.doc 4-4 We are also investigating the modal distributio ns of the Aitken and accumulation modes in CMAQ. Although these modes are typi cally assumed to be entirely PM 2.5 , 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

PM25 a 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) 10 b + 3*f(RH) c (1.375*SO4 + 1.29*NO3) + 4* OC + 10* EC + SOIL + 0.6* CM 10 b + 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.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec4.gst.doc 4-5 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 coefficientJuly 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec5.gst.doc 5-1 5.0 SOUTHEASTERN AEROSOL RE SEARCH AND CHARACTERIZATION (SEARCH)

SEARCH is a monitoring network for Southeastern Aerosol Research and Characterization. There are 8 monitoring sites located in four st ates in the SEARCH network whose locations are shown in Figure 5-1. Daily PM 2.5 data are measured for 46 sp ecies, while daily coarse PM data are measured for 18 species in SEARCH netw ork. 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 PM 2.5 species and 18 coarse PM species. The SEARCH data are currently available for two of the VISTAS Ph ase I modeling episodes (July 9-22, 1999 and July 7-28, 2001).

The frequency of the PM 2.5 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 PM 2.5 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 . PM 2.5 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 VanadiumJuly 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec5.gst.doc 5-2 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 PM 2.5 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.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec5.gst.doc 5-3 Figure 5-1. Locations of the SEARCH monitoring sites (Source: Atmospheric Research and Analysis, Inc., www.atmopheric-research.com).July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec6.gst.doc 6-1 6.0 SPECIATION TR ENDS NETWORK (STN)

EPA’s Speciation Trends Network (STN) includes about 215 monitoring stations nationwide. It appears that among these 215 sites may include IMPROVE site s or other data from other networks. This, however, need s to be verified. Daily PM 2.5 data are measured for 64 species in the STN network. Some archived ST N 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 PM 2.5 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 881 70 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 883 02 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 LCJuly 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec7.gst.doc 7-1 7.0 NATIONAL ATMOSPHERIC DE POSITION PROGRAM (NADP)

The National Atmospheric Deposition Program/N ational Trends Network (NADP/NTN) is designed to measure wet deposition. The netw ork is a cooperative effort between State Agricultural Experiment Stations, the U.S. Geological Survey, U.S. Department of Agriculture, and other governmental and privat e entities. It includes over 200 sites in the continental United States, Alaska, and Puerto Ri co, and the Virgin Islands whose locations are shown in Figure 7-1. The purpose of the netw ork 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 Nation al 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 sa mpling period the data has poor temporal resolution and the sample chemistry can be a ffected by chemical and biological activity. The NADP also includes the Atmospheric Integrat ed Research Monitoring Network (AIRMoN) which was designed to study precipitation chemistr y 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 ne xt. NADP measures weekly deposition of compounds in units of mg/l as well as precipitati on in units of l. The PM models will output deposition in terms of hourly mass flux per gr id cell (e.g., 12 km x 12 km). The modeled results will be accumulated to weekly de position fluxes per unit area (e.g., gm/km 2 /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 we t deposition components. Figure 7-2 shows the locations of monitoring sites for both program s. The dry deposition data is described at their web page: http://www.arl.noaa.gov/resear ch/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 AIRM oN network by combining estimates of sitespecific and time-evolving depos ition velocities with measuremen ts of air concentrations obtained using a weekly sampling protocol. The intent has been to mirror the 0900 Tuesday sample change standards adopted by the Nationa l Atmospheric Deposition Program. At times, the AIRMoN dry samples are obtained over s ubstantially different periods, because ofJuly 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec7.gst.doc 7-2 operator absence or problems with instrumenta tion. 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 sulf ur 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 th e 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 ammoni um nitrate deposited on the teflon filter is incompletely disassociated with minor conseque nces 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 th e 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 he re 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 rate s are computed as the product of the weekly average deposition velocities and the weekly average con centration, 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. O mitting consideration of a correlation term is like ly 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 th e 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.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec7.gst.doc 7-3 AIRMoN Wet Deposition Data:

The AIRMoN wet deposition program is described at the web page: http://www.arl.noaa.gov/resear ch/projects/airmon_wet.html The AIRMoN wet deposition moni toring 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 quan tification of ammonium deposition. At the National Atmospheric Deposition Program Te chnical 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 da ta users to specific problems.

UCR is currently downloading data from both wet and dry AIRMoN networks.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec7.gst.doc 7-4 Figure 7-1. Locations of NADP National Trends Network (NTN) monitors (Figure obtained from the EPA NADP website).July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec7.gst.doc 7-5 Figure 7-2. Locations of the AIRMoN wet and dry deposition monitoring sites (Figure obtained from the EPA AIRMoN website).July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec7.gst.doc 7-6 Figure 7-3. Locations of the AIRMoN wet deposition monitoring sites (Figure obtained from the EPA AIRMoN website).July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec8.gst.doc 8-1 8.0 CLEAN AIR STATUS A ND TREND NETWORK (CASTNET)

CASTNET is designed to measure dry depositio n and it is comprised of 123 sites across the United States as shown in Figure 8-1. It incl udes 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 th e ambient concentration measurements to compare with the model. Dry deposition data ar e 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 ar e 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 f ilter 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 p acks are exposed for 1-week intervals (i.e., Tuesday to Tuesday) at a flow rate of 1.5 lite rs per minute (3.0 liters per minute for western sites), and sent to the Harding ESE, Gaines ville, FL laboratory for chemical analysis.

All three filters are extracted a nd analyzed for certain species:

Teflon filter: SO 4 2- , NO 3 - , NH 4 + Nylon filter: SO 4 2- , NO 3 - Cellulose filter (Whatman): SO 4 2- , NO 3 - The sulfate, nitrate and ammonium in the teflon filter extract are interpreted as particulate species (listed above as TSO4, TNO3 and T NH4). 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 volatil ized 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 nitr ate 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 direct ly 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 andJuly 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec8.gst.doc 8-2 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 mapp ing 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 HNO 3 NHNO3 (nylon filter) 2176.9*DENS a *HNO3

Particulate NO 3 TNO3 (Teflon filter) ANO3J + ANO3I

Total HNO 3 + NO 3 NHNO3 (nylon filter) + TNO3 (Teflon filter) 2176.9*DENS a *HNO3 + ANO3J + ANO3I

Particulate NH 4 TNH4 (Teflon filter) ANH4I + ANH4J

Gaseous SO 2 TOTAL_SO2 2211.5*DENS*SO2

Particulate SO 4 TSO4 (Teflon filter) ASO4I + ASO4J

a Air density obtained from MCIP outputsJuly 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec8.gst.doc 8-3 Figure 8-1. Locations of CASTNet monitoring sites (Figure obtained from EPA CASTNet website).July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec9.gst.doc 9-1 9.0 PM SUPERSITES

There are 8 PM Supersites. The locations of th ese 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 (N ew York, Pittsburgh, and Baltimore). As other researchers expressed interest in the pr ogram 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 bound ary layer. EPA plans to centralize all air quality and meteorological data in one location w ithin several relational databases to allow for easy access to the data and to help ensure that m odelers and data analysts will be working with uniform databases. Details on data collect ed 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 UniversityJuly 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec10.gst.doc 10-1 10.0 AIRCRAFT DATA

Of the three episodes selected for Phase I, onl y during episode 3 (13-21 July 1999) were there research aircraft flights performed that provi de aloft meteorological and air quality data suitable for model performance testing. Thes e data were collected during the Southern Oxidants Study (SOS) intensive field program pe rformed principally in Nashville, TN during the summer of 1999. An overview of the SOS/Na shville 1999 aircraft data has been submitted to VISTAS as Appendix A to the Task 2 repor t (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/pr oject/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.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec11.gst.doc 11-1 11.0 DATA QUALITY ASSURANCE

In gathering data from the monitoring networ ks we have assumed that QA was performed by the agency or researcher responsible for collect ing 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 pr ocessing 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 sp ecies. The plots should be inspected for the following:

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

• Periodicity of peaks, calibration carryover

• Unexpected diurnal beha vior (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 to tals, 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 measur ed 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 bala nce 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.

### Appendix A

Locations of Speciated PM and IMPROVE Monitors

### 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 PM 2.5 

# Speciation Networks

SS SS

SS



## 01/02 SS

SS SS SS

SS

### 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 Protocol & Rural Supplemental PM 2.5 

# Speciation Sites

2/02Air 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 

### (This page intentionally left blank)

# Appendix C.3 Mendenhall PM2.5 Data Imputation for 4Q2006 MemorandumMendenhall 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

Introduc tion ................................................................................................................... .........................................1 Methods........................................................................................................................ ..........................................2 Results ........................................................................................................................ ............................................2 Discussion ..................................................................................................................... .........................................4 Recommendations ................................................................................................................ ..................................4

List of Tables

Table 1 Regression Analysis using the Lexington, Cher ry Grove, Burlington and Ha ttie 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 Quarterl y Summaries.................................................................................................... .............................4 Table 5 We ighted Annual Means and Design Value........................................................................................... ...4

List of Figures

Figure 1 Map of Monitor Lo cations .............................................................................................. .........................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 (D avidson Co., ID 370570002) and Hattie (Forsyth Co., ID 370670022).

The design value calculations for 20 05-2008 for the Mendenhall site are in complete 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 constructedPM2.5 Data Imputation

immediately adjacent to the monitoring site (unpublis hed 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 calcula te 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 regre ssion 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 predic tors. 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 -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 R 2 = 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)



### HD LX MH *6322.0*3464.0 + = (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 Ta ble 5 shows the 3 annual m eans 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 -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 approp riate value to use for th e 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 Greensbo ro) 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.