Comparison of Integrated Filter and SemiContinuous Measurements of PM2.5 Nitrate, 

Sulfate, and Carbon Aerosols in the Speciation 

Trends Network (STN) 

EPA 454/R-05-004 

December 2005 

Comparison of Integrated Filter and Semi-continuous Measurements of PM2.5

Nitrate, Sulfate, and Carbon Aerosols in the Speciation Trends Network (STN) 

By: 

David Vaughn 

Theresa O’Brien 

Paul T. Roberts 

Sonoma Technology, Inc. 

1360 Redwood Way, Suite C 

Petaluma, CA 94954-1169 

Joann Rice 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Emissions, Monitoring and Analysis Division 

Research Triangle Park, NC 27711 

Contract No. EP-D-05-004 

Task Order 1-04 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Emissions, Monitoring and Analysis Division 

Research Triangle Park, NC 27711 
iv

ACKNOWLEDGMENTS 

The authors would like to thank Solomon Ricks and Kevin Cavender at the U.S. 

Environmental Protection Agency (EPA) for their guidance during this project. Obviously this 

work could not have been done without the hard work of staff at the various states operating the 

semi-continuous speciation monitors: Darcy Anderson and Brant Englund at the Arizona 

Department of Environmental Quality; Mark Bohlin of Cook County Department of 

Environmental Control, Illinois, and Bob Swinford at the Illinois Environmental Protection 

Agency; John Wicker and Don Klotz at the Indiana Department of Environmental Management; 

Ed Michel at the Texas Commission of Environmental Quality and Earle Wright, the site 

contractor in Texas; and Jim Frost and J.B. Dennison at the Washington Department of Ecology. 

In addition, Jim Homolya of EPA was the original field measurements and special study 

coordinator. At Sonoma Technology, Inc., Anna Frankel provided data processing and statistics 

support, and Liz Simon and Nicole Hyslop supported the Arizona field site early in the study. 

Sandy Smethurst of the STI publications staff provided editing support. 

EPA DISCLAIMER 

The information in this document has been funded wholly or in part by the United States 

Environmental Protection Agency (EPA) under Contract EP-D-05-004 to Sonoma Technology, 

Incorporated. Mention of trade manes or commercial products does not constitute EPA 

endorsement or recommendation for use. 
v

TABLE OF CONTENTS 

Section Page

ACKNOWLEDGMENTS ............................................................................................................. iv 

EPA DISCLAIMER ...................................................................................................................... iv 

LIST OF FIGURES ...................................................................................................................... vii 

LIST OF TABLES...........................................................................................................................x 

1. INTRODUCTION.............................................................................................................. 1-1 

2. OBJECTIVES .................................................................................................................... 2-1 

3. CONCLUSIONS AND RECOMMENDATIONS............................................................. 3-1 

3.1 R&P 8400N Nitrate Monitor .................................................................................... 3-2 

3.2 R&P 8400s Sulfate Monitor ..................................................................................... 3-2 

3.3 R&P 5400 Carbon Monitor ...................................................................................... 3-3 

3.4 Sunset Carbon Monitor............................................................................................. 3-3 

4. SITE DESCRIPTIONS ...................................................................................................... 4-1 

4.1 Chicago, Illinois........................................................................................................ 4-1 

4.2 Phoenix, Arizona ...................................................................................................... 4-1 

4.3 Seattle, Washington .................................................................................................. 4-1 

4.4 Houston, Texas ......................................................................................................... 4-1 

4.5 Indianapolis, Indiana................................................................................................. 4-2 

5. INSTRUMENTATION...................................................................................................... 5-1 

5.1 STN Filter-Based Instruments .................................................................................. 5-1 

5.1.1 MetOne SASS .............................................................................................. 5-1 

5.1.2 URG MASS400 and URG MASS450.......................................................... 5-1 

5.2 Semi-Continuous Monitors....................................................................................... 5-1 

5.2.1 R&P 8400N Nitrate Monitor........................................................................ 5-1 

5.2.2 R&P 8400S Sulfate Monitor ........................................................................ 5-2 

5.2.3 Carbon .......................................................................................................... 5-2 

5.3 Data Set Summary .................................................................................................... 5-4 

5.4 Statistical Approach.................................................................................................. 5-6 

5.4.1 Comparability............................................................................................... 5-6 

5.4.2 Predictability ................................................................................................ 5-7 

6. PREVIOUS PRECISION AND COMPARISON RESULTS FOR STN SAMPLERS .... 6-1 

6.1 Precision Estimates for Data From Collocated STN Samples.................................. 6-1 

6.2 Results for Data From Collocated STN and IMPROVE.......................................... 6-1 

7. RESULTS........................................................................................................................... 7-1 

7.1 Precision Estimates................................................................................................... 7-1 

7.1.1 Single- (Within-) Sampler Precision Estimates............................................ 7-1 

7.1.2 Between Sampler Precision Estimates ......................................................... 7-5 
vi

7.2 R&P 8400N Nitrate Versus STN Filter-Based Nitrate............................................. 7-8 

7.2.1 Nitrate Data Corrections............................................................................... 7-8 

7.3 R&P 8400S Sulfate Versus STN Filter-Based Sulfate........................................... 7-13 

7.4 Carbon..................................................................................................................... 7-15 

7.4.1 R&P 5400 Semi-Continuous Versus STN Filter-Based Carbon................ 7-16 

7.4.2 Sunset Semi-Continuous Versus STN Filter-Based Carbon ...................... 7-18 

8. OPERATIONAL ISSUES.................................................................................................. 8-1 

8.1 R&P 8400N and 8400S ............................................................................................ 8-1 

8.1.1 Distribution of Annual Hours....................................................................... 8-1 

8.1.2 Flash Strips................................................................................................... 8-1 

8.1.3 Aqueous Standards....................................................................................... 8-3 

8.1.4 Water Reservoir and Hydration Tube .......................................................... 8-3 

8.1.5 Orifice Cleaning ........................................................................................... 8-3 

8.1.6 Molybdenum Converter ............................................................................... 8-3 

8.1.7 Data Screen Failures..................................................................................... 8-4 

8.1.8 Data Acquisition and Reduction................................................................... 8-4 

8.2 R&P 5400 ................................................................................................................. 8-4 

8.3 Sunset Carbon Monitor............................................................................................. 8-6 

9. REFERENCES................................................................................................................... 9-1 

APPENDIX A: SCATTER PLOTS AND REGRESSIONS FOR PHOENIX, SEATTLE, 

AND INDIANAPOLIS................................................................................................... A-1 
vii

LIST OF FIGURES 

Figure Page

Figure 5-1. Range of deployment dates for each of the monitors in at the five sites in the 

study. Vertical lines denote yearly increments. .............................................................. 5-5 

Figure 7-1. Twenty-four-hour average R&P 8400N PM2.5 nitrate versus 24-hr integrated 

STN filter nitrate at Chicago, from May 2, 2002, to May 10, 2005. ............................. 7-11 

Figure 7-2. Twenty-four-hour average R&P 8400N PM2.5 nitrate versus 24-hr integrated 

STN filter nitrate at Houston, from January 27, 2003, to April 13, 2005...................... 7-11 

Figure 7-3. Average diurnal variability in R&P 8400N nitrate concentrations in Phoenix 

during the winter (November through February) and summer (March through 

October). Adjustments to the R&P 8400N data were made based on the regression 

of 8400N data with the filter data. ................................................................................. 7-12 

Figure 7-4. Nitrate data from Phoenix, Chicago, Seattle, and Indianapolis illustrating the 

typical non-linearity of the semi-continuous data relative to the STN filter-based 

data. Houston data are atypical and excluded............................................................... 7-13 

Figure 7-5. Twenty-four-hour average R&P 8400S PM2.5 sulfate versus 24-hr integrated 

STN filter sulfate at Chicago, May 2, 2002 to May 10, 2005........................................ 7-14 

Figure 7-6. Twenty-four-hour average R&P 8400S PM2.5 sulfate versus 24-hr integrated 

STN filter sulfate at Seattle, May 2, 2002 to February 3, 2005..................................... 7-15 

Figure 7-7. Twenty-four-hour average R&P 5400 PM2.5 total carbon versus 24-hr 

integrated STN filter total carbon at Chicago, from May 2, 2002 to May 7, 2005. ...... 7-16 

Figure 7-8. Twenty-four-hour average R&P 5400 PM2.5 organic carbon versus 24-hr 

integrated STN filter organic carbon at Chicago, May 2, 2002 to May 7, 2005. .......... 7-17 

Figure 7-9. Twenty-four-hour average R&P 5400 PM2.5 elemental carbon versus 24-hr 

integrated STN filter elemental carbon at Chicago, May 2, 2002 to May 7, 2005........ 7-18 

Figure 7-10. Twenty-four-hour average Sunset PM2.5 total carbon versus 24-hr integrated 

STN filter total carbon at Chicago, August 1, 2004 to May 7, 2005............................. 7-19 

Figure 7-11. Twenty-four-hour average Sunset PM2.5 organic carbon versus 24-hr 

integrated STN filter organic carbon at Chicago, August 1, 2004 to May 7, 2005. ...... 7-20 

Figure 7-12. Twenty-four-hour average Sunset PM2.5 elemental carbon versus 24-hr 

integrated STN filter elemental carbon at Chicago, August 1, 2004 to May 7, 2005.... 7-20 
viii

LIST OF FIGURES 

Figure Page

Figure 7-13. Twenty-four-hour average Sunset PM2.5 total carbon versus 24-hr integrated 

STN filter total carbon at Seattle, August 13, 2004 to April 10, 2005. ......................... 7-21 

Figure 7-14. Twenty-four-hour average Sunset PM2.5 organic carbon versus 24-hr 

integrated STN filter organic carbon at Seattle, August 13, 2004 to April 10, 2005. ... 7-21 

Figure 7-15. Twenty-four-hour average Sunset PM2.5 elemental carbon versus 24-hr 

integrated STN filter elemental carbon at Seattle, August 13, 2004 to April 10, 

2005................................................................................................................................ 7-22 

Figure 7-16. Twenty-four-hour average Sunset PM2.5 total carbon versus 24-hr integrated 

STN filter total carbon at Phoenix, July 2, 2004 to May 13, 2005. ............................... 7-22 

Figure 7-17. Twenty-four-hour average Sunset PM2.5 organic carbon versus 24-hr 

integrated STN filter organic carbon at Phoenix, July 2, 2004 to May 13, 2005. ......... 7-23 

Figure 7-18. Twenty-four-hour average Sunset PM2.5 elemental carbon versus 24-hr 

integrated STN filter elemental carbon at Phoenix, July 2, 2004 to May 13, 2005....... 7-23 

APPENDIX A 

Figure A-1. Twenty-four-hour average R&P 8400N PM2.5 nitrate versus 24-hr integrated 

STN filter nitrate at Phoenix, from September 26, 2002, to May 13, 2005.................... A-2 

Figure A-2. Twenty-four-hour average R&P 8400N PM2.5 nitrate versus 24-hr integrated 

STN filter nitrate at Seattle, from May 2, 2002, to April 14, 2005................................. A-2 

Figure A-3. Twenty-four-hour average R&P 8400N PM2.5 nitrate versus 24-hr integrated 

STN filter nitrate at Indianapolis, from June 13, 2002, to March 11, 2005.................... A-3 

Figure A-4. Twenty-four-hour average R&P 8400S PM2.5 sulfate versus 24-hr integrated 

STN filter sulfate at Phoenix, from September 30, 2003, to May 12, 2004. .................. A-3 

Figure A-5. Twenty-four-hour average R&P 8400S PM2.5 sulfate versus 24-hr integrated 

STN filter sulfate at Indianapolis, from June 19, 2002, to July 8, 2004......................... A-4 

Figure A-6. Twenty-four-hour average R&P 8400S PM2.5 sulfate versus 24-hr integrated 

STN filter sulfate at Houston, from January 27, 2003, to April 13, 2005. ..................... A-4 

Figure A-7. Twenty-four-hour average R&P 5400 PM2.5 total carbon versus 

24-hr integrated STN filter total carbon at Phoenix, from December 13, 2002, to 

May 13, 2005. ................................................................................................................. A-5 
ix

LIST OF FIGURES 

Figure Page

Figure A-8. Twenty-four-hour average R&P 5400 PM2.5 total carbon versus 24-hr 

integrated STN filter total carbon at Seattle, from May 2, 2002, to March 29, 2005..... A-5 

Figure A-9. Twenty-four-hour average R&P 5400 PM2.5 total carbon versus 24-hr 

integrated STN filter total carbon at Indianapolis, from June 26, 2002, to April 9, 

2004................................................................................................................................. A-6 

Figure A-10. Twenty-four-hour average R&P 5400 PM2.5 total carbon versus 24-hr 

integrated STN filter total carbon at Houston, from January 24, 2003, to March 26, 

2005................................................................................................................................. A-6 

Figure A-11. Twenty-four-hour average R&P 5400 PM2.5 organic carbon versus 24-hr 

integrated STN filter organic carbon at Phoenix, from December 13, 2002, to May 

13, 2005........................................................................................................................... A-7 

Figure A-12. Twenty-four-hour average R&P 5400 PM2.5 organic carbon versus 24-hr 

integrated STN filter organic carbon at Seattle, from May 2, 2002, to March 29, 

2005................................................................................................................................. A-7 

Figure A-13. Twenty-four-hour average R&P 5400 PM2.5 organic carbon versus 24-hr 

integrated STN filter organic carbon at Indianapolis, from June 26, 2002, to April 

9, 2004............................................................................................................................. A-8 

Figure A-14. Twenty-four-hour average R&P 5400 PM2.5 organic carbon versus 24-hr 

integrated STN filter organic carbon at Houston, from January 24, 2003, to March 

26, 2005........................................................................................................................... A-8 

Figure A-15. Twenty-four-hour average R&P 5400 PM2.5 elemental carbon versus 24-hr 

integrated STN filter elemental carbon at Phoenix, from December 13, 2002, to 

May 13, 2005. ................................................................................................................. A-9 

Figure A-16. Twenty-four-hour average R&P 5400 PM2.5 elemental carbon versus 24-hr 

integrated STN filter elemental carbon at Seattle, from May 2, 2002, to March 29, 

2005................................................................................................................................. A-9 

Figure A-17. Twenty-four-hour average R&P 5400 PM2.5 elemental carbon versus 24-hr 

integrated STN filter elemental carbon at Indianapolis, from June 26, 2002, to April 

9, 2004........................................................................................................................... A-10 

Figure A-18. Twenty-four-hour average R&P 5400 PM2.5 elemental carbon versus 24-hr 

integrated STN filter elemental carbon at Houston, from January 24, 2003, to 

March 26, 2005. ............................................................................................................ A-10
x

LIST OF TABLES 

Table Page

3-1. Summary of comparisons of semi-continuous nitrate, sulfate, and carbon monitor 

data with collocated filter-based STN data at the Phoenix, Chicago, Indianapolis, 

Deer Park, and Seattle sites.............................................................................................. 3-1 

5-1. Effect on regression statistics with two methods of data completeness evaluations 

for the 10-minute nitrate records...................................................................................... 5-5 

6-1. Collocated precision estimates for speciated components in the STN network .............. 6-1 

6-2. Summary of regression results between IMPROVE and STN filter-based data ............. 6-2 

7-1. Coefficients of variation of measured nitrate mass for the 8400N blind performance 

evaluations #4 and #5 of nitrate aqueous standards......................................................... 7-3 

7-2. Coefficients of variation of measured mass for the 8400S blind performance 

evaluations #4 and #5 of sulfate aqueous standards ........................................................ 7-4 

7-3. Coefficients of variation for the Sunset blind PE of EC, OC, and TC conducted in 

spring 2005....................................................................................................................... 7-5 

7-4. Summary statistics for comparison of semi-continuous speciated PM2.5 data with 

filter-based STN data ....................................................................................................... 7-6

7-5. Theoretical conversion efficiencies from aqueous standards tests and gas analyzer 

efficiencies from routine span audit................................................................................. 7-9 

8-1. Percent of annual hours spent on operational and maintenance tasks for the R&P 

8400 series monitors ........................................................................................................ 8-2

8-2. Percent of annual hours spent on operational and maintenance tasks for the R&P 

5400 OCEC monitor ........................................................................................................ 8-5 
1-1

1. INTRODUCTION 

The U.S. Environmental Protection Agency (EPA) established the National Ambient Air 

Quality Standard (NAAQS) for PM2.5 gravimetric mass in 1997. A chemical Speciation Trends 

Network (STN) of 54 sites was established across the United States in support of the NAAQS to 

evaluate the chemical components of PM2.5. Major components of PM2.5 include nitrate (NO3), 

sulfate (SO4), elemental carbon (EC), and organic carbon (OC). At STN sites, approved 

speciation samplers are used to collect 24-hr filter-based measurements, nominally on every third 

day. Nitrate, sulfate, and carbon fractions are routinely analyzed, along with a suite of elements 

and PM2.5 mass. Research Triangle Institute (RTI) in North Carolina performs all filter analyses 

for the STN. PM2.5 mass is determined from Teflon® filters using a gravimetric method that is 

similar to the method used in the PM2.5 mass attainment network. Elemental analysis is 

performed on the Teflon® filters (subsequent to gravimetric mass) by Energy-Dispersive X-Ray 

Fluorescence (EDXRF). The major inorganic ions of filter-based PM2.5 STN samples are 

determined from nylon filters by ion chromatography (IC) following extraction in water. EC and 

OC fractions are collected using QMA-quartz filters that are analyzed by a thermal-optical 

transmittance (TOT) method that is a modified version of NIOSH 5040 (NIOSH, 1999). 

Semi-continuous instruments have been developed to measure ambient aerosol 

concentrations of nitrate, sulfate, EC, and OC on a near real-time basis. At this time, no semicontinuous or continuous monitors have been developed for determination of elemental 

composition of PM2.5 in ambient air. Collocated comparisons of the instruments with the 

standard methods used in the STN are needed to demonstrate their bias and precision. In the 

spring 2001, the EPA initiated a study to evaluate these monitors and their potential use in the 

routine speciation monitoring network. The semi-continuous monitors were collocated with 

STN samplers at five monitoring sites. Ideally, the quality of data from the semi-continuous 

monitors will be adequate to supplement speciation data collection; the data will have higher 

time resolution and be acquired more rapidly; and the monitors will require less maintenance. 

Proven continuous monitoring would eliminate transportation and laboratory analysis costs 

associated with filter-based samples and would benefit public outreach and air quality 

forecasting by increasing the timeliness of the data. Continuous-monitoring data may support 

more accurate source apportionment and help elucidate the processes leading to the occurrence 

of nitrate, sulfate, EC, and OC in PM2.5. 

Because the spatial requirements of the comparison study are geographically diverse, a 

wide range of environments was included in the study design. The participants in the study were 

the Arizona Department of Environmental Quality (Phoenix site); Illinois Environmental 

Protection Agency (Chicago site, operated by Cook County); Washington Department of 

Ecology (Seattle site); Indiana Department of Environmental Management (Indianapolis site); 

and Texas Council on Environmental Quality (Houston site). Although data were available from 

two sites in Illinois, only data from the Chicago site were analyzed here. Monitors have been 

collecting data for nearly three years at most of these sites. 
2-1

2. OBJECTIVES 

EPA contracted with Sonoma Technology, Inc. (STI) to conduct an analysis of the 

collocated filter-based and semi-continuous data collected during this study. Beyond the 54 STN 

sites, the EPA speciation network has an additional component of state and local-directed 

supplemental monitoring sites called SLAMS (State and Local Air Monitoring Stations). The 

SLAMS comprise a very dynamic network that is currently comprised of about 200 sites placed 

to meet state and local air monitoring needs. The goal is to determine if the commerciallyavailable semi-continuous carbon, nitrate, and sulfate monitors used in this study are sufficiently 

robust to allow routine application in the speciation monitoring network. 

Using filter-based samplers in the STN as the benchmark, the semi-continuous monitors 

were evaluated for comparability and predictability (see Section 4.4 for definitions). Monitors 

meeting comparability criteria would be useful for detecting spatial or temporal differences and 

would be acceptable for use in supplementing the data collected by the routine STN network. 

Comparable monitors would also be acceptable for use in the SLAMS network. Comparable 

monitors would not require and corrections or adjustments to the data. Criteria for predictability 

are the less stringent, but would allow calibration of the high time-resolution, semi-continuous 

data with the filter-based data, leading to more accurate representations of the diurnal variation 

that is known to exist for the major components of PM2.5. 
3-1

3. CONCLUSIONS AND RECOMMENDATIONS 

The results of the comparisons between the semi-continuous nitrate, sulfate, and carbon 

data and collocated filter-based nitrate, sulfate, and carbon data from the STN network were 

reviewed. The comparisons were based on 24-hr average data from the semi-continuous 

monitors and 24-hr filter sampler data from the STN network. The results were then compared 

with the criteria for comparability and predictability, as discussed in Section 5.4. Table 3-1 

summarizes whether the data from the monitors, operated at each of the five sites, met the 

comparability and predictability criteria. 

Table 3-1. Summary of comparisons of semi-continuous nitrate, sulfate, and carbon 

monitor data with collocated filter-based STN data at the Phoenix (AZ), Chicago (IL), 

Indianapolis (IN), Deer Park (TX), and Seattle (WA) sites. Blank cells indicate that 

the comparison did not meet the criteria. 

Comparability 

(R2

, slope and intercept)

Comparability 

(R2, ratio of means) 

Predictability 

Instrument (r) 

AZ IL IN TX WA AZ IL IN TX WA AZ IL IN TX WA

R&P 8400Na NA NA NA NA NA 9e 9 9 9 d

Pre-molyb NA NA NA NA NA 9e 9 9 9 9

Post-molyc NA NA NA NA NA 9 9g 9 9 9 d

R&P 8400S NA NA NA NA NA 9f 9f 9 9

R&P 5400 EC 

R&P 5400 OC g 9

R&P 5400 TC g d 9

Sunset EC 9 9 e e d 9 g e e g 9 9 e e 9

Sunset OC e e g g e e 9 9 9 e e 9

Sunset TC e e g g e e 9 9 9 e e 9

a

 Entire data set; both prior to and after converter replacement 

b

 Only the R&P 8400N data subset prior to converter replacement c

 Only the R&P 8400N data subset after converter replacement d

 Close to meeting criteria 

e

 No monitor installed 

f

 Met ratio-of-means criteria, but not correlation criteria 

g

 Met correlation, but not ratio-of-means criteria 

NA Criteria did not apply

When evaluating comparability, neither the 8400N nor the 8400S provided results that 

consistently met both the ratio-of-means and the correlation comparability criteria suggested for 

this study. Only one 8400N monitor in Phoenix met both comparability criteria after converter 
3-2

replacement. It is clear that more improvements and enhancements are required to improve 

comparability of these monitors with filter-based measurements. However, both the 8400N and 

8400S did a better job at meeting the predictability (correlation) criterion. The 8400N monitors 

at four of five sites met the predictability criterion; and the fifth site was close to meeting it. The 

8400S met predictability at two sites (Indianapolis and Seattle). These monitors largely do not 

provide data that are comparable to filter-based STN measurements; however, where monitors 

met the predictability criterion, the data may be adequate for adjustment using the filter-based 

STN measurements to more accurately represent diurnal patterns at these sites. 

The comparability of the 5400 OCEC monitor, as compared to the filter-based STN 

carbon measurements, was poor. Predictability was also poor. The predictability criterion for 

OC was met at only one site (Seattle). 

The Sunset carbon monitor did a better job at meeting, or came close to meeting, the 

ratio-of-means and correlation criteria for carbon. However, it met the slope and intercept 

comparability criteria on only one site. The Sunset carbon monitor met the predictability 

criterion at all five sites, indicating that adjustment of the data against STN filter-based 

measurements may be appropriate to more adequately represent diurnal patterns in speciation 

monitoring network. 

3.1 R&P 8400N NITRATE MONITOR 

Data from the R&P 8400N met the correlation and ratio-of-the-means comparability 

criteria at the Phoenix site, only for data after the converter was replaced. Data from 

Indianapolis and Chicago met the ratio-of-means criteria, but not the correlation criterion. Data 

from the other sites did not meet either of the comparability criteria. Data from the R&P 8400N 

met the predictability criteria at four of the five sites and were close at the fifth site. 

Compared with the STN nitrate data, there was significant bias in the R&P 8400N data at 

all five sites; the bias was negative at four sites and positive at the Houston site. The nitrate 

response appears to be non-linear, with a stronger bias evident at higher ambient nitrate 

concentrations. The nitrate monitors seemed to have had consistently high conversion 

efficiencies and gas analyzer efficiencies, although there were some indications that problems 

with the gas analyzer converter efficiency could cause different results. The most timeconsuming activity for the nitrate monitor was data acquisition and data processing; running 

aqueous standards and dealing with the flash strip failures were the next most time-consuming 

activities. 

3.2 R&P 8400S SULFATE MONITOR 

Data from the R&P 8400S at the Phoenix and Houston sites met the ratio-of-means 

criteria, but not the correlation criterion. The predictability criteria were met at only two of the 

five sites. When compared with the STN sulfate data, there was significant bias in the R&P 

8400S data at all five sites; the bias was negative at four sites and positive at the Seattle site. The 
3-3

sulfate monitors had significantly different conversion efficiencies at the various sites, although 

the gas analyzer efficiencies were consistently high. The most time-consuming activity for the 

sulfate monitor was data acquisition and data processing; running aqueous standards and dealing 

with the flash strip failures were the next most time-consuming activities. 

3.3 R&P 5400 CARBON MONITOR 

Of the EC, OC, and TC data from the R&P 5400, only the OC and TC data at the Seattle 

site met the predictability criteria while the TC data at the Indianapolis site almost met the 

predictability criteria. R&P 5400 EC, OC, and TC data did not meet the comparability criteria at 

any site. When compared with the STN carbon data, there was significant negative bias in the 

R&P 5400 TC, OC, and EC data at all five sites; regression slopes typically averaged 0.4 to 0.6. 

The most time-consuming activity for the 5400 carbon monitor was data acquisition and data 

processing; flow problems, along with oven and afterburner problems, were the most frequent 

and recurring operational issues. 

3.4 SUNSET CARBON MONITOR 

The results for the Sunset carbon monitor operated at three (Phoenix, Chicago, and 

Seattle) of the sites were significantly better. The EC data at Phoenix met the slope and intercept 

comparability criteria; the EC, OC, and TC data from all three sites either met or almost met the 

ratio-of-means and correlation comparability criteria; and EC, OC, and TC at all three sites met 

the predictability criteria. Compared with the STN TC and OC data, there was noticeable 

positive bias in the Sunset data at low concentrations, but little bias at higher concentrations. 

The most significant operational problems with the Sunset carbon monitor were failures with the 

associated laptop computer and differences in the version of analysis software used to calculate 

final concentrations. 
4-1

4. SITE DESCRIPTIONS 

4.1 CHICAGO, ILLINOIS 

The Chicago Com Ed site is located in a trailer at the Commonwealth Edison (Com Ed) 

Maintenance facility, 7801 Lawndale Avenue, Chicago, Illinois. The AIRS Site ID is 

170310076. The locational coordinates are latitude 41.885864 (41 deg 45 min 4.9 sec N) and 

longitude –87.625729 (87 deg 42 min 49.5 sec W). The site elevation is 186 m above mean sea 

level. 

The Com Ed facility covers an area of several square blocks to the east of Lawndale 

Avenue. Residential areas are located immediately east, south, and west. Industrial areas, 

including large rail yards, are located 1 km to the northwest and 1.5 km to the east. Chicago 

Midway Airport is located 4 km to the northwest. 

4.2 PHOENIX, ARIZONA 

Phoenix is located in the central Arizona desert where many sources contribute to 

observed PM2.5. It differs from the other locations in the study because most of its PM is 

generally in the coarse fraction (PM10-2.5). The Phoenix data included in the this study were 

collected at the Phoenix JLG Supersite, AIRS Site ID 04-013-9997, located at 

4530 N. 17th Avenue (latitude 33.502959, 33 deg 30 min 13.0 sec N and longitude –112.095785, 

112 deg 5 min 42.0 sec W). The site is in a residential neighborhood about 1.5 km east of 

Interstate 17. The elevation is 346 m above mean sea level. 

4.3 SEATTLE, WASHINGTON 

The Seattle Beacon Hill site is located in a sampling trailer at the Beacon Hill Reservoir 

at Charleston Street and 15th Avenue South. The AIRS Site ID is 53-033-0080. The locational 

coordinates are latitude 47.569722 (47 deg 34 min 11 sec N) and longitude –122.312500 

(122 degrees 18 min 45 sec W). The site elevation is 91 m above mean sea level. The Beacon 

Hill reservoir covers an area of approximately 50 acres and is located to the south and east of 

downtown Seattle. The site is adjacent to Jefferson Park and a golf course and is surrounded by 

mixed residential and light commercial development. The Duwamish industrial area and 

Interstate 5 are located approximately 1 km to the west of the site. 

4.4 HOUSTON, TEXAS 

The Houston Deer Park site used in this study is located at 4514-1/2 Durant St., Houston, 

Texas. The site is in a residential neighborhood, but the Houston Ship Channel and associated 

petrochemical and refining industries are a short distance to the north. The AIRS Site ID is 

48-201-1039. The coordinates of the site are latitude 29.669722 (29 deg 40 min 11 sec N) and 

longitude –95.128611 (95 deg 07 min 43 sec W). The site is 6 m above mean sea level. 
4-2

4.5 INDIANAPOLIS, INDIANA 

The Indianapolis monitoring site is located in Washington Park, a public park adjacent to 

a police station parking lot to the west. It is surrounded by both commercial and residential 

properties, with mild traffic volume on the main road. The AIRS Site ID is 180970078. The 

locational coordinates are latitude 39.811097 (39 deg 48 min 39.9 sec N) and longitude 

–86.114469 (86 deg 6 min 52.1 sec W). The site elevation is 235 m above mean sea level. 
5-1

5. INSTRUMENTATION 

5.1 STN FILTER-BASED INSTRUMENTS 

The every-third-day filter-based measurements at the five sites were made with 

speciation sampler types. The Spiral Ambient Speciation Sampler (SASSTM; MetOne, Grants 

Pass, Oregon) were used at Phoenix and Indianapolis, and the Mass Aerosol Speciation Sampler 

(MASS 400 or MASS 450, URG, Chapel Hill, North Carolina) were used at Chicago, Houston, 

and Seattle. 

5.1.1 MetOne SASS 

Sites with MetOne SASS samplers hosted either a MetOne SASS or a MetOne 

SuperSASS. There are no significant operational differences between the two versions (the 

SuperSASS has additional channels for multiple-event sampling). Parallel channels allow 

simultaneous sampling, and each channel has a removable cartridge with a sharp-cut cyclone. 

One cartridge contains a Teflon filter for gravimetric analysis. A second cartridge contains a 

denuder to capture ammonia and nitric acid followed by a nylon filter. This sample filter is 

extracted and analyzed by IC for nitrate and sulfate. A third cartridge contains a quartz filter 

which is analyzed by thermal oxidation to determine OC and EC concentrations. 

5.1.2 URG MASS400 and URG MASS450 

The URG MASS400 (Mass Aerosol Speciation Sampler for Anions, Cations, and Trace 

Metals) is the filter-based sampler (with Teflon and Nylon filters) used for nitrate and sulfate 

measurements. The URG MASS450 (Mass Aerosol Speciation Sampler for Organic and 

Elemental Carbon and Semi-Volatile Organic Compounds) is the filter-based sampler (with a 

quartz filter) used for aerosol carbon measurements.

5.2 SEMI-CONTINUOUS MONITORS 

Speciation monitors manufactured by Rupprecht and Patashnik (R&P, Inc., Albany, New 

York) deployed in this study include the R&P 5400 (carbon), R&P 8400N (nitrate), and R&P 

8400S (sulfate) instruments. In mid-2004, a semi-continuous carbon monitor from Sunset 

Laboratory, Inc., Tigard, Oregon, was added at three of the five sites. 

5.2.1 R&P 8400N Nitrate Monitor 

A two-step process is used by the R&P 8400N Nitrate Monitor (8400N) to collect and 

analyze the aerosol sample. In this study, a 10-minute cycle time was used, with the first 8.75 

minutes allotted to sample collection and the remaining time to analysis. In the collection step, 

ambient air is drawn through a system that includes a PM2.5 sharp-cut cyclone, a carbon 

honeycomb denuder that removes interfering gases, and a Nafion humidifier that provides for 
5-2

uptake of water by the particles and minimizes particle bounce from the NiChrome® collection 

strip. The air sample is maintained near ambient temperature by a sheath of ambient air 

surrounding the sample inlet line. During the analysis period, purified nitrogen is used to purge 

the collection cell; the purified nitrogen also acts as a carrier gas to the chemiluminescence 

oxides of nitrogen (NOx) analyzer. A baseline reading is followed by the flash volatilization that 

catalytically reduces the nitrate to NOx, which is carried to the high sensitivity gas analyzer for 

analysis. The difference between the integrated areas under the baseline and sample gas stream 

curves (adjusted for theoretical conversion efficiency) is combined with sampled volume to yield 

an ambient nitrate concentration. 

Previous field evaluations of data from the prototype instrument and the commercialized 

version against various filter nitrate data have yielded inconsistent results. Intercomparisons 

between data from the prototype instrument and filter-based data in initial field tests gave 

favorable results (Stolzenburg and Hering, 2000; Liu et al., 2000; Hering and Stolzenburg, 

1998). Following commercialization of the R&P instrument, data from field comparisons 

against filter-based data suggest that significant bias in nitrate concentrations exists compared to 

data from STN monitors (Hogrefe et al., 2004; Wittig et al., 2004; Harrison et al., 2004; Reid et 

al., 2005). Some researchers have found good data precision with the 8400N, allowing 

calibration of the high time-resolution data by the collocated filter data (Harrison et al., 2004; 

Wittig et al., 2004). Calibration of the data provides potential for additional temporal analyses 

but does not alleviate the equivalency requirement that would allow use of the semi-continuous 

monitors in place of STN monitors. 

5.2.2 R&P 8400S Sulfate Monitor 

The R&P 8400S Sulfate Monitor (8400S) follows a sampling protocol that parallels the 

R&P 8400N. The Pulse Generator component—the instrument that collects and flash volatilizes 

the sample—is identical to the 8400N except that platinum flash strips are used, a shorter flash 

duration (but a higher flash temperature) is used, and purified air is used as the carrier gas. A 

high sensitivity SO2-pulsed UV fluorescence analyzer is used to measure the evolved gas. 

Comparisons with filter-based sulfate data during a one-month study conducted at the 

Atlanta Supersite revealed that data from the 8400S and from measurements were “fairly well 

correlated” (Weber et al., 2003). Data from the 8400S were lumped with data from four other 

semi-continuous instruments and then regressed against the 24-hr filter-based data, yielding a 

slope of 1.15 and a correlation (r) of 0.92. Sample size for the 8400S was small (13 samples). 

Drewnick et al. (2003) found high correlation (r = 0.99) between 8400S and STN filterbased measurements of sulfate, with a slope of 0.935. Sample size was small (7 samples). 

5.2.3 Carbon 

Thermo Optical Analysis (TOA) is routinely used for analysis of OC and EC from filterbased samples. It utilizes combustion in controlled atmospheres at selected temperatures, 

combined with light transmittance or reflectance to allow correction for pyrolyzed (charred) OC. 
5-3

The gases produced in TOA are either converted to carbon dioxide (CO2) and detected directly 

or converted to methane (CH4) and detected by flame ionization. 

OC and EC fractions are defined operationally by the method used. Changes in 

temperature regimes and dwell times, as well as the nature of the sample, can have significant 

effects on the OC/EC split (Schauer et al., 2003). Different thermal evolution protocols will 

yield different results; consistency in those protocols is needed to compare data sets. Thermal 

Optical Reflectance (TOR) is the method used in the Interagency Monitoring of Protected Visual 

Environments (IMPROVE) network, while Thermal Optical Transmission (TOT) is the method 

used in the STN network. The IMPROVE TOR protocol uses a lower temperature protocol 

compared to STN TOT. Many other EC and OC analysis protocols are widely used, but 

generally the methods are insufficiently documented, making evaluations of their equivalence 

difficult. 

R&P 5400 Monitor

The R&P 5400 carbon monitor (5400) measures OC and total carbon (TC) mass in PM2.5. 

As used in this study, the monitor collects a sample for three hours, and analyzes the collected 

sample in about 20 minutes. The instrument has two separate sampling trains, allowing the 

collection and analysis of separate samples to occur in parallel. When sample collection ends on 

one train, sample collection begins on the second. The sample stream passes through a PM2.5

size-selective inlet, and the PM collects on an impaction surface. At the end of the 3-hr sampling 

period, collection stops. In a closed system of ambient air, the chamber containing the sample is 

heated to two temperature plateaus in succession—340oC and 750oC. In the first plateau of 

340oC (8 minutes long), the assumption is that all of the OC combusts in the presence of oxygen 

to form CO2 and that no EC combusts. In the second temperature plateau of 750oC, all EC 

combusts to CO2. A non-dispersive infrared sensor measures the CO2 concentrations during 

these periods and based on the cumulative CO2 concentration and sampled air volume, the 

ambient concentrations of OC and TC are calculated. The EC concentration is then determined 

by difference. There is no correction for pyrolysis in the 5400. 

Measurements from the 5400 monitor have been compared with filter-based 

measurements in a few field studies. R&P 5400 TC measurements were not comparable to or 

predictable from collocated filter-based measurements, analyzed by TOR methods, at the Fresno 

Supersite (Watson and Chow, 2002), where frequent instrument problems led to low data 

recovery for the 5400. Average TC was 40-60% higher than filter-based TC. Correlation 

coefficients (r) between the 5400 and filter-based measurements for TC, OC, and EC were 0.46, 

0.38, and 0.61, respectively. 

In a comparison with samples collected with a filter-based Reference Ambient Aerosol 

Sampler (RAAS), analyzed by TOT at RTI (Rice, 2004), the 5400 underestimated TC and OC by 

64% and 78%, respectively, while the EC component was overestimated by 89%. Correlation 

coefficients (r) for TC and OC were 0.64 and 0.67, respectively, and the correlation for EC was 

0.37. Aside from the lack of pyrolysis correction in the 5400, a positive artifact associated with 

the filter-based measurements made without a denuder may have exacerbated the observed 

differences. 
5-4

Comparisons conducted at the Atlanta Supersite showed very good agreement between 

data from the 5400 and from an in situ TOT method for TC and OC, with regression slopes and 

correlation coefficients close to 1.0 (Lim et al., 2003). 

Sunset Carbon Monitor 

In mid-2004, a Sunset Laboratory, Inc., Carbon Aerosol Analysis Field Instrument 

(Sunset) employing TOT was installed at three of the five sites: Chicago, Phoenix, and Seattle. 

This instrument is a field version of the Sunset laboratory-based analyzer, with the analysis 

section of the analyzer built and operated similarly to the laboratory-based analyzer described by 

Turpin et al. (1990). The monitor reports hourly concentrations of OC and EC, based on 47 

minutes of sampling at a nominal flow rate of 8 lpm. The monitors at the three sites utilize a 

modified NIOSH 5040 protocol. Following sampling, the filter is heated in an oxygen-free, 

high-purity helium atmosphere to 600oC and then to 850oC, oxidizing OC to CO2 that is then 

measured by a non-dispersive infrared detector. A red-light laser is used to monitor pyrolytic 

conversion of OC to EC. Following the oxygen-free heating, the oven is switched to a 2% 

oxygen/helium mixture and heated to 600oC and then to 850oC. During this phase, both original 

EC and pyrolyzed OC burn in the presence of oxygen to form CO2. The point during the second 

heating cycle when the laser transmittance equals its beginning value is considered the split 

point—carbon measurements prior to this point are assigned to OC and those after to EC. 

Bae et al. (2004) operated two collocated Sunset carbon monitors concurrently with 

integrated a 24-hr filter-based sampler at the St. Louis-Midwest Supersite throughout 2002; the 

sampler was built by Jamie Schauer at the University of Wisconsin, Madison, based on the 

Caltech organics sampler. The semi-continuous analyzers operated on alternate hours, 

employing a full hour of sampling instead of just 47 minutes so a truly continuous OCEC 

measurement was obtained. The slopes of the linear regressions of Sunset TC and OC against 

the filter-based measurements were 0.97 and 0.93, respectively, and exhibited correlation 

coefficients (r) of 0.94 and 0.95, respectively. EC exhibited a slope of 0.95 but a poorer 

correlation coefficients (r=0.60) attributed to very low EC levels at the sampling site (annual 

average 0.70 μg/m3

), with a large fraction of EC measurements near the detection limit. 

5.3 DATA SET SUMMARY 

Figure 5-1 graphically illustrates the deployment dates for the monitors at each of the 

five sites. In constructing 24-hr average data from the semi-continuous monitors, at least 

75% data completeness was required. Hourly reported data for the Sunset carbon monitor 

required a minimum of 18 valid hourly averages per day. The R&P 5400 monitor reported 3-hr 

averages in this study, and five of the six daily samples (83%) served as the completeness 

criteria. 

The 8400N and the 8400S report data as 10-minute averages, so determination of data 

completeness is less straightforward. The method for assessing completeness of these data 

differed between sites. For the Phoenix data, the nitrate measurements were assessed for data 

completeness by the daily averages based on the validity of 108 of the possible 144 daily 

10-minute averages. In all other cases, hourly averages were calculated as an initial step and 
5-5

accepted as valid if five of the six (83%) of the 10-minute periods were valid. This procedure 

was followed with a secondary calculation of the 24-hr average, requiring 18 (75%) or more 

valid hourly values. 

Using the 10-minute nitrate data from the Phoenix site, a brief analysis was undertaken to 

determine the effect on regression statistics by these two alternative completeness methods. 

Results indicate minimal effects on the slopes, intercepts, and coefficients of determination for 

this particular data set (Table 5-1). 

4/15/2002 4/15/2003 4/14/2004 4/14/2005

8400N

8400S

5400

Sunset

Arizona

Illinois

Washington

Texas

Indiana

Figure 5-1. Range of deployment dates for each of the monitors in at the five sites 

in the study. Vertical lines denote yearly increments. 

Table 5-1. Effect on regression statistics with two methods of data completeness 

evaluations for the 10-minute nitrate records. 

Method Number of 

Records Slope Intercept R2

Hourly (greater than or equal to 5 of 

6 10-min averages), then daily 

(greater than or equal to 18 hourly 

averages) 

895 0.7853 0.3449 0.8742 

10-minute (greater than or equal to 

108 of 144) 

903 0.7846 0.343 0.8735 
5-6

5.4 STATISTICAL APPROACH 

Formal evaluations of comparability and predictability between the semi-continuous 

monitors and the STN monitors are based on ordinary least squares linear regressions. Average 

y/x ratios and y-minus-x differences, with associated standard deviations, are presented to 

supplement the regression statistics. The criteria used to decide whether the semi-continuous 

monitors are sufficiently robust to be used to supplement STN and SLAMS monitors is based on 

comparability and predictability. Below, we present criteria for comparability and predictability; 

the results for the speciated monitors in this study are compared against these criteria in Sections 

5 and 6. 

5.4.1 Comparability 

The criteria for comparability are less stringent than those, for example, used in PM2.5

mass equivalence, but the precisions involved are still sufficient to discern concentration 

differences in space and time. There are two sources of criteria for comparability, the criteria 

defined for STN samplers by the Expert Panel (Koutrakis, 1999), and the results obtained from 

evaluation of several speciation samplers in the Four-City Study (Solomon et al., 2000).

Comparability is achieved when data from a monitor collocated with an STN monitor meet the 

following criteria: 

Nitrate criteria 

o Squared correlation (r2) greater than or equal to 0.90 

o The ratio of the means of 1 ± 0.1 for nitrate (Koutrakis, 1999). The Four-City Study 

(Solomon et al., 2000), where a number of commercially-available speciation samplers 

were field-tested, suggested that the ratio-of-means criteria be 1 ± 0.15 for nitrate. This is 

the criteria used in this study to determine comparability for nitrate, since it reflects 

method performance in a field application. 

Sulfate criteria 

o Squared correlation (r2) greater than or equal to 0.95 

o The ratio of the means of 1 ± 0.05 for sulfate. 

o The Four-City Study report did not suggest an alternative criterion for sulfate. 

Carbon criteria 

o The Expert Panel did not provide criteria for carbon; however, the Four-City Study report 

suggested that the ratio-of-means criteria be 1 ± 0.15 for organic and elemental carbon. 

o Definitions for comparability used for evaluation of in situ carbon measurements have 

been suggested by Watson and Chow, 2002. The criteria for comparability were 1) a 

slope that was 1 ± 3 standard errors; and 2) intercept equal to 0 ± 3 standard errors; and 

3) a correlation (r) > 0.9. Both the ratio-of-means criteria and those proposed by Watson 

and Chow, 2002 will be used to assess comparability for carbon. 

There is no federal reference method (FRM) for speciation sampling; therefore, the STN 

samplers are used as the reference for evaluating collocated semi-continuous speciation 

samplers. Note that the collocated STN data for the most commonly used sampler (the 

MetOne SASS) meet both the Expert Panel and Four-City Study criteria above (see Section 
5-7

5.1). In addition, data from collocated STN and IMPROVE samplers are similar for nitrate 

and sulfate; thus, these criteria seem reasonable for an assessment of comparability. 

5.4.2 Predictability 

Predictability criteria are typically based on a minimum correlation coefficient (r) 

between two measured variables. Watson and Chow suggest a criterion for correlation of 0.9. 

The Data Quality Objectives (DQOs) for assessing the relationship between PM2.5 FRM and 

continuous PM2.5 measurements provided a range of squared correlations (R2

) from 0.73 to 0.84, 

depending on the acceptable amount of decision error (U.S. EPA, 2002). Therefore, a 

correlation of 0.9 seems to be a reasonable criterion for predictability in this study. The slope 

may deviate substantially from unity and the intercept may also deviate from zero. Predictability 

is of interest here, particularly with respect to accurately portraying highly time-resolved diurnal 

variability. Nitrate, sulfate, and carbon fractions in ambient air are susceptible to substantial 

diurnal variability from local sources. In cases where the high time-resolution, semi-continuous 

monitors exhibit substantial bias but good precision, benchmark filter-based data may be used to 

calibrate and adjust the high time-resolution data to more accurately portray these diurnal 

patterns. 
6-1

6. PREVIOUS PRECISION AND COMPARISON RESULTS FOR STN SAMPLERS 

In this section, we present STN collocated results and comparison results of data from the 

STN urban and IMPROVE (Interagency Monitoring of PROtected Visual Environments) rural 

networks. The purpose of presenting these results is to provide a context and examples of how 

well speciation data from filter-based STN samplers might be expected to compare with data 

from collocated STN filter-based samplers where the sampling and analysis protocols are the 

same and collocated IMPROVE samplers that use different sampling procedures, and in the case 

of carbon, different analysis methods. 

6.1 PRECISION ESTIMATES FOR DATA FROM COLLOCATED STN SAMPLES 

The EPA has performed collocated sampling within the STN network (Rice, 2005). 

Statistics for collocated samples for nitrate, sulfate, and carbon species are shown in Table 6-1. 

Detailed statistics for the MetOne sampler, which is the most-used, are also shown in Table 6-1; 

the slope and intercept results are from Deming regressions. The regression coefficients are 

quite high (r) greater than 0.92 for all these species, with slopes very near one and intercepts very 

near zero. 

Table 6-1. Collocated precision and regression estimates for component species 

in the STN network for 2002-2004. 

Sampler 

Type Parameter Number Regression 

Slope 

Intercept 

Slope 

Ratio of 

Mean 

Correlation 

(r) 

STN/STN 

% CV 

Nitrate 1048 .985 ± 

.01 

–.052 

± .05 

.977 .99 12.3 

Sulfate 1047 1.019 ± 

.01 

–.094 

± .02 

.990 .99 10.6 

OC 1042 .970 ± 

.01 

.016 ± 

.09 

.973 .92 15.5 

EC 1041 1.009 ± 

.01 

–.016 

± .01 

 .994 .95 17.0 

MetOne 

TC 1041 0.981 ± 

.01 

–.039 

± .09 

.984 .93 16.2 

6.2 RESULTS FOR DATA FROM COLLOCATED STN AND IMPROVE 

STN filter-based data are being used to evaluate the composition of PM2.5 throughout the 

United States. Publicly available STN and IMPROVE data from collocated samplers at three 

locations (Seattle, Washington; Phoenix, Arizona; and Washington, DC) were obtained from 

EPA’s Air Quality System (AQS), and regressions of IMPROVE data on STN data were 

conducted for nitrate, sulfate, EC, OC, and TC. A summary of the regression results is given in 

Table 6-2. 

In general, we used data for January 2002 through May 2004. However, sometimes 

either the STN or IMPROVE data are missing; thus, the records are shorter. For the 
6-2

Washington, DC, site, we used carbon data from January 2, 2002 through May 30, 2004, and ion 

data from January 2, 2002, through November 14, 2003. For the Seattle site, we used carbon and 

ion data from May 2, 2002, through May 30, 2004. For the Phoenix site, we used carbon data 

from December 13, 2002, through May 30, 2004, and ion data from August 12, 2002, through 

May 30, 2004 (see Hyslop et al., 2004). 

Correlation coefficients (r) of the nitrate and sulfate data from the two networks exceeded 

0.97 at all three sites; the Seattle location showed a slight positive bias for both particle-phase 

ions, and the Washington location a negative bias for nitrate. These results for nitrate using the 

MetOne sampler and for sulfate using the MetOne and Anderson samplers are quite similar to 

the collocated results shown in Table 6-1. 

Carbon analysis methods differ between the two networks; thus, these comparisons are 

not as good as those for collocated STN comparisons shown in Table 6-1. IMPROVE uses a 

lower temperature protocol than STN. IMPROVE uses TOR for char correction, while STN uses 

TOT. The Seattle comparison results indicate less OC and TC bias than do the results from the 

other two sites. The URG sampler at the Seattle site has a nominal flow rate of 16.7 LPM, while 

the MetOne and Andersen samplers have flow rates of 6.7 and 7.3 LPM respectively. The 

IMPROVE sampler has a nominal flow rate of 22.8 LPM. The bias at the Phoenix and 

Washington sites is likely due to the increased magnitude of the difference in sampling flow 

rates for the MetOne and Andersen samplers and resulting difference in filter face velocity. 

Correlations for the EC component were substantially lower than for OC and TC. 

Table 6-2. Summary of regression results between IMPROVE and STN filterbased data. Data are from the AQS. 

Sampler Ordinary Least Squares 

Observable Location 

y x 

Regression 

Slope ±

Standard Error

Intercept ± 

Standard Error 

Correlation 

(r) 

(Pearsons)

Number 

of Pairs

Phoenix, AZ Improve MetOne 0.965 ± 0.01 0.02 ± 0.02 0.99 196 

Seattle, WA Improve URG 1.34 ± 0.02 -0.05 ± 0.02 0.98 206 

Nitrate 

Washington, DC Improve Anderson 0.873 ± 0.02 -0.01 ± 0.04 0.97 175 

Phoenix, AZ Improve MetOne 0.979 ± 0.02 0.04 ± 0.02 0.98 82 

Seattle, WA Improve URG 1.101 ± 0.01 -0.04 ± 0.02 0.99 205 

Sulfate 

Washington, DC Improve Anderson 0.999 ± 0.02 0.20 ± 0.11 0.98 175 

Phoenix, AZ Improve MetOne 0.86 ± 0.02 -1.36 ± 0.16 0.96 158 

Seattle, WA Improve URG 1.1 ± 0.02 -0.32 ± 0.09 0.96 215 

TC 

Washington, DC Improve Anderson 0.69 ± 0.02 -0.14 ± 0.12 0.90 220 

Phoenix, AZ Improve MetOne 0.872 ± 0.03 -1.5 ± 0.2 0.94 158 

Seattle, WA Improve URG 1.15 ± 0.03 -0.41 ± 0.1 0.94 215 

OC 

Washington, DC Improve Anderson 0.657 ± 0.02 -0.23 ± 0.11 0.90 220 

Phoenix, AZ Improve MetOne 0.751 ± 0.04 0.18 ± 0.05 0.85 158 

Seattle, WA Improve URG 0.756 ± 0.05 0.15 ± 0.04 0.70 215 

EC 

Washington, DC Improve Anderson 0.665 ± 0.06 0.25 ± 0.04 0.63 220 
7-1

7. RESULTS 

The EPA’s NAREL (National Air and Radiation Environmental Laboratory) provides 

quality assurance support to the EPA speciation networks. One of NAREL’s tasks is to support 

the semi-continuous speciation study by preparing performance evaluation (PE) samples, which 

provide an estimate of the monitor bias. The PE samples are single-blind, meaning the operator 

knows that the sample is a PE, but does not have the known concentration. When the PE 

samples are analyzed in replicate, an estimate of within-monitor precision can obtained. This 

section provides the precision results from the PE samples and summarizes the comparisons 

between the semi-continuous and filter-based results (Section 7.1); detailed discussions of the 

comparison results for nitrate (Section 7.2), sulfate (Section 7.3), and carbon (Section 7.4) are 

also provided. 

7.1 PRECISION ESTIMATES 

This section provides the results of replicate, blind PE samples for the R&P nitrate and 

sulfate monitors and for the Sunset carbon monitor (Section 7.1.1); no precision estimates are 

provided for the R&P carbon monitor because this monitor’s collection and analysis system is 

closed; therefore, no PE sample can be introduced into the system and there is no way to easily 

introduce a sample through the inlet. Section 7.1.2 provides precision estimates and detailed 

statistics for the collocated semi-continuous and STN comparisons. 

7.1.1 Single- (Within-) Sampler Precision Estimates 

Collocation of semi-continuous instruments would provide the most robust method of 

determining measurement precision because it includes the error contribution of all sampling 

(e.g., flow rate) and analytical system components. No collocated semi-continuous instruments 

were operated at the STN sites during this study. However, replicate blind PE samples can 

provide estimates of single-sampler precision; these results are presented as coefficient of 

variation (CV). Such estimates do not completely characterize the total measurement system 

precision because they omit field errors, such as those associated with flow rate. In addition, 

error unrelated to the sampling system may be introduced through differences in site-operator 

techniques in administering the PE. 

To date, there have been five PE tests for the nitrate and sulfate monitors, and two PE 

tests for the Sunset carbon monitor. Replicate measurements were made at multiple levels of 

deposited nitrate and sulfate mass. Similarly, for the Sunset carbon monitor, replicate samples of 

filter punches were taken from sucrose-spiked filters and filters from STN ambient samplers. 

NAREL provided blind aqueous standards for nitrate and sulfate and carbon filter punches for 

the PE tests. The results are used here only for estimates of precision. Evaluation of the PE 

results, including a discussion of bias as well as precision, is presented elsewhere (Taylor, 2005). 

Within-sampler precision estimates (CV) for the R&P 8400N (Table 7-1) and 8400S 

(Table 7-2) are based on available data from PE4 (fall 2004) and PE5 (spring 2005) that were 

conducted at all five sites (Taylor, 2005). Precision estimates for the Sunset carbon monitors 
7-2

(Table 7-3) are based on the PE1 of fall 2004 and PE2 of spring 2005 at three sites (Taylor, 

2005). No precision estimates are provided for the R&P 5400 carbon monitor. 

For the Sunset monitor, CVs are presented from ambient samples only—sucrose-spiked 

filters have very high CVs for EC because of the absence of any EC in these spiked samples. 

Measurements of low-level chemical species such as EC are more likely to show large relative 

variability compared to PM2.5 measurements. The Sunset within-sampler precisions averaged 2% 

for OC and TC (Table 7-3). The EC precision of 8% was inflated by the high CV (28%) in 

Phoenix for PE 2. If the suspect data underlying the 28% CV are eliminated, the CV for EC 

drops to 4%. 

The average CV for nitrate, including all sites over all levels of deposited nitrate mass, 

was 5.2% in both PE4 and PE5 (Table 7-1). The average CV for sulfate was 10.7% in PE4 and 

5.1% in PE5 (Table 6-2); overall average is 8.3%. There were no Phoenix PE data for sulfate, 

and a sulfate PE was not conducted at the Indianapolis site in spring 2005. 
7-3

Table 7-1. Coefficients of variation of measured nitrate mass for the 8400N PE4 (fall 2004) and PE5 (sprnitrate aqueous standards. The overall mean CV is 5.2%. (Data courtesy of NAREL.) 

Mass Deposited (ng) 

10 20 30 100 200 250 300 

Site 

CV (%) 

Phoenix PE 4 1.4% – 2.9% 13.8% – 1.1% – Chicago PE 4 4.2% – 1.0% 2.6% – 0.6% – Indianapolis PE 4 9.7% – 5.1% 5.5% – 8.2% – Houston PE 4 5.0% – 4.8% 3.2% – 7.0% – Seattle PE 4 1.8% – 7.8% 1.9% – 4.4% – 

Average PE 4 4.4% – 4.3% 5.4% – 4.3% – 

Phoenix PE 5 – 6.3% – 3.2% 7.8% – 3.4% Chicago PE 5 – 3.2% – 1.3% 0.8% – 1.7% Indianapolis PE 5 – 10.4% – 1.2% 11.3% – 2.0% Houston PE 5 – 2.8% – 6.1% 12.6% – 10.3% Seattle PE 5 – 7.8% – 5.6% 9.3% – 6.9% 

Average PE 5 – 6.1% – 3.5% 8.3% – 4.9% 

Ov
7-4

Table 7-2. Coefficients of variation of measured mass for the 8400S PE4 (fall 2004) and PE5 (spring 200

aqueous standards. The overall average CV is 8.3%. (Data courtesy of NAREL.) 

Mass Deposited (ng) 

Site 30 60 100 150 240 300 600 900 

CV (%) 

Phoenix PE 4 – – – – – – – – Chicago PE 4 3.8% – 8.1% 2.8% – 5.6% – 4.9% Indianapolis PE 4 11.6% – 29.9% 13.9% – 30.6% – 13.0% Houston PE 4 15.5% – 21.4% 20.3% – 13.1% – 8.4% Seattle PE 4 12.1% – 1.2% 11.8% – 7.0% – 5.5% 

Average PE 4 10.7% 15.1% 12.2% 14.1% 8.0% 

8400S PE 5Phoenix PE 5 – – – – – – – – Chicago PE 5 – 4% – – 2% – 2% 1Indianapolis PE 5 – – – – – – – – Houston PE 5 – 13% – – 8% – 4% 2Seattle PE 5 – 6% – – 14% – 3% 7

Average PE 5 7.8% – – 8.4% – 3.2% 3.4

O
7-5

Table 7-3. Coefficients of variation for the Sunset blind PE1 (fall 2004) and PE2 

(spring 2005) of EC, OC, and TC. The overall mean CV is 4%. The average CV 

for EC drops from 8% to 4% if the Phoenix PE 2 value is eliminated. (Data 

courtesy of NAREL.) 

Sunset CV (%) 

Site EC OC TC 

Phoenix PE 1 0% 1% 1% 

Phoenix PE 2 28% 6% 4% 

Chicago PE 1 5% 2% 1% 

Chicago PE 2 2% 1% 1% 

Seattle PE 1 6% 1% 0% 

Seattle PE 2 6% 2% 2% 

Average CV 8% 2% 2% 

7.1.2 Between Sampler Precision Estimates 

Regression statistics and precision estimates (σ), as the standard deviation of the paired 

differences, for the nitrate, sulfate, and carbon comparisons are shown in Table 7-4. The slopes 

and intercepts, and their standard errors, from the ordinary least squares regression are presented 

for each set of collocated measurements. The regressions utilized the semi-continuous monitor 

data as the y variable and the filter-based data as the x variable. Also, Pearsons correlation 

coefficients (r), the number of pairs in the comparison, the average y/x ratios and their standard 

deviations, the average of the paired differences (y – x), and the standard deviation of the paired 

differences are given. The distribution of the data pairs whose difference is less than 1σ, 

between 1 and 2σ, between 2 and 3σ, and greater than 3σ are also provided; the standard 

deviation of the paired differences is represented by σ. Representative scatter plots and 

regressions are presented in the discussions of the nitrate results (Section 7.2), sulfate results 

(Section 7.3), and carbon results (Section 7.4); scatter plots not shown in the text are shown in 

Appendix A. Also discussed in Sections 7.2 and 7.3 are the results of nitrate and sulfate aqueous 

standards tests and of gas analyzer tests; these tests are important components of quality control 

activities for the semi-continuous speciation monitors. 

In the discussions in Sections 7.2 through 7.4, we compare the results shown in Table 7-4 

with the criteria discussed in Section 5.4. 
7-6

Table 7-4. Summary statistics for comparison of semi-continuous speciated PM2.5 data with filter-based 

Sampler Ordinary Least Squares 

Observable Location 

y x 

Regression 

Slope ±

Standard 

Error 

Intercept ±

Standard 

Error 

Correlation 

(r) 

(Pearsons) 

Number 

of Pairs

Average 

Ratio of y/x 

± Standard 

Deviation 

Avg 

Difference 

of y – x 

(μg/m3) 

S.D

A

Diffe

of y 

(μg

Chicago, IL R&P 8400N URG 0.54 ± 0.01 0.53 ± 0.05 0.91 277 0.88 ± 0.27 -0.64 1Phoenix, AZ R&P 8400N MetOne 0.79 ± 0.02 0.34 ± 0.04 0.94 270 1.30 ± 0.80 0.05 0Indianapolis, IN R&P 8400N MetOne 0.59 ± 0.02 0.41 ± 0.08 0.90 178 0.82 ± 0.26 -0.74 1Houston, TX R&P 8400N URG 1.97 ± 0.07 0.34 ± 0.09 0.89 194 2.65 ± 1.75 1.28 1

NO3 (all data) 

Seattle, WA R&P 8400N URG 0.85 ± 0.03 0.24 ± 0.03 0.87 297 1.26 ± 0.53 0.12 0

Chicago, IL R&P 8400N URG 0.50 ± 0.02 0.54 ± 0.06 0.92 197 0.85 ± 0.25 -0.69 1Phoenix, AZ R&P 8400N MetOne 0.85 ± 0.02 0.34 ± 0.04 0.94 208 1.37 ± 0.86 0.16 0Indianapolis, IN R&P 8400N MetOne 0.62 ± 0.03 0.39 ± 0.10 0.90 126 0.85 ± 0.28 -0.61 1Houston, TX R&P 8400N URG 2.02 ± 0.09 0.32 ± 0.12 0.90 122 2.51 ± 0.95 1.33 1

NO3 ("Pre-moly") 

Seattle, WA R&P 8400N URG 0.81 ± 0.03 0.29 ± 0.03 0.87 203 1.31 ± 0.56 0.14 0

Chicago, IL R&P 8400N URG 0.67 ± 0.03 0.43 ± 0.10 0.94 80 0.97 ± 0.28 -0.48 1Phoenix, AZ R&P 8400N MetOne 0.65 ± 0.02 0.27 ± 0.04 0.98 62 1.05 ± 0.52 -0.34 0Indianapolis, IN R&P 8400N MetOne 0.52 ± 0.02 0.40 ± 0.10 0.95 52 0.73 ± 0.16 -1.05 1Houston, TX R&P 8400N URG 1.90 ± 0.12 0.37 ± 0.16 0.87 72 2.89 ± 2.59 1.20 1

NO3 ("Post-moly") 

Seattle, WA R&P 8400N URG 0.96 ± 0.05 0.11 ± 0.05 0.89 94 1.16 ± 0.43 0.08 0

Chicago, IL R&P 8400S URG 0.59 ± 0.02 0.78 ± 0.10 0.85 263 0.94 ± 0.31 -0.58 1Phoenix, AZ R&P 8400S MetOne 0.76 ± 0.10 0.18 ± 0.10 0.71 62 0.99 ± 0.33 -0.04 0Indianapolis, IN R&P 8400S MetOne 0.66 ± 0.03 0.50 ± 0.18 0.93 94 0.81 ± 0.19 -1.33 1Houston, TX R&P 8400S URG 0.81 ± 0.06 0.40 ± 0.23 0.74 175 0.96 ±0.45 -0.31 1

SO4

Seattle, WA R&P 8400S URG 1.25 ± 0.03 0.24 ± 0.04 0.94 245 1.52 ± 0.27 0.55 0
7-7

Table 7-4. Summary statistics for comparison of semi-continuous speciated PM2.5 data with filter-base

Sampler Ordinary Least Squares 

Observable Location 

y x 

Regression 

Slope ±

Standard 

Error 

Intercept ±

Standard 

Error 

Correlation 

(r) 

(Pearsons) 

Number 

of Pairs

Average 

Ratio of y/x 

± Standard 

Deviation 

Avg 

Difference 

of y – x 

(μg/m3) 

S.D

A

Diffe

of y 

(μg

Chicago, IL R&P 5400 URG 0.53 ± 0.03 0.27 ± 0.11 0.78 270 0.61 ± 0.18 -1.48 0Phoenix, AZ R&P 5400 MetOne 0.35 ± 0.02 0.95 ± 0.14 0.85 170 0.52 ± 0.18 -3.89 2Indianapolis, IN R&P 5400 MetOne 0.51 ± 0.02 0.49 ± 0.13 0.89 142 0.63 ± 0.13 -2.07 1Houston, TX R&P 5400 URG 0.46 ± 0.03 0.81 ± 0.11 0.75 155 0.83 ± 0.36 -0.71 0Seattle, WA R&P 5400 URG 0.58 ± 0.01 0.47 ± 0.06 0.92 325 0.76 ± 0.24 -1.04 1Chicago, IL Sunset Lab URG 0.94 ± 0.04 0.80 ± 0.15 0.95 70 1.20 ± 0.23 0.56 0Phoenix, AZ Sunset Lab MetOne 0.76 ± 0.03 0.22 ± 0.19 0.95 91 0.81 ± 0.20 -1.23 1

TC 

Seattle, WA Sunset Lab URG 0.91 ± 0.02 0.58 ± 0.09 0.98 74 1.15 ± 0.32 0.21 0

Chicago, IL R&P 5400 URG 0.54 ± 0.02 0.35 ± 0.08 0.80 270 0.67 ± 0.18 -1.05 0Phoenix, AZ R&P 5400 MetOne 0.33 ± 0.02 0.72 ± 0.13 0.80 170 0.48 ± 0.17 -3.47 2Indianapolis, IN R&P 5400 MetOne 0.50 ± 0.03 0.60 ± 0.13 0.86 142 0.66 ± 0.15 -1.71 1Houston, TX R&P 5400 URG 0.47 ± 0.03 0.65 ± 0.10 0.73 155 0.81 ± 0.35 -0.65 0Seattle, WA R&P 5400 URG 0.53 ± 0.02 0.57± 0.05 0.90 325 0.80 ± 0.27 -0.81 0Chicago, IL Sunset Lab URG 0.84 ± 0.04 0.87 ± 0.13 0.93 70 1.19 ± 0.26 0.39 0Phoenix, AZ Sunset Lab MetOne 0.68 ± 0.03 0.39 ± 0.20 0.91 91 0.78 ± 0.23 -1.26 1

OC 

Seattle, WA Sunset Lab URG 0.79 ± 0.03 0.73 ± 0.11 0.96 74 1.14 ± 0.33 0.06 0

Chicago, IL R&P 5400 URG 0.31 ± 0.03 0.03 ± 0.03 0.49 270 0.37 ± 0.34 -0.42 0Phoenix, AZ R&P 5400 MetOne 0.40 ± 0.02 0.28 ± 0.03 0.80 170 0.83 ± 0.75 -0.41 0Indianapolis, IN R&P 5400 MetOne 0.39 ± 0.04 0.03 ± 0.03 0.65 142 0.45 ± 0.27 -0.36 0Houston, TX R&P 5400 URG 0.29 ± 0.14 0.21 ± 0.06 0.16 155 0.99 ± 1.29 -0.07 0Seattle, WA R&P 5400 URG 0.63 ± 0.02 0.02 ± 0.02 0.83 325 0.68 ± 0.30 -0.22 0Chicago, IL Sunset Lab URG 1.11 ± 0.04 0.09 ± 0.04 0.95 70 1.26 ± 0.29 0.17 0Phoenix, AZ Sunset Lab MetOne 1.04 ± 0.03 0.005 ± 0.03 0.97 91 1.05 ± 0.23 0.04 0

EC 

Seattle, WA Sunset Lab URG 1.23 ± 0.06 -0.03 ± 0.06 0.92 74 1.24 ± 0.68 0.15 0
7-8

7.2 R&P 8400N NITRATE VERSUS STN FILTER-BASED NITRATE 

7.2.1 Nitrate Data Corrections 

Aqueous standard salt solutions of potassium nitrate (KNO3) were used to evaluate the 

conversion efficiency of the 8400N at each site. Frequency of the tests ranged from monthly to 

quarterly. Replicate measurements were made at zero nitrate and four additional nitrate levels 

(nominally 20, 40, 60, and 80 ng applied nitrate). Slopes of the measured mass versus deposited 

mass were applied as corrections to the data, either as inputs in the 8400N setup under 

“Theoretical Conversion Factor”, or during post-processing of the data. Zero and span audits of 

the NO analyzer were undertaken every few days (e.g., every three days at the Phoenix site). 

Average results for each site are shown in Table 7-5. The average theoretical conversion 

efficiency is the percent of the nitrate placed via the aqueous standard on the strip that is 

measured in the gas analyzer; we expect these efficiencies to be consistent across monitors (i.e., 

from site to site in this study). We also expect the efficiency to be close to 100%, thus 

demonstrating that all nitrate placed via the aqueous standard on the strip was volatilized and 

subsequently measured in the gas analyzer. The efficiencies for nitrate are generally high (87% 

to 96%) and fairly consistent among the sites. 

Other corrections could potentially be applied to the semi-continuous 8400N nitrate data. 

Although none of the additional corrections discussed below have been applied to the data from 

this study, their potential to affect the summary statistics should be noted. 

In the Pittsburgh Air Quality Study (PAQS), Wittig et al., 2004 applied a correction for 

instrument offset (18% on average), determined by sampling HEPA-filtered air (dynamic zero) 

during a series of 10-minute cycles, that was subtracted from the 8400N measurements. 

Bimonthly dynamic zero measurements were made during particulate nitrate measurements at 

the St. Louis-Midwest Supersite (Reid et al., 2005), but the data were not used to correct for 

instrument offset because of high variability in the blank values and uncertainty as to whether the 

dynamic zero values reflected a true blank measurement. 

Corrections for sample flow drift may also be applied, based on measured sample flow 

rate and the flow rate indicated by the instrument. In PAQS (Wittig et al., 2004), flow drift 

corrections averaged –3% for nitrate. It is also possible to construct correction curves to account 

for reaction cell vacuum drift. The 8400N reaction cell vacuum set point is 5.0 in. Hg. 

Deviations from this set point can affect nitrate measurements through the instrument offset, 

conversion efficiency, and gas analyzer efficiency. During PAQS (Wittig et al., 2004), 

corrections for reaction cell vacuum drift averaged –1%. 

Note: None of the additional corrections described above, were applied to the data from 

this five site study. 
7-9

Table 7-5. Theoretical conversion efficiency from aqueous standards tests and gas 

analyzer efficiency from routine span audits. 

Nitrate Sulfate 

Aqueous Standards Tests Gas Analyzer 

Audits Aqueous Standards Tests Gas Analyzer Audits 

Location 

Average 

Theoretical 

Conversion 

Efficiency 

± St Dev (%) 

Average 

Correlation 

Coefficient 

(r) 

Average 

Analyzer 

Efficiency 

± St Dev (%) 

Average 

Theoretical 

Conversion 

Efficiency 

± St Dev (%) 

Average 

Correlation 

Coefficient 

(r) 

Average 

Analyzer 

Efficiency 

± St Dev (%) 

Phoenix, AZ 96.1 ± 14.2 0.998 96.3 ± 2.9 215.2 ± 95.2 0.978 94.6 ± 16.8 

Chicago, IL 94.4 ± 4.0 0.995 99.2 ± 1.3 63.3 ± 7.7 0.979 99.7 ± 0.8 

Seattle, WA 96.1 ± 4.7 0.992 98.1 ± 2.1 50.3 ± 8.9 0.981 100.1 ± 3.5 

Houston, TX 95.6 ± 13.1 0.996 N/A 64.9 ± 15.4 0.992 N/A 

Indianapolis, IN 87.5 ± 10.9 0.958 N/A 83.4 ± 9.9 0.955 N/A 

N/A = Data not available. 

Applying all the corrections above during PAQS (Wittig et al., 2004) resulted in good 

correlations with filter-based measurements, but significant bias in 8400N measurements still 

existed. 

The molybdenum catalyst in the NO2 converters has a fixed lifetime (nominally one year) 

and exhibits decreased efficiency with time. Operators at each of the five sites returned the NOx

analyzer of the 8400N monitors to R&P during 2004 to have the converters evaluated and 

replaced. These converter changes occurred at different times of the year for each site, and the 

instruments were inoperable at differing time periods. As a consequence, the 8400N nitrate data 

were evaluated in three ways: pre-converter replacement, post-converter replacement, and all 

data combined. Replacement of the molybdenum converter increased the regression slopes for 

the data from the Chicago and Seattle monitors, while the slopes for the data from the Phoenix, 

Indianapolis, and Houston monitors decreased following converter replacement (Table 7-4). 

From filter-based data, nitrate concentrations ranged up to 5 μg/m3

 in Seattle and 

Houston, 12 μg/m3 in Indianapolis, 16μg/m3 in Chicago, and to 18 μg/m3 in Phoenix (see 

Figures 7-1 to 7-4, for example). 

Data from the R&P 8400N met the correlation and ratio-of-means comparability criteria 

at the Phoenix site after converter replacement; data from the Illinois site met the ratio-of-means 

criteria in all cases, but not the correlation criteria; and data from the Indiana site met the ratioof-means criteria after converter replacement, but not the correlation criteria. The other sites did 

not meet either of the comparability criteria. Data from the R&P 8400N met the predictability 

criteria at four of the five sites and were close at the fifth site. With the exception of the Houston 

8400N, the semi-continuous monitors underestimated nitrate concentrations, as measured by the 

STN filter-based samplers, except at very low concentrations. Regression slopes ranged from 
7-10

0.50 to 0.96, with associated standard errors of 0.01 to 0.05. All the data sets showed positive 

intercepts, suggesting a positive sampling artifact. The intercepts ranged from 0.11 to 

0.54 μg/m3

, reflecting the manufacturer’s stated instrument error of 0.4 μg/m3. No dynamic zero 

data were available for correcting the nitrate data for instrument offset. 

In Chicago (Figure 7-1), the 8400N averaged only 54% of the filter-based concentrations 

using all data. The average ratio of 8400N nitrate to filter-based nitrate was 0.88 ± 0.27, and the 

average difference (y – x) in concentration was –0.64 μg/m3

. The Chicago nitrate data is typical 

of the response observed at all sites except Houston. 

In Phoenix and Seattle, the low slopes were coupled with high 8400N/STN (y/x) ratios. 

This is due to the preponderance of data at low concentrations and the positive intercepts. In 

Phoenix, even though the slope of 8400N versus STN indicated that the semi-continuous monitor 

was capturing only about 79% of the nitrate collected by the filters, the ratio of 8400N nitrate to 

filter-based nitrate was 1.3 ± 0.80, and the average concentration difference (y – x) was only 

0.05 μg/m3

. Approximately 75% of the concentrations in Phoenix were less than 2 μg/m3. 

These low concentrations combined with the positive intercept to yield a high ratio and low 

average difference. 

In Seattle, about 80% of the filter-based nitrate concentrations were less than 1 μg/m3

, 

yielding an average y/x ratio of 1.26 ± 0.53 and an average concentration difference of 

0.12 μg/m3. 

There was a substantial break in the time series of the Indianapolis nitrate data 

(Figure 5-1) so there are about one hundred fewer sample pairs than at the other sites. 

The response of the Houston 8400N was atypical among the five sites (Figure 7-2), with 

results in the opposite direction. Slopes approached 2 for all data subsets, indicating a 

substantial overestimate of STN filter-based nitrate. Average y/x ratios were between 2.5 and 

3.0 for the data subsets, and the average difference (y – x) exceeded 1.2 μg/m3

. This large 

discrepancy between the Houston site and the others is not explained, but raises additional 

concern about the true bias of the monitor compared to filter-based measurements. 

The Seattle and Houston sites use URG samplers. For nitrate, the URG sampler uses a 

Teflon® and backup nylon filter for the collection and analysis of total nitrate. Non-volatile 

nitrate is collected on the front Teflon® filter and volatilized nitrate is collected on the backup 

nylon filter. Teflon filters are also used for gravimetric mass determinations and EnergyDispersive X-Ray Fluorescence (EDXRF) for elemental analysis. The Teflon® filter is weighed 

first for mass, then subject to vacuum EDXRF, and finally extracted for ion analysis (including 

nitrate). The 4-City Study report (Solomon et al., 2002) noted that up to 40% of the nitrate 

collected on the Teflon filter can be lost due to vacuum XRF. Any losses on the Teflon® filter 

would affect the bias between the STN URG and 8400N at these sites. 
7-11

URG Filter-based Nitrate (μg/m3)

R&P Continuous Nitrate 8400N (μg/m3)

Post-moly

y = 0.6693x + 0.4345

R2

 = 0.8743

Pre-moly

y = 0.5027x + 0.5435

R2

 = 0.8392

All Data

y = 0.5406x + 0.534

R2

 = 0.8301

0

4

8

12

16

20

0 4 8 12 16 20

One-to-one

Pre-moly

Post-moly

all data

Best fit (post-moly)

Best fit (pre-moly)

Best fit (all data)

URG Filter-based Nitrate (μg/m3)

R&P Continuous Nitrate 8400N (μg/m3)

Post-moly

y = 0.6693x + 0.4345

R2

 = 0.8743

Pre-moly

y = 0.5027x + 0.5435

R2

 = 0.8392

All Data

y = 0.5406x + 0.534

R2

 = 0.8301

0

4

8

12

16

20

0 4 8 12 16 20

One-to-one

Pre-moly

Post-moly

all data

Best fit (post-moly)

Best fit (pre-moly)

Best fit (all data)

Figure 7-1. Twenty-four-hour average R&P 8400N PM2.5 nitrate versus 24-hr 

integrated STN filter nitrate at Chicago, from May 2, 2002, to May 10, 2005. 

Pre-moly

y = 2.0198x + 0.3201

R2

 = 0.804

Post-moly

y = 1.8921x + 0.3698

R2

 = 0.7639

All Data

y = 1.9716x + 0.3411

R2

 = 0.7885

0

4

8

12

16

0 4 8 12 16

One-to-one

Pre-moly

Post-moly

all data

Best fit (pre-moly)

Best fit (post-moly)

Best fit (all data)

URG Filter-based Nitrate (μg/m3)

R&P Continuous Nitrate 8400N (μg/m3)

Pre-moly

y = 2.0198x + 0.3201

R2

 = 0.804

Post-moly

y = 1.8921x + 0.3698

R2

 = 0.7639

All Data

y = 1.9716x + 0.3411

R2

 = 0.7885

0

4

8

12

16

0 4 8 12 16

One-to-one

Pre-moly

Post-moly

all data

Best fit (pre-moly)

Best fit (post-moly)

Best fit (all data)

URG Filter-based Nitrate (μg/m3)

R&P Continuous Nitrate 8400N (μg/m3) Figure 7-2. Twenty-four-hour average R&P 8400N PM2.5 nitrate versus 24-hr 

integrated STN filter nitrate at Houston, from January 27, 2003, to April 13, 2005. 

Although data from the R&P 8400N met both the correlation and ratio-of-means 

comparability criteria at only the Phoenix site, the R&P 8400N data did meet the predictability 

criteria at four of the five sites and were close at the fifth site. One benefit of this predictability 

is that the high time-resolution data may be calibrated by the filter data, yielding improved 

accuracy for examination of diurnal variability in nitrate concentrations. An example of this, 

using the nitrate data from Phoenix, is given in Figure 7-3, where the average diurnal nitrate 
7-12

concentrations for wintertime (November through February) and summertime (March through 

October) nitrate data are adjusted, based on the filter data. Wintertime concentrations increased 

following adjustment while summertime concentrations decreased, reflecting the overestimation 

of nitrate by the 8400N at low ambient concentrations and the underestimation at higher 

concentrations at the Phoenix monitor. 

Scatter plots and regressions for Phoenix, Seattle, and Indianapolis are included in 

Appendix A. With the exception of the atypical Houston data, the semi-continuous nitrate data 

reflect a non-linear component with respect to the filter-based data (Figure 7-4), where at low 

concentrations, the semi-continuous data exhibit minimal scatter and adhere more closely to the 

1:1 line. At higher concentrations the semi-continuous data are noticeably lower than the filterbased nitrate. This pattern is similar to those reported by other researchers (Reid et al., 2005; 

Wittig et al., 2004). One of the postulated explanations for this pattern is a matrix effect between 

8400N nitrate recovery and aerosol composition, where a deficiency of electron donors prevents 

complete reduction of nitrate to NO and NO2 (Reid et al., 2005). An experiment utilizing 

CO-doped purge gas (increasing available electron donors) yielded a sustained 20% increase in 

nitrate concentration for the 8400N, although 8400N nitrate remained substantially lower than 

the filter-based measurements (Reid et al., 2005). Continued evaluations may lead to further 

performance enhancements and the potential for future comparability of the 8400N with STN 

filter-based methods. 

0

0.5

1

1.5

2

2.5

3

3.5

12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM

Nitrate (ug/m3)

Summer

Winter

Summer-Adj.

Winter-Adj.

Figure 7-3. Average diurnal variability in R&P 8400N nitrate concentrations in 

Phoenix during the winter (November through February) and summer (March 

through October). Adjustments to the R&P 8400N data were made based on the 

regression of 8400N data with the filter data. 
7-13

0.00

5.00

10.00

15.00

20.00

0 5 10 15 20

STN Nitrate (μg m-3) 

R&P 8400N Nitrate (μg m-3)

Figure 7-4. Nitrate data from Phoenix, Chicago, Seattle, and Indianapolis 

illustrating the typical non-linearity of the semi-continuous data relative to the 

STN filter-based data. Houston data are atypical and excluded. 

7.3 R&P 8400S SULFATE VERSUS STN FILTER-BASED SULFATE 

Aqueous standard salt solutions of ammonium sulfate (NH4SO4) were used to evaluate 

the conversion efficiency of the 8400S at each site. Frequency of the tests ranged from monthly 

to quarterly. Replicate measurements were made at zero nitrate and four additional nitrate levels 

(nominally 60, 120, 180, and 240 ng applied sulfate). Slopes of the measured mass versus 

deposited mass were applied as corrections to the data, either as inputs in the 8400S setup under 

“Theoretical Conversion Factor”, or during post-processing of the data. Zero and span audits of 

the SO2 analyzer were undertaken every few days (e.g., every three days at the Phoenix site). 

Average results for each site are shown in Table 7-5. These results for sulfate are significantly 

worse than those for nitrate; the theoretical conversion efficiencies are neither near 100%, nor 

consistent among the sites, varying from 50% to over 200%. However, the SO2 gas analyzer 

efficiencies, as determined by aqueous standards, are quite high and close to 100%, as is 

expected. 

The Phoenix and Seattle sites had substantially lower sulfate concentrations than did the 

more easterly cities of Houston, Chicago, and Indianapolis. 

1:1 
7-14

Only the Phoenix and Houston sites met the ratio-of-means criterion, but none of the data 

from the 8400S monitors met both comparability criteria at any of the five sites. Predictability 

criteria for sulfate were met at two of the five sites (Indianapolis and Seattle). The Phoenix 

sulfate monitor was operationally problematic. This analyzer was moved from Phoenix to 

Houston in June 2004, so the data comparison here with the Phoenix STN data involved only 62 

data pairs covering the time period from September 2003 through May 2004 (Figure 5-1). Once 

the analyzer was moved to Houston, collocated 8400S measurements were made; however, at the 

time of this report, insufficient collocated data were available to analyze for precision estimates. 

With the exception of data from the Seattle 8400S monitor, all sulfate regression slopes 

were less than or equal to 0.8 with positive intercepts (Table 7-4). No dynamic zero data were 

available to apply corrections for instrument offset. Although slopes were comparable between 

Chicago, Phoenix, and Indianapolis, the average difference between 8400S and STN sulfate data 

(y – x) was more negative for the more eastern cities because of the higher concentrations 

measured there. The scatter plot and regression for Chicago (Figure 7-5) were similar to those 

for Phoenix and Indianapolis. 

y = 0.5938x + 0.7763

R2

 = 0.7285

0.00

4.00

8.00

12.00

16.00

20.00

0.00 4.00 8.00 12.00 16.00 20.00

URG Filter-based (ug/m3)

R&P Continuous 8400S (ug/m3)

One-to-one

Best fit

Figure 7-5. Twenty-four-hour average R&P 8400S PM2.5 sulfate versus 24-hr 

integrated STN filter sulfate at Chicago, May 2, 2002 to May 10, 2005. 

In contrast to the monitors at other sites, the Seattle 8400S sulfate monitor measured 

more sulfate than the STN filter-based sampler for all but 6 of the 245 data pairs, with a slope for 

the comparison of 1.25 ± 0.03 (Figure 7-6). All but a few measured concentrations in Seattle 
7-15

were below 3 μg/m3, and the linear fit at these low concentrations was very good. Both the 

Seattle (r = 0.94) and the Indianapolis (r = 0.92) sites met the predictability criteria. 

y = 1.2483x + 0.2439

R2

 = 0.8875

0

2

4

6

8

02468

URG Filter-based Sulfate (ug/m3)

R&P Continuous 8400S Sulfate(ug/m3)

One-to-one

Best fit

Figure 7-6. Twenty-four-hour average R&P 8400S PM2.5 sulfate versus 24-hr 

integrated STN filter sulfate at Seattle, May 2, 2002 to February 3, 2005. 

7.4 CARBON 

That operationally defined measurements of aerosol TC, OC, and EC can yield highly 

variable results has been well documented. In a summary of comparison studies of filter 

methods to measure OC and EC, EC concentrations were found to differ by up to a factor of 7 

among filter-based methods; factor of 2 differences were common (Watson et al., 2005). In the 

comparisons discussed in this report, the analytical methods differ substantially in such factors as 

the analysis atmosphere (e.g., ambient air versus pure helium), temperature regimes and dwell 

times, and correction for pyrolysis of OC. The STN method utilizing TOT is used as the 

benchmark, but the criteria for comparability or predictability must be viewed with the 

understanding that one analysis method is not necessarily better than another. 
7-16

7.4.1 R&P 5400 Semi-Continuous Versus STN Filter-Based Carbon 

There are substantial differences in analysis methods between the 5400 and the STN TOT 

procedure. The 5400 carbon analysis is undertaken in ambient air, while the TOT procedure 

uses pure helium for the OC step and a helium/oxygen mixture for the EC step. There is no 

correction for pyrolyzed OC (POC) in the 5400. Thus, any POC that is generated is potentially 

added to the EC fraction. Maximum temperatures differ (750oC in the 5400 and 920oC in STN 

TOT), and the OC versus EC split temperature is 600oC in TOT versus 275oC in the 5400. The 

TOT method converts the generated CO2 to methane and measures it with a flame ionization 

detector (FID) while the 5400 employs a non-dispersive infrared (NDIR) detector. That results 

are dissimilar is not unexpected. 

Of the EC, OC, and TC data from the R&P 5400 at the five sites, only OC and TC data 

from the Seattle site met the predictability criteria while the TC data from the Indianapolis site 

almost met the predictability criteria. No R&P 5400 EC, OC, and TC data met the comparability 

criteria at any site (see Table 7-4). 

Slopes for TC ranged from 0.35 to 0.58, with correlation coefficients from 0.78 to 0.92. 

TC was underestimated over all concentration ranges, yielding average y/x ratios from 0.52 to 

0.83, and average differences (y – x) from –0.71 to –3.89 μg/m-3. Figure 7-7 shows a typical 

regression for TC. Scatter plots of the 5400 versus STN TC for the other four sites are shown in 

Appendix A. 

y = 0.5339x + 0.2684

R2

 = 0.6065

0.00

2.00

4.00

6.00

8.00

10.00

12.00

0.00 2.00 4.00 6.00 8.00 10.00 12.00

URG Filter-based Total Carbon (ug/m3)

R&P 5400 Total Carbon (ug/m3)

One-to-one

Best fit

Figure 7-7. Twenty-four-hour average R&P 5400 PM2.5 total carbon versus 24-hr 

integrated STN filter total carbon at Chicago, from May 2, 2002 to May 7, 2005. 
7-17

Slopes for OC ranged from 0.33 to 0.54, with correlation coefficients from 0.73 to 0.90. 

OC was underestimated over all concentration ranges, yielding average y/x ratios from 0.48 to 

0.81, and average differences (y – x) from –0.65 to –3.47 μg/m3

. Figure 7-8 depicts the 

5400-to-STN relationship for OC in Chicago. Plots for the other locations are shown in 

Appendix A. 

y = 0.5447x + 0.3502

R2

 = 0.6398

0.00

2.00

4.00

6.00

8.00

10.00

12.00

0.00 2.00 4.00 6.00 8.00 10.00 12.00

URG Filter-based Organic Carbon (ug/m3)

R&P 5400 Organic Carbon (ug/m3)

One-to-one

Best filt

Figure 7-8. Twenty-four-hour average R&P 5400 PM2.5 organic carbon versus 24-

hr integrated STN filter organic carbon at Chicago, May 2, 2002 to May 7, 2005. 

Slopes for EC ranged from 0.29 to 0.63, with correlation coefficients from 0.16 to 0.83. 

EC was underestimated over all concentration ranges, yielding average y/x ratios from 0.37 to 

0.99, and average differences (y – x) from –0.07 to –0.42 μg/m3

. A typical regression plot, from 

Chicago, is shown in Figure 7-9. 

In previous studies comparing data from the 5400 with filter-based aerosol carbon data, 

EC was overestimated by the 5400 (Watson and Chow, 2002; Rice, 2004). At the five STN sites 

included in the current analysis, the 5400 consistently underestimated the EC fraction, as 

reflected in the low slopes, the average y/x ratios all less than 1, and the consistently negative y-x 

differences. 
7-18

y = 0.3088x + 0.0344

R2

 = 0.2384

0.00

1.00

2.00

3.00

4.00

0.00 1.00 2.00 3.00 4.00

URG Filter-based elemental Carbon (ug/m3)

R&P 5400 Elemental Carbon (ug/m3)

One-to-one

Best fit

Figure 7-9. Twenty-four-hour average R&P 5400 PM2.5 elemental carbon versus 24-hr 

integrated STN filter elemental carbon at Chicago, May 2, 2002 to May 7, 2005. 

7.4.2 Sunset Semi-Continuous Versus STN Filter-Based Carbon 

A Sunset Laboratory thermal-optical carbon monitor was added to the suite of 

instruments at the Chicago, Phoenix, and Seattle sites during 2004. The analysis method is 

similar to the STN TOT method—it uses a pure helium atmosphere for the OC step, followed by 

a helium/oxygen mixture for the EC step; maximum temperatures are similar (850oC for the 

Sunset carbon monitor and 920oC for STN TOT), and both methods have a light transmittancebased pyrolysis correction. The Sunset carbon monitor used in this comparison study use an 

NDIR to directly detect evolved CO2. This method is similar to the laboratory-based STN 

analysis method. 

The data sets for the Sunset comparisons are considerably smaller than the other data sets 

being evaluated here (70 to 90 pairs versus 150 to 300 pairs, see Table 7-1), but still sufficient 

for statistical analysis. 

The results for the Sunset carbon monitor operated at three of the sites were significantly 

better than the results for the R&P 5400—the EC data at one site (Phoenix) met the slope and 

intercept comparability criteria; the EC, OC, and TC data from all three sites either met or almost 

met the ratio-of-means and correlation comparability criteria; and EC, OC, and TC at all three 

sites met the predictability criteria. The comparability evaluations between the Sunset and filter-
7-19

based OC and TC data offer some interesting results and point out the difficulty in assessing 

statistical equivalence and comparability. 

Initial examination of the regression plots for Chicago (Figures 7-10 through 7-12) and 

Seattle (Figures 7-13 through 7-15) shows good agreement between the semi-continuous and 

filter-based data. The correlations are all greater than 0.90, and the regression slopes equal unity 

within 3 standard errors and/or average y/x ratios equal unity within 1 standard deviation (Table 

7-1). However, the TC and OC intercepts differ from zero by more than 3 standard errors, 

making the Sunset data not comparable to filter-based data when intercept is a criterion (Table 

7-1). How important is the intercept criterion? In the case of the Chicago Sunset TC, the slope 

is 0.91 and r = 0.98, and, as the regression plot (Figure 7-10) clearly demonstrates, the 

relationship over the range of measured concentrations is qualitatively good. However, under 

low ambient concentrations, an intercept of 0.80 ±0.15 imparts significant bias. This may 

indicate the need to determine a dynamic zero to adjust for background for the Sunset monitor. 

In addition, the Sunset monitor has an organic carbon gas-phase denuder and the STN does not. 

It is unclear how the denuder influences these results. 

The Phoenix Sunset monitor met the correlation and ratio-of-means comparability criteria 

for OC and TC even though the low slopes for the Phoenix TC (Figure 7-16) and OC (Figure 

7-17) were considerably outside the acceptable magnitude. Since OC is a major component of 

TC, OC concentrations dominated the TC slope, even though EC showed excellent agreement 

with the filter-based data (Figure 7-18). 

y = 0.9358x + 0.8049

R2

 = 0.9084

0.00

2.00

4.00

6.00

8.00

10.00

12.00

0.00 2.00 4.00 6.00 8.00 10.00 12.00

URG Filter-based Total carbon (ug/m3)

Sunset Continuous Total Carbon (ug/m3)

One-to-one

Best fit

Figure 7-10. Twenty-four-hour average Sunset PM2.5 total carbon versus 24-hr 

integrated STN filter total carbon at Chicago, August 1, 2004 to May 7, 2005. 
7-20

y = 0.8432x + 0.8696

R2

 = 0.8663

0.00

2.00

4.00

6.00

8.00

10.00

12.00

0.00 2.00 4.00 6.00 8.00 10.00 12.00

URG Filter-based total carbon (ug/m3)

Sunset Continuous total carbon (ug/m3)

One-to-one

Best fit

Figure 7-11. Twenty-four-hour average Sunset PM2.5 organic carbon versus 24-hr 

integrated STN filter organic carbon at Chicago, August 1, 2004 to May 7, 2005. 

y = 1.1051x + 0.0876

R2

 = 0.904

0.00

1.00

2.00

3.00

4.00

0.00 1.00 2.00 3.00 4.00

URG Filter-based (ug/m3)

Sunset Continuous (ug/m3)

One-to-one

Best fit

Figure 7-12. Twenty-four-hour average Sunset PM2.5 elemental carbon versus 24-hr 

integrated STN filter elemental carbon at Chicago, August 1, 2004 to May 7, 2005. 
7-21

y = 0.9072x + 0.5808

R2

 = 0.9698

0

4

8

12

16

20

0 4 8 12 16 20

URG Filter-based (ug/m3)

Sunset Continuous (ug/m3)

One-to-one

Linear (Sunset TC)

Figure 7-13. Twenty-four-hour average Sunset PM2.5 total carbon versus 24-hr 

integrated STN filter total carbon at Seattle, August 13, 2004 to April 10, 2005. 

y = 0.7911x + 0.7321

R2

 = 0.918

0

2

4

6

8

10

12

0 2 4 6 8 10 12

URG Filter-based organic carbon (ug/m3)

Sunset Continuous organic carbon (ug/m3)

One-to-one

Best fit

Figure 7-14. Twenty-four-hour average Sunset PM2.5 organic carbon versus 24-hr 

integrated STN filter organic carbon at Seattle, August 13, 2004 to April 10, 2005. 
7-22

y = 1.2272x - 0.0274

R2

 = 0.8491

0

1

2

3

4

01234

URG Filter-based organic carbon (ug/m3)

Sunset Continuous organic carbon (ug/m3)

One-to-one

Best fit

Figure 7-15. Twenty-four-hour average Sunset PM2.5 elemental carbon versus 24-hr 

integrated STN filter elemental carbon at Seattle, August 13, 2004 to April 10, 2005. 

y = 0.7603x + 0.2221

R2

 = 0.8964

0.00

5.00

10.00

15.00

20.00

25.00

0.00 5.00 10.00 15.00 20.00 25.00

MetOne Filter-based (ug/m3)

Sunset Continuous (ug/m3)

One-to-one

Best fit

Figure 7-16. Twenty-four-hour average Sunset PM2.5 total carbon versus 24-hr 

integrated STN filter total carbon at Phoenix, July 2, 2004 to May 13, 2005. 
7-23

y = 0.677x + 0.391

R2

 = 0.8196

0.00

4.00

8.00

12.00

16.00

20.00

0.00 4.00 8.00 12.00 16.00 20.00

MetOne Filter-based organic carbon (ug/m3)

Sunset Continuous Organic Carbon (ug/m3)

One-to-one

Best fit

Figure 7-17. Twenty-four-hour average Sunset PM2.5 organic carbon versus 24-hr 

integrated STN filter organic carbon at Phoenix, July 2, 2004 to May 13, 2005. 

y = 1.0385x + 0.0045

R2

 = 0.9488

0.00

1.00

2.00

3.00

4.00

5.00

0.00 1.00 2.00 3.00 4.00 5.00

MetOne Filter-based elemental carbon (ug/m3)

Sunset Continuous elemental carbon (ug/m3)

One-to-one

Best fit

Figure 7-18. Twenty-four-hour average Sunset PM2.5 elemental carbon versus 24-hr 

integrated STN filter elemental carbon at Phoenix, July 2, 2004 to May 13, 2005.
8-1

8. OPERATIONAL ISSUES 

A desirable characteristic of semi-continuous speciation monitors is ease of maintenance. 

Site operators at the five locations were polled, and their comments about operational issues are 

qualitatively summarized here. Prior to the study, Standard Operating Procedures (SOPs), 

quality control (QC) checklists, and other assistance documents were jointly prepared by the 

study participants. 

8.1 R&P 8400N AND 8400S 

The 8400N and 8400S instruments have few operational differences beyond those 

mentioned in Section 5.2.2, so their operational characteristics are discussed together. 

The general impression of the 8400 series operating manuals was that they were poorly 

written, lacked coherence, and, in some cases, contained erroneous information. Fortunately, the 

SOP and assistance documents were generally complete and well thought out. Site operators 

noted that the QC checklists should be continually updated as field experiences were discussed 

and evaluated during the field study. One example would be updating acceptable values for the 

nitrate flash duration range. Also mentioned was the importance of maintaining consistency in 

units between the nitrate and sulfate instrument QC checklists to avoid confusion for staff. 

8.1.1 Distribution of Annual Hours 

Site operators agreed that, as a very general estimate, about 400 hours were spent 

annually on each 8400 series monitor. Data acquisition and reduction alone are estimated to 

account for about 44% of all annual hours expended on these instruments, followed by aqueous 

standards tests at 14% of total hours. Most of these hours were within weekly or monthly visits, 

while PE tests dominated the hours attributed to quarterly tasks. Table 8-1 gives estimates of the 

proportion of annual hours allotted to specific tasks. Note that these estimates are for operations, 

maintenance, and data processing, and do not include reporting tasks. 

8.1.2 Flash Strips 

The 8400S exhibited more flash-strip-related problems than did the 8400N, apparently 

related to the washers that are used to hold the flash strip in place and the fact that the 8400S 

platinum strips are more delicate than the 8400N NiChrome® strips. The 8400S was originally 

supplied with round washers meant to hold the flash strips, but R&P replaced these round 

washers with square washers. Users reported that the jagged edges of the square washers 

apparently caused more strip failures. Both round and square washers are currently in use at the 

different sites. Some operators found no difference in the washers, while others strongly disliked 

the square ones. R&P was supposed to have distributed a flash strip installation guide tool to all 

operators. This tool was intended to hold the square washers in place while retaining nuts were 

tightened to avoid stressing the flash strip. One operator who received the tool stated that it was 

very helpful, but operators at some sites did not receive the tool. Site operators changed the 
8-2

strips nominally once a month and reported that it was important to check the washers at every 

flash strip change for black marks or burning, and to replace them if they looked or felt bad. One 

site operator stated that constant strip-breaking on the 8400S (every 2–3 days) was a significant 

problem, and the instrument was eventually shut down because of this problem. 

The posts on which the flash strips are mounted also tend to develop burrs or pits that can 

contribute to flash-strip failure, and these posts need to be sanded with a fine emery cloth. 

If the flash duration of the 8400S is too short (less than 8 ms), the strips are prone to 

“burning up”. A flash duration of between 10 and 16 ms will prolong the life of the 8400S flash 

strip. 

Table 8-1. Percent of annual hours spent on operational and maintenance tasks 

for the R&P 8400 series monitors. The annual total for all tasks is estimated at 

400 hours for each 8400N or 8400S monitor. 

Maintenance Item or Task Percent of Annual Hours 

Carrier Gas 4.8 

Calibration Gas 4.8 

Water Reservoir 4.0 

Flash strip 1.5 

Flash duration 3.3 

Carbon denuder maintenance 0.1 

Sample flow rate 3.3 

Orifice cleaning 1.5 

Make-up flow and filter check 0.3 

R-cell pressure adjustments 1.5 

Zero and span drift 4.5 

Manual analyzer audits 3.0 

Cyclone maintenance 1.5 

Field blanks 0.8 

Leak checks 1.5 

Flow checks 0.8 

Ambient T and P verification 0.4 

Aqueous standards 13.5 

Performance evaluations (blind tests) 4.0 

Data acquisition and reduction issues 43.5 

Pump Maintenance/ Repair 1.3 

Sample Line Replacement 0.5 
8-3

8.1.3 Aqueous Standards 

Testing against aqueous standards is time-consuming but necessary. The primary issues 

concerning aqueous standards for the 8400 series instruments are related to using varying 

volumes of the standard solution. One consequence is that for the larger volumes, there may not 

be adequate time for complete evaporation to occur, especially at lower temperatures. This issue 

was apparently a larger problem for the 8400S than for the 8400N. One suggested resolution of 

this problem was to use multiple concentrations of standards at one low volume (e.g., 

0.2 μl SO4, and 0.5 μl NO3). R&P promised to provide standard solutions in multiple 

concentrations, but none of the site operators actually received these standards. PE solutions 

provided by NAREL for routine aqueous standards tests are being used at at least one site. 

Another suggestion for ensuring complete evaporation of the syringe-applied standard was to 

provide a heated flash cell (e.g., 35 to 50o C). 

8.1.4 Water Reservoir and Hydration Tube 

Some participants commented on problems with discoloration of the hydration tube. One 

participant attributed the discoloration to bacteria growing in the humidifier because the water 

was not bacteria-free. This operator has been using ultra-pure bacteria-free water to fill the 

hydration bottle. Others used distilled water. The true cause of this discoloration is 

undetermined. Another possibility is a reaction with the metal fittings, since the discoloration 

tends to be on either end of the hydration tube, near the metal “T” fittings. Discoloration of the 

hydration tube was commonly reported; but according to R&P, the ability of the permeation 

tubing to hydrate the sample air is not affected. 

8.1.5 Orifice Cleaning 

Although the 8400 series manuals recommend monthly intervals between sample orifice 

cleaning, site operators reported that weekly schedules (or at each site visit) are more apt to 

minimize flow rate problems. 

8.1.6 Molybdenum Converter 

The molybdenum converter in the 8400N instruments was evaluated and replaced during 

2004. The efficiency of the “moly chips” that convert the nitrogen species to NO under high 

temperatures is reduced over time, and it is important to periodically check the conversion 

efficiency. There is some uncertainty about which nitrogen species should be used to check the 

converter efficiency. As discussed above, the effect of the converter replacement on the 

monitors’ efficiencies, as measured by the periodic aqueous standards tests, varied among sites. 
8-4

8.1.7 Data Screen Failures 

Some sites experienced “data screen failures” due to failures in the backlight controller. 

R&P delivered an upgrade kit that involves the installation of a “piggy-back” board on the 

existing backlight controller. 

8.1.8 Data Acquisition and Reduction 

Methods of data acquisition and reduction for the semi-continuous monitors differed 

among sites. Some sites logged analog data and other sites logged digital data. All site operators 

indicated that viewing the data every day, often via remote access, was a valuable activity. The 

operators viewed the data to determine if the instrument was operating, and operating properly, 

to evaluate various instrument operating parameters that are provided on these new instruments 

using serial outputs and to identify major problems. The review and evaluation of important 

operational data from these monitors, such as flash duration, flow rate, and R-cell pressures, was 

valuable. 

Several different systems were used to view the data on a daily basis. The Indiana site 

operator did a quick, daily review of the data through a special MeteoStar LEADS web site. The 

Texas operator performed his daily review via the Texas Commission on Environmental Quality 

(TCEQ) web site, which also uses MeteoStar LEADS, but the operator used an on-site PC to 

collect the data that were validated and used for reporting. The Illinois operators do not use the 

MeteoStar system. At the Phoenix site, an STI data acquisition system was used to collect the 

serial data. The data were then automatically screened against minimum validation criteria, 

plotted and made available on a web site and archived. 

8.2 R&P 5400 

Site operators agreed that, as a very general estimate, about 150 hours were spent 

annually on the R&P 5400 OCEC monitor. Data acquisition and reduction alone accounted for 

more than 50% of all annual hours spent on this instrument. Monthly and semi-annual checks 

and repairs of furnace lamps, afterburner lamps, and oven fuses combined to account for an 

additional 20% of annual hours. Table 8-2 shows estimates of the proportion of annual hours 

allotted to specific tasks. Note that these estimates are for operations, maintenance, and data 

processing but do not include subsequent reporting tasks. 
8-5

Table 8-2. Percent of annual hours spent on operational and maintenance tasks 

for the R&P 5400 OCEC monitor. The annual total for all tasks are estimated at 

150 hours for each 5400 monitor. 

Maintenance Item or Task Percent of Annual Hours

Flow audit 3.9 

Collection path leak test 3.9 

Analysis loop leak test 2.0 

Licor calibrations 3.3 

Furnace burners/afterburners fuses (monthly) 9.8 

Inlet cleaning 11.8 

Intake filters (cooling fans) 0.7 

In-line filters 0.7 

Data acquisition and reduction issues 52.3 

Collector replacement 5.2 

Furnace Lamp Checks/ Replacement (semi-annually) 10.5 

Pump Maintenance/ Replacement 2.0 

Flow problems, along with oven and afterburner problems, were the most frequent and 

recurring operational issues with the R&P 5400. It had been known that the sample flow through 

the collectors in the 5400 monitor tend to degrade over time, requiring replacement of the 

collectors, and this degradation was observed during the course of the Study. The collectors are 

expensive (~ $1,200 per pair), and it is recommended that both collectors be replaced whenever 

one of them starts to become clogged. Failure to reach set point temperatures was most 

frequently caused by blown fuses or burnt out oven lamps or afterburner lamps. The contact 

points for the lamps were also reported to have melted on occasion. 

The 5400 monitor in Phoenix experienced leak problems. Although regular leak checks 

yielded apparently acceptable results, evidence exists that a leak may have existed and gone 

undetected for an extended period of time. The hypothesis is that the pressure sensor in the 

optical bench has a maximum range and that this range was being exceeded during the manual 

leak tests. The R&P recommendation is to achieve a pressure of about 1200 mb for this test. 

Empirically, it appears that the pressure sensor tops out at 1206 mb. If this pressure were to be 

exceeded, a pressure drop could be occurring during the leak test but would not be detected. On 

February 4, 2005, after both sides of the analyzer failed an analysis loop leak check while a 

manual leak check was passed, this hypothesis was tested. A manual leak check using a 

maximum pressure of 1180 mb was initiated, a pressure drop was noted immediately, and the 

leak was isolated at the septum/buffer tank by pinching off the line above the buffer tank. This 

action stopped the pressure drop, and the septum fittings were replaced. It is recommended that 

all manual leak checks use a maximum pressure of 1180 mb. The rated maximum pressure of 

the optical bench pressure sensor is not known. 

In Phoenix, the replacement of some hardware in the Licor NDIR component in 

September 2004 caused false status codes during the auto-calibration procedure. R&P’s 
8-6

recommendation was to disable the auto-calibration and auto-leak check functions. This step 

increased the duties of the site operators because these checks had to be performed manually. 

Some site operators felt that the data from the R&P 5400 were so disappointing that they 

did not want to waste their time discussing operational issues. Most of the above issues are 

based on comments in the Phoenix site log. 

8.3 SUNSET CARBON MONITOR 

Generally, site operator experiences with the Sunset OCEC analyzer have been good. The 

Sunset analyzer has no analog output, so users without serial datalogging capability are at a 

disadvantage. This characteristic combined with a failure in the laptop PC control system to 

cause massive (one month) data loss at the Chicago site, which apparently had only analog 

capabilities. A hard-drive failure in the laptop occurred one day before the month’s data were 

due to be downloaded. A loss of six days’ data occurred at the same site when the laptop locked 

up just after a Thursday visit by the site operator and was not rebooted until the next visit the 

following Tuesday. All site operators reported problems with the laptop computer at some point. 

The burner coils in the Sunset have to be replaced periodically. The first report of burner 

failures came from Phoenix in August 2005, when both front and rear burners failed within a few 

weeks of each other. 

Over the past year, the three Sunset monitors in the evaluation differed in their analysis 

software. All comparisons presented here have been based on a recalculation of the 

concentrations from the raw data, employing the most recent version of the analysis software. It 

will be important for the manufacturer to ensure that the deployed instruments have up-to-date 

versions of software and employ identical procedures. 

Although the audit punches in the PE were easy to complete, the manual integration of 

the peaks was tedious. Modification of the software to enable the field results file to duplicate 

the post-calculated values would be helpful. 

A few comments noted that the Sunset manual was inadequate to acquaint an operator 

with operation of the instrument. While covering the instrument setup routines fairly well, the 

manual offers minimal information on the theory of operation and data output interpretation. 

There are no schematics or illustrations. More clarification of and details about filter 

replacement procedures would be helpful. The inclusion of the NIOSH laboratory SOP in the 

manual is confusing because of differences between the laboratory and field instruments (FID 

versus NDIR, for example). 

No sucrose spikes were ever applied by the site operators, except for those provided by 

NAREL for the PEs. This action would provide useful checks on the monitors’ performance. 
9-1

9. REFERENCES 

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of a semi-continuous instrument for elemental carbon and organic carbon using a 

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9-2

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particle nitrate in the atmosphere. Environ. Sci. Technol. 34 (5), 907-914. 

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at the U.S. Environmental Protection Agency Atlanta Supersite. Journal of Geophysical 

Research-Atmospheres 108 (D7). 

Wittig A.E., Takahama S., Khlystov A., Pandis S.N., Hering S., Kirby B., and Davidson C. 

(2004) Semi-continuous PM2.5 inorganic composition measurements during the 

Pittsburgh Air Quality Study. Atmos. Environ. 38, 3201-3213. 
A-1

APPENDIX A 

SCATTER PLOTS AND REGRESSIONS 

FOR PHOENIX, SEATTLE, AND INDIANAPOLIS 
A-2

A.1 NITRATE 

Pre-moly

y = 0.8507x + 0.345

R2

 = 0.8864

Post-moly

y = 0.6513x + 0.2726

R2

 = 0.9679

All data

y = 0.7854x + 0.3362

R2

 = 0.8756

0

4

8

12

16

20

0 4 8 12 16 20

One-to-one

Pre-moly

Post-moly

All data

Best fit (pre-moly)

Best fit (post-moly)

Best fit (all data)

MetOne Filter-based Nitrate (μg/m3)

R&P Continuous Nitrate 8400N (μg/m3)

Pre-moly

y = 0.8507x + 0.345

R2

 = 0.8864

Post-moly

y = 0.6513x + 0.2726

R2

 = 0.9679

All data

y = 0.7854x + 0.3362

R2

 = 0.8756

0

4

8

12

16

20

0 4 8 12 16 20

One-to-one

Pre-moly

Post-moly

All data

Best fit (pre-moly)

Best fit (post-moly)

Best fit (all data)

MetOne Filter-based Nitrate (μg/m3)

R&P Continuous Nitrate 8400N (μg/m3)

Figure A-1. Twenty-four-hour average R&P 8400N PM2.5 nitrate versus 24-hr 

integrated STN filter nitrate at Phoenix, from September 26, 2002, to May 13, 2005. 

Post-moly

y = 0.9571x + 0.1108

R2

 = 0.8002

All data

y = 0.8483x + 0.2413

R2

 = 0.7605

Pre-moly

y = 0.813x + 0.2891

R2

 = 0.7518

0

1

2

3

4

5

6

0123456

3

One-to-one

Pre-moly

Post-moly

All data

Best fit (post-moly)

Best filt (all data)

Best fit (pre-moly)

URG Filter-based Nitrate (μg/m3)

R&P Continuous Nitrate 8400N (μg/m3)

Post-moly

y = 0.9571x + 0.1108

R2

 = 0.8002

All data

y = 0.8483x + 0.2413

R2

 = 0.7605

Pre-moly

y = 0.813x + 0.2891

R2

 = 0.7518

0

1

2

3

4

5

6

0123456

3

One-to-one

Pre-moly

Post-moly

All data

Best fit (post-moly)

Best filt (all data)

Best fit (pre-moly)

URG Filter-based Nitrate (μg/m3)

R&P Continuous Nitrate 8400N (μg/m3)

Figure A-2. Twenty-four-hour average R&P 8400N PM2.5 nitrate versus 24-hr 

integrated STN filter nitrate at Seattle, from May 2, 2002, to April 14, 2005. 
A-3

Post-moly

y = 0.5203x + 0.3974

R2

 = 0.9002

Pre-moly

y = 0.6221x + 0.3928

R2

 = 0.8036

All data

y = 0.5851x + 0.4065

R2

 = 0.8137

0

4

8

12

16

20

0 4 8 12 16 20

One-to-one

Pre-moly

Post-moly

all data

Best fit (post-moly)

Best fit (pre-moly)

Best fit all data)

MetOne Filter-based Nitrate (μg/m3)

R&P Continuous Nitrate 8400N (μg/m3)

Post-moly

y = 0.5203x + 0.3974

R2

 = 0.9002

Pre-moly

y = 0.6221x + 0.3928

R2

 = 0.8036

All data

y = 0.5851x + 0.4065

R2

 = 0.8137

0

4

8

12

16

20

0 4 8 12 16 20

One-to-one

Pre-moly

Post-moly

all data

Best fit (post-moly)

Best fit (pre-moly)

Best fit all data)

MetOne Filter-based Nitrate (μg/m3)

R&P Continuous Nitrate 8400N (μg/m3) Figure A-3. Twenty-four-hour average R&P 8400N PM2.5 nitrate versus 24-hr 

integrated STN filter nitrate at Indianapolis, from June 13, 2002, to March 11, 2005. 

A.2 SULFATE 

y = 0.7622x + 0.1785

R2

 = 0.5028

0.00

1.00

2.00

3.00

4.00

0.00 1.00 2.00 3.00 4.00

Met One Filter-based Sulfate (ug/m3)

R&P Continuous 8400S Sulfate (ug/m3)

One-to-one

Best fit

Figure A-4. Twenty-four-hour average R&P 8400S PM2.5 sulfate versus 24-hr 

integrated STN filter sulfate at Phoenix, from September 30, 2003, to May 12, 2004. 
A-4

y = 0.6604x + 0.4971

R2

 = 0.866

0

5

10

15

20

25

30

0 5 10 15 20 25 30

Met One Filter-based Sulfate (ug/m3)

R&P Continuous 8400S Sulfate (ug/m3)

One-to-one

Best fit

Figure A-5. Twenty-four-hour average R&P 8400S PM2.5 sulfate versus 24-hr 

integrated STN filter sulfate at Indianapolis, from June 19, 2002, to July 8, 2004. 

y = 0.8059x + 0.4029

R2

 = 0.5485

0

5

10

15

20

0 5 10 15 20

URG Filter-based Sulfate (ug/m3) 

R&P Continuous 8400S Sulfate (ug/m3)

One-to-one

Best fit

Figure A-6. Twenty-four-hour average R&P 8400S PM2.5 sulfate versus 24-hr 

integrated STN filter sulfate at Houston, from January 27, 2003, to April 13, 2005. 
A-5

A.3 R&P 5400 VERSUS STN CARBON 

A.3.1 Total Carbon 

y = 0.3473x + 0.946

R2

 = 0.7289

0.00

5.00

10.00

15.00

20.00

25.00

0.00 5.00 10.00 15.00 20.00 25.00

Met One Filter-based Total Carbon (ug/m3)

R&P 5400 Total Carbon (ug/m3)

One-to-one

Best fit

Figure A-7. Twenty-four-hour average R&P 5400 PM2.5 total carbon versus 24-hr integrated 

STN filter total carbon at Phoenix, from December 13, 2002, to May 13, 2005. 

y = 0.5797x + 0.4674

R2

 = 0.8417

0

4

8

12

16

20

0 4 8 12 16 20

URG Filter-based Total Carbon (ug/m3)

R&P 5400 Total Carbon (ug/m3)

One-to-one

Best fit

Figure A-8. Twenty-four-hour average R&P 5400 PM2.5 total carbon versus 24-hr 

integrated STN filter total carbon at Seattle, from May 2, 2002, to March 29, 2005. 
A-6

y = 0.5124x + 0.4858

R2

 = 0.7943

0

5

10

15

20

0 5 10 15 20

Met One Filter-based Total Carbon (ug/m3)

R&P 5400 Total Carbon (ug/m3)

One-to-one

Best fit

Figure A-9. Twenty-four-hour average R&P 5400 PM2.5 total carbon versus 

24-hr integrated STN filter total carbon at Indianapolis, from June 26, 2002, to 

April 9, 2004. 

y = 0.4623x + 0.8104

R2

 = 0.5562

0

4

8

12

16

0 4 8 12 16

URG Filter-based Total Carbon (ug/m3)

R&P 5400 Total Carbon (ug/m3)

One-to-one

Best fit

Figure A-10. Twenty-four-hour average R&P 5400 PM2.5 total carbon versus 

24-hr integrated STN filter total carbon at Houston, from January 24, 2003, to 

March 26, 2005. 
A-7

A.3.2 Organic Carbon 

y = 0.3292x + 0.7216

R2

 = 0.6442

0.00

4.00

8.00

12.00

16.00

20.00

0.00 4.00 8.00 12.00 16.00 20.00

Met One Filter-based Organic Carbon (ug/m3)

R&P Organic Carbon 5400 (ug/m3)

One-to-one

Best fit

Figure A-11. Twenty-four-hour average R&P 5400 PM2.5 organic carbon versus 

24-hr integrated STN filter organic carbon at Phoenix, from December 13, 2002, to 

May 13, 2005. 

y = 0.5333x + 0.569

R2

 = 0.8039

0

2

4

6

8

10

12

0 2 4 6 8 10 12

URG Filter-based Organic Carbon (ug/m3)

R&P Organic Carbon 5400 (ug/m3)

Best fit

One-to-one

Figure A-12. Twenty-four-hour average R&P 5400 PM2.5 organic carbon versus 

24-hr integrated STN filter organic carbon at Seattle, from May 2, 2002, to March 29, 

2005. 
A-8

y = 0.5001x + 0.5906

R2 = 0.7315

0

4

8

12

16

20

0 4 8 12 16 20

Met One Filter-based Organic Carbon (ug/m3)

R&P Organic Carbon 5400 (ug/m3)

One-to-one

Best fit

Figure A-13. Twenty-four-hour average R&P 5400 PM2.5 organic carbon versus 

24-hr integrated STN filter organic carbon at Indianapolis, from June 26, 2002, to 

April 9, 2004. 

y = 0.4665x + 0.6502

R2

 = 0.5386

0

4

8

12

0 4 8 12

URG Filter-based Organic Carbon (ug/m3)

R&P Organic Carbon 5400 (ug/m3)

One-to-one

Best fit

Figure A-14. Twenty-four-hour average R&P 5400 PM2.5 organic carbon versus 

24-hr integrated STN filter organic carbon at Houston, from January 24, 2003, to 

March 26, 2005. 
A-9

A.3.3 Elemental Carbon 

y = 0.4005x + 0.2763

R2

 = 0.6357

0.00

1.00

2.00

3.00

4.00

5.00

0.00 1.00 2.00 3.00 4.00 5.00

Met One Filter-based Elemental Carbon (ug/m3)

R&P Elemental Carbon 5400 (ug/m3)

One-to-one

Best fit

Figure A-15. Twenty-four-hour average R&P 5400 PM2.5 elemental carbon versus 

24-hr integrated STN filter elemental carbon at Phoenix, from December 13, 2002, to 

May 13, 2005. 

y = 0.6307x + 0.0187

R2

 = 0.682

0

1

2

3

4

01234

URG Filter-based Elemental Carbon (ug/m3)

R&P 5400 Elemental Carbon (ug/m3)

One-to-one

Best fit

Figure A-16. Twenty-four-hour average R&P 5400 PM2.5 elemental carbon versus 

24-hr integrated STN filter elemental carbon at Seattle, from May 2, 2002, to 

March 29, 2005. 
A-10

y = 0.3941x + 0.0278

R2

 = 0.4255

0

1

2

3

0123

Met One Filter-based Elemental Carbon (ug/m3)

R&P 5400 Elemental Carbon (ug/m3)

One-to-one

Best fit

Figure A-17. Twenty-four-hour average R&P 5400 PM2.5 elemental carbon versus 

24-hr integrated STN filter elemental carbon at Indianapolis, from June 26, 2002, to 

April 9, 2004. 

y = 0.2851x + 0.2054

R2

 = 0.0267

0

1

2

3

0123

URG Filter-based Elemental Carbon (ug/m3)

R&P 5400 Elemental Carbon (ug/m3)

One-to-one

Best fit

Figure A-18. Twenty-four-hour average R&P 5400 PM2.5 elemental carbon versus 

24-hr integrated STN filter elemental carbon at Houston, from January 24, 2003, to 

March 26, 2005. 
United States 

Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Emissions, Monitoring and Analysis Division 

Research Triangle Park, NC 

Publication N