Annex A. Atmospheric Science 

A.1. Ambient Air Particle Monitoring 

A.1.1. Measurements and Analytical Specifications 

Table A-1. Summary of integrated and continuous samplers included in the field comparison. 

Abbreviation Instrument Manufacturer / Research Institute 

INTEGRATED PARTICLE OR GAS/PARTICLE INSTRUMENTS 

Dichot Dichotomous Sampler with Virtual Impactor Andersen Instruments (Smyrna, GA) 

AND-241 Dichot Thermo Andersen Series 241 Dichotomous Sampler Andersen Instruments 

AND-246 Dichot Thermo Andersen SA-246B Dichotomous Sampler Andersen Instruments 

AND-hIVOL10 

FRM Thermo Andersen GMW-1200 HiVol PM10 FRM Sampler Andersen Instruments 

ARA-PCM ARA Particle Composition Monitor Atmospheric Research and Analysis Inc. (Plano, TX) 

CMU CMU Speciation Sampler Carnegie Mellon University (CMU), (Pittsburgh, PA) 

DRI-SFS DRI Sequential Filter Sampler Desert Research Institute (Reno, NV) 

HEADS (or HI) Harvard EPA Annular Denuder System (or Harvard Impactor) Harvard School of Public Health (Boston, MA) 

IMPROVE_SSb IMPROVE Speciation Sampler URG Corp. (Chapel Hill, NC) 

URG-3000Nb Modified IMPROVE Module C Sampler for Carbon URG Corp. 

MASS-400b URG Mass Aerosol Speciation Sampler Model 400 URG Corp. 

MASS-450b URG Mass Aerosol Speciation Sampler Model 450 URG Corp. 

MiniVol Battery-Powered Portable Low-Volume Sampler Air Metrics Inc. (Eugene, OR) 

PC-BOSS Particle Concentrator-Brigham Young University Organic Sampling 

System Brigham Young University (Provo, UT) 

SAMPLING SYSTEM 

PQ-200 FRM BGI PQ-200 FRM Sampler BGI Inc. (Waltham, MA) 

PQ-200 FRMA BGI PQ-200A FRM Audit Sampler BGI Inc. 

R&P-ACCU R&P-Automated Cartridge Collector Unit Sampler Rupprecht & Patashnick, Co. (Albany, NY) 

R&P-2000 FRM R&P Partisol-2000 FRM Sampler Rupprecht & Patashnick, Co. 

R&P-2000 FRMA R&P Partisol-2000 FRM Audit Sampler Rupprecht & Patashnick, Co. 

R&P-2025 Dichotb R&P Partisol 2025 Dichotomous Sequential Air Sampler Rupprecht & Patashnick, Co. 

R&P-2025 FRM R&P Partisol-Plus Model 2025 PM2.5 Sequential Samplers Rupprecht & Patashnick, Co. 

R&P-2300b R&P Partisol 2300 Chemical Speciation Sampler Rupprecht & Patashnick, Co. 

 

Note: Hyperlinks to the reference citations throughout this document will take you to the NCEA HERO database (Health and 

Environmental Research Online) at http://epa.gov/hero. HERO is a database of scientific literature used by U.S. EPA in the process of 

developing science assessments such as the Integrated Science Assessments (ISA) and the Integrated Risk Information System (IRIS). 

December 2009 A-1 
Abbreviation Instrument Manufacturer / Research Institute 

RAAS-100 FRM Thermo Andersen Reference Ambient Air Sampler Model 100 Andersen Instruments 

FRM SAMPLER 

RAAS-200 FRM Thermo Andersen RAAS Model 200 FRM Audit Sampler Andersen Instruments 

RAAS-300 FRM Thermo Andersen RAAS Model 300 FRM Sampler Andersen Instruments 

RAAS-400b Thermo Andersen RAAS Model 400 Speciation Sampler Andersen Instruments 

SASSb MetOne Spiral Ambient Speciation Sampler Met One Instruments (Grants Pass, OR) 

SCS PM2.5 Sequential Cyclone Sampler New York University (New York, NY) 

URG-PCMb URG Particle Composition Monitor URG Corp. (Chapel Hill, NC) 

VAPS URG Versatile Air Pollution Sampler URG Corp. 

CONTINUOUS MASS INSTRUMENTS 

BAM Β-Attenuation Monitor Model 1020 Met One Instruments 

nano-BAM Met One BAM Model 1020 with 150 nm impactor Met One Instruments 

CAMM Continuous Ambient Mass Monitor Developed by Harvard School of Public Health, commercialized 

by Thermo Andersen Instruments; now withdrawn from market 

RAMS Real-Time Ambient Mass Sampler (modified Tapered Element 

Oscillation Microbalance with diffusion denuder and Nafion dryer) Brigham Young University 

TEOM Tapered Element Oscillating Microbalance Rupprecht & Patashnick, Co. 

30 °C-TEOM TEOM operated at 30 °C Rupprecht & Patashnick, Co. 

50 °C-TEOM TEOM operated at 50 °C Rupprecht & Patashnick, Co. 

SES-TEOM TEOM 1400a Series with Sample Equilibration System Rupprecht & Patashnick, Co. 

D-TEOM Differential TEOM Rupprecht & Patashnick, Co. 

FDMS-TEOM Filter Dynamics Measurement System TEOM Rupprecht & Patashnick, Co. 

ACCU-TEOM TEOM 1400 Series with an automated cartridge collection unit Rupprecht & Patashnick, Co. 

CONTINUOUS PARTICLE LIGHT SCATTERING INSTRUMENTS 

Dust Trak Dust Trak nephelometer TSI Inc. (Shoreview, MN) 

EcoTech EcoTech Model M9003 nephelometer EcoTech Pty Ltd., Australia (American EcoTech, Warren, RI) 

NGN NGN-2 nephelometer Optec Inc. (Lowell, MI) 

RR-M903 Radiance Research Nephelometer Model M903 Radiance Research Inc. (Seattle, WA) 

CONTINUOUS ELEMENT INSTRUMENTS 

GFAAS Graphite Furnace Atomic Absorption Spectrometry—aerosol 

collection as preconcentrate slurry University of Maryland (College Park, MD) 

SEAS Semicontinuous Elements in Aerosol Sampler University of Maryland 

CONTINUOUS NITRATE INSTRUMENTS 

ADI-N Aerosol Dynamics Inc. Flash Volatilization Analyzer Aerosol Dynamics Inc. (Berkeley, CA) 

ARA-N Atmospheric Research and Analysis NO3-Analyzer Atmospheric Research and Analysis Inc. 

R&P-8400N R&P-8400N Flash Volatilization Continuous NO3- Analyzer Rupprecht & Patashnick, Co. 

CONTINUOUS SULFATE INSTRUMENTS 

ADI-S Aerosol Dynamics Inc. Flash Volatilization Analyzer Aerosol Dynamics Inc. 

CASM Continuous Ambient Sulfate Monitor (prototype of the TE-5020 by 

Thermo Electron [Franklin, MA]) Harvard School of Public Health 

R&P-8400S R&P-8400S Flash Volatilization Continuous SO4

2– Analyzer Rupprecht & Patashnick, Co. 

TE-5020 Thermo Electron Model 5020 SO4

2– Particulate Analyzer Thermo Electron Corp. (Franklin, MA) 

December 2009 A-2 
Abbreviation Instrument Manufacturer / Research Institute 

CONTINUOUS MULTI-ION INSTRUMENTS 

AIM Ambient Ion Monitor Model 9000 (Cl–

,NO2

–

 ,NO3

–

,PO4

3–, 

SO4

2–, NH4+

,Na+,Mg2+,K+,Ca2+) URG Corp. 

Dionex-IC Dionex Ion Chromatograph (F–

, Cl–, NO2

– , Br–, NO3–

, PO4

3–, SO42– , 

Li+

, NH4

+

 ,Na+,Mg2+,K+,Ca2+) Dionex Corp. 

ECN Energy Research Center of the Netherlands IC-based sampler (Cl–

, 

NO3

–

, SO4

2–, NH4+

 ,Na+, Mg2+,K+, Ca2+) 

Energy Research Center of the Netherlands (Petten, the 

Netherlands 

PILS-IC Particle into Liquid Sampler, coupled with IC (Cl–

, NO2

–

 , NO3

–

, PO4

3–, 

SO4

2–, NH4+

 ,Na+,Mg2+,K+, Ca2+) Georgia Institute of Technology (Atlanta, GA) 

TT Texas Tech IC-based sampler (NO3

–

, SO4

2–) Texas Tech University (Lubbock, TX) 

CONTINUOUS CARBON INSTRUMENTS 

OC and EC 

ADI-C ADI Flash Volatilization Carbon Analyzer Aerosol Dynamics Inc. 

RU-OGI Rutgers University/Oregon Graduate Institute in-situ carbon analyzer 

(OC, EC) 

Rutgers University (Camden, NJ)/Oregon Graduate Institute 

(Beaverton, OR) 

R&P-5400 R&P-5400 continuous ambient carbon analyzer Rupprecht & Patashnick, Co. 

Sunset OCEC Sunset Semi-Continuous Real-Time Carbon Aerosol Analysis 

Instrument Sunset Laboratory, Inc. (Tigard, OR) 

BC 

Aethalometer Magee Scientific Co. (Berkeley, CA) 

 AE-16 Magee AE-16 aethalometer (BC) Magee Scientific Co. 

 AE-20 Magee AE-20 dual wavelength aethalometer (BC) Magee Scientific Co. 

 AE-21 Magee AE-21 dual-wavelength aethalometer (BC) Magee Scientific Co. 

 AE-31 Magee AE-31 seven color aethalometer (BC) Magee Scientific Co. 

DRI-PA DRI Photoacoustic Analyzer (BC) Droplet Measurement Technologies, Inc. (Boulder, CO) 

MAAP Multi-Angle Absorption Photometer, Model 5012 (BC) Thermo Scientific Corp. (Franklin, MA) 

PSAP Particle Soot Absorption Photometer (BC) Radiance Research Inc. (Seattle, WA) 

Other Carbon 

PAS-PAH Photo-Ionization Monitor for PAHs (Model PAS 2000) EcoChem Analytics (League City, TX) 

PILS-WSOC PILS-WSOC Analyzer, combination of PILS and total organic 

analyzer (TOA) Georgia Institute of Technology 

PARTICLE SIZING INSTRUMENTS FOR MASS AND CHEMICAL SPECIATION 

DRUM-3 Davis Rotating-Drum Uniform Size-Cut Monitor (0.1-2.5 μm in 3 

stages) University of California–Davis (Davis, CA) 

DRUM-8 Davis Rotating-Drum Uniform Size-Cut Monitor (0.09- > 5.0 μm in 8 

stages) University of California–Davis 

ELPI Electrical Low Pressure Impactor (0.007-10 μm in 12 stages) Dekati (Tampere, Finland) 

LPI Low Pressure Impactor (0.03-10 μm in 13 stages) Aerosol Dynamics, Inc. 

MOUDI Micro Orifice Uniform Deposit Impactor MSP Corp. (Minneapolis, MN) 

 MOUDI-100 MOUDI Model 100 (0.18-18 μm in 8 stages) MSP Corp. 

 MOUDI-110 MOUDI Model 110 (0.056-18 μm in 10 stages) MSP Corp. 

 Nano-MOUDI Nano MOUDI (0.010-0.056 μm in 3 stages coupled to MOUDI Model 

110) MSP Corp. 

PARTICLE NUMBER / VOLUME INSTRUMENTS 

APS Aerodynamic Particle Sizer TSI Inc. 

APS-3320 TSI Model 3320 (0.5-20 μm) TSI Inc. 

December 2009 A-3 
Abbreviation Instrument Manufacturer / Research Institute 

APS-3321 TSI Model 3321 (0.5-20 μm; replaced TSI Model 3320) TSI Inc. 

DMA Differential Mobility Analyzer TSI Inc. 

 DMA-3081 TSI Model 3081 (0.01-1.0 μm) TSI Inc. 

 DMA-3085 TSI Model 3085 (0.002-0.15 μm) TSI Inc. 

EEPS Engine Exhaust Particle Sizer (EEPS 0.056-0.56 μm) TSI Inc. 

FMPS Fast Mobility Particle Sizer (FMPS 0.056-0.56 μm) TSI Inc. 

GRIMM-1108 Optical Particle Counter (OPC; 0.3-20 μm) GRIMM Technologies, Inc. (Douglasville, GA) 

SMPS Scanning Mobility Particle Sizer TSI Inc. 

 SMPS-3936 TSI Model 3936L (0.01-1.0 μm) TSI Inc. 

 Nano-SMPS-3936 TSI Model 3936N (0.002-0.15 μm) TSI Inc. 

 SMPS + C SMPS and Condensation Nucleus Counter (0.005-0.35 or 

0.01-0.875 μm) GRIMM Technologies, Inc. 

 SMPS-custom DMA Model 3071 and CPC Model 3010 TSI Inc. 

WPS Wide-Range Particle Spectrometer (0.01-10.0 μm) MSP Corp. 

SINGLE PARTICLE INSTRUMENTS 

AMS Aerosol Mass Spectrometer (0.04-2 μm) Aerodyne Research Inc. (Billerica, MA) 

ATOFMS Aerosol Time of Flight Mass Spectrometer (0.3-2.5 μm) TSI Inc. 

CNC, CPC Condensation Nucleus Counters, Condensation Particle Counter Various vendors 

DAASS Dry-Ambient Aerosol Size Spectrometer consisting of two SMPS and 

One APS (0.003-10 μm) Carnegie Mellon University 

LIBS Laser-Induced Breakdown Spectroscopy National Research Council, Industrial Materials Institute 

(Boucherville, Quebec, Canada) 

PALMS Particle Analysis by Laser Mass Spectrometer (0.22-2.5 μm) NOAA (Boulder, CO) 

RSMS-II Rapid Single Particle Mass Spectrometer -II (0.035-1.1 μm) University of Delaware (Newark, DE) 

RSMS-III Rapid Single Particle Mass Spectrometer III (0.01-2.0 μm) University of Delaware 

LABORATORY INSTRUMENTS 

DRI Model 2001 

DRI Model 2001 Thermal/Optical Carbon Analyzer (OC, EC, Eight 

Carbon Fractions with reflectance and transmittance laser 

correction) 

Atmoslytic, Inc. (Calabasas, CA) 

SEM Scanning Electron Microscopy Various vendors 

a

Now with Thermo Scientific, Franklin, MA. 

b

EPA-approved speciation sampler used in the Speciation Trends Network (STN). 

c

Now commercialized by Applikon Analytical, the Netherlands, and marketed under the name “MARGA” (Monitor for Aerosols and Gases in Ambient Air). d

Not available. 

Source: Chow et al. (2008, 156355)

December 2009 A-4 
Table A-2. Summary of PM2.5 and PM10 FRM and FEM samplers. 

Manufacturera Sampler 

Name 

Size 

Cutb Description FRM or FEMc Designation # FRN 

BGI Inc. PQ-100 PM10 FRM RFPS-1298-124 Vol. 63, p. 69625, 

12/17/98 

BGI Inc. PQ-200 PM10

Louvered PM10 inlet; operates at flow rate of 16.7 L/min; 24-h 

integrated sampler; uses a mass flow meter to adjust to equivalent volumetric flow at ambient temperature and pressure. FRM RFPS-1298-125 Vol. 63, p. 69625, 

12/17/98 

Vol. 63, p. 18911, 

04/16/98 

BGI Inc. PQ-200 PM2.5 

Identical to PM10 sampler but uses a WINSd impactor downstream of 

the PM10 inlet for PM2.5 fractionation at 16.7 L/min; 24-h integrated 

sampler. 

FRM RFPS-0498-116 

Vol. 63, p. 31993, 

06/11/98 

BGI Inc. 

PQ-200-

VSCC or 

PQ-200AVSCC 

PM2.5 

Same as BGI PQ200 PM2.5 sampler but with BGI VSCC instead 

of WINS impactor; PQ200A is a portable audit sampler, similar in 

design to PQ-200, but more compact in nature. 

FEM (II) EQPM-0202-142 Vol. 67, p. 15567, 

04/02/02 

R&P R&P-2000 PM10 

R&P Partisol FRM Model 2000 PM10 sampler with louvered PM10

inlet; operates at flow rate of 16.7 L/min; 24-h integrated 

sampler; uses a mass flow meter to adjust to equivalent 

volumetric flow at ambient temperature and pressure; singlechannel sampler. 

FRM RFPS-1298-126 Vol. 63, p. 69625, 

12/17/98 

R&P R&P-2000 PM2.5 FRM RFPS-0498-117 Vol. 63, p. 18911, 

04/16/98 

R&P R&P2000A PM2.5 

R&P Partisol FRM Model 2000 PM2.5 sampler, identical to PM10

sampler but uses a WINS impactor downstream of the PM10 inlet 

for PM2.5 fractionation at 16.7 L/min; 24-h integrated sampler; 

R&P2000A is a portable audit sampler. FRM RFPS-0499-129 Vol. 64, p. 19153, 

04/19/99 

R&P R&P-2025 PM10 

R&P Partisol-Plus Model 2025 PM10 sequential sampler with 

louvered PM10 inlet; operates at 16.7 L/min; 24-h integrated 

sampler; uses a mass flow meter to adjust to equivalent 

volumetric flow at ambient temperature and pressure; sequential 

sampler with a capacity of 16 filter cassettes, allowing for two 

weeks of unattended daily sampling; filter exchange is 

performed pneumatically. 

FRM RFPS-1298-127 Vol. 63, p. 69625, 

12/17/98 

R&P R&P-2025 PM2.5 

R&P Partisol-Plus Model 2025 PM2.5 sequential sampler, 

identical to R&P-2025 PM10 sampler but uses a WINS impactor 

downstream of the PM10 inlet for PM2.5 fractionation at 16.7 

L/min. 

FRM RFPS-0498-118 Vol. 63, p. 18911, 

04/16/98 

R&P R&P2000-

VSCC PM2.5 Same as R&P-2000 PM2.5 sampler but with BGI VSCC, instead 

of WINS impactor for PM2.5 separation. FEM (II) EQPM-0202-143 Vol. 67, p. 15567, 

04/02/02 

R&P R&P2000AVSCC PM2.5 Same as R&P-2000A PM2.5 sampler but with BGI VSCC instead 

of WINS impactor for PM2.5 separation. FEM (II) EQPM-0202-144 Vol. 67, p. 15567, 

04/02/02 

R&P R&P-2025-

VSCC PM2.5 Same as R&P-2025 PM2.5 sampler but with BGI VSCC instead 

of WINS impactor, for PM2.5 separation. FEM (II) EQPM-0202-145 Vol. 67, p. 15567, 

04/02/02 

Andersen RAAS-100 PM10 

Andersen Instruments, Inc. Model RAAS10-100 PM10 sampler 

with louvered PM10 inlet; operates at flow rate of 16.7 L/min; 

24-h integrated sampler; volumetric flow measured by dry test 

meter at pump outlet modulates pump speed to maintain flow 

rate; single-channel. 

FRM RFPS-0699-130 Vol. 64, p. 33481, 

06/23/99 

Andersen RAAS-100 PM2.5 Graseby Andersen Model RAAS2.5-100 PM2.5 sampler, similar 

to RAAS-100 PM10 with a WINS impactor for PM2.5 separation. FRM RFPS-0598-119 Vol 63, p. 31991, 

06/11/98 

Andersen RAAS200A PM10 FRM RFPS-0699-131 Vol. 64, p. 33481, 

06/23/99 

Andersen RAAS-200A PM2.5 

Andersen Instruments, Inc. Model RAAS10-200 and RAAS2.5-

100 Audit Samplers, portable compact version; similar to RAAS100. FRM RFPS-0299-128 Vol. 64, p. 12167, 

03/11/99 

Andersen RAAS-300 PM10 

Andersen Instruments, Inc. Model RAAS10-300, sequential 

sampler with louvered PM10 inlet, operates at 16.7 L/min; capacity to hold eight filter-holders for multiple day operation. 

FRM RFPS-0699-132 Vol. 64, p. 33481, 

06/23/99 

Andersen RAAS-300 PM2.5 

Graseby Andersen Model RAAS2.5-300 PM2.5 sampler, similar 

to RAAS-300 PM10 sampler with a WINS impactor for PM2.5

separation. 

FRM RFPS-0598-120 Vol. 63, p. 31991, 

06/11/98 

December 2009 A-5 
Manufacturera Sampler 

Name 

Size 

Cutb Description FRM or FEMc Designation # FRN 

Thermo Scientific, 

Inc. CAPS PM2.5 Model 605 Computer Assisted Particle Sampler (CAPS), 24-h 

integrated. Not available commercially. FRM RFPS-1098-123 Vol. 63, p. 8036, 10/29/98 

Thermo Scientific, 

Inc. 

RAAS 100-

VSCC PM2.5 

Same as RAAS-100 PM2.5 sampler but with BGI VSCC, instead 

of WINS impactor. FEM (II) EQPM-0804-153 Vol. 69, p. 47924, 08/06/04 

Thermo Scientific, 

Inc. 

RAAS 200-

VSCC PM2.5 Same as RAAS-200 PM2.5 sampler but with BGI VSCC instead of WINS impactor. FEM (II) EQPM-0804-154 Vol. 69, p. 47924, 08/06/04 

Thermo Scientific, 

Inc. 

RAAS 300-

VSCC PM2.5 Same as RAAS-300 PM2.5 sampler but with BGI VSCC instead of WINS impactor. FEM (II) EQPM-0804-155 Vol. 69, p. 47925, 08/06/04 

URG Corp. MASS-100 PM2.5 

Model MASS100 PM2.5 sampler with louvered PM10 inlet 

followed by WINS impactor, operates at 16.7 L/min; 24-h 

integrated, volumetric flow measured by dry test meter at pump 

outlet modulates pump speed to maintain flow rate; single 

channel. 

FRM RFPS-0400-135 Vol. 65, p. 26603, 

05/08/00 

URG Corp. MASS-300 PM2.5 

Model MASS300 PM2.5 sampler with louvered PM10 inlet followed by WINS impactor, operates at 16.7 L/min; 24-h integrated, sequential sampler with circular tray holding six filters. 

FRM RFPS-0400-136 Vol. 65, p. 26603, 

05/08/00 

Tisch Environmental, Inc. TE-6070 

HiVol PM10

Model TE-6070 PM10 High-Volume Sampler, with TE-6001 PM10 

size selective inlet; 8” x 10” filter holder. FRM RFPS-0202-141 Vol. 67, p. 15566, 04/02/02 

Met One BAM PM10 

Models BAM 1020, GBAM 1020, BAM 1020-1, and GBAM 

1020-1, with BX-802 inlet; glass-fiber filter tape with 1-h filter 

change frequency. 

FEM EQPM-0798-122 Vol. 63, p. 41253, 

08/03/98 

a

 BGI Inc.: BGI Incorporated, Waltham, MA. R&P: Rupprecht & Patashnick Company, Inc., Albany, NY, now Thermo Scientific, Inc., Franklin, MA. Andersen: Graseby Andersen, later Andersen Instruments, 

Inc., Smyrna, GA, now Thermo Scientific, Inc., Franklin, MA. Thermo Environmental Instruments, Inc., now Thermo Scientific, Inc., Franklin, MA. URG Corp.: URG Corporation, Chapel Hill, NC. Tisch 

Environmental, Inc., Cleves, OH. Met One Instruments, Inc., Grants Pass, OR 

b The efficiency of an inlet (Watson et al., 1983, 045084) is determined by its 50% cut-point (d50, the diameter at which half of the particles penetrate through the inlet, while the other half is retained by the 

inlet, while the other half is retained by the inlet) and the geometric standard deviation (GSD, which is an indicator of the sharpness of the separation, and is derived by the square root of the ratio of particle 

diameters at penetrations of 16% andc 84%, [d16/d84]

0.5). c

 FRM: Federal Reference Method; FEM: Federal Equivalent Method. Roman numeral within parenthesis indicates FEM class. 

d

 Particle separation in WINS is achieved by means of a single-jet round nozzle with flow directed into an impaction reservoir. The impaction surface consists of a Gelman Type A/E glass-fiber filter immersed 

in 1 mL of Dow Corning (Midland, MI) 704 diffusion pump oil housed in a reservoir. 

Note: The geometric standard deviation (GSD, which is an indicator of the sharpness of the separation, and is derived by the square root of the ratio of particle diameters at penetrations of 16% and 84%, 

[d16/d84]0.5). 

Source: Chow et al. (2008, 156355) 

December 2009 A-6 
Table A-3. Measurement and analytical specifications for filter analysis of mass, elements, ions, 

and carbon. 

Observable Analytical 

Accuracya Precisionb 

 Minimum 

Detectable Limit 

(MDL) 

Interferences Comparability Data 

Completeness 

PM2.5 mass ± 5% 4 ± 10% 4 0.04 μg/m3 c

 to 

~1 μg/m3 d 5,6 

Electrostatic charges need to 

be neutralized before 

measurement; positive (e.g., 

OC adsorption) and negative 

artifacts (e.g., nitrate 

volatilization) 

Within 20% 4

 90 to 100% h 6,7 

Elements ± 2-5% 4 ± 10% 4

 XRF: 0.4-30 ng/m3 g 

8

 PIXE: 6-360 ng/m3

d 9 ICP/MS: 0.004-25 

ng/m3 10 0.05-11.7 

ng/m3 9,11 AAS: 0.02-

7.15 ng/m3 12 

Volatile compounds may evaporate from filters due to vacuum in XRF and PIXE. 

Potential contamination during 

extraction and incomplete 

extraction efficiency for ICPMS and AAS. Matrix 

interference and peak overlap 

may occur on heavily loaded 

samples. 

10 to 30% depending 

on species 4 90 to 100%h 6,7 

Nitrate 

± 6% with spiked 

concentrations on 

Teflon4 

and ± 1-14% on 

nylon filters13 

± 5 to 10% on replicate analysis 4,13,14

co-located 

precision ± 5-7%14-16 

0.06 μg/m3

 e to 

0.2 μg/m3 d 1,6,17 

Subject to volatilization from 

Teflon or quartz-fiber filters 

Within 35% and 

probably greater 4 85 to 100% 6,7 

Sulfate ± 5% 4 ± 6 to 10% 4,14,15 0.06 μg/m3 e to 

0.2 μg/m3 d 1,6,13 N/A 

Typically within 10%; 

MOUDIs 13 to 20% 

lower than speciation 

samplers 4,17-19 

85 to 100% 

6,7,20,21 

Ammonium ± 5% 4 ± 10% 4 0.06 μg/m3 e

 to 

0.07 μg/m3 d 1,6 

Subject to volatilization from 

Teflon or quartz-fiber filters Within 30% 4 86 to 100% 6,7 

OC: ± 20% OC: 0.1 μg/m3 f

 to 

0.8 μg/m3 d 

OC: Within 20 to 

50% 

EC: ± 20% EC: 0.03 μg/m3 d to 

0.1 μg/m3 f EC: Within 20 to 200% 

OC, EC, TC 

±

 5% for TC and 

OC. No standard 

exists to determine 

EC accuracy 

TC: ± 10% 4 TC: 0.8 μg/m3 d 1,6

Subject to adsorption (positive 

artifact) and volatilization 

(negative artifact) of organic 

gases to and from quartz-fiber 

filters 

TC: Within 20% 4,17,22

86 to 100% 6,7 

Total mass of 

WSOC 

DRI Model 2001 

Carbon 

Analyzer: ± 5%23 

TOA: ± 3-7% 24,25 

DRI Model 2001 

Carbon 

Analyzer: ± 10%23 

Sunset Carbon 

Analyzer: ± 3%26 

TOA: ± 5-10% 27 

DRI Model 2001 

Carbon Analyzer: 

0.1-0.23 μg C/m3 23 

Sunset Carbon 

Analyzer: 0.05-

0.22 μg C/m3 26,28 

Elemental High TOC 

II: 0.05 μg C/m3 29 

TOA: 0.12 μg 

C/m3 26 

Extraction efficiency and 

volume reduction steps Within 17% 26 N/A 

Elements in 

water soluble 

matter: C, H, N, 

and S 

C: 1.5%; 

H: 3%; 

N: 3%; 

S: 5% 30 

 ± 2%30 

C: 0.3 μg/m3 

H:0.09 μg/m3

 

N: 0.03 μg/m3 

S: 0.10 μg/m3 30 

Contamination during sample 

drying step N/A N/A 

Dissolved 

organic 

nitrogen 

N/A ± 5-30%31

 0.001μg N/m3 while 

inorganic nitrogen is 

low; ≥ 0.071μg N/m3

while inorganic 

nitrogen is high31 

Concentration of inorganic 

nitrogen 

Good correlation 

between UV and 

persulfate oxidation 

methods (R2 = 0.87) 31

N/A 

December 2009 A-7 
Observable Analytical 

Accuracya Precisionb 

 Minimum 

Detectable Limit 

(MDL) 

Interferences Comparability Data 

Completeness 

Neutral polyols 

and polyether GC/MS: ± 4-8% 32 

GC/MS: ± 23% 33,34 

Typically ± 20%, 

ranged from ± 10 

to ± 30% i 32,35,36,37,38 

HPLC/MS: 

± 5-26%39 

GC/MS: 

Levoglucosan: 10 

ng/m3 40 

2.08 ng/m3 j 31 

0.01-0.03 ng/m3 33,41

HPLC/MS: 9-648 

pg/m3 39 

GCMS: Extraction recovery 

interfered by sample matrix 

Derivatization efficiency 

IC/PAD: Overlapping peaks in 

chromatogram 

IC/PAD: Good 

correlation 

(R2

 = 0.97) with 

HPLC/MS; and 

(R2

 = 0.89) with 

GC/MS Method 42 

N/A 

Mono- and 

Di-carboxylic 

acids 

N/A 

GC/MS: ± 5-11% on 

3 replicates, ± 8 % in 

avg 43,44 

IC: ± 10-15%45 

GC/MS: 0.04-1.12 

ng/m3 46 

IC: 0.01-0.12 ng/m3 

47 

GC/MS: Extraction recovery 

interfered by sample matrix 

Derivatization efficiency 

IC: Overlapping peaks in 

chromatogram 

GC/MS: Within 50% 

for less volatile 

compounds 46 

N/A 

Amino acids N/A ± 9% 48 1.65-23.6 

pg/m3 k 48

Derivatization efficiency 

Stability of derivatives 

Overlapping peaks in 

chromatogram 

N/A N/A 

Mass of humiclike substances 

(HULIS) 

N/A N/A 0.083 ng/m3 l 49 Separation efficiency N/A N/A 

a

 Accuracy is the ability of analytical methods to quantify the observable of a standard reference material correctly; it does not refer to measurement accuracy if no standards available.50 b

 Refers to precision of co-located measurements, unless specified otherwise. 

c

 Based on 1 μg/filter limit of detection for 24-h samples, assuming a flow rate of 16.7 L/min 

d

 Based on field blanks collected with FRM samplers; μg/filter converted to μg/m3 basis assuming a flow rate of 16.7 L/min for 24-h e

 Based on 1⁄2 of a 47-mm filter extracted in 15 mL deionized-distilled water (DDW) for 24-h samples, assuming a flow rate of 16.7 L/min f

 Based on 0.2 μg/cm2 detection limit and 13.8 cm2 deposit area for a 47-mm filter, assuming a flow rate of 16.7 L/min for 24-h g

 Based on 24-h samples at a flow rate of 16.7 L/min and analyzed by XRF 

h Except for samples from one FRM sampler at Atlanta Supersite, for which data recovery was 50%7

; reason not reported. i

 Reported as uncertainty in literature 

j

 Based on 24-h samples at a flow rate of 16.7 L/min 

k

 Based on 13.8 cm2 deposit area for a 47-mm filter and extracted into a final volume of 200 μL, assuming a flow rate of 16.7 L/min for 24-h and molecular weight of amino acid = 150 l

 Based on 13.8 cm2 deposit area for a 47-mm filter and extracted into a final volume of 200 μL, assuming a flow rate of 16.7 L/min for 24-h 

N/A: Not available 

1

Chow (1995, 077012); 2Watson and Chow (2001, 157123); 3 Watson et al. (1983, 045084); 4Fehsenfeld et al. (2004, 157360); 5Solomon et al. (2001, 157193); 6Watson et al. (2005, 157124); 7Mikel (2001, 

156762); 8

Watson et al. (1999, 020949); 9Solomon and Sioutas (2006, 156995); 10Graney et al. (2004, 053756); 11Tanaka et al. (1998, 157041); 12Pancras et al. (2005, 098120); 13John et al. (1988, 045903); 14Hering and Cass (1999, 084958); 15Fitz et al. (1989, 077387); 16Hering et al. (1988, 036012); 17Solomon et al. (2003, 156994); 18Cabada et al. (2004, 148859); 19Fine et al. (2003, 155775); 20Hogrefe et al. 

(2004, 099003); 21Drewnick et al. (2003, 099160); 22Watson et al. (2005, 157125); 23Ho et al. (2006, 156552); 24Decesari et al. (2005, 144536); 25 Mayol-Bracero et al. (2002, 045010); 26Yang et al. (2003, 

156167); 27 Turšic et al. (2006, 157063); 28Mader et al.(2004, 156724); 29Xiao and Liu (2004, 056801); 30Kiss et al. (2002, 156646); 31Cornell and Jickells (1999, 156367); 32 Zheng et al. (2002, 026100); 33Fraser et al. (2002, 140741); 34 Fraser et al. (2003, 042231) 35Schauer er al. (2000, 012225); 36Fine et al. (2004, 141283); 37Yue et al. (2004, 157169); 38Rinehart et al. (2006, 115184); 39Wan and Yu (2006, 

157104); 40Poore (2000, 012839); 41Fraser et al. (2003, 040266); 42Engling et al. (2006, 156422); 43Yu et al. (2005, 157167); 44Tran et al. (2000, 013025); 45Yao et al. (2004, 102213); 46Li and Yu (2005, 

156692); 47Henning et al. (2003, 156539); 48Zhang and Anastasio (2003, 157182); 49Emmenegger et al. (2007, 156418); 50 Watson et al. (1989, 046318); 51Greaves et al. (1985, 156494); 52Waterman et al. 

(2000, 157116); 53Waterman et al. (2001, 157117); 54Falkovich and Rudich (2001, 156427); 55Chow et al. (2007, 157209); 56Miguel et al. (2004, 123260); 57Crimmins and Baker (2006, 097008); 58Ho and Yu 

(2004, 156551); 59Jeon et al. (2001, 016636); 60Mazzoleni et al. (2007, 098038); 61Poore (2002, 051444); 62Butler et al. (2003, 156313); 63Chow et al. (2006, 146622); 64Russell et al. (2004, 082453); 65Grover 

et al. (2006, 138080); 66Grover et al. (2005, 090044); 67Schwab et al. (2006, 098449); 68Hauck et al. (2004, 156525); 69Jaques et al. (2004, 155878); 70Rupprecht and Patashnick (2003, 157207); 71Pang et al 

(2002, 030353); 72Eatough et al. (2001, 010303); 73Lee et al. (2005, 128139); 74Lee et al. (2005, 156680); 75Babich et al. (2000, 156239); 76Lee et al. (2005, 155925); 77Lee et al. (2005, 128139); 78Anderson 

and Ogren (1998, 156213); 79Chung et al. (2001, 156357); 80Kidwell and Ondov (2004, 155898); 81Lithgow et al. (2004, 126616); 82Weber et al. (2003, 157129); 83Harrison et al. (2004, 136787); 84Rattigan et 

al. (2006, 115897); 85Wittig et al. (2004, 103413); 86Vaughn et al. (2005, 157089); 87Chow et al. (2005, 099030); 88Weber et al (2001, 024640) ; 89Schwab et al. (2006, 098785); 90Lim et al. (2003, 037037); 91Watson and Chow (2002, 037873); 92Venkatachari et al. (2006, 105918); 93Bae et al. (2004, 156243); 94Arhami et al. (2006, 156224); 95Park et al. (2005, 156843); 96Bae et al. (2004, 098680); 97Chow et al. 

(2006, 156350); 98Arnott et al. (2005, 156227); 99Bond et al. (1999, 156281); 100Virkkula et al. (2005, 157097); 101Petzold et al. (2002, 156863); 102Park et al. (2006, 098104); 103Arnott et al. (1999, 020650); 104Peters et al. (2001, 016925); 105Pitchford et al. (1997, 156872); 106Rees et al. (2004, 097164); 107Watson et al. (2000, 010354); 108Lee et al. (2005, 156680); 109Hering et al. (2004, 155837); 110Watson et al. 

(1998, 198805); 111Chakrabarti et al. (2004, 157426); 112Mathai et al. (1990, 156741); 113Kidwell and Ondov (2001, 017092); 114Stanier et al. (2004, 095955); 115Khlystov et al. (2005, 156635); 116Takahama et 

al. (2004, 157038); 117Chow et al. (2005, 156348); 118Zhang et al. (2002, 157181); 119Subramanian et al. (2004, 081203); 120Chow et al. (2006, 155207); 121Birch and Cary (1996, 026004); 122Birch (1998, 

024953); 123Birch and Cary (1996, 002352); 124NIOSH (1996, 156810); 125NIOSH (1999, 156811); 126Chow et al. (1993, 077459); 127Chow et al. (2007, 156354); 128Ellis and Novakov (1982, 156416); 129Peterson and Richards (2002, 156861); 130Schauer et al. (2003, 037014); 131Middlebrook et al. (2003, 042932); 132Wenzel et al. (2003, 157139); 133Jimenez et al. (2003, 156611); 134Phares et al. (2003, 

156866); 135Qin and Prather (2006, 156895); 136Zhang et al. (2005, 157185); 137Bein et al. (2005, 156265); 138Drewnick et al. (2004, 155754); 139Drewnick et al. (2004, 155755); 140Lake et al. (2003, 156669); 141Lake et al. (2004, 088411) 

Source: Chow et al. (2008, 156355))

December 2009 A-8 
Table A-4. Measurement and analytical specifications for filter analysis of organic species. 

Organic Analytical Accuracy Precision MDL Interferences 

Species TD Solvent 

Extraction 

TD Solvent 

Extraction 

TD Solvent 

Extraction 

TD Solvent 

Extraction 

Comparabil

ity 

PAHs ± 2.8-24.1%51

 ± 4.4-29.4%52 

13.8-26.5%53 

 ± 0.5-12.9%54 

0.05-4.83%55 

Z-score 

values 0 to -

1.9 56 

 ± 4-8%32 

 ± 6.5-22%57

Avg ± 3.2%, 

ranged 

from ± 0.05 to 

 ± 11.5%55 

Avg ± 8%, 

ranged from 

± 3.8 

to ± 15%56 

 ± 23%56 Avg 

 ± 2.6%, 

ranged 

from ± 0.6 to 

 ± 9.5%57

typically 

 ± 20%, 

ranged 

from ± 10 to 

 ± 30%c 32,35-37

0.016-0.48 

ng/m 3 a 58 

0.030-0.45 

ng/m3 a 55

0.83-1.66 

ng/m3 b 38 

0.033-3.85 

ng/m3 b 56

0.01-0.03 

ng/m3 33,34,37 

0.76-276 

pg/m3 b 57

Fragmentatio

n of labile 

compounds 

Possible 

contaminants 

from solvents 

and complicated extraction 

procedures. 

Loss of 

volatile 

compounds 

during the extraction and 

pretreatment 

steps. 

Possible 

carryover 

from injection 

port. 

R2s for solvent 

extraction 

were 0.95 58, 

0.9755, and 

0.98 59 

n-Alkanes N/A ± 4-8%32 Avg ± 3.2%, 

ranged 

from ± 0.05 

to ± 11.5%55 

 ± 23%56 

Typically ± 20

%, from ± 10 

to ± 30%c 32,35-

37 

0.081-0.86 

ng/m3 a 58 

0.061-0.97 

ng/m3 a 55 

0.01-0.03 

ng/m3 33,34,37 

Same as 

PAHs 

Same as 

PAHs 

R2s for solvent 

extraction are 

0.94 58, and 

0.98 55,59 

Hopanes N/A N/A Avg ± 3.2%, 

ranged 

from ± 0.05 

to ± 11.5%55 

 ± 23%56 

Typically ± 20

%, from ± 10 

to ± 30%c 32,35-

37

0.030-0.14 

ng/m3 a 55 

0.83-1.66 

ng/m3 b 38 

0.01-0.03 

ng/m3 33,41

0.01 ng/m3 37

Same as 

PAHs 

Same as 

PAHs 

R2s for solvent 

extraction are 

0.99 55 and 

0.998 59 

Steranes N/A N/A Avg ± 3.2%, 

ranged 

from ± 0.05 

to ± 11.5%55 

N/A 0.018-0.063 

ng/m3 a 55 

0.83-1.66 

ng/m3 b 60 

Same as 

PAHs 

Same as 

PAHs 

R2s for 

solvent 

extraction are 

0.97 55 and 

0.998 59 

Organic acids 

(including nalkanoic 

acids, nalkenoic 

acids, alkane 

dicarboxylic 

acids, 

aromatic 

carboxylic 

acids, resin 

acids) 

N/A ± 4-8%32 ± 10 

to ± 29%55 

 ± 24%41 

 ± 23%56

Typically ± 20

%, from ± 10 

to ± 30%c 32,35-

37

Monocarboxylic 

acids (C8, 

C12, and 

C16): 

0.79, 2.0, and 

3.2 ng/m3 a 54

0.01-0.03 

ng/m3 33,41 

Fragmentatio

n of labile 

compounds. 

Loss of polar 

species due 

to absorption 

onto the 

surface of the 

injector. 

Improper stationary phase 

column used 

during TD 

analysis. 

Incomplete 

thermal 

desorption of 

analytes 

because of 

strong affinity 

with filter 

matrix. 

Possible 

contaminants 

from solvents 

and complicated 

extraction 

procedures. 

Loss of 

volatile 

compounds 

during the extraction and 

pretreatment 

steps. 

Possible 

carryover 

from injection 

port . 

Low 

derivatization 

efficiency . 

Correlation 

with solvent 

extraction 

method 

R2

 = 0.731 59

December 2009 A-9 
Analytical Accuracy Precision MDL Interferences 

Polyols and 

sugars, 

including guaiacol and substituted 

guaiacols, syringol and 

substituted 

syringols, 

anhydrosugars 

N/A ± 4-8%32 N/A ± 23%56 

Typically ± 20

%, from ± 10 

to ± 30%c 32,35-

37

N/A Levoglucosa: 

10 ng/m3 61

2.08 ng/m3 b 38 

0.01-0.03 

ng/m3 33,41

Same as 

organic acids 

Same as 

organic acids 

N/A 

a

 Assumes 2.9 cm2 filter used in analysis from a deposit area of 13.8 cm2, and sample collection at a flow rate of 16.7 L/min for 24-h b

 Assumes sample collection at a flow rate of 16.7 L/min for 24-h. 

c

 Reported as uncertainty in literature. 

d

 Assumes a final extract volume of 1 mL and sample collection at a flow rate of 16.7 L/min for 24-h. 

N/A: Not available 

1

Chow (1995, 077012); 2Watson and Chow (2001, 157123); 3 Watson et al. (1983, 045084); 4Fehsenfeld et al. (2004, 157360); 5Solomon et al. (2001, 157193); 6Watson et al. (2005, 157124); 7Mikel (2001, 

156762); 8

Watson et al. (1999, 020949); 9Solomon and Sioutas (2006, 156995); 10Graney et al. (2004, 053756); 11Tanaka et al. (1998, 157041); 12Pancras et al. (2005, 098120); 13John et al. (1988, 045903); 14Hering and Cass (1999, 084958); 15Fitz et al. (1989, 077387); 16Hering et al. (1988, 036012); 17Solomon et al. (2003, 156994); 18Cabada et al. (2004, 148859); 19Fine et al. (2003, 155775); 20Hogrefe et al. 

(2004, 099003); 21Drewnick et al. (2003, 099160); 22Watson et al. (2005, 157125); 23Ho et al. (2006, 156552); 24Decesari et al. (2005, 144536); 25 Mayol-Bracero et al. (2002, 045010); 26Yang et al. (2003, 

156167); 27Turšic et al. (2006, 157063); 28Mader et al.(2004, 156724); 29Xiao and Liu (2004, 056801); 30Kiss et al. (2002, 156646); 31Cornell and Jickells (1999, 156367); 32 Zheng et al. (2002, 026100); 33Fraser et al. (2002, 140741); 34 Fraser et al. (2003, 042231) 35Schauer er al. (2000, 012225); 36Fine et al. (2004, 141283); 37Yue et al. (2004, 157169); 38Rinehart et al. (2006, 115184); 39Wan and Yu (2006, 

157104); 40Poore (2000, 012839); 41Fraser et al. (2003, 040266); 42Engling et al. (2006, 156422); 43Yu et al. (2005, 157167); 44Tran et al. (2000, 013025); 45Yao et al. (2004, 102213); 46Li and Yu (2005, 

156692); 47Henning et al. (2003, 156539); 48Zhang and Anastasio (2003, 157182); 49Emmenegger et al. (2007, 156418); 50 Watson et al. (1989, 046318); 51Greaves et al. (1985, 156494); 52Waterman et al. 

(2000, 157116); 53Waterman et al. (2001, 157117); 54Falkovich and Rudich (2001, 156427); 55Chow et al. (2007, 157209); 56Miguel et al. (2004, 123260); 57Crimmins and Baker (2006, 097008); 58Ho and Yu 

(2004, 156551); 59Jeon et al. (2001, 016636); 60Mazzoleni et al. (2007, 098038); 61Poore (2002, 051444); 62Butler et al. (2003, 156313); 63Chow et al. (2006, 146622); 64Russell et al. (2004, 082453); 65Grover 

et al. (2006, 138080); 66Grover et al. (2005, 090044); 67Schwab et al. (2006, 098449); 68Hauck et al. (2004, 156525); 69Jaques et al. (2004, 155878); 70Rupprecht and Patashnick (2003, 157207); 71Pang et al 

(2002, 030353); 72Eatough et al. (2001, 010303); 73Lee et al. (2005, 128139); 74Lee et al. (2005, 156680); 75Babich et al. (2000, 156239); 76Lee et al. (2005, 155925); 77Lee et al. (2005, 128139); 78Anderson 

and Ogren (1998, 156213); 79Chung et al. (2001, 156357); 80Kidwell and Ondov (2004, 155898); 81Lithgow et al. (2004, 126616); 82Weber et al. (2003, 157129); 83Harrison et al. (2004, 136787); 84Rattigan et 

al. (2006, 115897); 85Wittig et al. (2004, 103413); 86Vaughn et al. (2005, 157089); 87Chow et al. (2005, 099030); 88Weber et al (2001, 024640) ; 89Schwab et al. (2006, 098785); 90Lim et al. (2003, 037037); 91Watson and Chow (2002, 037873); 92Venkatachari et al. (2006, 105918); 93Bae et al. (2004, 156243); 94Arhami et al. (2006, 156224); 95Park et al. (2005, 156843); 96Bae et al. (2004, 098680); 97Chow et al. 

(2006, 156350); 98Arnott et al. (2005, 156227); 99Bond et al. (1999, 156281); 100Virkkula et al. (2005, 157097); 101Petzold et al. (2002, 156863); 102Park et al. (2006, 098104); 103Arnott et al. (1999, 020650); 104Peters et al. (2001, 016925); 105Pitchford et al. (1997, 156872); 106Rees et al. (2004, 097164); 107Watson et al. (2000, 010354); 108Lee et al. (2005, 156680); 109Hering et al. (2004, 155837); 110Watson et al. 

(1998, 198805); 111Chakrabarti et al. (2004, 157426); 112Mathai et al. (1990, 156741); 113Kidwell and Ondov (2001, 017092); 114Stanier et al. (2004, 095955); 115Khlystov et al. (2005, 156635); 116Takahama et 

al. (2004, 157038); 117Chow et al. (2005, 156348); 118Zhang et al. (2002, 157181); 119Subramanian et al. (2004, 081203); 120Chow et al. (2006, 155207); 121Birch and Cary (1996, 026004); 122Birch (1998, 

024953); 123Birch and Cary (1996, 002352); 124NIOSH (1996, 156810); 125NIOSH (1999, 156811); 126Chow et al. (1993, 077459); 127Chow et al. (2007, 156354); 128Ellis and Novakov (1982, 156416); 129Peterson and Richards (2002, 156861); 130Schauer et al. (2003, 037014); 131Middlebrook et al. (2003, 042932); 132Wenzel et al. (2003, 157139); 133Jimenez et al. (2003, 156611); 134Phares et al. (2003, 

156866); 135Qin and Prather (2006, 156895); 136Zhang et al. (2005, 157185); 137Bein et al. (2005, 156265); 138Drewnick et al. (2004, 155754); 139Drewnick et al. (2004, 155755); 140Lake et al. (2003, 156669); 141Lake et al. (2004, 088411) 

Source: Chow et al. (2008, 156355)

December 2009 A-10 
Table A-5. Measurement and analytical specifications for continuous mass and mass surrogate 

instruments. 

Instrument and 

Measurement Principle 

Averaging 

Time 

Analytical 

Accuracya Precisionb MDL Interferences Comparability Data Completeness

INERTIA INSTRUMENTS 

TEOM Air is drawn through a 

size-selective inlet onto the 

filter mounted on an 

oscillating hollow tube. The 

oscillation frequency 

changes with mass loading 

on the filter, which is used to 

calculate mass concentration 

by calibrating measured 

frequency with standards. 

10 min-24 h ± 0.75%c

 ± 5 μg/m3 for 

10-min avgc,d 

± 1.5 μg/m3

 for 

1-h avgc,d 

0.01 μg, which is 

0.06 μg/m3

 for 1-h 

avgc 

Loses semivolatile species 

at both 30°C 

and 50°C. 

SESTEOM, 

while less 

sensitive to 

relative 

humidity, does 

not completely 

eliminate loss 

of semi-volatile 

species 

Underestimated 

FRM mass by 

20 to 35% 62-64

99% 65 87-92% 6

FDMS TEOM. A self-referencing TEOM with a filter at 

4 °C that accounts for 

volatile species. It is 

equipped with a diffusion 

Nafion dryer to remove 

particle-bound water. The 

Teflon (PTFE)-coated borosilicate glass-fiber filter that 

is maintained at 4 °C removes particles during the 

reference flow cycle. The 

flow alternates between a 

base and reference flow 

every 6 min. If a negative 

mass is measured during the 

reference flow, due to loss of 

volatiles from the filter, it is 

added to the mass made 

during the prior particleladen samples to obtain total 

PM2.5 concentration. 

1 h-24 h ± 0.75%c < 10%65 0.01 μg, which is 

0.06 μg/m3

 

for 1-h avgc

 

N/A 9 to 30% higher 

than FRM mass 

Within 10% of 

mass by DTEOM, PCBOSS, RAMS 

and BAM 66,67 

95-99%65,68 

57-65%67 

Differential Tapered Element 

Oscillating Microbalance (DTEOM) 

Similar to FDMS, but an 

electrostatic precipitator is 

used in place of the glassfiber filter to remove particles 

during the 6 min reference 

flow cycle. 

1 h-24 h ± 0.75%c

 < 10%e 65,69,70 0.01 μg, or 

0.06 μg/m3 for 1-h 

avgc

 

N/A Within 10% of 

FDMS-TEOM 

65,66 

86%65 

RAMS. A TEOM with a 

cyclone inlet, diffusion 

denuders, and Nafion dryer. 

Particles are collected on a 

“sandwich” filter (Teflon followed by carbonimpregnated glass-fiber 

filter) on the tapered 

oscillating element. The 

various denuders remove 

gas phase organic compounds, nitric acid, sulfur 

dioxide, nitrogen dioxide, 

ammonia, and ozone, which 

could otherwise be adsorbed 

by the TEOM filter. 

10 min-24 h N/A < 10%f 71 ± 1 to 2 μg/m3

 for 

30-min avg 72 N/A 10 to 20% 

higher than avg 

72 FRM mass 73,74

N/A 

December 2009 A-11 
Instrument and 

Measurement Principle 

Averaging 

Time 

Analytical 

Accuracya Precisionb MDL Interferences Comparability Data Completeness

PRESSURE DROP INSTRUMENT 

Continuous Ambient Mass 

Monitor (CAMM) 

Air is drawn through a Teflon-membrane filter tape and 

the pressure drop across the 

filter is monitored 

continuously. The proportion 

of pressure drop to aerosol 

loading is related to the PM 

concentration. The filter tape 

advances every 30-60 min to 

minimize volatilization and 

adsorption artifacts during 

sampling. 

1 h-24 h N/A 28.1% for 1-h 

avg 

15.9% for 24-h 

avg 

(~3.5 μg/m3

) 75 

< 5 μg/m3

 for 1 h 

avg 75 Needs effective 

sealing for 

good 

performance; 

even slight 

leaks may 

result in highly 

variable 

baseline. 

Probably less 

sensitive than 

DTEOM or 

RAMS. 75,77 

Varied performance: within 

2% of SESTEOM and FRM 

at Houston, TX, 

while not 

correlated with 

D-TEOM or 

FRM at 

Rubidoux, 

CA.76,77 

N/A 

Β-ATTENUATION INSTRUMENT 

Β Attenuation Monitor (BAM) 

Β rays electrons are passed 

through a quartz-fiber filter 

tape on which particles are 

collected. The loss of 

electrons (Β attenuation) 

caused by the particle 

loading on the filter is 

converted to mass 

concentration, after subtraction of blank filter 

attenuation. 

1 h-24 h ± 3 μg for 

24-h avg 

concentrations 

< 100 μg/m3

and 2% for 

100 to 

1,000 μg/m3

 ± 8 μg for 1-h 

concentrations

< 100 μg/m3 

and 8% for 

100 to 

1000 μg/m3 

 ± 2 μg/m3 c,h 5 μg/m3

 for 1-h avg1 Water 

absorption by 

particles may 

result in higher 

mass measurements; maybe 

important at RH 

>85% 

Up to 30% 

higher than FRM 

mass and within 

2% of FDMS 

TEOM 63,67 

93-99%6,65,67 

LIGHT-SCATTERING INSTRUMENT 

Nephelometers (including 

DustTrak) 

A light source illuminates the 

sample air and the scattered 

light is detected at an angle 

(usually 90°) relative to the 

source. The signal is related 

to the concentration of the 

particles giving an estimate 

of the particle light-scattering 

coefficient. Zero air calibrations can be performed using 

particle-free air. 

5 min-24 h N/A Nephelometers: 

< 5% for TSI 

and NGNi 

nephelometers 

78,79 

DustTrak: 

Greater of 0.1% 

or 1 μg/m3 c,h 

Nephelometer: 

< 1.5 Mm-1 

DustTrak: ± 1 μg/m3 

for 24-h avgj 

Conversion factor to calculate 

mass concentration from 

bscat may vary 

depending on 

particle size, 

shape and 

composition. 

Light scattering 

by DustTrak 

proportional to 

dp 6 for dp 

< 0.25 μm 79 

Typically good 

correlation with 

SES-TEOM and 

D-TEOM (R2

>0.80). 

Comparability 

depends on 

conversion 

factor used. 

>80-98% for 

NGN2, RR-M903 

and GreenTek 

Nephelometers 6 

>80% for 

DustTrak 6 95 to 

98% for GRIMM 

optical particle 

counter65 

December 2009 A-12 
Instrument and 

Measurement Principle 

Averaging 

Time 

Analytical 

Accuracya Precisionb MDL Interferences Comparability Data Completeness

a

 Accuracy is the ability of analytical methods to quantify the observable of a standard reference material correctly; does not refer to measurement accuracy, since no standards available. 

b Refers to precision of co-located measurements, unless specified otherwise. 

c

 Manufacturer-specified measurement parameter. 

d

 Details not available on how the precision was obtained and whether it refers to co-located precision. 

e

 Includes a combination of estimates: based on co-located precision and based on regression slopes. 

f

 Co-located precision with respect to PC-BOSS reconstructed PM2.5 mass. g

 Using glass-fiber “sandwich” filter. 

h

 Specified as “resolution” by the manufacturer. 

i Co-located precision estimate based on regression slope for NGN nephelometer (slope = 1.01, intercept = -1.64 μg/m3

, R2 = 0.99). j

 Specified as “Zero stability” by the manufacturer. 

N/A: Not available. 

1

Chow (1995, 077012); 2Watson and Chow (2001, 157123); 3 Watson et al. (1983, 045084); 4Fehsenfeld et al. (2004, 157360); 5Solomon et al. (2001, 157193); 6Watson et al. (2005, 157124); 7Mikel (2001, 

156762); 8

Watson et al. (1999, 020949); 9Solomon and Sioutas (2006, 156995); 10Graney et al. (2004, 053756); 11Tanaka et al. (1998, 157041); 12Pancras et al. (2005, 098120); 13John et al. (1988, 045903); 14Hering and Cass (1999, 084958); 15Fitz et al. (1989, 077387); 16Hering et al. (1988, 036012); 17Solomon et al. (2003, 156994); 18Cabada et al. (2004, 148859); 19Fine et al. (2003, 155775); 20Hogrefe et al. 

(2004, 099003); 21Drewnick et al. (2003, 099160); 22Watson et al. (2005, 157125); 23Ho et al. (2006, 156552); 24Decesari et al. (2005, 144536); 25 Mayol-Bracero et al. (2002, 045010); 26Yang et al. (2003, 

156167); 27Turšic et al. (2006, 157063); 28Mader et al.(2004, 156724); 29Xiao and Liu (2004, 056801); 30Kiss et al. (2002, 156646); 31Cornell and Jickells (1999, 156367); 32 Zheng et al. (2002, 026100); 33Fraser et al. (2002, 140741); 34 Fraser et al. (2003, 042231) 35Schauer er al. (2000, 012225); 36Fine et al. (2004, 141283); 37Yue et al. (2004, 157169); 38Rinehart et al. (2006, 115184); 39Wan and Yu (2006, 

157104); 40Poore (2000, 012839); 41Fraser et al. (2003, 040266); 42Engling et al. (2006, 156422); 43Yu et al. (2005, 157167); 44Tran et al. (2000, 013025); 45Yao et al. (2004, 102213); 46Li and Yu (2005, 

156692); 47Henning et al. (2003, 156539); 48Zhang and Anastasio (2003, 157182); 49Emmenegger et al. (2007, 156418); 50 Watson et al. (1989, 046318); 51Greaves et al. (1985, 156494); 52Waterman et al. 

(2000, 157116); 53Waterman et al. (2001, 157117); 54Falkovich and Rudich (2001, 156427); 55Chow et al. (2007, 157209); 56Miguel et al. (2004, 123260); 57Crimmins and Baker (2006, 097008); 58Ho and Yu 

(2004, 156551); 59Jeon et al. (2001, 016636); 60Mazzoleni et al. (2007, 098038); 61Poore (2002, 051444); 62Butler et al. (2003, 156313); 63Chow et al. (2006, 146622); 64Russell et al. (2004, 082453); 65Grover 

et al. (2006, 138080); 66Grover et al. (2005, 090044); 67Schwab et al. (2006, 098449); 68Hauck et al. (2004, 156525); 69Jaques et al. (2004, 155878); 70Rupprecht and Patashnick (2003, 157207); 71Pang et al 

(2002, 030353); 72Eatough et al. (2001, 010303); 73Lee et al. (2005, 128139); 74Lee et al. (2005, 156680); 75Babich et al. (2000, 156239); 76Lee et al. (2005, 155925); 77Lee et al. (2005, 128139); 78Anderson 

and Ogren (1998, 156213); 79Chung et al. (2001, 156357); 80Kidwell and Ondov (2004, 155898); 81Lithgow et al. (2004, 126616); 82Weber et al. (2003, 157129); 83Harrison et al. (2004, 136787); 84Rattigan et 

al. (2006, 115897); 85Wittig et al. (2004, 103413); 86Vaughn et al. (2005, 157089); 87Chow et al. (2005, 099030); 88Weber et al (2001, 024640) ; 89Schwab et al. (2006, 098785); 90Lim et al. (2003, 037037); 91Watson and Chow (2002, 037873); 92Venkatachari et al. (2006, 105918); 93Bae et al. (2004, 156243); 94Arhami et al. (2006, 156224); 95Park et al. (2005, 156843); 96Bae et al. (2004, 098680); 97Chow et al. 

(2006, 156350); 98Arnott et al. (2005, 156227); 99Bond et al. (1999, 156281); 100Virkkula et al. (2005, 157097); 101Petzold et al. (2002, 156863); 102Park et al. (2006, 098104); 103Arnott et al. (1999, 020650); 104Peters et al. (2001, 016925); 105Pitchford et al. (1997, 156872); 106Rees et al. (2004, 097164); 107Watson et al. (2000, 010354); 108Lee et al. (2005, 156680); 109Hering et al. (2004, 155837); 110Watson et al. 

(1998, 198805); 111Chakrabarti et al. (2004, 157426); 112Mathai et al. (1990, 156741); 113Kidwell and Ondov (2001, 017092); 114Stanier et al. (2004, 095955); 115Khlystov et al. (2005, 156635); 116Takahama et 

al. (2004, 157038); 117Chow et al. (2005, 156348); 118Zhang et al. (2002, 157181); 119Subramanian et al. (2004, 081203); 120Chow et al. (2006, 155207); 121Birch and Cary (1996, 026004); 122Birch (1998, 

024953); 123Birch and Cary (1996, 002352); 124NIOSH (1996, 156810); 125NIOSH (1999, 156811); 126Chow et al. (1993, 077459); 127Chow et al. (2007, 156354); 128Ellis and Novakov (1982, 156416); 129Peterson and Richards (2002, 156861); 130Schauer et al. (2003, 037014); 131Middlebrook et al. (2003, 042932); 132Wenzel et al. (2003, 157139); 133Jimenez et al. (2003, 156611); 134Phares et al. (2003, 

156866); 135Qin and Prather (2006, 156895); 136Zhang et al. (2005, 157185); 137Bein et al. (2005, 156265); 138Drewnick et al. (2004, 155754); 139Drewnick et al. (2004, 155755); 140Lake et al. (2003, 156669); 141Lake et al. (2004, 088411) 

Source: Chow et al. (2008, 156355)

December 2009 A-13 
Table A-6. Measurement and analytical specifications for continuous elements. 

Instrument and 

Measurement 

Principle 

Averaging 

Time 

Analytical 

Accuracya Precision MDL Interferences Comparability Data Completeness 

Semi-continuous 

Elements in Aerosol 

System (SEAS) 

Particles are collected at 

30-min interval for 

subsequent laboratory 

atomic absorption 

analysis for elements. 

Aerosol collection is 

through condensational 

growth by direct steam 

injection. The grown 

particles are separated 

from the airstream using 

virtual impactor. The 

droplets accumulate in a 

slurry that is pumped to 

a separate sample vial 

for each time period. 

15-30 min ± 10%b

 for Mn, 

Fe, Ni, Cu, Zn, Se, 

Cd, and Sb 

 ± 20%b

 for Cr, As, 

and Pb 80 

20-43%c 80 Al: 440 pg 

Cr: 6.7 pg 

Mn: 9.9 pg 

Fe: 85 pg 

Ni: 42 pg 

Cu: 26 pg 

Zn: 43 pg 

As: 27 pg 

Se: 33 pg 

Cd: 3.2 pg 

Sb 160 pg 

Pb: 31 pg80 

Spectral 

interferences limit 

the number of 

elements detected 

simultaneously 

N/A N/A 

Laser-Induced 

Breakdown 

Spectroscopy (LIBS) 

Used for in-situ single 

particle analysis. A highpower pulsed laser is 

projected into particles 

producing hightemperature plasma. 

Photons emission from 

relaxing atoms in the 

excited states provides 

characteristics of 

individual elements. 

A few 

seconds 

N/A N/A Na: 143 fg 

Mg: 53 fg 

Al: 184 fg 

Ca: 50 fg 

Cr: 166 fg 

Mn: 176 fg 

Cu: 15 fg81 

N/A N/A N/A 

a

 Accuracy is the ability of analytical methods to quantify the observable of a standard reference material correctly; does not refer to measurement accuracy, since no standards are available. b

 Based on analysis of standard reference material (SRM) 1643d from National Institute of Standards and Technology (NIST). 

c Based on error propagation. 

N/A: Not available 

80(Kidwell and Ondov, 2004, 155898); 81(Lithgow et al., 2004, 126616). 

Source: Chow et al. (2008, 156355)

December 2009 A-14 
Table A-7. Measurement and analytical specifications for continuous NO3-. 

Instrument and Measurement 

Principle 

Averaging 

Time 

Analytical 

Accuracya Precision MDL Interferences Comparability Data Completion 

FLASH VOLATIZATION INSTRUMENTS 

Aerosol Dynamics Inc. continuous nitrate 

analyzer (ADIN) 

Particle collection by humidification and 

impaction followed by flash volatilization 

and detection of the evolved gases in a 

chemiluminescent NOX analyzer. 

10 min N/A N/A 0.1 μg/m3

 for 

10-min avg 82 N/A 

Within 30% of 

filter and 

continuous NO3

–

. 

See Weber et al. 82 for details. 

93%7

Rupprecht and Patashnick continuous 

nitrate analyzer (R&P-8400N) 

Particle collection by impaction followed 

by flash volatilization and detection of 

the evolved gases in a 

chemiluminescent NOX analyzer. A 

carbon honeycomb denuder, installed at 

the inlet to the Nafion humidifier 

removes nitric acid and ammonia vapor. 

10 min N/A 6.3%-23%b

83

0.17 to 

0.3 μg/m3

 for 

24-h avg 83,84 

0.24 μg/m3 to 

0.45 μg/m3

 for 

10-min avg 83,85

Conversion and 

volatilization 

efficiency appears 

to depend on 

ambient 

composition; extent 

of underestimation 

increases with 

higher 

concentrations. 84,86

20 to 45% lower 

than filter NO3

–

20,8285,87

>80->94%6,20,83-

85

DENUDER-DIFFERENCE INSTRUMENT 

Atmospheric Research and Analysis 

nitrate analyzer (ARAN) 

Sampled air passes through a 350oC 

molybdenum (Mo) mesh that converts 

particulate nitrate into NO. A pre-split 

stream with a Teflon filter installed 

upstream of an identical converter 

(i.e., particle-free air) is used as a 

reference. NO in both streams is 

quantified by chemiluminescence and 

their difference determines the 

particulate nitrate concentration. The 

instrument inlet contains a potassium 

iodide- coated denuder to remove HNO3

and NO2. 

30 s N/A N/A 0.5 μg/m3

 for 

30-s avg 82

N/A 

Within 30% of 

filter and 

continuous NO3

–

. 

See Weber et al. 82 for details. 

76%7

SAMPLE DISSOLUTION FOLLOWED BY IC ANALYSIS INSTRUMENTS 

Energy Research Center of the 

Netherlands (ECN) IC-based ion 

analyzer 

Collects particles into water drops using 

a steam jet aerosol collector, via 

cyclone. The combined flow from 

collected droplets containing dissolved 

aerosol components and wall steam 

condensate is directed to an anion IC for 

analysis of nitrate. Interfering gases are 

pre-removed by a rotating wet annular 

denuder system. 

1 h N/A N/A 0.1 μg/m3 82 N/A 

Within 30% of 

filter and 

continuous NO3

–

. 

See Weber et al. 82 for details. 

100%7

Texas Tech University (TT) ion analyzer

Particles in the sample stream are 

processed through a cyclone and a 

parallel plate wet denuder, then collected 

alternatively on one of two 2.5 cm prewashed glass fiber filters for a period of 

15 min. The particles on the freshly 

sampled filter are automatically 

extracted for 6.5 min with water and 

analyzed for nitrate by IC. 

15-30 min N/A N/A 

0.010 μg/m3 82 N/A 

Within 30% of 

filter and 

continuous NO3

–

. 

See Weber et al. 82 for details. 

97%7

Particle into Liquid Sampler-Ion 

Chromatography (PILS-IC) 

Ambient particles are mixed with 

saturated water vapor to produce 

droplets collected by impaction. The 

resulting liquid stream is analyzed with 

an IC to quantify aerosol ionic 

components. 

1 h N/A 10%-15%c

7,82,88 0.05-0.1 μg/m3 20,82,88

Consistent water 

quality is essential 

for good precision. 

Within 10% of 

nylon-filter NO3

–

and 37% higher 

than R&P-8400N 20

65-70%20

December 2009 A-15 
Instrument and Measurement 

Principle 

Averaging 

Time 

Analytical 

Accuracya Precision MDL Interferences Comparability Data Completion 

Dionex-IC 

The gas-denuded air stream enters the 

annular channel of a concentric nozzle, 

where deionized water generates a 

spray that entrains the particles. The 

flow is then drawn through a 0.5 μm pore 

size PTFE filter. The remaining solution 

is aspirated by a peristaltic pump and 

sent to IC for ion analysis. 

1 h N/A 14%d 65 N/A 

Consistent water 

quality is essential 

for good precision. 

Bias of < 10% 

relative to filter 

NO3

– 65 N/A 

Ambient Ion Monitor (AIM; Model 9000)

Air is drawn through a size-selective inlet 

into a liquid diffusion denuder where 

interfering gases are removed. The 

stream enters a supersaturation 

chamber where the resulting droplets 

are collected through impaction. The collected particles and a fraction of the 

condensed water are accumulated until 

the particles can be injected into IC for 

hourly analysis. 

1 h N/A N/A 0.1 μg/m3

 for 

1-h avge N/A N/A N/A 

PARTICLE MASS SPECTROMETER INSTRUMENT 

Aerosol Mass Spectrometer (AMS) 

Air stream is drawn through an 

aerodynamic lens and focused into a 

beam in a vacuum chamber. This 

aerosol beam is chopped by a 

mechanical chopper and the flight time 

of the particles through a particle-sizing 

chamber is determined by the timeresolved mass spectrometer 

measurement. The particle impacts onto 

a 600 °C heated plate where it 

decomposes and is analyzed by a 

quadruple mass spectrometer. The 

nitrate ion, along with other ions, is 

detected by the mass spectrometer. 

A few 

seconds N/A N/A 0.03 μg/m3 20

Subject to 

interferences from 

fragments of other 

species with mass 

to charge ratio in 

the same range as 

fragments of nitrate. 

Highly refractory 

materials are not 

detected. 

Within 10% of 

nylon-filter NO3

–

, 

and within 15% of 

PILS-IC and 30% 

of R&P8400N 20 

94-98%20

a

 Accuracy is the ability of analytical methods to quantify the observable of a standard reference material correctly; does not refer to measurement accuracy, since no standards are available. b

 Overall uncertainty estimated by error propagation. 

c

 Uncertainty estimated from uncertainties in flow rates and calibrations; does not refer to co-located precision. 

d Co-located precision with respect to PC-BOSS PM2.5 total particulate NO3 (the sum of the denuded front filter [non-volatilized NO3-] and HNO3-absorbing backup filter [volatilized NO3]). e

 Manufacturer specified measurement parameter 

N/A: Not available. 

1

Chow (1995, 077012); 2Watson and Chow (2001, 157123); 3 Watson et al. (1983, 045084); 4Fehsenfeld et al. (2004, 157360); 5Solomon et al. (2001, 157193); 6Watson et al. (2005, 157124); 7Mikel (2001, 

156762); 8

Watson et al. (1999, 020949); 9Solomon and Sioutas (2006, 156995); 10Graney et al. (2004, 053756); 11Tanaka et al. (1998, 157041); 12Pancras et al. (2005, 098120); 13John et al. (1988, 045903); 14Hering and Cass (1999, 084958); 15Fitz et al. (1989, 077387); 16Hering et al. (1988, 036012); 17Solomon et al. (2003, 156994); 18Cabada et al. (2004, 148859); 19Fine et al. (2003, 155775); 20Hogrefe et al. 

(2004, 099003); 21Drewnick et al. (2003, 099160); 22Watson et al. (2005, 157125); 23Ho et al. (2006, 156552); 24Decesari et al. (2005, 144536); 25 Mayol-Bracero et al. (2002, 045010); 26Yang et al. (2003, 

156167); 27Turšic et al. (2006, 157063); 28Mader et al.(2004, 156724); 29Xiao and Liu (2004, 056801); 30Kiss et al. (2002, 156646); 31Cornell and Jickells (1999, 156367); 32 Zheng et al. (2002, 026100); 33Fraser et al. (2002, 140741); 34 Fraser et al. (2003, 042231) 35Schauer er al. (2000, 012225); 36Fine et al. (2004, 141283); 37Yue et al. (2004, 157169); 38Rinehart et al. (2006, 115184); 39Wan and Yu (2006, 

157104); 40Poore (2000, 012839); 41Fraser et al. (2003, 040266); 42Engling et al. (2006, 156422); 43Yu et al. (2005, 157167); 44Tran et al. (2000, 013025); 45Yao et al. (2004, 102213); 46Li and Yu (2005, 

156692); 47Henning et al. (2003, 156539); 48Zhang and Anastasio (2003, 157182); 49Emmenegger et al. (2007, 156418); 50 Watson et al. (1989, 046318); 51Greaves et al. (1985, 156494); 52Waterman et al. 

(2000, 157116); 53Waterman et al. (2001, 157117); 54Falkovich and Rudich (2001, 156427); 55Chow et al. (2007, 157209); 56Miguel et al. (2004, 123260); 57Crimmins and Baker (2006, 097008); 58Ho and Yu 

(2004, 156551); 59Jeon et al. (2001, 016636); 60Mazzoleni et al. (2007, 098038); 61Poore (2002, 051444); 62Butler et al. (2003, 156313); 63Chow et al. (2006, 146622); 64Russell et al. (2004, 082453); 65Grover 

et al. (2006, 138080); 66Grover et al. (2005, 090044); 67Schwab et al. (2006, 098449); 68Hauck et al. (2004, 156525); 69Jaques et al. (2004, 155878); 70Rupprecht and Patashnick (2003, 157207); 71Pang et al 

(2002, 030353); 72Eatough et al. (2001, 010303); 73Lee et al. (2005, 128139); 74Lee et al. (2005, 156680); 75Babich et al. (2000, 156239); 76Lee et al. (2005, 155925); 77Lee et al. (2005, 128139); 78Anderson 

and Ogren (1998, 156213); 79Chung et al. (2001, 156357); 80Kidwell and Ondov (2004, 155898); 81Lithgow et al. (2004, 126616); 82Weber et al. (2003, 157129); 83Harrison et al. (2004, 136787); 84Rattigan et 

al. (2006, 115897); 85Wittig et al. (2004, 103413); 86Vaughn et al. (2005, 157089); 87Chow et al. (2005, 099030); 88Weber et al (2001, 024640) ; 89Schwab et al. (2006, 098785); 90Lim et al. (2003, 037037); 91Watson and Chow (2002, 037873); 92Venkatachari et al. (2006, 105918); 93Bae et al. (2004, 156243); 94Arhami et al. (2006, 156224); 95Park et al. (2005, 156843); 96Bae et al. (2004, 098680); 97Chow et al. 

(2006, 156350); 98Arnott et al. (2005, 156227); 99Bond et al. (1999, 156281); 100Virkkula et al. (2005, 157097); 101Petzold et al. (2002, 156863); 102Park et al. (2006, 098104); 103Arnott et al. (1999, 020650); 104Peters et al. (2001, 016925); 105Pitchford et al. (1997, 156872); 106Rees et al. (2004, 097164); 107Watson et al. (2000, 010354); 108Lee et al. (2005, 156680); 109Hering et al. (2004, 155837); 110Watson et al. 

(1998, 198805); 111Chakrabarti et al. (2004, 157426); 112Mathai et al. (1990, 156741); 113Kidwell and Ondov (2001, 017092); 114Stanier et al. (2004, 095955); 115Khlystov et al. (2005, 156635); 116Takahama et 

al. (2004, 157038); 117Chow et al. (2005, 156348); 118Zhang et al. (2002, 157181); 119Subramanian et al. (2004, 081203); 120Chow et al. (2006, 155207); 121Birch and Cary (1996, 026004); 122Birch (1998, 

024953); 123Birch and Cary (1996, 002352); 124NIOSH (1996, 156810); 125NIOSH (1999, 156811); 126Chow et al. (1993, 077459); 127Chow et al. (2007, 156354); 128Ellis and Novakov (1982, 156416); 129Peterson and Richards (2002, 156861); 130Schauer et al. (2003, 037014); 131Middlebrook et al. (2003, 042932); 132Wenzel et al. (2003, 157139); 133Jimenez et al. (2003, 156611); 134Phares et al. (2003, 

156866); 135Qin and Prather (2006, 156895); 136Zhang et al. (2005, 157185); 137Bein et al. (2005, 156265); 138Drewnick et al. (2004, 155754); 139Drewnick et al. (2004, 155755); 140Lake et al. (2003, 156669); 141Lake et al. (2004, 088411) 

Source: Chow et al. (2008, 156355)

December 2009 A-16 
Table A-8. Measurement and analytical specifications for continuous SO4

2–. 

Instrument and Measurement Principle Averaging 

Time 

Analytical 

Accuracya Precision MDL Interferences Comparability Data Completeness

FLASH VOLATILIZATION INSTRUMENTS 

Aerosol Dynamics, Inc. continuous sulfate 

analyzer (ADIS) 

Particle collection by impaction followed by flash 

volatilization and detection of the evolved gases 

by a UV-fluorescence SO2 analyzer. 

10 min N/A N/A 0.4 μg/m3 

82 N/A 

Within 15% of 

filter and 

continuous 

SO4

2– 

See Weber et al. 

82 for details. 

100%7

Rupprecht and Patashnick continuous sulfate 

analyzer (R&P-8400S) 

Particle collection by impaction followed by flash 

volatilization and detection of the evolved gases 

by a UV-fluorescence SO2 analyzer. An 

activated carbon denuder at the inlet to the 

Nafion humidifier removes SO2. 

10 min N/A 

25% on 

avg < 15% 

at conc. 

>9 μg/m3

and >30% 

at conc. 

< 2 μg/m3 b 

84 

0.48 μg/m3 

85 

SO4

2– to SO2

conversion and 

volatilization 

efficiency 

appears to 

depend on 

ambient 

composition 84 

10-30% lower 

than filter SO4

2– 

20,21,84 

84- 95%6,20,21,84,85

THERMAL REDUCTION INSTRUMENTS 

Continuous Ambient Sulfate Monitor (CASM) 

Sampled air passes through a Na2CO3 coated 

annular denuder to remove ambient SO2 and is 

subsequently split into independent sample and 

filter flows. The sample flow passes through a 

quartz tube containing a stainless steel rod 

maintained at 1000 °C that reduces sulfate to 

SO2. The flow then passes through a PTFE filter 

and into a trace-level SO2 fluorescence 

analyzer. 

15 min N/A N/A N/A N/A 

Up to 25% lower 

than filter SO4

2––

 and within 6% 

of R&P8400S, 

PILS-IC and 

AMS 20,21 

80-98%20,21

Thermo Electron Model 5020 sulfate particulate 

analyzer (TE-5020) 

The commercial version of CASM, with slight 

changes in the sample flow path. 

15 min N/A < 10%c 89

 0.3 μg/m3 

for 24-h 

avg 89 

0.5 μg/m3 

for 15-min 

avgd 

SO4

2– to SO2 

conversion 

efficiency 

depends on 

ambient 

composition 89 

~20% lower 

than filter SO4

2–

89 88-90%89

SAMPLE DISSOLUTION FOLLOWED BY IC ANALYSIS INSTRUMENTS 

Energy Research Center of the Netherlands 

(ECN) IC-based ion analyzer 

Entrains particles into water drops using the 

steam jet aerosol collector. The drops are 

collected using a cyclone and the combined 

flow from collected droplets containing dissolved aerosol components and wall steam 

condensate is directed to an anion IC for 

analysis of sulfate. Interfering gases are preremoved by a rotating wet annular denuder 

system. 

1 h N/A N/A N/A N/A 

Within 15% of 

filter and 

continuous 

SO4

2– 

See Weber et al. 

82 for details. 

100% 

Texas Tech University (TT) ion analyzer 

Particles in the sample stream, after being 

processed through a cyclone and a parallel 

plate wet denuder, are collected alternatively on 

one of two 2.5 cm pre-washed glass fiber filters 

for a period of 15 min. The particles on the 

freshly sampled filter are automatically 

extracted for 6.5 min with water and analyzed 

for sulfate by IC. 

30 min N/A N/A N/A N/A 

Within 15% of 

filter and 

continuous 

SO4

2– 

See Weber et al. 

82 for details. 

100%7

Particle into Liquid Sampler-Ion 

Chromatography (PILS-IC) 

Ambient particles are mixed with saturated 

water vapor to produce droplets collected by 

impaction. The resulting liquid stream is 

analyzed with an IC to quantify aerosol ionic 

components. 

1 h N/A 10%-15%e 

7,82,88 

0.1 to 

0.18 μg/m3 

82,88 

Consistent 

water quality is 

essential for 

good precision. 

Within 30% of 

filter and other 

continuous 

SO4

2– 20,21 

65-70%20,21

December 2009 A-17 
Instrument and Measurement Principle Averaging 

Time 

Analytical 

Accuracya Precision MDL Interferences Comparability Data Completeness

Dionex-IC 

The gas-denuded air stream enters the annular 

channel of a concentric nozzle, where deionized 

water generates a spray that entrains the 

particles. The flow is then drawn through a 0.5-

 μm pore size PTFE filter. The remaining 

solution is aspirated by a peristaltic pump and 

sent to IC for ion analysis. 

1h N/A 11%f 65 N/A 

Consistent 

water quality is 

essential for 

good precision. 

Within 10% of 

filter SO4

2– 65 N/A 

Ambient Ion Monitor (AIM; Model 9000) 

Air is drawn through a size-selective inlet into a 

liquid diffusion denuder where interfering gases 

are removed. The stream enters a super 

saturation chamber where the resulting droplets 

are collected through impaction. The collected 

particles and a fraction of the condensed water 

are accumulated until the particles can be 

injected into IC for hourly analysis. 

1h N/A N/A 0.1 μg/m3

for 1-h avg N/A N/A N/A 

PARTICLE MASS SPECTROMETER 

Aerosol Mass Spectrometer (AMS) 

Airstream is drawn through an aerodynamic 

lens and focused into a beam in a vacuum 

chamber. This aerosol beam is chopped by a 

mechanical chopper and the flight time of the 

particles through a particle-sizing chamber is 

determined by the time-resolved mass 

spectrometer measurement. The particle 

impacts onto a 600 °C heated plate where it 

decomposes and is analyzed by a quadruple 

mass spectrometer. The sulfate ion, along with 

other ions, is detected by the mass 

spectrometer. 

A few 

seconds N/A N/A N/A 

Subject to 

interferences 

from fragments 

of other species 

with mass to 

charge ratio in 

the same range 

as fragments of 

sulfate. Highly 

refractory 

materials are 

not detected. 

Up to 30% lower 

than filter SO4

2–

 and within 5% 

of R&P8400S, 

PILS-IC and 

CASM 20,21 

93-98%20,21

a

 Accuracy is the ability of analytical methods to quantify the observable of a standard reference material correctly; does not refer to measurement accuracy, since no standards available. 

b

 Overall uncertainty estimated by error propagation. 

c

 Co-located precision estimate based on regression slope (slope = 0.95, intercept = 0.01-0.2, R2>0.98). d

 Manufacturer specified measurement parameter. 

e

 Uncertainty estimated from uncertainties in flow rates and calibrations; does not refer to co-located precision. 

f

 Co-located precision with respect to PC-BOSS PM2.5 SO4

2–. 

N/A: Not available 

1

Chow (1995, 077012); 2Watson and Chow (2001, 157123); 3 Watson et al. (1983, 045084); 4Fehsenfeld et al. (2004, 157360); 5Solomon et al. (2001, 157193); 6Watson et al. (2005, 157124); 7Mikel (2001, 

156762); 8Watson et al. (1999, 020949); 9Solomon and Sioutas (2006, 156995); 10Graney et al. (2004, 053756); 11Tanaka et al. (1998, 157041); 12Pancras et al. (2005, 098120); 13John et al. (1988, 045903); 14Hering and Cass (1999, 084958); 15Fitz et al. (1989, 077387); 16Hering et al. (1988, 036012); 17Solomon et al. (2003, 156994); 18Cabada et al. (2004, 148859); 19Fine et al. (2003, 155775); 20Hogrefe et al. 

(2004, 099003); 21Drewnick et al. (2003, 099160); 22Watson et al. (2005, 157125); 23Ho et al. (2006, 156552); 24Decesari et al. (2005, 144536); 25 Mayol-Bracero et al. (2002, 045010); 26Yang et al. (2003, 

156167); 27Turšic et al. (2006, 157063); 28Mader et al.(2004, 156724); 29Xiao and Liu (2004, 056801); 30Kiss et al. (2002, 156646); 31Cornell and Jickells (1999, 156367); 32 Zheng et al. (2002, 026100); 33Fraser et al. (2002, 140741); 34 Fraser et al. (2003, 042231) 35Schauer er al. (2000, 012225); 36Fine et al. (2004, 141283); 37Yue et al. (2004, 157169); 38Rinehart et al. (2006, 115184); 39Wan and Yu (2006, 

157104); 40Poore (2000, 012839); 41Fraser et al. (2003, 040266); 42Engling et al. (2006, 156422); 43Yu et al. (2005, 157167); 44Tran et al. (2000, 013025); 45Yao et al. (2004, 102213); 46Li and Yu (2005, 

156692); 47Henning et al. (2003, 156539); 48Zhang and Anastasio (2003, 157182); 49Emmenegger et al. (2007, 156418); 50 Watson et al. (1989, 046318); 51Greaves et al. (1985, 156494); 52Waterman et al. 

(2000, 157116); 53Waterman et al. (2001, 157117); 54Falkovich and Rudich (2001, 156427); 55Chow et al. (2007, 157209); 56Miguel et al. (2004, 123260); 57Crimmins and Baker (2006, 097008); 58Ho and Yu 

(2004, 156551); 59Jeon et al. (2001, 016636); 60Mazzoleni et al. (2007, 098038); 61Poore (2002, 051444); 62Butler et al. (2003, 156313); 63Chow et al. (2006, 146622); 64Russell et al. (2004, 082453); 65Grover 

et al. (2006, 138080); 66Grover et al. (2005, 090044); 67Schwab et al. (2006, 098449); 68Hauck et al. (2004, 156525); 69Jaques et al. (2004, 155878); 70Rupprecht and Patashnick (2003, 157207); 71Pang et al 

(2002, 030353); 72Eatough et al. (2001, 010303); 73Lee et al. (2005, 128139); 74Lee et al. (2005, 156680); 75Babich et al. (2000, 156239); 76Lee et al. (2005, 155925); 77Lee et al. (2005, 128139); 78Anderson 

and Ogren (1998, 156213); 79Chung et al. (2001, 156357); 80Kidwell and Ondov (2004, 155898); 81Lithgow et al. (2004, 126616); 82Weber et al. (2003, 157129); 83Harrison et al. (2004, 136787); 84Rattigan et 

al. (2006, 115897); 85Wittig et al. (2004, 103413); 86Vaughn et al. (2005, 157089); 87Chow et al. (2005, 099030); 88Weber et al (2001, 024640) ; 89Schwab et al. (2006, 098785); 90Lim et al. (2003, 037037); 91Watson and Chow (2002, 037873); 92Venkatachari et al. (2006, 105918); 93Bae et al. (2004, 156243); 94Arhami et al. (2006, 156224); 95Park et al. (2005, 156843); 96Bae et al. (2004, 098680); 97Chow et al. 

(2006, 156350); 98Arnott et al. (2005, 156227); 99Bond et al. (1999, 156281); 100Virkkula et al. (2005, 157097); 101Petzold et al. (2002, 156863); 102Park et al. (2006, 098104); 103Arnott et al. (1999, 020650); 104Peters et al. (2001, 016925); 105Pitchford et al. (1997, 156872); 106Rees et al. (2004, 097164); 107Watson et al. (2000, 010354); 108Lee et al. (2005, 156680); 109Hering et al. (2004, 155837); 110Watson et al. 

(1998, 198805); 111Chakrabarti et al. (2004, 157426); 112Mathai et al. (1990, 156741); 113Kidwell and Ondov (2001, 017092); 114Stanier et al. (2004, 095955); 115Khlystov et al. (2005, 156635); 116Takahama et 

al. (2004, 157038); 117Chow et al. (2005, 156348); 118Zhang et al. (2002, 157181); 119Subramanian et al. (2004, 081203); 120Chow et al. (2006, 155207); 121Birch and Cary (1996, 026004); 122Birch (1998, 

024953); 123Birch and Cary (1996, 002352); 124NIOSH (1996, 156810); 125NIOSH (1999, 156811); 126Chow et al. (1993, 077459); 127Chow et al. (2007, 156354); 128Ellis and Novakov (1982, 156416); 129Peterson and Richards (2002, 156861); 130Schauer et al. (2003, 037014); 131Middlebrook et al. (2003, 042932); 132Wenzel et al. (2003, 157139); 133Jimenez et al. (2003, 156611); 134Phares et al. (2003, 

156866); 135Qin and Prather (2006, 156895); 136Zhang et al. (2005, 157185); 137Bein et al. (2005, 156265); 138Drewnick et al. (2004, 155754); 139Drewnick et al. (2004, 155755); 140Lake et al. (2003, 156669); 141Lake et al. (2004, 088411) 

Source: Chow et al. (2008, 156355)

December 2009 A-18 
Table A-9. Measurement and analytical specifications for ions other than NO3

–

 and SO4

2–. 

Instrument and Measurement 

Principle 

Averaging 

Time 

Analytical 

Accuracya Precision MDL Interferences Comparability Data Completeness

SAMPLE DISSOLUTION FOLLOWED BY IC ANALYSIS INSTRUMENTS 

NO2

–

 by Particle into Liquid Sampler-Ion 

Chromatography (PILS-IC) 

Ambient particles are mixed with saturated 

water vapor to produce droplets collected 

by impaction. The resulting liquid stream is 

analyzed with an IC to quantify aerosol 

ionic components. 

1 h N/A 10%b 88 0.14 μg/m3 20

Consistent water 

quality is 

essential for 

good precision 

N/A N/A 

NH4

+

 by Particle into Liquid Sampler-Ion 

Chromatography (PILS-IC) 

Ambient particles are mixed with saturated 

water vapor to produce droplets collected 

by impaction. The resulting liquid stream is 

analyzed with an IC to quantify aerosol 

ionic components. 

1 h N/A 10%b 88 0.05 μg/m3 

88

Consistent water 

quality is 

essential for 

good precision 

~5% lower than 

all-sampler avgc

at Atlanta 7 N/A 

Cl–, Na+, K+, Ca++ by Particle into Liquid 

Sampler-Ion Chromatography (PILS-IC) 

Ambient particles are mixed with saturated 

water vapor to produce droplets collected 

by impaction. The resulting liquid stream is 

analyzed with an IC to quantify aerosol 

ionic components. 

1 h N/A 10%b 88 0.1 μg/m3 88

Consistent water 

quality is 

essential for 

good precision 

N/A N/A 

Cl–, NO2

–

, NO3

–

, PO4

3–, SO42– , NH4+

, Na+,

Mg++, K+

, Ca++ by Ambient Ion Monitor 

(AIM; Model 9000) 

Air is drawn through a size-selective inlet 

into a liquid diffusion denuder where 

interfering gases are removed. The stream 

enters a super saturation chamber where 

the resulting droplets are collected through 

impaction. The collected particles and a 

fraction of the condensed water are 

accumulated until the particles can be 

injected into IC for hourly analysis. 

1 h N/A N/A 0.1 μg/m3 for 

1-h avgd N/A N/A N/A 

a Accuracy is the ability of analytical methods to quantify the observable of a standard reference material correctly; does not refer to measurement accuracy, since no standards are available. b

 Uncertainty estimated from uncertainties in flow rates and calibrations; does not refer to co-located precision. 

c

 All-sampler avg appears to include a combination of 10 integrated and 3 continuous samplers, although specific details are missing 7. Performance evaluations at sites dominated by semi-volatile ammonium 

nitrate are needed. 

d

 Manufacturer specified measurement parameter 

1

Chow (1995, 077012); 2Watson and Chow (2001, 157123); 3 Watson et al. (1983, 045084); 4Fehsenfeld et al. (2004, 157360); 5Solomon et al. (2001, 157193); 6Watson et al. (2005, 157124); 7Mikel (2001, 

156762); 8

Watson et al. (1999, 020949); 9Solomon and Sioutas (2006, 156995); 10Graney et al. (2004, 053756); 11Tanaka et al. (1998, 157041); 12Pancras et al. (2005, 098120); 13John et al. (1988, 045903); 14Hering and Cass (1999, 084958); 15Fitz et al. (1989, 077387); 16Hering et al. (1988, 036012); 17Solomon et al. (2003, 156994); 18Cabada et al. (2004, 148859); 19Fine et al. (2003, 155775); 20Hogrefe et al. 

(2004, 099003); 21Drewnick et al. (2003, 099160); 22Watson et al. (2005, 157125); 23Ho et al. (2006, 156552); 24Decesari et al. (2005, 144536); 25 Mayol-Bracero et al. (2002, 045010); 26Yang et al. (2003, 

156167); 27Turšic et al. (2006, 157063); 28Mader et al.(2004, 156724); 29Xiao and Liu (2004, 056801); 30Kiss et al. (2002, 156646); 31Cornell and Jickells (1999, 156367); 32 Zheng et al. (2002, 026100); 33Fraser et al. (2002, 140741); 34 Fraser et al. (2003, 042231) 35Schauer er al. (2000, 012225); 36Fine et al. (2004, 141283); 37Yue et al. (2004, 157169); 38Rinehart et al. (2006, 115184); 39Wan and Yu (2006, 

157104); 40Poore (2000, 012839); 41Fraser et al. (2003, 040266); 42Engling et al. (2006, 156422); 43Yu et al. (2005, 157167); 44Tran et al. (2000, 013025); 45Yao et al. (2004, 102213); 46Li and Yu (2005, 

156692); 47Henning et al. (2003, 156539); 48Zhang and Anastasio (2003, 157182); 49Emmenegger et al. (2007, 156418); 50 Watson et al. (1989, 046318); 51Greaves et al. (1985, 156494); 52Waterman et al. 

(2000, 157116); 53Waterman et al. (2001, 157117); 54Falkovich and Rudich (2001, 156427); 55Chow et al. (2007, 157209); 56Miguel et al. (2004, 123260); 57Crimmins and Baker (2006, 097008); 58Ho and Yu 

(2004, 156551); 59Jeon et al. (2001, 016636); 60Mazzoleni et al. (2007, 098038); 61Poore (2002, 051444); 62Butler et al. (2003, 156313); 63Chow et al. (2006, 146622); 64Russell et al. (2004, 082453); 65Grover 

et al. (2006, 138080); 66Grover et al. (2005, 090044); 67Schwab et al. (2006, 098449); 68Hauck et al. (2004, 156525); 69Jaques et al. (2004, 155878); 70Rupprecht and Patashnick (2003, 157207); 71Pang et al 

(2002, 030353); 72Eatough et al. (2001, 010303); 73Lee et al. (2005, 128139); 74Lee et al. (2005, 156680); 75Babich et al. (2000, 156239); 76Lee et al. (2005, 155925); 77Lee et al. (2005, 128139); 78Anderson 

and Ogren (1998, 156213); 79Chung et al. (2001, 156357); 80Kidwell and Ondov (2004, 155898); 81Lithgow et al. (2004, 126616); 82Weber et al. (2003, 157129); 83Harrison et al. (2004, 136787); 84Rattigan et 

al. (2006, 115897); 85Wittig et al. (2004, 103413); 86Vaughn et al. (2005, 157089); 87Chow et al. (2005, 099030); 88Weber et al (2001, 024640) ; 89Schwab et al. (2006, 098785); 90Lim et al. (2003, 037037); 91Watson and Chow (2002, 037873); 92Venkatachari et al. (2006, 105918); 93Bae et al. (2004, 156243); 94Arhami et al. (2006, 156224); 95Park et al. (2005, 156843); 96Bae et al. (2004, 098680); 97Chow et al. 

(2006, 156350); 98Arnott et al. (2005, 156227); 99Bond et al. (1999, 156281); 100Virkkula et al. (2005, 157097); 101Petzold et al. (2002, 156863); 102Park et al. (2006, 098104); 103Arnott et al. (1999, 020650); 104Peters et al. (2001, 016925); 105Pitchford et al. (1997, 156872); 106Rees et al. (2004, 097164); 107Watson et al. (2000, 010354); 108Lee et al. (2005, 156680); 109Hering et al. (2004, 155837); 110Watson et al. 

(1998, 198805); 111Chakrabarti et al. (2004, 157426); 112Mathai et al. (1990, 156741); 113Kidwell and Ondov (2001, 017092); 114Stanier et al. (2004, 095955); 115Khlystov et al. (2005, 156635); 116Takahama et 

al. (2004, 157038); 117Chow et al. (2005, 156348); 118Zhang et al. (2002, 157181); 119Subramanian et al. (2004, 081203); 120Chow et al. (2006, 155207); 121Birch and Cary (1996, 026004); 122Birch (1998, 

024953); 123Birch and Cary (1996, 002352); 124NIOSH (1996, 156810); 125NIOSH (1999, 156811); 126Chow et al. (1993, 077459); 127Chow et al. (2007, 156354); 128Ellis and Novakov (1982, 156416); 129Peterson and Richards (2002, 156861); 130Schauer et al. (2003, 037014); 131Middlebrook et al. (2003, 042932); 132Wenzel et al. (2003, 157139); 133Jimenez et al. (2003, 156611); 134Phares et al. (2003, 

156866); 135Qin and Prather (2006, 156895); 136Zhang et al. (2005, 157185); 137Bein et al. (2005, 156265); 138Drewnick et al. (2004, 155754); 139Drewnick et al. (2004, 155755); 140Lake et al. (2003, 156669); 141Lake et al. (2004, 088411) 

Source: Chow et al. (2008, 156355)

December 2009 A-19 
Table A-10. Measurement and analytical specifications for continuous carbon. 

Instrument and Measurement Principle Averaging 

Time 

Analytical 

Accuracya Precision MDL Interferences Comparability Data Completeness

PARTICLE COLLECTION ON IMPACTOR FOLLOWED BY FLASH VOLATILIZATION INSTRUMENT 

Aerosol Dynamic Inc. continuous carbon 

analyzer (ADI-C) 

Particle collection by impaction followed by 

flash oxidation and detection of the evolved 

gases by a non-dispersive infrared CO2 

analyzer. OC is estimated as twice the 

oxidizable carbon. EC is not quantified. 

10 min N/A N/A 

OC: 

2 μg/m3

EC, TC: 

not 

applicable, 

since it 

measures 

only OC 90 

N/A 

15-22% lower 

OC than that by 

R&P-5400 and 

RU-OGI 

83%7

 

PARTICLE COLLECTION ON FILTER / IMPACTOR FOLLOWED BY HEATING/ANALYSIS INSTRUMENTS 

Rupprecht and Patashnick 5400 continuous 

ambient carbon analyzer (R&P-5400) 

Particles collected on an impactor, which is 

heated to 275 °C to 350 °C, then to 700 °C 

after sample collection is complete. Evolved 

CO2 is measured by an infrared detector. OC 

is defined as the carbon measured at the 

lower temperature, and EC is the remaining 

carbon measured at the higher temperature. 

1 h N/A N/A 

OC: 

0.5 μg/m3

EC: 

0.5 μg/m3 

TC: 

0.5 μg/m3 90 

N/A 

20 to 60% lower 

TC than filter TC 

by TOR or 

TOT.91,92 

56-60%6,91 

Rutgers University-Oregon Graduate Institute 

(RU-OGI) in-situ thermal/optical transmittance 

carbon analyzer. 

Air is sampled through a quartz-fiber filter for 

1 h and then analyzed by heating through 

different temperature steps to determine OC 

and EC. Sample flow is pre-split into two 

identical systems that alternate every hour 

between sampling and analysis mode to 

achieve continuous measurements. 

30 min N/A 3%b,7 

OC: 

0.3 μg/m3

EC: 

0.5 μg/m3

TC: 

0.4 μg/m3 90 

N/A 

8% higher OC 

and 20% lower 

EC than R&P5400 90 

86%7

 

Sunset semi-continuous realtime carbon 

aerosol analysis instrument (Sunset OCEC) 

Particles collected on a quartz-fiber filter are 

subject to heating temperature ramps following 

the NIOSH 5040 TOT protocol and the 

resulting CO2 is analyzed by nondispersive 

infrared (NDIR) detector to quantify OC and 

EC. Instrument is alternated between sampling 

and analytical mode. 

1 h N/A 

OC: 10%c 

EC: 20%c

TC: 10%c

93,94 

OC: N/A 

EC: N/A 

TC: 

0.4 μg/m3 

(1-h avg)95 

N/A 

Within 7 to 25% 

of filter OC and 

EC and within 

15% for TC. 

Wide variation 

due to differentces in temperature and 

analysis 

protocols. 92,95,96 

80-89%6,95 

LIGHT ABSORPTION INSTRUMENTS 

Aethalometer (AE-16, AE-21, AE-31) 

Attenuation of light transmitted through a 

quartz-fiber filter tape that continuously samples aerosol is measured and converted to a 

BC mass concentration using σabs of 14625/λ

(m2

/g). 

5 min N/A 5 to 

10%d,7,97 BCe

: 0.1 μg/m3 90

Subject to multiple scattering 

effects by particle and filter 

matrix resulting 

in absorption 

enhancement. 

Empirical corrections have 

been proposed 

98 that can correct for such 

effects. 

Within ± 25% of 

RU-OGI, Sunset 

and filter EC by 

TOR/TOT.90-92 

75-90%6

 

December 2009 A-20 
Instrument and Measurement Principle Averaging 

Time 

Analytical 

Accuracya Precision MDL Interferences Comparability Data Completeness

Particle Soot Absorption Photometer (PSAP) 

Attenuation of light transmitted through a 

glass-fiber filter that continuously samples 

aerosol is measured to quantify light 

absorption (babs). 

1 min N/A 6 to 

8%99,100 BC f: 

0.1 μg/m3 90

Instrument includes an empirical correction 

for scattering 

and loading effects 99 and 

adjustments 

have been proposed for the 

three wavelength model 

100 

~50% lower 

than AE-16, RUOGI and R&P5400 EC.90 

N/A 

Multi-Angle Absorption Photometer (MAAP) 

Light transmittance at 0° and reflectance from 

a glass-fiber filter at 130° and 165° from the 

illumination direction are used in a radiative 

transfer model to estimate babs and is 

converted to BC using σabs of 6.6 m2/g. 

1 min N/A 12%g,101

BC h

: 

0.05 μg/m3

(or 

babs = 0.33 

Mm-1 for 

10-min 

avg) 

0.02 μg/m3

(or 

babs = 0.13 

Mm-1 for 

30-min 

avg)101 

The instrument 

is designed to 

minimize multiple scattering 

and loading effects by measuring both 

transmittance 

and reflectance 

and using a 

two-stream 

approximation 

radiative 

transfer model 

to calculate babs.

Within 18% of 

filter EC by 

IMPROVE_TOR 

(R2

 = 0.96) and 

up to 40% 

higher than 

Sunset EC. 102 

N/A 

DRI Photoacoustic Analyzer (DRI-PA) 

Light absorption by particles in air results in a 

heating of the surrounding air. The expansion 

of the heated air produces an acoustic (sound 

wave) signal which is detected by a microphone to determine babs, which is converted 

to BC using σabs = 5 m2

/g for the 1047 nm 

instrument and σabs = 10 m2/g for the 532 nm 

instrument. 

5 s N/A N/A 

BC i: 

0.04 μg/m3

(or 

babs = 0.4 

Mm-1 for 

10-min 

avg) at 

532 nm103 

At 532 nm, 

absorbance by 

NO2 interferes 

with that by 

particles. Accounted by 

either removing 

NO2 from sample line using 

denuders or by 

doing a periodic 

background 

(particle-free 

air) subtraction. 

Good correlation 

(R2

 >0.80), but 

more than 40% 

lower than 

aethalometer, 

MAAP and filter 

IMPROVE_TOR 

EC. Suggests 

need for a 

different σabs. 102 

N/A 

PHOTO-IONIZATION INSTRUMENTS 

Photoionization monitor for polycyclic aromatic 

hydrocarbons (PAS-PAH) The air stream is 

exposed to UV radiation, which ionizes the 

particle-bound PAH molecules. The charged 

particles are collected on a filter element and 

the piezoelectric current is proportional to the 

particle-bound PAH. 

5 min N/A N/A ~3 ng/m3 j,k N/A N/A >91%6

December 2009 A-21 
Instrument and Measurement Principle Averaging 

Time 

Analytical 

Accuracya Precision MDL Interferences Comparability Data Completeness

a

 Accuracy is the ability of analytical methods to quantify the observable of a standard reference material correctly; does not refer to measurement accuracy, since no standards are available. b

 No specific details on how the precision was estimated; appears to be based on replicate analysis, may not represent overall co-located measurement precision c

 Co-located precision estimates based on variation in avg ratios of replicate analysis using laboratory instrument and regression slopes (Slopes for OC = 1.01, EC = 0.82, TC = 0.94; R2 = 0.97-0.99) of colocated field measurements. 

d Estimated using co-located AE-21 and AE-31 BC measurements at Fresno, CA.97 

e

 While the default manufacturer recommended conversion factor (or mass absorption efficiency, σabs) is 16.6 m2/g at 880 nm, Lim et al. (2003, 037037) assumed a value of 12.6 m2/g. f

 Assuming a σabs of 10 m2/g. g

 Co-located precision estimate based on the variability of the avg ratio (0.99 ± 0.12). 

h

 Assuming a σabs of 6.5 m2/g. i Assuming a σabs of 10 m2

/g at 532 nm and 5 m2/g at 1047 nm. j

 Specified by manufacturer as “lower threshold”; needs to be calibrated with site-specific PAH. Typically used as a relative measure in terms of electrical output in femtoamps. k

 Manufacturer specified measurement parameter 

N/A: Not available. 

1

Chow (1995, 077012); 2Watson and Chow (2001, 157123); 3 Watson et al. (1983, 045084); 4Fehsenfeld et al. (2004, 157360); 5Solomon et al. (2001, 157193); 6Watson et al. (2005, 157124); 7Mikel (2001, 

156762); 8

Watson et al. (1999, 020949); 9Solomon and Sioutas (2006, 156995); 10Graney et al. (2004, 053756); 11Tanaka et al. (1998, 157041); 12Pancras et al. (2005, 098120); 13John et al. (1988, 045903); 14Hering and Cass (1999, 084958); 15Fitz et al. (1989, 077387); 16Hering et al. (1988, 036012); 17Solomon et al. (2003, 156994); 18Cabada et al. (2004, 148859); 19Fine et al. (2003, 155775); 20Hogrefe et al. 

(2004, 099003); 21Drewnick et al. (2003, 099160); 22Watson et al. (2005, 157125); 23Ho et al. (2006, 156552); 24Decesari et al. (2005, 144536); 25 Mayol-Bracero et al. (2002, 045010); 26Yang et al. (2003, 

156167); 27Turšic et al. (2006, 157063); 28Mader et al.(2004, 156724); 29Xiao and Liu (2004, 056801); 30Kiss et al. (2002, 156646); 31Cornell and Jickells (1999, 156367); 32 Zheng et al. (2002, 026100); 33Fraser et al. (2002, 140741); 34 Fraser et al. (2003, 042231) 35Schauer er al. (2000, 012225); 36Fine et al. (2004, 141283); 37Yue et al. (2004, 157169); 38Rinehart et al. (2006, 115184); 39Wan and Yu (2006, 

157104); 40Poore (2000, 012839); 41Fraser et al. (2003, 040266); 42Engling et al. (2006, 156422); 43Yu et al. (2005, 157167); 44Tran et al. (2000, 013025); 45Yao et al. (2004, 102213); 46Li and Yu (2005, 

156692); 47Henning et al. (2003, 156539); 48Zhang and Anastasio (2003, 157182); 49Emmenegger et al. (2007, 156418); 50 Watson et al. (1989, 046318); 51Greaves et al. (1985, 156494); 52Waterman et al. 

(2000, 157116); 53Waterman et al. (2001, 157117); 54Falkovich and Rudich (2001, 156427); 55Chow et al. (2007, 157209); 56Miguel et al. (2004, 123260); 57Crimmins and Baker (2006, 097008); 58Ho and Yu 

(2004, 156551); 59Jeon et al. (2001, 016636); 60Mazzoleni et al. (2007, 098038); 61Poore (2002, 051444); 62Butler et al. (2003, 156313); 63Chow et al. (2006, 146622); 64Russell et al. (2004, 082453); 65Grover 

et al. (2006, 138080); 66Grover et al. (2005, 090044); 67Schwab et al. (2006, 098449); 68Hauck et al. (2004, 156525); 69Jaques et al. (2004, 155878); 70Rupprecht and Patashnick (2003, 157207); 71Pang et al 

(2002, 030353); 72Eatough et al. (2001, 010303); 73Lee et al. (2005, 128139); 74Lee et al. (2005, 156680); 75Babich et al. (2000, 156239); 76Lee et al. (2005, 155925); 77Lee et al. (2005, 128139); 78Anderson 

and Ogren (1998, 156213); 79Chung et al. (2001, 156357); 80Kidwell and Ondov (2004, 155898); 81Lithgow et al. (2004, 126616); 82Weber et al. (2003, 157129); 83Harrison et al. (2004, 136787); 84Rattigan et 

al. (2006, 115897); 85Wittig et al. (2004, 103413); 86Vaughn et al. (2005, 157089); 87Chow et al. (2005, 099030); 88Weber et al (2001, 024640) ; 89Schwab et al. (2006, 098785); 90Lim et al. (2003, 037037); 91Watson and Chow (2002, 037873); 92Venkatachari et al. (2006, 105918); 93Bae et al. (2004, 156243); 94Arhami et al. (2006, 156224); 95Park et al. (2005, 156843); 96Bae et al. (2004, 098680); 97Chow et al. 

(2006, 156350); 98Arnott et al. (2005, 156227); 99Bond et al. (1999, 156281); 100Virkkula et al. (2005, 157097); 101Petzold et al. (2002, 156863); 102Park et al. (2006, 098104); 103Arnott et al. (1999, 020650); 104Peters et al. (2001, 016925); 105Pitchford et al. (1997, 156872); 106Rees et al. (2004, 097164); 107Watson et al. (2000, 010354); 108Lee et al. (2005, 156680); 109Hering et al. (2004, 155837); 110Watson et al. 

(1998, 198805); 111Chakrabarti et al. (2004, 157426); 112Mathai et al. (1990, 156741); 113Kidwell and Ondov (2001, 017092); 114Stanier et al. (2004, 095955); 115Khlystov et al. (2005, 156635); 116Takahama et 

al. (2004, 157038); 117Chow et al. (2005, 156348); 118Zhang et al. (2002, 157181); 119Subramanian et al. (2004, 081203); 120Chow et al. (2006, 155207); 121Birch and Cary (1996, 026004); 122Birch (1998, 

024953); 123Birch and Cary (1996, 002352); 124NIOSH (1996, 156810); 125NIOSH (1999, 156811); 126Chow et al. (1993, 077459); 127Chow et al. (2007, 156354); 128Ellis and Novakov (1982, 156416); 129Peterson and Richards (2002, 156861); 130Schauer et al. (2003, 037014); 131Middlebrook et al. (2003, 042932); 132Wenzel et al. (2003, 157139); 133Jimenez et al. (2003, 156611); 134Phares et al. (2003, 

156866); 135Qin and Prather (2006, 156895); 136Zhang et al. (2005, 157185); 137Bein et al. (2005, 156265); 138Drewnick et al. (2004, 155754); 139Drewnick et al. (2004, 155755); 140Lake et al. (2003, 156669); 141Lake et al. (2004, 088411) 

Source: Chow et al. (2008, 156355)

December 2009 A-22 
Table A-11. Summary of mass measurement comparisons. 

Site / Period / Sampler / Configuration Summary of Findings 

1. Birmingham, AL (11/04/96 To 11/23/96) 

2. Denver-Adams City, CO (12/11/96 To 1/7/97) 

3. Bakersfield, CA (1/21/97 To 3/19/97) 

4. Denver-Welby, Co (12/12/96 To 12/21/96) 

5. Phoenix, AZ (12/06/96 To 12/21/96) 

6. Azusa, CA (3/25/97 To 5/19/97) 

7. Research Triangle Park (RTP), NC (1/17/97 To 8/14/97) 

8. Rubidoux, CA (1/6/99 To 2/26/99) 

9. Atlanta, GA (8/3/99 To 8/31/99) 

Sampler Flow Rate (L/Min) Filter Typea Denuderb

RAAS2.5-100 

PM2.5 FRM 16.7 Teflon (N/A) None 

RAAS2.5-300 

PM2.5 FRM 16.7 Teflon (N/A) None 

RAAS2.5-200 

PM2.5 FRM 16.7 Teflon (N/A) None 

R&P Partisol 2000 

PM2.5 FRM 16.7 Teflon (N/A) None 

R&P Partisol-plus 

2025 PM2.5 FRM 16.7 Teflon (N/A) None 

BGI PQ200 PM2.5

FRM 16.7 Teflon (N/A) None 

Sierra Instruments 

SA-244 Dichot 16.7 Teflon (N/A) None 

IMPROVE PM2.5 22.8 Teflon (N/A) None 

Harvard PM2.5

Impactor 10 Teflon (N/A) None 

Airmetrics battery 

powered PM2.5

MiniVol 

5 Teflon (N/A) None 

Peters et al. (2001, 017108)

104: Pitchford et al. (1997, 156872)105

dataset 

Co-located precision (CV) for the RAAS2.5-100 samplers ranged 

from 1.5% at Bakersfield to 6.2% at Birmingham. 

In Birmingham, CV for two co-located Harvard Impactor was 1% and 

for three Dichots was 6.2%. The IMPROVE samplers had greater 

variability, with a CV of 11.3% (Denver-Adam City) and 10.8% 

(Bakersfield). 

Partisol and RAAS showed the strongest pairwise comparison 

(slope = 1.0 ± 0.06, intercept = 0.26 ± 1.81, and correlation = 1.0), 

within the EPA equivalency criteria. Strong relationships (correlation 

>0.96; slope = 0.9-1.12, intercept < 3σ) were observed for other 

samplers in reference to the RAAS. 

At Denver-Welby, 6 RAAS samplers were deployed (3 with and 3 

without temperature compensation for flow control). The units with 

temperature compensation had a positive bias relative to the nontemperature compensated units. 

Non-FRM samplers did not meet the EPA equivalency criteria, 

despite strong linear relationships with the FRM sampler. 

Peters et al. (2001, 016925)

104: RTP 97 dataset 

CV was 1.7%, 2.3%, 3.4%, 6.4% for the PQ200, Partisol 2000, 

RAAS2.5100, and Dichot, respectively. Dichot flows were valve 

controlled and set visually by the operator using rotameters. 

Good one-to-one correspondence was observed for FRM 

comparisons. The FRM averages were within -1.2% to 3.2%, within 

the acceptable ± 10% range 

Peters et al. (2001, 016925)

104: Rubidoux 99 and Atlanta 99 

dataset 

In Rubidoux, the precision for PQ200 was 6.1%, higher than at RTP 97. In Atlanta, the grouped data from PQ200, RAAS2.5-300, and 

Partisol yielded a precision of 1.7%. 

Linear regression results met the EPA equivalency criteria for all 

FRMs. 

Atlanta Supersite, GA: 8/3/99 to 9/1/99 

Four km NW of downtown, within 200 m of a bus maintenance yard and several 

warehouse facilities, representative of a mixed commercial-residential neighborhood. 

Sampler 

Flow 

Rate 

(L/Min) 

Filter Typea Denuderb

R&P-2000 FRM 16.7 Teflon (P) None 

RAAS-100 FRM 16.7 Teflon (P) None 

RAAS-400 24 Teflon (P) None 

SASS 6.7 Teflon (P) None 

MASS-400 16.7 Teflon (P) Na2CO3

R&P-2300 10 Teflon (P) None 

R&P-2025 Dichot: 

 PM2.5 15 Teflon (P) None 

 PM10-2.5 1.67 Polycarbonate None 

Na2CO3/Citric 

URG-PCM 16.7 Teflon (P) Acid 

ARA-PCM 16.7 Teflon (N/A) Na2CO3/Citric acid 

PC-BOSS 

(operated by TVA) 105 Teflon (W) CIF 

Solomon et al. (2003, 156994)

17

PM2.5 mass from individual samplers was compared to all-sampler 

avgs, called the filter relative reference (filter RR) value. Overall 

agreements were within ± 20% of filter RR. 

FRM samplers were within 3.5% of filter RR. 

Avg mass measured by RAAS-400, SASS and URG-PCM were 

within ± 10% of filter RR. Avg mass measured by MASS-400, R&P2300 and R&P-2025 dichot were greater than filter RR but 

within ± 20%. Avg mass measured by PC-BOSS (BYU) and ARAPCM were lower than filter RR within ± 10%. 

All samplers except PC-BOSS (TVA) had R2

 >0.80, relative to filter 

RR. 

While avg mass for each sampler was within 20%, daily variability 

was >50% of filter RR. 

Glycerol in the Na2CO3 denuder may have contaminated the filter in 

the MASS-400 sampler resulting in higher PM2.5 values. 

PC-BOSS samplers removed particles < 0.1 μm aerodynamic 

diameter from PM2.5 measurements. Corrections were made using 

sulfate (SO4

2–) concentrations in the major flow or immediately after 

the PM2.5 inlet, but before the flow split-up. This was insufficient to 

bring PC-BOSS mass close to filter RR. PC-BOSS was also 

equipped with upstream denuders ahead of the filters, which may 

have enhanced loss of semi-volatile components, resulting in a lower 

mass on the filter. 

December 2009 A-23 
Site / Period / Sampler / Configuration Summary of Findings 

PC-BOSS 

(operated by BYU) 150 Teflon (W) CIF 

PM2.5 Continuous 

Sampler 

Flow 

Rate 

(L/Min) 

Inlet Temperature Dryer Other 

TEOM 16.7 30 °C Nafion PM2.5

Butler et al. (2003, 156313)

62 

The sum of individual species accounted for ~78% of the RAAS-100 

FRM PM2.5 mass concentration. 

TEOM explained ~82 to 92% of the species sum of RAAS with 

R2

 = 0.86. 

Atlanta Supersite, GA: 11/21/01 to 12/23/01 

PM2.5 Sampler 

Flow 

Rate 

(L/Min) 

Filter Typea Denuderb

R&P-2025 FRM 16.7 Teflon (N/A) None 

PM2.5 Continuous 

Sampler 

Flow 

Rate 

(L/Min) 

Inlet Temperature Dryer Other 

TEOM 16.7 30 °C Nafion PM2.5 

SES-TEOM 16.7 30 °C Nafion PM2.5 

CAMM 0.3 N/A Nafion PM2.5 

RAMS 16.7 30 °C Nafion 

PM2.5 

TEA & CIF 

denuders With 

particle 

concentrator 

Radiance 

Research M903 N/A N/A Nafion bscat 

Radiance 

Research M903 N/A N/A None bscat 

Lee et al. (2005, 128139)

73 

RAMS PM2.5 adjusted using particle concentrator efficiency of 0.5. 

Good correlation between SES-TEOM and Radiance Research 

M903s (R2

 = 0.80), while medium correlation was found between 

CAMM and Radiance Research M903 (R2 = 0.64) or RAMS and 

Radiance Research M903 (R2

 = 0.63). 

CAMM = (0.75 ± 0.03) SES-TEOM + (2.51 ± 0.51); R2

 = 0.78; 

N = 196 

RAMS = (0.85 ± 0.06) SES-TEOM + (5.34 ± 1.04); R2

 = 0.52; N = 96 

RAMS = (0.91 ± 0.07) CAMM + (5.71 ± 1.20); R2

 = 0.43; N = 196 

Semi-volatile material explains the difference between RAMS and 

SES TEOM. 

CAMM = (0.75 ± 0.08) R&P-2025 FRM + (2.47 ± 1.02); R2

 = 0.76; 

N = 31 

RAMS = (0.97 ± 0.22) R&P-2025 FRM + (2.39 ± 3.42); R2

 = 0.64; 

N = 13 

SES-TEOM = (1.07 ± 0.05) R&P-2025 FRM + (-1.34 ± 0.71); 

R2

 = 0.95; N = 26 

CAMM vs. FRM yielded lower slopes (0.75) with high intercepts. 

PITTSBURGH SUPERSITE, PA: 7/1/01 to 6/1/02 6 km east of downtown in a 

park on the top of a hill 

Sampler 

Flow 

Rate 

(L/Min) 

Filter Typea Denuder 

MOUDI-110 30 Teflon (P,d) None 

And-241 Dichot 16.7 Teflon (P None 

R&P-2000 PM2.5

FRM 16.7 Teflon (W) None 

PM2.5 Continuous 

Sampler 

Flow 

Rate 

(L/Min) 

Inlet Temperature Dryer Other 

SES-TEOM 16.7 30 °C Nafion PM2.5 

DAASS N/A 30 °C Nafion or 

None PM2.5 

Cabada et al. (2004, 148859)

18: Rees et al. (2004, 097164)106

MOUDI PM10 = 0.80 Dichot PM10, R2 = 0.85 

MOUDI PM2.5 = 1.03 Dichot PM2.5, R2

 = 0.78 

MOUDI PM2.5 = 1.01 FRM PM2.5, R2

 = 0.78 

Dichot PM2.5 = 0.97 FRM PM2.5 + 0.02; R2

 = 0.94 

Good agreement for PM2.5 FRM, Dichot, and MOUDI. Lower slope for 

PM10 suggests loss of coarse particles in the MOUDI sampler. 

Ultrafine (< 100 nm) mass (PM0.10) measurements had high 

uncertainties (~30%) 

Ultrafine mass by MOUDI showed no correlation with ultrafine 

volume (V0.10) by DAASS. Ratio of PM0.10/PM2.5 mass ratio showed 

reasonable agreement with volume ratio (V0.10/V2.5, R2

 = 0.55, 

slope = 0.76). Bounce of large particles to smaller stages in MOUDI 

was small, since mass ratio (PM0.10/PM2.5) did not exceed volume 

ratio (V0.10/V2.5). Low correlation between ultrafine mass and 

volume could be due to the ultrafine mass measurement uncertainty 

or due to fundamental differences in the measurement methods 

employed by MOUDI and DAASS. Ambient conditions and 

characteristics of the aerosols (such as non-spherical shapes of fresh 

particles) could also influence these estimates. 

Rees et al. (2004, 097164)

106 

SES-TEOM PM2.5 = 1.02 FRM PM2.5 + 0.65; R2 = 0.95 

Volatilization did not affect SES-TEOM performance when PM2.5

mass >20-30 μg/m3. When ambient temperature was < -6 °C, and 

when mass was low, SES-TEOM was lower (up to 50%) than FRM or 

Dichot. 

December 2009 A-24 
Site / Period / Sampler / Configuration Summary of Findings 

FRESNO SUPERSITE, CA and other CRPAQS sites; 12/2/99 to 2/3/01. Some 

comparisons included data till 12/29/03 . Fresno Supersite was located 5.5 km 

northeast of downtown in a mixed residential-commercial neighborhood. 107 

Sampler 

Flow 

Rate 

(L/Min) 

Filter Typea Denuder 

RAAS-100 PM2.5

FRM 16.7 Teflon (P) None 

RAAS-300 PM2.5

FRM 16.7 Teflon (P) None 

R&P-2000 PM2.5

FRM 16.7 Teflon (P) None 

R&P-2025 PM2.5

FRM 16.7 Teflon (P) None 

RAAS-400 PM2.5 24 Teflon (P) None 

SASS PM2.5 6.7 Teflon (P) None 

And-246 Dichot 

PM2.5 15 Teflon (P) None 

PM10-2.5 1.67 Teflon (P) None 

DRI-SFS PM2.5 113 Teflon (P) None 

MiniVol PM2.5 5 Teflon (P) None 

MOUDI-100 30 FEPb Teflon (P) None 

And-hIVOL PM10

FRM 1130 Teflon (P) None 

Chow et al. (2006, 146622)

63

PM2.5 measurements from the 11 filter samplers were within ~20% of 

each other, except for MiniVols, which were 20 to 30% lower than 

RAAS-300 FRM. 

All the FRM samplers were within ± 10% of each other. 

All the filter samplers were well correlated with each other (R2

>0.90).e 

DRI-SFS (with HNO3 denuder) and And-246 Dichot PM2.5 were lower 

(~5% and 7%, respectively, on avg) than FRM, possibly due to nitrate 

(NO3

–

 volatilization. 

Poor correlation (R2

) found between TEOM PM2.5 concentrations and 

RAAS-100 FRM. TEOM PM2.5 was lower than RAAS-100 FRM by 

22%. Heating of TEOM inlet to 50 °C resulted in loss of semi-volatile 

components such as ammonium nitrate (NH4NO3) and possibly some 

semi-volatile organic compounds. 

TEOM PM10 concentrations were 28% lower than the And-hIVOL10 

FRM on avg, ranging from 13% in summer to 43% in winter. 

TEOM was neither equivalente nor comparablee to the FRM sampler 

for PM2.5 or PM10. 

BAM PM2.5 concentrations showed high correlation (R2

 >0.90) with 

the RAAS-100 and RAAS-300 FRM samplers, with slopes ranging 

from 0.92 to 0.97. BAM PM2.5 was typically higher than FRM (17 to 

30%) except at Bakersfield, CA, where it was 21% lower, suggesting 

a BAM calibration difference between Bakersfield and other sites. 

BAM PM10 concentrations were 26% higher than And-hIVOL PM10

FRM concentration on avg (R2

 >0.92). 

Higher BAM measurements were attributed to water absorption by 

hygroscopic particles. BAM PM2.5 and PM10 deviations were larger for 

concentrations < 25 μg/m3

. 

Grover et al. (2006, 138080) 65 

PC-BOSS PM2.5 = (0.88 ± 0.04) FDMS-TEOM + (6.7 ± 4.3); R2 = 95; 

n = 29 

PC-BOSS PM2.5 = (1.11 ± 0.07) D-TEOM + (7.5 ± 6.1); R2

 = 0.90; 

n = 29 

TEOM50C PM2.5 = (0.80 ± 0.01) TEOm3

0C + (1.1 ± 3.1); R2 = 0.91; 

n = 507 

TEOm3

0C PM2.5 = (0.50 ± 0.01) FDMS-TEOM -(1.7 ± 6.9); R2 = 0.68; 

n = 516 

Heated GRIMM PM concentrations were lower than FDMS-TEOM 

and ambient temperature GRIMM, suggesting loss of semi-volatile 

matter. 

Data recovery was greater than 95% for all continuous instruments, 

except for D-TEOM, which had 86% recovery. 

Reasonable agreement was seen between FDMS-TEOM, D-TEOM, 

BAM, and GRIMM PM2.5 when semi-volatile matter was dominated by 

NH4NO3. However, the FDMS-TEOM was higher than the other 

instruments during high concentration periods, associated with days 

with a high fraction of semi-volatile organic compounds (SVOCs). 

Possible differences in SVOCs may have contributed to the 

differences between FDMS and other instruments. 

Continuous Sampler Flow Rate (L/Min) Inlet 

Temperature Dryer Other 

TEOM 16.7 50 °C None PM2.5 and PM10

BAM 16.7 Ambient None PM2.5 and PM10

Sampler Flow Rate (L/Min) Filter Typea Denuderb

PC-BOSS PM2.5 150 Teflon (W) CIF 

December 2009 A-25 
Site / Period / Sampler / Configuration Summary of Findings 

Continuous Sampler Flow Rate (L/Min) Inlet 

Temperature Dryer Other 

TEOM 16.7 50 °C None PM2.5 

TEOM 16.7 30 °C None PM2.5 

FDMSTEOM 16.7 30 °C Nafion PM2.5 

D-TEOM 16.7 30 °C Nafion PM2.5 

GRIMM1100 1.2 Ambient None bscat 

GRIMM1100 1.2 

80 °C heater, 

resulting in 

aerosol 

temperature 

Heater bscat 

BAM 16.7 Ambient None PM2.5 

HOUSTON SUPERSITE, TX; 1/1/00 to 2/28/02 

The Houston Supersite included three sites located in southeast Texas including one on the grounds 

of a municipal airport at the edge of a small community, one adjacent to the highly industrial ship 

channel and one on the grounds of a middle school in a suburban community. 

PM2.5 Sampler Flow Rate (L/Min) Filter Typea Denuder 

R&P-2025 FRM 16.7 Teflon (N/A) None 

Continuous Sampler Flow Rate (L/Min) Inlet 

Temperature Dryer Otherb

TEOM 16.7 50 °C None PM2.5 

SES-TEOM 16.7 30 °C Nafion PM2.5 

Aug-Sep ‘00 

CAMM 0.3 Ambient Nafion PM2.5 

Aug-Sep ‘00 

RAMS 16.7 30 °C Nafion 

PM2.5 

TEA & CIF 

denuders; AugSep ‘00 

Radiance Research 

M903 N/A N/A Nafion Bscat Aug-Sep ‘00 

Russell et al. (2004, 082453)

64; Lee et al. (2005, 

156680)

108 

Good correlations between 24-h SES-TEOM PM2.5

and R&P-2025 FRM mass. 

CAMM = (0.93 ± 0.03) RAMS + (3.14 ± 0.74); 

R2

 = 0.81 

SES-TEOM = (0.92 ± 0.03) RAMS + (1.52 ± 0.77); 

R2

 = 0.80 

SES-TEOM = (1.01 ± 0.03) CAMM + (-1.91 ± 0.79); 

R2

 = 0.83 

Correlation of Radiance Research M903 and SESTEOM was good (R2

 = 0.95), while that of Radiance 

Research M903 with CAMM or RAMS was poor (R2

~ 0.4). 

RAMS >SES-TEOM at high temperature and low 

RH (< 60%), suggesting loss of water and 

particulate NO3

–

 from SES-TEOM. 

CAMM = (1.02 ± 0.08) R&P-2025 + (1.62 ± 1.35); 

R2

 = 0.89 

RAMS = (1.10 ± 0.08) R&P-2025 + (0.68 ± 1.28); 

R2

 = 0.89 

SES-TEOM = (1.09 ± 0.07) R&P-2025 + 

(0.21 ± 1.27); R2

 = 0.94 

Integrated mass < Continuous PM2.5 mass. 

Difference possibly related to loss of SVOCs and 

NO3

–

 from integrated sampler 

LOS ANGELES SUPERSITE, CA; 9/01 to 8/02 

The Los Angeles Supersite consisted of multiple sampling locations in the South Coast Air Basin to 

provide wide geographical and seasonal coverage, including urban “source” sites and downwind 

“receptor” sites. 

Sampler Flow Rate (L/Min) Filter Typea Denuderb

R&P-2025 Dichot 

PM2.5 15 Teflon (P) None 

PM10-2.5 16.7 N/A None 

MOUDI-110 30 Teflon (P None 

HEADS PM2.5 10 Teflon (N/A) NaHCO3 

Continuous Sampler Flow Rate (L/Min) Inlet 

Temperature Dryer Other 

D-TEOM 16.7 30 °C Nafion PM2.5 

Nano-BAM 

(BAM-1020 with d50 

148 ± 10 nm inlet) 

16.7 Ambient None 

~150 nm cutpoint at 16.7 

L/min 

Jaques et al. (2004, 155878)

69; Hering et al. 

(2004, 155837)

109 

Dichot PM2.5 = 0.83 MOUDI + 1.23; R2 = 0.83 

(n = 37) 

Dichot PM2.5 showed higher NO3

–

 loss than MOUDI, 

consistent with anodized aluminum surfaces serving 

as efficient denuders that remove volatilized NO3

–

2,110. 

D-TEOM PM2.5 = 1.18 MOUDI – 1.28; R2

 = 0.86 

(n = 20) 

Over-estimation of D-TEOM may be due to particle 

losses in the MOUDI. 

PM2.5 by D-TEOM during ESP-off phase (net artifact 

effect) tracked well with the NO3

–

 concentrations. 

NO3

–

 vaporization from the TEOM was caused by 

the temperature of the TEOM filter (~30-50 °C) 

rather than the pressure drop across the filter. 

Vaporization from the TEOM had a time constant 

between 10 and 100 min depending on ambient and 

TEOM filter temperatures, the vapor pressure, and 

December 2009 A-26 
Site / Period / Sampler / Configuration Summary of Findings 

SMPS-3936 0.3 Ambient None 

Number to 

mass assuming spherical 

particles of 1.6 

g/cc density 

the extent of vapor saturation upstream and 

downstream of the TEOM filter. The mass measured 

during 5-min periods (ESP-on and off cycle in DTEOM) provides an estimate of the dynamic 

vaporization losses. 

Chakrabarti et al. (2004, 157426)

111 

Good agreement between MOUDI PM0.15 and NanoBAM PM0.15 (MOUDI PM0.15 = 0.97 Nano-BAM 

PM0.15 + 0.60; R2

 = 0.92; n = 24) 

Nano-BAM captured peak PM0.15 concentrations not 

quantified by SMPS. Potential particle 

agglomeration (with resulting high surface areas) 

caused SMPS to include particles in the 

accumulation- rather than ultrafine-mode, since 

mobility diameter is a function of surface area. 

RUBIDOUX, CA; 08/15/01 to 09/07/01, 07/01/03 to 07/31/03. Rubidoux is located in the eastern 

section of the South Coast Air Basin (SoCAB) in the northwest corner of Riverside County, 78 km 

downwind of the central Los Angeles metropolitan area and in the middle of the remaining agricultural 

production area in SoCAB. 

Sampler Flow Rate (L/Min) Filter Typea Denuderb

PC-BOSS PM2.5 150 Teflon (W) CIF 

R&P-2025 PM2.5 FRM 16.7 Teflon (N/A) None 

Continuous Sampler Flow Rate (L/Min) Inlet 

Temperature Dryer Other 

TEOM 16.7 50 °C None PM2.5 

FDMS-TEOM 16.7 30 °C Nafion PM2.5 

D-TEOM 16.7 30 °C Nafion PM2.5 

RAMS 16.7 30 °C Nafion PM2.5 

Denuders used

CAMM 0.3 N/A None PM2.5 

Radiance Research 

M903 N/A N/A Nafion bscat 

Radiance Research 

M903 N/A N/A None bscat 

Grover et al. (2005, 090044)

66 (2003 

measurements): 

D-TEOM = (0.98 ± 0.02) FDMS-TEOM + 

(-0.6 ± 5.3); R2

=0.85; n = 426; excludes 38 data 

points when FDMS-TEOM PM2.5 was higher than DTEOM PM2.5 by ~21 μg/m3

. 

RAMS = (0.93 ± 0.02) FDMS-TEOM + (2.4 ± 8.2); 

R2

 = 0.81; n = 337 

FDMS-TEOM = (0.96 ± 0.06) PC-BOSSconstructed 

mass + (-0.3 ± 3.9); R2

 = 0.90; n = 33 

R&P-2025 FRM = (0.96 ± 0.06) FDMS-TEOM + 

(-9.3 ± 3.9); R2

 = 0.90; n = 29 

The R&P-2025 FRM PM2.5 was, on avg, ~32% lower 

than FDMSTEOM. Losses of NH4NO3 and organics 

can account for the difference. 

TEOM @ 50 °C PM2.5 was consistently lower than 

FDMS-TEOM, DTEOM or RAMS and was, on avg, 

~ 50% lower than FDMS-TEOM. This difference is 

due to loss of semi-volatile NO3- and organics from 

the heated TEOM. 

FDMS-TEOM and D-TEOM needed little attention 

from site operators. 

Lee et al. (2005, 155925)

76 (2001 measurements) 

D-TEOM PM2.5 and Radiance Research M903s light 

scattering (with and without dryers) showed good 

correlation. 

D-TEOM = (3.69 ± 0.09) Radiance Research 

M903no-dryer + (2.74 ± 0.89); R2

 = 0.84; n = 299 

D-TEOM = (3.79 ± 0.10) Radiance Research 

M903dryed + (4.08 ± 0.84); R2

 = 0.83; n = 312 

Radiance Research M903no-dryer = (1.03 ± 0.01) 

Radiance Research M903dryed + (0.34 ± 0.05); 

R2

 = 0.98; n = 513; absorbed water did not affect 

relationship to PM2.5. 

CAMM and RAMS compared poorly (R2

 = 0 to 0.25) 

with D-TEOM, Radiance Research M903s and 

among themselves. 

RAMS correlated well with D-TEOM for PM2.5 

>30 μg/m3 due to RAMS’s efficient particle collection 

of larger particle sizes (historically associated with 

high mass loadings at this site) in the PM2.5 size 

range. 

D-TEOM PM2.5 correlated well with ADI-N sized NO3

(R2

 = 0.62) and OC by Sunset OCEC (R2 = 0.61) 

suggesting that D-TEOM measured PM2.5 mass with 

minimum loss of SVOCs. RAMS showed R2

 of 0.20 

(NO3

–

) to 0.30 (OC), while CAMM showed no 

correlation. 

December 2009 A-27 
Site / Period / Sampler / Configuration Summary of Findings 

LINDON, UT; 01/29/03 to 02/12/03 

Sampler Flow Rate (L/Min) Filter Typea Denuderb

PC-BOSS PM2.5 150 Teflon (W) CIF 

CONTINUOUS 

SAMPLER FLOW RATE (L/MIN) INLET TEMPERATURE DRYER OTHER 

TEOM 16.7 30 °C None PM2.5 

FDMS-TEOM 16.7 30 °C Nafion PM2.5 

RAMS 16.7 30 °C Nafion PM2.5 Denuder 

used 

Grover et al. (2005, 090044)

66 

RAMS required regular maintenance. 

RAMS = (0.92 ± 0.03) FDMS-TEOM + (1.3 ± 3.9); 

R2

 = 0.69; n = 332 

PC-BOSS constructed mass = (0.89 ± 0.21) FDMSTEOM + (1.8 ± 2.8); R2

 = 0.66; n = 11 

TEOM @ 30 °C PM2.5 was consistently lower than 

FDMS-TEOM and the difference was consistent 

with concentrations SVOCs and NH4NO3 measured 

by PC-BOSS. 

PHILADELPHIA, PA; 07/02/01 to 08/01/01 At water treatment center in a grassy field surrounded by 

mixed deciduous and pine trees on three sides and a river on the other. Within 0.5 km of Interstate I95 and within 30 km from downtown Philadelphia. 

Sampler Flow Rate (L/Min) Filter Typea Denuderb

Harvard Impactor PM2.5 10 Teflon (N/A) N/A 

Continuous Sampler Flow Rate (L/Min) Inlet Temperature Dryer Other 

SES-TEOM 16.7 35 °C Nafion PM2.5 

CAMM 0.3 N/A Nafion PM2.5 

RAMS 16.7 30 °C Nafion 

PM2.5 TEA & 

CIF denuders 

With particle 

concentrator 

Radiance Research 

M903 N/A N/A Nafion bscat 

Radiance Research 

M903 N/A N/A None bscat 

Lee et al. (2005, 128139)

73 

Radiance Research M903dryer = (0.78 ± 0.01) 

Radiance Research M903no dryer + (0.30 ± 0.03); 

R2

 = 0.95 

Radiance Research M903s vs. CAMM, R2

 = 0.78 

Radiance Research M903s vs. RAMS, R2

 = 0.63 

Radiance Research M903s vs. SES-TEOM, 

R2

 = 0.72 

CAMM = (0.60 ± 0.03) SES-TEOM + (2.0 ± 0.42); 

R2

 = 0.71; N = 185 

RAMS = (0.71 ± 0.04) SES-TEOM + (2.51 ± 0.59); 

R2

 = 0.63; N = 185 

RAMS = (0.93 ± 0.06) CAMM + (2.44 ± 0.68); 

R2

 = 0.55; N = 185 

Both RAMS and CAMM under-measured ambient 

PM2.5. 

CAMM = (0.70 ± 0.06) HI + (0.16 ± 0.96); R2

 = 0.87; 

N = 22 

SES-TEOM = (1.0 ± 0.10) HI + (-0.68 ± 1.74); 

R2

 = 0.89; N = 15 

BALTIMORE SUPERSITE, MD; 05/17/01 to 06/11/01. Located near a freeway and bus yard. 

Sampler Flow Rate (L/Min) Filter Type Denuder 

RAAS-100 PM2.5 FRM 16.7 Teflon None 

Continuous Sampler Flow Rate (L/Min) Inlet Temperature Dryer Other 

SES-TEOM 16.7 35 °C Nafion PM2.5 

CAMM 0.3 N/A Nafion PM2.5 

RAMS 16.7 30 °C Nafion 

PM2.5 TEA & 

CIF denuders; 

No particle 

Radiance Research 

M903 N/A N/A Nafion bscat 

Radiance Research 

M903 N/A N/A None bscat 

Lee et al. (2005, 128139)

73 

Radiance Research M903dryed = (0.65 ± 0.02) 

Radiance Research M903no dryer + (1.80 ± 0.20); 

R2

 = 0.75, suggesting influence from particle-bound 

water. 

High correlation (R2

 = 0.75) between Radiance 

Research M903s. 

Poor correlation among the continuous instruments. 

Radiance Research M903s did not follow PM2.5

concentrations measured by other continuous 

instruments. 

CAMM = (0.32 ± 0.07) SES-TEOM + (9.45 ± 1.61); 

R2

 = 0.14; N = 120 

RAMS = (0.82 ± 0.10) SES-TEOM + (6.41 ± 2.09); 

R2

 = 0.38; N = 120 

RAMS = (0.71 ± 0.12) CAMM + (11.3 ± 2.23); 

R2

 = 0.21; N = 120 

CAMM = (0.80 ± 0.29) RAAS-100 FRM + 

(-0.83 ± 5.85); R2

 = 0.60; N = 7 

RAMS = (1.05 ± 0.12) RAAS-100 FRM + 

(4.80 ± 2.60); R2

 = 0.90; N = 11 

SES-TEOM = (0.86 ± 0.10) RAAS-100 FRM + 

(2.96 ± 1.99); R2

 = 0.90; N = 10 

December 2009 A-28 
Site / Period / Sampler / Configuration Summary of Findings 

SEATTLE, WA; 01/28/01 to 02/21/01 

Urban area near major highway and interstate, 8 km southeast of downtown. 

SAMPLER FLOW RATE (L/MIN) 

Lee et al. (2005, 156680)

108

Radiance Research M903dryed = 0.94 ± 0.00 

Radiance Research M903no dryer; R2

FILTER TYPE = 1.0. a DENUDERb

MASS PM2.5 16.7 Teflon (N/A) Na2CO3 

Continuous Sampler 

Correlation of Radiance Research M903 vs. SESTEOM, R2

 = 0.80, while that of Radiance Research 

M903 with CAMM was R2

 = 0.84 and with RAMS 

was R2 = 0.72. 

CAMM = (1.07 ± 0.05) RAMS + (1.03 ± 0.55); 

R2

 = 0.61 

SES-TEOM = (0.95 ± 0.03) RAMS + (1.24 ± 0.38); 

R2

 = 0.72 

SES-TEOM = (0.87 ± 0.03) CAMM + (0.55 ± 0.37); 

R2

 = 0.74 

SES-TEOM likely lost semi-volatile organic matter. 

Continuous PM2.5 samplers were similar to filter 

PM2.5 sampler. Number of samples was small (~7). 

Some SES-TEOM mass values were less than 

MASS filter values suggesting that loss of mass is 

likely for a SES-TEOM at 30°C, particularly during 

the cold season. 

Flow Rate (L/Min) Inlet Temperature Dryer Other 

SES-TEOM 16.7 30 °C Nafion PM2.5 

CAMM 0.3 Ambient Nafion PM2.5 

RAMS 16.7 30 °C Nafion PM2.5 TEA & 

CIF denuders 

Radiance Research 

M903 N/A N/A Nafion bscat 

Radiance Research 

M903 N/A N/A None bscat 

NEW YORK SUPERSITE, NY; 01/01/03 to 12/31/04 

Urban site located at Queens College, NY, about 14 km west of Manhattan, within 2 km of freeways, 

and within 12 km of international airports. A rural site was located at Pinnacle State Park surrounded 

by golf course, picnic areas, undeveloped forest lands, and no major cities within 15 km. 

Sampler Flow Rate (L/Min) Filter Typea Denuderb

R&P-2025 PM2.5 FRM 16.7 Teflon (N/A) None 

R&P-2300 PM2.5 16.7 Teflon (N/A) None 

Continuous Sampler Flow Rate (L/Min) Inlet Temperature Dryer Other 

TEOM 16.7 50 °C None PM2.5 

FDMS-TEOM 16.7 30 °C Nafion PM2.5 

BAM 16.7 “smart” heater on @ RH >44% PM2.5 

Schwab et al. (2006, 098449)

67 

FDMS-TEOM had operational difficulties resulting in 

low data capture (65% at urban site and 57% at 

rural site). 

BAM had data captures greater than 95% at both 

sites. 

Urban site: 

BAM = (1.02 ± 0.02) FDMS-TEOM + 1.72; 

R2

 = 0.93; n = 244 

FDMS-TEOM = (1.25 ± 0.02) FRM – (0.63 ± 0.26); 

R2

 = 0.95; n = 238 

BAM = (1.28 ± 0.03) FRM + (1.27 ± 0.38); 

R2

 = 0.88; n = 320 

Rural site: 

FDMS-TEOM = (1.09 ± 0.02) FRM – (0.004 ± 0.18); 

R2

 = 0.95; n = 349 

PM2.5 FDMS-TEOM >FRM >TEOM50°C, suggesting 

that FRM captured a fraction, but not all, of the 

volatile components. TEOM50°C volatilizes PM2.5, 

particularly during winter. 

December 2009 A-29 
Site / Period / Sampler / Configuration Summary of Findings 

a

Filter Manufacturer in parentheses - W: Whatman, Clifton, NJ; P: Pall-Gelman, Ann Arbor, MI; S: Schleicher & Schnell. Keene, NH; N/A: not available or not reported. b

Na2CO3: Sodium carbonate; NaHCO3: Sodium bicarbonate CIF: Charcoal Impregnated Filter; FEP: Fluorinated Ethylene Propylene copolymer; TEA: Triethanolamine; TSP: Total Suspended PM. c

37 mm filter. 

d

37 mm after-filter for stages smaller than 0.16 μm and 47-mm for higher stages. 

e

Equivalence requires correlation coefficient (r) ≥ 0.97, linear regression slope 1.0 ± 0.05 and an intercept 0 ± 1 μg/m3; Comparability requires r>0.9 and linear regression slope equal 1 within 3 standard errors 

and intercept equal zero within 3 standard errors; Predictability requires r>0.9. 91, 112 

1

Chow (1995, 077012); 2Watson and Chow (2001, 157123); 3 Watson et al. (1983, 045084); 4Fehsenfeld et al. (2004, 157360); 5Solomon et al. (2001, 157193); 6Watson et al. (2005, 157124); 7Mikel (2001, 

156762); 8

Watson et al. (1999, 020949); 9Solomon and Sioutas (2006, 156995); 10Graney et al. (2004, 053756); 11Tanaka et al. (1998, 157041); 12Pancras et al. (2005, 098120); 13John et al. (1988, 045903); 14Hering and Cass (1999, 084958); 15Fitz et al. (1989, 077387); 16Hering et al. (1988, 036012); 17Solomon et al. (2003, 156994); 18Cabada et al. (2004, 148859); 19Fine et al. (2003, 155775); 20Hogrefe et al. 

(2004, 099003); 21Drewnick et al. (2003, 099160); 22Watson et al. (2005, 157125); 23Ho et al. (2006, 156552); 24Decesari et al. (2005, 144536); 25 Mayol-Bracero et al. (2002, 045010); 26Yang et al. (2003, 

156167); 27Turšic et al. (2006, 157063); 28Mader et al.(2004, 156724); 29Xiao and Liu (2004, 056801); 30Kiss et al. (2002, 156646); 31Cornell and Jickells (1999, 156367); 32 Zheng et al. (2002, 026100); 33Fraser et al. (2002, 140741); 34 Fraser et al. (2003, 042231) 35Schauer er al. (2000, 012225); 36Fine et al. (2004, 141283); 37Yue et al. (2004, 157169); 38Rinehart et al. (2006, 115184); 39Wan and Yu (2006, 

157104); 40Poore (2000, 012839); 41Fraser et al. (2003, 040266); 42Engling et al. (2006, 156422); 43Yu et al. (2005, 157167); 44Tran et al. (2000, 013025); 45Yao et al. (2004, 102213); 46Li and Yu (2005, 

156692); 47Henning et al. (2003, 156539); 48Zhang and Anastasio (2003, 157182); 49Emmenegger et al. (2007, 156418); 50 Watson et al. (1989, 046318); 51Greaves et al. (1985, 156494); 52Waterman et al. 

(2000, 157116); 53Waterman et al. (2001, 157117); 54Falkovich and Rudich (2001, 156427); 55Chow et al. (2007, 157209); 56Miguel et al. (2004, 123260); 57Crimmins and Baker (2006, 097008); 58Ho and Yu 

(2004, 156551); 59Jeon et al. (2001, 016636); 60Mazzoleni et al. (2007, 098038); 61Poore (2002, 051444); 62Butler et al. (2003, 156313); 63Chow et al. (2006, 146622); 64Russell et al. (2004, 082453); 65Grover 

et al. (2006, 138080); 66Grover et al. (2005, 090044); 67Schwab et al. (2006, 098449); 68Hauck et al. (2004, 156525); 69Jaques et al. (2004, 155878); 70Rupprecht and Patashnick (2003, 157207); 71Pang et al 

(2002, 030353); 72Eatough et al. (2001, 010303); 73Lee et al. (2005, 128139); 74Lee et al. (2005, 156680); 75Babich et al. (2000, 156239); 76Lee et al. (2005, 155925); 77Lee et al. (2005, 128139); 78Anderson 

and Ogren (1998, 156213); 79Chung et al. (2001, 156357); 80Kidwell and Ondov (2004, 155898); 81Lithgow et al. (2004, 126616); 82Weber et al. (2003, 157129); 83Harrison et al. (2004, 136787); 84Rattigan et 

al. (2006, 115897); 85Wittig et al. (2004, 103413); 86Vaughn et al. (2005, 157089); 87Chow et al. (2005, 099030); 88Weber et al (2001, 024640) ; 89Schwab et al. (2006, 098785); 90Lim et al. (2003, 037037); 91Watson and Chow (2002, 037873); 92Venkatachari et al. (2006, 105918); 93Bae et al. (2004, 156243); 94Arhami et al. (2006, 156224); 95Park et al. (2005, 156843); 96Bae et al. (2004, 098680); 97Chow et al. 

(2006, 156350); 98Arnott et al. (2005, 156227); 99Bond et al. (1999, 156281); 100Virkkula et al. (2005, 157097); 101Petzold et al. (2002, 156863); 102Park et al. (2006, 098104); 103Arnott et al. (1999, 020650); 104Peters et al. (2001, 016925); 105Pitchford et al. (1997, 156872); 106Rees et al. (2004, 097164); 107Watson et al. (2000, 010354); 108Lee et al. (2005, 156680); 109Hering et al. (2004, 155837); 110Watson et al. 

(1998, 198805); 111Chakrabarti et al. (2004, 157426); 112Mathai et al. (1990, 156741); 113Kidwell and Ondov (2001, 017092); 114Stanier et al. (2004, 095955); 115Khlystov et al. (2005, 156635); 116Takahama et 

al. (2004, 157038); 117Chow et al. (2005, 156348); 118Zhang et al. (2002, 157181); 119Subramanian et al. (2004, 081203); 120Chow et al. (2006, 155207); 121Birch and Cary (1996, 026004); 122Birch (1998, 

024953); 123Birch and Cary (1996, 002352); 124NIOSH (1996, 156810); 125NIOSH (1999, 156811); 126Chow et al. (1993, 077459); 127Chow et al. (2007, 156354); 128Ellis and Novakov (1982, 156416); 129Peterson and Richards (2002, 156861); 130Schauer et al. (2003, 037014); 131Middlebrook et al. (2003, 042932); 132Wenzel et al. (2003, 157139); 133Jimenez et al. (2003, 156611); 134Phares et al. (2003, 

156866); 135Qin and Prather (2006, 156895); 136Zhang et al. (2005, 157185); 137Bein et al. (2005, 156265); 138Drewnick et al. (2004, 155754); 139Drewnick et al. (2004, 155755); 140Lake et al. (2003, 156669); 141Lake et al. (2004, 088411) 

Source: Chow et al. (2008, 156355)

December 2009 A-30 
Table A-12. Summary of element and liquid water content measurement comparisons. 

SITE / PERIOD / SAMPLER SUMMARY OF FINDINGS 

College Park, MD; 11/18/1999 to 11/19/1999, 11/22/1999 

Adjacent to a parking lot in the University of Maryland 

campus, influenced by motor vehicles, coal-fired power plants 

and incinerators ~21 km southwest of site and regionally 

transported material. 

Concentrated Slurry/Graphite Furnace Atomic Absorption 

Spectrometry (GFAAS) (collectively known as SemiContinuous Elements in Aerosol Sampler, SEAS) 

Ambient air is pulled in at a flow rate of 170 L/min. Particles 

are grown using steam injection to about 3 to 4 μm in 

diameter, which are then concentrated and separated from 

the air stream in the form of a slurry using impactors. The 

slurry is collected in glass sample vials, which are 

subsequently analyzed by GFAAS in the laboratory. 

Kidwell and Ondov (2001, 017092; 2004, 155898)

Overall collection efficiency (of the entire system) measured using latex particles was 40% 

for particles initially 0.1 to 0.5 μm in diameter, increasing with size to 68% for particles 3 μm 

in diameter. Major losses were in the virtual impactor major flow channel and in the 

condensers. 

Six elements were detected simultaneously, limited by spectral interference and the 

minimum detectable limit (MDL). Twelve elements (Al, Cr, Mn, Fe, Ni, Cu, Zn, As, Se, Cd, 

Sb, and Pb) were measured. 

MDLs ranged from 3.2 picogram (pg = 10-12 gram) to 440 pg. 

Comparison with NIST standards showed good agreement, except for Al, Cr and Fe, due to 

poor atomization. The method was valid for dissolved solutions, but not for large particles 

(>10 μm). 

Overall avg relative standard deviation (RSD) was 20 to 43% by error propagation, mainly 

due to the collection and analytical efficiencies. 

There were possible memory effects due to particle adhesion to impactor collection 

surfaces. 

Lower MDLs may be possible through redesign and introduction of a wash cycle between 

samples. A 2.5 μm inlet might improve analytical efficiency by removing coarse particles. 

Pittsburgh Supersite, PA; 08/26/2002 to 09/02/2002 

6 km east of downtown in a park on the top of a hill. 

Laser Induced Breakdown Spectroscopy (LIBS) 

Ambient air was concentrated using a PM2.5 inlet and a virtual 

impactor. The concentrated stream was transported through a 

Teflon tube to the sample cell of the LIBS system. The sample 

cell was excited using a Nd: YAG laser. The resulting plasma 

was collected and focused into a spectrometer, generating 

spectra characteristic of different elements. 

Lithgow et al. (2004, 126616)

Calibration was done by sampling particle-laden streams with known metal concentrations. 

Good linear fits with correlation coefficients 0.97 to 0.99 

Seven metals (Na, Mg, Al, Ca, Cr, Mn, and Cu) were analyzed. 

The MDLs were in the order of femtograms (fg = 10-15 gram) per sample. 

This system has the capability of identifying the components, quantifying them and also 

giving a particle size distribution. Mass was underestimated because of missing small 

particles. 

Pittsburgh Supersite, PA; 07/01/2001 to 08/31/2001, 

01/01/2002 to 07/01/2002. 

6 km east of downtown in a park on the top of a hill. 

Dry Ambient Aerosol Size Spectrometer (DAASS) 

Measures the aerosol size distribution (using nano-SMPS, 

SMPS and APS) alternatively, at ambient relative humidity 

(RH) (ambient channel) and at low RH (18 ± 6%) (dry 

channel). A comparison of the two size distributions provides 

information on the water absorption and change in size due to 

RH. 

Stanier et al. (2004, 095955); Khlystov et al. (2005, 156635) 

Measured water content ranging from less than 1 μg/m3 to 30 μg/m3, constituting < 5% to 

100% of the dry aerosol mass. 

Small differences between dry and ambient channels of the DAASS. Number concentrations 

were within 5% of each other. 

Additional sources of error are associated with temperature differences between measured 

outdoor ambient temperature and the temperature at which the ambient measurement 

channel was maintained. Although the measurement system was placed in a ventilated 

enclosure, it was ~4 °C higher than ambient temperature during July 2001. During winter, 

the system was maintained at a minimum temperature of 9 °C, while the outdoor 

temperature dropped to -5 °C. This caused differences in RH sensed by the system in the 

ambient channel versus the actual outdoor RH. 

RH differences cause underestimation of the particle number at sizes < 200 nm and an 

overestimation at sizes >200 nm. This causes the volume growth factor to be higher by 2 to 

14%, with the highest bias occurring at high RH and low temperature (92% outside RH and -

5 °C). 

The difference in temperature might also lead to evaporation of semi-volatile components 

such as NH4NO3. For the winter period, it was estimated that, for the worst case, the volume 

growth factor would be underestimated by about 10% for 60-90% RH. 

Insufficient purging of dry air between the dry and ambient cycles (implying the need for 

supplemental vacuum power during the vent stages) causes uncertainties in estimated 

growth factors. Correction factors were between 0.97 and 1.03. 

Water content estimated by DAASS can be used to evaluate the thermodynamic models. 

For the Pittsburgh study, the models underestimated the water content by 37%. 

Data from DAASS showed that the aerosol was wet even at ambient RH less than 30%. 

Source: Chow et al. (2008, 156355)

December 2009 A-31 
Table A-13. Summary of PM2.5 NO3

-

 measurement comparisons. 

SITE / PERIOD / SAMPLER / CONFIGURATION SUMMARY OF FINDINGS 

ATLANTA SUPERSITE, GA: 8/3/99 to 9/1/99 Four km NW of downtown, within 200 m of a bus 

maintenance yard and several warehouse facilities, representative of a mixed commercial-residential 

neighborhood. 

Sampler Flow Rate (L/Min) Filter Typea Denuderb

R&P-2000 FRM 16.7 Quartz (P) None 

RAAS-400 24 Nylon (P) MgO 

SASS 6.7 Nylon (P) MgO 

MASS-400 16.7 Teflon (P)-Nylon (P Na2CO3

MASS-450 16.7 Quartz (P) None 

R&P-2300 10 Nylon (P) Na2CO3

VAPS 15 Polycarbonatec (front & 

back-up) Na2CO3

URG-PCM 16.7 Teflon (P)-Cellulose-fiber 

(W Na2CO3

ARA-PCM 16.7 Teflon (N/A)-Nylon (N/A) Na2CO3/Citric acid 

PC-BOSS (TVA) 105 Teflon (W)- 

Nylon (P) CIF 

PC-BOSS (BYU) 150 Teflon (W)- 

Nylon (P) CIF 

PC-BOSS (BYU) 150 Quartz (P)- 

CIF (S) CIF 

MOUDI-100 30 Teflon (N/AQuartz (N/A None 

Continuous Sampler Flow Rate (L/Min) Denuder Analysis Methodb

ADI-N 1 Activated Carbon NOX Chemiluminescence 

ARA-N 3 

Potassium iodide 

(Kl) and dual sodium 

chlorite (NaClO2) 

NOX Chemiluminescence 

PILS-IC 5 

Two URG annular 

glass denuders in 

series containing 

citric acid and 

CaCO3

IC 

ECN 16.7 Rotating annular wet 

denuder system IC 

TT 5 Wet parallel plate 

denuder IC 

Solomon et al.(2003, 156994)

17

PM2.5 NO3

-

 from each sampler was compared to 

the all-sampler avgs, called the filter relative 

reference (filter RR) value. Overall agreements 

were within 30-35% of filter RR. 

Wide scatter from paired comparisons, possibly 

due to volatilized NO3

-

, differences in denuder 

design and filter types, and low concentrations 

(close to analytical uncertainty). 

A small positive artifact (few tenths of μg/m3

) might 

be present when using Na2

CO3 impregnated filters, due to possible collection 

(and subsequent oxidation) of HONO and NO2 on 

carbonate-impregnated filters. In addition, glycerol 

in Na2CO3 coated denuders may contaminate the 

filters downstream. 

PM2.5 NO3- R&P-2000 FRM and MOUDI-100 

samplers are consistently lower than other 

samplers. 

Weber et al. (2003, 157129)

82

Hourly PM2.5 NO3-were compared to all-sampler 

averages (continuous RR), similar to the approach 

used for integrated filter samplers. Overall 

agreements were within ± 20-30% (or ± 0.2 μg/m3

) 

except for ARA-N. 

Except for ARA-N, good correlations (R2

 = 0.70 to 

0.90) were found during the second half of the 

study. The poor performance of ARA-N was 

probably due to an inefficient denuder (25-60% 

efficient) resulting in high background. 

Large discrepancies between continuous and filter 

RR, probably due to low ambient concentrations 

(study avg = 0.5 μg/m3

) near the detection limit 

(~0.1 μg/m3

, except for ARA-N, which had 

0.5 μg/m3). 

The ARA-N was within 13%, ADI-N, ECN and 

PILS-IC within 18% and TT within 26% of filter RR 

(all <0.2 μg/m3

 difference). 

Filter samples showed more variability (Relative 

Standard Deviation, RSD = 22%) than continuous 

measurements (RSD = 13%). This is probably due 

to sampling artifacts in filter samples; NO3-

volatilization in continuous monitors is expected to 

be minimal due to shorter averaging times and 

rapid stabilization in solutions. 

December 2009 A-32 
SITE / PERIOD / SAMPLER / CONFIGURATION SUMMARY OF FINDINGS 

PITTSBURGH SUPERSITE, PA; 7/1/01 to 8/1/02 6km east of downtown in a park on the top of a hill 

Sampler Flow Rate (L/Min) Filter Typea Denuderb

MOUDI-110 30 Teflon (W) 

Teflon (W) None 

CMU 16.7 Nylon (W) MgO/Citric acid 

R&P-2000 FRM 16.7 Teflon (W) None 

Cabada et al. (2004, 148859)

18; Takahama et al. 

(2004, 157038)

116 

More than 70% (~0.5 μg/m3) of NO3 mass was lost 

from MOUDI samplers during summer. 

MOUDI NO3 = 0.27 CMU; R2

 = 0.40; Summer 

MOUDI NO3 = 0.99 CMU; R2

 = 0.49; winter 

Wittig et al. (2004, 103413)

85 

Avg conversion efficiency to NOX (tested using 

NH4NO3 solution) was 0.85 ± 0.08. Gas analyzer 

efficiency was stable at 0.99 ± 0.04. 

Corrections were made for instrument offset, 

software calculation error, conversion efficiency, 

gas analyzer efficiency, vacuum drift, and sample 

flow drift. The overall avg correction was 8%, 

ranging from -62% to 93%. 

Data Recovery >80%. Data loss was associated 

with vacuum pump failures and excessive flash 

strip breakage. 

R&P-8400N = 0.83 CMU + 0.20 μg/m3

; R2 = 0.84 

Underestimation in the R&P-8400N could be due 

to incomplete particle collection or incomplete 

conversion of various forms of NO3

-

. 

Used co-located filter measurements for final 

calibration. 

FRESNO SUPERSITE, CA and other CRPAQS sites; 12/2/99 to 2/3/01 

Located 5.5 km northeast of downtown in a mixed residential-commercial neighborhood. 107 

Sampler Flow Rate (L/Min) Filter Typea Denuderb

DRI-SFS 113 Quartz (Pellulose Al2O3 

RAAS-400 24 Quartz (P)-Nylon (P) Na2CO3 

RAAS-400 24 Quartz (P)-Quartz (P) None 

RAAS-100 FRM 16.7 Quartz (P) None 

Continuous Sampler Flow Rate (L/Min) Denuder Analysis Methodb

R&P-8400N 5 Activated Carbon NOX

Chemiluminescence 

Sampler Flow Rate (L/Min) Filter Typea Denuderb

PC-BOSS 150 Teflon (W)- Nylon (P) CIF 

Continuous Sampler Flow Rate (L/Min) Denuder Analysis Methodb

R&P-8400N 5 Activated Carbon NOX 

Chemiluminescence 

Dionex-IC 5 Parallel plate wet denuder IC 

Chow et al. (2005, 099030)

87 

Maximum NO3- volatilization was observed during 

summer (Jun-Aug), while the lowest volatilization 

was observed during winter (Dec-Feb). 

Seasonal avg volatilized NO3- in particulate NO3

-

(PNO3

-

, the sum of non-volatilized and volatilized 

NO3

-

) ranged from less than 10% during winter to 

more than 80% during summer. 

Volatilized NH4NO3 accounted for 44% of actual 

PM2.5 mass (i.e., measured mass plus volatilized 

NH4NO3) in Fresno during summer. 

Front-quartz non-volatilized NO3- concentrations 

were similar for DRISFS (0.52 ± 0.26 μg/m3) and 

RAAS-100 FRM (0.81 ± 0.33 μg/m3

) for warm 

months (May-Sep). With preceding denuders, the 

DRI-SFS PNO3 concentration (3 ± 1.9 μg/m3) was 

much higher than the RAAS100 FRM NO3

–

, 

suggesting that the FRM sampler removed 

gaseous nitric acid (HNO3) resulting in NO3- 

volatilization. FRM Teflon-membrane filters are 

subject to similar NO3

– losses. 

Chow et al. (2005, 156348)

117

December 2009 A-33 
SITE / PERIOD / SAMPLER / CONFIGURATION SUMMARY OF FINDINGS 

High correlation (R2

 >0.90) between 24-h avg 

R&P-8400N NO3 and SFS filter NO3

–

concentrations, but R&P-8400N NO3- was 7 to 

25% lower than filter NO3-. 

Limited comparison (n < 15) with filter samples at 

Bakersfield showed that the slopes were close to 

unity during early morning hours, while they 

decreased during the afternoon hours, indicating 

possible loss of NO3

–

 by the R&P-8400N 

instrument. 

The R&P-8400N required substantial maintenance 

and careful operation. 

Grover et al. (2006, 138080)

65

Dionex-IC NO3 = (0.71 ± 0.04) PC-BOSS NO3 + 

(3.2 ± 1.1); R2

 = 0.91; n = 29 

R&P-8400N = (1.10 ± 0.06) PC-BOSS NO3 -

(0.8 ± 1.8); R2

 = 0.93; n = 29 

R&P-8400N = (0.55 ± 0.01) Dionex-IC + 

(1.4 ± 1.8); R2

 = 0.75; n = 493 

R&P-8400N measured less than Dionex-IC, 

particularly at high RH. R&P-8400N may suffer 

incomplete flash vaporization under conditions of 

high RH. 

December 2009 A-34 
SITE / PERIOD / SAMPLER / CONFIGURATION SUMMARY OF FINDINGS 

BALTIMORE SUPERSITE, MD; 2/14/02 to 11/30/02 

Adjacent to a parking lot in the University of Maryland campus, influenced by motor vehicles, coal-fired 

power plants and incinerators ~21 km southwest of site and regionally transported material. 

Sampler Flow Rate (L/Min) Filter Typea Denuderb

SASS 6.7 Nylon (N/A) MgO 

Continuous Sampler Flow Rate (L/Min) Denuder Analysis Methodb

R&P-8400N 5 Activated Carbon NOX

Chemiluminescence 

Harrison et al. (2004, 136787)

 83

Corrections were made to R&P-8400N data for 

software calculation error, conversion efficiency, 

gas analyzer efficiency, vacuum drift and sample 

flow drift. 

The relative uncertainty of R&P-8400N 

measurements averaged 8.7%, ranging from 6.3% 

to 23%. 

Data capture >95%. 

R&P-8400N underestimated SASS filter NO3

–

 by 

~33%, attributed to variations in conversion 

efficiency, matrix effects, and impaction efficiency. 

This suggested a true conversion efficiency of 

68% as compared to an avg conversion efficiency 

of R&P-8400N to NOX (tested using potassium 

nitrate solution) of 0.90 ± 0.04. 

Large errors occurred when the concentrations 

were near the detection limit, when the 

temperature difference (between instrument and 

ambient) was large, and when the ambientRH was 

< 40%. Ridged flash strips produced lower 

dissociation losses than flat strips. 

Reliable measurements were obtained when the 

instrument-outdoor temperature differences were 

minimal and when grooved/ridged flash strips were 

used. A co-located filter measurement was used 

for final corrections. 

NEW YORK SUPERSITE, NY; 06/29/01 to 08/05/01 and 07/09/02 to 08/07/02 

Urban site located at Queens College, NY, about 14 km west of Manhattan, within 2 km of freeways, 

and within 12 km of international airports. Rural site located at Whiteface mountain, 600 m above sea 

level, in a clearing surrounded by deciduous and evergreen trees and no major cities within 20 km of 

the site. 

Sampler Flow Rate (L/Min) Filter Typea Denuderb

R&P-2300 10 Nylon (N/A) Na2CO3

Continuous Sampler Flow Rate (L/Min) Denuder Analysis Methodb

R&P-8400N 5 Activated Carbon NOX Chemiluminescence 

PILS-IC 5 Na2CO3 and citric 

acid IC 

AMS 0.1 None Mass Spectrometry 

Hogrefe et al. (2004, 099003)

20

Data completeness: 86-88% for R&P-8400N, 94 -

98% for AMS, and 65-70% for PILS-IC. 

Some PILS measurements were invalidated owing 

to larger aqueous flow caused by bigger tubing. 

Larger aqueous flow and inconsistent water quality 

affected NO3

–

 concentrations. 

R&P-8400N NO3- was lower than R&P-2300 filter 

NO3

–

. PILS-IC was within 5% of R&P-2300 filter 

NO3- concentrations. 

At the urban site, AMS was within 10% of the filter 

NO3 concentration. At the rural site, AMS had a 

slope of 0.51 and R2 of 0.46, compared with filter 

NO3

–

. 

NEW YORK SUPERSITE, NY; 10/01 to 07/05 (urban), 07/02 to 07/05 (rural) Urban site located at a 

school in South Bronx, NY in a residential area, within a few kilometers away from major highways and 

a freight yard (experiencing significant truck traffic). Rural site located at Whiteface mountain, 600 m 

above sea level, in a clearing surrounded by deciduous and evergreen trees and no major cities within 

20 km of the site. 

Sampler Flow Rate (L/Min) Filter Typea Denuderb

R&P-2300 10 Nylon (N/A) Na2CO3 

TEOM-ACCU 16.7 Zefluor None 

Continuous Sampler Flow Rate (L/Min) Denuder Analysis Methodb

R&P-8400N 5 Activated Carbon NOX Chemiluminescence 

Rattigan et al. (2006, 115897)

84 

Data capture was more than 94%. 

Data were adjusted for span and zero drifts, 

conversion efficiency, flow drift, and blanks. 

R&P-8400N NO3

– was systematically lower than 

R&P-2300 filter NO3 over all concentration ranges, 

except at <1 μg/m3

. 

Urban: R&P-8400N = 0.59 R&P-2300 NO3 + 0.28; 

R2 = 0.88; n = 305 

Rural: R&P-8400N = 0.73 R&P-2300 NO3 + 0.01; 

R2

 = 0.90; n~161; however concentrations were 

low with 95% of data < 1 μg/m3. 

Required weekly or biweekly maintenance by 

trained personnel. 

LOS ANGELES SUPERSITE, CA; 7/13/01 to 9/15/01 (Rubidoux) and 9/15/01 to 2/10/02 

(Claremont) 

Multiple sampling locations in the South Coast Air Basin (SoCAB), including urban “source” sites and 

downwind “receptor” sites. 

Sampler Flow Rate (L/Min) Filter Typea Denuderb

Fine et al. (2003, 155775)

19 

MOUDI = 0.68 HEADS; R2 = 0.88 

ADI-N Sized = 0.80 HEADS; R2

 = 0.79 

December 2009 A-35 
SITE / PERIOD / SAMPLER / CONFIGURATION SUMMARY OF FINDINGS 

MOUDI 30 Teflon (P) None 

HEADS 10 Teflon (N/A) -GF--

GF Carbonate 

Continuous Sampler Flow Rate (L/Min) Denuder Analysis Methodb

ADI-N Sized 0.9 Activated Carbon NOX Chemiluminescence 

ADI-N Sized = 1.12 MOUDI; R2

 = 0.53 

ADI-N NO3- showed better agreement with HEADS 

at lower concentrations, the ADI-N deviated 

(biased low) from the HEADS concentrations at 

higher NO3

–

concentrations. This deviation was 

attributed to NO3- vaporization, loss of NO3

–

associated with particles less than 0.1 μm not 

collected by the ADI-N sampler, or loss of particles 

in the ADI-N inlet tubing. 

The underestimation of NO3- by MOUDI compared 

to HEADS may be due to NO3

–

volatilization from 

MOUDI stages, since SO4

2– comparisons showed 

MOUDI to explain 85% of HEADS SO4

2–. 

ADI-N and MOUDI showed better correlation 

(R2

 = 0.67) for the 1-2 μm size range NO3 relative 

to other size ranges (R2 < = 0.56). This is possibly 

due to NO3

– in the form of non-volatilized sodium 

nitrate (NaNO3) than volatilized NH4NO3 in the 1-

2 μm size range. Single particle analysis also 

indicated this possibility of NaNO3 in the 1-2 μm 

range. 

RUBIDOUX, CA; 07/01/03 to 07/31/03

Located in the eastern section of SoCAB in the northwest corner of Riverside County, 78 km downwind 

of the central Los Angeles metropolitan area and in the middle of the remaining agricultural production 

area in SoCAB. 

Grover et al. (2005, 090044)

66 

R&P-8400N = (0.65 ± 0.07) PC-BOSS + 

(3.3 ± 2.4); R2

 = 0.73; n = 31 

At higher concentrations (no numerical value 

reported), R&P-8400N NO3-was lower than PCBOSS NO3-, possibly due to incomplete 

volatilization of NH4NO3 in R&P-8400N at higher 

concentrations (and higher relative humidity). 

At the urban site, the continuous instruments 

correlated well with filter NO3

–

- measurements and 

among themselves (R2 ≥ 0.89). At the rural site, R2

ranged from 0.61 to 0.83, except for the AMS 

versus R&P2300 comparison, with an R2 of 0.46. 

Sampler Flow Rate (L/Min) Filter Typea Denuderb

PC-BOSS 150 Teflon (W)-Nylon (P) CIF 

Continuous Sampler Flow Rate (L/Min) Denuder Analysis Methodb

R&P-8400N 5 Activated Carbon NOX Chemiluminescence 

R&P-8400N 5 Activated Carbon NOX Chemiluminescence 

PILS-IC 5 Na2CO3 and Citric acid IC 

AMS 0.1 None Mass Spectrometry 

December 2009 A-36 
SITE / PERIOD / SAMPLER / CONFIGURATION SUMMARY OF FINDINGS 

a

Filter Manufacturer in parenthesis - W: Whatman, Clifton, NJ; P: Pall-Gelman, Ann Arbor, MI; S: Schleicher & Schnell. Keene, NH; N/A: not available or not reported. b

Al2O3: Aluminum oxide; GF: Na2CO3 impregnated Glass Fiber Filters; IC: Ion chromatography; MgO: Magnesium oxide; Na2CO3: Sodium carbonate; NaHCO3: Sodium bicarbonate NOX: Oxides of nitrogen; 

CIF: Charcoal Impregnated Filter; FEP: Fluorinated Ethylene Propylene copolymer; TEA: Triethanolamine; TSP: Total Suspended PM. c

Na2CO3 impregnated. d

37 mm filter. 

1

Chow (1995, 077012); 2Watson and Chow (2001, 157123); 3 Watson et al. (1983, 045084); 4Fehsenfeld et al. (2004, 157360); 5Solomon et al. (2001, 157193); 6Watson et al. (2005, 157124); 7Mikel (2001, 

156762); 8

Watson et al. (1999, 020949); 9Solomon and Sioutas (2006, 156995); 10Graney et al. (2004, 053756); 11Tanaka et al. (1998, 157041); 12Pancras et al. (2005, 098120); 13John et al. (1988, 045903); 14Hering and Cass (1999, 084958); 15Fitz et al. (1989, 077387); 16Hering et al. (1988, 036012); 17Solomon et al. (2003, 156994); 18Cabada et al. (2004, 148859); 19Fine et al. (2003, 155775); 20Hogrefe et al. 

(2004, 099003); 21Drewnick et al. (2003, 099160); 22Watson et al. (2005, 157125); 23Ho et al. (2006, 156552); 24Decesari et al. (2005, 144536); 25 Mayol-Bracero et al. (2002, 045010); 26Yang et al. (2003, 

156167); 27Turšic et al. (2006, 157063); 28Mader et al.(2004, 156724); 29Xiao and Liu (2004, 056801); 30Kiss et al. (2002, 156646); 31Cornell and Jickells (1999, 156367); 32 Zheng et al. (2002, 026100); 33Fraser et al. (2002, 140741); 34 Fraser et al. (2003, 042231) 35Schauer er al. (2000, 012225); 36Fine et al. (2004, 141283); 37Yue et al. (2004, 157169); 38Rinehart et al. (2006, 115184); 39Wan and Yu (2006, 

157104); 40Poore (2000, 012839); 41Fraser et al. (2003, 040266); 42Engling et al. (2006, 156422); 43Yu et al. (2005, 157167); 44Tran et al. (2000, 013025); 45Yao et al. (2004, 102213); 46Li and Yu (2005, 

156692); 47Henning et al. (2003, 156539); 48Zhang and Anastasio (2003, 157182); 49Emmenegger et al. (2007, 156418); 50 Watson et al. (1989, 046318); 51Greaves et al. (1985, 156494); 52Waterman et al. 

(2000, 157116); 53Waterman et al. (2001, 157117); 54Falkovich and Rudich (2001, 156427); 55Chow et al. (2007, 157209); 56Miguel et al. (2004, 123260); 57Crimmins and Baker (2006, 097008); 58Ho and Yu 

(2004, 156551); 59Jeon et al. (2001, 016636); 60Mazzoleni et al. (2007, 098038); 61Poore (2002, 051444); 62Butler et al. (2003, 156313); 63Chow et al. (2006, 146622); 64Russell et al. (2004, 082453); 65Grover 

et al. (2006, 138080); 66Grover et al. (2005, 090044); 67Schwab et al. (2006, 098449); 68Hauck et al. (2004, 156525); 69Jaques et al. (2004, 155878); 70Rupprecht and Patashnick (2003, 157207); 71Pang et al 

(2002, 030353); 72Eatough et al. (2001, 010303); 73Lee et al. (2005, 128139); 74Lee et al. (2005, 156680); 75Babich et al. (2000, 156239); 76Lee et al. (2005, 155925); 77Lee et al. (2005, 128139); 78Anderson 

and Ogren (1998, 156213); 79Chung et al. (2001, 156357); 80Kidwell and Ondov (2004, 155898); 81Lithgow et al. (2004, 126616); 82Weber et al. (2003, 157129); 83Harrison et al. (2004, 136787); 84Rattigan et 

al. (2006, 115897); 85Wittig et al. (2004, 103413); 86Vaughn et al. (2005, 157089); 87Chow et al. (2005, 099030); 88Weber et al (2001, 024640) ; 89Schwab et al. (2006, 098785); 90Lim et al. (2003, 037037); 91Watson and Chow (2002, 037873); 92Venkatachari et al. (2006, 105918); 93Bae et al. (2004, 156243); 94Arhami et al. (2006, 156224); 95Park et al. (2005, 156843); 96Bae et al. (2004, 098680); 97Chow et al. 

(2006, 156350); 98Arnott et al. (2005, 156227); 99Bond et al. (1999, 156281); 100Virkkula et al. (2005, 157097); 101Petzold et al. (2002, 156863); 102Park et al. (2006, 098104); 103Arnott et al. (1999, 020650); 104Peters et al. (2001, 016925); 105Pitchford et al. (1997, 156872); 106Rees et al. (2004, 097164); 107Watson et al. (2000, 010354); 108Lee et al. (2005, 156680); 109Hering et al. (2004, 155837); 110Watson et al. 

(1998, 198805); 111Chakrabarti et al. (2004, 157426); 112Mathai et al. (1990, 156741); 113Kidwell and Ondov (2001, 017092); 114Stanier et al. (2004, 095955); 115Khlystov et al. (2005, 156635); 116Takahama et 

al. (2004, 157038); 117Chow et al. (2005, 156348); 118Zhang et al. (2002, 157181); 119Subramanian et al. (2004, 081203); 120Chow et al. (2006, 155207); 121Birch and Cary (1996, 026004); 122Birch (1998, 

024953); 123Birch and Cary (1996, 002352); 124NIOSH (1996, 156810); 125NIOSH (1999, 156811); 126Chow et al. (1993, 077459); 127Chow et al. (2007, 156354); 128Ellis and Novakov (1982, 156416); 129Peterson and Richards (2002, 156861); 130Schauer et al. (2003, 037014); 131Middlebrook et al. (2003, 042932); 132Wenzel et al. (2003, 157139); 133Jimenez et al. (2003, 156611); 134Phares et al. (2003, 

156866); 135Qin and Prather (2006, 156895); 136Zhang et al. (2005, 157185); 137Bein et al. (2005, 156265); 138Drewnick et al. (2004, 155754); 139Drewnick et al. (2004, 155755); 140Lake et al. (2003, 156669); 141Lake et al. (2004, 088411) 

Source: Chow et al. (2008, 156355)

December 2009 A-37 
Table A-14. Summary of PM2.5 SO4

2– measurement comparisons 

SITE/PERIOD/SAMPLER/ CONFIGURATION SUMMARY OF FINDINGS 

ATLANTA SUPERSITE, GA: 08/03/99 to 09/01/99 

Four km NW of downtown, within 200 m of a bus maintenance yard and several warehouse facilities, 

representative of a mixed commercial-residential neighborhood. 

Sampler Flow Rate (L/Min) Filter Typea Denuderb

R&P-2000 FRM 16.7 Quartz (P) None 

RAAS-400 24 Teflon (P) None 

SASS 6.7 Teflon (P) None 

MASS-450 16.7 Quartz (P) None 

R&P-2300 10 Quartz (P) None 

VAPS 15 Quartz (P) XAD-4 

URG-PCM 16.7 Teflon (P)-Cellulose-fiber 

(W) 

ARA-PCM 16.7 Teflon (N/A) Na2CO3/Citric acid 

ARA-PCM 16.7 Nylon (N/A) Na2CO3/Citric acid 

PC-BOSS (TVA) 105 Teflon (W) CIF 

PC-BOSS (TVA) 105 Quartz (P) CIF 

PC-BOSS (BYU) 150 Teflon (W) CIF 

PC-BOSS (BYU) 150 Quartz (P) CIF 

MOUDI-100 30 Teflon (N/A) 

Quartz (N/A) None 

Continuous Sampler Flow Rate (L/Min) Denuder Analysis Methodb

ADI-S 2.7 Activated Carbon SO2, UV 

Fluorescence 

PILS-IC 5 

Two URG annular glass 

denuders in series 

containing citric acid & 

CaCO3

IC 

ECN 16.7 Rotating annular wet 

denuder system IC 

TT 5 Wet parallel plate 

denuder IC 

Solomon et al. (2003, 156994)

17 

PM2.5 SO4

2– from each sampler was 

compared to all-sampler averages, called the 

filter relative reference (filter RR) value. The 

samplers agreed to within 10% of filter RR, 

except for the PC-BOSS (TVA) and MOUDI100. 

While avg mass was within 10%, daily 

variability was >50% of filter RR. 

All samplers, except for the PC-BOSS (TVA), 

correlated well (R2

 >0.90) with daily filter RR. 

PC-BOSS (TVA) had instrument leaks. 

The R&P-2000 FRM, on avg, agreed within 

1% of filter RR. 

MOUDI-100 was ~13% low compared to filter 

RR. 

Weber et al. (2003, 157129)

82; Zhang et al. 

(2002, 157181)

118

Hourly PM2.5 SO4

2– were compared to allsampler averages (continuous RR), similar to 

the approach used for filter samplers. Overall 

agreement was within 16% or 2 μg/m3. 

Good correlations (R2

 = 0.76 to 0.94) were 

found during the second half of the study, 

except for TT versus ADI. 

Good correlation (R2

 = 0.84) was found 

between continuous and filter-based SO4

2– 

Continuous RR = (1.15 ± 0.15), Filter RR + 

(0.41 ± 1.73) 

Variability among continuous SO4

2–

 instruments (RSD = 13%) was similar to that 

for NO3- instruments. Filter sample variability 

was low (RSD = 8%) indicating more 

uniformity among samplers. 

The ECN and TT instruments were within 

15%, PILS-IC was within 20% and ADI-S was 

within 26% of filter RR. 

PITTSBURGH SUPERSITE, PA; 070/1/01 to 08/01/02 

6 km east of downtown in a park on the top of a hill 

Sampler Flow Rate (L/Min) Filter Typea Denuderb

MOUDI-110 30 Teflon (W) None 

CMU 16.7 Teflon (W) MgO/Citric acid 

Cabada et al. (2004, 148859)

18; Takahama 

et al. (2004, 157038)

116 

MOUDI SO4

2– 0.80 CMU; R2

 = 0.95; Summer 

MOUDI SO4

2– 0.97 CMU; R2

 = 0.48; winter 

Wittig et al. (2004, 103413)

85 

 

December 2009 A-38 
SITE/PERIOD/SAMPLER/ CONFIGURATION SUMMARY OF FINDINGS 

R&P-2000 FRM 16.7 Teflon (W) None 

Continuous Sampler Flow Rate 

(L/Min) Denuder Analysis Methodb

R&P-8400S 5 Activated Carbon SO2 UV Fluorescence 

Avg conversion efficiency to SO2 (tested 

using ammonium sulfate [(NH4)2SO4] 

solution) was 0.65 ± 0.07. Gas analyzer 

efficiency was stable at 0.99 ± 0.06. 

Corrections were made for instrument offset, 

software calculation error, conversion 

efficiency, gas analyzer efficiency, vacuum 

drift, and sample flow drift. The overall 

correction was, on avg, -1% and ranged from 

-90% to 100% for individual samples. 

Data Recovery >90%. Data loss was 

associated with vacuum pump failures or 

excessive flash strip breakage. 

R&P-8400S (SO4

2–) = 0.71 CMU + 

0.42 μg/m3

; R2 = 0.83 

Underestimation is attributed to incomplete 

particle collection or incomplete conversion of 

various forms of SO4

2–. 

Used co-located filter measurements for final 

calibration. 

LOS ANGELES SUPERSITE, CA; 07/13/01 to 09/15/01 (Rubidoux) and 09/15/01 to 02/10/02 

(Claremont)

Multiple sampling locations in the South Coast Air Basin (SoCAB), including urban “source” sites and 

downwind “receptor” sites. 

Sampler Flow Rate 

(L/Min) Filter Typea Denuderb

MOUDI 30 Teflon (P) None 

HEADS 10 Teflon (N/A) GF-GFc Carbonate 

Fine et al. (2003, 155775)

19 

MOUDI explained 85% of HEADS SO4

2–

 (R2 = 0.89; n = 40) 

NEW YORK SUPERSITE, NY; 06/29/01 to 08/05/01 and 07/09/02 to 08/07/02 

Urban site located at Queens College, NY, about 14 km west of Manhattan, within 2 km of freeways, and 

within 12 km of international airports. Rural site located at Whiteface mountain, 60m above sea level, in a 

clearing surrounded by deciduous and evergreen trees and no major cities within 20 km of the site. 

Sampler Flow Rate 

(L/Min) Filter Typea Denuderb

R&P-2300 10 Nylon (N/A) Na2CO3 

SCS 42 Zefluor (N/A) None 

TEOM-ACCU 16.7 Zefluor (N/A) None 

Continuous Sampler Flow Rate 

(L/Min) Denuder Analysis Methodb

R&P-8400S 5 Activated Carbon SO2 UV Fluorescence 

PILS-IC 5 Na2CO3 and Citric acid IC 

AMS 0.1 None Mass Spectrometry 

CASM 5 Na2CO3 and Carbon 

and a Nafion dryer 

SO2 UV Fluorescence 

Drewnick et al. (2003, 099160)

21; Hogrefe 

et al. (2004, 099003)

20

Data completeness: 89-93% for R&P-8400S, 

94-98% for AMS, 81-98% for CASM, and 

65-70% for PILS-IC. 

The urban site data showed good 

correlations (R2

 = 0.87 to 0.94) with slopes 

ranging from 0.97 to 1.01. At the rural site, 

the variability was large (R2

 = 0.73 to 0.91) 

with slopes ranging from 0.76 to 1.32. SO4

from PILS-IC was overestimated by ~25% 

when compared to the AMS at the rural site. 

Filter samples were within 5% of each other, 

except for comparison of ACCU with R&P2300 at the rural site, with high correlations 

(R2

 = 0.97 to 1.0). ACCU underestimated 

SO4

2– by ~15%. 

Continuous versus 6-h SCS filter 

comparisons showed high R2

 (0.91 to 0.95) at 

the urban site. Continuous instruments 

consistently measured lower SO4

2–

 concentrations compared to the SCS filter 

measurements (slopes 0.68 to 0.73) 

On avg, 85% of the filter-based SO4

2– was measured by the continuous instruments with 

consistent relationships. At the rural site, 

PILS-IC overestimated SO4

2– concentrations 

(slopes 1.11 to 1.15), AMS and R&P-8400S 

showed slopes of 0.71-0.74 against SCS and 

ACCU, while it ranged from 0.53- 0.68 

against R&P-2300. 

Error estimates: 

Sampling losses: 2-3% for AMS and PILS-IC, 

5-10% for R&P-8400S and none for CASM. 

Continuous instruments probably 

experienced more inlet transport losses (~ 

December 2009 A-39 
SITE/PERIOD/SAMPLER/ CONFIGURATION SUMMARY OF FINDINGS 

25%) than filter samplers due to longer inlet 

lines. 

Small (< 2%) positive artifact was found in 

filters. 

NEWYORK SUPERSITE, NY; 10/01 to 07/05 (urban), 07/02 to 07/05 (rural) 

Urban site located at a school in South Bronx, NY in a residential area, within a few kilometers from major 

highways and a freight yard (experiencing significant truck traffic). Rural site located at Whiteface mountain, 

600m above sea level, in a clearing surrounded by deciduous and evergreen trees and no major cities within 

20 km of the site. The study by Schwab et al.89 was based at a rural site located at Pinnacle State Park 

surrounded by golf course, picnic areas and undeveloped forest lands and no major cities within 15 km. 

Integrated Sampler Flow Rate 

(L/Min) Filter Typea Denuderb

R&P-2300 10 Nylon (N/A) Na2CO3 

TEOM-ACCU 16.7 Zefluor None 

Continuous Sampler Flow Rate (L/Min) Denuder Analysis Methodb

R&P-8400S 5 Activated 

Carbon SO2 pulsed fluorescence 

TE-5020 

(07/14/04 to 11/01/04) 

5 Na2CO3 SO2 pulsed fluorescence 

Rattigan et al. (2006, 115897)

84 

Data capture was above 85%. Data loss was 

primarily due to frequent flash strip failures, 

every 2 wk and without warning. 

Data were adjusted for span and zero drifts, 

measured conversion efficiency, flow drift, 

and blanks. 

Calibrations used aqueous standards of 

(NH4)2SO4 and oxalic acid solution in 1:4 

ratio. Lower fractions of oxalic acid showed 

lower conversion efficiencies. 

Urban South Bronx site: 

R&P-8400S = 0.82 TEOM-ACCU + 1.15; 

R2

 = 0.84; n = 513 

R&P-8400S = 0.74 R&P-2300 + 1.14; 

R2

 = 0.81; n = 322 

Rural Whiteface mountain: 

R&P-8400S = 0.75 TEOM-ACCU + 0.22; 

R2

 = 0.95; n = 207 

R&P-8400S = 0.78 R&P-2300 + 0.17; 

R2

 = 0.85; n = 198 

Required weekly or biweekly maintenance by 

trained personnel 

Schwab et al. (2006, 098785)

89 

TE-5020 = 0.78 ACCU – 0.2; R2 = 0.94 

Similar studies at St. Louis, MO, show slopes 

near unity. This suggests that the instrument 

is sensitive to aerosol composition. 

Low maintenance and calibration 

requirements for TE-5020 compared to PILSIC and R&P-8400S. 

FRESNO SUPERSITE,CA; 12/01/03 to 12/23/03 

Located 5.5 km northeast of downtown in a mixed residential-commercial neighborhood. Flow Sampler 

(L/min) Filter Typea Denuderb 

Sampler Flow Rate (L/Min) Filter Typea Denuderb

PC-BOSS 150 Teflon (W)- 

Nylon (P) CIF 

Continuous Sampler Flow Rate (L/Min) Denuder Analysis Methodb

R&P-8400S 5 Activated 

Carbon SO2 pulsed fluorescence 

Dionex-IC 5 Parallel plate 

wet denuder IC 

Grover et al. (2006, 138080)

65

Dionex-IC SO4

2– (1.03 ± 0.03) PC-BOSS SO4

+ (0.2 ± 0.3); R2

 = 0.98; n = 27 

R&P-8400S SO4

2– (0.95 ± 0.05) Dionex-IC 

SO4 + (0.3 ± 0.6); R2

 = 0.68; n = 195 

December 2009 A-40 
SITE/PERIOD/SAMPLER/ CONFIGURATION SUMMARY OF FINDINGS 

a

Filter Manufacturer in parentheses - W: Whatman, Clifton, NJ; P: Pall-Gelman, Ann Arbor, MI; S: Schleicher & Schnell. Keene, NH; N/A: not available. b

Al2O3: Aluminum oxide; IC: Ion chromatography; CIF: Charcoal Impregnated Filter; FEP: Fluorinated Ethylene Propylene copolymer; MgO: Magnesium oxide; Na2CO3: Sodium carbonate; NaHCO3: Sodium 

bicarbonate NOX: Oxides of nitrogen; SO2: Sulfur dioxide; TEA: Triethanolamine; TSP: Total Suspended PM; UV: Ultraviolet; XAD-4: Hydrophobic, non-polar polyaromatic resin. c

Na2CO3 impregnated. d

37 mm filter. 

1

Chow (1995, 077012); 2Watson and Chow (2001, 157123); 3 Watson et al. (1983, 045084); 4Fehsenfeld et al. (2004, 157360); 5Solomon et al. (2001, 157193); 6Watson et al. (2005, 157124); 7Mikel (2001, 

156762); 8

Watson et al. (1999, 020949); 9Solomon and Sioutas (2006, 156995); 10Graney et al. (2004, 053756); 11Tanaka et al. (1998, 157041); 12Pancras et al. (2005, 098120); 13John et al. (1988, 045903); 14Hering and Cass (1999, 084958); 15Fitz et al. (1989, 077387); 16Hering et al. (1988, 036012); 17Solomon et al. (2003, 156994); 18Cabada et al. (2004, 148859); 19Fine et al. (2003, 155775); 20Hogrefe et al. 

(2004, 099003); 21Drewnick et al. (2003, 099160); 22Watson et al. (2005, 157125); 23Ho et al. (2006, 156552); 24Decesari et al. (2005, 144536); 25 Mayol-Bracero et al. (2002, 045010); 26Yang et al. (2003, 

156167); 27Turšic et al. (2006, 157063); 28Mader et al.(2004, 156724); 29Xiao and Liu (2004, 056801); 30Kiss et al. (2002, 156646); 31Cornell and Jickells (1999, 156367); 32 Zheng et al. (2002, 026100); 33Fraser et al. (2002, 140741); 34 Fraser et al. (2003, 042231) 35Schauer er al. (2000, 012225); 36Fine et al. (2004, 141283); 37Yue et al. (2004, 157169); 38Rinehart et al. (2006, 115184); 39Wan and Yu (2006, 

157104); 40Poore (2000, 012839); 41Fraser et al. (2003, 040266); 42Engling et al. (2006, 156422); 43Yu et al. (2005, 157167); 44Tran et al. (2000, 013025); 45Yao et al. (2004, 102213); 46Li and Yu (2005, 

156692); 47Henning et al. (2003, 156539); 48Zhang and Anastasio (2003, 157182); 49Emmenegger et al. (2007, 156418); 50 Watson et al. (1989, 046318); 51Greaves et al. (1985, 156494); 52Waterman et al. 

(2000, 157116); 53Waterman et al. (2001, 157117); 54Falkovich and Rudich (2001, 156427); 55Chow et al. (2007, 157209); 56Miguel et al. (2004, 123260); 57Crimmins and Baker (2006, 097008); 58Ho and Yu 

(2004, 156551); 59Jeon et al. (2001, 016636); 60Mazzoleni et al. (2007, 098038); 61Poore (2002, 051444); 62Butler et al. (2003, 156313); 63Chow et al. (2006, 146622); 64Russell et al. (2004, 082453); 65Grover 

et al. (2006, 138080); 66Grover et al. (2005, 090044); 67Schwab et al. (2006, 098449); 68Hauck et al. (2004, 156525); 69Jaques et al. (2004, 155878); 70Rupprecht and Patashnick (2003, 157207); 71Pang et al 

(2002, 030353); 72Eatough et al. (2001, 010303); 73Lee et al. (2005, 128139); 74Lee et al. (2005, 156680); 75Babich et al. (2000, 156239); 76Lee et al. (2005, 155925); 77Lee et al. (2005, 128139); 78Anderson 

and Ogren (1998, 156213); 79Chung et al. (2001, 156357); 80Kidwell and Ondov (2004, 155898); 81Lithgow et al. (2004, 126616); 82Weber et al. (2003, 157129); 83Harrison et al. (2004, 136787); 84Rattigan et 

al. (2006, 115897); 85Wittig et al. (2004, 103413); 86Vaughn et al. (2005, 157089); 87Chow et al. (2005, 099030); 88Weber et al (2001, 024640) ; 89Schwab et al. (2006, 098785); 90Lim et al. (2003, 037037); 91Watson and Chow (2002, 037873); 92Venkatachari et al. (2006, 105918); 93Bae et al. (2004, 156243); 94Arhami et al. (2006, 156224); 95Park et al. (2005, 156843); 96Bae et al. (2004, 098680); 97Chow et al. 

(2006, 156350); 98Arnott et al. (2005, 156227); 99Bond et al. (1999, 156281); 100Virkkula et al. (2005, 157097); 101Petzold et al. (2002, 156863); 102Park et al. (2006, 098104); 103Arnott et al. (1999, 020650); 104Peters et al. (2001, 016925); 105Pitchford et al. (1997, 156872); 106Rees et al. (2004, 097164); 107Watson et al. (2000, 010354); 108Lee et al. (2005, 156680); 109Hering et al. (2004, 155837); 110Watson et al. 

(1998, 198805); 111Chakrabarti et al. (2004, 157426); 112Mathai et al. (1990, 156741); 113Kidwell and Ondov (2001, 017092); 114Stanier et al. (2004, 095955); 115Khlystov et al. (2005, 156635); 116Takahama et 

al. (2004, 157038); 117Chow et al. (2005, 156348); 118Zhang et al. (2002, 157181); 119Subramanian et al. (2004, 081203); 120Chow et al. (2006, 155207); 121Birch and Cary (1996, 026004); 122Birch (1998, 

024953); 123Birch and Cary (1996, 002352); 124NIOSH (1996, 156810); 125NIOSH (1999, 156811); 126Chow et al. (1993, 077459); 127Chow et al. (2007, 156354); 128Ellis and Novakov (1982, 156416); 129Peterson and Richards (2002, 156861); 130Schauer et al. (2003, 037014); 131Middlebrook et al. (2003, 042932); 132Wenzel et al. (2003, 157139); 133Jimenez et al. (2003, 156611); 134Phares et al. (2003, 

156866); 135Qin and Prather (2006, 156895); 136Zhang et al. (2005, 157185); 137Bein et al. (2005, 156265); 138Drewnick et al. (2004, 155754); 139Drewnick et al. (2004, 155755); 140Lake et al. (2003, 156669); 141Lake et al. (2004, 088411) 

Source: Chow et al. (2008, 156355)

Table A-15. Summary of PM2.5 carbon measurement comparisons. 

SITE/PERIOD/SAMPLER/ CONFIGURATION SUMMARY OF FINDINGS 

ATLANTA SUPERSITE, GA: 08/03/99 to 09/01/99 

Four km NW of downtown, within 200 m of a bus maintenance yard and several warehouse facilities, 

representative of a mixed commercial-residential neighborhood. 

Sampler Flow Rate 

(L/Min) Filter Typea Denuderb Analysis Methodc

R&P-2000 FRM 16.7 Quartz (P) None NIOSH 5040-TOT 

RAAS-400 24 Quartz (P) None NIOSH 5040-TOT 

SASS 6.7 Quartz (P)- 

Quartz (P) None NIOSH 5040-TOT 

MASS-450 16.7 Quartz (P) None NIOSH 5040-TOT 

R&P-2300 10 Quartz (P)- 

Quartz (P) None NIOSH 5040-TOT 

VAPS 15 Quartz (P) XAD-4 NIOSH 5040-TOT 

URG-PCM 16.7 Quartz (P)- 

Quartz (P) XAD-4 Front: NIOSH 5040-TOT; Backup: custom-TOTd 

ARA-PCM 16.7 Quartz (N/A)- 

Quartz (N/A) CIF IMPROVE_TOR 

PC-BOSS (TVA) 150 Quartz (P)- 

CIF (N/A) CIF Front: IMPROVE_TOR; Backup: TPV 

PC-BOSS (BYU) 150 Quartz (P)-CIF 

(S) CIF TPB 

MOUDI-100 30 Al Foil-Quartz 

(N/A)f None Custom-TOR to suit Ale

Continuous Sampler Flow Rate 

(L/Min) Denuder OC EC Comments 

Solomon et al. (2003, 156994)

17 

Organic Carbon (OC); 

PM2.5 OC from each sampler was compared to 

the all-sampler avg, called the relative 

reference (RR) value. The samplers agreed to 

within 20 to 50% of RR. Only front filter OC is 

reported without artifact correction. 

Denuded samplers showed lower OC (20 to 

35%) than RR, while non-denuded sampler 

OC was higher (5 to 35%). 

Among non-denuded samplers, as filter face 

velocity decreased, OC increased, with the 

exception of R&P-2300. 

OC positive artifacts ranged from 2 to 4 μg/m3

EC: 

PM2.5 EC from each sampler was compared to 

the all-sampler avg, called the relative 

reference (RR) value. The samplers agreed to 

within 20 to 200% of RR. 

TOT samples showed less EC than RR by 15 

to 30%, while TOR samples showed more EC 

than RR by 40 to 90%. PCBOSS (BYU) >RR 

value by 140%. EC by TOR is ~twice EC by 

TOT. 

Major difference in EC is due to the carbon 

analysis protocol and optical monitoring 

correction (i.e., transmittance, reflectance). 

Lim et al. (2003, 037037)

90 

December 2009 A-41 
SITE/PERIOD/SAMPLER/ CONFIGURATION SUMMARY OF FINDINGS 

ADI-C 2.7 Activated Carbon Not 

known N/A 

Part of SO4

2– instrument 

w/CO2 non-dispersive 

infrared (NDIR) analyzer; 

data corrected for avg field 

blank; OC = 2 oxidized OC

RU-OGI 16.1 None 700 in 

He 

850 in 2% 

O2

TOT; Dynamic blank for 

adsorption correction 

R&P-5400 16.7 None 275 in 

air 750 in air No pyrolysis correction 

PSAP 1.26 None babs@ 

565 nm 10m2

/g factor 

AE-16 4 None babs@ 

880 nm 

12.6 m2/g factor 

TC concentrations measured by the RU-OGI 

and R&P-5400 correlated reasonably well 

(R2

 = 0.83), with a slope of 0.96. The ratio of 

the mean RU-OGI to mean R&P-5400 TC was 

1.02. 

R&P-5400 OC was 8% lower than the RU-OGI 

(R2

 = 0.73), while the R&P-5400 EC was 20% 

higher than RU-OGI (R2

 = 0.74). 

OC measured by ADI-C was lower than R&P5400 and RUOGI by 15% and 22%, 

respectively. 

EC from PSAP and AE-16 correlated well 

(R2

 = 0.97). PSAP was lower by ~50%, 

compared with AE-16, R&P-5400 and RUOGI. 

EC measured by AE-16 was ~12% higher 

than RU-OGI. Calibration factors for the light 

absorption instruments need to be adjusted for 

better correlation. 

Calibration factor might be non-linear over the 

range of absorbance measured. 

The mean OC from R&P-5400 and RU-OGI 

were within 10% of filter RR values. Mean 

ADI-C OC was 14% lower than filter RR OC. 

EC from continuous instruments was 2-2.5 

times filter RR EC; continuous TC was also 

greater than filter RR TC by 17% (R&P-400) to 

27% (RU-OGI). 

December 2009 A-42 
SITE/PERIOD/SAMPLER/ CONFIGURATION SUMMARY OF FINDINGS 

PITTSBURGH SUPERSITE, PA; 06/01/01 to 07/31/02 

Six km east of downtown in a park on the top of a hill. 

Sampler Flow Filter Type/Packa Denuder Analysis Methodc

16.7 Non-denuded 

sample 

Teflon 

(P/W)-

Quartz (P) 

(QBT) 

None NIOSH 5040-TOT 

CMU Custom-1 

16.7 Non-denuded 

sample 

Quartz (P)-

Quartz (P) 

(QBQ) 

None NIOSH 5040-TOT 

16.7 Denuded 

sample 

DenuderQuartz (P)-

CIG (S) 

Activated Carbon NIOSH 5040-TOT 

16.7 Dynamic 

blank (DYN) 

Teflon 

(P/W)-

DenuderQuartz (P)-

CIG (S) 

Activated Carbon NIOSH 5040-TOT 

CMU Custom-2 

16.7 Non-denuded 

blank (UDB) 

Teflon 

(P/W)- 

Quartz (P)-

CIG (S) 

None NIOSH 5040-TOT 

Subramanian et al. (2004, 081203)

119 

Particulate OC (POC) was estimated from 

denuded sample (Quartz OC + CIG OC) after 

subtracting DYN POC. 

Denuder efficiency (1-DYN POC/UDB POC) 

was 94 ± 3%. No seasonal variability or 

deterioration in denuder performance was 

observed. 

Positive artifact due to denuder breakthrough 

was 18.3 ± 12.5% of the denuded sample 

POC. 

Negative artifact (CIGsample-CIGDYN) was, 

on avg, 6.3 ± 6.2% of POC. 

Positive artifact was 34 ± 10% from QBT, and 

was 13 ± 5% from QBQ. QBT >>QBQ. 

QBT over-corrected the positive artifact by 

20%. OC volatilization from the front Teflon 

filter that subsequently adsorbed on the backup quartz filter, resulted in an overestimation 

of the positive artifact. 

Non-denuded QBQ provided a more 

representative estimate of the positive artifact 

on the non-denuded front quartz filter for 24-h 

samples. However, it was not suitable for 4- to 

6-h samples, because the filters were not in 

equilibrium with the air stream. 

Positive artifact dominated when sampling 

with a non-denuded quartz filter. 

Comparison of 24-h avg non-denuded front 

quartz OC versus denuded POC over the year 

showed an intercept of 0.53 μg/m3

, indicative 

of a positive artifact on quartz filter samples. 

The artifacts were higher in summer on an 

absolute basis; however, they showed no 

seasonal variation when expressed as a 

fraction of POC. 

ST. LOUIS SUPERSITE, IL, MO; 01/01/02 to 12/31/02

Three km east of St. Louis, MO City center, also impacted by industrial sources, and located in a mixed 

residential light commercial neighborhood. 

Sampler Flow Rate 

(Lmin) 

Filter 

Type/Packa Denuderb Analysis Methodc

Quartz (P) None ACE Asia TOT 

University of 

Wisconsin Custom-1 24 Denuder-Quartz 

(P) CIF ACE Asia TOT 

Denuder-Quartz 

(P) CIF ACE Asia TOT 

University of 

Wisconsin Custom-2 24 Teflon (N/A)-

Denuder-Quartz 

(P) 

CIF ACE Asia TOT 

Continuous Sampler Flow Rate 

(L/Min) Denuder OC EC Comments 

Bae et al. (2004, 156243)

93; Bae et al. (2004, 

098680)

96 

Denuder breakthrough was 0.17 ± 0.15 μg/m3, 

and constituted less than 5% of annual avg 

OC concentration. 

Non-denuded OC = (1.06 ± 0.02) × denuded 

OC + (0.34 ± 0.10) 

Equivalence of OC intercept and denuder 

breakthrough implies that the low-level artifact 

is caused by denuder breakthrough. 

Non-denuded EC = (1.04 ± 0.03) × denuded 

EC + (0.07 ± 0.03), indicating negligible EC 

artifact. 

Results suggested higher summertime OC 

artifact, on an absolute basis. 

December 2009 A-43 
SITE/PERIOD/SAMPLER/ CONFIGURATION SUMMARY OF FINDINGS 

Sunset OCEC 8 CIF 340, 

500, 615, 

870°C in 

100% He 

550, 625, 

700, 775, 

850, 900 

°C in 2% 

O2, 98% 

He 

ACE Asia TOT; CH4 

FID detector 

Comparison of continuous Sunset TC and OC 

with 24-h filter samples showed good 

correlations (R2

) of 0.89 and 0.90, 

respectively. 

Continuous Sunset TC 

in μg/m3

 = (0.97 ± 0.02) × filter TC + 

(0.83 ± 0.11), indicating comparability with the 

filter measurements. 

Continuous Sunset OC = (0.93 ± 0.02) × filter 

OC + (0.94 ± 0.09) 

Positive intercept was interpreted to be a 

blank correction for the continuous 

measurements. 

EC comparison was poor with large scatter in 

data (R2

 = 0.60), probably due to low EC 

concentrations (avg = 0.70 μg/m3), close to 

the detection limit (0.5 μg/m3

). 

FRESNO SUPERSITE, CA and other CRPAQS sites; 12/02/99 to 02/03/01, 12/1/03 to 11/30/04 

Fresno Supersite was located 5.5 km northeast of downtown in a mixed residential-commercial 

neighborhood. 

Sampler Flow Rate 

(Lmin) 

Filter 

Type/Packa Denuderb Analysis Methodc

Quartz (P) None IMPROVE_TOR 

DRI-SFS 113 Teflon (P)-

Quartz (P) 

(QBT) 

None IMPROVE_TOR 

RAAS-400 24 

(P) (QBT) 

Quartz (P)-

Quartz (P) 

(QBQ) 

None IMPROVE_TOR 

RAAS-400 24 

Quartz (P)-

Quartz (P) 

(QBQ) 

XAD-4 / CIF IMPROVE_TOR 

RAAS-100 FRM 16.7 Quartz (P) None IMPROVE_TOR 

Continuous Sampler Flow Rate 

(L/Min) Denuder OC EC Comments 

R&P-5400 16.7 None 275°C in air 750°C in 

air 

No pyrolysis 

correction 

Sunset OCEC 8.5 CIG 

650, 750, 

850, 

940°C in 

2% O2 in 

He 

Transmittance 

MAAP 16.7 None babs @ 

670 nm 

Transmittance 

6.5 m2/g factor 

AE-16 6.8 None babs @ 

880 nm 

AE-21 6.8 None 

babs @ 

370, 880 

nm 

AE-31 6.8 None 

babs @ 

370, 470, 

520, 590, 

660, 880 

and 950 

nm 

Transmittance 

14625/λ m2

/g factor, 

where λ is in nm 

DRI-PA 3 None 

250, 500, 

650, 850°C 

in He 

babs @ 

1047 nm 

Absorption, 5 m2/g 

factor 

Sampler Flow Rate 

(Lmin) 

Filter 

Type/Packa Denuderb Analysis Methodc

Watson and Chow (2002, 037873)

91; Chow 

et al. (2005, 156348)

117; Chow et al. (2006, 

155207)

120; Watson et al. (2005, 157124)6

; 

Park et al. (2006, 098104)

102 

Non-denuded RAAS-400 and RAAS-100 FRM 

measured equivalent TC. DRI-SFS, RAAS400 and RAAS-100 FRM samplers showed 

comparability for front filter TC, OC and EC 

measurements. 

Positive OC artifact was 1.62 ± 0.58 μg/m3 

(~24% of non-denuded front quartz OC) from 

QBT, and 1.12 ± 0.91 μg/m3 (~17% of nondenuded front quartz OC) from QBQ. QBT 

>>QBQ 

Results from CRPAQS showed, on avg, a 

positive OC artifact of 34% (of the nondenuded front quartz OC) from QBT and 

17.5% (of the non-denuded front quartz OC) 

from QBQ. 

Positive artifact was higher during summer 

than winter. 

Negative artifact was, on avg, 

0.61 ± 0.58 μg/m3

 (~10% of POC) at Fresno. 

Over all the CRPAQS sites, it ranged from 

2.3% in winter to 11% in summer, with an avg 

of 4.9%. 

Positive artifact is estimated to be 0.5 μg/m3

. 

No difference in denuded quartz backup OC 

was found between using XAD and CIF 

denuders. 

Comparison of R&P-5400 TC, OC, and EC 

against filter samples showed poor correlation 

(R2

 < 0.55). 

TC from R&P-5400 was 40-60% higher than 

filter TC by TOR. None of the R&P-5400 

versus TOR filter comparisons were 

comparable or predictable, due to several 

frequent instrument malfunctions during the 

experiment and the small data set (~35 data 

points). 

IMPROVE_TOR EC was consistently 20-25% 

higher than aethalometer BC. 

IMPROVE_TOR EC was comparable to 

MAAP BC. 

Comparison of light absorption (babs) from 

DRI-PA (1047 nm), MAAP (670 nm), and AE 

December 2009 A-44 
SITE/PERIOD/SAMPLER/ CONFIGURATION SUMMARY OF FINDINGS 

PC-BOSS 150 Quartz (P)-CIG 

(S)† CIF TPV 

Continuous Sampler Flow Rate 

(L/Min) Denuder OC EC Comments 

R&P-5400 16.7 None 375°C in air 750°C in 

air No pyrolysis 

Sunset OCEC 8.0 CIG 

250, 500, 

650, 850°C 

in He 

650, 750, 

850°C in 

2% O2 & 

98% He 

NIOSH 5040_TOT 

NDIR CO2 detector 

(880 nm) analyzers with the filter 

IMPROVE_TOR EC, gave a σabs of 2.3, 5.5 

and 10 m2

/g, differing from the default 

conversion factors of 5, 6.5, and 16.6 m2/g 

used for each instrument at the specified 

wavelength. 

Grover et al. (2006, 138080)

65 

R&P-5400 TC = (0.50 ± 0.01) Sunset TC + 

(3.6 ± 1.5); R2

 = 0.73; n = 480 

Sunset TC = (0.63 ± 0.05) PC-BOSS TC + 

(4.1 ± 3.2); R2

 = 0.86; n = 29 

R&P-5400 TC = (0.41 ± 0.02) PC-BOSS TC + 

(6.7 ± 1.6); R2

 = 0.91; n = 29 

December 2009 A-45 
SITE/PERIOD/SAMPLER/ CONFIGURATION SUMMARY OF FINDINGS 

BALTIMORE SUPERSITE, MD; 02/15/2002 to 11/30/2002

East of downtown in an urban residential area. Within 91 m of bus maintenance facility. 

Sampler Flow Rate 

(L/Min) 

Filter 

Type/Packa Denuderb Analysis Methodc

SASS 6.7 Quartz (P)-

Quartz (P) None STN_TOT 

Continuous Sampler Flow Rate 

(L/Min) Denuderb OC EC Comments 

Sunset OCEC 8 Carbon 

600°C, 

then 

870°C in 

He 

870°C in 

2% O2 in He

TOT; CH4 FID 

detector; Denuder 

breakthrough ~ 

0.5-1 μg C/m3

; Used 

0.5 to correct OC 

concentrations 

Park et al. (2005, 156843)

95

Data capture 93.8% 

Compared to SASS, Sunset underestimated 

OC and EC by 22% and ~11.5%, respectively. 

Higher OC in SASS was attributed to the 

absence of a denuder (i.e., positive artifact by 

gaseous adsorption) and to temperature 

differences between the STN_TOT and 

Sunset_TOT carbon analysis temperature 

protocols. 

EC discrepancy was probably related to the 

differences in temperature protocol. 

RUBIDOUX, CA; 07/13/03 to 07/26/03 

Rubidoux is located in the eastern section of the South Coast Air Basin (SoCAB) in the northwest corner of 

Riverside County, 78 km downwind of the central Los Angeles metropolitan area and in the middle of the 

remaining agricultural production area in SoCAB. 

Sampler Flow Rate 

(L/Min) 

Filter 

Type/Packa Denuderb Analysis Methodc

PC-BOSS 150 Quartz (P)-CIG 

(S) CIF TPB (CIG heated to 450 °C in N2) 

Continuous Sampler Flow Rate 

(L/Min) Denuderb OC EC Comments 

Sunset OCEC 8 CIF N/A N/A 

TOT; NDIR detector; 

NIOSH 5040 

protocol 

Sunset OCEC 8 CIF N/A Not measured 

TOT; has blank 

quartz filter before 

entering analyzer. 

Used as “blank” 

stream for 

quantifying OC 

artifacts; 3-step 

analysis only in He. 

Grover et al. (2005, 090044)

66 

Sunset OCEC TC = (0.90 ± 0.06) PC-BOSS + 

(2.0 ± 2.1); R2

 = 0.93; n = 21 

Sunset TC was adjusted for carbon artifacts 

measured by second (blank) instrument. 

NEW YORK SUPERSITE, NY; 01/12/04 to 02/05/04

Urban site located at Queens College, NY, about 14 km west of Manhattan, within 2 km of freeways, and 

within 12 km of international airports. 

Integrated Sampler Flow Rate 

(L/Min) 

Filter 

Type/Packa Denuderb Analysis Methodc

R&P-2300 10 Quartz None STN_TOT 

Continuous Sampler Flow Rate 

(L/Min) Denuderb OC EC Comments 

R&P-5400 16.7 None 340 °C in 

air 

750 °C in 

air No pyrolysis 

correction 

Sunset OCEC N/A CIF 600, 870 

°C in He 

870 °C at 

10% O2 in 

He 

Transmittance 

AE-20 N/A None babs @ 370, 

880 nm 

Transmittance, 

14625 λ m2

/g factor, 

where λ is in nm 

AMS N/A None N/A N/A ~ 1 μm cut-point 

Venkatachari et al. (2006, 105918)

92 

Regression of OC from Sunset OCEC against 

PM2.5 mass concentration yielded an intercept 

of 1.14 μg/m3, which was used as a measure 

of the positive artifact on the Sunset data. The 

Sunset OC data was corrected for this artifact. 

AE-20 BC concentrations were ~86% of 

Sunset EC and R&P2300 filter EC 

concentrations. 

AE-20 versus R&P-5400 showed high scatter. 

Sunset Optical EC = 0.58 ± 0.05 Sunset 

Thermal EC; R2

 = 0.86; n = 506 

Sunset Optical EC = 0.62 ± 0.05 AE-20 BC; 

R2

 = 0.96; n = 539 

R&P-5400 TC tracked filter TC closely, but 

differed widely for OC and EC. 

Sunset OC = (0.75 ± 0.76) R&P-2300 OC + 

(0.08 ± 0.36); R2

 = 0.67; n = 16 

Sunset OC = (0.98 ± 0.11) R&P-5400 OC - 

(0.47 ± 0.17); R2

 = 0.44; n = 327 

December 2009 A-46 
SITE/PERIOD/SAMPLER/ CONFIGURATION SUMMARY OF FINDINGS 

R&P-5400 OC = (0.60 ± 0.47) R&P-2300 OC 

+ (0.58 ± 0.82); R2

 = 0.58; n = 17 

Organic matter measurements by AMS 

showed reasonable correlation (R2

 = 0.76) 

with filter (R&P-2300) OC, while being poorly 

correlated with continuous OC by Sunset 

(R2

 = 0.32) and R&P-5400 (R2 = 0.36) 

Sunset EC = (1.21 ± 0.44) R&P-2300 EC – 

(0.03 ± 0.13); R2

 = 0.94; n = 16 

Sunset EC = (1.35 ± 0.12) R&P-5400 EC + 

(0.06 ± 0.04); R2

 = 0.61; n = 327 

R&P-5400 EC = (0.49 ± 0.46) R&P-2300 EC + 

(0.09 ± 0.26); R2 = 0.77; n = 15 

Sunset TC = (0.86 ± 0.39) R&P-2300 TC – 

(0.06 ± 0.69); R2

 = 0.77; n = 16 

Sunset TC = (1.31 ± 0.10) R&P-5400 TC – 

(1.15 ± 0.15); R2

 = 0.59; n = 327 

R&P-5400 TC = (0.77 ± 0.58) R&P-2300 TC + 

(0.35 ± 1.37); R2

 = 0.83; n = 16 

a

Filter Manufacturer in parentheses - W: Whatman, Clifton, NJ; P: Pall-Gelman, Ann Arbor, MI; S: Schleicher & Schnell. Keene, NH; N/A: not available. QBT: quartz backup filter behind Teflon front filter. QBQ: 

quartz backup filter behind Quartz front filter. 

b

Al2O3: Aluminum oxide; IC: Ion chromatography; CIF: Charcoal Impregnated Filter; CIG: Charcoal Impregnated Glass-Fiber Filter; FEP: Fluorinated Ethylene Propylene copolymer; MgO: Magnesium oxide; 

Na2CO3: Sodium carbonate; NaHCO3: Sodium bicarbonate NOX: Oxides of nitrogen; SO2: Sulfur dioxide; TEA: Triethanolamine; TSP: Total Suspended PM; UV: Ultraviolet; XAD-4: (hydrophobic, non-polar 

polyaromatic resin. 

c

NIOSH 5040_TOT: National Institute of Occupational Safety and Health Method 5040 Thermal Optical Transmittance Protocol. 121, 122, 123, 124, 125 OC: 250, 500, 650, 850 °C for OC1, OC2, OC3, and OC4 

fractions, respectively, for 60, 60, 60, 90 sec respectively, in 100% He atmosphere. EC: 650, 750, 850, 940 °C for EC1, EC2, EC3, and EC4 fractions, respectively, 30, 30, 30, >120 sec respectively, in 98% He 

and 2% O2 atmosphere. OPT: Pyrolysis correction by transmittance. IMPROVE_TOR: Interagency Monitoring of Protected Visual Environments Thermal Optical Reflectance Protocol. 126 OC fractions: 120, 

250, 450, 550 °C for OC1, OC2, OC3, and OC4 fractions, respectively, until a well defined peak has evolved at each step, with a time limit of min 80 sec and max of 580 sec, in 100% He atmosphere. EC 

fractions: 550, 700, 800 °C for EC1, EC2, and EC3 fractions, respectively, until a well defined peak has evolved at each step, with a time limit of min 80 sec and max of 580 sec, in 2% O2 and 98% He 

atmosphere. OPR: Pyrolysis correction for pyrolyzed organic carbon (OP) by reflectance. OC = OC1+OC2+OC3+OC4+OP EC = EC1+EC2+EC3-OP TC = OC+EC. IMPROVE_A TOR: 127 Note that as of 

May, 2007, the U.S. EPA is switching samples from the Speciation Trends Network thermal optical transmittance protocol to the IMPROVE_A protocol. OC: 140, 280, 480, 580 °C for OC1, OC2, OC3, and 

OC4, fractions, respectively, until a well defined peak has evolved at each step, with a time limit of 80 sec and max of 580 sec, in 100% He atmosphere EC: 580, 740, 840 °C for EC1, EC2, and EC3 fractions, 

respectively, until a well defined peak has evolved at each step, with a time limit of min 80 sec and max of 580 sec, in 2% O2 and 98% He atmosphere. OPR: Pyrolysis correction for pyrolyzed organic carbon 

(OP) by reflectance. OPT: Pyrolysis correction by transmittance. TPV: Temperature Programmed Volatilization. 17, 81, 128 For CIF Filters: Heated from 50 °C to 300 °C at a ramp rate of 10 °C/min in N2. For 

Quartz filters: Heated from 50 °C to 800 °C at a ramp rate of 28 °C/min in 70% N2 and 30% O2; EC estimated from high temperature peak (>450 °C) on thermogram obtained from quartz-fiber filter analysis; 

No pyrolysis correction. STN_TOT: Speciation Trends Network Thermal Optical Transmittance Protocol.129 OC: 310, 480, 615, 920 °C for 60, 60, 60, 90 sec respectively, in 100% He atmosphere. EC: 600, 

675, 750, 825, 920 °C for 45, 45, 45, 45, 120 sec respectively, in 98% He and 2% O2 atmosphere. ACE Asia TOT: Aerosol Characterization Experiments in Asia Thermal Optical Transmittance Protocol. 130 

OC: 340, 500, 615, 870 °C for 60, 60, 60, 90 sec respectively, in 100% He atmosphere. EC: 550, 625, 700, 775, 850, 900 °C for45, 45, 45, 45, 45, 120 sec respectively, in 98% He, 2% O2. Pyrolysis correction 

by transmittance. 

d

Custom TOT: XAD-4 impregnated quartz, analyzed in He-only atmosphere with a maximum temperature 176 °C; EC is not measured. e

Custom TOR to suit Al substrate; details not reported. 

f

37 mm filter 

1

Chow (1995, 077012); 2Watson and Chow (2001, 157123); 3 Watson et al. (1983, 045084); 4Fehsenfeld et al. (2004, 157360); 5Solomon et al. (2001, 157193); 6Watson et al. (2005, 157124); 7Mikel (2001, 

156762); 8Watson et al. (1999, 020949); 9Solomon and Sioutas (2006, 156995); 10Graney et al. (2004, 053756); 11Tanaka et al. (1998, 157041); 12Pancras et al. (2005, 098120); 13John et al. (1988, 045903); 14Hering and Cass (1999, 084958); 15Fitz et al. (1989, 077387); 16Hering et al. (1988, 036012); 17Solomon et al. (2003, 156994); 18Cabada et al. (2004, 148859); 19Fine et al. (2003, 155775); 20Hogrefe et al. 

(2004, 099003); 21Drewnick et al. (2003, 099160); 22Watson et al. (2005, 157125); 23Ho et al. (2006, 156552); 24Decesari et al. (2005, 144536); 25 Mayol-Bracero et al. (2002, 045010); 26Yang et al. (2003, 

156167); 27Turšic et al. (2006, 157063); 28Mader et al.(2004, 156724); 29Xiao and Liu (2004, 056801); 30Kiss et al. (2002, 156646); 31Cornell and Jickells (1999, 156367); 32 Zheng et al. (2002, 026100); 33Fraser et al. (2002, 140741); 34 Fraser et al. (2003, 042231) 35Schauer er al. (2000, 012225); 36Fine et al. (2004, 141283); 37Yue et al. (2004, 157169); 38Rinehart et al. (2006, 115184); 39Wan and Yu (2006, 

157104); 40Poore (2000, 012839); 41Fraser et al. (2003, 040266); 42Engling et al. (2006, 156422); 43Yu et al. (2005, 157167); 44Tran et al. (2000, 013025); 45Yao et al. (2004, 102213); 46Li and Yu (2005, 

156692); 47Henning et al. (2003, 156539); 48Zhang and Anastasio (2003, 157182); 49Emmenegger et al. (2007, 156418); 50 Watson et al. (1989, 046318); 51Greaves et al. (1985, 156494); 52Waterman et al. 

(2000, 157116); 53Waterman et al. (2001, 157117); 54Falkovich and Rudich (2001, 156427); 55Chow et al. (2007, 157209); 56Miguel et al. (2004, 123260); 57Crimmins and Baker (2006, 097008); 58Ho and Yu 

(2004, 156551); 59Jeon et al. (2001, 016636); 60Mazzoleni et al. (2007, 098038); 61Poore (2002, 051444); 62Butler et al. (2003, 156313); 63Chow et al. (2006, 146622); 64Russell et al. (2004, 082453); 65Grover 

et al. (2006, 138080); 66Grover et al. (2005, 090044); 67Schwab et al. (2006, 098449); 68Hauck et al. (2004, 156525); 69Jaques et al. (2004, 155878); 70Rupprecht and Patashnick (2003, 157207); 71Pang et al 

(2002, 030353); 72Eatough et al. (2001, 010303); 73Lee et al. (2005, 128139); 74Lee et al. (2005, 156680); 75Babich et al. (2000, 156239); 76Lee et al. (2005, 155925); 77Lee et al. (2005, 128139); 78Anderson 

and Ogren (1998, 156213); 79Chung et al. (2001, 156357); 80Kidwell and Ondov (2004, 155898); 81Lithgow et al. (2004, 126616); 82Weber et al. (2003, 157129); 83Harrison et al. (2004, 136787); 84Rattigan et 

al. (2006, 115897); 85Wittig et al. (2004, 103413); 86Vaughn et al. (2005, 157089); 87Chow et al. (2005, 099030); 88Weber et al (2001, 024640) ; 89Schwab et al. (2006, 098785); 90Lim et al. (2003, 037037); 91Watson and Chow (2002, 037873); 92Venkatachari et al. (2006, 105918); 93Bae et al. (2004, 156243); 94Arhami et al. (2006, 156224); 95Park et al. (2005, 156843); 96Bae et al. (2004, 098680); 97Chow et al. 

(2006, 156350); 98Arnott et al. (2005, 156227); 99Bond et al. (1999, 156281); 100Virkkula et al. (2005, 157097); 101Petzold et al. (2002, 156863); 102Park et al. (2006, 098104); 103Arnott et al. (1999, 020650); 104Peters et al. (2001, 016925); 105Pitchford et al. (1997, 156872); 106Rees et al. (2004, 097164); 107Watson et al. (2000, 010354); 108Lee et al. (2005, 156680); 109Hering et al. (2004, 155837); 110Watson et al. 

(1998, 198805); 111Chakrabarti et al. (2004, 157426); 112Mathai et al. (1990, 156741); 113Kidwell and Ondov (2001, 017092); 114Stanier et al. (2004, 095955); 115Khlystov et al. (2005, 156635); 116Takahama et 

al. (2004, 157038); 117Chow et al. (2005, 156348); 118Zhang et al. (2002, 157181); 119Subramanian et al. (2004, 081203); 120Chow et al. (2006, 155207); 121Birch and Cary (1996, 026004); 122Birch (1998, 

024953); 123Birch and Cary (1996, 002352); 124NIOSH (1996, 156810); 125NIOSH (1999, 156811); 126Chow et al. (1993, 077459); 127Chow et al. (2007, 156354); 128Ellis and Novakov (1982, 156416); 129Peterson and Richards (2002, 156861); 130Schauer et al. (2003, 037014); 131Middlebrook et al. (2003, 042932); 132Wenzel et al. (2003, 157139); 133Jimenez et al. (2003, 156611); 134Phares et al. (2003, 

156866); 135Qin and Prather (2006, 156895); 136Zhang et al. (2005, 157185); 137Bein et al. (2005, 156265); 138Drewnick et al. (2004, 155754); 139Drewnick et al. (2004, 155755); 140Lake et al. (2003, 156669); 141Lake et al. (2004, 088411) 

Source: Chow et al. (2008, 156355)

December 2009 A-47 
Table A-16. Summary of particle mass spectrometer measurement comparisons. 

ATLANTA SUPERSITE, GA: 08/03/99 to 09/01/99 

Four km NW of downtown, within 200 m of a bus maintenance yard and several warehouse facilities, representative of a mixed commercial-residential neighborhood. 

Spectrometer 

Inlet Characteristics (Flow Rate 

[L/Min]; Size Inlet; Dryer 

Aerodynamic Diameter, μm; 

Particle Sizing Method) 

Volatilization/ 

Ionization 

Methoda Hit Ratesb Mass Spectrometerc Particle Analysis/ 

Classification Other 

PALMS 

N/A 

PM2.5 cyclone 

Nafion (17 days) / None (4 days) 

0.35-2.5 

Light scattering 

LDI, 

ArF 193 nm 

2x109

 to 5x109

W/cm2

14 to 100%, 

overall 87% 

Single TOF reflectron; 

Ion polarity needs to 

be pre-selected 

Peak ID/regression 

tree analysis 

ATOFMS 

1 

None 

None 

0.2-2.5 

Aerosol TOF 

LDI, Nd: YAG 266 

nm laser 

~ 1x108

 W/cm2

25-30%, 

occasionally 

as low as 5% 

Dual TOF reflectron; 

Detects both positive 

and negative ions 

Aerosol TOF 

RSMS-II 

N/A 

None 

Nafion 

0.015-1.3 

Aerodynamic focusing; Need to 

pre-select sizes to be analyzed 

LDI, Arf laser, 193 

nm 

1x108

 to 2x108

W/cm2

N/A 

Single linear TOF; Ion 

polarity needs to be 

pre-selected 

Peak ID/artificial 

neural network 

Pure sulfuric 

acid (H2SO4), 

(NH4)2SO4, 

and water 

(H2O) 

have relatively

high ionization 

thresholds (i.e. 

difficult to 

ionize). 

Fraction of 

molecules 

ionized in the 

particles is on 

the order of 

10-5 to 10-6. 

AMS 

N/A 

PM2.5 cyclone 

None 

0.05-2.5 

Aerosol TOF 

T~550°C/ EI N/A 

Quadrupole; 

Mass weighted size 

distributions on preselected positive ions 

only. 

ID using standard 

EI ionization 

databases 

Does not 

detect/ analyze 

highly 

refractory 

materials such 

as metals, sea 

salt, soot etc. 

Fraction of 

molecules 

ionized in the 

particles is on 

the order of 

10-6 to 10-7 

Middlebrook et al. (2003, 042932)

131; Wenzel et al. (2003, 157139)132; Jimenez et al. (2003, 156611)133 

Particle sizing is approximate in PALMS, while ATOFMS, RSMS-II and AMS provide relatively accurate particle sizing. 

Particle transmission in AMS is ~100% (i.e., it uses all particles in the sampled air) between 60 and 600 nm, while that for PALMS, ATOFMS and RSMS-II 

range from 10-6 for submicron particles to 2% for supermicron (>0.8 μm) particles. 

AMS has fewer matrix effects (due to separate volatilization and ionization steps) compared to single-step LDI instruments. 

While four major particle classifications (organic/SO4

2–, sodium/potassium sulfate, soot/hydrocarbon and mineral) were observed by all three laser 

instruments, they differed in the classification frequencies. Differences in frequencies that are detected and grouped are related to the differences in the 

laser ionization conditions (e.g., wavelength), particle transmission, sizing method and the way the spectra were classified. 

Shorter ionization wavelengths are able to produce ions more easily than longer ones. 

Low hit rates in ATOFMS corresponded to periods of high SO4

2– concentrations. Low hit rates in PALMS were related to a variety of factors including high 

SO4

2– concentrations, differing laser fluence and laser position relative to particle beam. Use of a dryer in PALMS enhanced ionization of particles that were 

difficult to ionize at high ambient RH. 

The RSMS-II and ATOFMS were less sensitive to SO4

2– and hence may have fewer organic/SO42– particles (i.e., underestimate SO42– , pure sulfuric acid 

etc.). 

The PALMS, ATOFMS and RSMS (laser based instruments) are qualitative, while the AMS can be quantitative. The relative ratio of ion intensities from the 

laser instruments, however, may be indicative of relative concentrations, thus giving semi-quantitative information. 

Comparison of the ratio of NO3 to SO4 peaks with the results from the semi continuous instruments showed better correlation with the AMS (R2

 = 0.93) than 

PALMS (R2

 = 0.65 for non-dry particles to 0.70 for dry particles). While reasonable correlations between the PALMS and the composite semi-continuous 

data indicate the possibility for calibration of laser-based data for certain ions, the calibration factors may vary depending on the particle matrix, water 

content and laser ionization parameters, and averaging the spectra according to these factors may minimize these effects. 

Comparison of AMS SO4 with PILS SO4 showed good correlation (R2

 = 0.79), and the data uniformly scattered around a 1:1 line. NO3 comparison was poor 

(R2

 = 0.49) because of the low signal to noise ratio at low concentrations 

The continuum between particle classifications indicates that the particles were not adequately represented by non-overlapping classifications. 

December 2009 A-48 
ATLANTA SUPERSITE, GA: 08/03/99 to 09/01/99 

Four km NW of downtown, within 200 m of a bus maintenance yard and several warehouse facilities, representative of a mixed commercial-residential neighborhood. 

HOUSTON SUPERSITE, TX; 08/23/00 to 09/18/00 

Houston Regional Monitoring Site was located < 1.0 km north of the Houston ship channel, where chemical and other industries are present. The site was 

located between a railway to the south and a chemical plant to the north. Major freeways were located just to the north and east of the sampling site. 

Spectrometer 

Inlet Characteristics (Flow Rate 

[L/Min]; Size Inlet; Dryer 

Aerodynamic Diameter, μm; 

Particle Sizing Method) 

Volatilization/ 

Ionization 

Methoda Hit Ratesb Mass Spectrometerc Particle Analysis/ 

Classification Other 

RSMS-II 

N/A 

None 

Nafion 

0.035-1.14 

Aerodynamic focusing; Need to 

pre-select sizes to be analyzed 

LDI, ArF laser, 

193 nm N/A 

Single linear TOF; Ion 

polarity needs to be 

pre-selected 

Peak ID/artificial 

neural network 

At each size 

point, aerosol 

was sampled 

in each cycle 

for either 10 

min or until 

mass spectra 

for 30 particles 

per major 

class were 

collected, 

whichever 

came first. 

Phares et al. (2003, 156866)

134

27,000 spectra were classified using a neural network into 15 particle types 

Fifteen particle type mass spectra were presented along with their size distribution, avg time of day occurrence, and wind direction dependence 

Major classes were a K+

 dominant, Si/Silicon Oxide, Carbon, Sea Salt, Fe, Zn, Amines, Lime, Vanadium, Organic Mineral, Pb and K, Al, and a Pb salt 

particle type. 

FRESNO SUPERSITE, CA: 11/30/00 to 2/4/01 

Urban location in a residential neighborhood. 

Spectrometer 

Inlet Characteristics (Flow Rate 

[L/Min]; Size Inlet; Dryer 

Aerodynamic Diameter, μm; 

Particle Sizing Method) 

Volatilization/ 

Ionization 

Methoda Hit Ratesb Mass Spectrometerc Particle Analysis/ 

Classification Other 

ATOFMS 

1 

None 

None 

0.3-2.5 

Aerodynamic 

LDI, ND: YAG 

266 nm N/A Dual reflectron TOF 

Peak ID/artificial 

neural network 

ATOFMS 

unscaled 

detected 

particles 

tracked β

attenuation 

monitor PM2.5

mass 

concentration 

Qin and Prather (2008, 156985)

135 

Biomass burning particles reached a maximum at night and a minimum during the day. These particles were less than 1 μm in diameter and accounted for 

more than 60% of the particles detected at night. 

Another particle class characterized by high mass carbon fragments had a similar diurnal pattern. These particles were larger than 1 μm and were 

interpreted as biomass particles that have undergone gas to particle conversion of semi-volatile species followed by dissolution in a water droplet. 

PITTSBURGH SUPERSITE, PA; 09/07/02 TO 09/22/02 FOR AMS; 09/20/01 to 09/26/02 for RSMS-III

6 km east of downtown in a park on the top of a hill 

Spectrometer 

Inlet Characteristics (Flow Rate [L/Min]; 

Size Inlet; Dryer Aerodynamic 

Diameter, μm; Particle Sizing Method) 

Volatilization/ 

Ionization 

Methoda Hit Ratesb Mass Spectrometerc

AMS 

1.4 cc/s 

PM2.5 cyclone 

None 

0.05-1.0 

Aerosol TOF 

T - 600°C/ EI 

Quadrupole; 

Mass weighted size distributions on 

pre-selected positive ions only. 

Particle size-cut of ~1 μm 

RSMS-III 

N/A 

None 

Nafion 

0.03-1.1 

Aerodynamic focusing; Need to preselect sizes to be analyzed. 

LDI, ArF laser, 193 

nm 

Dual TOF feflectron; Detects both 

positive and negative ions 

At each size point, aerosol 

was sampled in each cycle for 

either 10 min or until mass 

spectra for 30 particles per 

major class were collected, 

whichever came first 

December 2009 A-49 
ATLANTA SUPERSITE, GA: 08/03/99 to 09/01/99 

Four km NW of downtown, within 200 m of a bus maintenance yard and several warehouse facilities, representative of a mixed commercial-residential neighborhood. 

Zhang et al. (2005, 157185)

136; Bein et al. (2005, 156265)137 

The AMS observed 75% of the SO4

2– measured by R&P-8400S (R2

 = 0.69). 

Collection efficiency (CE) of 0.5 used for SO4

2– , NO3 and NH4+ and 0.7 for organics to correct mass concentrations for incomplete detection. Use of a 

constant CE irrespective of size and shape may overestimate accumulation mode (mostly, oxygenated) organics (true CE ~ 0.5) and underestimate smaller 

mode (primary) organics (true CE ~ 1.0). 

Comparison of AMS organics (organic matter, OM) with OC measured by a continuous Sunset OCEC instrument showed good correlation (R2

 = 0.88) with 

a slope of 1.69. A 24-h avg comparison, showed a slope of 1.45. These values are in the typical range of 1.2 to 2.0 for OM/OC ratios. 

AMS could be used along with the SMPS to estimate particle density. The AMS did not always agree with SMPS, probably due to non-spherical particles 

(irregular) such as soot from fresh traffic emissions, whose mass may be overestimated by the SMPS. 

Comparison of AMS mass with the MOUDI, showed differences for aerodynamic diameters >600 nm, probably due to the AMS transmission being less than 

unity for particles larger than 600 nm. 

For RSMS-III, 54% of the detected particles were assigned to one class (carbonaceous ammonium nitrate). This class was preferentially detected during 

the colder months and was detected from many different wind directions. 

The next largest RSMS-III class was EC/OC/K class at 11%, and is believed to be from biomass burning. 

An unidentified organic carbon RSMS-III class (3.3% of all detected particles) was seen to be highly dependent on wind direction dependence and was 

primarily detected during August and September of 2002. These particles likely originated from a landfill. 

NEW YORK SUPERSITE; 06/30/01 to 08/05/01 (urban); 07/09/02 to 08/07/02: (rural)

Urban Site: Queens College, Queens, New York, located at the edge of a parking lot and within 1 km from expressways and highways in New York City 

Metropolitan area. 

Rural Site: Whiteface Mountain, New York, located in a cleared area surrounded by mix of deciduous and evergreen trees, ~2 km away from the closest 

highway with no major cities within 20 km. 

Spectrometer 

Inlet Characteristics (Flow 

Rate [L/Min]; Size Inlet; Dryer 

Aerodynamic Diameter, μm; 

Particle Sizing Method) 

Volatilization/ 

Ionization 

Methoda Hit Ratesb Mass Spectrometerc

AMS 

0.1 

PM2.5 cyclone 

None 

0.02-2.5 

Aerosol TOF 

T – 700°C/ EI 

Quadrupole; 

Mass weighted size 

distributions on preselected positive ions 

only. 

Data are 10-min 

averages 

Drewnick et al. (2004, 155754)

138; Drewnick et al. (2004, 155755)139; Hogrefe et al. (2004, 099003)20 

Transport losses were 1.3% on avg. 

Inlet losses (at the inlet of AMS) were 1.9%, on avg, ranging from 11% for a 20 nm particle to 9% for a 2.5 μm particle, with a minimum of 0.7% for a 350 nm 

particle 

Overall measurement uncertainty of particle diameter was ~11%. 

The AMS was reliable with proper calibration, care, and maintenance. Valid 10 min averages were obtained for all components more than 93% of the time. 

The mass to charge ratios (m/z) of fragments from different components may overlap (e.g., NH+, a fragment of NH4

+

 and CH3

+

, a fragment of organic 

species, have m/z = 15) resulting in an interference (called as isobaric interference) Interfering signals were not used to calculate concentrations. This loss 

in concentration was adjusted by applying a correction factor determined from laboratory studies. 

Typical interferences were from fragments of organic species, water and oxygen. 

With adjustments, the SO4

2– , NO3–, and ammonium concentrations measured by the AMS were consistently lower than that measured by other co-located 

instruments, probably due to incomplete focusing of the (NH4)2SO4 and NH4NO3 particles by the aerodynamic lens. 

At the urban site, AMS NO3 was within 10% of the filter NO3 concentration. At the rural site, it had a slope of 0.51 and R2

 of 0.46. 

AMS SO4 showed good agreement with R&P-8400S at both the rural and urban locations (R2

 = 0.89 to 0.92, slope = 0.99, n = 407 to 695) and was within 

70 to 85% of filter SO4

2– concentration. 

Comparison of the total non-refractory mass measured by the AMS with the PM2.5 TEOM mass (operated at 50°C or with dryer) at the urban location, 

showed good correlation (R2 = 0.91) with near zero intercept (0.22 μg/m3). On avg, the AMS observed 64% of the mass measured by the TEOM. 

The unexplained mass (36%) was attributed to transport losses, transmission and optical losses, and refractory components in the aerosol sample (e.g., 

metals, EC). The mass closure was within the estimated uncertainty of the AMS mass measurements (5-10%). 

December 2009 A-50 
ATLANTA SUPERSITE, GA: 08/03/99 to 09/01/99 

Four km NW of downtown, within 200 m of a bus maintenance yard and several warehouse facilities, representative of a mixed commercial-residential neighborhood. 

BALTIMORE SUPERSITE, MD; 04/01/02 to 11/30/02

East of downtown in an urban residential area. Within 91 m of a bus maintenance facility. 

Spectrometer 

Inlet Characteristics (Flow 

Rate [L/Min]; Size Inlet; Dryer 

Aerodynamic Diameter, μm 

Particle Sizing Method) 

Volatilization/ 

Ionization 

Methoda Hit Ratesb Mass Spectrometerc

RSMS-III 

0.2-18, based on particle size 

chosen 

None 

Nafion 

0.045-1.3 

Aerodynamic focusing; Need 

to pre-select sizes to be 

analyzed 

LDI, ArF laser, 193 nm TOF with dual ion polarity 

At each size set point, 

aerosol was sampled 

in each cycle for either 

10 min or until mass 

spectra from 30 

particles were 

collected, whichever 

came first. 

Lake et al. (2003, 156669)

140, Lake et al. (2004, 088411)141 

Utilizing both positive and negative ion detection enables detection of more species. However, detection efficiencies of negative ions decreased for smaller 

particles. 

SO4

+

 concentration (number or mass) was not accurately quantified. 

RSMS-III was most efficient in 0.050 to 0.77 μm range. 

Particle compositions could be related to specific source categaories. 

aEI: Electon Impact; LDI: Laser Desorption / Ionization 

bHit rate refers to the number of particles with a mass spectrum as a fraction of the number of particles detected. It does not apply to RSMS and AMS because there is no separate detection 

cTOF: Time fo Flight 

1

Chow (1995, 077012); 2Watson and Chow (2001, 157123); 3 Watson et al. (1983, 045084); 4Fehsenfeld et al. (2004, 157360); 5Solomon et al. (2001, 157193); 6Watson et al. (2005, 157124); 7Mikel (2001, 

156762); 8

Watson et al. (1999, 020949); 9Solomon and Sioutas (2006, 156995); 10Graney et al. (2004, 053756); 11Tanaka et al. (1998, 157041); 12Pancras et al. (2005, 098120); 13John et al. (1988, 045903); 14Hering and Cass (1999, 084958); 15Fitz et al. (1989, 077387); 16Hering et al. (1988, 036012); 17Solomon et al. (2003, 156994); 18Cabada et al. (2004, 148859); 19Fine et al. (2003, 155775); 20Hogrefe et al. 

(2004, 099003); 21Drewnick et al. (2003, 099160); 22Watson et al. (2005, 157125); 23Ho et al. (2006, 156552); 24Decesari et al. (2005, 144536); 25 Mayol-Bracero et al. (2002, 045010); 26Yang et al. (2003, 

156167); 27Turšic et al. (2006, 157063); 28Mader et al.(2004, 156724); 29Xiao and Liu (2004, 056801); 30Kiss et al. (2002, 156646); 31Cornell and Jickells (1999, 156367); 32 Zheng et al. (2002, 026100); 33Fraser et al. (2002, 140741); 34 Fraser et al. (2003, 042231) 35Schauer er al. (2000, 012225); 36Fine et al. (2004, 141283); 37Yue et al. (2004, 157169); 38Rinehart et al. (2006, 115184); 39Wan and Yu (2006, 

157104); 40Poore (2000, 012839); 41Fraser et al. (2003, 040266); 42Engling et al. (2006, 156422); 43Yu et al. (2005, 157167); 44Tran et al. (2000, 013025); 45Yao et al. (2004, 102213); 46Li and Yu (2005, 

156692); 47Henning et al. (2003, 156539); 48Zhang and Anastasio (2003, 157182); 49Emmenegger et al. (2007, 156418); 50 Watson et al. (1989, 046318); 51Greaves et al. (1985, 156494); 52Waterman et al. 

(2000, 157116); 53Waterman et al. (2001, 157117); 54Falkovich and Rudich (2001, 156427); 55Chow et al. (2007, 157209); 56Miguel et al. (2004, 123260); 57Crimmins and Baker (2006, 097008); 58Ho and Yu 

(2004, 156551); 59Jeon et al. (2001, 016636); 60Mazzoleni et al. (2007, 098038); 61Poore (2002, 051444); 62Butler et al. (2003, 156313); 63Chow et al. (2006, 146622); 64Russell et al. (2004, 082453); 65Grover 

et al. (2006, 138080); 66Grover et al. (2005, 090044); 67Schwab et al. (2006, 098449); 68Hauck et al. (2004, 156525); 69Jaques et al. (2004, 155878); 70Rupprecht and Patashnick (2003, 157207); 71Pang et al 

(2002, 030353); 72Eatough et al. (2001, 010303); 73Lee et al. (2005, 128139); 74Lee et al. (2005, 156680); 75Babich et al. (2000, 156239); 76Lee et al. (2005, 155925); 77Lee et al. (2005, 128139); 78Anderson 

and Ogren (1998, 156213); 79Chung et al. (2001, 156357); 80Kidwell and Ondov (2004, 155898); 81Lithgow et al. (2004, 126616); 82Weber et al. (2003, 157129); 83Harrison et al. (2004, 136787); 84Rattigan et 

al. (2006, 115897); 85Wittig et al. (2004, 103413); 86Vaughn et al. (2005, 157089); 87Chow et al. (2005, 099030); 88Weber et al (2001, 024640) ; 89Schwab et al. (2006, 098785); 90Lim et al. (2003, 037037); 91Watson and Chow (2002, 037873); 92Venkatachari et al. (2006, 105918); 93Bae et al. (2004, 156243); 94Arhami et al. (2006, 156224); 95Park et al. (2005, 156843); 96Bae et al. (2004, 098680); 97Chow et al. 

(2006, 156350); 98Arnott et al. (2005, 156227); 99Bond et al. (1999, 156281); 100Virkkula et al. (2005, 157097); 101Petzold et al. (2002, 156863); 102Park et al. (2006, 098104); 103Arnott et al. (1999, 020650); 104Peters et al. (2001, 016925); 105Pitchford et al. (1997, 156872); 106Rees et al. (2004, 097164); 107Watson et al. (2000, 010354); 108Lee et al. (2005, 156680); 109Hering et al. (2004, 155837); 110Watson et al. 

(1998, 198805); 111Chakrabarti et al. (2004, 157426); 112Mathai et al. (1990, 156741); 113Kidwell and Ondov (2001, 017092); 114Stanier et al. (2004, 095955); 115Khlystov et al. (2005, 156635); 116Takahama et 

al. (2004, 157038); 117Chow et al. (2005, 156348); 118Zhang et al. (2002, 157181); 119Subramanian et al. (2004, 081203); 120Chow et al. (2006, 155207); 121Birch and Cary (1996, 026004); 122Birch (1998, 

024953); 123Birch and Cary (1996, 002352); 124NIOSH (1996, 156810); 125NIOSH (1999, 156811); 126Chow et al. (1993, 077459); 127Chow et al. (2007, 156354); 128Ellis and Novakov (1982, 156416); 129Peterson and Richards (2002, 156861); 130Schauer et al. (2003, 037014); 131Middlebrook et al. (2003, 042932); 132Wenzel et al. (2003, 157139); 133Jimenez et al. (2003, 156611); 134Phares et al. (2003, 

156866); 135Qin and Prather (2006, 156895); 136Zhang et al. (2005, 157185); 137Bein et al. (2005, 156265); 138Drewnick et al. (2004, 155754); 139Drewnick et al. (2004, 155755); 140Lake et al. (2003, 156669); 141Lake et al. (2004, 088411) 

Source: Chow et al. (2008, 156355)

December 2009 A-51 
Table A-17. Summary of key parameters for TD-GC/MS and pyrolysis-GC/MS. 

Reference Sample Type TD Unit Analytical Instrument Total Analysis 

Time 

TD-GC/MS WITH RESISTIVELY HEATED EXTERNAL OVEN 

Greaves et al. (1985, 156494; 

1987, 156495); Veltkamp et 

al. (1996, 081594) 

Aerosol sample and NIST SRM 

1649 

A cylindrical aluminum block 

containing a heating cartridge 

connected to a thermocouple 

HP 5892A GC/MS in EI 

mode 

ambient sample: 

55.5 min NIST 

standard: 45.5 

min 

Waterman et al. (2000, 

157116) NIST SRM 1640a External oven mounted on the top 

of the GC/MS system 

HP 5890 GC/Fisons 

MD 800 MS, scan 

range: 40-520 amu 

90 min 

Waterman et al. (2001, 

157117) NIST SRM 1649a Same as above 

HP 5890 GC/Fisons 

MD 800 MS, scan 

range: m/z 40 to 520 

90 mins 

Sidhu et al. (2001, 155202) 

Aerosol collected on glass fiber 

filters from combustion of alternative 

diesel fuel. 

A stainless steel tube (0.635 cm 

O.D.) laced in a GC oven 

Two GCs and one MS. The 

first GC is used as the TE 

unit. The second GC 

separates the desorbent. 

Ua 

Hays et al. (2003, 156529; 

2004, 156530); Dong et al. 

(2004, 156409) 

Aerosol collected from residential 

wood combustion, residential oil 

furnace and fireplace appliance 

A glass tube placed in an external 

oven (TDS2 Gerstel Inc.) 

Aglient 6890 GC/5793 MSD, 

scan range: 50 to 500 amu 99 min 

CURIE POINT TD-GC/MS 

Jeon et al. (2001, 016636) 

High-volume PM10 ambient samples 

collected along the U.S./Mexico 

border 

Curie point pyrolyzer HP 5890 GC/5792 MSD Ua 

Neususs et al. (2000, 

156804) 

Ambient aerosol collected during the 

2nd Aerosol Characterization 

Experiment 

Curie point pyrolyzer Fisons Trio 1000 35 min 

IN-INJECTION PORT TED-GC/MS 

Helmig et al. (1990, 156536) Aerosol samples collected on glassfiber filters at a forest site 

GC injector port, with modified 

septum cap 

Carlo Erba Mega 5160 

GC/VG 250/70 SE MS, scan 

range: 45-400 amu 

47 min 

Hall et al. (1999, 156512) NIST SRM 1649 Micro-scale sealed vessel placed 

inside the injector port 

HP 5890 GC/Fisons MD 800 

MS, scan range: 40-500 

amu 

82.5 min 

Blanchard and Hopper (1997) 

(1997, 156277); Blanchard et 

al. (2002, 047598) 

Aerosol samples collected on 

quartz-and-glass filters in Ontario 

A GC injection port was added with 

three minor components, including 

a small T-connector, 3-way valve, 

and needle valve 

HP 5892A GC/5972A MS in 

EI mode 71 min 

Falkovich and Rudich (2001, 

156427) ; Falkovich et al. 

(2004, 156428); Graham et 

al. (2004, 156490) 

NIST SRM 1649a; urban aerosols 

collected with an 8-stage impactor in 

Tel-Aviv, Israel 

Direct Sample Introduction (DSI) 

device (ChromatoProbe, Varian 

Co.) 

Varian Saturn 3400 GC/MS 64.2 min 

Ho and Yu (2004, 156551); 

Yang et al. (2005, 102388) 

Ambient aerosol samples collected 

on Teflon-impregnated glass-fiber 

filters in Hong Kong and on quartz 

filters at Nanjing, China 

Conventional GC injection port. No 

modification of GC injector and liner 

HP 5890 GC/5791 MSD, 

scan range: 50-650 amu 41.5 min 

TD-GC X GC-MS 

Welthagen et al. (2003, 

104056); Schnelle-Kreis et al. 

(2005, 112944) 

Ambient samples in Augsburg, 

Germany 

Injection port Optic III with 

autoloader (ATAS-GL, Veldhoven, 

NL) 

Agilent 6890 GC/LECO 

Pegasus III TOF/MS with a 

LECO Pegasus 4D GCxGC 

modulator 

175 min 

Hamilton et al. (2004, 

156516) PM2.5 aerosol collected in London Conventional GC injection port The same as above, scan 

range: 20-350 amu 93.7 min 

Hamilton et al. (2005, 

088173) 

Secondary organic aerosol formed 

during the photo-oxidation of 

toluene with OH radicals 

The same as above The same above 102.5 min 

December 2009 A-52 
Reference Sample Type TD Unit Analytical Instrument Total Analysis 

Time 

IN SITU SEMI-CONTINUOUS AND CONTINUOUS TD SYSTEMS 

Williams et al. (2006, 156157) In situ aerosol samples collected in 

Berkley, CA 

Collection-TE cell with conventional 

GC injection port 

Agilent 6890 GC/5793 MSD, 

scan range: 29-550 amu 59 min 

PYROLYSIS TD-GC/MS 

Voorhees et al. (1991, 

157101) 

PM0.6 and PM>0.45 collected on 

quartz fiber in pristine regions of 

Colorado 

A tube furnace directly interfaced to 

an GC/MS 

Extrel Simulscan GC/MS, 

scan range: 35-450 amu 31.7 min 

Subbalakshmi et al. (2000, 

157023) 

Ambient aerosol collected on glassfiber filters in Jakarta, Indonesia A pyroinjector Agilent 6890 GC/5973 MS, 

scan range: 50-550 amu 63.5 min 

Fabbri et al. (2002, 156426) PM10 collected on glass-fiber filters 

in an industrial area of Italy 

A pyrolyzer directly connected to the 

GC injector port through an 

interface heated at 250° C 

Varian 3400 GC/Saturn II ion 

trap MS, scan range: 45-400 

amu 

57 min 

Blazso et al. (2003, 156278) 

PM2.6 collected on quartz-fiber filters 

and size-segregated aerosol 

sampled collected on A1 foils in 

Brazil 

A pyrolyzer Agilent 6890 GC/5973 MS 30.3 min 

Labban et al. (2006, 156665) PM10 of re-suspended soil collected 

on quartz-fiber filters Curie point pyrolyzer HP 5890 GC/5972 MS 25.5. min 

a

Total analysis time could not be determined because of insufficient experimental details 

Source: Chow et al. (2007, 157209)

December 2009 A-53 
A.1.2. Networks 

Table A-18. Relevant Spatial Scales for PM10, PM2.5, and PM10-2.5 Measurement 

Spatial 

Scales 

PM10 PM2.5 PM10-2.5

Microscale 

(~5-100 m) 

This scale would typify areas such as 

downtown street canyons, traffic corridors, and 

fence line stationary source monitoring 

locations where the general public could be 

exposed to maximum PM10 concentrations. 

Microscale PM sites should be located near 

inhabited buildings or locations where the 

general public can be expected to be exposed 

to the concentration measured. Emissions from 

stationary sources such as primary and 

secondary smelters, power plants, and other 

large industrial processes may, under certain 

plume conditions, likewise result in high ground 

level concentrations at the microscale. In the 

latter case, the microscale would represent an 

area impacted by the plume with dimensions 

extending up to approximately 100 m. Data 

collected at microscale sites provide 

information for evaluating and developing hot 

spot control measures. 

This scale would typify areas such as downtown 

street canyons and traffic corridors where the 

general public would be exposed to maximum 

concentrations from mobile sources. In some 

circumstances, the microscale is appropriate for 

particulate sites; community-oriented SLAMS sites 

measured at the microscale level should, 

however, be limited to urban sites that are 

representative of long-term human exposure and 

of many such microenvironments in the area. In 

general, microscale PM sites should be located 

near inhabited buildings or locations where the 

general public can be expected to be exposed to 

the concentration measured. Emissions from 

stationary sources such as primary and secondary 

smelters, power plants, and other large industrial 

processes may, under certain plume conditions, 

likewise result in high ground level concentrations 

at the microscale. In the latter case, the 

microscale would represent an area impacted by 

the plume with dimensions extending up to 

approximately 100 m. Data collected at 

microscale sites provide information for evaluating 

and developing hot spot control measures. Unless 

these sites are indicative of population-oriented 

monitoring, they may be more appropriately 

classified as SPM. 

This scale would typify relatively small areas 

immediately adjacent to: industrial sources; 

locations experiencing ongoing construction, 

redevelopment, and soil disturbance; and 

heavily traveled roadways. Data collected at 

microscale stations would characterize 

exposure over areas of limited spatial extent 

and population exposure, and may provide 

information useful for evaluating and 

developing source-oriented control measures. 

Middle Scale 

(~100-500 m) 

Much of the short-term public exposure to 

coarse fraction particles (PM10) is on this scale 

and on the neighborhood scale. People moving 

through downtown areas or living near major 

roadways or stationary sources, may 

encounter particulate pollution that would be 

adequately characterized by measurements of 

this spatial scale. Middle scale PM10 

measurements can be appropriate for the 

evaluation of possible short-term exposure 

public health effects. In many situations, 

monitoring sites that are representative of 

micro-scale or middle-scale impacts are not 

unique and are representative of many similar 

situations. This can occur along traffic corridors 

or other locations in a residential district. In this 

case, one location is representative of a 

neighborhood of small scale sites and is 

appropriate for evaluation of long-term or 

chronic effects. This scale also includes the 

characteristic concentrations for other areas 

with dimensions of a few hundred meters such 

as the parking lot and feeder streets 

associated with shopping centers, stadia, and 

office buildings. In the case of PM10, unpaved 

or seldomly swept parking lots associated with 

these sources could be an important source in 

addition to the vehicular emissions themselves.

People moving through downtown areas, or living 

near major roadways, encounter particle 

concentrations that would be adequately 

characterized by this spatial scale. Thus, 

measurements of this type would be appropriate 

for the evaluation of possible short-term exposure 

public health effects of PM pollution. In many 

situations, monitoring sites that are representative 

of microscale or middle-scale impacts are not 

unique and are representative of many similar 

situations. This can occur along traffic corridors or 

other locations in a residential district. In this case, 

one location is representative of a number of 

small scale sites and is appropriate for evaluation 

of long-term or chronic effects. This scale also 

includes the characteristic concentrations for other 

areas with dimensions of a few hundred meters 

such as the parking lot and feeder streets 

associated with shopping centers, stadia, and 

office buildings. 

People living or working near major roadways 

or industrial districts encounter particle 

concentrations that would be adequately 

characterized by this spatial scale. Thus, 

measurements of this type would be 

appropriate for the evaluation of public health 

effects of PM10-2.5 exposure. Monitors located in 

populated areas that are nearly adjacent to 

large industrial point sources of PM10-2.5 

provide suitable locations for assessing 

maximum population exposure levels and 

identifying areas of potentially poor air quality. 

Similarly, monitors located in populated areas 

that border dense networks of heavily-traveled 

traffic are appropriate for assessing the 

impacts of resuspended road dust. This scale 

also includes the characteristic concentrations 

for other areas with dimensions of a few 

hundred meters such as school grounds and 

parks that are nearly adjacent to major 

roadways and industrial point sources, 

locations exhibiting mixed residential and 

commercial development, and downtown areas 

featuring office buildings, shopping centers, 

and stadiums. 

December 2009 A-54 
Spatial 

Scales 

PM10 PM2.5 PM10-2.5

Neighborhood 

Scale 

(~500 m-4 km) 

Measurements in this category represent 

conditions throughout some reasonably 

homogeneous urban sub-region with 

dimensions of a few kilometers and of 

generally more regular shape than the middle 

scale. Homogeneity refers to the PM 

concentrations, as well as the land use and 

land surface characteristics. In some cases, a 

location carefully chosen to provide 

neighborhood scale data would represent not 

only the immediate neighborhood but also 

neighborhoods of the same type in other parts 

of the city. Neighborhood scale PM10 sites 

provide information about trends and 

compliance with standards because they often 

represent conditions in areas where people 

commonly live and work for extended periods. 

Neighborhood scale data could provide 

valuable information for developing, testing, 

and revising models that describe the largerscale concentration patterns, especially those 

models relying on spatially smoothed emission 

fields for inputs. The neighborhood scale 

measurements could also be used for 

neighborhood comparisons within or between 

cities. 

Measurements in this category would represent 

conditions throughout some reasonably 

homogeneous urban sub- region with dimensions 

of a few kilometers and of generally more regular 

shape than the middle scale. Homogeneity refers 

to the PM concentrations, as well as the land use 

and land surface characteristics. Much of the 

PM2.5 exposures are expected to be associated 

with this scale of measurement. In some cases, a 

location carefully chosen to provide neighborhood 

scale data would represent the immediate 

neighborhood as well as neighborhoods of the 

same type in other parts of the city. PM2.5 sites of 

this kind provide good information about trends 

and compliance with standards because they 

often represent conditions in areas where people 

commonly live and work for periods comparable to 

those specified in the NAAQS. In general, most 

PM2.5 monitoring in urban areas should have this 

scale. 

Measurements in this category would 

represent conditions throughout some 

reasonably homogeneous urban sub-region 

with dimensions of a few kilometers and of 

generally more regular shape than the middle 

scale. Homogeneity refers to the PM 

concentrations, as well as the land use and 

land surface characteristics. This category 

includes suburban neighborhoods dominated 

by residences that are somewhat distant from 

major roadways and industrial districts but still 

impacted by urban sources, and areas of 

diverse land use where residences are 

interspersed with commercial and industrial 

neighborhoods. In some cases, a location 

carefully chosen to provide neighborhood scale 

data would represent the immediate 

neighborhood as well as neighborhoods of the 

same type in other parts of the city. The 

comparison of data from middle scale and 

neighborhood scale sites would provide 

valuable information for determining the 

variation of PM10-2.5 levels across urban areas 

and assessing the spatial extent of elevated 

concentrations caused by major industrial point 

sources and heavily traveled roadways. 

Neighborhood scale sites would provide 

concentration data that are relevant to 

informing a large segment of the population of 

their exposure levels on a given day. 

Urban Scale 

(~4-50 km) 

This class of measurement would be used to 

characterize the PM concentration over an entire 

metropolitan or rural area ranging in size from 4 to 

50 kilometers. Such measurements would be 

useful for assessing trends in area-wide air 

quality, and hence, the effectiveness of large scale 

air pollution control strategies. Communityoriented PM2.5 sites may have this scale. 

Regional 

Scale 

(~50-100s km) 

These measurements would characterize 

conditions over areas with dimensions of as much 

as hundreds of kilometers. As noted earlier, using 

representative conditions for an area implies 

some degree of homogeneity in that area. For this 

reason, regional scale measurements would be 

most applicable to sparsely populated areas. Data 

characteristics of this scale would provide 

information about larger scale processes of PM 

emissions, losses and transport. PM2.5 transport 

contributes to elevated particulate concentrations 

and may affect multiple urban and State entities 

with large populations such as in the eastern 

United States. Development of effective pollution 

control strategies requires an understanding at 

regional geographical scales of the emission 

sources and atmospheric processes that are 

responsible for elevated PM2.5 levels and may also 

be associated with elevated O3 and regional haze.

December 2009 A-55 
Table A-19. Major routine operating air monitoring networksa

Network Lead 

Agency 

Number 

of Sites 

Initiated Measurement 

Parameters 

Location of Information and/or Data 

STATE / LOCAL / FEDERAL NETWORKS 

NCoreb – National Core 

Monitoring Network 

EPA 75 2008 O3, NO/NO2/NOY, SO2, 

CO, PM2.5/PM10-2.5, 

PM2.5 speciation, NH3, 

HNO3, surface 

meteorologyc

http://www.epa.gov/ttN/Amtic/monstratdoc.html

SLAMS1 – State and 

Local Ambient 

Monitoring Stations 

EPA ~3000 1978 O3, NOX/NO2, SO2, 

PM2.5/PM10, CO, Pb 

http://www.epa.gov/air/oaqps/qa/monprog.html

STN—PM2.5 Speciation 

Trends Network 

EPA 300 1999 PM2.5, PM2.5

speciation, major 

Ions, metals 

http://www.epa.gov/ttnamti1/specgen.html

PAMS—Photochemical 

Assessment 

Monitoring Network 

EPA 75 1994 O3, NOX/NOY, CO, 

speciated VOCs, 

carbonyls, surface 

meteorology & Upper 

Air 

http://www.epa.gov/air/oaqps/pams/

IMPROVE—

Interagency Monitoring 

of Protected Visual 

Environments 

NPS 110 plus 

67 protocol 

sites 

1988 PM2.5/PM10, major 

ions, metals, light 

extinction, scattering 

coefficient 

http://vista.cira.colostate.edu/IMPROVE/

CASTNet – Clean Air 

Status and Trends 

Network 

EPA 80+ 1987 O3, SO2, major ions, 

calculated dry 

deposition, wet 

deposition, total 

deposition for 

sulfur/nitrogen, 

surface meteorology 

http://www.epa.gov/castnet/

GPMN—Gaseous 

Pollutant Monitoring 

Network 

NPS 33 1987 O3, NOX/NO/NO2, 

SO2, CO, surface 

meteorology, (plus 

enhanced monitoring 

of CO, NO, NOX, 

NOY, and SO2 plus 

canister samples for 

VOC at 3 sites) 

http://www2.nature.nps.gov/air/Monitoring/network.cfm#data

POMS—Portable 

Ozone Monitoring 

Stations 

NPS 14 2002 O3, surface 

meteorology, with 

CASTNet-protocol 

filter pack (optional) 

sulfate, nitrate, 

ammonium, nitric 

acid, sulfur dioxide 

http://www2.nature.nps.gov/air/studies/portO3.cfm

Passive Ozone 

Sampler Monitoring 

Program 

NPS 43 1995 O3 dose (weekly) http://www2.nature.nps.gov/air/Studies/Passives.cfm

NADP/NTN—National 

Atmospheric 

Deposition Program / 

National Trends 

Network 

USGS 200+ 1978 Major Ions from 

precipitation 

chemistry 

http://nadp.sws.uiuc.edu/

NADP/MDN—National 

Atmospheric 

Deposition Program / 

Mercury Deposition 

Network 

None 90+ 1996 Mercury from 

precipitation 

chemistry 

http://nadp.sws.uiuc.edu/mdn/

December 2009 A-56 
Network Lead 

Agency 

Number 

of Sites 

Initiated Measurement 

Parameters 

Location of Information and/or Data 

AIRMoN—National 

Atmospheric 

Deposition Program / 

Atmospheric Integrated 

Research Monitoring 

Network 

NOAA 8 1992 Major Ions from 

precipitation 

chemistry 

Note: some sites 

began in 1976 as part 

of the DOE MAP3S 

program; early data 

are archived on 

NADP and ARL 

servers. 

http://nadp.sws.uiuc.edu/AIRMoN/

IADN—Integrated 

Atmospheric 

Deposition Network 

EPA 20 1990 PAHs, PCBs, and 

organochlorine 

compounds are 

measured in air and 

precipitation samples 

http://www.epa.gov/glnpo/monitoring/air/

NAPS—National Air 

Pollution Surveillance 

Network 

Canada 152+ 1969 SO2, CO, O3, NO, 

NO2, NOX, VOCs, 

SVOCs, PM10, PM2.5, 

TSP, metals 

http://www.etc-cte.ec.gc.ca/NAPS/index_e.html

CAPMoN—Canadian 

Air and Precipitation 

Monitoring Network 

Canada 29 2002 O3, NO, NO2, NOY, 

PAN, NH3, PM2.5, 

PM10 and coarse 

fraction mass, PM2.5

speciation, major ions 

for particles and trace 

gases, precipitation 

chemistry for major 

ions 

http://www.msc.ec.gc.ca/capmon/index_e.cfm

Mexican Air Quality 

Network 

Mexico 52-62 Late 

1960s 

O3, NOX, CO, SO2, 

PM10, TSP,VOC 

http://www.ine.gob.mx/dgicur/calaire/indicadores.html

Mexican City Ambient 

Air Quality Monitoring 

Network 

Mexico 49 Late 

1960s 

O3, NOX, CO, SO2, 

PM10, TSP,VOC 

http://www.ine.gob.mx/dgicur/calaire/indicadores.html

AIR TOXICS MONITORING NETWORKS 

NATTS—National Air 

Toxics Trends Stations 

EPA 23 2005 VOCs, Carbonyls, 

PM10 metalsd

, Hg 

http://www.epa.gov/ttN/Amtic/airtoxpg.html

State/Local Air Toxics 

Monitoring 

EPA 250+ 1987 VOCs, Carbonyls, 

PM10 metalsd

, Hg 

http://www.epa.gov/ttN/Amtic/airtoxpg.html

NDAMN—National 

Dioxin Air Monitoring 

Network 

EPA 34 1998-2005 CDDs, CDFs, dioxinlike PCBs http://cfpub.epa.gov/ncea/CFM/recordisplay.cfm?deid=54811

TRIBAL MONITORING NETWORKS 

Tribal Monitoringf EPA 120+ 1995 O3, NOX/NO2, SO2, 

PM2.5/PM10, CO, Pb 

http://www.epa.gov/air/tribal/airprogs.html#ambmon

INDUSTRY / RESEARCH NETWORKS 

New Source Permit 

Monitoring 

None variable variable O3, NOX/NO2, SO2, 

PM2.5/PM10, CO, Pb 

Contact specific industrial facilities 

HRM Network—

Houston Regional 

Monitoring Network 

None 9 1980 O3, NOX, PM2.5/PM10, 

CO, SO2, Pb, VOCs, 

surface meteorology 

http://hrm.radian.com/houston/how/index.htm

ARIES / SEARCH—

Aerosol Research 

Inhalation 

Epidemiology Study / 

SouthEastern Aerosol 

Research and 

Characterization Study 

experiment 

None 8 1992 O3, NO/NO2/NOY, 

SO2, CO, PM2.5/PM10, 

PM2.5 speciation, 

major Ions, NH3, 

HNO3, scattering 

coefficient, surface 

meteorology 

http://www.atmospheric-research.com/studies/SEARCH/index.html

December 2009 A-57 
Network Lead 

Agency 

Number 

of Sites 

Initiated Measurement 

Parameters 

Location of Information and/or Data 

SOS – SERON—

Southern Oxidant 

Study - Southeastern 

Regional Oxidant 

Networks 

EPA ~40 1990 O3, NO, NOY, VOCs, 

CO, surface 

meteorology 

http://www.ncsu.edu/sos/pubs/sos3/State_of_SOS_3.pdf

NATIONAL/GLOBAL RADIATION NETWORKS 

RadNet—formerly 

Environmental 

Radiation Ambient 

Monitoring System 

(ERAMS) 

EPA 200+ 1973 Radionuclides and 

radiation 

http://www.epa.gov/enviro/html/erams/

SASP – Surface Air 

Sampling Program 

DHS 41 1963 89Sr, 90Sr, naturally 

occurring 

radionuclides, 7Be, 

210Pb 

http://www.eml.st.dhs.gov/databases/sasp/

NEWNET–

Neighborhood 

Environmental Watch 

Network 

DOE 26 1993 Ionizing gamma 

radiation, surface 

meteorology 

http://newnet.lanl.gov/

SOLAR RADIATION NETWORKS 

UV Index – EPA Sunrise 

Programg EPA ~50 U.S. cities 

2002 Calculated UV 

radiation index 

http://www.epa.gov/sunwise/uvindex.html

UV Net – Ultraviolet 

Monitoring Program 

EPA 21 1995/2004 Ultraviolet solar 

radiation (UV-B and 

UV-A bands), 

irradiance, ozone, 

NO2

http://www.epa.gov/uvnet/access.html

NEUBrew (NOAA-EPA 

Brewer 

Spectrophotometer UV 

and Ozone Network 

NOAA 6 2005 Ultraviolet solar 

radiation (UV-B and 

UV-A bands), 

irradiance, ozone, 

SO2

http://www.esrl.noaa.gov/gmd/grad/neubrew/

UV-B Monitoring and 

Research Program 

USDA 35 1992 Ultraviolet-B radiation http://uvb.nrel.colostate.edu/UVB/index.jsf

SURFRAD – Surface 

Radiation Budget 

Network 

NOAA 7 1993 Solar and infrared 

radiation, direct and 

diffuse solar 

radiation, 

photosynthetically 

active radiation, UVB, 

spectral solar, and 

meteorological 

parameters 

http://www.srrb.noaa.gov/surfrad/index.html

AERONET – Aerosol 

RObotic NETwork 

NASA 

colocated 

networks 

22 + other 

participants 

1998 Aerosol spectral 

optical depths, 

aerosol size 

distributions, and 

precipitable water 

http://aeronet.gsfc.nasa.gov/index.html

MPLNET – Micro-pulse 

Lidar Network 

8 2000 Aerosols and cloud 

layer heights 

http://mplnet.gsfc.nasa.gov/

PRIMENet – Park 

Research 

& Intensive Monitoring of 

Ecosystems NETworkh

NPS 14 1997 ozone, wet and dry 

deposition, visibility, 

surface meteorology, 

and ultraviolet 

radiation 

http://www.cfc.umt.edu/primenet/Assets/Announcements/99PReport.pdf

a

Some networks listed separately may also serve as subcomponents of other larger listed networks; as a result, some double counting of the number of individual monitors is likely. b

NCore is a network proposed to replace NAMS, as a component of SLAMS; NAMS are currently designated as national trends sites. 

c

surface meteorology includes wind direction and speed, temperature, precipitation, relative humidity, solar radiation (PAMS only). d

PM10 metals may include arsenic, beryllium, cadmium, chromium, lead, manganese, nickel, and others. e

The number of sites indicated for tribal monitoring is actually the number of monitors, rather than sites. The number of sites with multiple monitors is probably <80. f

 Sunrise program estimates UV exposure levels through modeling - does not include measurements. 

g

NEUBREW is a subset Original UV brewer network (UV Net); PRIMENET participated in UV Net program. 

December 2009 A-58 
A.1.3. Monitor Distribution with Respect to Population Density 

Figure A-1. PM2.5 monitor distribution in comparison with population density, Atlanta, GA. 

December 2009 A-59 
Figure A-2. PM10 monitor distribution in comparison with population density, Atlanta, GA. 

December 2009 A-60 
Figure A-3. PM2.5 monitor distribution in comparison with population density, Birmingham, 

AL. 

December 2009 A-61 
Figure A-4. PM10 monitor distribution in comparison with population density, Birmingham, 

AL. 

December 2009 A-62 
Figure A-5. PM2.5 monitor distribution in comparison with population density, Boston, MA. 

December 2009 A-63 
Figure A-6. PM10 monitor distribution in comparison with population density, Boston, MA. 

December 2009 A-64 
Figure A-7. PM2.5 monitor distribution in comparison with population density, Chicago, IL. 

December 2009 A-65 
Figure A-8. PM10 monitor distribution in comparison with population density, Chicago, IL. 

December 2009 A-66 
Figure A-9. PM2.5 monitor distribution in comparison with population density, Denver, CO. 

December 2009 A-67 
Figure A-10. PM10 monitor distribution in comparison with population density, Denver, CO. 

December 2009 A-68 
Figure A-11. PM2.5 monitor distribution in comparison with population density, Detroit, MI. 

December 2009 A-69 
Figure A-12. PM10 monitor distribution in comparison with population density, Detroit, MI. 

December 2009 A-70 
Figure A-13. PM2.5 monitor distribution in comparison with population density, Houston, TX. 

December 2009 A-71 
Figure A-14. PM10 monitor distribution in comparison with population density, Houston, TX. 

December 2009 A-72 
Figure A-15. PM2.5 monitor distribution in comparison with population density, Los Angeles, 

CA. 

December 2009 A-73 
Figure A-16. PM10 monitor distribution in comparison with population density, Los Angeles, 

CA. 

December 2009 A-74 
Figure A-17. PM2.5 monitor distribution in comparison with population density, New York, NY. 

December 2009 A-75 
Figure A-18. PM10 monitor distribution in comparison with population density, New York, NY. 

December 2009 A-76 
Figure A-19. PM2.5 monitor distribution in comparison with population density, Philadelphia, 

PA. 

December 2009 A-77 
Figure A-20. PM10 monitor distribution in comparison with population density, Philadelphia, 

PA. 

December 2009 A-78 
Figure A-21. PM2.5 monitor distribution in comparison with population density, Phoenix, AZ. 

December 2009 A-79 
Figure A-22. PM10 monitor distribution in comparison with population density, Phoenix, AZ. 

December 2009 A-80 
Figure A-23. PM2.5 monitor distribution in comparison with population density, Pittsburgh, PA. 

December 2009 A-81 
Figure A-24. PM10 monitor distribution in comparison with population density, Pittsburgh, PA. 

December 2009 A-82 
Figure A-25. PM2.5 monitor distribution in comparison with population density, Riverside, CA. 

December 2009 A-83 
Figure A-26. PM10 monitor distribution in comparison with population density, Riverside, CA. 

December 2009 A-84 
Figure A-27. PM2.5 monitor distribution in comparison with population density, Seattle, WA. 

December 2009 A-85 
Figure A-28. PM10 monitor distribution in comparison with population density, Seattle, WA. 

December 2009 A-86 
Figure A-29. PM2.5 monitor distribution in comparison with population density, St. Louis, MO. 

December 2009 A-87 
Figure A-30. PM10 monitor distribution in comparison with population density, St. Louis, MO. 

December 2009 A-88 
A.2. Ambient PM Concentration 

A.2.1. Speciation Trends Network Site Data 

Figure A-31. Three-yr avg of 24-h PM2.5 Cu concentrations measured at CSN sites across the 

U.S., 2005-2007. 

December 2009 A-89 
Figure A-32. Three-yr avg of 24-h PM2.5 Fe concentrations measured at CSN sites across the 

U.S., 2005-2007 

December 2009 A-90 
Figure A-33. Three-yr avg of 24-h PM2.5 Ni concentrations measured at CSN sites across the 

U.S., 2005-2007 

December 2009 A-91 
Figure A-34. Three-yr avg of 24-h PM2.5 Pb concentrations measured at CSN sites across the 

U.S., 2005-2007 

December 2009 A-92 
Figure A-35. Three-yr avg of 24-h PM2.5 Se concentrations measured at CSN sites across the 

U.S., 2005-2007 

December 2009 A-93 
Figure A-36. Three-yr avg of 24-h PM2.5 V concentrations measured at CSN sites across the 

U.S., 2005-2007 

December 2009 A-94 
A.2.2. Intraurban Variability 

The following figures and tables exemplify the intraurban variability among PM2.5, PM10-2.5

and PM10 measurements for select CSAs/CBSAs (2005-2007) including Atlanta, Birmingham, 

Chicago, Denver, Detroit, Houston, New York City, Philadelphia, Phoenix, Riverside, Seattle and St. 

Louis. Maps are included to show monitor locations relative to major roadways. Box plots show the 

median and interquartile range of concentrations with whiskers extending to the 5th and 95th 

percentiles at each site during (1) winter (December-February); (2) spring (March-May); (3) summer 

(June-August); and (4) fall (September-November). Tables of inter-sampler comparison statistics and 

scatter plots of inter-sampler correlation vs. distance illustrate variability present in each area. 

December 2009 A-95 
Figure A-37. PM2.5 monitor distribution and major highways, Atlanta, GA. 

December 2009 A-96 
Figure A-38. Box plots illustrating the seasonal distribution of 24-h avg PM2.5 concentrations 

for Atlanta, GA. 

December 2009 A-97 
Table A-20. Inter-sampler correlation statistics for each pair of PM2.5 monitors reporting to AQS for 

Atlanta, GA. 

A B C D E F G H I 

A 1.00 0.88 0.87 0.93 0.89 0.91 0.85 0.72 0.85 

 (0.0, 0.00) (5.2, 0.11) (6.2, 0.12) (3.9, 0.11) (5.3, 0.12) (4.6, 0.11) (6.9, 0.15) (8.7, 0.19) (7.2, 0.15) 

351 330 310 330 315 334 207 319 326 

B 1.00 0.96 0.89 0.88 0.91 0.88 0.78 0.88 

(0.0, 0.00) (4.1, 0.08) (5.7, 0.12) (4.6, 0.10) (3.6, 0.08) (5.6, 0.13) (9.0, 0.17) (6.5, 0.13) 

352 309 327 314 333 205 313 321 

C 1.00 0.87 0.86 0.88 0.85 0.79 0.90 

 (0.0, 0.00) (5.2, 0.12) (5.6, 0.11) (4.4, 0.10) (5.8, 0.13) (7.9, 0.17) (4.5, 0.11) 

 339 315 304 324 193 298 303 

D 1.00 0.89 0.80 0.87 0.74 0.82 

(0.0, 0.00) (4.8, 0.12) (3.7, 0.11) (5.8, 0.13) (8.3, 0.18) (7.3, 0.15) 

1014 883 978 208 314 322 

E 1.00 0.79 0.88 0.74 0.83 

 (0.0, 0.00) (3.8, 0.11) (5.3, 0.12) (7.8, 0.17) (6.4, 0.14) 

 946 904 208 305 309 

F 1.00 0.88 0.70 0.84 

(0.0, 0.00) (5.3, 0.12) (8.5, 0.19) (6.3, 0.14) 

 

LEGEND 

R 

(P90, COD) 

N 1036 213 321 327 

G 1.00 0.73 0.79 

 (0.0, 0.00) (8.8, 0.17) (7.4, 0.15) 

 221 195 198 

H 1.00 0.76 

(0.0, 0.00) (8.7, 0.17) 

336 309 

I 1.00 

 (0.0, 0.00) 

 344 

 

December 2009 A-98 
0.00

0.20

0.40

0.60

0.80

1.00

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

Figure A-39. PM2.5 inter-sampler correlations as a function of distance between monitors for 

Atlanta, GA. 

December 2009 A-99 
Figure A-40. PM2.5 monitor distribution and major highways, Birmingham, AL. 

December 2009 A-100 
Figure A-41. Box plots illustrating the seasonal distribution of 24-h avg PM2.5 concentrations 

for Birmingham, AL. 

December 2009 A-101 
Table A-21. Inter-sampler correlation statistics for each pair of PM2.5 monitors reporting to AQS for 

Birmingham, AL. 

A B C D E F G H I J 

A 1.00 0.91 0.86 0.91 0.88 0.91 0.87 0.88 0.88 0.84 

 (0.0, 0.00) (10.4, 0.15) (13.7, 0.21) (9.7, 0.13) (8.1, 0.13) (10.8, 0.15) (12.6, 0.18) (11.7, 0.18) (12.3, 0.18) (12.5, 0.19) 

 1087 360 356 182 1072 361 360 357 358 348 

B 1.00 0.93 0.93 0.85 0.96 0.91 0.93 0.93 0.89 

(0.0, 0.00) (5.3, 0.12) (4.7, 0.09) (8.3, 0.15) (3.6, 0.08) (5.4, 0.11) (5.1, 0.11) (4.9, 0.10) (6.1, 0.12) 

363 356 181 359 358 360 355 358 348 

C 1.00 0.93 0.81 0.93 0.91 0.94 0.90 0.90 

 (0.0, 0.00) (5.9, 0.13) (10.1, 0.20) (4.6, 0.12) (4.3, 0.12) (4.0, 0.10) (4.9, 0.12) (4.9, 0.11) 

 359 180 355 354 355 350 353 343 

D 1.00 0.88 0.96 0.95 0.95 0.93 0.89 

(0.0, 0.00) (7.9, 0.12) (3.6, 0.08) (3.8, 0.09) (4.7, 0.10) (4.7, 0.10) (6.1, 0.12) 

182 179 179 181 179 180 174 

E 1.00 0.87 0.85 0.85 0.86 0.81 

 (0.0, 0.00) (8.1, 0.15) (8.7, 0.16) (8.8, 0.17) (9.2, 0.16) (10.6, 0.18) 

 1079 360 359 356 357 347 

F 1.00 0.95 0.95 0.95 0.90 

(0.0, 0.00) (3.9, 0.09) (4.1, 0.10) (3.4, 0.09) (5.6, 0.11) 

 

LEGEND 

R 

(P90, COD) 

N 364 359 354 357 348 

G 1.00 0.96 0.92 0.89 

 (0.0, 0.00) (3.3, 0.08) (4.5, 0.10) (4.9, 0.11) 

 363 356 359 350 

H 1.00 0.91 0.93 

(0.0, 0.00) (5.0, 0.11) (4.3, 0.09) 

360 354 344 

I 1.00 0.87 

 (0.0, 0.00) (5.8, 0.12) 

 361 349 

J 1.00 

(0.0, 0.00)

351 

 

December 2009 A-102 
0.00

0.20

0.40

0.60

0 10 20 30 40 50 60 70 80 90 10

Distance Between Samplers (km)

Correlation

0.80

1.00

0

Figure A-42. PM2.5 inter-sampler correlations as a function of distance between monitors for 

Birmingham, AL. 

December 2009 A-103 
Figure A-43. PM2.5 monitor distribution and major highways, Boston, MA. 

December 2009 A-104 
Figure A-44. Box plots illustrating the seasonal distribution of 24-h avg PM2.5 concentrations 

for Boston, MA. 

December 2009 A-105 
Table A-22. Inter-sampler correlation statistics for each pair of PM2.5 monitors reporting to AQS for 

Boston, MA. 

Site A B C D E F G H I J 

A 1.00 0.80 0.77 0.71 0.84 0.79 0.78 0.79 0.79 0.77 

(0.0, 0.00) (6.6, 0.21) (6.2, 0.22) (6.9, 0.23) (4.8, 0.19) (8.1, 0.23) (7.7, 0.24) (6.8, 0.22) (7.9, 0.25) (7.5, 0.24) 

341 326 318 323 329 318 319 325 338 310 

B 1.00 0.92 0.87 0.87 0.90 0.90 0.90 0.90 0.85 

(0.0, 0.00) (4.1, 0.17) (4.1, 0.18) (4.7, 0.19) (6.3, 0.21) (6.2, 0.23) (4.9, 0.19) (7.1, 0.26) (5.5, 0.21) 

350 328 331 339 326 323 333 343 317 

C 1.00 0.90 0.85 0.90 0.89 0.90 0.88 0.86 

(0.0, 0.00) (3.5, 0.17) (5.3, 0.21) (6.3, 0.23) (6.3, 0.24) (5.0, 0.20) (6.8, 0.26) (6.2, 0.21) 

342 321 331 316 318 326 336 311 

D 1.00 0.80 0.88 0.88 0.86 0.86 0.87 

(0.0, 0.00) (5.6, 0.20) (5.8, 0.21) (5.8, 0.22) (4.6, 0.19) (7.0, 0.26) (5.8, 0.19) 

355 336 324 329 332 345 313 

E 1.00 0.90 0.90 0.89 0.87 0.87 

LEGEND 

Pearson R 

(P90, COD) 

n (0.0, 0.00) (5.9, 0.19) (5.8, 0.21) (5.0, 0.19) (6.9, 0.24) (5.4, 0.20) 

357 330 333 340 350 322 

F 1.00 0.94 0.94 0.92 0.92 

(0.0, 0.00) (3.8, 0.14) (3.5, 0.15) (4.5, 0.17) (5.4, 0.18) 

349 324 324 339 310 

G 1.00 0.94 0.94 0.89 

(0.0, 0.00) (4.0, 0.16) (4.3, 0.15) (5.7, 0.20) 

398 325 338 308 

H 1.00 0.93 0.89 

(0.0, 0.00) (4.7, 0.19) (5.0, 0.17) 

349 342 318 

I 1.00 0.86

(0.0, 0.00) (6.9, 0.23) 

1015 330 

J 1.00

(0.0, 0.00)

335 

 

Site K L M N O P Q R S 

A 0.77 0.61 0.71 0.68 0.73 0.87 0.81 0.85 0.86 

(8.1, 0.23) (8.3, 0.29) (8.0, 0.23) (7.9, 0.23) (7.0, 0.22) (5.3, 0.18) (7.2, 0.23) (5.6, 0.20) (5.2, 0.18) 

320 173 324 334 331 326 292 285 306 

B 0.86 0.80 0.87 0.83 0.88 0.86 0.80 0.85 0.85 

(6.6, 0.21) (6.2, 0.23) (5.3, 0.19) (6.0, 0.21) (4.7, 0.18) (5.6, 0.19) (7.9, 0.26) (5.7, 0.21) (6.0, 0.19) 

329 175 331 341 336 335 300 288 314 

C 0.86 0.89 0.93 0.90 0.93 0.83 0.79 0.81 0.82 

(6.9, 0.21) (4.8, 0.23) (4.4, 0.17) (4.6, 0.19) (3.8, 0.18) (5.9, 0.21) (7.8, 0.26) (6.2, 0.23) (6.0, 0.21) 

321 173 323 335 328 329 290 281 309 

D 0.88 0.79 0.91 0.85 0.86 0.80 0.75 0.79 0.80 

(6.4, 0.19) (5.7, 0.25) (3.5, 0.16) (4.7, 0.19) (4.2, 0.18) (6.2, 0.20) (7.8, 0.25) (6.2, 0.21) (5.8, 0.20) 

325 174 329 339 334 342 300 287 321 

E 0.87 0.72 0.83 0.79 0.84 0.91 0.86 0.88 0.91 

(6.3, 0.20) (8.3, 0.27) (5.8, 0.17) (6.3, 0.20) (4.8, 0.18) (4.5, 0.17) (6.3, 0.22) (4.9, 0.18) (3.9, 0.17) 

333 179 338 347 343 343 306 295 324 

F 0.91 0.78 0.90 0.85 0.85 0.89 0.86 0.88 0.89 

(4.7, 0.17) (9.6, 0.33) (5.3, 0.18) (6.4, 0.20) (7.5, 0.22) (5.2, 0.16) (6.0, 0.16) (4.9, 0.16) (5.5, 0.17) 

323 168 323 334 330 336 295 281 316 

G 0.90 0.77 0.90 0.85 0.87 0.88 0.86 0.87 0.88 

(5.0, 0.19) (9.0, 0.33) (5.3, 0.19) (6.3, 0.20) (7.0, 0.22) (5.5, 0.17) (5.3, 0.17) (5.2, 0.17) (5.7, 0.19) 

320 172 326 335 329 383 296 282 356 

H 0.90 0.75 0.88 0.83 0.84 0.89 0.86 0.87 0.88 

(4.4, 0.17) (9.4, 0.30) (4.9, 0.18) (5.6, 0.21) (6.8, 0.21) (4.5, 0.16) (6.0, 0.19) (4.5, 0.16) (5.1, 0.17) 

327 175 332 341 336 335 299 289 314 

I 0.87 0.75 0.86 0.82 0.83 0.88 0.84 0.85 0.87 

(6.1, 0.20) (10.0, 0.36) (6.7, 0.22) (7.2, 0.23) (8.2, 0.25) (6.1, 0.20) (6.0, 0.16) (6.0, 0.18) (6.3, 0.21) 

341 181 352 356 357 957 314 306 936 

J 0.95 0.73 0.87 0.84 0.80 0.90 0.86 0.87 0.88 

(3.0, 0.14) (9.2, 0.28) (5.2, 0.18) (5.9, 0.20) (7.5, 0.22) (5.0, 0.17) (5.9, 0.20) (5.3, 0.17) (5.2, 0.18) 

316 167 314 326 323 321 283 272 302 

K 1.00 0.71 0.88 0.85 0.81 0.89 0.86 0.87 0.88 

(0.0, 0.00) (10.3, 0.31) (6.0, 0.16) (6.5, 0.19) (8.2, 0.22) (5.2, 0.16) (5.8, 0.18) (5.5, 0.16) (5.5, 0.18) 

346 170 326 337 332 331 296 286 313 

L 1.00 0.89 0.91 0.90 0.68 0.63 0.72 0.69 

(0.0, 0.00) (6.7, 0.24) (5.9, 0.23) (4.8, 0.21) (10.0, 0.29) (12.1, 0.35) (9.1, 0.30) (9.8, 0.29) 

183 176 181 177 181 153 149 164 

M 1.00 0.94 0.90 0.83 0.81 0.82 0.84 

(0.0, 0.00) (3.8, 0.13) (4.6, 0.16) (5.5, 0.16) (7.4, 0.20) (5.8, 0.17) (5.1, 0.16) 

361 341 336 345 300 288 326 

N 1.00 0.90 0.77 0.75 0.78 0.78 

(0.0, 0.00) (4.4, 0.17) (6.7, 0.19) (8.1, 0.22) (6.4, 0.20) (6.2, 0.19) 

362 346 347 309 297 327 

O 

LEGEND 

Pearson R 

(P90, COD) 

n 1.00 0.80 0.75 0.79 0.80 

(0.0, 0.00) (5.8, 0.19) (8.8, 0.25) (6.8, 0.21) (6.0, 0.19) 

 362 348 304 292 330 

P 1.00 0.95 0.97 0.97 

(0.0, 0.00) (3.6, 0.14) (2.0, 0.09) (2.1, 0.08) 

December 2009 A-106 
Site K L M N O P Q R S 

1027 307 299 943 

Q 1.00 0.92 0.94 

(0.0, 0.00) (3.1, 0.13) (4.0, 0.16) 

 321 268 290 

R 1.00 0.94 

 (0.0, 0.00) (2.7, 0.12) 

 313 280 

S 1.00 

 (0.0, 0.00) 

 998 

 

0

0.2

0 10 20 30 40 50 60 70 80 90 10

Distance Between Samplers (km)

0.4

0.6

0.8

1

0

Correlation

Figure A-45. PM2.5 inter-sampler correlations as a function of distance between monitors for 

Boston, MA. 

December 2009 A-107 
Figure A-46. PM2.5 monitor distribution and major highways, Chicago, IL. 

December 2009 A-108 
December 2009 A-109 
Figure A-47. Box plots illustrating the seasonal distribution of 24-h avg PM2.5 concentrations 

for Chicago, IL. 

December 2009 A-110 
Table A-23. Inter-sampler correlation statistics for each pair of PM2.5 monitors reporting to AQS for 

Chicago, IL. 

A B C D E F G H I J K L M N O 

A 1.00 0.98 0.93 0.94 0.97 0.95 0.97 0.94 0.96 0.91 0.95 0.95 0.91 0.92 0.89 

 (0.0, 0.00) (3.1, 0.08) (5.5, 0.12) (4.7, 0.11) (3.9, 0.09) (5.7, 0.13) (3.9, 0.09) (4.6, 0.12) (4.2, 0.11) (6.8, 0.16) (5.8, 0.14) (4.6, 0.12) (5.7, 0.15) (6.6, 0.15) (6.0, 0.16) 

 178 156 176 149 154 154 151 156 164 163 166 141 165 152 156 

B 1.00 0.94 0.95 0.97 0.95 0.97 0.95 0.96 0.93 0.93 0.95 0.92 0.93 0.90 

(0.0, 0.00) (4.6, 0.11) (3.6, 0.10) (3.3, 0.08) (5.2, 0.13) (2.7, 0.07) (4.3, 0.11) (3.4, 0.09) (6.3, 0.16) (6.5, 0.15) (4.0, 0.10) (5.1, 0.15) (5.8, 0.14) (5.2, 0.15) 

343 320 276 300 296 296 289 312 315 306 288 157 152 150 

C 1.00 0.96 0.92 0.91 0.90 0.94 0.92 0.90 0.91 0.92 0.88 0.92 0.86 

 (0.0, 0.00) (4.4, 0.11) (5.7, 0.11) (4.8, 0.11) (6.0, 0.12) (4.3, 0.11) (5.5, 0.11) (8.8, 0.18) (7.2, 0.17) (4.5, 0.12) (7.5, 0.16) (7.9, 0.16) (7.5, 0.17) 

 984 313 325 318 324 312 336 332 337 311 178 175 173 

D 1.00 0.94 0.93 0.94 0.95 0.94 0.93 0.93 0.92 0.89 0.96 0.88 

(0.0, 0.00) (3.8, 0.10) (4.2, 0.12) (3.8, 0.10) (4.1, 0.13) (3.3, 0.10) (6.2, 0.15) (5.2, 0.14) (3.6, 0.10) (5.3, 0.14) (5.1, 0.13) (4.5, 0.15) 

333 286 280 283 270 299 296 289 273 151 146 145 

E 1.00 0.95 0.98 0.95 0.98 0.92 0.92 0.95 0.95 0.94 0.92 

 (0.0, 0.00) (5.0, 0.11) (2.4, 0.06) (4.5, 0.11) (2.6, 0.07) (5.8, 0.16) (5.7, 0.15) (4.4, 0.10) (4.8, 0.11) (5.0, 0.11) (4.6, 0.13) 

 351 306 304 292 320 321 313 286 159 154 152 

F 1.00 0.95 0.95 0.96 0.89 0.91 0.94 0.94 0.94 0.94 

(0.0, 0.00) (5.1, 0.12) (4.5, 0.12) (4.5, 0.10) (8.5, 0.20) (7.9, 0.19) (5.7, 0.12) (7.0, 0.15) (7.9, 0.17) (7.9, 0.16) 

345 301 294 322 323 311 285 161 157 154 

G 1.00 0.95 0.97 0.90 0.91 0.94 0.95 0.95 0.95 

 (0.0, 0.00) (4.9, 0.12) (3.0, 0.07) (6.3, 0.15) (5.8, 0.14) (4.7, 0.10) (4.2, 0.11) (5.0, 0.12) (4.4, 0.12) 

 350 284 315 318 309 287 154 149 148 

H 1.00 0.95 0.91 0.92 0.94 0.93 0.94 0.91 

 (0.0, 0.00) (4.3, 0.11) (7.4, 0.19) (6.4, 0.18) (4.4, 0.13) (6.4, 0.16) (7.1, 0.16) (5.9, 0.17) 

335 311 309 302 275 164 157 156 

I 1.00 0.90 0.92 0.96 0.96 0.95 0.93 

 (0.0, 0.00) (6.7, 0.17) (5.9, 0.16) (3.9, 0.10) (4.6, 0.12) (5.3, 0.13) (4.6, 0.14) 

 361 341 328 304 173 169 166 

J 1.00 0.92 0.90 0.91 0.94 0.89 

(0.0, 0.00) (4.7, 0.13) (7.0, 0.17) (5.7, 0.14) (4.4, 0.12) (5.4, 0.16) 

356 330 304 171 165 164 

K 1.00 0.93 0.94 0.96 0.92 

 (0.0, 0.00) (5.9, 0.15) (5.2, 0.13) (4.0, 0.10) (4.9, 0.15) 

 361 292 173 166 167 

L 1.00 0.94 0.95 0.92 

 

LEGEND 

R 

(P90, COD) 

N 

(0.0, 0.00) (6.4, 0.13) (5.9, 0.13) (6.0, 0.14) 

331 147 142 142 

M 1.00 0.97 0.95 

 (0.0, 0.00) (3.9, 0.09) (2.7, 0.11) 

 179 160 165 

N 1.00 0.95

(0.0, 0.00) (3.8, 0.11) 

176 152 

O 1.00 

 (0.0, 0.00) 

 174 

 

December 2009 A-111 
P Q R S T U V W X Y Z AA AB AC AD AE 

A 0.90 0.89 0.91 0.83 0.96 0.83 0.93 0.95 0.96 0.95 0.98 0.94 0.93 0.95 0.94 0.88 

 (8.0, 0.19) (7.5, 0.19) (5.6, 0.16) (8.0, 0.24) (4.4, 0.11) (7.2, 0.16) (5.9, 0.13) (4.7, 0.12) (4.4, 0.10) (4.5, 0.12) (3.4, 0.10) (5.8, 0.17) (6.8, 0.17) (6.0, 0.16) (5.4, 0.15) (7.1, 0.17) 

 166 151 157 145 154 162 159 149 156 160 160 154 159 158 159 162 

B 0.90 0.90 0.91 0.83 0.95 0.81 0.94 0.95 0.96 0.96 0.98 0.94 0.92 0.93 0.87 0.87 

(8.0, 0.20) (6.7, 0.17) (5.5, 0.16) (8.0, 0.24) (3.9, 0.10) (6.7, 0.15) (5.2, 0.11) (5.0, 0.11) (4.0, 0.10) (4.2, 0.11) (2.9, 0.09) (5.9, 0.17) (6.8, 0.17) (6.5, 0.18) (5.3, 0.16) (7.2, 0.17) 

159 290 153 143 292 310 300 289 292 300 309 288 308 299 305 311 

C 0.89 0.91 0.86 0.78 0.90 0.76 0.89 0.90 0.90 0.90 0.93 0.87 0.88 0.88 0.84 0.79 

 (10.2, 0.22) (8.3, 0.19) (7.1, 0.17) (10.4, 0.25) (6.9, 0.13) (8.5, 0.18) (6.4, 0.13) (7.7, 0.15) (7.1, 0.14) (7.9, 0.15) (6.7, 0.15) (8.6, 0.20) (8.9, 0.20) (9.6, 0.21) (8.7, 0.18) (8.5, 0.20) 

 180 324 172 164 309 327 315 305 311 317 323 491 323 313 323 333 

D 0.90 0.94 0.89 0.80 0.92 0.74 0.91 0.94 0.93 0.93 0.95 0.92 0.90 0.90 0.85 0.87 

(7.6, 0.19) (6.5, 0.16) (5.5, 0.14) (8.6, 0.22) (5.0, 0.12) (7.8, 0.19) (5.9, 0.13) (5.4, 0.13) (5.4, 0.13) (5.8, 0.13) (4.8, 0.13) (6.4, 0.17) (6.9, 0.18) (7.0, 0.19) (6.2, 0.17) (6.9, 0.17) 

153 278 147 135 280 294 283 273 282 284 292 274 291 287 290 297 

E 0.91 0.92 0.94 0.87 0.94 0.77 0.94 0.96 0.95 0.96 0.96 0.93 0.91 0.90 0.86 0.87 

 (8.3, 0.18) (5.6, 0.16) (4.9, 0.12) (7.1, 0.20) (4.1, 0.10) (7.5, 0.17) (5.6, 0.12) (4.3, 0.10) (4.4, 0.11) (3.9, 0.09) (3.9, 0.11) (5.8, 0.17) (6.9, 0.17) (6.8, 0.18) (6.3, 0.16) (7.3, 0.17) 

 160 294 155 142 300 320 310 299 303 310 317 292 314 304 313 318 

F 0.91 0.92 0.92 0.87 0.90 0.74 0.93 0.92 0.92 0.92 0.93 0.91 0.90 0.87 0.83 0.84 

(10.5, 0.23) (8.6, 0.20) (8.5, 0.17) (10.0, 0.25) (6.9, 0.14) (9.2, 0.19) (5.4, 0.11) (8.2, 0.16) (7.2, 0.15) (7.6, 0.15) (6.3, 0.16) (8.5, 0.22) (9.4, 0.21) (9.1, 0.23) (8.3, 0.20) (9.3, 0.21) 

163 295 159 144 302 320 308 297 305 311 316 292 317 306 317 322 

G 0.91 0.91 0.95 0.88 0.94 0.76 0.95 0.97 0.96 0.97 0.96 0.92 0.92 0.90 0.87 0.85 

 (7.9, 0.19) (5.9, 0.16) (4.1, 0.12) (7.1, 0.21) (3.9, 0.10) (7.5, 0.17) (4.7, 0.10) (3.7, 0.09) (3.6, 0.09) (3.5, 0.08) (3.4, 0.11) (5.7, 0.17) (6.8, 0.16) (6.8, 0.18) (5.9, 0.15) (7.5, 0.17) 

 156 292 154 140 293 315 303 293 296 303 312 288 311 300 308 314 

H 0.91 0.93 0.92 0.82 0.92 0.78 0.92 0.91 0.92 0.92 0.93 0.90 0.89 0.88 0.83 0.88 

(9.3, 0.23) (7.1, 0.20) (6.6, 0.17) (9.6, 0.26) (5.7, 0.13) (7.5, 0.17) (6.1, 0.13) (6.8, 0.15) (5.9, 0.15) (6.7, 0.14) (5.9, 0.15) (7.7, 0.22) (8.1, 0.22) (8.3, 0.22) (8.1, 0.20) (7.6, 0.20) 

165 284 158 145 287 307 297 288 292 299 303 281 301 293 301 307 

I 0.91 0.92 0.95 0.87 0.93 0.78 0.94 0.95 0.94 0.95 0.95 0.92 0.91 0.89 0.85 0.86 

 (8.2, 0.21) (6.1, 0.17) (4.7, 0.12) (7.7, 0.22) (4.2, 0.10) (7.1, 0.17) (5.0, 0.10) (4.6, 0.11) (4.8, 0.11) (4.7, 0.10) (4.2, 0.13) (6.5, 0.18) (6.8, 0.18) (7.4, 0.19) (6.6, 0.17) (7.0, 0.18) 

 175 314 168 154 318 338 327 316 322 328 335 306 334 323 334 339 

J 0.92 0.91 0.90 0.85 0.89 0.73 0.89 0.89 0.89 0.89 0.91 0.89 0.88 0.87 0.82 0.87 

(5.6, 0.14) (5.1, 0.14) (6.2, 0.16) (6.7, 0.21) (6.1, 0.17) (8.6, 0.22) (8.7, 0.20) (6.0, 0.16) (6.3, 0.16) (6.2, 0.16) (5.9, 0.16) (5.6, 0.16) (5.8, 0.16) (6.3, 0.17) (6.3, 0.17) (6.4, 0.15) 

173 313 167 153 319 341 329 317 327 329 337 307 335 327 336 340 

K 0.94 0.93 0.93 0.86 0.91 0.78 0.89 0.91 0.90 0.91 0.92 0.92 0.91 0.90 0.85 0.91 

 (5.2, 0.12) (4.2, 0.12) (6.1, 0.16) (7.2, 0.20) (5.1, 0.16) (8.0, 0.21) (8.4, 0.19) (5.2, 0.15) (5.5, 0.15) (5.2, 0.15) (5.4, 0.14) (5.2, 0.15) (4.9, 0.15) (5.3, 0.16) (5.3, 0.16) (5.1, 0.13) 

 176 298 169 155 310 327 319 304 313 319 325 301 325 315 323 328 

L 0.90 0.92 0.93 0.86 0.93 0.75 0.92 0.93 0.93 0.92 0.95 0.92 0.92 0.90 0.85 0.89 

(9.3, 0.20) (6.7, 0.17) (6.7, 0.14) (8.9, 0.21) (5.5, 0.12) (8.2, 0.19) (6.2, 0.13) (5.7, 0.13) (5.8, 0.13) (6.2, 0.13) (5.0, 0.13) (7.2, 0.17) (7.6, 0.17) (7.4, 0.18) (7.3, 0.16) (7.1, 0.17) 

151 285 144 132 285 301 290 282 286 293 299 277 299 286 294 301 

M 0.92 0.95 0.96 0.88 0.91 0.74 0.90 0.94 0.92 0.93 0.92 0.91 0.91 0.88 0.89 0.89 

 (6.2, 0.16) (4.5, 0.14) (3.4, 0.09) (6.3, 0.19) (5.9, 0.14) (9.0, 0.22) (8.0, 0.17) (4.7, 0.12) (5.2, 0.14) (5.0, 0.13) (5.8, 0.16) (6.4, 0.17) (5.5, 0.15) (6.9, 0.19) (6.2, 0.17) (6.9, 0.17) 

 175 157 165 152 162 171 166 159 164 168 169 158 168 165 168 171 

N 0.92 0.98 0.94 0.89 0.92 0.81 0.91 0.92 0.91 0.93 0.93 0.91 0.91 0.88 0.89 0.90 

(5.4, 0.13) (2.8, 0.08) (4.1, 0.12) (5.8, 0.17) (6.2, 0.13) (7.9, 0.20) (8.3, 0.17) (4.9, 0.12) (5.6, 0.14) (4.6, 0.13) (4.9, 0.15) (5.4, 0.15) (4.9, 0.14) (6.5, 0.17) (5.4, 0.16) (6.0, 0.14) 

162 151 153 140 156 165 160 157 158 162 165 153 162 158 161 165 

O 0.88 0.92 0.93 0.86 0.89 0.75 0.89 0.92 0.90 0.91 0.90 0.89 0.90 0.87 0.87 0.87 

 (7.5, 0.18) (4.9, 0.15) (3.8, 0.13) (6.5, 0.20) (5.9, 0.15) (8.8, 0.22) (7.6, 0.17) (5.1, 0.13) (5.8, 0.15) (5.5, 0.14) (5.6, 0.16) (6.1, 0.17) (5.7, 0.16) (7.0, 0.19) (6.2, 0.17) (7.0, 0.18) 

 166 152 157 145 155 166 161 154 158 161 162 151 161 159 162 166 

P 1.00 0.92 0.90 0.84 0.88 0.73 0.87 0.89 0.89 0.89 0.90 0.89 0.90 0.89 0.89 0.92 

(0.0, 0.00) (5.2, 0.13) (7.1, 0.17) (7.2, 0.20) (8.5, 0.20) (12.0, 0.26) (10.9, 0.24) (6.7, 0.18) (7.4, 0.18) (6.9, 0.18) (7.6, 0.19) (6.1, 0.16) (5.7, 0.15) (6.3, 0.17) (6.4, 0.17) (5.7, 0.13) 

181 159 166 152 164 174 168 160 166 169 171 158 170 167 170 173 

Q 1.00 0.92 0.85 0.88 0.71 0.89 0.90 0.88 0.90 0.90 0.89 0.88 0.86 0.82 0.91 

 (0.0, 0.00) (5.4, 0.16) (7.2, 0.19) (6.1, 0.18) (9.3, 0.24) (9.1, 0.21) (5.5, 0.16) (6.5, 0.16) (5.5, 0.16) (6.3, 0.16) (5.3, 0.16) (5.3, 0.16) (6.3, 0.18) (5.9, 0.17) (5.5, 0.14) 

 347 154 139 290 309 296 289 294 302 306 292 303 293 303 310 

R 1.00 0.91 0.93 0.76 0.90 0.94 0.93 0.94 0.91 0.92 0.92 0.88 0.89 0.89 

(0.0, 0.00) (5.8, 0.18) (5.1, 0.13) (8.6, 0.22) (7.5, 0.17) (4.4, 0.11) (5.0, 0.13) (4.0, 0.12) (5.8, 0.17) (6.2, 0.17) (5.6, 0.16) (7.1, 0.19) (6.4, 0.17) (7.1, 0.17) 

175 143 157 167 161 153 160 161 164 153 164 160 163 166 

S 1.00 0.83 0.66 0.81 0.86 0.83 0.87 0.83 0.84 0.84 0.81 0.82 0.80 

 (0.0, 0.00) (8.5, 0.22) (11.3, 0.28) (11.6, 0.26) (6.7, 0.20) (8.0, 0.21) (7.2, 0.19) (7.3, 0.22) (6.1, 0.21) (7.4, 0.20) (7.8, 0.23) (7.1, 0.22) (9.0, 0.22) 

 164 144 153 148 143 146 148 151 141 151 149 148 153 

T 1.00 0.81 0.93 0.95 0.97 0.96 0.97 0.92 0.91 0.92 0.87 0.85 

(0.0, 0.00) (5.9, 0.15) (6.2, 0.12) (3.4, 0.10) (3.2, 0.09) (2.9, 0.08) (3.2, 0.12) (5.2, 0.17) (5.5, 0.16) (5.4, 0.18) (4.9, 0.15) (6.6, 0.18) 

330 318 307 297 302 305 315 284 312 311 313 319 

U 1.00 0.77 0.79 0.81 0.81 0.81 0.78 0.76 0.79 0.74 0.69 

 (0.0, 0.00) (7.6, 0.17) (6.6, 0.17) (6.0, 0.15) (6.3, 0.16) (6.4, 0.17) (8.1, 0.22) (8.4, 0.22) (7.2, 0.21) (7.0, 0.19) (10.0, 0.23) 

 351 327 319 322 326 336 305 334 324 333 338 

V 1.00 0.96 0.97 0.95 0.95 0.93 0.91 0.90 0.88 0.83 

(0.0, 0.00) (5.9, 0.11) (4.8, 0.10) (5.8, 0.12) (5.7, 0.14) (7.7, 0.20) (8.6, 0.20) (8.3, 0.21) (6.9, 0.17) (9.3, 0.22) 

339 306 314 316 325 292 323 314 321 325 

W 1.00 0.98 0.98 0.96 0.95 0.93 0.92 0.89 0.85 

 (0.0, 0.00) (2.8, 0.06) (2.5, 0.07) (3.6, 0.11) (4.5, 0.15) (4.8, 0.15) (5.4, 0.16) (3.9, 0.13) (6.9, 0.17) 

 328 299 306 312 281 310 306 311 316 

X 1.00 0.98 0.97 0.95 0.93 0.94 0.91 0.85 

 (0.0, 0.00) (2.3, 0.07) (3.3, 0.10) (4.6, 0.14) (4.9, 0.14) (4.9, 0.15) (3.6, 0.11) (6.8, 0.17) 

334 311 318 286 319 305 316 321 

Y 1.00 0.97 0.95 0.93 0.92 0.89 0.85 

 (0.0, 0.00) (3.6, 0.11) (4.7, 0.16) (5.0, 0.15) (5.3, 0.17) (4.4, 0.14) (6.7, 0.18) 

 340 322 296 322 311 321 326 

Z 1.00 0.95 0.93 0.94 0.89 0.86 

(0.0, 0.00) (4.6, 0.15) (5.3, 0.15) (4.9, 0.15) (4.1, 0.14) (6.8, 0.17) 

347 305 331 321 328 335 

AA 1.00 0.98 0.97 0.89 0.88 

 (0.0, 0.00) (2.4, 0.07) (2.9, 0.08) (3.2, 0.11) (5.9, 0.17) 

 532 305 287 300 304 

AB 

LEGEND 

R 

(P90, COD) N 1.00 0.96 0.89 0.86 

 (0.0, 0.00) (3.1, 0.09) (3.7, 0.11) (6.5, 0.17) 

 346 317 328 333 

AC 1.00 0.91 0.85 

 (0.0, 0.00) (2.8, 0.10) (6.7, 0.17) 

 336 320 322 

AD 1.00 0.79

 (0.0, 0.00) (7.2, 0.18) 

 346 332 

AE 1.00 

December 2009 A-112 
P Q R S T U V W X Y Z AA AB AC AD AE 

 (0.0, 0.00) 

 355 

 

0

0.2

0 10 20 30 40 50 60 70 80 90 10

Distance Between Samplers (km)

0.4

0.6

0.8

1

0

Correlation

Figure A-48. PM2.5 inter-sampler correlations as a function of distance between monitors for 

Chicago, IL. 

December 2009 A-113 
Figure A-49. PM2.5 monitor distribution and major highways, Denver, CO. 

December 2009 A-114 
Figure A-50. Box plots illustrating the seasonal distribution of 24-h avg PM2.5 concentrations 

for Denver, CO. 

December 2009 A-115 
Table A-24. Inter-sampler correlation statistics for each pair of PM2.5 monitors reporting to AQS for 

Denver, CO. 

A B C D E F G H 

A 1.00 0.74 0.84 0.68 0.86 0.91 0.76 0.83 

 (0.0, 0.00) (6.0, 0.21) (5.4, 0.17) (7.9, 0.26) (4.1, 0.14) (3.0, 0.11) (5.9, 0.19) (4.6, 0.14) 

369 353 347 332 362 339 341 325 

B 1.00 0.58 0.76 0.92 0.84 0.50 0.49 

(0.0, 0.00) (5.7, 0.19) (3.9, 0.17) (3.2, 0.13) (4.4, 0.17) (7.8, 0.23) (6.6, 0.21) 

363 344 328 356 336 337 323 

C 1.00 0.74 0.71 0.75 0.83 0.88 

 (0.0, 0.00) (4.4, 0.19) (4.5, 0.17) (5.4, 0.18) (3.5, 0.14) (3.7, 0.13) 

 361 326 354 336 333 320 

D 1.00 0.82 0.77 0.54 0.57 

(0.0, 0.00) (5.6, 0.21) (6.0, 0.24) (7.2, 0.24) (6.4, 0.24) 

354 347 332 318 305 

E 1.00 0.94 0.64 0.60 

 (0.0, 0.00) (2.3, 0.09) (7.1, 0.21) (5.6, 0.18) 

 1046 969 353 330 

F 1.00 0.68 0.69 

 

LEGEND 

R 

(P90, COD) 

N (0.0, 0.00) (6.6, 0.21) (5.9, 0.17) 

1006 333 317 

G 1.00 0.88 

 (0.0, 0.00) (3.4, 0.13) 

 359 313 

H 1.00 

(0.0, 0.00)

334 

 

December 2009 A-116 
0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

Figure A-51. PM2.5 inter-sampler correlations as a function of distance between monitors for 

Denver, CO. 

December 2009 A-117 
Figure A-52. PM2.5 monitor distribution and major highways, Detroit, MI. 

December 2009 A-118 
Figure A-53. Box plots illustrating the seasonal distribution of 24-h avg PM2.5 concentrations 

for Detroit, MI. 

December 2009 A-119 
Table A-25. Inter-sampler correlation statistics for each pair of PM2.5 monitors reporting to AQS for 

Detroit, MI. 

A B C D E F G H I J K L M 

A 1.00 0.91 0.86 0.91 0.89 0.90 0.89 0.88 0.89 0.91 0.92 0.87 0.88 

 (0.0, 0.00) (5.9, 0.17) (7.8, 0.19) (6.7, 0.17) (7.6, 0.18) (5.9, 0.18) (8.1, 0.20) (8.3, 0.22) (8.0, 0.19) (7.3, 0.17) (5.5, 0.16) (11.0, 0.26) (7.8, 0.21)

 356 299 333 301 296 341 349 334 284 301 293 336 333 

B 1.00 0.90 0.94 0.92 0.92 0.93 0.90 0.92 0.91 0.92 0.89 0.91 

(0.0, 0.00) (6.8, 0.17) (5.3, 0.14) (5.9, 0.16) (5.8, 0.17) (6.2, 0.18) (7.5, 0.21) (5.8, 0.18) (4.9, 0.16) (5.4, 0.17) (10.2, 0.24) (6.1, 0.19)

306 286 296 290 294 300 288 277 297 286 292 288 

C 1.00 0.90 0.87 0.91 0.93 0.90 0.91 0.90 0.89 0.87 0.93 

 (0.0, 0.00) (7.0, 0.16) (8.8, 0.20) (5.5, 0.15) (5.9, 0.14) (7.2, 0.17) (6.3, 0.16) (6.2, 0.14) (6.2, 0.16) (10.4, 0.20) (4.9, 0.13)

 342 289 284 326 335 320 273 286 279 321 319 

D 1.00 0.93 0.94 0.96 0.92 0.94 0.94 0.94 0.91 0.92 

(0.0, 0.00) (6.3, 0.15) (4.5, 0.14) (4.3, 0.13) (5.8, 0.16) (4.5, 0.12) (3.8, 0.11) (3.6, 0.13) (8.2, 0.18) (6.2, 0.15)

308 292 296 303 291 281 297 291 290 290 

E 1.00 0.90 0.90 0.89 0.90 0.90 0.90 0.87 0.87 

 (0.0, 0.00) (7.5, 0.18) (7.3, 0.20) (8.2, 0.22) (7.0, 0.19) (6.4, 0.18) (6.9, 0.18) (10.7, 0.25) (7.7, 0.21)

 303 291 297 286 276 292 284 288 288 

F 1.00 0.95 0.90 0.92 0.92 0.95 0.89 0.93 

(0.0, 0.00) (4.5, 0.13) (6.2, 0.17) (5.7, 0.15) (5.2, 0.14) (3.9, 0.12) (9.8, 0.21) (5.7, 0.15)

 350 343 326 280 297 288 329 326 

G 1.00 0.94 0.95 0.92 0.93 0.90 0.95 

 (0.0, 0.00) (5.1, 0.14) (4.9, 0.12) (4.5, 0.14) (5.6, 0.16) (8.2, 0.18) (4.7, 0.12)

 1049 336 549 302 295 337 335 

H 1.00 0.93 0.91 0.89 0.91 0.91 

(0.0, 0.00) (4.8, 0.15) (5.4, 0.15) (6.9, 0.18) (7.6, 0.16) (6.1, 0.15)

 342 273 290 288 321 319 

I 1.00 0.92 0.90 0.92 0.93 

 (0.0, 0.00) (4.4, 0.13) (6.1, 0.14) (7.9, 0.18) (5.8, 0.14)

 572 279 271 274 274 

J 

LEGEND 

R 

(P90, COD) 

N 1.00 0.91 0.90 0.91 

 (0.0, 0.00) (5.3, 0.15) (8.1, 0.17) (5.6, 0.13)

 308 288 291 291 

K 1.00 0.88 0.91 

 (0.0, 0.00) (9.5, 0.21) (6.3, 0.16)

 301 281 283 

L 1.00 0.91 

 (0.0, 0.00) (8.5, 0.17)

 344 322 

M 1.00 

 (0.0, 0.00)

 342 

 

December 2009 A-120 
0

0.2

0.4

0.6

0 10 20 30 40 50 60 70 80 90 10

Distance Between Samplers (km)

Correlation

0.8

1

0

Figure A-54. PM2.5 inter-sampler correlations as a function of distance between monitors for 

Detroit, MI. 

December 2009 A-121 
Figure A-55. PM2.5 monitor distribution and major highways, Houston, TX. 

December 2009 A-122 
Figure A-56. Box plots illustrating the seasonal distribution of 24-h avg PM2.5 concentrations 

for Houston, TX. 

Table A-26. Inter-sampler correlation statistics for each pair of PM2.5 monitors reporting to AQS for 

Houston, TX. 

A B 

A 1.00 0.66 

 (0.0, 0.00) (10.0, 0.24) 

 326 310 

B 1.00 

 (0.0, 0.00) 

 1016 

 

LEGEND 

R 

(P90, COD) 

N

 

December 2009 A-123 
0

0.2

0.4

0.6

0 10 20 30 40 50 60 70 80 90 10

Distance Between Samplers (km)

Correlation

0.8

1

0

Figure A-57. PM2.5 inter-sampler correlations as a function of distance between monitors for 

Houston, TX. 

December 2009 A-124 
Figure A-58. PM2.5 monitor distribution and major highways, Los Angeles, CA. 

December 2009 A-125 
Figure A-59. Box plots illustrating the seasonal distribution of 24-h avg PM2.5 concentrations 

for Los Angeles, CA. 

December 2009 A-126 
Table A-27. Inter-sampler correlation statistics for each pair of PM2.5 monitors reporting to AQS for 

Los Angeles, CA. 

A B C D E F G H I J K 

A 1.00 0.86 0.87 0.81 0.80 0.88 0.68 0.64 0.30 0.70 0.82 

 (0.0, 0.00) (9.0, 0.18) (7.7, 0.16) (9.0, 0.19) (9.7, 0.21) (5.8, 0.14) (11.5, 0.22) (12.4, 0.23) (18.0, 0.36) (10.5, 0.21) (11.4, 0.23) 

862 252 803 238 262 269 761 793 179 804 259 

B 1.00 0.92 0.87 0.83 0.88 0.77 0.73 0.31 0.74 0.71 

(0.0, 0.00) (5.5, 0.11) (9.1, 0.19) (9.0, 0.15) (7.6, 0.15) (9.8, 0.17) (11.6, 0.18) (24.1, 0.38) (11.9, 0.19) (15.0, 0.27) 

308 293 250 278 279 268 282 177 292 277 

C 1.00 0.80 0.89 0.92 0.84 0.79 0.29 0.82 0.78 

 (0.0, 0.00) (9.6, 0.20) (5.8, 0.11) (6.4, 0.13) (9.0, 0.15) (10.0, 0.17) (18.6, 0.38) (9.4, 0.16) (13.2, 0.25) 

 1004 274 315 319 880 913 213 920 305 

D 1.00 0.69 0.77 0.63 0.60 0.41 0.64 0.60 

(0.0, 0.00) (10.9, 0.23) (7.4, 0.18) (11.3, 0.22) (11.1, 0.22) (14.8, 0.31) (9.6, 0.21) (11.6, 0.23) 

291 263 263 256 268 164 274 261 

E 1.00 0.79 0.95 0.92 0.34 0.88 0.76 

 (0.0, 0.00) (9.1, 0.19) (5.9, 0.11) (7.6, 0.13) (19.7, 0.39) (8.2, 0.15) (13.7, 0.27) 

327 301 289 301 192 307 291 

F 1.00 0.70 0.70 0.33 0.69 0.72 

(0.0, 0.00) (10.5, 0.18) (9.2, 0.19) (14.8, 0.34) (9.8, 0.19) (9.9, 0.21) 

334 290 302 184 311 293 

G 1.00 0.96 0.23 0.92 0.78 

 (0.0, 0.00) (4.0, 0.09) (17.0, 0.35) (5.4, 0.12) (11.0, 0.21) 

 

LEGEND 

Pearson R 

(P90, COD) 

n 946 859 194 882 277 

H 1.00 0.26 0.91 0.78 

(0.0, 0.00) (15.3, 0.34) (5.9, 0.12) (9.5, 0.21) 

 990 208 914 294 

I 1.00 0.21 0.31 

 (0.0, 0.00) (18.3, 0.35) (9.7, 0.28) 

 221 205 180 

J 1.00 0.84 

 (0.0, 0.00) (9.8, 0.19) 

 999 298 

K 1.00 

 (0.0, 0.00) 

 318 

 

0

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

0.2

0.4

0.6

0.8

1

Correlation

Figure A-60. PM2.5 inter-sampler correlations as a function of distance between monitors for 

Los Angeles, CA. 

December 2009 A-127 
Figure A-61. PM2.5 monitor distribution and major highways, New York City, NY. 

December 2009 A-128 
December 2009 A-129 
Figure A-62. Box plots illustrating the seasonal distribution of 24-h avg PM2.5 concentrations 

for New York, NY. 

December 2009 A-130 
Table A-28. Inter-sampler correlation statistics for each pair of PM2.5 monitors reporting to AQS for 

New York, NY. 

Site A B C D E F G H I J K L M N O P Q R 

A 1.00 0.89 0.97 0.97 0.82 0.96 0.96 0.96 0.96 0.93 0.91 0.91 0.92 0.88 0.84 0.87 0.89 0.84 

 (0.0, 0.00) (5.3, 0.15) (3.6, 0.09) (4.8, 0.11) (11.8, 0.33) (3.8, 0.11) (4.0, 0.11) (3.4, 0.10) (4.6, 0.12) (5.1, 0.12) (5.8, 0.12) (5.7, 0.12) (5.5, 0.13) (6.6, 0.16) (9.1, 0.19) (8.3, 0.16) (7.6, 0.16) (9.3, 0.21) 

 349 322 316 322 325 328 321 324 326 335 329 316 331 301 296 321 318 316 

B 1.00 0.93 0.91 0.78 0.91 0.92 0.91 0.91 0.92 0.83 0.84 0.85 0.82 0.79 0.82 0.82 0.78 

(0.0, 0.00) (4.5, 0.13) (5.3, 0.14) (10.4, 0.32) (4.7, 0.13) (4.6, 0.13) (4.6, 0.14) (5.0, 0.14) (4.5, 0.13) (7.3, 0.17) (7.1, 0.17) (7.8, 0.19) (7.2, 0.19) (7.7, 0.20) (7.6, 0.18) (6.6, 0.18) (8.4, 0.22) 

339 312 315 319 316 313 313 315 330 319 305 321 291 292 310 307 305 

C 1.00 0.98 0.82 0.96 0.95 0.96 0.97 0.94 0.91 0.91 0.91 0.89 0.84 0.88 0.89 0.84 

 (0.0, 0.00) (3.4, 0.08) (10.8, 0.32) (3.9, 0.10) (4.1, 0.11) (3.6, 0.10) (4.0, 0.11) (4.8, 0.11) (5.7, 0.13) (5.8, 0.14) (6.5, 0.15) (5.4, 0.15) (6.9, 0.17) (6.3, 0.14) (6.2, 0.15) (8.2, 0.20) 

 332 314 309 310 308 307 310 319 314 299 316 287 289 307 305 297 

D 1.00 0.85 0.96 0.96 0.94 0.96 0.92 0.90 0.89 0.91 0.88 0.87 0.89 0.90 0.86 

(0.0, 0.00) (8.4, 0.29) (3.4, 0.11) (3.8, 0.11) (5.0, 0.13) (3.0, 0.10) (5.5, 0.13) (7.1, 0.15) (6.9, 0.15) (6.7, 0.18) (6.3, 0.17) (6.5, 0.16) (6.0, 0.15) (5.5, 0.14) (6.6, 0.18) 

565 314 316 532 315 313 325 319 308 517 506 288 311 309 330 

E 1.00 0.82 0.82 0.79 0.83 0.81 0.80 0.77 0.76 0.76 0.79 0.78 0.87 0.87 

 (0.0, 0.00) (10.0, 0.31) (10.7, 0.33) (11.4, 0.33) (8.8, 0.28) (10.3, 0.32) (12.5, 0.34) (13.0, 0.34) (13.8, 0.39) (11.6, 0.35) (9.1, 0.30) (10.4, 0.32) (7.9, 0.28) (7.3, 0.24) 

 341 321 313 317 319 330 322 305 323 294 291 316 311 305 

F 1.00 0.99 0.98 0.98 0.94 0.88 0.89 0.89 0.86 0.85 0.88 0.87 0.83 

(0.0, 0.00) (2.1, 0.07) (2.9, 0.09) (2.8, 0.09) (4.7, 0.11) (6.7, 0.14) (6.8, 0.15) (6.8, 0.16) (6.4, 0.17) (6.8, 0.18) (6.1, 0.15) (7.3, 0.16) (7.5, 0.21) 

341 314 319 321 328 321 308 323 293 295 312 310 308 

G 1.00 0.96 0.98 0.93 0.88 0.89 0.89 0.84 0.84 0.86 0.87 0.82 

 (0.0, 0.00) (2.9, 0.10) (3.6, 0.11) (5.2, 0.12) (7.1, 0.15) (6.7, 0.15) (6.9, 0.16) (6.9, 0.18) (8.0, 0.19) (7.6, 0.16) (8.1, 0.17) (8.4, 0.23) 

 992 315 319 326 319 309 526 513 286 310 306 327 

H 1.00 0.98 0.94 0.88 0.89 0.89 0.84 0.82 0.85 0.85 0.79 

(0.0, 0.00) (3.7, 0.10) (3.7, 0.10) (7.1, 0.14) (7.1, 0.14) (6.6, 0.16) (6.7, 0.18) (8.1, 0.20) (7.8, 0.17) (7.5, 0.17) (9.2, 0.23) 

338 320 324 318 303 321 292 285 310 307 304 

I 1.00 0.95 0.89 0.90 0.89 0.87 0.85 0.87 0.88 0.83 

 (0.0, 0.00) (4.1, 0.11) (7.0, 0.16) (7.0, 0.16) (7.7, 0.20) (6.4, 0.18) (6.6, 0.17) (6.5, 0.16) (6.5, 0.15) (7.6, 0.19) 

 344 327 324 307 323 296 291 313 313 310 

J 1.00 0.87 0.87 0.87 0.84 0.79 0.82 0.84 0.79 

 (0.0, 0.00) (7.0, 0.16) (7.2, 0.16) (8.5, 0.17) (6.9, 0.18) (7.9, 0.20) (8.1, 0.18) (7.5, 0.17) (9.0, 0.22) 

 352 332 316 334 303 299 321 322 316 

K 1.00 0.95 0.93 0.88 0.86 0.90 0.92 0.86 

 (0.0, 0.00) (3.4, 0.09) (4.5, 0.12) (6.4, 0.15) (7.5, 0.17) (5.7, 0.13) (5.8, 0.14) (8.7, 0.20)

 345 314 330 301 296 317 319 312 

L 

LEGEND 

R 

(P90, COD) 

N 1.00 0.97 0.91 0.86 0.94 0.93 0.87 

 (0.0, 0.00) (4.1, 0.10) (6.4, 0.14) (8.0, 0.18) (5.2, 0.12) (5.9, 0.13) (8.3, 0.20) 

 334 321 289 288 309 303 301 

M 1.00 0.91 0.86 0.93 0.92 0.85 

 (0.0, 0.00) (5.5, 0.14) (8.4, 0.21) (6.7, 0.15) (7.5, 0.18) (9.7, 0.25) 

 559 499 300 326 318 337 

N 1.00 0.93 0.95 0.91 0.88 

 (0.0, 0.00) (4.7, 0.14) (4.1, 0.11) (5.8, 0.15) (7.2, 0.20) 

 545 270 293 292 316 

O 1.00 0.93 0.91 0.94 

 (0.0, 0.00) (4.3, 0.12) (4.9, 0.14) (4.3, 0.14) 

 313 294 287 279 

P 1.00 0.94 0.91 

 (0.0, 0.00) (4.9, 0.12) (5.5, 0.16) 

 336 308 303 

Q 1.00 0.95 

 (0.0, 0.00) (3.8, 0.13) 

 336 307 

R 1.00

 (0.0, 0.00)

 357 

 

December 2009 A-131 
S T U V W X Y Z AA AB AC AD AE AF AG AH AI AJ 

A 0.75 0.89 0.90 0.90 0.88 0.92 0.94 0.93 0.93 0.88 0.89 0.94 0.88 0.89 0.93 0.90 0.89 0.96 

 (10.4, 0.21) (6.1, 0.13) (7.1, 0.15) (6.0, 0.13) (7.2, 0.15) (7.2, 0.16) (4.0, 0.11) (4.7, 0.12) (5.5, 0.13) (7.6, 0.18) (7.3, 0.18) (4.4, 0.11) (7.2, 0.19) (6.7, 0.16) (5.1, 0.12) (6.2, 0.15) (7.5, 0.16) (4.4, 0.12) 

 323 315 337 299 316 332 342 348 325 320 340 346 326 323 299 317 318 338 

B 0.68 0.84 0.83 0.83 0.84 0.84 0.88 0.85 0.84 0.81 0.81 0.86 0.81 0.92 0.84 0.87 0.86 0.88 

(10.8, 0.23) (5.9, 0.16) (8.6, 0.20) (6.5, 0.18) (6.8, 0.18) (9.0, 0.21) (5.9, 0.16) (6.8, 0.17) (7.3, 0.18) (7.9, 0.20) (8.4, 0.22) (7.0, 0.17) (8.8, 0.23) (5.8, 0.16) (6.6, 0.17) (6.8, 0.18) (7.1, 0.18) (5.5, 0.15) 

314 307 328 290 305 325 334 338 317 313 331 336 315 316 292 311 309 329 

C 0.76 0.89 0.88 0.89 0.89 0.92 0.95 0.93 0.92 0.89 0.88 0.93 0.88 0.89 0.93 0.91 0.89 0.96 

 (8.5, 0.20) (6.1, 0.14) (7.8, 0.18) (6.4, 0.15) (6.0, 0.15) (7.8, 0.18) (4.4, 0.11) (5.4, 0.13) (5.6, 0.15) (6.1, 0.16) (7.5, 0.20) (5.3, 0.12) (7.4, 0.20) (6.7, 0.15) (4.7, 0.11) (5.7, 0.14) (6.0, 0.15) (3.5, 0.10) 

 307 304 321 283 297 317 326 331 311 306 325 330 308 312 282 305 302 322 

D 0.80 0.88 0.88 0.87 0.88 0.89 0.94 0.92 0.91 0.89 0.87 0.92 0.85 0.90 0.92 0.90 0.91 0.96 

(7.7, 0.19) (7.3, 0.16) (8.1, 0.20) (7.1, 0.17) (6.9, 0.17) (9.7, 0.21) (5.6, 0.14) (6.2, 0.15) (7.0, 0.17) (5.9, 0.16) (9.6, 0.23) (6.6, 0.15) (9.2, 0.23) (5.4, 0.14) (4.8, 0.12) (6.5, 0.16) (5.3, 0.14) (3.7, 0.10) 

509 306 537 326 304 324 332 548 315 313 330 336 315 313 496 308 310 328 

E 0.67 0.79 0.74 0.76 0.75 0.75 0.80 0.78 0.76 0.73 0.74 0.79 0.70 0.88 0.77 0.75 0.80 0.84 

 (9.8, 0.32) (11.3, 0.34) (14.9, 0.40) (11.7, 0.36) (12.1, 0.36) (15.2, 0.41) (11.5, 0.34) (13.1, 0.36) (13.9, 0.38) (10.1, 0.33) (15.7, 0.43) (13.1, 0.35) (15.0, 0.42) (7.6, 0.26) (11.3, 0.32) (12.5, 0.36) (9.4, 0.31) (9.8, 0.29) 

 315 306 329 290 307 324 334 340 319 314 332 338 316 316 294 310 309 331 

F 0.79 0.87 0.86 0.86 0.87 0.89 0.93 0.91 0.90 0.90 0.87 0.91 0.86 0.89 0.93 0.89 0.89 0.94 

(7.9, 0.19) (6.7, 0.15) (8.5, 0.19) (6.8, 0.16) (6.6, 0.16) (8.2, 0.19) (5.0, 0.12) (6.4, 0.14) (6.7, 0.15) (5.6, 0.16) (8.4, 0.20) (6.3, 0.14) (8.0, 0.21) (6.4, 0.16) (4.6, 0.12) (5.7, 0.15) (5.3, 0.15) (4.1, 0.12) 

316 306 329 293 309 325 335 340 320 317 334 339 319 317 290 312 310 332 

G 0.77 0.87 0.87 0.86 0.88 0.89 0.92 0.90 0.90 0.88 0.86 0.91 0.85 0.88 0.90 0.89 0.88 0.93 

 (8.7, 0.21) (6.3, 0.15) (7.8, 0.18) (7.0, 0.16) (6.3, 0.15) (8.3, 0.17) (5.4, 0.13) (5.7, 0.14) (7.1, 0.15) (7.5, 0.17) (8.1, 0.19) (6.4, 0.14) (8.2, 0.20) (6.7, 0.17) (5.2, 0.13) (6.1, 0.15) (6.1, 0.16) (5.0, 0.14) 

 513 304 928 327 303 319 329 958 315 308 327 333 314 311 856 312 309 325 

H 0.74 0.86 0.86 0.87 0.87 0.90 0.92 0.89 0.90 0.88 0.87 0.91 0.86 0.88 0.90 0.88 0.86 0.93 

(9.6, 0.22) (6.6, 0.15) (8.4, 0.18) (7.1, 0.16) (6.9, 0.16) (7.5, 0.17) (5.2, 0.13) (5.6, 0.14) (6.4, 0.15) (7.3, 0.19) (7.9, 0.19) (5.7, 0.13) (7.3, 0.20) (6.9, 0.17) (5.6, 0.14) (6.8, 0.16) (6.4, 0.17) (5.0, 0.13) 

314 304 326 289 306 322 331 337 315 310 329 335 313 313 290 308 308 327 

I 0.76 0.88 0.87 0.88 0.88 0.90 0.93 0.92 0.91 0.87 0.87 0.92 0.86 0.90 0.93 0.89 0.89 0.94 

 (8.1, 0.20) (7.1, 0.17) (8.7, 0.21) (7.4, 0.17) (6.9, 0.17) (9.4, 0.22) (5.7, 0.15) (6.5, 0.16) (7.2, 0.18) (6.2, 0.17) (9.6, 0.24) (6.3, 0.16) (9.2, 0.24) (5.5, 0.13) (5.1, 0.14) (6.7, 0.18) (5.8, 0.16) (4.1, 0.12) 

 315 308 332 293 309 326 334 343 323 313 332 338 318 319 296 310 311 330 

J 0.67 0.84 0.85 0.86 0.86 0.88 0.90 0.88 0.86 0.81 0.85 0.89 0.84 0.90 0.88 0.87 0.84 0.91 

(11.1, 0.22) (6.6, 0.16) (9.0, 0.19) (6.7, 0.16) (6.8, 0.17) (8.8, 0.19) (6.1, 0.14) (7.1, 0.16) (7.3, 0.17) (8.2, 0.19) (9.0, 0.21) (6.9, 0.15) (8.9, 0.22) (6.4, 0.16) (6.4, 0.15) (7.5, 0.16) (7.7, 0.18) (5.6, 0.14) 

327 316 343 301 318 337 345 351 330 324 343 349 327 329 301 321 320 341 

K 0.74 0.94 0.92 0.95 0.92 0.92 0.93 0.92 0.93 0.84 0.90 0.93 0.88 0.89 0.90 0.91 0.89 0.92 

 (10.9, 0.21) (3.9, 0.11) (5.7, 0.14) (3.4, 0.10) (4.3, 0.12) (6.0, 0.15) (3.8, 0.12) (4.2, 0.12) (4.3, 0.12) (8.5, 0.19) (6.2, 0.17) (3.8, 0.11) (6.2, 0.18) (7.4, 0.17) (5.9, 0.13) (5.0, 0.13) (6.5, 0.15) (4.8, 0.13) 

 320 317 336 302 317 330 339 344 324 318 338 343 321 321 294 318 314 335 

L 0.78 0.94 0.97 0.98 0.95 0.94 0.93 0.94 0.95 0.86 0.91 0.95 0.91 0.89 0.92 0.96 0.92 0.92 

(9.8, 0.20) (3.9, 0.11) (4.5, 0.12) (2.9, 0.08) (4.0, 0.10) (6.3, 0.15) (4.5, 0.12) (4.2, 0.11) (4.1, 0.11) (8.1, 0.18) (6.3, 0.17) (4.2, 0.10) (5.9, 0.17) (6.8, 0.17) (5.4, 0.12) (4.0, 0.11) (6.4, 0.14) (5.4, 0.13) 

313 303 325 292 303 314 323 333 306 305 322 327 309 306 283 305 299 319 

M 0.80 0.93 0.97 0.97 0.96 0.95 0.95 0.96 0.97 0.88 0.94 0.96 0.93 0.88 0.95 0.96 0.93 0.93 

 (9.9, 0.22) (5.4, 0.13) (3.8, 0.09) (3.5, 0.09) (4.7, 0.11) (4.9, 0.11) (4.7, 0.12) (3.5, 0.10) (3.4, 0.09) (8.3, 0.20) (4.5, 0.12) (3.5, 0.10) (4.5, 0.13) (8.5, 0.21) (5.0, 0.14) (4.3, 0.10) (6.8, 0.16) (5.7, 0.17) 

 504 318 534 341 319 331 342 545 326 320 339 345 326 323 484 319 318 338 

N 0.88 0.86 0.90 0.91 0.91 0.86 0.90 0.89 0.91 0.89 0.85 0.90 0.85 0.85 0.91 0.92 0.92 0.90 

(6.4, 0.17) (6.5, 0.16) (6.5, 0.15) (5.7, 0.13) (4.5, 0.13) (8.2, 0.18) (5.9, 0.14) (5.4, 0.15) (5.3, 0.14) (5.6, 0.17) (8.1, 0.18) (5.3, 0.14) (7.7, 0.18) (7.5, 0.20) (4.9, 0.14) (4.6, 0.13) (5.3, 0.14) (5.4, 0.16) 

492 287 519 313 290 301 309 529 297 289 308 313 294 292 477 292 293 306 

O 0.87 0.82 0.86 0.87 0.88 0.84 0.87 0.86 0.88 0.89 0.82 0.88 0.81 0.84 0.88 0.89 0.92 0.88 

 (5.6, 0.16) (7.2, 0.18) (9.9, 0.22) (7.3, 0.18) (6.4, 0.16) (11.1, 0.24) (6.7, 0.18) (8.6, 0.19) (8.2, 0.20) (5.2, 0.15) (10.3, 0.25) (8.4, 0.18) (10.6, 0.25) (7.0, 0.18) (6.1, 0.16) (6.1, 0.18) (4.7, 0.14) (5.4, 0.16) 

 295 289 302 280 284 299 308 312 292 295 307 311 290 290 269 290 283 304 

P 0.86 0.89 0.92 0.94 0.95 0.88 0.90 0.89 0.92 0.89 0.87 0.91 0.87 0.87 0.91 0.94 0.95 0.91 

(6.2, 0.15) (6.2, 0.14) (7.4, 0.17) (5.0, 0.12) (4.0, 0.11) (8.9, 0.19) (5.9, 0.14) (6.8, 0.15) (6.4, 0.14) (5.3, 0.14) (8.9, 0.21) (6.4, 0.14) (8.3, 0.20) (6.7, 0.16) (5.5, 0.13) (4.7, 0.12) (3.5, 0.10) (5.0, 0.14) 

312 307 325 296 305 319 329 335 312 309 327 333 313 311 285 307 306 326 

Q 0.79 0.92 0.91 0.93 0.92 0.89 0.90 0.90 0.90 0.83 0.90 0.91 0.87 0.91 0.89 0.90 0.91 0.92 

 (8.1, 0.19) (5.0, 0.14) (8.2, 0.20) (6.2, 0.14) (5.4, 0.15) (9.7, 0.22) (6.3, 0.16) (7.3, 0.17) (7.3, 0.18) (6.9, 0.19) (9.9, 0.24) (6.9, 0.16) (9.5, 0.24) (4.8, 0.14) (6.0, 0.15) (6.3, 0.17) (5.3, 0.14) (5.5, 0.13) 

 313 303 327 287 304 321 328 335 314 306 329 332 311 312 287 303 302 324 

R 0.82 0.86 0.84 0.85 0.87 0.82 0.87 0.84 0.86 0.83 0.81 0.86 0.78 0.88 0.86 0.87 0.90 0.88 

(6.5, 0.20) (7.6, 0.21) (10.9, 0.26) (8.2, 0.21) (7.0, 0.20) (11.6, 0.28) (8.6, 0.21) (10.0, 0.23) (9.1, 0.24) (7.0, 0.21) (11.2, 0.30) (9.5, 0.22) (11.0, 0.30) (6.2, 0.17) (7.6, 0.20) (7.8, 0.22) (5.7, 0.17) (6.3, 0.17) 

330 296 347 291 304 314 323 355 309 301 324 327 309 308 304 298 302 320 

S 1.00 0.69 0.77 0.75 0.78 0.72 0.78 0.79 0.82 0.88 0.74 0.78 0.73 0.69 0.85 0.82 0.88 0.79 

 (0.0, 0.00) (10.4, 0.22) (10.5, 0.24) (10.5, 0.21) (9.2, 0.19) (12.5, 0.25) (8.6, 0.19) (9.3, 0.21) (9.4, 0.20) (5.0, 0.16) (11.6, 0.26) (9.9, 0.20) (11.5, 0.25) (10.3, 0.22) (7.2, 0.17) (8.5, 0.18) (5.5, 0.14) (8.1, 0.19) 

 550 306 525 324 306 325 336 536 319 314 333 339 322 316 478 310 312 331 

T 1.00 0.92 0.93 0.93 0.92 0.91 0.91 0.90 0.80 0.87 0.93 0.85 0.89 0.88 0.90 0.86 0.91 

(0.0, 0.00) (6.0, 0.15) (4.5, 0.12) (4.8, 0.13) (6.7, 0.16) (5.2, 0.14) (4.9, 0.13) (5.6, 0.14) (8.4, 0.20) (6.9, 0.18) (4.9, 0.12) (7.2, 0.20) (6.3, 0.17) (6.2, 0.14) (5.8, 0.14) (7.3, 0.17) (5.9, 0.14) 

330 319 293 301 313 323 329 308 303 321 327 306 308 281 306 298 319 

U 1.00 0.98 0.97 0.92 0.91 0.93 0.94 0.84 0.91 0.94 0.90 0.86 0.90 0.96 0.91 0.90 

 (0.0, 0.00) (3.9, 0.10) (5.0, 0.12) (5.4, 0.12) (6.9, 0.15) (5.2, 0.13) (4.9, 0.12) (9.9, 0.22) (5.0, 0.12) (5.2, 0.12) (5.9, 0.14) (9.6, 0.23) (6.9, 0.17) (5.8, 0.12) (8.4, 0.18) (7.5, 0.19) 

 1017 341 325 337 347 987 332 326 346 351 330 330 878 325 323 343 

V 1.00 0.97 0.93 0.92 0.92 0.94 0.83 0.90 0.94 0.91 0.88 0.89 0.96 0.91 0.91 

(0.0, 0.00) (2.8, 0.09) (6.1, 0.14) (5.0, 0.13) (4.4, 0.12) (4.2, 0.12) (8.2, 0.19) (6.6, 0.16) (4.3, 0.11) (6.1, 0.16) (7.0, 0.18) (5.8, 0.15) (4.0, 0.10) (6.4, 0.15) (5.3, 0.15) 

352 288 300 307 351 294 290 305 311 294 290 301 291 287 304 

W 1.00 0.90 0.91 0.91 0.92 0.85 0.89 0.93 0.88 0.87 0.89 0.96 0.92 0.90 

 (0.0, 0.00) (7.0, 0.16) (5.5, 0.13) (5.3, 0.13) (4.8, 0.13) (7.0, 0.18) (6.8, 0.17) (5.0, 0.12) (6.9, 0.18) (6.8, 0.18) (5.1, 0.14) (3.7, 0.10) (4.9, 0.13) (5.0, 0.15) 

 332 316 325 331 310 309 323 328 308 310 281 304 303 320 

X 1.00 0.96 0.97 0.95 0.86 0.94 0.97 0.93 0.88 0.93 0.92 0.88 0.93 

(0.0, 0.00) (5.8, 0.13) (4.4, 0.11) (5.0, 0.11) (10.0, 0.23) (3.3, 0.09) (4.5, 0.11) (4.1, 0.11) (9.8, 0.24) (6.9, 0.17) (6.5, 0.14) (9.2, 0.20) (8.2, 0.19) 

349 344 349 328 324 342 348 326 326 301 319 319 340 

Y 1.00 0.97 0.96 0.90 0.93 0.98 0.93 0.89 0.97 0.93 0.92 0.97 

 (0.0, 0.00) (3.2, 0.08) (3.9, 0.09) (6.5, 0.16) (5.4, 0.15) (2.8, 0.08) (5.4, 0.15) (6.5, 0.18) (3.3, 0.09) (4.9, 0.12) (5.3, 0.13) (3.5, 0.11) 

 359 359 338 333 352 358 337 335 308 328 329 350 

Z 1.00 0.97 0.90 0.94 0.98 0.92 0.88 0.95 0.93 0.91 0.95 

(0.0, 0.00) (2.9, 0.09) (7.2, 0.17) (4.4, 0.13) (1.8, 0.07) (4.6, 0.14) (7.8, 0.19) (4.0, 0.10) (4.7, 0.11) (6.1, 0.14) (4.9, 0.14) 

 1059 342 337 357 363 341 342 919 337 335 355 

AA 1.00 0.92 0.94 0.98 0.93 0.87 0.97 0.95 0.94 0.95 

 (0.0, 0.00) (7.1, 0.18) (3.8, 0.11) (2.9, 0.07) (4.1, 0.11) (8.1, 0.20) (4.0, 0.11) (4.3, 0.10) (6.6, 0.15) (5.1, 0.15)

 342 317 336 341 319 319 292 313 312 335 

AB 1.00 0.85 0.89 0.86 0.79 0.95 0.90 0.93 0.90 

(0.0, 0.00) (9.2, 0.24) (7.2, 0.17) (9.0, 0.23) (8.1, 0.20) (4.1, 0.13) (6.1, 0.16) (3.8, 0.12) (5.5, 0.16)

 

LEGEND 

R 

(P90, COD) N 337 330 337 316 313 291 310 310 329 

AC 1.00 0.95 0.98 0.84 0.96 0.91 0.89 0.91 

 (0.0, 0.00) (4.4, 0.13) (3.0, 0.08) (10.4, 0.26) (6.6, 0.18) (6.6, 0.15) (9.3, 0.21) (7.4, 0.22) 

 357 356 334 336 304 326 326 348 

AD 1.00 0.93 0.89 0.97 0.95 0.93 0.96 

 (0.0, 0.00) (4.6, 0.14) (7.1, 0.18) (4.0, 0.10) (4.4, 0.10) (6.0, 0.13) (4.6, 0.13) 

 363 341 339 311 333 333 354 

AE 1.00 0.82 0.94 0.92 0.89 0.89 

December 2009 A-132 
S T U V W X Y Z AA AB AC AD AE AF AG AH AI AJ 

 (0.0, 0.00) (10.0, 0.26) (6.2, 0.18) (5.6, 0.15) (8.4, 0.20) (8.0, 0.22) 

 341 319 290 313 314 332 

AF 1.00 0.86 0.87 0.87 0.91 

 (0.0, 0.00) (7.0, 0.16) (7.1, 0.18) (6.4, 0.16) (5.5, 0.14) 

 342 289 310 313 331 

AG 1.00 0.93 0.94 0.96 

 (0.0, 0.00) (4.8, 0.12) (4.5, 0.11) (3.7, 0.11) 

 951 289 283 304 

AH 1.00 0.97 0.92

 (0.0, 0.00) (4.1, 0.10) (4.9, 0.15) 

 337 307 327 

AI 1.00 0.92 

 (0.0, 0.00) (4.8, 0.14) 

 335 324 

AJ 1.00

 (0.0, 0.00)

 355 

 

0

0.2

0.4

0.6

0 10 20 30 40 50 60 70 80 90 10

Distance Between Samplers (km)

Correlation

0.8

1

0

Figure A-63 PM2.5 inter-sampler correlations as a function of distance between monitors for 

New York, NY. 

December 2009 A-133 
Figure A-64. PM2.5 monitor distribution and major highways, Philadelphia, PA. 

December 2009 A-134 
Figure A-65. Box plots illustrating the seasonal distribution of 24-h avg PM2.5 concentrations 

for Philadelphia, PA. 

December 2009 A-135 
Table A-29. Inter-sampler correlation statistics for each pair of PM2.5 monitors reporting to AQS for 

Philadelphia, PA. 

A B C D E F G H I J K L M N O 

A 1.00 0.94 0.96 0.98 0.92 0.96 0.93 0.89 0.95 0.92 0.86 0.96 0.96 0.95 0.97 

 (0.0, 0.00) (4.7, 0.12) (3.1, 0.08) (3.2, 0.08) (4.8, 0.12) (3.5, 0.10) (4.2, 0.11) (5.3, 0.13) (4.2, 0.12) (4.6, 0.14) (4.7, 0.15) (3.5, 0.08) (3.7, 0.10) (4.5, 0.12) (3.2, 0.08) 

 335 305 282 318 311 312 308 289 247 298 277 283 243 236 236 

B 1.00 0.95 0.93 0.94 0.92 0.88 0.83 0.90 0.87 0.81 0.91 0.92 0.88 0.89 

(0.0, 0.00) (4.3, 0.12) (6.4, 0.15) (3.4, 0.11) (5.2, 0.14) (6.0, 0.15) (6.8, 0.17) (6.7, 0.17) (6.5, 0.18) (5.9, 0.18) (6.5, 0.14) (5.0, 0.14) (7.3, 0.17) (5.9, 0.13) 

346 288 329 318 313 315 293 253 302 285 293 253 238 246 

C 1.00 0.96 0.95 0.94 0.88 0.88 0.93 0.88 0.84 0.93 0.93 0.91 0.93 

 (0.0, 0.00) (4.3, 0.09) (3.5, 0.11) (4.7, 0.12) (5.3, 0.14) (6.0, 0.14) (3.5, 0.12) (6.6, 0.16) (5.5, 0.17) (5.0, 0.12) (4.8, 0.13) (6.0, 0.14) (4.6, 0.11) 

 331 312 289 292 286 270 242 278 261 281 245 225 237 

D 1.00 0.91 0.94 0.92 0.88 0.94 0.90 0.85 0.95 0.93 0.93 0.95 

(0.0, 0.00) (6.5, 0.15) (4.9, 0.12) (5.0, 0.14) (6.3, 0.15) (4.1, 0.12) (5.3, 0.14) (5.8, 0.18) (4.3, 0.11) (5.6, 0.14) (4.2, 0.10) (4.5, 0.11) 

999 325 490 317 297 257 312 287 801 732 540 704 

E 1.00 0.91 0.87 0.83 0.90 0.86 0.86 0.88 0.90 0.87 0.89 

 (0.0, 0.00) (5.6, 0.14) (6.1, 0.15) (6.7, 0.16) (6.6, 0.16) (7.1, 0.19) (5.7, 0.15) (6.8, 0.15) (5.3, 0.13) (7.0, 0.18) (5.7, 0.13) 

 348 320 321 301 255 310 287 296 255 242 254 

F 1.00 0.95 0.90 0.92 0.89 0.87 0.96 0.96 0.95 0.96 

(0.0, 0.00) (3.4, 0.09) (5.3, 0.13) (5.4, 0.14) (5.9, 0.16) (4.4, 0.15) (3.7, 0.10) (3.6, 0.10) (4.5, 0.13) (3.4, 0.09) 

539 317 296 261 309 284 466 437 414 396 

G 1.00 0.90 0.90 0.87 0.85 0.93 0.97 0.92 0.96 

 (0.0, 0.00) (4.8, 0.14) (5.9, 0.16) (6.2, 0.17) (4.7, 0.16) (3.7, 0.09) (3.1, 0.09) (5.7, 0.13) (3.5, 0.08) 

 340 295 258 305 289 288 251 235 240 

H 1.00 0.84 0.83 0.89 0.90 0.94 0.87 0.89 

(0.0, 0.00) (5.7, 0.16) (8.0, 0.19) (4.4, 0.13) (5.0, 0.13) (4.0, 0.12) (5.9, 0.17) (4.8, 0.13) 

317 240 288 275 273 234 215 227 

I 1.00 0.87 0.81 0.91 0.92 0.90 0.92 

 (0.0, 0.00) (5.5, 0.17) (5.7, 0.17) (4.9, 0.14) (5.4, 0.15) (5.2, 0.16) (5.1, 0.14) 

 277 248 228 235 215 196 195 

J 1.00 0.79 0.89 0.89 0.89 0.91 

(0.0, 0.00) (7.4, 0.21) (5.8, 0.15) (6.4, 0.17) (5.7, 0.13) (5.0, 0.14) 

 

LEGEND 

R 

(P90, COD) N 331 278 282 246 237 231 

K 1.00 0.87 0.95 0.84 0.86 

 (0.0, 0.00) (4.7, 0.15) (3.7, 0.13) (6.8, 0.20) (4.3, 0.13) 

 307 268 230 211 212 

L 1.00 0.98 0.95 0.97 

(0.0, 0.00) (3.1, 0.09) (3.7, 0.11) (3.4, 0.07) 

890 672 512 630 

M 1.00 0.95 0.96 

 (0.0, 0.00) (4.7, 0.14) (3.2, 0.09) 

 805 495 563 

N 1.00 0.97

(0.0, 0.00) (3.5, 0.10)

596 447 

O 1.00 

 (0.0, 0.00) 

 780 

 

December 2009 A-136 
0

0 10 20 30 40 50 60 70 80 90 10

Distance Between Samplers (km)

0.2

0.4

0.6

0.8

1

0

Correlation

Figure A-66. PM2.5 inter-sampler correlations as a function of distance between monitors for 

Philadelphia, PA. 

December 2009 A-137 
Figure A-67. PM2.5 monitor distribution and major highways, Phoenix, AZ. 

December 2009 A-138 
Figure A-68. Box plots illustrating the seasonal distribution of 24-h avg PM2.5 concentrations 

for Phoenix, AZ. 

December 2009 A-139 
Table A-30. Inter-sampler correlation statistics for each pair of PM2.5 monitors reporting to AQS for 

Phoenix, AZ. 

A B C D E 

A 1.00 0.87 0.92 0.50 0.12 

 (0.0, 0.00) (6.4, 0.15) (6.5, 0.16) (10.4, 0.25) (14.4, 0.40) 

 370 345 355 222 321 

B 1.00 0.89 0.54 0.23 

(0.0, 0.00) (6.8, 0.17) (9.6, 0.25) (13.2, 0.40) 

 352 338 212 307 

C 1.00 0.54 0.18 

 (0.0, 0.00) (7.2, 0.20) (9.3, 0.33) 

 360 216 315 

D 1.00 0.51 

(0.0, 0.00) (7.8, 0.27) 

 227 200 

E 

LEGEND 

R 

(P90, COD) 

N 1.00 

 (0.0, 0.00) 

 325 

 

December 2009 A-140 
0

0.2

0.4

0.6

0 10 20 30 40 50 60 70 80 90 10

Distance Between Samplers (km)

Correlation

0.8

1

0

Figure A-69. PM2.5 inter-sampler correlations as a function of distance between monitors for 

Phoenix, AZ. 

December 2009 A-141 
Figure A-70. PM2.5 monitor distribution and major highways, Pittsburgh, PA. 

December 2009 A-142 
Figure A-71. Box plots illustrating the seasonal distribution of 24-h avg PM2.5 concentrations 

for Pitsburgh, PA. 

December 2009 A-143 
Table A-31. Inter-sampler correlation statistics for each pair of PM2.5 monitors reporting to AQS for 

Pittsburgh, PA. 

A B C D E F G H I J K L 

A 1.00 0.79 0.95 0.92 0.93 0.95 0.95 0.85 0.90 0.93 0.91 0.88 

 (0.0, 0.00) (15.9, 0.19) (5.6, 0.13) (4.7, 0.11) (4.7, 0.11) (4.9, 0.10) (3.8, 0.10) (6.4, 0.13) (6.4, 0.13) (5.0, 0.12) (6.0, 0.13) (5.6, 0.12) 

1063 1035 298 164 323 329 170 319 344 337 934 340 

B 1.00 0.71 0.65 0.80 0.85 0.76 0.69 0.71 0.68 0.68 0.67 

(0.0, 0.00) (16.9, 0.24) (17.4, 0.25) (14.4, 0.19) (12.5, 0.14) (15.7, 0.20) (17.0, 0.19) (15.7, 0.21) (17.8, 0.23) (19.3, 0.25) (15.9, 0.21) 

1066 303 165 329 335 171 324 350 341 938 346 

C 1.00 0.93 0.90 0.91 0.94 0.80 0.93 0.96 0.95 0.91 

 (0.0, 0.00) (2.8, 0.09) (6.6, 0.16) (8.7, 0.17) (6.0, 0.14) (9.4, 0.19) (6.7, 0.15) (4.6, 0.12) (4.5, 0.10) (6.5, 0.15) 

306 144 282 282 148 268 290 286 270 286 

D 1.00 0.84 0.87 0.91 0.79 0.89 0.91 0.97 0.85 

(0.0, 0.00) (6.4, 0.15) (8.5, 0.16) (5.8, 0.13) (9.2, 0.17) (5.9, 0.13) (4.6, 0.11) (3.1, 0.08) (6.5, 0.15) 

165 153 161 158 156 158 155 146 157 

E 1.00 0.90 0.90 0.84 0.85 0.86 0.88 0.83 

 (0.0, 0.00) (6.4, 0.13) (6.5, 0.13) (6.8, 0.14) (8.3, 0.16) (7.7, 0.16) (7.6, 0.15) (7.3, 0.15) 

332 313 157 295 320 315 290 318 

F 1.00 0.91 0.82 0.88 0.88 0.89 0.86 

(0.0, 0.00) (6.7, 0.13) (7.4, 0.14) (7.1, 0.15) (7.9, 0.15) (8.8, 0.17) (7.0, 0.14) 

337 167 302 327 319 296 322 

G 

LEGEND 

Pearson R 

(P90, COD) n 1.00 0.78 0.94 0.93 0.90 0.91 

 (0.0, 0.00) (7.3, 0.16) (4.0, 0.10) (5.0, 0.11) (6.6, 0.15) (5.0, 0.13) 

 171 159 163 159 149 161 

H 1.00 0.80 0.78 0.82 0.70 

(0.0, 0.00) (8.4, 0.15) (8.2, 0.17) (9.0, 0.18) (9.2, 0.18) 

328 317 309 288 314 

I 1.00 0.93 0.89 0.88 

 (0.0, 0.00) (5.0, 0.11) (7.2, 0.16) (6.0, 0.13) 

 354 334 310 339 

J 1.00 0.93 0.88 

 (0.0, 0.00) (5.5, 0.12) (5.9, 0.13) 

 345 302 331 

K 1.00 0.86 

 (0.0, 0.00) (6.9, 0.15) 

 966 306 

L 1.00

 (0.0, 0.00)

 350 

 

December 2009 A-144 
0

0 10 20 30 40 50 60 70 80 90 10

Distance Between Samplers (km)

0.2

0.4

0.6

0.8

1

0

Correlation

Figure A-72. PM2.5 inter-sampler correlations as a function of distance between monitors for 

Pittsburgh, PA. 

December 2009 A-145 
Figure A-73. PM2.5 monitor distribution and major highways, Riverside, CA. 

December 2009 A-146 
Figure A-74. Box plots illustrating the seasonal distribution of 24-h avg PM2.5 concentrations 

for Riverside, CA. 

December 2009 A-147 
Table A-32. Inter-sampler correlation statistics for each pair of PM2.5 monitors reporting to AQS for 

Riverside, CA. 

A B C D E F G 

A 1.00 0.45 0.96 0.92 0.36 0.94 0.90 

 (0.0, 0.00) (20.6, 0.32) (5.0, 0.10) (7.2, 0.13) (22.1, 0.35) (6.0, 0.12) (5.7, 0.13) 

314 269 297 282 191 281 273 

B 1.00 0.49 0.49 0.42 0.49 0.50 

(0.0, 0.00) (22.7, 0.35) (20.9, 0.34) (8.2, 0.25) (19.7, 0.33) (18.8, 0.31) 

310 289 270 203 285 266 

C 1.00 0.91 0.37 0.92 0.91 

 (0.0, 0.00) (8.2, 0.14) (26.6, 0.37) (6.9, 0.12) (7.6, 0.12) 

 934 300 227 302 287 

D 1.00 0.36 0.93 0.82 

(0.0, 0.00) (20.1, 0.35) (6.7, 0.14) (9.6, 0.17) 

319 195 289 274 

E 1.00 0.40 0.41 

 (0.0, 0.00) (21.1, 0.36) (21.6, 0.34) 

 

LEGEND 

R 

(P90, COD) 

N 236 201 190 

F 1.00 0.90 

(0.0, 0.00) (6.7, 0.12) 

328 276 

G 1.00 

 (0.0, 0.00) 

 310 

December 2009 A-148 
0

0.2

0.4

0.6

0.8

0 10 20 30 40 50 60 70 80 90 10

Distance Between Samplers (km)

Correlation

1

1.2

0

Figure A-75. PM2.5 inter-sampler correlations as a function of distance between monitors for 

Riverside CA. 

December 2009 A-149 
Figure A-76. PM2.5 monitor distribution and major highways, Seattle, WA. 

December 2009 A-150 
Figure A-77. Box plots illustrating the seasonal distribution of 24-h avg PM2.5 concentrations 

for Seattle, WA. 

December 2009 A-151 
Table A-33. Inter-sampler correlation statistics for each pair of PM2.5 monitors reporting to AQS for 

Seattle, WA. 

A B C 

A 1.00 0.89 0.86 

 (0.0, 0.00) (6.3, 0.16) (4.5, 0.14) 

 352 337 331 

B 1.00 0.80 

(0.0, 0.00) (7.8, 0.20) 

354 335 

C 

LEGEND 

R 

(P90, COD) 

N 1.00 

 (0.0, 0.00) 

 591 

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80 90 10

Distance Between Samplers (km)

Correlation

0

Figure A-78. PM2.5 inter-sampler correlations as a function of distance between monitors for 

Seattle, WA. 

December 2009 A-152 
Figure A-79. PM2.5 monitor distribution and major highways, St. Louis, MO. 

December 2009 A-153 
Figure A-80. Box plots illustrating the seasonal distribution of 24-h avg PM2.5 concentrations 

for St. Louis, MO. 

December 2009 A-154 
Table A-34. Inter-sampler correlation statistics for each pair of PM2.5 monitors reporting to AQS for 

St. Louis, MO. 

Site A B C D E F G H I J K L 

A 1.00 0.85 0.93 0.89 0.88 0.86 0.85 0.93 0.86 0.84 0.84 0.88 

 (0.0, 0.00) (10.5, 0.23) (4.7, 0.17) (5.0, 0.17) (7.3, 0.20) (6.2, 0.18) (4.8, 0.17) (4.1, 0.13) (4.4, 0.16) (6.0, 0.18) (5.7, 0.19) (5.3, 0.17) 

 173 156 129 162 146 156 167 158 162 168 169 166 

B 1.00 0.89 0.86 0.85 0.82 0.88 0.89 0.88 0.86 0.87 0.89 

(0.0, 0.00) (8.6, 0.16) (7.4, 0.16) (7.7, 0.16) (8.6, 0.17) (7.8, 0.17) (8.2, 0.18) (7.9, 0.17) (7.7, 0.17) (7.5, 0.16) (6.8, 0.14) 

329 135 301 156 306 312 305 318 316 316 315 

C 1.00 0.94 0.91 0.88 0.90 0.96 0.94 0.90 0.89 0.94 

 (0.0, 0.00) (4.0, 0.11) (6.4, 0.13) (5.7, 0.13) (5.5, 0.13) (3.9, 0.11) (5.3, 0.11) (5.7, 0.13) (5.6, 0.14) (4.4, 0.11) 

 163 139 124 133 158 141 144 158 160 156 

D 1.00 0.89 0.84 0.89 0.94 0.92 0.89 0.88 0.92 

(0.0, 0.00) (5.7, 0.13) (6.0, 0.15) (4.9, 0.12) (4.3, 0.12) (4.5, 0.11) (4.7, 0.13) (4.6, 0.12) (3.9, 0.11) 

349 156 314 331 315 326 335 332 336 

E 1.00 0.90 0.91 0.90 0.91 0.93 0.91 0.95 

 (0.0, 0.00) (5.5, 0.12) (6.2, 0.13) (5.8, 0.16) (5.3, 0.14) (5.1, 0.13) (4.9, 0.13) (3.7, 0.10) 

 166 152 159 153 157 160 163 160 

F 1.00 0.89 0.86 0.88 0.88 0.85 0.88 

(0.0, 0.00) (5.4, 0.12) (6.1, 0.16) (5.4, 0.13) (5.3, 0.14) (5.6, 0.14) (5.4, 0.13) 

349 333 317 332 337 332 334 

G 1.00 0.93 0.94 0.96 0.93 0.94 

 (0.0, 0.00) (4.3, 0.10) (3.3, 0.08) (2.9, 0.08) (3.9, 0.10) (3.8, 0.10) 

 

LEGEND 

R 

(P90, COD) 

N 1040 533 586 994 987 992 

H 1.00 0.96 0.95 0.95 0.96 

(0.0, 0.00) (3.0, 0.08) (4.1, 0.12) (3.8, 0.12) (4.0, 0.11) 

566 550 552 546 544 

I 1.00 0.96 0.95 0.96 

 (0.0, 0.00) (3.1, 0.09) (3.1, 0.10) (3.4, 0.09) 

 619 605 599 598 

J 1.00 0.96 0.97 

(0.0, 0.00) (2.5, 0.09) (2.5, 0.08) 

1049 1001 1007 

K 1.00 0.97 

 (0.0, 0.00) (1.9, 0.07) 

 1038 991 

L 1.00 

(0.0, 0.00)

1046 

 

December 2009 A-155 
0

0 10 20 30 40 50 60 70 80 90 10

Distance Between Samplers (km)

0.2

0.4

0.6

0.8

1

0

Correlation

Figure A-81 PM2.5 inter-sampler correlations as a function of distance between monitors for 

St. Louis, MO. 

December 2009 A-156 
Figure A-82. PM10 monitor distribution and major highways, Atlanta, GA. 

December 2009 A-157 
Figure A-83. Box plots illustrating the seasonal distribution of 24-h avg PM10 concentrations 

for Atlanta, GA. 

December 2009 A-158 
Table A-35. Inter-sampler correlation statistics for each pair of PM10 monitors reporting to AQS for 

Atlanta, GA. 

Site A B C D E F 

A 1.00 0.69 0.74 0.78 0.70 0.59 

 (0.0, 0.00) (18.0, 0.22) (15.0, 0.20) (13.0, 0.20) (16.0, 0.22) (20.0, 0.24) 

172 169 162 165 158 164 

B 1.00 0.88 0.79 0.71 0.82 

(0.0, 0.00) (6.0, 0.12) (14.5, 0.17) (16.0, 0.18) (10.0, 0.14) 

178 167 170 162 169 

C 1.00 0.88 0.84 0.82 

 (0.0, 0.00) (9.0, 0.13) (10.0, 0.13) (9.0, 0.15) 

 171 162 155 161 

D 1.00 0.75 0.74 

(0.0, 0.00) (12.0, 0.15) (15.0, 0.20) 

174 158 166 

E 

LEGEND 

R 

(P90, COD) 

N 1.00 0.67 

 (0.0, 0.00) (17.0, 0.19) 

 995 163 

F 1.00 

(0.0, 0.00) 

178 

 

December 2009 A-159 
0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

Figure A-84. PM10 inter-sampler correlations as a function of distance between monitors for 

Atlanta, GA. 

December 2009 A-160 
Figure A-85. PM10 monitor distribution and major highways, Birmingham, AL. 

December 2009 A-161 
Figure A-86. Box plots illustrating the seasonal distribution of 24-h avg PM10 concentrations 

for Birmingham, AL. 

December 2009 A-162 
Table A-36. Inter-sampler correlation statistics for each pair of PM10 monitors reporting to AQS for 

Birmingham, AL. 

A B C D E F G H I J 

A 1.00 0.80 0.88 0.86 0.78 0.84 0.77 0.78 0.41 0.29 

 (0.0, 0.00) (23.0, 0.16) (11.0, 0.11) (12.0, 0.13) (12.0, 0.14) (13.0, 0.13) (15.0, 0.18) (14.0, 0.15) (41.0, 0.30) (68.0, 0.34) 

 180 180 174 180 176 171 180 178 179 177 

B 1.00 0.82 0.74 0.61 0.73 0.75 0.71 0.26 0.23 

(0.0, 0.00) (23.0, 0.17) (25.0, 0.21) (26.0, 0.20) (26.0, 0.19) (25.0, 0.20) (25.0, 0.22) (51.0, 0.33) (57.0, 0.36) 

1095 224 183 179 179 1090 181 1087 1080 

C 1.00 0.84 0.66 0.78 0.74 0.80 0.33 0.41 

 (0.0, 0.00) (10.0, 0.12) (15.0, 0.16) (12.0, 0.14) (14.0, 0.17) (13.0, 0.15) (43.0, 0.32) (62.0, 0.34) 

 224 175 171 168 224 173 222 221 

D 1.00 0.67 0.79 0.76 0.84 0.45 0.41 

(0.0, 0.00) (15.0, 0.17) (12.0, 0.15) (14.0, 0.17) (11.0, 0.12) (42.0, 0.30) (65.5, 0.34) 

183 178 173 183 180 182 180 

E 1.00 0.67 0.64 0.56 0.33 0.12 

 (0.0, 0.00) (16.0, 0.15) (18.0, 0.18) (19.0, 0.20) (45.0, 0.32) (71.0, 0.39) 

 179 169 179 176 178 176 

F 1.00 0.75 0.74 0.36 0.21 

(0.0, 0.00) (14.0, 0.16) (15.0, 0.17) (43.0, 0.32) (71.0, 0.38) 

179 179 171 178 177 

G 1.00 0.76 0.59 0.15 

 

LEGEND 

R 

(P90, COD) 

N (0.0, 0.00) (15.0, 0.19) (43.0, 0.27) (63.0, 0.39) 

 1090 181 1083 1075 

H 1.00 0.58 0.50 

(0.0, 0.00) (38.0, 0.27) (59.0, 0.31) 

181 180 178 

I 1.00 0.05 

 (0.0, 0.00) (72.0, 0.40) 

 1087 1072 

J 1.00

(0.0, 0.00)

1080 

 

December 2009 A-163 
0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

Figure A-87 PM10 inter-sampler correlations as a function of distance between monitors for 

Birmingham, AL. 

December 2009 A-164 
Figure A-88. PM10 monitor distribution and major highways, Boston, MA. 

December 2009 A-165 
Figure A-89. Box plots illustrating the seasonal distribution of 24-h avg PM10 concentrations 

for Boston, MA. 

Table A-37. Inter-sampler correlation statistics for each pair of PM10 monitors reporting to AQS for 

Boston, MA. 

Site A B C D E F G H 

A 1.00 0.69 0.69 0.73 0.71 0.84 0.70 0.79 

 (0.0, 0.00) (15.0, 0.22) (12.0, 0.20) (10.0, 0.22) (13.0, 0.30) (8.0, 0.14) (15.0, 0.20) (10.0, 0.17) 

191 169 179 173 171 182 169 167 

B 1.00 0.66 0.56 0.45 0.69 0.77 0.65 

(0.0, 0.00) (17.0, 0.24) (19.0, 0.28) (24.0, 0.39) (15.0, 0.21) (12.0, 0.17) (16.0, 0.20) 

174 167 161 158 169 156 154 

C 1.00 0.72 0.47 0.62 0.64 0.59 

 (0.0, 0.00) (10.0, 0.22) (17.0, 0.33) (12.0, 0.21) (16.0, 0.26) (16.0, 0.24) 

 182 170 168 179 166 164 

D 1.00 0.63 0.68 0.59 0.69 

 (0.0, 0.00) (11.0, 0.29) (10.0, 0.23) (19.0, 0.30) (13.0, 0.26) 

 175 163 173 161 158 

E 1.00 0.84 0.58 0.80 

 (0.0, 0.00) (13.0, 0.29) (22.0, 0.38) (15.0, 0.33) 

 171 171 161 157 

F 

LEGEND 

Pearson R 

(P90, COD) 

n 1.00 0.81 0.95 

(0.0, 0.00) (11.0, 0.16) (5.0, 0.11) 

182 169 167 

G 1.00 0.79 

 (0.0, 0.00) (10.0, 0.13) 

 169 154 

H 1.00

 (0.0, 0.00) 

 168 

 

December 2009 A-166 
0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

Figure A-90 PM10 inter-sampler correlations as a function of distance between monitors for 

Boston, MA. 

December 2009 A-167 
Figure A-91. PM10 monitor distribution and major highways, Chicago, IL. 

December 2009 A-168 
Figure A-92. Box plots illustrating the seasonal distribution of 24-h avg PM10 concentrations 

for Chicago, IL. 

December 2009 A-169 
Table A-38. Inter-sampler correlation statistics for each pair of PM10 monitors reporting to AQS for 

Chicago, IL. 

Site A B C D E F G H I J K L M N O 

A 1.00 0.78 0.68 0.83 0.93 0.92 0.86 0.79 0.75 0.14 0.69 0.89 0.55 0.27 0.75 

 (0.0, 0.00) (15.0, 0.18) (23.0, 0.24) (25.0, 0.22) (8.0, 0.10) (11.0, 0.13) (12.0, 0.17) (12.0, 0.18) (13.0, 0.18) (22.0, 0.28) (15.0, 0.21) (13.0, 0.22) (21.0, 0.30) (16.0, 0.24) (15.0, 0.23) 

179 176 173 174 171 173 171 167 179 173 169 166 170 171 166 

B 1.00 0.66 0.74 0.76 0.84 0.79 0.74 0.68 0.36 0.73 0.81 0.66 0.33 0.77 

(0.0, 0.00) (23.0, 0.23) (23.0, 0.21) (14.0, 0.17) (12.0, 0.15) (13.0, 0.18) (17.0, 0.23) (16.0, 0.19) (22.0, 0.24) (16.0, 0.19) (18.0, 0.27) (23.0, 0.31) (19.0, 0.25) (20.0, 0.26) 

1077 173 1040 171 173 171 173 179 1041 169 166 170 1033 166 

C 1.00 0.63 0.72 0.74 0.64 0.62 0.62 0.19 0.49 0.66 0.39 0.27 0.61 

 (0.0, 0.00) (26.0, 0.23) (21.0, 0.21) (18.5, 0.19) (19.0, 0.21) (22.0, 0.27) (23.0, 0.20) (26.5, 0.28) (24.0, 0.23) (29.0, 0.37) (33.0, 0.40) (26.0, 0.26) (31.0, 0.35) 

176 171 169 170 168 164 176 170 166 163 167 168 163 

D 1.00 0.79 0.85 0.79 0.74 0.70 0.23 0.69 0.82 0.61 0.29 0.76 

(0.0, 0.00) (27.0, 0.21) (19.0, 0.17) (23.0, 0.19) (27.0, 0.28) (20.0, 0.19) (32.0, 0.29) (24.0, 0.23) (31.0, 0.36) (36.0, 0.39) (31.0, 0.29) (31.0, 0.33) 

1058 169 171 169 171 177 1022 168 166 168 1020 164 

E 1.00 0.93 0.84 0.86 0.74 0.17 0.70 0.89 0.53 0.34 0.73 

 (0.0, 0.00) (9.0, 0.10) (13.0, 0.16) (10.0, 0.16) (13.0, 0.16) (22.0, 0.26) (15.0, 0.19) (15.0, 0.25) (22.0, 0.33) (17.0, 0.22) (18.0, 0.25) 

174 168 166 163 174 168 164 161 166 166 163 

F 1.00 0.84 0.86 0.77 0.21 0.75 0.89 0.62 0.32 0.80 

(0.0, 0.00) (12.0, 0.15) (13.0, 0.19) (12.0, 0.14) (23.0, 0.25) (16.0, 0.17) (18.0, 0.28) (25.0, 0.34) (20.0, 0.23) (20.0, 0.27) 

176 169 165 176 170 166 163 167 168 163 

G 1.00 0.77 0.69 0.28 0.74 0.86 0.52 0.33 0.70 

 (0.0, 0.00) (15.0, 0.22) (14.0, 0.18) (23.0, 0.26) (14.0, 0.18) (19.0, 0.31) (24.0, 0.36) (19.0, 0.24) (22.0, 0.30) 

174 162 174 168 165 161 165 166 163 

H 1.00 0.71 0.18 0.66 0.83 0.59 0.36 0.76 

(0.0, 0.00) (16.0, 0.23) (27.0, 0.30) (18.0, 0.25) (13.0, 0.23) (19.0, 0.29) (17.0, 0.25) (14.0, 0.22) 

176 170 169 161 157 161 168 157 

I 1.00 0.24 0.69 0.75 0.50 0.39 0.68 

 (0.0, 0.00) (22.0, 0.24) (12.0, 0.15) (20.0, 0.32) (26.0, 0.37) (16.0, 0.21) (21.0, 0.30) 

 182 176 172 169 173 174 169 

J 1.00 0.49 0.38 0.22 0.48 0.22 

(0.0, 0.00) (15.0, 0.20) (25.0, 0.34) (28.0, 0.36) (22.0, 0.21) (27.0, 0.33) 

LEGEND 

R 

(P90, COD) 

N 1059 166 163 168 1018 164 

K 1.00 0.80 0.54 0.49 0.65 

 (0.0, 0.00) (17.0, 0.32) (24.0, 0.35) (14.0, 0.19) (21.0, 0.31) 

 172 161 165 164 162 

L 1.00 0.60 0.33 0.78 

 (0.0, 0.00) (15.0, 0.26) (19.0, 0.31) (10.0, 0.20) 

 169 161 161 158 

M 1.00 0.24 0.84 

 (0.0, 0.00) (21.0, 0.35) (8.0, 0.16) 

 173 165 161 

N 1.00 0.31

 (0.0, 0.00) (19.0, 0.29) 

 1051 161 

O 1.00 

 (0.0, 0.00) 

 169 

 

December 2009 A-170 
0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

Figure A-93. PM10 inter-sampler correlations as a function of distance between monitors for 

Chicago, IL. 

December 2009 A-171 
Figure A-94. PM10 monitor distribution and major highways, Denver, CO. 

December 2009 A-172 
Figure A-95. Box plots illustrating the seasonal distribution of 24-h avg PM10 concentrations 

for Denver, CO. 

December 2009 A-173 
Table A-39. Inter-sampler correlation statistics for each pair of PM10 monitors reporting to AQS for 

Denver, CO. 

Site A B C D E F 

A 1.00 0.84 0.43 0.70 0.72 0.67 

 (0.0, 0.00) (20.0, 0.16) (36.0, 0.34) (29.0, 0.24) (26.0, 0.21) (27.0, 0.28) 

1043 1022 164 987 980 339 

B 1.00 0.57 0.72 0.74 0.72 

(0.0, 0.00) (28.0, 0.27) (17.0, 0.18) (15.0, 0.16) (18.0, 0.22) 

1074 169 1019 1007 348 

C 1.00 0.75 0.72 0.51 

 (0.0, 0.00) (17.0, 0.23) (16.0, 0.23) (16.0, 0.23) 

 169 169 156 164 

D 1.00 0.89 0.52 

(0.0, 0.00) (9.0, 0.13) (17.0, 0.22) 

LEGEND 

R 

(P90, COD) 

N 1039 976 341 

E 1.00 0.58 

 (0.0, 0.00) (17.0, 0.23) 

 1028 330 

F 1.00 

(0.0, 0.00) 

353 

 

December 2009 A-174 
0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

Figure A-96. PM10 inter-sampler correlations as a function of distance between monitors for 

Denver, CO. 

December 2009 A-175 
Figure A-97. PM10 monitor distribution and major highways, Detroit, MI. 

December 2009 A-176 
Figure A-98. Box plots illustrating the seasonal distribution of 24-h avg PM10 concentrations 

for Detroit, MI. 

December 2009 A-177 
Table A-40. Inter-sampler correlation statistics for each pair of PM10 monitors reporting to AQS for 

Detroit, MI. 

Site A B C 

A 1.00 0.77 0.74 

 (0.0, 0.00) (14.0, 0.18) (28.0, 0.26) 

 174 169 172 

B 1.00 0.79 

(0.0, 0.00) (21.0, 0.21) 

 176 174 

C 1.00 

 

LEGEND 

R 

(P90, COD) 

N (0.0, 0.00) 

 1057 

 

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

Figure A-99. PM10 inter-sampler correlations as a function of distance between monitors for 

Detroit, MI. 

December 2009 A-178 
Figure A-100. PM10 monitor distribution and major highways, Houston, TX. 

December 2009 A-179 
Figure A-101. Box plots illustrating the seasonal distribution of 24-h avg PM10 concentrations 

for Houston, TX. 

December 2009 A-180 
Table A-41. Inter-sampler correlation statistics for each pair of PM10 monitors reporting to AQS for 

Houston, TX. 

SITE A B C D E F G 

A 1.00 0.84 0.78 0.76 0.43 0.56 0.75 

 (0.0, 0.00) (9.0, 0.12) (11.0, 0.16) (12.0, 0.16) (15.0, 0.20) (77.0, 0.37) (17.0, 0.28) 

174 163 158 165 167 159 156 

B 1.00 0.86 0.86 0.38 0.52 0.79 

(0.0, 0.00) (9.0, 0.11) (9.0, 0.12) (15.0, 0.19) (74.0, 0.39) (16.0, 0.26) 

178 156 160 163 158 152 

C 1.00 0.83 0.41 0.38 0.85 

 (0.0, 0.00) (10.0, 0.14) (17.0, 0.19) (74.0, 0.40) (14.5, 0.25) 

 174 156 159 151 150 

D 1.00 0.32 0.43 0.76 

(0.0, 0.00) (18.0, 0.20) (81.0, 0.43) (16.0, 0.23) 

175 163 155 154 

E 1.00 0.15 0.38 

 (0.0, 0.00) (78.0, 0.43) (20.0, 0.28) 

 174 158 157 

F 1.00 0.37 

LEGEND 

R 

(P90, COD) 

N (0.0, 0.00) (92.0, 0.54) 

 359 149 

G 1.00 

 (0.0, 0.00) 

 163 

December 2009 A-181 
Houston

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

Figure A-102. PM10 inter-sampler correlations as a function of distance between monitors for 

Houston, TX. 

December 2009 A-182 
Figure A-103. PM10 monitor distribution and major highways, Los Angeles, CA. 

December 2009 A-183 
Figure A-104. Box plots illustrating the seasonal distribution of 24-h avg PM10 concentrations 

for Los Angeles, CA. 

December 2009 A-184 
Table A-42. Inter-sampler correlation statistics for each pair of PM10 monitors reporting to AQS for 

Los Angeles, CA. 

Site A B C D E F G 

A 1.00 0.73 0.44 0.73 0.47 0.41 0.65 

 (0.0, 0.00) (17.0, 0.17) (27.0, 0.24) (24.0, 0.22) (28.0, 0.26) (29.0, 0.24) (30.0, 0.28) 

169 153 154 157 169 155 143 

B 1.00 0.61 0.57 0.52 0.42 0.73 

(0.0, 0.00) (14.0, 0.14) (21.0, 0.24) (23.0, 0.23) (15.0, 0.16) (20.0, 0.23) 

175 159 159 173 162 149 

C 1.00 0.65 0.43 0.93 0.73 

 (0.0, 0.00) (27.0, 0.28) (22.0, 0.24) (11.0, 0.11) (21.0, 0.22) 

 178 158 176 159 148 

D 1.00 0.70 0.65 0.57 

(0.0, 0.00) (16.0, 0.20) (26.0, 0.28) (19.5, 0.24) 

LEGEND 

Pearson R 

(P90,tCOD) 

n 176 175 161 150 

E 1.00 0.29 0.38 

 (0.0, 0.00) (26.0, 0.25) (20.0, 0.24) 

 985 173 159 

F 1.00 0.65 

 (0.0, 0.00) (21.5, 0.22) 

 175 150 

G 1.00 

 (0.0, 0.00) 

 162 

 

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

Figure A-105. PM10 inter-sampler correlations as a function of distance between monitors for 

Los Angeles, CA. 

December 2009 A-185 
Figure A-106. PM10 monitor distribution and major highways, New York, NY. 

December 2009 A-186 
concentration (ug/m3)

0

10

20

30

40

50

60

winter

spring

summer

fall

winter

spring

summer

fall

winter

spring

summer

fall

Figure A-107. Box plots illustrating the seasonal distribution of 24-h avg PM10 concentrations 

for New York, NY. 

December 2009 A-187 
Table A-43. Inter-sampler correlation statistics for each pair of PM10 monitors reporting to AQS for 

New York, NY. 

Site A B C 

A 1.00 0.88 0.82 

 (0.0, 0.00) (11.0, 0.20) (12.0, 0.16) 

 167 156 164 

B 1.00 0.74 

(0.0, 0.00) (18.0, 0.25) 

 169 166 

C 1.00 

 

LEGEND 

R 

(P90, COD) 

N (0.0, 0.00) 

 178 

 

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

Figure A-108. PM10 inter-sampler correlations as a function of distance between monitors for 

New York, NY. 

December 2009 A-188 
Figure A-109. PM10 monitor distribution and major highways, Philadelphia, PA. 

December 2009 A-189 
Figure A-110. Box plots illustrating the seasonal distribution of 24-h avg PM10 concentrations 

for Philadelphia, PA. 

December 2009 A-190 
Table A-44. Inter-sampler correlation statistics for each pair of PM10 monitors reporting to AQS for 

Philadelphia, PA. 

Site A B C D 

A 1.00 0.81 0.64 0.84 

 (0.0, 0.00) (13.0, 0.21) (14.0, 0.19) (12.0, 0.20) 

1059 1005 1025 1013 

B 1.00 0.71 0.93 

(0.0, 0.00) (11.0, 0.20) (6.0, 0.12) 

1040 1006 994 

C 1.00 0.73 

 (0.0, 0.00) (11.0, 0.19) 

 1059 1014 

D 1.00 

LEGEND 

R 

(P90, COD) 

N (0.0, 0.00) 

1049 

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

Figure A-111. PM10 inter-sampler correlations as a function of distance between monitors for 

Philadelphia, PA. 

December 2009 A-191 
Figure A-112. PM10 monitor distribution and major highways, Phoenix, AZ. 

December 2009 A-192 
December 2009 A-193 
Figure A-113. Box plots illustrating the seasonal distribution of 24-h avg PM10 concentrations 

for Phoenix, AZ. 

December 2009 A-194 
Table A-45. Inter-sampler correlation statistics for each pair of PM10 monitors reporting to AQS for 

Phoenix, AZ. 

Site A B C D E F G H I J K L M 

A 1.00 0.71 0.85 0.85 0.67 0.94 0.86 0.77 0.73 0.83 0.77 0.70 0.87 

 (0.0, 0.00) (38.0, 0.25) (33.0, 0.21) (21.0, 0.12) (38.0, 0.23) (14.0, 0.09) (22.0, 0.13) (34.0, 0.21) (35.0, 0.18) (59.0, 0.24) (34.0, 0.24) (30.0, 0.17) (28.5, 0.16) 

790 178 181 788 181 779 335 180 772 781 177 789 170 

B 1.00 0.84 0.82 0.85 0.67 0.74 0.81 0.67 0.68 0.75 0.60 0.63 

(0.0, 0.00) (13.0, 0.12) (23.0, 0.19) (11.0, 0.11) (37.0, 0.29) (47.0, 0.30) (13.0, 0.13) (49.0, 0.30) (84.0, 0.43) (16.0, 0.15) (51.0, 0.31) (56.0, 0.32) 

179 179 177 179 175 179 178 175 176 175 178 164 

C 1.00 0.88 0.81 0.78 0.80 0.81 0.70 0.73 0.81 0.63 0.75 

 (0.0, 0.00) (20.0, 0.16) (12.0, 0.11) (38.0, 0.27) (44.0, 0.28) (13.0, 0.13) (48.0, 0.29) (84.0, 0.41) (15.0, 0.14) (49.0, 0.29) (55.0, 0.30) 

182 180 182 178 182 181 178 179 178 181 167 

D 1.00 0.76 0.88 0.81 0.82 0.76 0.78 0.79 0.65 0.83 

(0.0, 0.00) (23.0, 0.17) (22.0, 0.14) (29.0, 0.16) (18.0, 0.17) (39.0, 0.20) (71.0, 0.31) (22.0, 0.19) (35.0, 0.20) (42.0, 0.21) 

1084 180 778 334 179 1062 1072 176 1080 172 

E 1.00 0.64 0.68 0.74 0.66 0.59 0.67 0.51 0.61 

 (0.0, 0.00) (40.0, 0.27) (47.0, 0.29) (16.0, 0.14) (48.0, 0.29) (88.0, 0.42) (15.0, 0.15) (49.0, 0.30) (58.0, 0.31) 

182 178 182 181 178 179 178 181 167 

F 1.00 0.83 0.76 0.75 0.86 0.74 0.69 0.87 

(0.0, 0.00) (22.0, 0.13) (36.0, 0.25) (32.0, 0.17) (54.0, 0.21) (41.0, 0.28) (30.0, 0.17) (25.0, 0.15) 

780 331 177 762 772 175 779 167 

G 1.00 0.77 0.65 0.78 0.71 0.65 0.80 

 (0.0, 0.00) (44.0, 0.26) (38.0, 0.19) (48.0, 0.19) (46.0, 0.30) (36.0, 0.19) (33.0, 0.16) 

 336 181 326 333 178 335 169 

H 1.00 0.79 0.81 0.79 0.69 0.72 

(0.0, 0.00) (47.0, 0.26) (79.0, 0.39) (16.0, 0.14) (43.0, 0.27) (53.0, 0.29) 

181 177 178 177 180 167 

I 1.00 0.79 0.76 0.69 0.68 

 

LEGEND 

R 

(P90, COD) 

N (0.0, 0.00) (52.0, 0.22) (48.0, 0.29) (33.0, 0.17) (38.0, 0.20) 

 1073 1061 174 1068 171 

J 1.00 0.78 0.73 0.80 

 (0.0, 0.00) (83.0, 0.42) (57.0, 0.23) (51.0, 0.22) 

 1083 175 1078 171 

K 1.00 0.72 0.68 

 (0.0, 0.00) (45.0, 0.29) (56.0, 0.32) 

 178 177 164 

L 1.00 0.63 

 (0.0, 0.00) (42.0, 0.20) 

 1090 173 

M 1.00 

 (0.0, 0.00) 

 174 

 

December 2009 A-195 
N O P Q R S T U V W X 

A 0.87 0.68 0.47 0.53 0.68 0.40 0.69 0.50 0.27 0.56 0.65 

 (39.0, 0.18) (28.0, 0.17) (29.0, 0.19) (49.0, 0.42) (34.0, 0.27) (64.0, 0.57) (40.0, 0.34) (82.0, 0.31) (49.0, 0.27) (48.0, 0.43) (31.0, 0.20) 

784 783 406 171 171 171 174 475 474 169 262 

B 0.59 0.75 0.75 0.73 0.63 0.55 0.59 0.53 0.66 0.65 0.64 

(67.0, 0.37) (15.0, 0.15) (22.0, 0.17) (23.0, 0.27) (30.0, 0.25) (32.0, 0.43) (21.0, 0.24) (94.0, 0.41) (62.0, 0.34) (24.0, 0.30) (46.0, 0.29) 

178 179 175 169 168 169 172 172 177 167 155 

C 0.70 0.87 0.80 0.70 0.71 0.48 0.64 0.56 0.71 0.62 0.60 

 (69.0, 0.35) (11.0, 0.12) (19.0, 0.15) (24.0, 0.28) (26.0, 0.24) (36.0, 0.44) (22.0, 0.24) (91.0, 0.40) (59.0, 0.32) (28.0, 0.31) (43.0, 0.28) 

181 182 178 172 171 172 175 175 180 170 157 

D 0.78 0.86 0.73 0.63 0.68 0.49 0.65 0.66 0.45 0.58 0.70 

(57.0, 0.25) (15.0, 0.12) (30.0, 0.19) (38.0, 0.38) (27.0, 0.25) (46.0, 0.53) (31.0, 0.31) (87.0, 0.34) (59.0, 0.30) (38.0, 0.39) (32.0, 0.21) 

1075 1056 405 170 169 170 173 474 473 168 318 

E 0.60 0.73 0.68 0.72 0.64 0.43 0.48 0.42 0.69 0.51 0.52 

 (67.0, 0.35) (14.0, 0.14) (21.0, 0.17) (21.0, 0.28) (27.0, 0.24) (33.0, 0.44) (21.0, 0.25) (93.0, 0.41) (63.0, 0.32) (25.0, 0.32) (46.0, 0.28) 

181 182 178 172 171 172 175 175 180 170 157 

F 0.91 0.68 0.46 0.48 0.63 0.38 0.63 0.47 0.28 0.42 0.66 

(35.0, 0.14) (31.0, 0.21) (30.0, 0.22) (60.0, 0.46) (37.0, 0.30) (68.0, 0.60) (45.0, 0.39) (80.0, 0.31) (50.0, 0.27) (57.0, 0.47) (34.0, 0.22) 

774 773 403 169 167 168 172 470 469 166 259 

G 0.77 0.57 0.47 0.55 0.65 0.46 0.62 0.49 0.44 0.57 0.64 

 (35.0, 0.16) (41.0, 0.25) (36.5, 0.24) (61.0, 0.47) (41.0, 0.30) (73.0, 0.61) (58.0, 0.41) (78.0, 0.28) (45.0, 0.24) (59.0, 0.48) (32.0, 0.22) 

332 336 330 172 171 172 175 329 334 170 185 

H 0.70 0.75 0.82 0.63 0.74 0.55 0.62 0.60 0.76 0.64 0.76 

(66.0, 0.33) (15.0, 0.14) (18.0, 0.15) (29.0, 0.31) (24.5, 0.22) (37.0, 0.46) (24.0, 0.25) (84.0, 0.38) (58.0, 0.29) (30.0, 0.33) (39.0, 0.25) 

180 181 177 171 170 171 174 174 179 169 156 

I 0.76 0.61 0.52 0.57 0.71 0.51 0.58 0.59 0.37 0.51 0.80 

 (42.0, 0.18) (49.0, 0.27) (39.0, 0.22) (66.0, 0.47) (41.0, 0.27) (77.0, 0.60) (60.0, 0.40) (72.0, 0.27) (46.0, 0.23) (63.0, 0.47) (30.0, 0.16) 

1064 1045 397 169 168 168 171 461 461 167 314 

J 0.91 0.58 0.41 0.48 0.65 0.48 0.65 0.51 0.28 0.46 0.74 

(29.0, 0.12) (83.0, 0.38) (68.0, 0.31) (103.0, 0.58) (75.0, 0.40) (115.0, 0.69) (92.0, 0.51) (69.0, 0.26) (59.0, 0.27) (101.0, 0.58) (62.0, 0.27) 

1074 1055 404 169 168 169 172 473 472 167 319 

K 0.69 0.71 0.75 0.52 0.64 0.52 0.62 0.71 0.68 0.55 0.68 

 (73.0, 0.36) (16.0, 0.16) (19.0, 0.18) (28.0, 0.29) (27.0, 0.23) (34.0, 0.44) (22.0, 0.24) (89.0, 0.40) (59.0, 0.33) (28.0, 0.32) (44.0, 0.29) 

177 178 174 168 167 168 171 171 176 166 153 

L 0.68 0.55 0.51 0.47 0.57 0.48 0.49 0.59 0.33 0.50 0.68 

(48.0, 0.20) (44.0, 0.26) (37.0, 0.22) (66.0, 0.47) (44.5, 0.29) (71.0, 0.60) (62.0, 0.40) (75.0, 0.27) (53.0, 0.24) (67.0, 0.48) (29.0, 0.18) 

1081 1063 406 171 170 171 174 475 474 169 321 

M 0.86 0.81 0.75 0.48 0.64 0.37 0.62 0.46 0.65 0.44 0.59 

 (32.0, 0.16) (53.0, 0.29) (47.0, 0.30) (74.0, 0.48) (51.0, 0.32) (80.0, 0.61) (58.5, 0.41) (62.0, 0.31) (48.0, 0.26) (68.0, 0.49) (42.0, 0.24) 

173 174 165 157 158 158 160 165 168 156 145 

N 1.00 0.58 0.41 0.48 0.67 0.42 0.63 0.42 0.26 0.40 0.60 

(0.0, 0.00) (66.0, 0.32) (51.0, 0.27) (88.0, 0.53) (62.5, 0.35) (98.0, 0.65) (75.0, 0.46) (71.0, 0.29) (55.0, 0.27) (88.0, 0.54) (48.0, 0.24) 

1086 1059 403 171 170 171 174 470 469 169 319 

O 1.00 0.90 0.61 0.64 0.39 0.60 0.72 0.59 0.55 0.64 

 (0.0, 0.00) (35.0, 0.22) (28.0, 0.31) (25.0, 0.24) (38.0, 0.47) (22.0, 0.26) (94.0, 0.39) (69.0, 0.35) (29.0, 0.33) (44.0, 0.26) 

1067 407 172 171 172 175 475 473 170 317 

P 1.00 0.67 0.81 0.58 0.78 0.82 0.64 0.71 0.67 

(0.0, 0.00) (32.0, 0.29) (22.0, 0.19) (44.0, 0.45) (21.0, 0.21) (80.0, 0.30) (52.0, 0.23) (32.0, 0.31) (39.0, 0.24) 

407 169 170 169 172 400 404 167 197 

Q 1.00 0.72 0.65 0.57 0.36 0.58 0.68 0.47 

 (0.0, 0.00) (40.0, 0.33) (15.0, 0.28) (23.0, 0.24) (104.0, 0.53) (78.0, 0.46) (15.0, 0.22) (62.0, 0.43) 

172 162 163 167 165 171 161 148 

R 1.00 0.66 0.68 0.53 0.82 0.68 0.68 

(0.0, 0.00) (55.0, 0.48) (32.0, 0.27) (75.0, 0.35) (47.0, 0.25) (40.0, 0.34) (39.0, 0.24) 

171 162 165 164 171 160 148 

S 1.00 0.60 0.46 0.59 0.72 0.52 

 (0.0, 0.00) (28.0, 0.35) (115.0, 0.65) (86.0, 0.59) (19.0, 0.28) (74.0, 0.58) 

 172 167 165 171 162 149 

T 1.00 0.56 0.66 0.68 0.61 

(0.0, 0.00) (94.0, 0.47) (71.0, 0.39) (18.0, 0.24) (51.5, 0.37) 

175 169 174 165 150 

U 1.00 0.54 0.52 0.71 

 (0.0, 0.00) (66.0, 0.24) (101.0, 0.53) (61.0, 0.25) 

 

LEGEND 

R 

(P90, COD) N 476 464 165 204 

V 1.00 0.60 0.64 

 (0.0, 0.00) (78.0, 0.47) (35.0, 0.20) 

 475 169 206 

W 1.00 0.56 

 (0.0, 0.00) (63.0, 0.44) 

 170 145 

X 1.00

(0.0, 0.00)

322 

 

December 2009 A-196 
0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

Figure A-114. PM10 inter-sampler correlations as a function of distance between monitors for 

Phoenix, AZ. 

December 2009 A-197 
Figure A-115. PM10 monitor distribution and major highways, Pittsburgh, PA. 

December 2009 A-198 
Figure A-116. Box plots illustrating the seasonal distribution of 24-h avg PM10 concentrations 

for Pittsburgh, PA. 

December 2009 A-199 
Table A-46. Inter-sampler correlation statistics for each pair of PM10 monitors reporting to AQS for 

Pittsburgh, PA. 

Site A B C D E F G H I

A 1 00 0 93 0 93 0 80 0 92 0 89 0 93 0 79 0 86 (0.0, 0.00) (9.0, 0.15) (8.0, 0.14) (23.0, 0.21) (8.0, 0.12) (14.0, 0.18) (8.0, 0.14) (16.0, 0.17) (18.0, 0.18) t 1077 1002 1065 1070 175 178 960 181 1005 B 1.00 0.96 0.80 0.91 0.92 0.97 0.81 0.89 (0.0, 0.00) (8.0, 0.15) (29.0, 0.24) (11.0, 0.20) (6.0, 0.16) (5.0, 0.10) (25.0, 0.29) (22.0, 0.20) 1019 1007 1012 163 166 911 169 954 C 1.00 0.81 0.94 0.93 0.94 0.77 0.87 (0.0, 0.00) (23.0, 0.20) (6.0, 0.11) (7.0, 0.12) (8.0, 0.13) (21.0, 0.22) (19.0, 0.17) 1083 1075 173 176 966 179 1010 D 1.00 0.72 0.66 0.76 0.83 0.88 (0.0, 0.00) (21.0, 0.20) (26.0, 0.24) (27.0, 0.24) (14.0, 0.18) (16.0, 0.14) 1087 176 179 970 182 1014 E 1.00 0.90 0.90 0.78 0.77 (0.0, 0.00) (10.0, 0.14) (10.0, 0.17) (20.0, 0.20) (20.0, 0.19) 176 173 154 175 166 F 1.00 0.94 0.70 0.74 (0.0, 0.00) (7.0, 0.12) (25.0, 0.27) (25.0, 0.22) 179 157 178 168 G

LEGEND 

Pearson R 

(P90, COD) 

n 1.00 0.70 0.87 (0.0, 0.00) (22.0, 0.28) (20.0, 0.19) 978 160 910 H 1.00 0.76 (0.0, 0.00) (17.0, 0.20) 182 171 I 1.00 (0.0, 0.00) 1022

J K L M N O P Q

A 0 84 0 76 0 88 0 85 0 86 0 77 0 78 0 86 (14.0, 0.20) (40.0, 0.30) (15.0, 0.18) (16.0, 0.19) (11.0, 0.16) (16.0, 0.22) (15.0, 0.19) (11.0, 0.15) 176 1044 1033 1052 1074 166 177 1061 B 0.93 0.76 0.88 0.81 0.91 0.76 0.83 0.88 (7.0, 0.16) (43.0, 0.36) (19.0, 0.23) (20.0, 0.26) (10.0, 0.16) (12.0, 0.19) (18.0, 0.28) (10.0, 0.18) 164 986 982 994 1016 157 165 1003 C 0.90 0.75 0.88 0.83 0.89 0.78 0.88 0.90 (8.0, 0.13) (39.0, 0.30) (14.0, 0.17) (15.0, 0.19) (9.0, 0.12) (12.0, 0.18) (13.0, 0.19) (9.0, 0.12) 174 1049 1039 1057 1080 164 175 1067 D 0.73 0.84 0.80 0.78 0.76 0.57 0.64 0.74 (24.0, 0.22) (24.0, 0.22) (20.0, 0.18) (20.0, 0.20) (25.0, 0.20) (28.0, 0.26) (20.0, 0.25) (26.0, 0.21) 177 1055 1043 1061 1084 167 178 1071 E 0.86 0.65 0.83 0.80 0.84 0.77 0.84 0.85 (10.0, 0.16) (36.0, 0.29) (16.0, 0.16) (14.0, 0.17) (12.0, 0.14) (14.0, 0.19) (13.0, 0.16) (11.0, 0.15) 171 169 169 172 176 161 172 174 F 0.90 0.57 0.82 0.75 0.86 0.83 0.84 0.86 (7.0, 0.12) (41.0, 0.34) (20.0, 0.20) (19.0, 0.22) (11.0, 0.14) (9.0, 0.15) (16.0, 0.22) (9.0, 0.14) 174 172 172 175 179 164 175 177 G 0.92 0.73 0.87 0.78 0.89 0.81 0.84 0.86 (7.0, 0.13) (45.0, 0.35) (18.0, 0.21) (19.0, 0.24) (9.0, 0.15) (11.0, 0.17) (17.0, 0.26) (10.0, 0.16) 156 955 938 952 975 146 157 967 H 0.74 0.68 0.77 0.78 0.74 0.60 0.65 0.76 (23.0, 0.26) (26.0, 0.22) (15.0, 0.18) (17.0, 0.18) (21.0, 0.22) (27.0, 0.29) (19.0, 0.22) (21.5, 0.24) 176 175 175 178 182 167 177 180 I 0.79 0.83 0.82 0.78 0.81 0.66 0.69 0.78 (22.0, 0.20) (30.0, 0.25) (16.0, 0.17) (18.0, 0.20) (20.0, 0.17) (26.0, 0.24) (21.0, 0.25) (22.0, 0.19) 166 992 978 998 1019 158 167 1009 J 1.00 0.66 0.79 0.72 0.88 0.78 0.86 0.86 (0.0, 0.00) (44.5, 0.33) (18.0, 0.20) (18.0, 0.22) (8.0, 0.13) (11.0, 0.17) (16.0, 0.21) (8.0, 0.15) 177 170 170 173 177 163 173 175 K 1.00 0.74 0.75 0.70 0.47 0.58 0.68 (0.0, 0.00) (31.0, 0.26) (33.0, 0.24) (40.0, 0.30) (44.0, 0.36) (34.0, 0.30) (43.0, 0.30) 1061 1017 1035 1058 160 171 1048 L 1.00 0.87 0.85 0.70 0.74 0.80 (0.0, 0.00) (13.0, 0.16) (16.0, 0.17) (22.0, 0.24) (17.0, 0.21) (18.0, 0.19) 1051 1025 1048 160 171 1035 M 1.00 0.74 0.64 0.67 0.77 (0.0, 0.00) (18.0, 0.21) (19.0, 0.26) (17.0, 0.22) (18.0, 0.19) 1069 1067 163 174 1053 N 1.00 0.72 0.86 0.86 (0.0, 0.00) (13.0, 0.18) (14.0, 0.20) (10.0, 0.14) 1092 167 178 1076 O 1.00 0.75 0.69 

LEGEND 

Pearson R 

(P90, COD) n (0.0, 0.00) (18.0, 0.25) (14.0, 0.19) 167 163 165 P 1.00 0.84 (0.0, 0.00) (15.0, 0.21) 178 176 Q 1.00 (0.0, 0.00) 1079

December 2009 A-200 
1

0

0.2

0.4

0.6

0.8

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

Figure A-117. PM10 inter-sampler correlations as a function of distance between monitors for 

Pittsburgh, PA. 

December 2009 A-201 
Figure A-118. PM10 monitor distribution and major highways, Riverside, CA. 

December 2009 A-202 
Figure A-119. Box plots illustrating the seasonal distribution of 24-h avg PM10 concentrations 

for Riverside, CA. 

December 2009 A-203 
Table A-47. Inter-sampler correlation statistics for each pair of PM10 monitors reporting to AQS for 

Riverside, CA. 

A B C D E F G H I J K L M 

A 1.00 0.09 0.15 0.90 0.94 0.25 0.94 0.24 0.12 0.83 0.27 0.46 0.78 

 (0.0, 0.00) (50.0, 0.31) (36.0, 0.32) (33.0, 0.19) (37.0, 0.24) (41.0, 0.38) (16.0, 0.13) (25.0, 0.22) (40.0, 0.39) (38.5, 0.24) (30.0, 0.23) (32.0, 0.25) (33.0, 0.21)

 174 170 155 165 172 169 171 174 173 160 158 169 164 

B 1.00 0.86 0.07 0.13 0.31 0.12 0.32 0.29 0.13 0.31 0.35 0.29 

(0.0, 0.00) (48.0, 0.37) (47.0, 0.28) (45.0, 0.27) (57.0, 0.47) (49.0, 0.26) (48.0, 0.33) (55.0, 0.49) (51.0, 0.25) (49.0, 0.35) (51.0, 0.31) (44.0, 0.24)

315 161 167 298 173 176 309 302 172 163 173 168 

C 1.00 0.13 0.21 0.36 0.20 0.34 0.36 0.23 0.38 0.50 0.40 

 (0.0, 0.00) (49.0, 0.37) (58.0, 0.42) (24.0, 0.31) (40.0, 0.35) (27.0, 0.28) (24.0, 0.30) (57.5, 0.41) (24.0, 0.27) (30.0, 0.25) (41.0, 0.34)

 170 151 162 156 160 170 168 150 147 159 154 

D 1.00 0.93 0.19 0.83 0.11 0.05 0.73 0.13 0.38 0.69 

(0.0, 0.00) (29.0, 0.17) (52.0, 0.43) (23.0, 0.17) (38.0, 0.27) (52.0, 0.46) (26.0, 0.18) (43.0, 0.30) (40.0, 0.26) (24.5, 0.16)

173 169 167 168 173 172 157 155 165 160 

E 1.00 0.23 0.93 0.26 0.16 0.86 0.27 0.57 0.82 

 (0.0, 0.00) (63.0, 0.48) (27.0, 0.17) (46.0, 0.33) (63.5, 0.51) (18.0, 0.13) (54.0, 0.36) (40.0, 0.28) (26.0, 0.15)

 358 174 179 351 340 175 165 175 171 

F 1.00 0.27 0.73 0.32 0.35 0.43 0.44 0.48 

(0.0, 0.00) (44.0, 0.41) (28.0, 0.33) (27.0, 0.32) (57.0, 0.46) (24.5, 0.32) (35.0, 0.35) (46.0, 0.43)

177 173 177 176 162 160 170 164 

G 1.00 0.27 0.20 0.90 0.35 0.58 0.85 

 (0.0, 0.00) (30.0, 0.25) (46.5, 0.45) (25.0, 0.16) (34.0, 0.27) (29.0, 0.24) (24.0, 0.15)

 181 181 180 165 163 174 168 

H 1.00 0.26 0.47 0.48 0.40 0.44 

(0.0, 0.00) (27.0, 0.33) (45.0, 0.32) (18.0, 0.18) (29.0, 0.25) (34.0, 0.26)

1060 983 178 172 178 175 

I 1.00 0.20 0.45 0.38 0.35 

 (0.0, 0.00) (62.0, 0.51) (25.0, 0.32) (41.0, 0.39) (48.0, 0.46)

 1015 177 172 177 173 

J 1.00 0.42 0.70 0.85 

(0.0, 0.00) (49.0, 0.35) (37.0, 0.27) (20.0, 0.15)

 

LEGEND 

R 

(P90, COD) 

N 178 155 163 157 

K 1.00 0.49 0.48 

 (0.0, 0.00) (30.0, 0.26) (38.0, 0.29)

 173 162 157 

L 1.00 0.84 

(0.0, 0.00) (24.0, 0.20)

178 167 

M 1.00 

 (0.0, 0.00) 

 175 

 

December 2009 A-204 
0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

Figure A-120. PM10 inter-sampler correlations as a function of distance between monitors for 

Riverside, CA. 

December 2009 A-205 
Figure A-121. PM10 monitor distribution and major highways, Seattle, WA. 

December 2009 A-206 
Figure A-122. Box plots illustrating the seasonal distribution of 24-h avg PM10 concentrations 

for Seattle, WA. 

Table A-48. Inter-sampler correlation statistics for each pair of PM10 monitors reporting to AQS for 

Seattle, WA. 

A B 

A 1.00 0.77 

 (0.0, 0.00) (14.0, 0.24) 

 1059 1041 

B 1.00 

(0.0, 0.00) 

 1077 

LEGEND 

R 

(P90, COD) 

N 

 

December 2009 A-207 
0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

Figure A-123. PM10 inter-sampler correlations as a function of distance between monitors for 

Seattle, WA. 

December 2009 A-208 
Figure A-124. PM10 monitor distribution and major highways, St. Louis, MO. 

December 2009 A-209 
Figure A-125. Box plots illustrating the seasonal distribution of 24-h avg PM10 concentrations 

for St. Louis, MO. 

December 2009 A-210 
Table A-49. Inter-sampler correlation statistics for each pair of PM10 monitors reporting to AQS for 

St. Louis, MO. 

A B C D E F G H I 

A 1.00 0.50 0.75 0.67 0.47 0.65 0.67 0.73 0.55 

 (0.0, 0.00) (30.0, 0.28) (14.0, 0.17) (23.0, 0.24) (16.0, 0.29) (16.0, 0.18) (13.0, 0.17) (18.0, 0.19) (52.0, 0.33) 

171 161 158 156 158 163 166 168 164 

B 1.00 0.65 0.63 0.46 0.68 0.68 0.64 0.52 

(0.0, 0.00) (20.0, 0.21) (20.0, 0.19) (37.0, 0.42) (23.0, 0.20) (28.0, 0.28) (22.0, 0.20) (36.0, 0.28) 

173 161 158 160 167 169 170 166 

C 1.00 0.75 0.57 0.80 0.76 0.82 0.65 

 (0.0, 0.00) (17.0, 0.17) (23.0, 0.33) (12.0, 0.13) (13.0, 0.18) (12.0, 0.13) (41.0, 0.27) 

174 157 158 165 169 169 168 

D 1.00 0.44 0.82 0.81 0.80 0.59 

(0.0, 0.00) (30.0, 0.40) (16.0, 0.15) (21.0, 0.24) (14.0, 0.15) (36.0, 0.27) 

176 157 163 165 166 169 

E 1.00 0.53 0.62 0.56 0.34 

 (0.0, 0.00) (22.0, 0.34) (17.0, 0.26) (25.0, 0.35) (55.0, 0.42) 

185 164 166 167 179 

F 1.00 0.89 0.86 0.67 

(0.0, 0.00) (11.0, 0.16) (12.0, 0.11) (41.0, 0.27) 

176 173 174 169 

G 1.00 0.83 0.65 

 (0.0, 0.00) (16.0, 0.19) (47.0, 0.32) 

 

LEGEND 

R 

(P90, COD) 

N 

 179 177 173 

H 1.00 0.64 

 (0.0, 0.00) (41.0, 0.27) 

 180 173 

I 1.00 

 (0.0, 0.00) 

 1050 

December 2009 A-211 
1

0

0.2

0.4

0.6

0.8

0 10 20 30 40 50 60 70 80 90 100

Distance Between Samplers (km)

Correlation

Figure A-126. PM10 inter-sampler correlations as a function of distance between monitors for St. 

Louis, MO. 

Table A-50. Correlation coefficients of hourly and daily average particle number, surface and volume 

concentrations in selected particle size ranges. 

Hourly averages Daily avg Size range 

(nm) All days (N = 5481) Sundays (N = 701) Weekdays (N = 3227) Event days (N = 577) No events (N = 4904) All days (N = 263) 

3-10 0.40 0.24 0.42 0.73 0.37 0.32 

10-30 0.35 0.22 0.31 0.57 0.33 0.27 

30-50 0.38 0.42 0.29 0.56 0.36 0.36 

50-100 0.46 0.56 0.39 0.57 0.45 0.46 

100-500 0.55 0.65 0.49 0.62 0.55 0.55 

500-800 0.73 0.75 0.70 0.76 0.72 0.71 

10-100 0.31 0.28 0.24 0.52 0.29 0.24 

10-800 0.55 0.65 0.49 0.62 0.55 0.55 

Total number 0.30 0.24 0.24 0.58 0.28 0.20 

Total surface 0.57 0.63 0.51 0.65 0.56 0.57 

Total volume 0.66 0.69 0.62 0.73 0.65 0.67 

Source: Tuch et al. (2006)

December 2009 A-212 
A.2.3. Speciation 

Figure A-127. Seasonally averaged PM2.5 speciation data for 2005-2007 for a) annual, b) spring, 

c) summer, d) fall and e) winter derived using the SANDWICH method in Atlanta, 

GA. 

December 2009 A-213 
Figure A-128. Seasonally averaged PM2.5 speciation data for 2005-2007 for a) annual, b) spring, 

c) summer, d) fall and e) winter derived using the SANDWICH method in 

Birmingham, AL. 

December 2009 A-214 
Figure A-129. Seasonally averaged PM2.5 speciation data for 2005-2007 for a) annual, b) spring, 

c) summer, d) fall and e) winter derived using the SANDWICH method in Boston, 

MA. 

December 2009 A-215 
Figure A-130. Seasonally averaged PM2.5 speciation data for 2005-2007 for a) annual, b) spring, 

c) summer, d) fall and e) winter derived using the SANDWICH method in Chicago, 

IL. 

December 2009 A-216 
Figure A-131. Seasonally averaged PM2.5 speciation data for 2005-2007 for a) annual, b) spring, 

c) summer, d) fall and e) winter, derived using the SANDWICH method in Denver, 

CO. 

December 2009 A-217 
Figure A-132. Seasonally averaged PM2.5 speciation data for 2005-2007 for a) annual, b) spring, 

c) summer, d) fall and e) winter derived using the SANDWICH method in Detroit, 

MI. 

December 2009 A-218 
Figure A-133. Seasonally averaged PM2.5 speciation data for 2005-2007 for a) annual, b) spring, 

c) summer, d) fall and e) winter derived using the SANDWICH method in Houston, 

TX. 

December 2009 A-219 
Figure A-134. Seasonally averaged PM2.5 speciation data for 2005-2007 for a) annual, b) spring, 

c) summer, d) fall and e) winter derived using the SANDWICH method in Los 

Angeles, CA. 

December 2009 A-220 
Figure A-135. Seasonally averaged PM2.5 speciation data for 2005-2007 for a) annual, b) spring, 

c) summer, d) fall and e) winter derived using the SANDWICH method in New 

York, NY. 

December 2009 A-221 
Figure A-136. Seasonally averaged PM2.5 speciation data for 2005-2007 for a) annual, b) spring, 

c) summer, d) fall and e) winter derived using the SANDWICH method in 

Philadelphia, PA. 

December 2009 A-222 
Figure A-137. Seasonally averaged PM2.5 speciation data for 2005-2007 for a) annual, b) spring, 

c) summer, d) fall and e) winter derived using the SANDWICH method in Phoenix, 

AZ. 

December 2009 A-223 
Figure A-138. Seasonally averaged PM2.5 speciation data for 2005-2007 for a) annual, b) spring, 

c) summer, d) fall and e) winter derived using the SANDWICH method in 

Pittsburgh, PA. 

December 2009 A-224 
Figure A-139. Seasonally averaged PM2.5 speciation data for 2005-2007 for a) annual, b) spring, 

c) summer, d) fall and e) winter derived using the SANDWICH method in 

Riverside, CA. 

December 2009 A-225 
Figure A-140. Seasonally averaged PM2.5 speciation data for 2005-2007 for a) annual, b) spring, 

c) summer, d) fall and e) winter derived using the SANDWICH method in Seattle, 

WA. 

December 2009 A-226 
Figure A-141. Seasonally averaged PM2.5 speciation data for 2005-2007 for a) annual, b) spring, 

c) summer, d) fall and e) winter derived using the SANDWICH method in St. Louis, 

MO. 

December 2009 A-227 
Figure A-142. Seasonal patterns in PM2.5 chemical composition from city-wide monthly average 

values for Atlanta, GA, 2005-2007. The gray line represents the difference in OCM 

calculated using material balance and blank corrected OC×1.4. 

Figure A-143. Seasonal patterns in PM2.5 chemical composition from city-wide monthly average 

values for Birmingham, AL, 2005-2007. The gray line represents the difference in 

OCM calculated using material balance and blank corrected OC×1.4. 

December 2009 A-228 
Figure A-144. Seasonal patterns in PM2.5 chemical composition from city-wide monthly average 

values for Boston, MA, 2005-2007. The gray line represents the difference in OCM 

calculated using material balance and blank corrected OC×1.4. 

Figure A-145. Seasonal patterns in PM2.5 chemical composition from city-wide monthly average 

values for Chicago, IL, 2005-2007. The gray line represents the difference in OCM 

calculated using material balance and blank corrected OC×1.4. 

December 2009 A-229 
Figure A-146. Seasonal patterns in PM2.5 chemical composition from city-wide monthly average 

values for Denver, CO, 2005-2007. The gray line represents the difference in OCM 

calculated using material balance and blank corrected OC×1.4. 

Figure A-147. Seasonal patterns in PM2.5 chemical composition from city-wide monthly average 

values for Detroit, MI, 2005-2007. The gray line represents the difference in OCM 

calculated using material balance and blank corrected OC x 1.4. 

December 2009 A-230 
Figure A-148. Seasonal patterns in PM2.5 chemical composition from city-wide monthly average 

values for Houston, TX, 2005-2007. The gray line represents the difference in 

OCM calculated using material balance and blank corrected OC×1.4. 

Figure A-149. Seasonal patterns in PM2.5 chemical composition from city-wide monthly average 

values for Los Angeles, CA, 2005-2007. The gray line represents the difference in 

OCM calculated using material balance and blank corrected OC×1.4. 

December 2009 A-231 
Figure A-150. Seasonal patterns in PM2.5 chemical composition from city-wide monthly average 

values for New York, NY, 2005-2007. The gray line represents the difference in 

OCM calculated using material balance and blank corrected OC×1.4. 

Figure A-151. Seasonal patterns in PM2.5 chemical composition from city-wide monthly average 

values for Philadelphia, PA, 2005-2007. The gray line represents the difference in 

OCM calculated using material balance and blank corrected OC×1.4. 

December 2009 A-232 
Figure A-152. Seasonal patterns in PM2.5 chemical composition from city-wide monthly average 

values for Phoenix, AZ, 2005-2007. The gray line represents the difference in OCM 

calculated using material balance and blank corrected OC×1.4. 

Figure A-153. Seasonal patterns in PM2.5 chemical composition from city-wide monthly average 

values for Pittsburgh, PA, 2005-2007. The gray line represents the difference in 

OCM calculated using material balance and blank corrected OC×1.4. 

December 2009 A-233 
Figure A-154. Seasonal patterns in PM2.5 chemical composition from city-wide monthly average 

values for Riverside, CA, 2005-2007. The gray line represents the difference in 

OCM calculated using material balance and blank corrected OC×1.4. 

Figure A-155. Seasonal patterns in PM2.5 chemical composition from city-wide monthly average 

values for Seattle, WA, 2005-2007. The gray line represents the difference in OCM 

calculated using material balance and blank corrected OC×1.4. 

December 2009 A-234 
Figure A-156. Seasonal patterns in PM2.5 chemical composition from city-wide monthly average 

values for St. Louis, MO, 2005-2007. The gray line represents the difference in 

OCM calculated using material balance and blank corrected OC×1.4. 

A.2.4. Diel Trends 

Figure A-157. Diel plots generated from all available hourly FRM-like PM2.5 data, stratified by 

weekday (left) and weekend (right), in Atlanta, GA. Included are the number of 

monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for 

each hour. 

December 2009 A-235 
Figure A-158. Diel plots generated from all available hourly FRM-like PM2.5 data, stratified by 

weekday (left) and weekend (right), in Chicago, IL. Included are the number of 

monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for 

each hour. 

Figure A-159. Diel plots generated from all available hourly FRM-like PM2.5 data, stratified by 

weekday (left) and weekend (right), in Houston, TX. Included are the number of 

monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for 

each hour. 

December 2009 A-236 
Figure A-160. Diel plots generated from all available hourly FRM-like PM2.5 data, stratified by 

weekday (left) and weekend (right), in New York, NY. Included are the number of 

monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for 

each hour. 

Figure A-161. Diel plots generated from all available hourly FRM-like PM2.5 data, stratified by 

weekday (left) and weekend (right), in Pittsburgh, PA. Included are the number of 

monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for 

each hour. 

December 2009 A-237 
Figure A-162. Diel plots generated from all available hourly FRM-like PM2.5 data, stratified by 

weekday (left) and weekend (right), in Seattle, WA. Included are the number of 

monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for 

each hour. 

Figure A-163. Diel plots generated from all available hourly FRM-like PM2.5 data, stratified by 

weekday (left) and weekend (right), in St. Louis, MO. Included are the number of 

monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for 

each hour. 

December 2009 A-238 
Figure A-164. Diel plot generated from all available hourly FRM/FEM PM10 data, stratified by 

weekday (left) and weekend (right), in Atlanta, GA. Included are the number of 

monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for 

each hour. 

Figure A-165. Diel plot generated from all available hourly FRM/FEM PM10 data, stratified by 

weekday (left) and weekend (right), in Chicago, IL. Included are the number of 

monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for 

each hour. 

December 2009 A-239 
Figure A-166. Diel plot generated from all available hourly FRM/FEM PM10 data, stratified by 

weekday (left) and weekend (right), in Denver, CO. Included are the number of 

monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for 

each hour. 

Figure A-167. Diel plot generated from all available hourly FRM/FEM PM10 data, stratified by 

weekday (left) and weekend (right), in Detroit, MI. Included are the number of 

monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for 

each hour. 

December 2009 A-240 
Figure A-168. Diel plot generated from all available hourly FRM/FEM PM10 data, stratified by 

weekday (left) and weekend (right), in Los Angeles, CA. Included are the number 

of monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for 

each hour. 

Figure A-169. Diel plot generated from all available hourly FRM/FEM PM10 data, stratified by 

weekday (left) and weekend (right), in Philadelphia, PA. Included are the number 

of monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for 

each hour. 

December 2009 A-241 
Figure A-170. Diel plot generated from all available hourly FRM/FEM PM10 data, stratified by 

weekday (left) and weekend (right), in Phoenix, AZ. Included are the number of 

monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for 

each hour. 

Figure A-171. Diel plot generated from all available hourly FRM/FEM PM10 data, stratified by 

weekday (left) and weekend (right), in Pittsburgh, PA. Included are the number of 

monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for 

each hour. 

December 2009 A-242 
Figure A-172. Diel plot generated from all available hourly FRM/FEM PM10 data, stratified by 

weekday (left) and weekend (right), in Riverside, CA. Included are the number of 

monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for 

each hour. 

Figure A-173. Diel plot generated from all available hourly FRM/FEM PM10 data, stratified by 

weekday (left) and weekend (right), in Seattle, WA. Included are the number of 

monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for 

each hour. 

December 2009 A-243 
Figure A-174. Diel plot generated from all available hourly FRM/FEM PM10 data, stratified by 

weekday (left) and weekend (right), in St. Louis, MO. Included are the number of 

monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for 

each hour. 

December 2009 A-244 
A.2.5. Copollutant Measurements 

 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {

{

{{

{{

{ {{

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{

{

{{

{{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{{

{{

{

{{

{{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{ {

{

{{

{{

Figure A-175. Correlations between 24-h PM2.5 and co-located 24-h avg PM10, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Atlanta, GA, stratified by season (2005-

2007). One point is included for each available monitor pair. 

December 2009 A-245 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{ {{{{{

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{

{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{{{{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{

{{{{{

Figure A-176. Correlations between 24-h PM2.5 and co-located 24-h avg PM10, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Birmingham, AL, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-246 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{{

{{

{ { {{ { {

{ {{ {{

{{{ {

{{{ { {{

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{ { {

{{

{ { { {{

{ {{{{{

{ {{

{ {

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{{

{{

{ {{ {{

{{{

{{ { {

{ { { {{{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{{{

{ {

{ {{ {{ {

{{ { { {

{{ { {

{{{{

Figure A-177. Correlations between 24-h PM2.5 and co-located 24-h avg PM10, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Boston, MA, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-247 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ { {{{{

{

{{ {{

{ { {{ {

{{

{ {{ { {{{ {

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ { {{{{{{{

{

{{{{

{ {{{

{

{{{{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ { { { {

{

{ {{{

{ {{{

{{

{{ { { {{{{ {

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{{{{{

{

{ {{

{ { { {{

{ {

{{{{{

Figure A-178. Correlations between 24-h PM2.5 and co-located 24-h avg PM10, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Chicago, IL, stratified by season (2005-

2007). One point is included for each available monitor pair. 

December 2009 A-248 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ { {{

{

{

{

{

{

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{{

{

{

{

{

{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{

{

{

{

{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{{

{

{

{

{

{

Figure A-179. Correlations between 24-h PM2.5 and co-located 24-h avg PM10, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Denver, CO, stratified by season (2005-

2007). One point is included for each available monitor pair. 

December 2009 A-249 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{ {{ {

{{

{ { {

{{ {{{ { {{

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{{{{{

{

{ {{

{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{{{{

{{

{ {{{

{ {{{ {{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{

{{ {{{

{{

{ {{{

{{{{{ {{

Figure A-180. Correlations between 24-h PM2.5 and co-located 24-h avg PM10, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Detroit, MI, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-250 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{

{

{

{

{

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{

{

{

{

{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{

{

{

{

{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{

{

{

{

{

Figure A-181. Correlations between 24-h PM2.5 and co-located 24-h avg PM10, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Houston, TX, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-251 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{{{

{ { {

{ {{{ { {{{

{ {{{{{ { {{

{{{ { {{ { {

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{{{

{{{

{{{{{{

{{{{{ {{

{{{ {{{{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ { {{{{

{ { {

{{{{ {

{{ {{{{{

{ { {{{{ { {{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{ { {

{{{

{ { {{{{{{

{ { { {{{ {{

{ {{ { { { {

Figure A-182. Correlations between 24-h PM2.5 and co-located 24-h avg PM10, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Los Angeles, CA, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-252 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{{

{{{ {{{{{

{{ { {{{

{{{{ {

{{ { {{{{{

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{{

{ {{{{{

{ { { {{{

{{{{

{{{{{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{{

{ { { {{{{

{ {{ { {{{{

{{{{{{

{ {{{{{{{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{{

{ { { { { { {{{

{ {{{ {{{

{ { { {{{

{{{{{{{{{

Figure A-183. Correlations between 24-h PM2.5 and co-located 24-h avg PM10, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for New York, NY, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-253 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{{ { {{ { {{

{{{{{{

{ { {{ {

{{ { { { {{ {

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{{

{{{{{{

{{{{{

{ { {{{

{{{{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{ {{{{{{

{{{{{

{{{ { {{

{{{{ {{ { { { {

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{{

{ { { {{ {{

{ { { { {{ {

{ { { {{{

{{{{{{{{

Figure A-184. Correlations between 24-h PM2.5 and co-located 24-h avg PM10, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Philadelphia, PA, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-254 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{{{

{

{

{{

{ { {

{{{

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{{{

{

{

{{

{{{

{{{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{{

{

{

{

{{{

{ {{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{ {{

{

{

{{

{{

{{{

Figure A-185. Correlations between 24-h PM2.5 and co-located 24-h avg PM10, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Phoenix, AZ, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-255 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{{{{{

{ { {{{ { {

{ { {{{

{ { {

{ { {{{{{

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ { {{{{{

{{{ {{ {{

{{{

{ { {

{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{{{

{{{{{{

{ { {{{{

{{{

{ { { {{{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{{{{

{ {{ {{{

{{ {{{{

{ {{

{{{{{

Figure A-186. Correlations between 24-h PM2.5 and co-located 24-h avg PM10, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Pittsburgh, PA, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-256 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ { { { {{

{ {{

{ {{{

{{{

{{{{{

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ { { {{

{ {{

{ {{{

{{{{

{{{{{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{ {{{

{{{

{ {{{

{{{{

{ { {{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ { { { {{

{ {{

{ {{

{{{

{ {{{{

Figure A-187. Correlations between 24-h PM2.5 and co-located 24-h avg PM10, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Riverside, CA, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-257 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{{

{ { {

{{

{{

{{ { { {{

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{ {{

{ { {

{ {

{{

{{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{{

{{

{{

{{

{ { {{{{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM10 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{ { {

{ {

{{

{ {{ {{{

Figure A-188. Correlations between 24-h PM2.5 and co-located 24-h avg PM10, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for St. Louis, MO, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-258 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {

{

{

{

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{

{

{

{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{

{

{

{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {

{

{

{

Figure A-189. Correlations between 24-h PM10 and co-located 24-h avg PM2.5, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Atlanta, GA, stratified by season (2005-

2007). One point is included for each available monitor pair. 

December 2009 A-259 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{

{ {

{{{

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{

{

{ {

{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{

{{

{{{{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{

{

{{

{{{{

Figure A-190. Correlations between 24-h PM10 and co-located 24-h avg PM2.5, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Birmingham, AL, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-260 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{{

{{

{ { {{

{{{{

{{{

{{{ {

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{ { {

{{

{{{{

{ {{

{ { {

{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{{

{

{{{{

{ {{{

{ {{

{ { { {

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{{{

{ {

{ { {{

{ {{{ {

{ {{

{{

Figure A-191. Correlations between 24-h PM10 and co-located 24-h avg PM2.5, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Boston, MA, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-261 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ { {{{{

{

{

{

{ {{

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ { {{{{{{{

{

{

{

{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ { { { {

{

{

{

{ { {

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{{{{{

{

{

{

{ {{

Figure A-192. Correlations between 24-h PM10 and co-located 24-h avg PM2.5, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Chicago, IL, stratified by season (2005-

2007). One point is included for each available monitor pair. 

December 2009 A-262 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ { {{

{

{{

{{

{{

{{

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{{

{

{{

{{

{{

{{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{

{{

{{

{{

{{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{{

{

{{

{{

{

{{

Figure A-193. Correlations between 24-h PM10 and co-located 24-h avg PM2.5, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Denver, CO, stratified by season (2005-

2007). One point is included for each available monitor pair. 

December 2009 A-263 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{

{

{{

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{

{

{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{

{

{ {

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{

{

{

{ {

Figure A-194. Correlations between 24-h PM10 and co-located 24-h avg PM2.5, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Detroit, MI, stratified by season (2005-

2007). One point is included for each available monitor pair. 

December 2009 A-264 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{

{{

{{{{

{{{

{ { {{ {

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{

{ {

{{{{

{ {{{

{ { {{ {{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{

{{

{ {{{

{ { {

{ {{{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{

{{

{ {{

{ { {{

{ {{{{

Figure A-195. Correlations between 24-h PM10 and co-located 24-h avg PM2.5, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Houston, TX, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-265 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{{{

{ {

{ {{ { {{

{ { {{ {{

{{ { { { { {

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{{{

{{

{ {{{{

{{{{{

{ { { {{{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ { {{{{

{ {

{{ { {{{

{{{ { {{{

{{{{ {{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{ { {

{{

{ { { { {{

{ { {{{ {{

{ {{ {{{

Figure A-196. Correlations between 24-h PM10 and co-located 24-h avg PM2.5, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Los Angeles, CA, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-266 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{ {

{ {

{

{

{

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{{

{{

{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{{

{ {

{

{{

{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{{

{ {

{

{{

{

Figure A-197. Correlations between 24-h PM10 and co-located 24-h avg PM2.5, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for New York, NY, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-267 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{ { { {

{{{{

{ {{

{ {

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{{

{ {{{

{{{{

{ { {

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{{ {{

{ {{{

{ {{

{ {{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{{

{ {{ {

{ {{

{{{

{ {{

Figure A-198. Correlations between 24-h PM10 and co-located 24-h avg PM2.5, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Philadelphia, PA, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-268 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{{{

{

{{{

{ {{{{{

{ { {{ { {{{{

{ { {{{ {{{

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{{{

{

{{ {

{ {{{{

{{ {{{{{

{{{ {{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{{

{

{ {{

{ { {{{

{ { { { {{{ {{

{{{ { { { { {{{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{ {{

{

{{{

{{{{{

{{{{

{ { { {{ { {{{{

Figure A-199. Correlations between 24-h PM10 and co-located 24-h avg PM2.5, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Phoenix, AZ, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-269 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{{{{{

{ {{ { { {{{{

{ {{{

{ {{{ {

{ {{ {{

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ { {{{{{

{{{ {{{ {

{ {{{

{ { { {{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{{{

{ { { { { {{

{ { {{

{{{{{

{ {{{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{{{{

{ { {{{{ {

{ {{ { {

{ { {{ {

{{{{

Figure A-200. Correlations between 24-h PM10 and co-located 24-h avg PM2.5, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Pittsburgh, PA, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-270 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ { { { {{

{ {{

{ { {{ {

{ { { 

{ { {{{ { { {

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ { { {{

{ {{

{{{ { { {

{ {{ {

{{ { { {{{{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{ {{{

{{ {{

{ {{ { {{

{ {{

{{{{ {{{{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ { { { {{

{{ { {

{ { { {{ {

{{{

{{ {{{{{{{

Figure A-201. Correlations between 24-h PM10 and co-located 24-h avg PM2.5, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for Riverside, CA, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-271 
 Spring

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{ {{{

{ { {{

{ {

{ { {

{ { {{ {

 Winter

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{ {{

{ { { {

{

{{{

{{{

 Fall 

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{{

{{{{

{{

{ {{

{{{{

 Summer

O3 (daily max 8-hr)

CO (daily avg)

NO2 (daily avg)

SO2 (daily avg)

PM10-2.5 (daily avg)

PM2.5 (daily avg)

r (correlation coefficient)

-1 .0

-0 .9

-0 .8

-0 .7

-0 .6

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

{{{

{ { { {

{ {

{{{

{{{{

Figure A-202. Correlations between 24-h PM10 and co-located 24-h avg PM2.5, PM10-2.5, SO2, NO2

and CO and daily maximum 8-h avg O3 for St. Louis, MO, stratified by season 

(2005-2007). One point is included for each available monitor pair. 

December 2009 A-272 
A.3. Source Apportionment 

A.3.1. Type of Receptor Models 

Table A-51. Different receptor models used in the Supersite source apportionment studies: chemical 

mass balance. 

Receptor Model Description Strengths and 

Weaknesses 

Effective Variance CMB 42,121 

(Note that all models based on 

eq 1 or 2 are CMB equations. 

The term CMB used here 

reflects the historical solution in 

which source profiles are 

explicitly used as model input 

and a single sample effective 

variance solution is reported.) 

CMB software is currently 

distributed by EPA. The most 

recent version is the CMB 8.2, 

which is run in the Microsoft 

Windows system. 

Principle 

Ambient chemical concentrations are expressed as the sum of products of species 

abundances in source emissions and source contributions (Equations A-1 or A-2). These 

equations are solved for the source contribution estimates when ambient concentrations 

and source profiles are input. The single-sample effective variance least squares is the 

most commonly used solution method because it incorporates uncertainties of ambient 

concentrations and source profiles in the estimate of source contributions and their 

uncertainties. This reduced to the tracer solution when it is assumed that there is one 

unique species for each source. Choices of source profiles should avoid collinearity, which 

occurs when chemical compositions of various source emissions are not sufficiently 

different.121 

 for i = 1 to I 

Equation A-1

∑=

=

J

j

iklmn ijklmnijm iklmn C STF

1

 ∑ +=

j

it ESFC itjtij

 

Equation A-2

Data Needs 

CMB requires source profiles, which are the mass fractions of particulate or gas species in 

source emissions. The species and particle size fraction measured in source emissions 

should match those in ambient samples to be apportioned. Several sampling and analysis 

methods provide time-integrated speciation of PM2.5 and volatile organic compounds 

(VOCs) for CMB. Source profiles are preferably obtained in the same geographical region 

as the ambient samples, although using source profiles from different regions is commonly 

practiced in the literature. The practitioner needs to decide the source profiles and species 

being included in the model, on the basis of the conceptual model and model performance 

measures. 

Output 

Effective variance CMB determines, if converged, source contributions to each sample in 

terms of PM or VOC mass. CMB also generates various model performance measures, 

including correlation R2

, deviation Χ2, residue/ uncertainty ratio, and MPIN matrix that are 

useful for refining the model inputs to obtain the best and most meaningful source 

apportionment resolution. 

Strengths 

Software available providing 

a good user interface. 

Provides quantitative 

uncertainties on source 

contribution estimates based 

on input concentrations, 

measurement uncertainties, 

and collinearity of source 

profiles. 

Quantifies contributions from 

source types with single 

particle and organic 

compound measurements. 

Weaknesses 

Completely compatible 

source and receptor 

measurements are not 

commonly available. 

Assumes all observed mass 

is due to the sources 

selected in advance, which 

involves some subjectivity. 

Chemically similar sources 

may result in collinearity 

without more specific 

chemical markers. 

Typically does not apportion 

secondary particle 

constituents to sources. Must 

be combined with profile 

aging model to estimate 

secondary PM. 

42 Hidy and Friedlander (1972, 156546) 121 Watson et al. (1997, 157121) 122 (1984, 045693) 

Source: Watson et al. (2008, 157128) 

December 2009 A-273 
Table A-52. Different receptor models used in the Supersites source apportionment studies: factor 

analysis. 

Receptor Model Description Strengths and 

Weaknesses 

PMF 

PMFx (PMF2 and PMF3) 

software is available from 

Dr. Pentti Paatero at the 

University of Helsinki, 

Finland. This software is a 

Microsoft DOS application. 

EPA distributes EPA PMF76 

version 1.1 as a Microsoft 

Windows application with 

better user interface. 

Principle 

PMFx contains PMF2 and PMF3. PMF2 solves the CMB equations (i.e., Equations A-2 and A3) using an iterative minimization algorithm. Source profiles Fij and contribution Sjt are solved 

simultaneously. The non-negativity constraint is implemented in the algorithm to decrease 

the number of possible solutions (local minimums) in the PMF analyses, because both 

source profile and contribution should not contain negative values. There is rotational 

ambiguity in all two-way factor analyses (i.e.,Fjt and Sjt matrices may be rotated and still fit 

the data). PMF2 allows using the FPEAK parameter to control the rotation. A positive FPEAK 

value forces the program to search such solutions where there are many zeros and large 

values but few intermediate values in the source matrix Fjt.Fkey can further bind individual 

elements in Fjt to zero On the basis of a similar algorithm, PMF3 solves a three-way problem. 

PMFx and UNMIX estimate Fij and Sjt by minimizing: 

 Equation A-3

Where the weighing factor, σit, represents the magnitude of Eit, PMFx limits solutions of 

Equation A-2 to non-negative Fij and Sjt. 

Data Needs 

A large number of ambient samples (usually much more than the number of factors in the 

model) are required to produce a meaningful solution. Species commonly used in PMF are 

also those in CMB. Weighting factors associated with each measurement need to be 

assigned before analysis. The practitioner also needs to decide the number of factors, 

FPEAK, and Fkey in the model. 

Output 

PMFx reports all the elements in Fij and Sjt matrices (PMF2). It also calculates model 

performance measures such as deviation Χ2 and standard deviation of each matrix element. 

The practitioner needs to interpret the results linking them to source profiles and source 

contributions. 

Strengths 

Software available. 

Can handle missing or belowdetection-limit data. 

Weights species 

concentrations by their 

analytical precisions. 

Downweight outliers in the 

robust mode. 

Derives source profiles from 

ambient measurements as 

they would appear at the 

receptor (does not require 

source measurements). 

Weaknesses 

Requires large (>100) ambient 

datasets. 

Need to determine the number 

of retaining factors. 

Requires knowledge of source 

profiles or existing profiles to 

verify the representativeness 

of calculated factor profiles 

and uncertainties of factor 

contributions. 

Relies on many 

parameters/initial conditions 

adjustable to model input; 

sensitive to the preset 

parameters. 

ME2125 

ME2 code is available from 

Dr. Pentti Paatero at the 

University of Helsinki, 

Finland as a Microsoft DOS 

application. 

Principle 

The PMFx algorithm is derived from ME2. Unlike PMFx that is limited to questions in the 

form of Equation A-1 or A-2, ME2 solves all models in which the data values are fitted by 

sums of products of unknown (and known) factor elements. The first part of the algorithm 

interprets instructions from the user and generates a table that specifies the model. The 

second part solves the model using an iterative minimization approach. Additional constraints 

could be programmed into the model to reduce the ambiguity in source apportionment. 

These constraints may include known source profiles and/or contributions (e.g., contributions 

are known to be zero in some cases). 

Data Needs 

Data needs are similar to those of PMFx but are more flexible. In theory, any measured or 

unknown variables may be included in the model as long as they satisfy linear relationships. 

The users need to specify the model structure, the input, and the output. 

Output 

ME2 calculates and reports all unknown variables in the model. 

Strengths 

Software available. 

Can handle user-specified 

models. 

Possibility to include all 

measured variables into the 

model, such as speciated 

concentration over different 

time scales, size distributions, 

meteorological variables, and 

noise parameters. 

Weaknesses 

Require substantial training to 

access the full feature of the 

software and develop a model. 

Generally requires large 

ambient datasets. 

Need to assume linear 

relationships between all 

variables. 

Relies on many 

parameters/initial conditions 

adjustable to model input; 

sensitive to the preset 

parameters. 

December 2009 A-274 
Receptor Model Description Strengths and 

Weaknesses 

UNMIX 29,44,126 

UNMIX code is available 

from Dr. Ron Henry at the 

University of Southern 

California as an MatLab 

application. A stand-alone 

version (UNMIX version 6) is 

also available from EPA. 

Principle 

UNMIX views each sample as a data point in a multidimensional space with each dimension 

representing a measured species. UNMIX solves Equations A-2 and A-3 by using a principle 

component analysis (PCA) approach to reduce the number of dimensions in the space to the 

number of factors that produce the data, followed by an unique “edge detection” technique to 

identify “edges” defined by the data points in the space of reduced dimension (e.g., Figures 1 

and 3). The number of factors is estimated by the NUMFACT algorithm in advance127, which 

reports the R2

 and signal-to-noise (S/N) ratio associated with the first N principle components 

(PCs) in the data matrix. The number of factors should coincide with the number of PCs with 

S/N ratio >2. Once the data are plotted on the reduced space, an edge is actually a 

hyperplan that signifies missing or small contribution from one or more factors. Therefore, 

UNMIX searches all the edges and uses them to calculate the vertices of the simplex, which 

are then converted back to source composition and contributions. Geometrical concepts of 

self-modeling curve resolution are used to ensure that the results obey (to within error) nonnegativity constraints on source compositions and contributions. 

Data Needs 

A large number of ambient samples (usually much more than the number of factors in the 

model) are required to achieve a meaningful solution. Species commonly used in UNMIX are 

also those in CMB. The measurement precision is not required. The practitioner needs to 

specify the number of factors on the basis of the NUMFACT results. 

Output 

UNMIX determines all the elements in the factor (Fij) and contribution (Sjt) matrices. It also 

calculates the uncertainty associated with the factor elements and model performance 

measures including: (1) R2

, (2) S/N ratio, and (3) strength. 

Strengths 

Software available with 

graphical user interface. 

Does not require source 

measurements. 

Provide graphical problem 

diagnostic tools (e.g., species 

scatter plot). 

Provide evaluation tools (e.g., 

R2

, S/N ratio). 

Weaknesses 

Requires large (>100) ambient 

datasets. 

Need to assume or 

predetermine number of 

retained factors. 

Does not make explicit use of 

errors or uncertainties in 

ambient measurements. 

Cannot use samples 

containing missing data in any 

species. 

Limited to a maximum of 7 or 

14 (UNMIX version 6) factors. 

Can report multiple or no 

solutions. 

Requires knowledge of 

existing source profiles to 

evaluate the solutions. 

December 2009 A-275 
Receptor Model Description Strengths and 

Weaknesses 

PDRM97 

PDRM was developed under 

the Supersites Program and 

requires MatLab or 

equivalent software to 

perform the calculation. 

Principle 

PDRM estimates contributions from selected stationary sources for a receptor site using high 

time-resolution measurements and meteorological data. In PDRM, Equation A-2 is modified 

to: 

 Equation A-4

where ERi,j is interpreted as the emission rate of species i from stationary source j and (Χ/Q,t 

is the meteorological dispersion factor averaged over the time interval t. Equation A-4 is 

solved for ERi,j and (Χ/Q,t simultaneously by a nonlinear fit minimizing the objective function, 

FUN: 

 Equation A-5

Because the number of solutions for a product of unknowns is infinite, additional constraints 

are set up for (Χ/Q,t on the basis of the Gaussian plume model, thus: 

 Equations A-6 & A-7 

Equations A-6 and A-7 limit the solution of Equation A-5 within the lower (LB) and upper (UB) 

bound of those predicted by the Gaussian plume model using different parameterizations. 

Data Needs 

PDRM requires speciated measurements at a higher time-resolution than typical CMB or 

PMF applications because of the fast-changing meteorological parameters. PDRM also 

requires data for Equation A-7: transport speed (u), lateral and vertical dispersion parameters 

(σy and σz), and stack height (h). 

Output 

PDRM determines emission rates and contributions from each point source considered in 

the model at the same time resolution as the measurement. 

Strengths 

Explicitly include 

meteorological information and 

stack configuration of 

stationary sources into the 

model. 

Do not require source 

measurements. 

Do not need to interpret the 

relations between factors and 

sources. 

Commercial software (e.g., 

MatLab) available for 

performing nonlinear fit. 

Suitable for high timeresolution measurement. 

Weaknesses 

Can only handle stationary 

sources but not area or mobile 

sources. 

Need to assume that only 

stationary sources are 

considered in the model 

contribute significantly for a 

measurement at the receptor 

site. 

Do not account for uncertainty 

in the measurement. 

Meteorological data may not 

be always available or 

accurate. 

Gaussian plume model may 

not be representative of the 

actual atmospheric dispersion. 

Sensitive to the imposed 

constraints (UB and LB). 

December 2009 A-276 
Receptor Model Description Strengths and 

Weaknesses 

PLS128 Principle Strengths 

PLS examines the relationships between a set of predictor (independent) and response 

(dependent) variables. It assumes that the predictor and response variables are controlled 

by independent “latent variables” less in number than either the predictor or the response 

variables. In recent applications, 96 PM chemical composition and size distribution are used 

as predictor (X) and response (Y) variables, respectively. Equation A- 2 is modified to: 

Fit two types of measurements 

(e.g., chemistry and size) with 

common factors. Provide more 

information to identify sources. 

Analyze strongly collinear and 

noisy dataset. 

 Do not require source 

measurements. 

Weaknesses 

Equation A-8 

 

Equation A-9 

where T and U are matrices of so-called “latent variables,” and P and C are loading matrices. 

If X and Y are correlated to some degree, T and U would show some similarity. Equations 

A-8 and A-9 are solved by an iterative algorithm “NIPALS,” which attempts to minimize E,D, 

and the difference between T and U simultaneously. If T and U end up being close enough, 

the X and Y variables can be explained by the same latent variables. These latent variables 

may then be interpreted as source or source categories. 

Data Needs 

Typical applications of PLS require both chemical speciated and size-segregated 

measurements. The practitioner needs to decide the number of latent variables on the basis 

of the correlation of resulting T and U matrices. 

Output 

PLS calculates latent variables, which are common factors best explaining the predictor and 

response variables, and the residues from fitting. Rx and Ry, 

 

Equation A-10 

 

Equation A-11 

indicate the degree to which variables X and Y are explained by the latent variables. 

Requires large (>100) ambient 

datasets. 

Difficult to relate latent 

variables to any physical 

quantities. 

Do not provide quantitative 

source contribution estimates. 

Need to decide the number of 

latent variables. 

Do not explicitly make use of 

measurement uncertainties. 

Can result in no solution. 

 Henry (1997, 020941) 

 Lewis et al. (2003, 088413) 

 Ogulei et al. (2006, 119975) 

 Park et al. (2005, 156844) 

 Paatero (1997, 087001) 

 Paatero et al. (2002, 156836) 

 Paatero (1999, 156835) 

 Henry (2003, 156540) 

Source: Watson et al. (2008, 157128)

December 2009 A-277 
Table A-53. Different receptor models used in the Supersites source apportionment studies: tracerbased methods. 

Receptor Model Description Strengths and Weaknesses 

EF 129,130 

The EF method may use a MLR 

algorithm, which is available in 

most statistical and spreadsheet 

software 

Principle 

A tracer (or marker) for a particular source or source category is a species 

enriched heavily in the source emission against other species and other sources. 

Using EFs-, concentration of the ith pollutant at a receptor site at time t (i.e., Ci,t) 

can be expressed as: 

 

Equation A-12

where the enrichment factor EFi,pj is the ratio of emission rate of the pollutant of 

interest (Fij) and tracer species (Fpj) from source j. Cpj,t is the concentration of 

tracer species for source j at time t, and Zi,t represents contributions from all other 

sources (including the background level). The solution for eq 12 is situationdependent. EFi,pj is usually unknown but may be estimated from source profiles, 

edges of a two-way scatter plot or the ratio of Ci,t to Cpj,t for a particular period 

when it is believed that a single source is dominant. In cases where Zi,t is a 

constant, EFi,pj may be derived from MLR. 

Data Needs 

The minimum data needs include concentrations of all primary tracers at the 

receptor site. Known EFs or background levels are helpful. 

Output 

The EF method determines contributions to species i from each source 

considered in the model. 

Strengths 

No special software needed. 

Indicate presence or absence of 

particular emitters. 

Provides evidence of secondary PM 

formation and changes in source 

impacts by changes in ambient 

composition. 

Could use a large (>100) dataset or a 

small (e.g., < 10) dataset. 

Weaknesses 

Semiquantitative method, not specific 

especially when the EFs are 

unknown in advance. 

Limited to sources with unique 

markers. 

Tracer species must be exclusively 

from the sources or source 

categories examined. 

Provide very limited error estimates. 

More useful for source/process 

identification than for quantification. 

NNLS 131,132 

The MatLab Optimization Toolbox 

provides a function “lsqnonneg” 

for performing the NNLS 

calculation. 

Principle 

NNLS also solves the EF equation (Equation A-12 or equivalent) with known 

target species and tracer concentrations. Conventional MLR solutions to eq 12 

may lead to negative EFs due to the uncertainty in measurements or colinearity in 

source contributions. This is avoided in the NNLS approach since additional nonnegative constraints are built into the algorithm, i.e.: 

 

Equation A-13

Utilizing orthogonal decomposition, a NNLS problem can be reduced to the more 

familiar least-distance programming and solved by a set of iterative subroutines 

developed and tested by Lawson and Hanson.131 In a more general sense, 

NNLS linearly relates a response variable to a set of independent variables with 

only non-negative coefficients. 

Data Needs 

When applied to EF or MLR problems, NNLS requires the concentration of target 

(response) and tracer (independent) species. 

Output 

NNLS generates non-negative regression coefficients for an EF/MLR problem and 

these coefficients can be related to the source contributions. 

Strengths 

Implemented by many statistical 

software packages. 

Generate only non-negative EFs or 

regression coefficients. 

Do not require source 

measurements. 

Possible to include meteorological or 

other (besides chemistry) data into 

the model. 

Weaknesses 

Require a large (>100) set of ambient 

measurements. 

Semiquantitative method, not 

specific. 

Do not explicitly consider 

measurement uncertainties. 

Tracer species must be exclusively 

from the sources or source 

categories examined. 

Non-negative constraints may not be 

appropriate in some cases. 

December 2009 A-278 
Receptor Model Description Strengths and Weaknesses 

FAC Principle 

FAC provides a simple mean of estimating the SOA production rate using the 

emission inventories of primary precursor VOCs. FAC is actually a sourceoriented modeling technique but it does not take into account all the atmospheric 

processes. FAC is defined as the fraction of SOA that would result from the 

reactions of a particular VOC: 

 

Equation A-14

where [VOCi]0 is the emission rate of VOCi and [SOA] is the formation rate of 

SOA. Equation A-14 can be viewed as an extension of Equation -12 but 

concentrations are replaced with emission rates and EFs are replaced with FACs. 

FAC and the fraction of VOC reacted under typical ambient conditions have been 

developed for a large number of hydrocarbons >C6

111. The most significant SOA 

precursors are aromatic compounds (especially toluene, xylene, and 

trimethylbenzenes) and terpenes. In most applications, these FACs are used 

directly to estimate SOA. 

Data Needs 

FAC requires the VOC emission inventory in the region of interest. The knowledge 

of O3 and radiation intensity is also helpful for slight modifications of the FACs. 

Output 

FAC method estimates the total production rate of SOA. 

Strengths 

Link SOA to primary VOC emissions 

so that SOA can also be treated as 

primary particles in the PM modeling. 

Simple and inexpensive. 

Weaknesses 

Ignore the influence of aerosol 

concentration and temperaturedependent gas-particle partitioning on 

SOA yield. 

Limited by the accuracy of VOC 

emission inventory. 

Do not directly infer the contribution 

of each source to ambient SOA 

concentration. 

Difficult to verify. 

Grosjean and Seinfeld (1989, 045643) 

Darns et al. (1970, 156379) 

Reimann and De Caritat (2000, 013269) 

Lawson and Hanson (1974, 156673) 

 Wang and Hopke (1989, 157105) 

Source: Watson et al. (2008, 157128)

December 2009 A-279 
Table A-54. Different receptor models used in the Supersites source apportionment studies: 

meteorology-based methods. 

Receptor Model Description Strengths and 

Weaknesses 

CPF 134,135 Principle 

CPF estimates the probability that a given source contribution from a given wind direction will 

exceed a predetermined threshold criterion (e.g., upper 25th percentile of the fractional contribution 

from the source of interest). The calculation of CPF uses source contributions (i.e., O3 in Equation 

A-2) determined for the receptor site and local wind direction data matching each of the source 

contributions in time. These data are then segregated to several sectors according to wind direction 

and the desired resolution (usually 36 sectors at a 10° resolution). Data with very low wind speed 

(e.g., < 0.1 m/sec) are usually excluded from analysis because of the uncertain wind direction. CPF 

is then determined by: 

 

Equation A-15 

where mΔθ is the number of occurrences in the direction sector θ → θ + Δθ that exceeds the 

specified threshold, and nΔθ is the total number of wind occurrences in that sector. Because wind 

direction is changing rapidly, high-time resolution measurements (e.g., minutes to hours) are 

preferred for a CPF analysis. If the calculated source contributions represent long-term averages, 

wind direction needs to be averaged over the same duration. In addition to source contribution, 

CPF can be applied directly to pollutant concentration measurements at a receptor site. 

Data Needs 

CPF requires the time series of source contributions at a receptor site, which is usually determined 

by CMB or factor analysis methods using speciated measurements at the site. CPF also requires 

wind direction and wind speed data averaged over the same time resolution as the sampling 

duration. 

Output 

CPF reports the probability of “high” contribution from a particular source or factor occurring within 

each wind direction sector. The results are often presented in a wind rose plot. 

Strengths 

Infer the direction of 

sources or factors 

relative to the 

receptor site. 

Provide verification 

for the source 

identification made 

by factor analysis 

method. 

Easy to implement. 

Weaknesses 

Criterion for the 

threshold is 

subjective. 

Absolute source 

contribution (or 

fractional 

contribution) may be 

influenced by other 

factors besides wind 

direction (e.g., wind 

speed, mixing 

height). 

Local and nearsurface wind 

direction only has a 

limited implication for 

long-range transport. 

Easy to be biased by 

a small number of 

wind occurrences in 

a particular sector. 

Work better for 

stationary sources 

than area or mobile 

sources. 

December 2009 A-280 
Receptor Model Description Strengths and 

Weaknesses 

NPR 136,137 Principle 

NPR calculates the expected (averaged) source contribution as a function of wind direction 

following: 

 

Equation A-16 

where Wi is the wind direction for the ith sample and Si is the contribution from a specific source to 

that sample, determined from measurements at the receptor site. K is a weighting function called 

the kernel estimator. There are many possible choices for K. Henry et al.136 recommend either 

Gaussian or Epanechnikov functions. The most important decision in NPR is the choice of the 

smoothing parameter Δθ. If Δθ is too large, S(θ) will be too smooth and meaningful peaks could be 

lost. If it is too small, S(θ) will have too many small, meaningless peaks. Δθ needs to be chosen 

according to the project-specific spatial distribution of sources. NPR also estimates the confidence 

intervals of S(θ) based on the asymptotic normal distribution of the kernel estimates, thus: 

 

Equation A-17 

Data Needs 

NPR requires the same data as the CPF method, including the time series of source/factor 

contributions (or fractional contributions) at the receptor site and local wind direction data matching 

the sampling duration in time. 

Output 

NPR reports the distribution of source contribution as a function of wind direction and the 

confidence level associated with it. 

Strengths 

Infer the direction of 

sources or factors 

relative to the 

receptor site. 

Provide verification 

for the source 

identification made 

by factor analysis 

method. 

Require no 

assumption about 

the function form of 

the relationship 

between wind 

direction and source 

contribution. 

Provide uncertainty 

estimates. 

Easy to implement. 

Weaknesses 

Choices for the 

kernel estimator and 

smoothing factor are 

subjective. 

Absolute source 

contribution (or 

fractional 

contribution) may be 

influenced by other 

factors besides wind 

direction (e.g., wind 

speed, mixing 

height). 

Local and nearsurface wind 

direction only has a 

limited implication for 

long-range transport. 

Easy to be biased by 

a small number of 

wind occurrences in 

a particular sector. 

Work better for 

stationary sources 

than area or mobile 

sources. 

December 2009 A-281 
Receptor Model Description Strengths and 

Weaknesses 

TSA 138 

TSA requires the calculation of 

air parcel back trajectory, which 

is often accomplished using the 

HY-SPLIT model.115,139 HYSPLIT version 4.5 is available 

at http://www.arl.noaa.gov/-

ready/hysplit4.html. 

Principle 

Similar to CPF, TSA clusters the measured pollutant concentration or calculated source contribution 

according to the wind pattern. However, air parcel back trajectory, rather than local wind direction, 

is used. A back trajectory traces the air parcel backward in time from a receptor. The initial height is 

often between 200 and 1000 m above ground level where the wind direction could differ from the 

surface wind direction substantively. For each sample i, TSA obtains one or more trajectories and 

calculates their total residence time in the jth directional sector (τi,j, i.e., the total number of 1-h 

trajectory end points that fall into the sector). The pollutant concentration or source contribution in 

the sample, Si, is then linearly apportioned into each directional sector according to τi,j and 

averaged over all samples to produce the directional dependent pollutant concentration/source 

contribution for the period of interest: 

 

Equation A-18 

where N is the number of samples. Compared with CPF and NPR, TSA considers the entire air 

mass history rather than just the wind direction at the receptor. 

Data Needs 

TSA requires the time series of pollutant concentration or source contribution at the receptor site, 

and back trajectories initiated over the site during the sampling duration. Trajectory is usually 

calculated once every hour so TSA is more suitable for analyzing measurements of >1-h resolution. 

Output 

TSA reports the avg pollutant concentration or source contribution as a function of wind direction 

based on back trajectory calculations. 

Strengths 

Infer the direction of 

sources or factors 

relative to the 

sampling site. 

Provide verification 

for the source 

identification made 

by factor analysis 

method. 

Account for air mass 

transport over 

hundreds to 

thousands of 

kilometers and on 

the order of several 

days. 

Can represent plume 

spread from vertical 

wind shear at 

different hours of day 

by adjusting the 

initial height of back 

trajectories. 

Weaknesses 

Need to generate 

and analyze the back 

trajectory data. 

Uncertainty in back 

trajectory calculation 

increases with its 

length in time. 

Source contribution 

depends on not only 

trajectory residence 

time but also 

entrainment 

efficiency, dispersion, 

and deposition. 

Difficult to resolve 

the direction of more 

localized sources. 

December 2009 A-282 
Receptor Model Description Strengths and 

Weaknesses 

PSCF 140

PSCF requires the calculation 

of air parcel back trajectory, 

which is often accomplished 

using the HY-SPLIT 

model.115,139 HY-SPLIT version 

4.5 is available at 

http://www.arl.noaa.gov/-

ready/hysplit4.html. 

Principle 

Ensemble air parcel trajectory analysis refers to the statistical analysis on a group of trajectories to 

retrieve useful patterns regarding the spatial distribution of sources. Uncertainties associated with 

individual trajectory calculations largely cancel out for a sufficient number of trajectories or 

trajectory segments. As a popular ensemble back trajectory analysis, PSCF estimates the 

probability that an upwind area contributes to high pollutant concentration or source contribution. 

Back trajectories are first calculated for each sample at the receptor site. To determine the PSCF, a 

study domain containing the receptor site is divided into an array of grid cells. Trajectory residence 

time (the time it spends) in each grid cell is calculated for all back trajectories and for a subset of 

trajectories corresponding to “high” pollutant concentration or source contribution at the site. PSCF 

in cell (i,j) is then defined as: 

 

Equation A-19 

The criterion for high pollutant concentration or source contribution is critical for the PSCF 

calculation. The 75th or 90th percentile of the concentration or factor is often used.113,141,142

Residence time can be represented by the number of trajectory end points in a cell. 

Data Needs 

Similar to TSA, PSCF calculation requires the time series of pollutant concentration or source 

contribution at the receptor site, and back trajectories initiated over the site during the sampling 

period. Trajectories should be calculated with 1-to 3-h segment to reduce the uncertainty from 

interpolation (if needed). 

Output 

PSCF reports the probability that an upwind area contributes to high pollutant concentrations or 

source contribution at the downwind receptor site. The results are often presented as a contour plot 

on the map. A high probability usually suggests potential source region. 

Strengths 

Infer the location of 

sources or factors 

relative to the 

sampling site. 

Provide verification 

for the source 

identification made 

by factor analysis 

method 

Account for air mass 

transport over 

hundreds to 

thousands of 

kilometers and on 

the order of several 

days. 

Resolve the spatial 

distribution of source 

strength 

(qualitatively). 

Weaknesses 

Need to generate 

and analyze the back 

trajectory data. 

Need to correct for 

the central tendency 

(residence time 

always increases 

toward the receptor 

site regardless of 

source contribution). 

Uncertainty in back 

trajectory calculation 

increases with its 

length in time. 

Source contribution 

depends on not only 

trajectory residence 

time but also 

entrainment 

efficiency, dispersion, 

and deposition. 

Difficult to resolve 

the location of more 

localized sources. 

December 2009 A-283 
Receptor Model Description Strengths and 

Weaknesses 

SQTBA 117, 143

SQTBA requires the calculation 

of air parcel back trajectory, 

which is often accomplished 

using the HY-SPLIT 

model.115,139 HY-SPLIT version 

4.5 is available at 

http://www.arl.noaa.gov/-

ready/hysplit4.html. 

Principle 

SQTBA is another type of ensemble air parcel trajectory analysis. The concept of SQTBA is to 

estimate the “transport field” for each trajectory ignoring the effects of chemical reactions and 

deposition. Back trajectories are first calculated for each sample at the receptor site, and a study 

domain containing the receptor site is divided into an array of grid cells. SQTBA assumes that the 

transition probability that an air parcel at (x’,y’,t’), where x’ and y’ are spatial coordinates and t’ 

means time, will reach a receptor site at (x,y,t) is approximately normally distributed along the 

trajectory with a standard deviation that increases linearly with time upwind144,145, thus: 

 

Equation A-20 

where (X,Y) is the coordinate of the grid center, a is the dispersion speed, and x’(t’) and x’ (t’) 

represent the trajectory. The probability field, Q, for a given trajectory is then integrated over the 

upwind period, τ, to produce a two-dimensional “natural” (nonweighted) transport field: 

 

Equation A-21 

After the transport field for each trajectory is established, they are weighted by the corresponding 

pollutant concentration or source contribution at the receptor site and summed to yield the overall 

SQTBA field.117 

Data Needs 

SQTBA requires the time series of pollutant concentration or source contribution at the receptor 

site, and back trajectories initiated over the site during the sampling period. Trajectories should be 

calculated with 1to 3-h segment to reduce the uncertainty from interpolation (if needed). 

Output 

SQTBA put more weight on trajectories associated higher pollutant concentration or source 

contribution and therefore the resulting field may imply the major transport path. 

Strengths 

Imply the location of 

sources or factors 

relative to the 

sampling site. 

Account for air mass 

transport over 

hundreds to 

thousands of 

kilometers and on 

the order of several 

days. 

Resolve the spatial 

distribution of source 

strength 

(qualitatively). 

Weaknesses 

Need to generate 

and analyze the back 

trajectory data. 

Need to correct for 

the central tendency 

(residence time 

always increases 

toward the receptor 

site regardless of 

source contribution). 

Need to estimate 

dispersion velocity. 

Involve complicated 

calculations. 

Physical meaning of 

the SQTBA field is 

unclear. 

Difficult to resolve 

the location of more 

localized sources. 

December 2009 A-284 
Receptor Model Description Strengths and 

Weaknesses 

RTWC 146

RTWC requires the calculation 

of air parcel back trajectory, 

which is often accomplished 

using the HY-SPLIT 

model.115,139 HY-SPLIT version 

4.5 is available at 

http://www.arl.noaa.gov/ 

ready/hysplit4.html 

Principle 

As an ensemble air parcel trajectory analysis, RTWC requires back trajectories calculated for each 

sample at the receptor site, and a study domain containing the receptor site divided into an array of 

grid cells. RTWC assumes that no major pollutant sources are located along “clean” (associated 

with low pollutant concentrations) trajectories and that “polluted” trajectories picked up emissions 

along their paths. In practice, RTWC distributes pollutant concentrations at the receptor to upwind 

grid cells along the back trajectories according to the trajectory residence times in those cells.117,146 

 

Equation A-22 

where Sk is the pollutant concentration or source contribution determined upon the arrival of 

trajectory k and Si,k is the redistributed pollutant concentration or source contribution for cell i 

upwind. 

RTWC is known for the problem of “tailing effect,” i.e., spurious source areas can be identified 

when cells are crossed by a very small number of trajectories. Although some corrections were 

proposed147 these approaches are purely empirical. 

Strengths 

Imply the location of 

sources or factors 

relative to the 

sampling site. 

Account for air mass 

transport over 

hundreds to 

thousands of 

kilometers and on 

the order of several 

days. 

Resolve the spatial 

distribution of source 

strength 

(qualitatively). 

Weaknesses 

Need to generate 

and analyze the back 

trajectory data. 

Need to correct for 

the central tendency 

and tailing effect. 

The amount of 

emission entrainment 

should not be 

proportional to the 

residence time of 

trajectories (so there 

is no linear 

relationship between 

RTWC field and 

source strength). 

Physical meaning of 

the RTWC field is 

unclear. 

Difficult to resolve 

the location of more 

localized sources. 

113 (Pekney et al., 2006, 086115) 117 (Zhou et al., 2004, 157190) 134 (Ashbaugh, 1983, 156229) 135 (Ashbaugh et al., 1984, 045148) 136 (Henry et al., 2002, 136097) 137 (Yu et al., 2004, 101779) 138 (Parekh and Husain, 1981, 156840) 140 (Hopke et al., 1995, 156566) 143 (Keeler and Samson, 1989, 156633) 144 (Samson, 1978, 188974) 145 (Samson, 1980, 073010) 146 (Stohl, 1996, 157014) 147 (Cheng et al., 1993, 052294) 

Source: (Watson et al., 2008, 157128) 

December 2009 A-285 
A.3.2. Source Profiles 

Table A-55. Source Profiles: Part I 

Motor Vehicle Exhaust - 

Gasoline Coal Combustion Highway Road Dust Unpaved Road Dust Refinery Element Symbol 

Weight % Uncertainty Weight % Uncertainty Weight % Uncertainty Weight % Uncertainty Weight % Uncertainty

Aluminum Al 0.1 N/A 5.968 0.5247 5.729 0.4058 7.4822 0.9315 8.4853 2.3478 

Antimony Sb 0.01 N/A 0 0.0625 0 0.0335 0 0.1601 0 0.0285 

Arsenic As 0 0.0164 0 0.0123 0 0.0226 0 0.0045 

Barium Ba 0.01 N/A 1.3315 1.0801 0.1377 0.1027 0 0.5473 0 0.0979 

Cadmium Cd 0 0.0341 0 0.019 0 0.0881 0 0.0155 

Calcium Ca 0.42 N/A 3.4536 1.0411 2.5657 0.1388 2.163 1.0444 0.1236 0.056 

Chloride ion Cl- 0.39 N/A 

Chromium Cr 0.01 N/A 0.0176 0.0041 0.0271 0.0023 0.0312 0.0161 0.0443 0.0127 

Cobalt Co 0 0.0432 0 0.0668 0 0.0869 0 0.0218 

Copper Cu 0.02 N/A 0.0179 0.0112 0.0219 0.0101 0.0474 0.0307 0.0299 0.0082 

Total carbon TC 4.2763 4.2579 14.3927 2.3449 4.2671 3.7193 0 1.6175 

Gallium Ga 0.014 0.014 0 0.005 0 0.0233 0 0.0059 

Gold Au 

Indium In 0 N/A 0 0.0404 0 0.022 0 0.1041 0 0.0183 

Iron Fe 1.27 N/A 2.916 0.3827 4.5713 0.2661 5.5128 2.1152 1.4708 0.2216 

Lanthanum La 0 N/A 0 0.2462 0 0.1341 0 0.6521 0 0.1146 

Lead Pb 0.08 N/A 0.068 0.0336 0.067 0.0074 0.0288 0.0284 0.0097 0.0063 

Magnesium Mg 0.14 N/A 

Manganese Mn 0.01 N/A 0.0284 0.0139 0.087 0.009 0.1372 0.0509 0.016 0.002 

Mercury Hg 0 N/A 0 0.0154 0 0.0083 0 0.0383 0 0.0073 

Molybdenum Mo 0 0.0134 0 0.0071 0 0.0331 0.0079 0.0088 

Nickel Ni 0.01 N/A 0.0072 0.0019 0.0081 0.0015 0.0091 0.0057 0.04 0.0065 

Nitrate NO3 ̄ 0.06 N/A 0 0.2116 0 0.094 0 0.6371 0 0.0772 

Organic 

carbon OC 59.37 N/A 0 2.9263 12.7127 2.1296 4.2671 2.2637 0 1.5288 

Palladium Pd 0 0.0263 0 0.0151 0 0.0701 0 0.0127 

Phosphorus P 0.27 N/A 0.9372 0.6322 0 0.0324 0.1603 0.044 0.0689 0.0144 

Potassium K 0.01 N/A 0.4644 0.0602 2.7161 0.3069 2.8299 0.4949 0.0825 0.0234 

Rubidium Rb 0.0053 0.0043 0.0184 0.0023 0.0184 0.0093 0 0.002 

Selenium Se 0.0406 0.0407 0 0.0024 0 0.0108 0 0.0021 

Silicon Si 1.61 N/A 9.0112 0.5675 17.596 1.4183 24.2969 4.0089 17.9733 5.1834 

Silver Ag 0 0.0312 0 0.0175 0 0.083 0 0.0151 

Sodium Na 0.01 N/A 

Strontium Sr 0.1964 0.0686 0.0395 0.0078 0.0313 0.0112 0.0094 0.0031 

Sulfate SO4 ̄ 10.1716 8.9405 1.1604 0.2003 0.8688 1.3788 2.3243 3.4523 

Sulfur S 0.37 N/A 2.948 2.729 0.598 0.0509 0.2808 0.3884 0.6304 0.9627 

December 2009 A-286 
Motor Vehicle Exhaust - 

Gasoline Coal Combustion Highway Road Dust Unpaved Road Dust Refinery 

Thallium Tl 

Tin Sn 0 0.0527 0 0.0298 0 0.1464 0 0.0254 

Titanium Ti 0.4315 0.0651 0.3612 0.0313 0.5258 0.1289 0.6178 0.0711 

Uranium U 

Vanadium V 0 0.0734 0.0288 0.0074 0 0.0646 0.0432 0.0084 

Yttrium Y 0 0.006 0.0046 0.0012 0 0.0146 0 0.0029 

Zinc Zn 0.49 N/A 0.0797 0.0341 0.0932 0.0256 0.0502 0.021 0.0166 0.003 

Zirconium Zr 0.0247 0.0043 0.0128 0.0025 0.0219 0.0168 0.0166 0.0022 

Ammonium NH4+ 0.34 N/A 0.3476 0.1352 0 0.025 0 0.1317 0.3281 0.5565 

Sodium ion Na+ 

Carbonate CO3

–

 

Organic 

carbon II OC2

Organic 

carbon III OC3

Organic 

carbon IV OC4

EC I EC1 

Chlorine 

atom Cl- 0.0629 0.0221 3.4403 0.5505 0.1519 0.0755 0.0186 0.0074 

EC III EC3 

EC EC 16.44 N/A 4.2763 3.0931 1.68 0.9817 0 2.9512 0 0.5283 

Bromine 

Atom Br 0.0147 0.0154 0.0037 0.0011 0 0.0078 0 0.0017 

Organic 

carbon I OC1

EC II EC2 

Sulfur 

dioxide SO2 7262.6687 7677.5681 

Potassium 

ion K+ 0.1109 0.0571 0.2295 0.1046 0.1263 0.0744 0.0115 0.0059 

Source: USA EPA Speciate database http://www.epa.gov/ttnchie1/software/speciate/index.html

December 2009 A-287 
Part II 

Residential Wood 

Burning Oil Combustion DE Fly Ash Incinerator 

Element Symbol 

Weight % Uncertainty Weight 

% Uncertainty Weight % Uncertainty Weight % Uncertainty Weight % Uncertainty

Aluminum Al 0.0034 0.0103 0 0.05 0 0.01 1.5708 0.4755 1.15 0.83 

Antimony Sb 0.0002 0.0108 0 0.01 0 0.01 0.007 0.0218 0.01 0.15 

Arsenic As 0.0003 0.0016 0.02 0 0 0 0.001 0.0023 0 0.04 

Barium Ba 0.0093 0.0369 0 0.03 0.01 0.04 0.0303 0.0655 0.14 0.55 

Cadmium Cd 0.0013 0.0058 0 0.01 0 0.01 0 0.0154 0.01 0.08 

Calcium Ca 0.0664 0.0165 0 0.04 0.01 0.01 10.1398 1.7825 2.37 0.62 

Chloride ion Cl- 0.0028 0.0004 17.5498 1.5419 

Chromium Cr 0.0003 0.0012 0.01 0.01 0 0 0.0054 0.001 0.02 0.02 

Cobalt Co 0.0005 0.0005 0.05 0.01 0 0 0.0015 0.0128 0 0.03 

Copper Cu 0.0002 0.0007 0.01 0.01 0 0 0.017 0.0013 0.08 0.1 

Total carbon TC 70.6416 7.1435 3.55 1.0855 98.94 17.859 1.4329 0.2009 55.79 27.5948 

Gallium Ga 0 0.0016 0.01 0 0 0 0.0013 0.0018 0 0.02 

Gold Au 0.0008 0.0033 

Indium In 0.0021 0.0069 0 0.01 0 0.01 0 0.0164 0.01 0.1 

Iron Fe 0.0038 0.0017 0.68 0.1 0 0 0.8306 0.059 1.72 0.31 

Lanthanum La 0.0086 0.0431 0 0.04 0.02 0.05 0.0046 0.0868 8.43 61.15 

Lead Pb 0.0031 0.0018 0 0 0 0 0.0031 0.0031 14.56 11.69 

Magnesium Mg 0.4455 0.0465 

Manganese Mn 0.003 0.0013 0 0 0 0 0.0426 0.0033 0.04 0.01 

Mercury Hg 0.0004 0.0027 0 0 0 0 0.0008 0.0025 27.63 47.27 

Molybdenum Mo 0 0.0024 0 0 0 0 0.0041 0.001 0.01 0.04 

Nickel Ni 0.0002 0.0005 2.36 0.23 0 0 0.0028 0.0004 0.01 0 

Nitrate NO3

– 0.2025 0.0156 0 0 0.06 0.01 0 0.2192 5.5 4.55 

Organic carbon OC 49.4961 5.481 1.71 0.56 90.8 14.79 1.4329 0.1592 37.21 18.03 

Palladium Pd 0.0006 0.0047 0 0 0 0 0 0.0126 0.02 0.07 

Phosphorus P 0 0.0051 0 0.65 0.01 0.02 0.5808 0.2447 0.05 0.16 

Potassium K 0.6346 0.1008 0 0 0 0 24.4341 5.0076 1.28 0.86 

Rubidium Rb 0.0007 0.0007 0 0 0 0 0.0351 0.0026 0 0.02 

Selenium Se 0.0001 0.0008 0.03 0 0 0 0.0018 0.0003 0.01 0.01 

Silicon Si 0.0443 0.0167 0 0.09 0.01 0.01 4.0201 1.2886 4.42 1.82 

Silver Ag 0.0023 0.0054 0 0 0 0.01 0 0.0143 0.02 0.08 

Sodium Na 2.8137 0.2174 

Strontium Sr 0.0006 0.0009 0 0 0 0 0.0406 0.0029 0.02 0.01 

Sulfate SO4

2– 0.4553 0.0359 25.29 5.62 0.53 0.07 8.0717 0.6409 10.46 2.6 

Sulfur S 0.1533 0.0173 16.48 1.62 0.59 0.21 2.6349 0.1873 3.16 0.63 

Thallium Tl 0.0011 0.0025 

Tin Sn 0.0006 0.0092 0 0.01 0 0.01 0.0067 0.0198 0.04 0.14 

Titanium Ti 0.001 0.012 0.01 0.01 0 0.01 0.058 0.0093 0.11 0.17 

Uranium U 0.0021 0.0052 

December 2009 A-288 
Residential Wood 

Burning Oil Combustion DE Fly Ash Incinerator 

Vanadium V 0.0007 0.005 0.4 0.04 0 0.01 0.0038 0.011 0.01 0.07 

Yttrium Y 0.0001 0.0011 0 0 0 0 0.0013 0.0021 0 0.02 

Zinc Zn 0.0762 0.0054 0.01 0 0.02 0.02 0.031 0.0023 0.57 0.39 

Zirconium Zr 0 0.0014 0 0 0 0 0.0039 0.0008 0 0.02 

Ammonium NH4+ 0.1132 0.014 0.84 0.24 0.03 0.01 0.0234 0.022 7.41 7.81 

Sodium ion Na+ 0.11 0.02 0 0.01 4.7518 0.3438 1.81 2.63 

Carbonate CO3

– 0 0.0214 0.2577 0.4463 

Organic carbon 

II OC2 7.513 0.6675 

Organic carbon 

III OC3 8.9627 1.4665 

Organic carbon 

IV OC4 2.7683 1.1919 

EC I EC1 20.342 2.9324 

Chlorine atom Cl– 0.2874 0.0404 0.05 0.01 0.03 0.01 27.5797 8.1193 6.35 10.46 

EC III EC3 2.2878 0.4252 

EC EC 21.1455 4.5813 1.84 0.93 8.14 10.01 0 0.1227 18.58 20.89 

Bromine Atom Br 0.0029 0.0011 0 0 0 0 0.0441 0.0032 0.19 0.3 

Organic carbon I OC1 25.1452 4.6648 

EC II EC2 2.9362 1.2422 

Sulfur dioxide SO2

Potassium ion K+ 0.5208 0.0795 0.01 0.01 0 0.01 14.5473 1.3393 1.01 0.42 

Source: U.S. EPA SPECIATE database http://www.epa.gov/ttnchie1/software/speciate/index.html

A.3.3. Receptor Model Results 

Table A-56. PM2.5 receptor model results (μg/m3) 

Sampling Site Measured PM2.5

Concentration 

Vegetative 

Burning 

Road 

Dust, 

Soil 

(NH4)2SO4 NH4NO3 Tailpipe Brake 

Wear 

Albany, NY 2000-2001 20.9 5.5 1.9 2.4 4.6 2.9 0.0 

Birmingham, AL, 2000-2001 16.2 3.3 1.4 3.7 2.4 5.7 0.0 

Houston, TX, 2000-2001 12.4 3.1 2.6 1.6 2.2 2.6 0.0 

Long Beach, CA, 2000-2001 30.0 4.6 1.3 2.1 16.3 4.1 0.4 

Las Vegas, NV, 2000-2001 2.5 1.0 2.0 0.5 0.3 1.5 0.0 

El Paso, TX, 2000-2001 5.5 0.7 2.8 0.7 0.3 2.0 0.3 

Westbury, NY, 2000-2001 11.5 1.7 0.7 5.2 2.2 5.3 0.0 

Source: Abu-Allaban et al. (2007, 098575) 

December 2009 A-289 
Table A-57. PM10 receptor model results (mass percent) 

Sampling Site Wood 

Smoke Diesel Gasoline Vehicles 

Natural Gas 

Combustion 

Vegetative 

Detritus 

Tire Wear 

Debris 

Apline, CA, 1994-1995 15.00 33.19 46.46 5.31 

Apline, CA, 1995 9.92 58.78 11.47 19.63 

Apline, CA, 1995 10.97 65.64 10.81 12.66 

Atascadero, CA, 1994-1995 44.22 22.16 26.44 6.91 

Atascadero, CA, 1995 21.36 38.99 12.41 17.89 9.43 

Atascadero, CA, 1995 73.45 18.11 3.14 5.31 

Lake Arrowhead, CA, 1994-1995 6.86 46.55 33.92 2.73 9.85 

Lake Arrowhead, CA, 1995 4.85 65.20 7.40 4.95 17.65 

Lake Arrowhead, CA, 1995 9.91 38.90 46.70 0.79 3.66 

Lake Elsinore, CA, 1994-1995 12.72 44.01 18.61 4.21 20.42 

Lake Elsinore, CA, 1995 17.13 74.72 0.26 7.81 

Lake Elsinore, CA, 19952 6.84 38.48 10.85 0.21 15.55 28.01 

Lancaster, CA, 1994-1995 22.49 43.14 20.56 0.45 3.73 9.78 

Lancaster, CA, 1995 3.69 46.18 12.66 0.20 8.21 29.17 

Lancaster, CA, 1995 34.89 37.30 7.33 0.61 7.78 11.93 

Lompoc, CA, 1994-1995 18.16 49.65 5.89 26.38 

Lompoc, CA, 1995 13.09 51.27 14.73 20.73 

Lompoc, CA, 1995 79.42 10.19 10.87 

Long Beach, CA, 1994-1995 10.12 43.24 16.49 0.13 3.97 26.00 

Long Beach, CA, 1995 2.38 70.25 5.47 0.86 6.79 14.11 

Long Beach, CA, 1995 14.32 56.80 6.15 0.72 5.34 16.61 

Mira Loma, CA, 1994-1995 4.68 48.87 18.10 8.82 19.52 

Mira Loma, CA, 1995 5.20 53.72 6.65 18.79 15.71 

Mira Loma, CA, 1995 27.97 41.88 8.87 11.50 9.85 

Riverside, CA, 1994-1995 14.14 46.67 12.03 6.83 20.31 

Riverside, CA, 1995 6.20 52.15 7.93 0.16 14.54 19.06 

Riverside, CA, 1995 25.28 47.65 6.91 20.17 

San Dimas, CA, 1995 7.62 71.35 4.87 0.15 8.35 

San Dimas, CA, 1995 22.01 61.34 4.48 0.23 3.70 7.85 

Santa Maria, CA, 1994-1995 18.66 23.99 22.03 5.58 8.15 

Santa Maria, CA, 1995 12.94 52.57 11.87 0.27 9.63 12.78 

Santa Maria, CA, 1995 12.24 48.13 10.79 0.47 18.04 15.05 

Upland, CA, 1994-1995 20.33 46.39 14.08 4.49 14.70 

Upland, CA, 1995 7.33 68.69 3.50 0.17 9.19 11.25 

Upland, CA, 1995 28.10 46.52 4.90 0.33 10.30 9.81 

Source: Manchester-Neesvig et al. (2003, 098102) 

December 2009 A-290 
A.4. Exposure Assessment 

A.4.1. Exposure Assessment Study Findings 

Table A-58. Exposure Assessment Study Summaries 

Adar et al. (2007, 098635) 

 Study Design Cohort 

Period March 2002-June 2002 

 Location St. Louis, Missouri 

 Population Senior citizens exposed to traffic-related PM 

 Age Groups 60 

 Indoor Source NR 

 Personal Method Samples of FeNO were collected between 8:00 and 9:00 a.m. on the mornings before and after each trip. In the hours 

surrounding these samples, group-level measurements of particle concentrations also were collected using several continuous 

instruments installed on two portable carts. These carts were first positioned in a central location inside the participants’ living 

facilities 24-h before each trip. The carts remained at the facilities until it was time for the trips, at which point they followed the 

participants from the health testing room, onto the bus, to the group activity, and to lunch. After the trip home aboard the bus, 

the carts were returned to the central location in the living facility where they remained until the conclusion of the health testing 

on the following morning. Continuous measurements of ambient particles and gases also were collected from a central 

monitoring station in East St. Louis, Illinois. Two portable carts containing continuous air pollution monitors were used to 

measure group-level micro-environmental exposures to traffic related pollutants, including PM2.5, BC, and size-specific particle 

counts. PM2.5 concentrations were measured continuously using a DustTrak aerosol monitor model 8520 with a Nafion diffusion 

dryer. Integrated samples of PM2.5 mass also were collected using a Harvard Impactor for daily calibration of the trip and 

facility. 

Periods Continuous BC concentrations were measured using a portable aethalometer with a 2.5-μm impaction inlet. Particle counts 

were measured using a model CI500 optical particle counter with a modified flow rate of 0.1 cubic feet per minute. 

 Personal Size NR 

 Microenvironment Size PM2.5 

 Ambient Size PM2.5, PM10

 Component(s) BC, pollen and mold also assessed 

 Primary Findings PM2.5 exposures resulted in increased levels of FeNO in elderly adults, suggestive of increased airway inflammation. These 

associations were best assessed by microenvironmental exposure measurements during periods of high personal particle 

exposures. In pre-trip samples, both microenvironmental and ambient exposures to PM2.5 were positively associated with 

FeNO. For example, an interquartile increase of 4 μg/m3

 in the daily microenvironmental PM2.5 concentration was associated 

with a 13% [95% CI: 2-24) increase in FeNO. After the trips, however, FeNO concentrations were associated predominantly 

with microenvironmental exposures, with significant associations for concentrations measured throughout the whole day. 

Associations with exposures during the trip also were strong and statistically significant with a 24% (95% CI: 15-34) increase in 

FeNO predicted per interquartile increase of 9 μg/m3

 in PM2.5. Although pre-trip findings were generally robust and the post-trip 

findings were generally robust, the post-trip findings were sensitive to several influential days. 

Adgate et al. (2002, 030676) 

Study Design Comparison of outdoor, indoor and personal PM2.5 in three communities. 

 Period April-June, June-August, September-November, 1999 

Location Battle Creek, East St. Paul, and Phillips, Minnesota, constituting the Minneapolis-St. Paul metropolitan area. 

Population Adults in urban areas 

Age Groups Mean age 42 ± 10, range 24-64 yr 

 Indoor Source No 

 Personal Method Inertial impactors (PEM) in a foam-insulated bag with shoulder strap with the inlet mounted on the front. 

 Personal Size PM2.5 

 Microenvironment Size PM2.5 

 Ambient Size PM2.5 

 Component(s) NR 

 Primary Findings The relative level of concentrations report in other studies was duplicated. Outdoor < indoor < personal. On days with paired 

samples (n = 29), outdoor concentrations were significantly lower (mean difference 2.9 μg/m3

, p = 0.026) than outdoor at home. 

December 2009 A-291 
Adgate et al. (2007, 156196) 

 Study Design NR 

Period 1999-; April 26-June 20, June 21-August 11, September 23-November 21 

Location Minneapolis-St. Paul metropolitan area 

 Population NR 

 Indoor Source Cigarette smoke, resuspension of house dust from carpets, furniture and clothes, and emissions from stoves and kerosene 

heaters (Leaderer et al., 1993; Ferro et al., 2004). 

 Personal Method Personal monitoring was conducted for two consecutive days, and was conducted so that the two 24-h averages matched 

indoor (I) and personal (P) measurements were collected in concert with outdoor (O) samples in each community. Gravimetric 

concentrations for P and I were collected using inertial impactor environmental monitoring inlets and air sampling pumps. To 

obtain I measurements, monitors were placed inside each residence in a room where the participants reported spending the 

most waking hours. P measurements were obtained by carrying personal pumps in small bags. O samples were collected near 

the approximate geographic center of each neighborhood and monitors ran from midnight to midnight for two consecutive 24-h 

periods, followed by a day to change filters. Gravimetric O PM2.5 concentrations were obtained using a federal reference 

method sampler. 

 Personal Size PM2.5

 Microenvironment Size PM2.5

 Ambient Size PM2.5

 Component(s) Ag, Al, Ca, Cd, Co, Cr, Cs, Cu, Fe, K, La, Mg, Mn, Na, Ni, Pb, S, Sb, Sc, Ti, Tl, V, Zn 

 Primary Findings The relationships among P, I, and O concentrations varied across trace elements (TE). Unadjusted mixed-model results 

demonstrated that O monitors are more likely to underestimate than overestimate exposure to many of the TEs that are 

suspected to play a role in the causation of air pollution related health effects. These data also support the conclusion that TE 

exposures are more likely to be underestimated in a lower income and centrally located community than in a comparatively 

higher income community. Within the limits of statistical power for this sample size, the adjusted models indicated clear 

seasonal and community related effects that should be incorporated in long-term exposure estimates for this population. 

Adgate et al. (2003, 040341) 

Study Design Time-series epidemiologic study 

Period April-November 1999; spring: 26 April-20 June; summer: 21 June-11 August; fall: 23 September-21 November 

Location Minneapolis-St. Paul, Minnesota 

Population Healthy non-smoking results 

Age Groups 24-64 yr (mean age 42 ± 10) 

 Indoor Source NR 

 Personal Method Personal and indoor gravimetric PM concentrations were collected using PM2.5 inertial impactor environmental monitoring inlets 

and air sampling pumps. Monitors were placed inside each participant’s residence in the room where he/she reported spending 

the majority of their waking hours to obtain I measurements. Participants also carried personal pumps in small bags to obtain P

measurements. Start times for indoor and personal monitors were always within a few minutes of each other. Gravimetric O 

and central site PM2.5 concentrations were obtained using a federal reference method sampler and EPA site requirements for 

ambient sampling. Gravometric samples were collected near the approximate geographic center of each neighborhood, and 

monitors ran from midnight to midnight for 2 consecutive 24-h periods, followed by a day to change filters. 

 Personal Size NR 

 Microenvironment Size NR 

 Ambient Size NR 

 Component(s) NR 

 Primary Findings PM2.5 concentrations were higher than I concentrations, which were higher than O concentrations. In healthy non-smoking 

adults, moderate median for correlation between P and I; modest median for correlation between I and O; and minimal median 

correlation between P and O longitudinal were observed for PM2.5 measurements. A sensitivity analysis indicated that 

correlations did not increase if the days with exposures to environmental tobacco smoke or occupational exposures were 

excluded. In the sample population neither P nor I monitors provided a highly correlated estimate of exposure to O PM2.5 over 

time. These results suggest that the studies showing relatively strong longitudinal correlation coefficients between P and O 

PM2.5 for individuals sensitive to air pollution health effects do not necessarily predict exposure to PM2.5 in the general 

population. 

Allen et al. (2003, 053578) 

Study Design Use of continuous light scattering data to separate indoor PM into indoor- and outdoor-generated components to enhance 

knowledge of the outdoor contribution to total indoor and personal PM exposures. 

Period November 1999-May 2001 

 Location Seattle, WA 

Population Elderly people and children spending most of their time (up to 70%) indoors. The study included healthy elderly subjects, elderly 

with COPD and coronary heart disease (CHD), and child subjects with asthma. 

Age Groups Age n; 0-29 25; 30-59 36; >60 22; unknown 2 

 Indoor Source Suggested (not identified) 

 Personal Method NR. Indoor and outdoor sampling conducted 

 Personal Size NR 

 Microenvironment Size PM2.5 

December 2009 A-292 
 Ambient Size PM2.5 

 Component(s) S 

 Primary Findings A recursive mass balance model can be successfully used to attribute indoor PM to its outdoor and indoor components and to 

estimate an avg Penetration, air exchange rate, deposition rate, and NH4+ for each residence. 

Allen et al. (2007, 154226) 

 Period Heating season October-February; Non-heating season March-September 

 Location Seattle, WA 

 Population NR 

 Age Groups NR 

 Indoor Source NR 

 Personal Method Indoor and outdoor PM2.5 was measured using a 10-l/min single-stage Harvard Impactor (HI) with 37-mm Teflon filters. The 

relationship between particle mass concentration and light scattering coefficient (bsp) was also measured on a continuous 

basis indoors and outdoors using nephelometers (model 902 and 903). 

 Personal Size NR 

 Microenvironment Size PM2.5 

 Ambient Size PM2.5 

 Component(s) S (measured by XRF) 

 Primary Findings The authors showed that RM can reliably estimate Finf. Simulation results suggest that the RM Finf estimates are minimally 

impacted by measurement error. In addition, the average light scattering response per unit mass concentration was greater 

indoors than outdoors. Results show that the RM method is unable to provide satisfactory estimates of the individual 

components of Finf. Individual homes vary in their infiltration efficiencies, thereby contributing to exposure misclassification in 

epidemiologic studies that assign exposures using ambient monitoring data. This variation across homes indicates the need for 

home-specific estimation methods, such as RM or S, instead of techniques that give average estimates of infiltration across 

homes. 

Annesi-Maesano et al. (2007, 093180) 

Study Design Population based 

Period March 1999 to October 2000 

Location Bordeaux, France; Clermont-Ferrand, France; Créteil, France; Marseille, France; Strasbourg, France; Reims, France 

 Population School children 

Age Groups 10.4 ± 0.7 yr 

 Indoor Source NR 

 Personal Method PM2.5 was monitored simultaneously in both schoolyards (proximity level) and fixed-site monitoring stations (city level) using 

4L/min battery operated pumps attached to polyethylene filter sampling cartridges. 

 Personal Size NR 

 Microenvironment Size NR 

 Ambient Size PM2.5 

 Component(s) NR 

 Primary Findings Results show an increased risk for EIB and flexural dermatitis at the period of the survey, past year atopic asthma and SPT 

positivity to indoor allergens in children exposed to high levels of traffic-related air pollution (PM2.5 concentrations exceeding 

10 μg/m3

). Population based findings are also consistent with experimental data that have demonstrated that inhalation of 

traffic-related air pollutants either individually or in combination, can enhance the immune responses and airway response to 

inhaled allergens, such as pollens or house dust mites, in atopic subjects. 

Balasubramanian and Lee (2007, 156248) 

Study Design Case study of 3 rooms of 1 flat on the 8th floor, and “outside the home.” 

Period May 12-23, 2004 

 Location Singapore 

Population Residents of an urban area in a densely populated country. 

 Age Groups NR 

 Indoor Source Time-activity logs identified tobacco smoking, cooking, household cleaning and general resident movements. 

 Personal Method NR 

 Personal Size NR 

 Microenvironment Size PM2.5 

 Ambient Size PM2.5 

 Primary Findings I/O suggest that chemicals such as Cl-

, Na+, Al, Co, Cu, Fe, Mn, Ti, V, Zn, and EC were derived from the migration of outdoor 

particles (I/O <1 or ~1). 

December 2009 A-293 
Barn et al. (2008, 156252) 

Study Design Measure indoor Finf of PM2.5 from forest fires/wood smoke, effectiveness of high-efficiency particulate air (HEPA) filter air 

cleaners in reducing indoor PM2.5, and to analyze the home determinants of Finf and air cleaner effectiveness (ACE). 

Period 2004-2005 (summer 2004 and 2005, winter 2004) 

Location British Columbia, Canada 

Population Homes affected by either forest fire smoke or residential wood smoke 

 Age Groups NR 

 Indoor Source NR 

 Personal Method Personal Data RAM for ambient air sampling 

 Personal Size Indoor home PM2.5 

 Microenvironment Size NR 

Ambient Size Outdoor home PM2.5 

 Component(s) NR 

 Primary Findings Use of HEPA filter air cleaners can dramatically reduce indoor PM2.5 concentrations. Number of windows and season predict Finf

(p< 0.001). 

Baxter et al. (2007, 092726) 

Study Design Part of a prospective birth cohort study performed by the Asthma Coalition for Community, Environment, and Social Stress 

(ACCESS) 

Period 2003-2005. Non-heating season: May to October; Heating season: December to March 

 Location Boston (urban) 

Population Lower socio-economic status (SES) households 

 Age Groups NR 

 Indoor Source NR 

 Personal Method PM2.5 samples were collected with Harvard personal environmental monitors (PEM). NO concentrations were measured using 

Yanagisawa passive filter badges. 

 Personal Size NR 

 Microenvironment Size PM2.5 

 Ambient Size PM2.5 

 Component(s) EC 

 Primary Findings The authors’ regression models indicated that PM2.5 was influenced less by local traffic but had significant indoor sources, while 

EC was associated with local traffic and NO2 was associated with both traffic and indoor sources. However, local traffic was 

found to be a larger contributor to indoor NO2 where traffic density is high and windows are opened, whereas indoor sources 

are a larger contributor when traffic density is low or windows are closed. Similarly, traffic contributed up to 0.2 μg/m3

 to indoor 

EC for homes with open windows, with an insignificant contribution for homes where windows were closed.; Comparing models 

based on p-values and using a Bayesian approach yielded similar results, with traffic density volume within a 50 m buffer of a 

home and distance from a designated truck route as important contributors to indoor levels of NO2 and EC, respectively. 

However, results from the Bayesian approach also suggested a high degree of uncertainty in selecting the best model. The 

authors concluded that by utilizing public databases and focused questionnaire data they could identify important predictors of

indoor concentrations for multiple air pollutants in a high-risk population. 

Baxter et al. (2007, 092725) 

Study Design Simultaneous indoor and outdoor samples taken in 43 low SES homes in heating and non-heating seasons. Homes were 

selected from a prospective birth cohort study of asthma etiology (n = 25). Non-cohort homes were in similar neighborhoods 

(n = 18). 

 Period 2003-2005 

 Location Boston, Massachusetts 

Population Lower SES populations in urban areas 

 Indoor Source Home type, year built, tobacco smoke, opening windows, time spent cooking, use of candles or air freshener, cleaning activities, 

air conditioner use. 

 Personal Method NR 

 Personal Size NR 

 Microenvironment Size PM2.5 

 Ambient Size NR 

 Component(s) EC (m-1 x 10-5); Ca (ng/m3

); Fe (ng/m3); K (ng/m3); Si (ng/m3); Na (ng/m3); CI (ng/m3); Zn (ng/m3); S (ng/m3); V (ng/m3) 

 Copollutant(s) NO2

 Primary Findings The effect of indoor sources may be more pronounced in high-density multi-unit dwellings. Cooking times, gas stoves, occupant 

density and humidifiers contributed to indoor pollutants. 

December 2009 A-294 
BéruBé et al. (2004, 007894) 

Study Design 6 homes in Wales and Cornwall were monitored four times per year, inside samples in the living areas and outside the home. 

Period NR but < 2003 

Location Wales and Cornwall, UK 

Population Urban, suburban, and rural homes 

 Indoor Source ETS, pets, cleaning, traffic load 

 Personal Method NR 

 Personal Size NR 

 Microenvironment Size PM10 

 Ambient Size NR 

 Component(s) NR 

 Primary Findings There are greater masses of PM10 indoors, and that the composition of the indoor PM10 is controlled by outdoor sources and to 

a lesser extent by indoor anthropogenic activities, except in the presence of tobacco smokers. The indoor and outdoor PM10

collected was characterized as being a heterogeneous mixture of particles (soot, fibers, sea salt, smelter, gypsum, pollen and 

fungal spores). 

Branis et al. (2005, 156290) 

Study Design Human exposure assessment in a university lecture hall 

Period Oct. 8, 2001-Nov. 11, 2001 

Location Prague, Czech Republic 

 Population University students 

 Age Groups NR 

 Indoor Source Presence of people identified as a source of coarse particles; outdoor air identified as a source of indoor fine particles (PM1.0

and PM2.5) 

 Personal Method Harvard impactors (HI) with membrane Teflon filters 

 Personal Size PM1, PM2.5, PM10

 Microenvironment Size PM1, PM2.5, PM10

 Ambient Size PM10

 Component(s) NR 

 Primary Findings Presence of people is an important source of coarse particles indoors; Outdoor air may be an important source of fine indoor 

particles. 

Brunekreef et al. (2005, 090486) 

Study Design Exposure assessment 

Period Winter and spring 1998-1999 

Location Amsterdam and Helsinki 

 Population Elderly 

Age Groups 50-84 yr 

 Indoor Source NR 

 Personal Method Amsterdam Gillian with made to fit bags with belt with GK2.05 cyclone samplers 4L/min; Helsinki BGI with shoulder strap or 

backpack with GK2.05 cyclone samplers 4 L/min. 

 Personal Size PM2.5 

 Microenvironment Size PM2.5 

 Ambient Size PM2.5 

 Component(s) SO4

2- 

 Primary Findings In both cities, personal and indoor PM2.5 were highly correlated with outdoor concentrations. 

Chillrud et al. (2004, 054799) 

Study Design Repeated measures on a cohort of high school students in New York City 

Period Summer and winter of 1999 (eight weeks each) 

 Location Manhattan, Bronx, Queens, Brooklyn, NY 

 Population Persons traveling the subway 

Age Groups 14-18 yr 

 Indoor Source No 

 Personal Method Sampling packs carried by subjects 

 Personal Size PM2.5 

 Microenvironment Size PM2.5 (home indoor and home outdoor) 

 Ambient Size PM2.5. Urban fixed-site and upwind fixed site operated for three consecutive 48-h periods each week. 

 Component(s) Elemental Fe, Mn, and Cr are reported in this study out of 28 elements sampled. 

 Primary Findings Personal samples had significantly higher concentration of Fe, Mn, and Cr than home indoor and ambient samples. The ratios 

of Fe (ng/μg of PM2.5) vs Mn (pg/μg PM2.5) showed personal samples to be twice the ratio for crustal material. Similarly for the 

Cr/Mn ratio. The ratios and strong correlations between pairs of elements suggested steel dust as the source. Time-activity 

data suggested subways as a source of the elevated personal metal levels. 

December 2009 A-295 
Conner and Williams (2004, 156364) 

Study Design This is part of the EPA Baltimore PM Study of the Elderly. 

 Period July-August, 1998 

 Location Towson, Maryland 

 Population 65+ adults 

Age Groups 65+ yr 

 Indoor Source Personal sampling devices (PEM) 

 Personal Method PM2.5 

 Personal Size PM2.5 

 Microenvironment Size NR 

 Ambient Size NR 

 Primary Finding(s) A greater variety of particles was observed in the personal samples compared to the fixed-location apartment samples. 

Cortez-Lugo et al. (2008, 156368) 

 Study Design Cohort 

 Period Feb-Nov 2000 

Location Mexico City, Mexico 

Population Ambulatory adults with moderate to severe COPD, active smokers excluded 

 Age Groups Adults 

 Indoor Source carpeting, aerosol sprays used, boiler use and location, animals, mold, tobacco smoking, windows closed 

 Personal Method Personal pumps with 37-mm Teflon filters, flow rate 4 l/min in a bag with shoulder strap. The impactor was near the breathing 

zone 

 Personal Size PM2.5 

 Microenvironment Size PM2.5, PM10

 Ambient Size PM2.5, PM10

 Component(s) NR 

 Primary Findings Indoor PM2.5 concentrations explained 40% of the variability of personal exposure. The best predictors of personal exposure 

were indoor contact with animals (12%), mold (27%), being present during cooking (27%), and aerosol use (17%). 

Crist et al. (2008, 156372) 

Study Design Indoor, outdoor, and personal monitoring 

Period January 1999-August 2000 

 Location Ohio 

Population Fourth & fifth-grade children 

Age Groups 9-11 yr old 

 Indoor Source Filter, portable pump 

 Personal Method Filter, PM2.5 

 Personal Size Indoor school; Filter, PM2.5 

 Microenvironment Size Outdoor school; Filter, PM2.5 

 Ambient Size PM2.5

 Component(s) NR 

 Primary Findings Higher correlation was observed between P and I compared with the correlation between either P and ambient (A) or I and A. 

Delfino et al. (2004, 056897) 

Study Design Panel study with repeated measures 

Period Sep-Oct 1999 or Apr-Jun 2000 

 Location Alpine, California 

 Population Children 

Age Groups 9-17 yr 

 Indoor Source No 

 Personal Method Personal dataRAM (pDR) carried at waist level using a fanny pack, shoulder harness, or vest. 

 Personal Size 0.1-10 μm 

 Microenvironment Size PM10 and PM2.5; measured immediately outside the house and in the living room of the home. 

 Ambient Size PM10

 Copollutant(s) O3 and NO2 measured at central site 

 Primary Findings Percent predicted FEV1 was inversely associated with personal exposure to fine particles. Also with indoor, outdoor and central 

site gravimetric PM2.5, PM10, and with hourly TEOM PM10. 

December 2009 A-296 
Delfino et al. (2006, 090745) 

Study Design Cohort. Measured daily expired NO (FeNO) 

 Period Aug-Dec 2003 

Location Riverside and Whittier, California 

Population Children with asthma exacerbations in previous 12 months, non-smokers, non-smoking households 

Age Groups 9-18 yr 

 Indoor Source No 

 Personal Method Wore a backpack during waking hours for PM2.5, EC and OC, NO2, temperature, and relative humidity. Exhaled air collected in 

Mylar bags to analyze for NO. 

 Personal Size 24-h PM2.5; 1-h max PM2.5; 8-h max PM2.5; 24-h NO2

 Microenvironment Size NR 

Ambient Size 24-h PM2.5; 24-h PM10; 8-h max O3; 8-h max NO2; 24-h NO2; 8-h max CO 

 Component(s) 24-h PM2.5 EC; 24-h PM2.5 OC 

 Primary Findings The strongest positive associations were between FeNO and 2-day average pollutant concentrations. Per IQR increases 1.1 

ppb FeNO/24 μg/m3

 personal PM2.5.; 0.7 ppb FeNO/0.6 μg/m3 personal EC; 1.6 ppb FeNO / 17 ppb personal NO2 Ambient 

PM2.5 and personal and ambient EC were significant only when subjects were taking inhaled corticosteroids. Subjects taking 

both inhaled steroids and antileukotrienes had no significant associations. Distributed lag models showed personal PM2.5 in the 

preceding 5 h was associated with FeNO. 

Diapouli et al. (2007, 156397) 

Study Design Exposure assessment. Sampling of schools, residence, private vehicle 

Period Schools- 11/2003-02/2004 and 10/2004-12/2004.; Residence- 10/2004; Vehicle- 10/204-12/2004 

 Location Athens, Greece 

Population Primary school children 

 Age Groups NR 

 Indoor Source NR 

 Personal Method Handheld portable Condensation Particle Counters (TSI, Model 3007) were used for all sampling locations. Primary schools 

indoor measurements were primarily conducted inside classrooms, at table height. However, at three of the schools, rooms of 

different uses were selected. These included a teachers’ office (where smoking was permitted), a computer day lab (used by 

students only part of the day), and a library and gymnasium (where intense activity took place almost all day long). Outdoor 

measurements took place in the yard of each school. Residence samples were taken in a bedroom at breathing height and on 

the terrace, for indoor and outdoor samples, respectively. In-vehicle samples were taken by placing the CPC 3700 on the 

passenger seat while the vehicle drove along predetermined routes. 

 Personal Size NR 

 Microenvironment Size 0.01-1 μm 

Ambient Size 0.01-1 μm 

 Component(s) NR 

 Primary Findings The results showed that children attending primary schools in the Athens area are exposed to significant PM concentration 

levels, both indoors and outdoors. Vehicular emissions seem to be a major contributor to the measured outdoor concentration 

levels at the studied sites. Indoor PM concentrations appeared to be influenced by both vehicular emissions and indoor 

sources including cleaning activities, smoking, a high number of people in relation to room volume and furniture material (i.e., 

carpet). UFPs concentrations diurnal variation, both outside the schools and the residence, supports the close relation of UFPs

levels with traffic density. Indoor concentrations within schools exhibited variability during the school day only when there were 

significant changes in room occupancy. 24-h variation of indoor concentrations at the residence were well correlated with the 

outdoor concentration (R2

 = 0.89). 

Diapouli et al. (2008, 190893) 

Study Design Indoor, outdoor air monitoring of PM. To determine children exposure in school environment. To evaluate relationship between 

indoor and outdoor levels. 

 Period Athens, Greece 

 Location Primary schools 

 Population NR 

 Indoor Source Indoor PM1, PM2.5, PM10, presence of children and activities of children in classrooms, infiltrated vehicular exhaust 

 Personal Method Harvard PEM, Teflon filters Dust Trak Condensation particle counter 

 Personal Size NR 

 Microenvironment Size PM1, PM2.5, PM10

 Ambient Size PM1, PM2.5, PM10

 Component(s) NO3

-

, SO4

2–

 Primary Findings High levels of PM10 and PM2.5 measured indoors and outdoors. PM10 more variable spatially than PM2.5. I/O ratio for PM10 and 

PM2.5 close to 1 at almost all sites. Ratio of PM1 smaller than 1 in all cases. Vehicular traffic presumed to be the main source of 

PM1. Indoor PM2.5 and PM10 levels dependent on the amount of activity in classroom and outdoor levels. Indoor SO4

2–

concentrations strongly associated with outdoor levels. Result suggests that SO4

2– can be used as a proper surrogate for 

indoor PM of outdoor origin. 

December 2009 A-297 
Ebelt et al. (2005, 056907) 

Study Design Personal exposure assessment related to health outcomes for a sensitive sub-population 

 Period Summer 1998 

Location Vancouver, British Columbia, Canada 

Population 16 persons who had COPD 

Age Groups Mean subject age 74 yr, Range 54 to 86 

 Indoor Source Separated total personal exposure into “ambient” and “non-ambient” based on sulfate results and modeling. 

 Personal Method 24-h integrated filter sample 

 Personal Size PM2.5 

 Microenvironment Size PM2.5, PM10, PM10-2.5

 Ambient Size PM2.5, PM10, PM10-2.5

 Component(s) SO4

2- 

 Primary Findings Ambient exposures and (to a lesser extent) ambient concentrations were associated with health outcomes. Total and 

nonambient particle exposures were not. 

Farmer et al. (2003, 089017) 

Study Design Case control molecular epidemiology studies of carcinogenic environmental pollutants, particularly PAHs 

 Period 12 months 

Location Prague, Czech Republic (2 sites); Košice, Slovak republic; Sofia, Bulgaria 

Population Policeman and busdrivers usually working through busy streets in 8-10 h shifts and a control population. 

Age Groups Variable, range not stated 

 Indoor Source NR 

 Personal Method Personal Monitoring Devices; Blood and Urine Samples; Stationary Versatile Air Pollution Samplers (VAPS) 

 Personal Size PM10

 Microenvironment Size NR 

 Ambient Size PM10; PM2.5 (not reported) 

 Component(s) Extractable organic matter (EOM), B[a]P, c-PAHs 

 Primary Findings EOM per PM10 was at least 2-fold higher in winter than in summer, and c-PAHs over 10-fold higher in winter than in summer. 

Personal exposure to B[a]P and to total c-PAHs in Prague ca. was 2-fold higher in the exposed group compared to the control 

group, in Košice ca. 3-fold higher, and in Sofia ca. 2.5-fold higher. 

Ferro et al. (2004, 055387) 

Study Design Case study, 1 home 

Period Redwood City, California 

 Location NR 

 Population NR 

 Age Groups NR 

 Personal Method Co-located real-time particle counters and integrated filter samplers (Met-One Model 237B) were used to measure personal 

(PEM), indoor (SIM) and outdoor (SAM) PM concentrations. The PEM was attached to a backpack frame and worn by the 

investigator while performing prescribed activities. The SIM was attached to a six foot step-ladder with the intake at breathing 

height. The SAM was located under a two-sided roofed shed in the backyard of the home with the filter samplers supported by 

a metal stand and the real-time particle counters sitting on a table. 

 Personal Size PM5

 Microenvironment Size PM2.5; PM5

 Ambient Size PM2.5; PM5

 Component(s) NR 

 Primary Findings The results of this study indicate that house dust resuspended from a range of human activities increases personal PM 

concentrations and this resuspension effect significantly contributes to the personal cloud. The results of this study also 

suggest that normal human activities that resuspend house dust may contribute significantly to the strong correlations found 

between personal exposure and indoor PM concentrations in previous studies. The PEM/SIM ratios for human activity 

presented in this paper are also in the range of those reported by previous studies. 

December 2009 A-298 
Gadkari and Pervez (2007, 156459) 

Study Design Evaluation of relative source contribution estimates of various routes of personal RPM in different urban residential 

environments. 

Period Summer 2004 (March 15-June 15) 

 Location Chattisgarh, India 

Population All likely. Not specified 

Age Groups 21-61 yr, average age 40 ± 15 yr 

 Indoor Source No 

 Personal Method Personal respirable dust samplers (RDS) with GFF 

 Personal Size RPM 

 Microenvironment Size NR 

 Ambient Size RPM 

 Component(s) Fe, Ca, Mg, Na K, Cd, Hg, Ni, Cr, Zn, As, Pb, Mn and Li 

 Primary Findings Authors concluded that “(1) indoor activities and poor ventilation qualities are responsible for major portion of high level of 

indoor RPM, (2) majority of personal RPM is greatly correlated with residential indoor RPM, (3) time–activity diary of individuals 

has much impact on relationship investigations of their personal RPM with their respective indoor and ambient-outdoor RPM 

levels; as reported in earlier reports and (4) residential indoors, local road-traffic and soil-borne RPMs are the dominating 

routes of personal exposure compared to ambient outdoor RPM levels.” 

Gauvin et al. (2002, 034893) 

Study Design Fine particle exposure assessment for children in French urban environments, part of VESTA study 

Period March 1998-December 2000 

 Location Paris, Grenoble, Toulouse, France 

Population Children aged 8-14 yr 

 Indoor Source ETS from mother, rodents at home. 

 Personal Method SKC pump 4 Lpm with PM2.5 inlet and 37 mm, 2 micron Teflon filter 

 Personal Size PM2.5 

 Microenvironment Size NR 

 Ambient Size PM10

 Component(s) NR 

 Primary Findings The final model explains 36% of the between subjects variance in PM2.5 exposure, with ETS contributing more than a third to 

this. 

Graney et al. (2004, 053756) 

Study Design The study was designed to assess the trace metal quantification abilities of several analytical methods to measure the total as 

well as soluble amounts of metals with PM2.5 collected from indoor and PM samples. (X-ray fluorescence and instrumental 

neutron activation analysis) 

Location Retirement facility in Towson, Maryland 

Population Retirement facility with subjects who spent 94% of their time indoors 

Age Groups Mean age = 84 yr 

 Indoor Source NR 

 Personal Method Measured using personal exposure monitors (MSP Inc) with nozzle to remove particles > 4 μm 

 Personal Size PM2.5 

 Microenvironment Size PM2.5 

 Ambient Size NR 

 Component(s) 42 elements were analyzed for in the PM2.5 samples collected from personal and well as indoor samples 

 Primary Findings Most of the extractable components of the metals were in a water-soluble form suggesting a high potential for bioavailability of 

elements from respiratory exposure to PM2.5. Based on comparison of trace metals in central I site vs. P samples, resident 

activities result in exposure to higher concentration of soluble trace metals. 

December 2009 A-299 
Haverinen-Shaughnessy et al. (2007, 156526) 

 Study Design Cross-sectional 

Period Winter, year not reported 

 Location Eastern Sweden 

Population Elementary school teachers 

 Age Groups NR 

 Personal Method Button inhalable aerosol samplers 

 Personal Size Particle mass 

 Microenvironment Size Particle mass 

 Ambient Size NR 

 Component(s) Absorbance coefficient/m x 10-5; Total fungi (spores/m3

); Total bacteria (cells/m3); Viable fungi MEA (CFU/m3); Viable fungi 

DG18 (CFU/m3

); Viable bacteria (CFU/m3) 

 Primary Findings The recall period of 7 days provided the most reliable data for health effect assessment. Both personal exposure and 

concentrations of pollutants at home were more frequently associated with health symptoms than work exposures. 

Ho et al. (2004, 056804) 

Study Design Human exposure assessment 

Period 25 Sept. 2002 to 8 March 2003 

 Location Hong Kong 

Population Occupied buildings located near major roadways 

 Age Groups NR 

 Indoor Source Yes. Regression of indoor versus outdoor concentrations of OC and EC revealed an indoor source of OC not present for EC, 

presumably due to such activities of cooking, smoking, and cleaning. 

 Personal Method Co-located mini-volume samplers (flow rate 5 L/min) and Partisol model 2000 sampler with 2.5 μm inlet. All samples on 47 mm 

Whatman quartz microfiber filters, weighed on an electronic microbalance. Analyzed for OC and EC using DRI Model 2001 

Thermal/Optical Carbon Analyzer. 

 Personal Size PM2.5 

 Microenvironment Size NR 

 Ambient Size PM2.5 

 Component(s) OC, EC, OM, TC 

 Primary Findings The major source of indoor EC, OC, and PM2.5 appears to be penetration of outdoor air, with a much greater attenuation in 

mechanically ventilated buildings. 

Hoek et al. (2008, 156554) 

Study Design Exposure assessment, characterizing indoor/outdoor particle relationships 

Period October 2002-March 2004 

Location 4 European cities Amsterdam, Athens, Birmingham, Helsinki 

 Population Urban populations 

 Age Groups NR 

 Indoor Source Smoking, candle burning, cooking/frying 

 Personal Method No personal exposure assessment was conducted 

 Personal Size NR 

 Microenvironment Size PM10, PM2.5, PM10-2.5, Ultrafine (UFP) 

 Ambient Size PM10, PM2.5, PM10-2.5, UFP 

 Component(s) soot, sulfate 

 Primary Findings Correlation between 24-h average central site and indoor concentrations was lower for UFP than for PM2.5, soot, or SO4

2-, 

probably related to greater losses during infiltration due to smaller particle size. Infiltration factors for UFP and PM2.5 were low. 

Hopke et al. (2003, 095544) 

Study Design Exposure assessment 

Period 26 July to 22 August 1998 

Location Retirement facility in Towson, MD 

 Population Elderly residents 

Age Groups Mean age of 84 

 Indoor Source Ammonium sulfate and ammonium nitrate, secondary sulfate, OC, and motor vehicle exhaust 

 Personal Method Inertial impactor PEM in the breathing zone of the subjects 

 Personal Size PM2.5 

 Microenvironment Size PM2.5 

 Ambient Size PM2.5 

 Component(s) SO4

2- 

 Primary Findings Personal exposures were influenced by a combination of indoor and outdoor factors. Indoor factors included gypsum, personal 

grooming products, and an unknown indoor source. Outdoor factor included SO4

2- , soil, and an unknown factor. Outdoor 

factors accounted for 63% of personal exposure, and SO42- was the largest ambient contributor to personal exposure (48%). 

December 2009 A-300 
Jacquemin et al. (2007, 156600) 

Study Design Assessment of relationship between outdoor and personal concentrations of PM2.5 absorbance and sulfur among postmyocardial infarction patients 

 Period January 2004-June 2004 

 Location Barcelona, Spain 

Population Survivors of a myocardial infarction exposed to ETS 

Age Groups n = 38, including 32 and 15 over age 64. 

 Indoor Source ETS 

 Personal Method Personal samplers (BGI GK2.05 cyclones and battery operated BGI AFC400S pumps) 

 Personal Size PM2.5 

 Microenvironment Size NA 

 Ambient Size PM2.5 

 Component(s) S 

 Primary Findings Ambient measurements of light extinction and S can be used as surrogates to personal PM2.5 exposure, especially for those 

exposed to ETS. 

Janssen et al. (2005, 088692) 

Study Design Panel Study 

 Period Amsterdam 11/2/1998-6/18/1999; Helsinki 11/1/1998-4/30/1999 

 Location Amsterdam, The Netherlands; Helsinki, Finland 

Population Elderly Cardiovascular Patients 

Age Groups 50-84 yr 

 Indoor Source No 

 Personal Method Personal PM2.5 GK2.05; cyclones; indoor & outdoor Harvard Impactors; Reflectance EEL 43 reflectometers; Elemental 

Composition Tracor Spectrace 5000 ED-XRF system 

 Personal Size PM2.5 

 Microenvironment Size PM2.5 

 Ambient Size PM2.5 

 Component(s) Estimated EC, elemental composition of a subset of personal, indoor and outdoor samples 

 Primary Findings For most elements, personal and indoor; concentrations were lower than and highly correlated with outdoor concentrations. The 

highest correlations (median r = 0.9) were found for sulfur and particle absorbance (EC), which both represent fine; mode 

particles from outdoor origin. Low correlations were observed for elements that represent the coarser part of the PM2.5 particles 

(Ca, Cu, Si, Cl-

). 

Jedrychowski et al. (2006, 156606) 

Study Design Prospective cohort 

 Period 11/2000-3/2003 

 Location Krakow, Poland 

Population Non-smoking pregnant women 

 Age Groups Yes 

 Personal Method Personal Exposure Monitor Sampler (PEMS, Harvard; School of Public Health) 

 Personal Size PM2.5 

 Microenvironment Size NR 

 Ambient Size PM10

 Component(s) NR 

 Primary Findings The contribution of the background ambient PM10 level was a very strong determinant of the total personal exposure to PM2.5, 

and it explained about 31% of variance between the subjects. 

Johannesson et al. (2007, 156614) 

 Study Design Cohort 

Period Spring and fall seasons of 2002 and 2003 

 Location Gothenburg, Sweden 

Population General adult population 

Age Groups 23-51 yr 

 Indoor Source NR 

 Personal Method Fine particles were measured for 24 h using both personal and stationary monitoring equipment. Personal monitoring of PM2.5

and PM1 was carried out simultaneously with parallel measurements of PM2.5 and PM1 indoors in living rooms and outside the 

house on a balcony, porch, etc. In addition, urban background PM2.5 levels were measured. Personal monitoring was 

performed in two ways. The 20 randomly selected subjects carried personal monitoring equipment for PM2.5 only, while the 10 

staff members carried two pieces of personal monitoring equipment at the same time. On the first measuring occasion, the staff 

members carried one PM2.5 cyclone and one PM1 cyclone. On the second occasion, duplicate monitors for PM2.5 were used. 

For personal and residential monitoring, the BGI Personal Sampling Pump was used together with the GK2.05 cyclone for 

PM2.5 sampling and the Triplex cyclone SCC1.062 for PM1sampling. The personal sampling pump was placed in a small 

December 2009 A-301 
shoulder bag and the cyclone attached to the shoulder strap near the subject’s breathing zone. The personal monitoring 

equipment was carried by the subject during awake time. During the night, it was placed in the living room. For indoor 

monitoring in living rooms, cyclones (PM2.5 and PM1) were placed at about 1.5 m above the floor. The same setup was used for 

residential outdoor monitoring. The urban background monitor was placed on top of a roof somewhat south of the city center 

but not near any major highway. 

 Personal Size PM2.5; PM1

 Microenvironment Size PM2.5; PM1

 Ambient Size PM2.5; PM1

 Component(s) BS 

 Primary Findings Personal exposure of PM2.5 correlated well with indoor levels, and the associations with residential outdoor and urban 

background concentrations were also acceptable. Statistically significantly higher personal exposure compared with residential 

outdoor levels of PM2.5 was found for nonsmokers. PM1 made up a considerable proportion (about 70–80%) of PM2.5. For BS, 

significantly higher levels were found outdoors compared with indoors, and levels were higher outdoors during the fall than 

during spring. There were relatively low correlations between particle mass and BS. The urban background station provided a 

good estimate of the residential outdoor concentrations of both PM2.5 and BS2.5 within the city. The air mass origin affected the 

outdoor levels of both PM2.5 and BS2.5; however, no effect was seen on personal exposure or indoor levels. 

Kaur et al. (2005, 086504) 

Study Design Exposure assessment, evaluation of exposures between modes of transport, routes, timing 

Period April 28-May 23, 2003 

Location Street canyon intersection in Central London, UK 

Population Users of an urban street canyon intersection 

 Age Groups NR 

 Indoor Source NR 

 Personal Method PM2.5 measured usnig high-flow gravimetric personal samplers (PM2.5) operating at a flow rate of 16 l/min carried in a backpack 

with sampling head positioned in personal breathing zone. UFP measured using TSI P-TRAK particle counters in which 

isopropyl alcohol condenses to form droplets that can be easily counted by a photodetector as they pass through a laser beam. 

 Personal Size PM2.5, UFP (0.02-1.0um) 

 Microenvironment Size PM2.5, UFP (0.02-1.0um) 

 Ambient Size PM2.5 

 Component(s) NR 

 Primary Findings Personal exposures to PM2.5 while walking were significantly lower then while riding in a car or taxi, likely a function of greater 

distance to roadside. No significant differences in PM2.5 were observed between exposures on the high traffic road compared 

with the backroad. Personal exposure levels were lowest during midday measurements for PM2.5 and highest in the early 

evening. Personal exposures to ultrafine particles were lowest while walking and highest while riding the bus. Exposures to 

ultrafine particles were also significantly higher on the high traffic road and during morning measurements. Exposure to 

ultrafine particles were highest in the morning, likely the result of peak traffic density in the morning. Exposure assessment also 

revealed that the background and curbside monitoring stations were not representative of the personal exposure of individuals 

to PM2.5 and CO at and around a street canyon intersection. 

Kaur et al. (2005, 088175) 

Study Design Personal exposure assessment of pedestrians walking along high-traffic urban road 

Period April 19, 2004-June 11, 2004 

Location Central London, UK 

 Population Pedestrians 

 Age Groups NR 

 Indoor Source NR 

 Personal Method PM2.5 gravimetric filter measurement, UFP (0.02-1 μm) P-TRAK device, reflectance reflectometer measurement of PM2.5 filter 

 Personal Size PM2.5, UFP (0.02-1 μm) 

 Microenvironment Size NR 

 Ambient Size PM2.5, UFP (0.02-1 μm) 

 Component(s) Absorbance of PM2.5 filter 

 Primary Findings PM2.5 pedestrian exposure was well correlated with and above background fixed-site monitoring levels. PM pedestrian exposure 

was influenced by proximity to curbside and the side of the road walked on. 

December 2009 A-302 
Kim et al. (2005, 156640) 

Study Design Panel study 

 Period 8/1999-11/2001 

 Location Toronto, Canada 

 Population Cardiac-compromised patients 

Age Groups Mean age 64 yr 

 Indoor Source Gas range (68%); indoor grill (11%); outdoor barbeque (30%); Gas heating fuel (68%); Oil heating fuel (7%) 

 Personal Method Rupprecht and Patashnick ChemPass Personal Sampling System 

 Personal Size PM2.5 

 Microenvironment Size NR 

 Ambient Size PM2.5 

 Component(s) NR 

 Primary Findings Personal PM2.5 exposures were higher than outdoor ambient levels. Personal PM2.5 exposures levels were correlated with 

ambient levels, mean r = 0.58 

Koistinen et al. (2004, 156655) 

 Study Design Representative Population-based study 

Period Oct 1996-Dec 1997 

 Location Helsinki, Finland 

Population Non-smoking adults not exposed to environmental tobacco smoke. 

Age Groups Adults 25-55 yr 

 Indoor Source Soil from outdoors, cooking, smoking, aerosol cleaners, sea salt, combustion sources 

 Personal Method Integrated 24-h filter sample 

 Personal Size PM2.5

 Microenvironment Size PM2.5

 Ambient Size PM2.5

 Component(s) BS 

 Primary Findings Population exposure assessment of PM2.5, based on outdoor fixed-site monitoring, overestimates exposures to outdoor sources 

like traffic and long-range transport and does not account for the contribution of significant indoor sources. 

Kousa et al. (2001, 025270) 

Study Design Population based exposure assessment 

Period October 1996 to June 1998 

Location Helsinki, Finland; Basel, Switzerland; Prague, Czech Republic; Athens, Greece 

Population Adult urban populations 

Age Groups 25-55 yr 

 Indoor Source Sometimes ETS 

 Personal Method Integrated 48-h filter sample 

 Personal Size PM2.5 

 Microenvironment Size PM2.5 

 Ambient Size PM2.5, PM10

 Component(s) NR 

 Primary Findings Throughout the study, the highest correlations were those between personal exposures and indoor concentrations, which 

suggests that indoor sources were important. Correlations were generally lower between ambient concentrations and personal 

exposures. 

December 2009 A-303 
Koutrakis et al. (2005, 095800) 

Study Design Panel study 

 Period Baltimore 6/28/98-8/22/98 (summer), 2/1/99-3/16/99 (winter); Boston 6/13/99-7/23/99 (summer), 2/1/00-3/12/00 (winter) 

 Location Baltimore, MD Boston, MA 

Population Healthy older adults, children, adults with COPD 

Age Groups Children 9-13 y/o; Seniors 65+ y/o 

 Indoor Source NR 

 Personal Method Personal exposure samples of PM2.5; were collected using a specially designed multipollutant sampler (Demokritou et al. 2001). 

PM2.5 was collected using personal environmental monitors (PEMs) and 37-mm; Teflon filters (Teflo, Gelman Sciences, Ann 

Arbor MI). 

 Personal Size PM2.5 

 Microenvironment Size NR 

 Ambient Size PM2.5 

 Component(s) EC, SO4

2–

 Primary Findings Ambient PM2.5 and SO4

2– are strong predictors of respective personal exposures. Ambient SO42– is a strong predictor of 

personal exposure to PM2.5. Because PM2.5 has substantial indoor sources and SO4

2– does not, the investigators; concluded 

that personal exposure to SO4

2– accurately reflects exposure to ambient PM2.5 and therefore the ambient component of 

personal exposure to PM2.5 as well. 

Lai et al. (2004, 056811) 

Study Design Personal exposure study 

Period December 1998-February 2000 

 Location Oxford, UK 

 Population Adults 

Age Groups 25-55 yr (avg = 41) 

 Indoor Source Cooking, active smoking, passive smoking heating by gas heater 

 Personal Method Integrated 48-h filter samples 

 Personal Size PM2.5 

 Microenvironment Size PM2.5 

 Ambient Size PM2.5 

 Component(s) Ag, Cr, Mn, Si, Al, Cu, Na, Sm, As, Fe, Ni, Sn, Ba, Ga, P, Sr, Br, Ge, Pb, Ti, Ca, Hg, Rb, Tl, Cd, I, S, Tm, Cl, K, Sb, V, Co, Mg, 

Se, Zn, Zr 

 Primary Findings Personal exposures were influenced by both indoor and ambient sources, and indoor levels exceeded ambient levels for PM2.5

as well as for VOCs and eight other compounds. Correlation between personal and indoor PM2.5 was 0.60 (p < 0.001). 

Larson et al. (2004, 098145) 

Study Design Time-series epidemiologic study 

Period Sep 26, 2000-May 25, 2001 

 Location Seattle, Washington 

 Population “Susceptible Populations” 

Age Groups Time-activity diary 

 Personal Method Harvard Personal Environmental Monitor 

 Personal Size PM2.5 

 Microenvironment Size PM2.5 outside subject’s residence, and inside residence 

 Ambient Size PM2.5 at Central outdoor site (downtown Seattle) 

 Component(s) Light absorbing carbon (LAC) and trace elements 

 Primary Findings Five sources of PM2.5 identified vegetative burning, mobile emissions, secondary sulfate, a source rich in chlorine, and crustalderived material. The burning of vegetation (in homes) contributed more PM2.5 mass on average than any other sources in all 

microenvironments. 

December 2009 A-304 
Li et al. (2003, 047845) 

Study Design Concurrent 10-min avg indoor and outdoor concentrations of PM10 and PM2.5 were recorded for 2 days each in 10 homes with 

swamp coolers 

 Period Summer 2001 

 Location El Paso, Texas 

 Population Cooking, cleaning, walking 

 Age Groups NR 

 Indoor Source NR 

 Personal Method PM2.5 and PM10; indoor and outdoor; tapered element oscillating microbalance (TEOM) instruments. 2 days were monitored for 

PM2.5, and 2 for PM10. 

 Personal Size NR 

 Microenvironment Size NR 

 Primary Findings Evaporative coolers were found to act as PM filters, creating indoor concentrations approximately 40% of outdoor PM10 and 

35% of outdoor PM2.5, regardless of cooler type. 

Liu et al. (2003, 073841) 

Study Design Comprehensive exposure assessment 

 Period 1999-2001 

 Location Seattle, WA 

Population High-risk sub populations 

Age Groups Children 6-13 yr, elderly 65-90 yr (one person was below 65, but his/her age was not specified) 

 Personal Method Harvard Personal Environmental Monitor for PM2.5 (HPEM2.5) 

 Personal Size PM2.5, PM10

 Microenvironment Size PM2.5, PM10

 Ambient Size PM2.5, PM10

 Primary Findings Average personal PM2.5 exposure was similar to ambient PM2.5 concentrations but much higher than average indoor 

concentrations. Personal, indoor, and outdoor PM2.5 and PM10, as well as the ratio PM2.5/PM10, were all significantly higher 

during the winter. Personal PM2.5 and PM10 exposures were highest for the children in the study. 

Lung et al. (2007, 156719) 

Period Weekdays between Nov 1998 and Feb 1999 

Location 6 communities in Taiwan, China 2 in Taipei, 2 in Taichung, and 2 in Kaohsiung. Sites are industrial, commercial, residential and 

mixed. 

Age Groups 18 to >70 

 Indoor Source Being in kitchen, park, major boulevard, stadium, incense burning, household work, factory, environmental tobacco smoke, 

traffic, ventilation conditions 

 Personal Method Personal Environmental Monitor with a SKC personal pump at 2 L/min, 37 mm Teflon filters 

 Personal Size PM10

 Microenvironment Size PM10

 Ambient Size PM10

 Component(s) None 

 Primary Findings Outdoor rather than indoor levels contributed significantly to personal exposure. Important factors include time spend outdoors 

and on transportation, riding a motorcycle, passing by factories, cooking or being in the kitchen, incense burning at home. 

Meng et al. . (2005, 081194) 

Study Design Evaluation of the use of central-site PM, rather than actual exposure, in PM epidemiology 

Period Summer 1999-spring 2001 

Location 3 cities: Houston (TX), Los Angeles County (CA), and Elizabeth (NJ) 

 Population NR 

 Age Groups NR 

 Indoor Source NR 

 Personal Method MSP monitors on the front strap of the sampling bag near the breathing zone. Pump, battery, and motion sensor were on the 

hip or back. 

 Personal Size PM2.5 

 Microenvironment Size PM2.5

 Ambient Size PM2.5

 Component(s) EC, OC, S, Si 

 Primary Findings Use of central-site PM2.5 as an exposure surrogate underestimates the bandwidth of the distribution of exposures to PM of 

ambient origin. 

December 2009 A-305 
Meng et al. (2005, 058595) 

Study Design RIOPA study matched indoor home & outdoor exposure assessment 

 Period May-October (hot); November-April (cool); (1999-2001) 

Location Los Angeles County, CA; Elizabeth, NJ; Houston, TX 

 Population Non-smoking homes 

 Indoor Source Combustion (primary); atmospheric (secondary); sulfate, organics, nitrates; mechanically (abrasion) generated. 

 Personal Method Filter (not specified) 

 Personal Size NR 

 Microenvironment Size Indoor home.; PM2.5 

 Ambient Size PM2.5, outdoor home 

 Component(s) Organic and elemental carbon; 24 elements (metals). 

 Primary Findings The median contribution of ambient sources to indoor PM2.5 using the mass balance approach was 56% for all study homes, 

63% for California, 52% for New Jersey, and 33% for Texas. 

Molnár et al. (2005, 156772) 

Study Design Indoor/outdoor exposure assessment related to domestic wood burning 

Period 10 February to 12 March 2003 

 Location Hagfors, Sweden 

Population Adult residents of Hagfors 

 Age Groups NR 

 Indoor Source NR 

 Personal Method Integrated filter samples with a dichotomous virtual impactor to separate PM10-2.5 from PM2.5

 Personal Size PM2.5 

 Microenvironment Size PM10-2.5, PM2.5 

 Ambient Size PM10-2.5, PM2.5 

 Component(s) BS, S, Cl, K, Ca, Mn, Fe, Cu, Zn, Br, Rb, Pb 

 Primary Findings Wood burning made statistically significant contributions to personal exposure to K, Ca, and Zn. Cl, Mn, Cu, Rb, Pb, and BS 

were found to be potential personal exposures from wood smoke, but their association was not always statistically significant. 

S had no significant association with personal exposure to wood smoke. 

Molnár et al. (2006, 156773) 

 Study Design Cross-sectional 

Period Autumn and spring in 2002 and 2003 

 Location Goteborg, Sweden, 

 Population Persons living in urban settings 

Age Groups 20 subjects 20-50 yr randomly selected from the population and 10 from departmental colleagues. 

 Indoor Source NR 

 Personal Method Integrated filter samples with cyclones for PM2.5 and PM1 cut points 

 Personal Size PM2.5 and PM1

 Microenvironment Size NR 

 Ambient Size NR 

 Component(s) S, Cl, K, Ca, Ti, V, Mn, Fe, Ni, Cu, Zn, Br, Pb 

 Primary Findings Personal exposure to Cl, K, Ca, Ti, Fe, and Cu in PM2.5 were significantly higher than outdoor and central site ambient 

concentrations, and personal exposure to Cl, Ca, Ti, Fe, and Br were also significantly higher than indoor levels. In most 

cases, indoor concentrations were not higher than outdoor concentrations. 

Na and Cocker (2005, 156790) 

Study Design Human exposure assessment 

Period Sept. 2001-January 2002 

Location Mira Loma, CA 

Population Residential homes and a high school 

 Age Groups NR 

 Indoor Source Indoor EC (elemental carbon) concentrations primarily of outside origin; Indoor PM2.5 significantly influenced by indoor OC 

(organic carbon) sources, including indoor smoking. 

 Personal Method Integrated filter samples for PM2.5 

 Personal Size PM2.5 

 Microenvironment Size NR 

 Ambient Size PM2.5 

 Component(s) EC, OC 

 Primary Findings Indoor PM2.5 was significant influenced by indoor OC sources. Indoor EC sources were predominantly of outdoor origin. 

December 2009 A-306 
Naumova et al. (2003, 089213) 

Study Design RIOPA Study-PAH partitioning indoor and outdoor pollutants to evaluate the hypothesis that outdoor air pollution contributed 

strongly to indoor air pollution. 

Period July 1999-June 2000 

Location Los Angeles, CA, Houston, TX, Elizabeth, NJ 

 Population Houses 

 Age Groups NR 

 Indoor Source NR 

 Personal Method Modified MSP Samplers, 37 mm quartz filter 

 Personal Size PM2.5

 Microenvironment Size PM2.5 

 Ambient Size PM2.5 

 Component(s) OC, EC 

 Primary Findings Both EC and OC were associated with gas/particle partitioning of PAHs, with EC being a better predictor. High correlation 

between EC and OC suggests that PAHs adsorb onto PM containing EC during combustion. 

Nerriere et al. (2005, 089481) 

Study Design Exposure assessment with stratified sampling of children and adults in 3 environments: high traffic emissions, local industrial 

sources, and urban background. 

Period “Hot” season May-June and “cold” season Feb-Mar. Grenoble in 2001, Paris in 2002, Rouen in 2002-2003, Strasbourg 2003. 

 Location Grenoble, Paris, Rouen, and Strasbourg, France 

Population Persons living, working, or going to school in 3 urban areas one highly exposed to traffic emissions, one influenced by local 

industrial sources, and a background urban environment. Industrial sources of pollution were present in each city. 

Age Groups 6-13 yr and 20-71 yr. All non-smokers and not exposed to environmental tobacco smoke or industrial air pollution. 

 Indoor Source NR 

 Personal Method Rucksack with Harvard ChemPass 

 Personal Size PM2.5, PM10

 Microenvironment Size NR 

 Ambient Size PM2.5, PM10

 Copollutant(s) NO2

 Primary Findings The difference between ambient air concentrations and average total exposure is pollutant specific. PM2.5 and PM10

concentrations underestimate population exposures across almost all cities, season, and age groups, but the opposite is true 

for NO2. 

Noullett et al (2006, 155999) 

 Study Design Cohort 

Period 5 February to 16 March 2001 

Location Prince George, British Columbia 

 Population Children 

Age Groups 10-12 yr 

 Indoor Source NR 

 Personal Method PM2.5 Harvard Personal Environment Monitors (HPEM2.5) 

 Personal Size PM2.5 

 Microenvironment Size NR 

 Ambient Size PM2.5 

 Component(s) SO4

2-, ABS (light absorbing carbon) 

 Primary Findings Thermal inversions were associated with personal exposures as well as ambient PM2.5 concentrations and likely caused 

observed spatial variability. However, ambient sampling locations were correlated in time. Similar observations were made for

SO4

2- and ABS. 

December 2009 A-307 
Rojas-Bracho et al. (2004, 054772) 

Study Design Cohort study with repeated measures. 

Period Winter or summer of 1996-1997 

 Location Boston, Massachusetts 

 Population COPD patients 

 Age Groups Adult 

 Indoor Source Housecleaning, cooking, transport in motor vehicles, low-effort home activities, moderate-effort home activities, activities in 

public places, and resting or sleeping. 

 Personal Method PEM 

 Personal Size PM2.5, PM10, and PM10-2.5

 Microenvironment Size PM2.5, PM10, & PM10-2.5

 Ambient Size NR 

 Component(s) NR 

 Primary Findings During both seasons, personal exposures were higher than indoor or outdoor means, except during the winter when indoor 

concentrations were higher than the personal or outdoor. 

Rotko et al. (2002, 037240) 

Study Design European multi-city air pollution study 

Period Athens, Greece:26 January 1997–4 June 1998 

 Basel, Switzerland 3 February 1997–23 January 1998 

 Milan, Italy 10 March 1997–23 May 1998 

 Oxford, UK November 1998–7 October 1999 

 Prague, Czech Republic 3 June 1997–4 June 1998 

 Helsinki, Finland 26 September 1996–10 December 1997 

Location Athens, Greece; Basel, Switzerland; Milan, Italy; Oxford, UK; Prague, Czech Republic; Helsinki, Finland 

 Population Adults 

Age Groups 25-55 yr 

 Indoor Source NR 

 Personal Method Integrated 48-h PM2.5 filter samples 

 Personal Size PM2.5 

 Microenvironment Size PM2.5 

 Ambient Size PM2.5 

 Copollutant(s) NO2

 Primary Findings Personal PM2.5 and NO2 levels were associated with subjects' level of annoyance. Highest annoyance levels occurred while in 

traffic. 

Sanderson and Farant (2004, 156942) 

Study Design Indoor and outdoor air monitoring of PAH. Investigate the relationship between indoor and outdoor PAH. 

 Period NR 

 Location Canada 

Population Residential homes in neighborhoods around aluminum smelting plant 

 Age Groups NR 

 Indoor Source NR 

 Personal Method Indoor quartz filter sample 

 Personal Size PM2.5

 Microenvironment Size NR 

 Ambient Size NR 

 Component(s) 4-6 ring PAHs on indoor particle 

 Primary Findings Indoor concentration of 4-6-ring PAH were linked to outdoor industrial sources in residences without any major indoor source, 

but with industrial facility as the main outdoor source. This study suggests that simultaneous measurements of indoor and 

outdoor concentrations of PAH >4 rings predominantly associated with fine PM could provide useful estimates of particle 

infiltration efficiency. 

December 2009 A-308 
Sarnat et al. (2006, 089166) 

Study Design Outdoor-indoor pollutant infiltration, occupied residences 

Period July 28, 2001-February 25, 2002 

 Location Los Angeles, CA 

 Population NR 

 Indoor Source Yes; cleaning, cooking, home ventilation (open windows/doors), kitchen fans, air conditioner/heating usage, number of 

occupants, nearby roadways 

 Personal Method NR 

 Personal Size NR 

 Microenvironment Size PM2.5, Particle number 

 Ambient Size PM2.5 

 Component(s) BC (nonvolatile component); NO3 (volatile component) 

 Primary Findings Infiltration rate for PM2.5 was intermediate, while BC was highest and NO3 lowest. Infiltration rate varied with particle size, air 

exchange rate, outdoor NO3. PM2.5 infiltration was lowest for volatile components. Outdoor volatile PM2.5 components may be 

less representative of indoor exposure to volatile PM2.5 of ambient origin. Outdoor nonvolatile PM2.5 components may be more 

representative of indoor exposure to nonvolatile PM2.5 of ambient origin. 

Sarnat et al. (2006, 090489) 

Study Design Personal and ambient exposure assessment 

Period June 14-August 18 (summer); Sep 24-Dec 15 (fall), 2000 

 Location Steubenville, OH 

 Population Non-smoking, older adults 

 Age Groups NR 

 Personal Method Integrated filter gravimetric measurement 

 Personal Size PM2.5 

 Microenvironment Size NR 

 Ambient Size PM2.5 

 Component(s) SO4

2-; EC 

 Primary Findings 24-h ambient measurements are more representative of personal particle exposure than gases, and ventilation is an important 

exposure modifier. 

Sarnat et al. (2005, 087531) 

Study Design Time-series epidemiologic study 

Period Summer 1999 and winter 2000 

Location Boston, MA. Comparisons to a previous study in Baltimore are also made. 

Population School children and seniors 

 Age Groups NR 

 Indoor Source PM2.5 

 Personal Method NR 

 Personal Size PM2.5 

 Microenvironment Size NR 

 Ambient Size PM2.5

 Component(s) SO4, 

 Copollutant(s) O3, NO2, SO2

 Primary Findings Substantial correlations between ambient PM2.5 concentrations and corresponding personal exposures. Summertime gaseous 

pollutant concentrations may be better surrogates of personal PM2.5 exposures (especially personal exposures to PM2.5 of 

ambient origin) than they are surrogates of personal exposures to the gases themselves. 

Shalat et al. (2007, 156971) 

Study Design Indoor home exposure assessment; sampling technology demonstration 

Period Winter heating season 

 Location Residential home 

 Population Children 

Age Groups Pre-toddler (6- to 12-month-old) children 

 Indoor Source NR 

 Personal Method Integrated filter and real-time nephelometer at floor height and at a height of 110 cm 

 Personal Size TSP, inhalable PM 

 Microenvironment Size NR 

 Ambient Size NR 

 Copollutant(s) NR 

 Primary Findings The study results suggest that young children are exposed to more inhalable PM and TSP because PM becomes resuspended 

from the floor with motion. 

December 2009 A-309 
Shao et al. (2007, 156973) 

Study Design Exposure assessment 

Period July and Winter 2003 

 Location Beijing, China 

 Population General population 

 Age Groups NR 

 Indoor Source NR 

 Personal Method PM10 measured with integrated filter samples 

 Personal Size PM10

 Microenvironment Size PM10

 Ambient Size PM10

 Component(s) NR 

 Primary Findings Plasmid scission assay, coupled with the image analysis, can be used to evaluate the relationship between particle physicochemistry and toxicity. 

Shilton et al. (2002, 049602) 

Study Design Respirable particulates inside and outside of a building were collected and compared 

Period 24-h sampling from 12:45 pm Mondays to Fridays between 9/19/00 to 5/01/01 

Location Wolverhampton city center, University of Wolverhampton, UK 

 Population NR 

 Indoor Source Mn,Al, NO3, Cl-

 (wind-blown dust), Cu and Zn-

 Personal Method Active sampling using Casella sampler (filter)- 

 Personal Size Respirable PM 

 Microenvironment Size Respirable PM 

Ambient Size Respirable PM 

 Component(s) NO3

-

, metals (Zn, Cu, Mn, Al), SO4

2-, Cl-

 Primary Findings The indoor particulate concentration was driven by ambient concentration, and meteorological-induced changes in ambient PM 

were detected indoors. 

Strand et al. (2007, 157018) 

 Study Design Cohort 

Period Winter of 1999-2000 and winter of 2000-2001 

 Location Denver, Colorado 

 Population Asthmatic children 

 Indoor Source NR 

 Personal Method Modeling/extrapolation from fixed-site ambient monitoring (multiple methods) 

 Personal Size NR 

 Microenvironment Size NR 

 Ambient Size PM2.5 

 Component(s) NR 

 Primary Findings Using modeled or extrapolated personal ambient PM exposure results in a deattenuation of decrements in FEV1 associated with 

PM exposure, relative to use of fixed-site ambient monitoring PM levels. Associations between FEV1 decrements and the 

various estimation procedures (modeling and extrapolation) were similar to each other. 

Tang et al. (2007, 091269) 

Study Design Cohort Study 

 Period 12/2003-2/2005 

 Location Sin-Chung City, Taiwan 

 Population Asthmatic children 

Age Groups 6-12 yr 

 Indoor Source No 

 Personal Method Portable particle monitor; DUSTcheck Portable Dust Monitor, model 1.108, GRIMM Labortechnik Ltd., Germany 

 Personal Size PM10, PM2.5, PM1, PM10-2.5, PM2.5-1

 Microenvironment Size NR 

 Ambient Size PM10, PM2.5, PM10-2.5-

 Component(s) NR 

 Primary Findings Results of linear mixed-effect model analysis suggested that personal PM data was more suitable for the assessment of change 

in children’s PEFR than ambient monitoring data. 

December 2009 A-310 
Thornburg et al. (2004, 157052) 

Study Design PM exposure studies 

Period RTP: Summer 2000-spring 2001 

 Tampa: October-November 2002 

Location Research Triangle Park (RTP), NC and Tampa, FL 

Population Residential home occupants 

 Age Groups NR 

 Indoor Source Resuspension of PM10 from a carpet and cooking 

 Personal Method Harvard impactors and PEMs, MIE pdr1000 nepholometer 

 Personal Size PM2.5, PM10

 Microenvironment Size NR 

 Ambient Size PM2.5, PM10

 Component(s) NR 

 Primary Findings The association of duty cycle with indoor-outdoor (I/O) ratio was confounded by the short time span of ventilation system 

operation and the presence of strong indoor sources. 

Toivola et al. (2002, 026571) 

Study Design Random sample of teachers 

Period Nov 1998-Mar 1999 and Nov-Dec 1999 

Location 2 cities in eastern Finland 

 Age Groups Adult 

 Indoor Source Fungi, bacteria 

Population Elementary school teachers 

 Personal Method Button inhalable aerosol sampler 

 Personal Size Particle Mass; BS 

 Microenvironment Size Particle Mass; BS 

 Ambient Size NR 

 Component(s) Total fungi, total bacteria, viable fungi, viable bacteria 

 Primary Findings Personal BS exposure correlated with both home and work BS exposures. BS concentrations explained best the variation of 

particle mass in personal and home concentrations. 

Trenga et al. (2006, 155209) 

Study Design Panel study with repeated measures 

Period 3 sampling periods Oct 1999-Aug 2000, Oct 2000-May 2001, Oct 2001-Feb 2002 

 Location Seattle, Washington 

Population Adults with and without COPD and children with asthma 

Age Groups adults ages 56-89 and children ages 6-13 

 Indoor Source NR 

 Personal Method Carrying personal monitor (Harvard Personal Environmental Monitor for PM2.5) 

 Personal Size PM2.5 

 Microenvironment Size PM2.5 

 Ambient Size PM10-2.5, PM2.5 for residential outdoor, PM2.5 for central site 

 Component(s) NR 

 Primary Findings FEV1 decrements associated with 1-day lagged central site PM2.5 in adult subjects with COPD. Associations between PM and 

lung function decrements were significant only in asthmatic children not receiving anti-inflammatory medication. 

Turpin et al. (2007, 157062) 

Study Design RIOPA Study 24-h integrated indoor, outdoor, and personal samples collected in 3 cites. 

Period Summer 1991-spring 2001 

Location Elizabeth, NJ, Houston, TX, and Los Angeles County, CA 

Population 309 adults and 118 children (89-18) 

 Indoor Source NR 

 Personal Method PEM on the front strap of a harness near the breathing zone. The bag on the hip contained the pump, battery pack, and motion 

sensor 

 Personal Size PM2.5 

 Microenvironment Size PM2.5, in the main living area (not kitchen) 

 Ambient Size PM2.5, in the front or back yard 

 Component(s) 18 volatile organics, 17 carbonyl, PM2.5 mass and >23 PM2.5 species, organic carbon, elemental carbon, and PAHs 

 Primary Findings The best estimate of the mean contribution of outdoor to indoor PM2.5 was 73% and the outdoor contribution to personal was 

26%. 

December 2009 A-311 
Vallejo et al. (2006, 157081) 

Study Design Panel study 

 Period 4/2002-8/2002 

Location Mexico City, Mexico 

 Population Health young, non-smoking adults 

Age Groups Mean age 27 yr 

 Indoor Source NR 

 Personal Method pDR nephelometric method 

 Personal Size PM2.5 

 Microenvironment Size NR 

 Ambient Size NR 

 Component(s) NR 

 Primary Findings The descriptive analysis showed that overall outdoor median concentration of PM2.5 was higher than the indoor concentration. 

In the indoor microenvironment, the highest concentrations occurred in the subway followed by the school, and the lowest was 

at home. The outdoor microenvironment with the highest concentrations was the public transportation (bus), while the 

automobile had the lowest. It was found that PM2.5 concentration levels had a circadian-like behavior probably related to an 

increase in the population daily activities during the morning hours, which decrease in the evening, especially at indoor 

microenvironments. The Center city area was found to have the highest concentrations of PM2.5.; Multivariate analysis 

corroborated that PM2.5 concentrations are mainly determined by geographical locations and hour of the day, but not by the 

type of microenvironment. 

van Roosbroeck et al. (2006, 090773) 

Study Design Personal exposure assessment, effect of traffic-related pollutants 

 Period March-June 2003 

 Location Amsterdam, The Netherlands 

 Population Schoolchildren 

Age Groups 9-12 yr 

 Indoor Source ETS, cooking 

 Personal Method Integrated filter gravimetric measurement. Light absorbance. 

 Personal Size PM2.5 

 Microenvironment Size NR 

 Ambient Size PM2.5 

 Component(s) Absorbance 

 Primary Findings Children living near busy roads had 35% higher personal exposure to ‘soot’ than children living in urban background locations. 

Vinzents et al. (2005, 087482) 

Study Design Panel study 

 Period 3/2003-6/2003 

 Location Copenhagen, Denmark 

Population Healthy young adults 

Age Groups Mean age = 25 yr 

 Indoor Source No 

 Personal Method Condensation particle counters 

 Personal Size UFP (10-100 nm) 

 Microenvironment Size UFP (10–100 nm) 

 Ambient Size PM10

 Primary Findings UFP exposure predicted oxidative DNA damage. 

Wallace and Williams (2005, 057485) 

 Study Design Cohort 

 Period 2000-2001 

Location Raleigh, North Carolina 

 Population African-American persons with elevated risk from exposure to particles. 

 Age Groups NR 

 Indoor Source NR 

 Personal Method PEM PM2.5 monitor 

 Personal Size PM2.5 

 Microenvironment Size Indoors PM2.5 

Ambient Size Outdoors near residence PM2.5 PM2.5 

 Component(s) S 

 Primary Findings Using outdoor particles to determine the effect on health is not accurate. The infiltration factor is a good estimator for personal 

exposure. Indoor and outdoor measurements of sulfur could be used in the absence of personal exposure measurement to 

estimate the contribution of outdoor fine particles to personal exposures. 

December 2009 A-312 
Wallace et al. (2006, 088211) 

Study Design Time series continuous monitoring of subjects with controlled hypertension or implanted defibrillators were monitored for 7 

consecutive days in 4 seasons. 

 Period 2000-2001 

 Location North Carolina 

 Population Health-compromised adults, non-smokers 

 Age Groups Adults 

 Indoor Source Cooking, cleaning, personal care, smoking 

 Personal Method PEM 

 Personal Size PM2.5 

 Microenvironment Size PM2.5; Indoor and outdoor 

 Ambient Size NR 

 Component(s) NR 

 Primary Findings Use of continuous particle measuring instruments allowed more precise identification of sources, frequency and magnitude of 

short-term peaks, and more accurate calculation of individual personal clouds. 

Wang et al. (2006, 157108) 

Study Design Exposure assessment, identification of sources of outdoor and indoor PM and trace elements 

Period Aug 4 -Sep 10, 2004 

 Location Guangzhou, China 

 Population 4 hospitals 

 Age Groups NR 

 Indoor Source NR 

 Personal Method No personal exposure assessment was conducted. 

 Personal Size NR 

 Microenvironment Size PM10, PM2.5 

 Ambient Size PM10, PM2.5 

 Component(s) Na, Al, Ca, Fe, Mg, Mn, Ti, K, V, Cr, Ni, Cu, Zn, Cd, Sn, Pb, As, Se 

 Primary Findings High correlation between PM2.5 and PM10 suggest that they came from similar emission sources. Outdoor infiltration could lead 

to direct transportation of PM indoors. Human activities and ventilation types could also influence indoor PM. levels. 

Ward et al. (2007, 157112) 

Study Design Indoor air sampling to determine size fractionated concentrations of PM, OC, EC, and total carbon 

 Period Jan-Mar 2005 

 Location Libby, Montana 

 Population Children exposed to wood-burning stoves in elementary and middle schools 

 Indoor Source Burning wood in stoves for heating 

 Personal Method NR 

 Personal Size NR 

 Microenvironment Size PM >2.5, 1.0-2.5, 0.5-1.0, 0.25-0.5, and < 0.25 μm 

Ambient Size PM >2.5, 1.0-2.5, 0.5-1.0, 0.25-0.5, and < 0.25 μm 

 Component(s) OC and EC 

 Primary Findings Total measured PM mass concentrations were much higher inside the elementary schools, with particle size fraction (>2.5, 0.5-

1.0, 0.25-0.5, and < 0.25 mm) concentrations between 2 and 5 times higher when compared to the middle school. The 1.0-2.5 

mm fraction had the largest difference between the two sites, with elementary school concentrations nearly 10 times higher 

than the; middle school values. 

Weisel et al. (2005, 157131) 

Study Design Matched indoor, outdoor, and personal concentrations in proximity to pollution sources. 

Period May 1999-Feb 2001 

Location Elizabeth, NJ, Houston, TX, and Los Angeles County, CA 

Population urban children and adults 

Age Groups Children and adults (6-89 yr) 

 Indoor Source Age of house, recent renovations (< 1 yr), type of home (single, multiple family), attached garage, carpet indoors, local pollution 

sources. 

 Personal Method PEM on a harness with inlet near breathing zone. 

 Personal Size PM2.5 

 Microenvironment Size PM2.5 

 Ambient Size PM2.5 

 Component(s) NR 

 Primary Findings Personal PM2.5 was significantly higher than indoor and outdoor PM2.5 concentrations. 

December 2009 A-313 
Wichmann et al. (2005, 086240) 

Study Design Exposure assessment 

Period November 29, 1993-March 30, 1994; October 17, 1994-December 22, 1994 

 Location Amsterdam, The Netherlands 

Population Adults and schoolchildren living near high-traffic or low-traffic roads 

Age Groups Adults (50-70 yr), schoolchildren (10-12 yr) 

 Indoor Source NR 

 Personal Method Personal impactor 

 Personal Size PM10

 Microenvironment Size PM10

 Ambient Size PM10

 Component(s) Absorbance coefficient measurements 

 Primary Findings Found tentative support for using type of road as a proxy for indoor and personal exposure to traffic-related absorbance PM. 

Williams et al. (2003, 053338) 

Study Design Cohort study, longitudinal 

Period Summer 2000, fall 2000, winter 2001, and spring 2001 

Location Raleigh and Chapel Hill, North Carolina 

 Population Elderly persons 

Age Groups > 50 yr 

 Indoor Source Occasional ETS 

 Personal Method Integrated filter samples 

 Personal Size PM2.5 

 Microenvironment Size PM2.5; PM10; PM10-2.5

 Ambient Size PM2.5; PM10; PM10-2.5

 Component(s) NR 

 Primary Findings When comparing cohorts, there was no statistically significant difference between PM2.5 exposure. Little spatial variability was 

observed regarding PM2.5 concentrations; this was observed to a lesser extent for PM10 as well. 

Wilson and Brauer (2006, 088933) 

Study Design Exposure assessment 

 Period April-September 1998 

 Location Vancouver, Canada 

Population Subjects with physician-diagnosed COPD 

 Age Groups 54-86-years-old 

 Indoor Source No 

 Personal Method Personal integrated filter gravimetric measurement; TEOM outdoor ambient 

 Personal Size PM2.5 

 Microenvironment Size NR 

 Ambient Size NR 

 Component(s) SO4

2- 

 Primary Findings It was observed that ambient PM2.5 exposure, estimated with the SO4

2- method, accounted for 71% of measured ambient 

concentration and 44% of measured total personal exposure. No correlation between nonambient exposure and ambient 

concentration was observed. 

Wu et al. (2006, 179950) 

Study Design Panel study 

 Period 9/3/2002-11/1/2002 

 Location Pullman, WA 

 Population Asthmatic adults 

Age Groups 18-52 yr 

 Indoor Source No 

 Personal Method Co-located Harvard Personal Environmental Monitors (HPEM2.5; Harvard School of Public Health, Boston, MA)\ 

 Personal Size PM2.5 

 Microenvironment Size PM2.5 

Ambient Size PM2.5 

 Component(s) Levoglucosan (LG); Elemental Carbon (EC); Organic Carbon (OC) 

 Primary Findings The authors observed significant variability between subjects for burning and nonburning episodes. The authors postulated that 

activity patterns contribute to this variability and that central-site measurements of LG might not be a good surrogate for 

biomass combustion smoke exposurefor this reason. 

December 2009 A-314 
Wu et al. (2005, 086397) 

Study Design Panel study 

 Period 1999-2000 

 Location Alpine, CA 

 Population Asthmatic children 

Age Groups 9-17 yr 

 Indoor Source NR 

 Personal Method pDR 

 Personal Size PM2.5 

 Microenvironment Size PM2.5

 Ambient Size PM2.5 

 Component(s) NR 

 Primary Findings Personal exposure was higher than those at fixed sites. Subjects received only 45.0% of their exposure indoors at, although 

they spent more than 60% of their time there. In contrast, 29.2% of their exposure was received at school where they spent 

only 16.4% of their time. Thus, exposures in microenvironments with high PM levels where less time is spent can make 

significant contributions to the total exposure. 

Yeh and Small (2002, 040077) 

Study Design Comparative assessment of AME and IES models 

Period 1997 (364 days) spring March-May, summer June-August, Fall September-November, winter December-February 

Location Los Angeles County, CA 

 Population General population; ETS and non-ETS Homes 

 Age Groups NR 

 Indoor Source Indoor Cooking, ETS, Other sources and unexplained particulates that maybe generated with engaging in various activities 

 Personal Method NR 

 Personal Size PM10 PM2.5 

 Microenvironment Size NR 

 Ambient Size PM10 PM2.5 

 Component(s) NR 

 Primary Findings Adjusting from outdoor concentrations to personal exposures and correcting dose-response bias produce nearly equal results. 

Roughly the same premature mortalities associated with short-term exposure to both ambient PM2.5 and PM10 are predicted by 

both models 

Yip et al. (2004, 157166) 

Study Design A panel study with repeated measures with personal & home monitoring for 8 2-week Periods. Children were stratified into 

smoking and non-smoking households. 

 Period 2000-2001 

 Location Detroit, Michigan 

Population School-age children with asthma 

Age Groups 7-11 yr 

 Personal Method PEM in a backpack 

 Personal Size PM10

 Microenvironment Size PM10; indoor at home & indoor at school 

 Ambient Size PM10

 Component(s) NR 

 Primary Findings Personal PM concentrations were significantly correlated with home environment (r = 0.38 to 0.70), with the strongest 

relationships in home with non-smokers. 

December 2009 A-315 
Zhao et al. (2006, 156181) 

Study Design Aerosol source apportionment under four environments (personal, residential indoor, residential outdoor and ambient) to 

evaluate the relationship between different environments through exposure analysis, and to demonstrate the utility of the 

combined receptor model on air quality studies of various environments. 

Period June 2000 to May 2001 

Location Raleigh and Chapel Hill, NC 

Population NR. People with respiratory ailments most likely. 

 Age Groups NR 

 Indoor Source 4 main sources to residential indoor PM Cu-factor mixed with indoor soil, secondary sulfate, Personal care and activity, ETS and 

its mixture 

 Personal Method PEM and HI 

 Personal Size NR 

 Microenvironment Size NR 

 Ambient Size NR 

 Component(s) SO4

2- , OC, EC, and trace elements 

 Primary Findings Secondary SO4

2- and vehicle emissions were significant contributors of personal PM exposure and residential indoor PM 

concentrations. 

Zhao et al. (2007, 156182) 

Study Design Comprehensive analysis of the sources of PM15 exposure on children with moderate to severe asthma in urban-poor settings. 

Period Two winter periods (October 2002-March 2003 and October 2003-March 2004) 

Location Elementary school for children with significant asthma, Denver, CO 

Population Schoolchildren in urban-poor settings suffering from moderate to severe asthma 

Age Groups 6-13 yr (60% in the range 10-13 yr, rest in the range 6-9 yr) 

 Indoor Source Yes, House cleaning compounds, and smoking were identified as primary internal sources. 

 Personal Method PEM 

 Personal Size PM2.5 

 Microenvironment Size PM2.5 

 Ambient Size PM2.5 

 Component(s) EC, Cl, Si, NO3

 Primary Findings Four external sources and three internal sources were resolved in this study. Secondary nitrate and motor vehicle were two 

major outdoor PM2.5 sources. Cooking was the largest contributor to the personal and indoor samples. Indoor environmental 

tobacco smoking also has an important impact on the composition of the personal exposure samples. 

Zhu et al. (2005, 157191) 

Study Design 4 apartments near the freeway were monitored at 2 times for 6 consecutive days, 24 h per day. Subjects did not enter the 

bedrooms where the samplers were, no cooking, cleaning, children, or pets. 

Period Oct. 2003-Dec. 2003 and Dec. 2003-Jan. 2004 

 Location Los Angles, CA 

 Population Urban Populations near major freeways. 

 Age Groups NR 

 Indoor Source NR 

 Personal Method NR 

 Personal Size Indoor and Outdoor ultrafine particles (6-220 nm) 

 Microenvironment Size NR 

 Component(s) CO 

 Primary Findings The size distributions of indoor aerosols showed less variability than the adjacent outdoor aerosols. Indoor to outdoor ratios for 

ultrafine particle concentrations depended strongly on particle size. I/O ratios were dependent on the indoor ventilation 

mechanisms applied. Size-dependent particle penetration factors and deposition rates were predicted from data by fitting a 

dynamic mass balance model. 

December 2009 A-316 
Zöllner et al. (2007, 157192) 

Study Design Exposure assessment 

 Period Winter Period of 2005 and 2006 

 Location Baden-Wuerttemberg, Germany 

 Population School children 

 Age Groups NR 

 Personal Method NR 

 Personal Size NR 

 Microenvironment Size They only reported concentrations for PM2.5. PM ranging in size from 0.02 to >20 μm were collected and analyzed but only 

PM2.5 concentration were reported. 

Ambient Size They only reported concentrations for PM2.5. PM ranging in size from 0.02 to >20 μm were collected and analyzed but only 

PM2.5 concentration were reported. 

 Primary Findings The impaction of PM was strongly influenced by specific weather conditions. Time resolution of measurements in classrooms 

showed variation in particle concentration depending on the type of building and indoor activities. E'[Concentrations of very 

small particles indoors and in ambient air measured by condensation particle counter were influenced by traffic emissions. 

Table A-59. Examples of studies showing developments with UFP sampling methods since the 2004 

PM AQCD. 

Reference PM Size 

Ranges 

PM 

Constituents Instruments Study Description 

Biswas et al. 

(2005, 150694) CPC (water) Water-based CPC performance evaluation. 

Feldpausch et 

al. (2006, 

155773) 

20-100 

nm 

Carbonaceous 

aerosols 

DS with CPC, compared with 

DMA 

The DS with CPC compared fairly well with the DMA for particle sizes 

up to 40 nm with 20-40% underestimation depending on discharge 

frequency settings. The DS sampling period is 3-5 s in comparison with 

the 1 min scanning time of the DMA. 

Hering et al. 

(2005, 155838) CPC (water) Water-based CPC performance evaluation. 

Hermann et al. 

(2007, 155840) 3-40 nm Ag, NaCl CPC (water and butanol) 

Roughly 95% collection efficiency for d >5 nm for TSI models 3776 and 

3786, 95% efficiency for d >20 nm for model 3775, near 90% efficiency 

for d>20 nm for model 3785, near 90% efficiency for d >25 nm for 

model 3772. 

Kinsey et al. 

(2006, 130654) 

10 nm-5 

μm DE 

TEOM, SMPS, CPC, DustTrak, EBAM, ELPI, integrated filter 

samples 

TEOM compared best with gravimetric filter among mass concentration 

analyzers, ELPI and SMPS comparable for differential number 

distribution but ELPI not useful for gravimetric analysis because mass is 

not significant at small end of distribution. 

Kulmala et al. 

(2007, 097838) CPC Changing temperature difference between saturator and condenser 

within CPC allowed for differences in cut-off diameters. 

Kulmala et al. 

(2007, 155911) 2-20 nm Atmospheric 

aerosol, Ag 

Battery of CPCs (water, butanol, 

n-butanol) 

Used the battery to discriminate between water-soluble, water-insoluble, 

butanol-soluble, and butanol-insoluble nucleation-mode particles. 

Ntziachristos 

and Samaras 

(2006, 116722) 

7 nm-1 

μm 

Automobile 

exhaust 

5 instruments used 

simultaneously to reduce 

uncertainty: Teflon-coated filter 

downstream of constant volume 

sampling, ELPI with 

thermodenuder, CPC, SMPS, 

diffusion charger 

Use of four reduced variables combining output from all instruments 

(ratio of particle number concentration from CPC and ELPI, estimated 

mean geometric mobility diameter from signal of diffusion charger and 

number concentration from CPC, ratio of signal of diffusion charger to 

constant volume sampler mass, ratio of constant volume sampler mass 

to volume collected by ELPI) resulted in identification of clear outliers 

and factors related to driving and fuel properties rather than 

measurement errors. 

Olfert et al. 

(2008, 156004) 

30-100 

nm NaCl, ambient FIMS (compared with SMPS) 

Particle number concentrations reported by the FIMS were 8-23% 

higher than the SMPS using an inversion technique designed to correct 

for particle residence time in the FIMS, which operates at 0.1 s 

resolution. 

Petäjä et al. 

(2006, 156021) CPC (water) Water-based CPC performance evaluation. 

Winkler et al. 

(2008, 156160) 1.5-4 nm Tungsten oxide CPC (n-Propanol) 

Authors remove excess charge on particles with ion trap to detect 

particles down to ~ 1 nm (by eliminating electrostatic attraction to 

agglomerate). 

December 2009 A-317 
Table A-60. Summary of in-vehicle studies of exposure assessment. 

Reference Study Design Mode of Transport Exposures Primary Findings 

Briggs et al. 

(2008, 156294) 

UFP (P-Trak), PM10, PM2.5, and PM1

(OSIRIS light scatter) were operated in a 

car while driving or walking on one of 48 

routes in London. Trips ranged 1.5-15 min 

by car and were repeated up to 5 times to 

improve statistics. 

Study Period: Weekdays in May and June 

2005. 

Car 

Walking 

Units: PM1-PM10 (μg/m3

), 

UFP (p cm-3) 

Avg Car Exposure: 

PM105.87 (3.09) 

PM2.5 3.01 (1.10) 

PM1 1.82 (1.10) 

UFP 21639 (14379) 

Avg Walking Exposure: 

PM1027.56 (13.16) 

PM2.5 6.59 (3.12) 

PM1 3.37 (3.40) 

UFP 30334 (17245) 

In-car concentrations of PM2.5, PM1, and UFP 

correlated well with walking concentrations (R = 0.806, 

0.800, 0.799 respectively). Avg walking concentrations 

were 1.4-4.7 times higher than average in-car 

concentrations. Cumulative walking exposures (not 

shown here) were 4.4-15.2 times higher than those in 

cars, likely resulting from longer transit times for 

walking. 

Diapouli et al. 

(2007, 156397) 

UFP (CPC) concentrations were 

measured at school, residential, and invehicle environments in Athens, Greece. 

Study Period: school hours, Nov 

2003-Feb 2004 and Oct-Dec 2004 

Car 15-min median 

(1000p/cm3): 

School indoor 13.6 

School outdoor 16.6 

Residence indoor 11.2 

Residence outdoor 24.0 

In-vehicle 78.0 

In-vehicle UFP concentrations were roughly 3.5-7 

times higher than school or residence 

concentrations. Indoor concentration diel patterns 

were also shown to follow outdoor levels, which 

suggests that indoor levels are of outdoor origin. 

Fruin et al. (2008, 

097183); 

Westerdahl et al. 

(2005, 086502) 

[Note: same data 

presented.] 

On-road zero emissions vehicle driven on 

33-mi arterial road and 75-mi freeway 

measured UFP (CPCs, SMPS, EAD), BC 

(aethalometer), NOX (chemiluminescence), 

PM-bound PAHs (UV-photoionization), and 

CO (Q-Trak). DVD analysis of traffic 

density and car speed. 

Study Period: Feb-Apr 2003 for 2- to 4-h 

periods. 

Car Arterial range of 

medians: 

UFP (1000p/cm3

) 13-43 

PM (μg/m3

2.5 ) 7.9-45 

BC (μg/m3) 0.74-3.3 

Freeway range of 

medians: 

UFP (1000p/cm3

) 47-190 

PM (μg/m3

2.5 ) 25-110 

BC (μg/m3) 2.4-13 

Measurements of freeway UFP, BC, PM-bound PAH, 

and NOx concentrations were roughly one order of 

magnitude higher than ambient measurements. 

Multiple regression analysis suggests these 

concentrations were a function of truck density and 

total truck count. (Only PM measurements reported 

here). 

Gomez-Perales et 

al. (2004, 

054418) 

PM2.5 (personal filter pump), CO (T15 

electrochemical cell), and benzene 

(canister) were measured on transit 

routes, and PM2.5 filters were analyzed 

for mass, OC/EC, SO4

2–, NO3-, and trace 

metals. 

Study period: 3-h morning and evening 

rush hour May-June 2002 

Bus 

Minibus 

Metro 

PM2.5 (μg/m3

): 

Bus 68 

Minibus 71 

Metro 61 

Generally, PM2.5 concentration was higher in the 

morning than evening rush hour, but variability was 

higher for minibuses than other modes of transport. 

Wind speed was found to be associated with PM2.5

concentration on minibuses. 

December 2009 A-318 
Reference Study Design Mode of Transport Exposures Primary Findings 

Gomez-Perales et 

al. (2007, 138816) 

PM2.5 (personal filter pump), CO (T15 

electrochemical cell), and benzene 

(canister) were measured on transit 

routes, and PM2.5 filters were analyzed 

for mass, OC/EC, SO4

2–, NO3-, and trace 

metals. 

Study period: 3-h morning and evening 

rush hour Jan-March 2003 

Bus 

Minibus 

Metro 

Units: PM2.5 mass 

(μg/m3

), components (% 

of mass) 

Bus: 

PM2.5 20-58 

(NH4O3 5-8 

(NH4)2SO4 10-18 

OC 17-39 

EC 8-20 

Crustal 15-18 

Non-crustal 2-3 

Unknown 6-24 

Minibus: 

PM2.5 25-55 

(NH4O3 4-13 

(NH4)2SO4 7-22 

OC 22-37 

EC 9-19 

Crustal 12-13 

Non-crustal 3-3 

Unknown 4-26 

Metro: 

PM2.5 24-41 

(NH4O35-8 

(NH4)2SO4 10-21 

OC 35-42 

EC 9-13 

Crustal 10-16 

Non-crustal 2-4 

Unknown 5-20 

Buses and minibuses had similar concentration 

levels for PM2.5 mass, and metro exposures were 

lower. CO and benzene concentrations were higher 

on minibuses than buses. OC was the largest PM 

constituent for all modes of transport. Measured 

concentrations were higher in the morning than in 

the evening rush hour periods. Maximum historical 

wind speeds (1995-2003) appeared to be inversely 

associated with measured concentration. 

Gulliver and 

Briggs (2004, 

053238) 

PM10, PM2.5, and PM1 sampled (OSIRIS 

light-scatter devices) in a car while 

driving or walking on northern corridor of 

Northhampton UK. 

Study Period: 1-h interval of morning and 

evening rush hour during Winter 

1999-2000. 

Car 

Walk 

Walking concentrations, 

Units: μg/m3

Walk, Car, Background 

PM10 38.2, 43.2, 26.6 

PM2.5 15.1, 15.5 

PM1 7.1, 7.0 

In-car PM10 concentrations were elevated 

compared with walking and background. PM2.5 and 

PM1 concentrations were comparable for walking 

and background. Periods of elevated PM2.5

compared with PM10 generally corresponded to 

times when SO4

2– levels were also high. 

Gulliver and 

Briggs (2007, 

155814) 

TSP, PM10, PM2.5, and PM1 sampled 

(OSIRIS light-scatter devices) in a car 

while driving or walking on one of 48 

routes in London. Trips ranged 1.5-15 

min by car and were repeated up to 4 

times to improve statistics. 

Study Period: Jan-Mar 2005. 

Car 

Walk 

Mean concentrations, 

Units: μg/m3

Walk, Car, Background 

TSP-PM10 19.1 (19.8)

 18.2 (18.0) 4.9 (5.1) 

PM10-2.5 22.1 (22.8) 15.1 

(14.2) 10.0 (9.0) 

PM2.5 -1 10.9 (10.4) 8.3 

(8.4) 7.6 (7.1) 

PM1 4.8 (3.4) 2.9 (2.6) 4.2 

(2.4) 

Walking exposures larger than car and 

background, and car exposures were generally 

larger than background except for PM1. Peak 

exposures during walking were significantly higher 

than peak in-car exposures. 

Rossner et al. 

(2008, 156927) 

Measured PM2.5 exposure of 50 city bus 

drivers and 50 controls in Prague, Czech 

Republic using personal samplers (type 

not specified) and VOCs using passive 

samplers. PM2.5 filters analyzed for cPAHs. Focus of study is oxidative stress 

biomarkers in drivers. 

Study period: winter 2005, summer 

2006, winter 2006. 

Bus Units: ng/m3

Winter 2005: Bus Control

c-PAH 7.1 (3.7) 9.4 (5.5) 

B[a]P 1.3 (0.7) 1.8 (1.0) 

Smmer 2006: Bus Control 

c-PAH 1.8 (0.5) 2.0 (0.8) 

B[a]P 0.2 (0.1) 0.3 (0.2) 

Wnter 2006: Bus Control 

c-PAH 5.4 (3.5) 4.1 (1.7) 

B[a]P 1.0 (0.5) 0.8 (0.4) 

c-PAH and B[a]P exposure to bus drivers was 

significantly higher in Winter 2006, but control 

exposure was significantly higher in Winter 2005 

for c-PAH and B[a]P and in summer 2006 for cPAH. No significant difference in VOC exposure 

between bus drivers and controls was observed. 

Oxidative stress markers were significantly higher 

in bus drivers than controls for all seasons. 

December 2009 A-319 
Reference Study Design Mode of Transport Exposures Primary Findings 

Sabin et al. (2005, 

088300) 

BC (aethalometer), particle-bound PAH 

(UV-photoionization), and NO (luminol 

reaction) were measured on 3 diesel 

school buses, 1 diesel school bus with a 

particle trap, and one compressed gas 

bus during before- and after-school 

commutes. 

Study Period: May-June 2002. 

School bus (diesel, diesel 

with particle trap (TO), 

compressed gas (CNG)) 

In-bus mean 

concentration 

Units: BC (μg/m3

) 

PAH (ng/m3

) 

Windows closed: 

BC PAH 

Ambient: 2.5, 27 

CNG: 2.3, 57 

TO: 7.1, 190 

Diesel: 11, 290 

Windows open: 

BC PAH 

Ambient: 1.9, 26 

CNG: 1.5, 43 

TO: 2.3, 42 

Diesel: 3.9, 58 

Mean concentrations on diesel buses without 

newer emissions control technologies were 2-4.4 

times higher than background. On buses with 

particle traps, concentrations were 1.2-2.5 times 

higher than background, while concentrations on 

compressed gas-fueled school buses were actually 

lower than background. 

Table A-61. Summary of personal PM exposure studies with no indoor source during 2002-2008. 

Reference / Location Personal Micro Ambient 

SOUTHWEST 

Delfino et al. (2004, 056897) 

Alpine, California 

Method: pDR, Units = μg/m3

Last 2-h PM2.5 34.4 (33.7) 

Diurnal PM2.5 55.7 (31.6) 

Nocturnal PM2.5 22.3 (13.6) 

1-h max PM2.5 151.0 (120.3) 

4-h max PM2.5 87.5 (55.3) 

8-h max PM2.5 67.6 (39.0) 

24-h PM2.5 37.9 (19.9) 

Method: HI, Units = μg/m3

Indoor 24-h PM1030.3 (11.9) 

Indoor 24-h PM2.5 12.1 (5.4) 

Outdoor 24-h PM10 25.9 (10.4) 

Outdoor 24-h PM2.5 11.0 (5.4) 

Method: TEOM, Units = μg/m3

Diurnal PM10 35.1 (11.3) 

Nocturnal PM10 23.3 (8.4) 

1-h max PM10 54.4 (13.8) 

4-h max PM10 44.5 (12.4) 

8-h max PM10 39.8 (11.2) 

24-h PM10 23.6 (9.1) 

24-h PM2.5 10.3 (5.6) 

Delfino et al. (2006, 090745) 

Riverside and Whittier, California 

Method: PEM, Units = μg/m3

Riverside: 

n 13 

24-h PM2.5 32.78 (21.84) 

1-h max PM2.5 97.94 (70.29) 

8-h max PM2.5 47.21 (30.0) 

Whittier: 

n 32 

24-h PM2.5 36.2 (21.84) 

1-h max PM2.5 93.63 (75.19) 

8-h max PM2.5 51.75 (36.88) 

Method: FRM, Units = μg/m3

Riverside: 

24-h PM2.5 36.63 (23.46) 

24-h PM10 70.82 (29.36) 

Whittier: 

24-h PM2.5 18.0 (12.14) 

24-h PM10 35.73 (16.6) 

Turpin et al. (2007, 157062) 

Los Angeles County, CA (and Elizabeth, 

NJ, Houston, TX) 

Method: PEM, Units = μg/m3

Avg of 48-h PM2.5 

Child 40.2 

Adult 29.2 

Method: HI, Units = μg/m3

Avg of 48-h PM2.5: 16.2 

Method: HI, Units = μg/m3

Avg of 48-h PM2.5: 19.2 

Wu et al. (2005, 157155) 

Alpine, CA 

Method: pDR, Units = μg/m3

n 11 

Avg of 24-h PM2.5 11.4 (7.8) 

Method: pDR, Units = μg/m3

n 14 

Avg of 24-h PM2.5 5.6 (2.9) 

Method: HI 

n 14 

Avg of 24-h PM2.5 9.8 (2.5) 

Method: pDR, Units = μg/m3

n 8 

Avg of 24-h PM2.5 14.0 (11.4) 

Method: HI 

n 8 

Avg of 24-h PM2.5 14.3 (7.8) 

December 2009 A-320 
Reference / Location Personal Micro Ambient 

NORTHWEST 

Jansen et al. (2005, 082236) 

Seattle, Washington, USA 

NR Method: HI, Units = μg/m3

Indoor home: 

PM10 11.93 

PM2.5 7.29 

Outdoor home: 

PM10 13.47 

PM2.5 10.47 

Method: HI, Units = μg/m3

PM10 18.0 

PM2.5 14.0 

Koenig et al. (2003, 156653) 

Seattle, WA 

13.4 ± 3.2 μg/m3 Inside homes = 11.1 ± 4.9 Outside homes = 13.3 ± 1.4 

3 Central-sites = 10.1 ± 5.7 

Liu S et al. (2003, 073841) 

Seattle, WA 

Summary of PM concentrations 

(μg/m3

) between October 1999 and 

May 2001 by study group. 

 

Group Mean ± SD Personal PM2.5 COPD 10.5 ± 7.2 Healthy 9.3 ± 8.4 

Asthmatic 13.3 ± 8.2 CHD 10.8 ± 8.4 

Summary of PM concentrations (μg/m3

) 

between October 1999 and May 2001 by 

study group. 

Group Mean ± SD 

Indoor 

PM2.5 COPD 8.5 ± 5.1 

Healthy 7.4 ± 4.8 

Asthmatic 9.2 ± 6.0 

CHD 9.5 ± 6.8 

PM10 COPD 14.1 ± 6.6 

Healthy 12.7 ± 7.8 

Asthmatic 19.4 ± 11.1 

CHD 16.2 ± 11.3 

Summary of PM concentrations (μg/m3

) 

between October 1999 and May 2001 by 

study group. 

Location Pollutant 

Group Mean ± SD 

Outdoor PM2.5 COPD 9.2 ± 5.1 

Healthy 9.0 ± 4.6 

Asthmatic 11.3 ± 6.4 

CHD 12.7 ± 7.9 

PM10 COPD 14.3 ± 6.8 

Healthy 14.5 ± 7.0 

Asthmatic 16.4 ± 7.4 

CHD 18.0 ± 9.0 

Mar et al. (2005, 087566) 

Seattle, WA USA 

Method: HI, Units = μg/m3

PM2.5: 

Healthy: 9.3 (8.4) 

CVD: 10.8 (8.4) 

COPD: 10.5 (7.2) 

Method: HI, Units = μg/m3

PM2.5: 

Healthy: 7.4 (4.8) 

CVD: 9.5 (6.8) 

COPD: 8.5 (5.1) 

PM10: 

Healthy: 12.7 (7.8) 

CVD: 16.2 (11.3) 

COPD: 14.1 (6.6) 

Method: HI, Units = μg/m3

PM2.5: 

Healthy: 9.0 (4.6) 

CVD: 12.7 (7.9) 

COPD: 9.2 (5.1) 

PM10: 

Healthy: 14.5 (7.0) 

CVD: 18.0 (9.0) 

COPD: 14.3 (6.8) 

Trenga et al. (2006, 155209) 

Seattle, Washington 

Method: PEM, Units = μg/m3

Median PM2.5 

 Child 11.3 

 Adult 8.5 

Method: HI, Units = μg/m3

Median PM2.5 

Child 7.5 

Adult 7.6 

Method: HI, Units = μg/m3

Residential Outdoor 

Median PM2.5 

Child 9.6 

Adult 8.6 

Residential Outdoor 

Median PMcoarse 

Child 4.7 

Adult 5.0 

Residential Outdoor 

Median PM2.5 central site 

Child 11.2 

Adult 10.3 

Wu et al. (2006, 179950) 

Pullman, WA 

During non-burning times: 13.8 (11.1) 

During burning episodes: 19.0 (11.8) 

 

SOUTHCENTRAL 

Turpin et al. (2007, 157062) 

Houston (and Elizabeth, NJ, and Los 

Angeles County, CA) 

Houston, Units = μg/m3 (48-h avg) 

Child: 36.6 

Adult: 37.2 

Houston: 17.1 Houston: 14.7 

December 2009 A-321 
Reference / Location Personal Micro Ambient 

MIDWEST 

Adgate et al. (2002, 030676) 

Battle Creek, East St. Paul, and Phillips, 

Minnesota, constituting the MinneapolisSt. Paul metropolitan area. 

PM2.5 , Units = μg/m3

Battle Creek 

All Seasons: 118, 22.7, (25.7), 16.2 

(2.2) 

Spring: 41, 26.3 (25.7), 19.4 (2.1) 

summer: 31, 28.5 (36.1), 20.3 (2.1) 

Fall 46, 15.5 (13.4), 11.9 (2.1) 

E. St. Paul 

All Seasons: 107, 30.5 (38.7), 20.6 

(2.3) 

Spring: 44, 33.9 (34.4), 23.9 (2.3) 

summer: 25, 20.5 (15.0), 17.2 (1.8) 

Fall: 38, 33.1(51.9), 19.5 (2.5) 

Phillips 

All Seasons: 107, 26.5 (24.3), 20.9 

(2.0) 

Spring: 28, 37.5 (37.6), 30.0 (1.8) 

summer: 40, 22.7 (15.3), 19.2 (1.7) 

Fall: 39, 22.7 (16.7), 17.6 (2.1) 

PM2.5 , Units = μg/m3

Battle Creek 

All Seasons: 108, 10.6 (6.6), 9.0 (1.8) 

Spring: 25, 12.7 (7.7), 11.0 (1.7) 

summer: 36, 8.9 (3.8), 8.1 (1.5) 

Fall: 47, 10.9 (7.4), 8.8 (2.0) 

E. St. Paul 

All Seasons: 97, 17.4 (20.3), 12.2 (2.2) 

Spring: 30, 20.7 (26.4), 13.6 (2.4) 

summer: 26, 15.8 (11.4), 13.7 (1.6) 

Fall 41 16.0 19.6 10.4 2.4 

Phillips 

All Seasons: 89, 14.2 (13.0), 11.3 (1.9) 

Spring: 15, 16.9 (14.2), 13.0 (2.1) 

summer: 36, 13.2 (6.4), 11.4 (1.7) 

Fall: 38,14.4 (16.7), 10.6 (2.0) 

PM2.5 , Units = μg/m3

Battle Creek 

All Seasons: 88 9.4 (6.2), 7.8 (1.8) 

Spring: 36, 10.5 (7.1), 8.5 (2.0) 

summer: 22, 8.7 (4.4), 7.8 (1.6) 

Fall: 30, 8.4 (6.2), 7.1 (1.7) 

E. St. Paul 

All Seasons: 95, 10.8 (6.6), 9.3 (1.8) 

Spring: 36, 12.0 (7.3), 10.1 (1.9) 

summer: 25, 8.5 (3.2), 7.8 (1.6) 

Fall: 34, 11.3 (7.5), 9.6 (1.8) 

Phillips 

All Seasons: 88, 10.0 (5.8), 8.7, (1.7) 

Spring: 30 (12.1), 7.2 (10.5) 

summer: 30, 8.6 (3.8), 7.8 (1.6) 

Fall: 28, 9.3 (5.5), 8.1 (1.7) 

Crist et al. (2008, 156372) 

Ohio River Valley near Columbus 

PM2.5 , Units = μg/m3

Athens (rural): 17.61 (17.81) 

Koebel (urban): 14.59 (13.05) 

New Albany (suburb): 13.93 (12.25) 

PM2.5 , Units = μg/m3

Indoor 

Athens (rural): 17.20 (13.56) 

Koebel (urban): 14.98 (12.30) 

New Albany (suburb): 16.52 (13.53) 

PM2.5 , Units = μg/m3 

Athens (rural): 13.66 (8.91) 

Koebel (urban): 13.89 (9.29) 

New Albany (suburb): 12.72 (8.86) 

Sarnat et al. (2006, 089784) 

Steubenville, OH 

Mean (SD): PM2.5 , Units = μg/m3

Summer 

n = 169 

mean (SD) = 19.9 (9.4) 

Fall 

mean (SD) = 20.1 (11.6) 

Mean (SD): PM2.5 , Units = μg/m3

Summer 

n = 65 

mean (SD) = 20.1 (9.3) 

Fall 

mean (SD) = 19.3 (12.2) 

SOUTHEAST 

Wallace and Williams (2005, 057485) 

Raleigh, North Carolina 

Units = μg/m3

PM2.5 pers = 23.0 (16.4) 

PM2.5 pers/PM2.5 out = 1.31 (0.99) 

Units = μg/m3

PM2.5 in = 19.4 (16.5) 

PM2.5 in/PM2.5 out = 1.08 (1.05) 

Units = μg/m3

PM2.5 out = 19.5 (8.6) 18.1 (8.1) 

Williams et al. (2003, 053338) 

SE Raleigh, North Carolina 

Chapel Hill, North Carolina 

Pooled PM mass concentrations 

(μg/m3

) across all subjects, 

residences, seasons, and cohorts 

Variable N Geo mean Mean RSD(a) 

Personal PM2.5 (b) 712 19.2 23.0 70.1 

(a) Relative standard deviation of the 

presented arithmetic mean. 

(b) measured using PEMs. 

Pooled PM mass concentrations (μg/m3) 

across all subjects, residences, seasons, 

and cohorts 

Variable N Geo mean Mean RSD(a) 

Indoor PM2.5 (c) 761, 15.3, 19.1, 80.1 

Outdoor PM2.5 (c) 761, 17.5, 19.3, 43.7 

Indoor PM10 (b) 761, 23.2, 27.7, 70.6 

Outdoor PM10 (b) 761, 27.5, 30.4, 46.4 

Indoor PM10-2.5 (d) 761, 6.3, 8.6, 111.8 

Outdoor PM10-2.5 (d) 761, 8.5, 11.1, 86.9 

 

(a) Relative standard deviation of the 

presented arithmetic mean. 

(b) measured using PEMs. 

(c) measured using HI samplers. 

(d) measured by difference in PEM PM10

monitor and co-located HI PM2.5 mass 

concentrations. 

Pooled PM mass concentrations (μg/m3

) 

across all subjects, residences, seasons, 

and cohorts 

Variable N Geo mean Mean RSD(a) 

Ambient PM2.5 (c) 746, 17.3, 19.2, 44.9 

Ambient PM10 (b) 752, 27.9, 31.4, 51.5 

Ambient PM10-2.5 (d) 210, 8.6, 10.0, 62.3 

(a) Relative standard deviation of the 

presented arithmetic mean. 

(b) measured using PEMs. 

(c) measured using HI samplers. 

(d) measured by difference in PEM PM10

monitor and co-located HI PM2.5 mass 

concentrations. 

December 2009 A-322 
Reference / Location Personal Micro Ambient 

NORTHEAST 

Koutrakis et al. (2005, 095800) 

Baltimore, MD Boston, MA 

PM2.5, Units = μg/m3: 

(Baltimore, Boston) 

Winter: Seniors: 15.1 (14.6), 14.1 (6.0) 

Children: 24.0 (21.8), 18.5 (12.8) 

COPD: 16.4 (12.7), NR 

Summer: Seniors: 22.1 (10.1), 18.8 

(9.7) 

Children: 18.6 (8.1), 30.3 (14.2) 

COPD: NR, NREC: 

(Baltimore, Boston) 

Winter: Seniors: NR, 1.4 (0.9) 

Children: 2.8 (1.8), 1.6 (1.6) 

COPD: 2.0 (1.2), NR 

Summer: Seniors: NR, NR 

Children: NR, NR 

COPD: NR, NRSO4: 

(Baltimore, Boston) 

Winter: Seniors: 1.9 (1.1), 1.9 (1.2) 

Children: NR, 2.3 (1.7) 

COPD: 1.5 (0.8), NR 

Summer: Seniors: 5.7 (3.5), 2.9 (1.9) 

Children: NR, NR 

COPD: NR, NR 

NR PM2.5, Units = μg/m3

: 

 

(Baltimore, Boston) 

Winter: 

All: 20.1 (9.4), 11.6 (6.8) 

summer: 

Seniors: 25.2 (11.5), 12.7 (5.4) 

Children: 23.2 (14.0), 17.0 (11.5) 

COPD: NR, NREC: 

(Baltimore, Boston) 

Winter: 

All: 1.2 (0.6) 

summer: NR, NRSO4: 

(Baltimore, Boston) 

Winter: 

All: 4.0 (1.7), 3.1 (1.8) 

summer: 

Seniors: 10.5 (7.1), 3.1 (1.8) 

Children: NR, 6.5 (6.0) 

Sarnat et al. (2005, 087531) 

Boston, Massachusetts. Comparisons to a 

previous study in Baltimore are made. 

Units = μg/m3: 

Winter-Children: 

PM2.5: 17.4-25.8 

SO4: 1.6-3.3 

Winter-Seniors: 

PM2.5: 10.8-16.2 

SO4: 1.6-2.6 

Summer-Children 

PM2.5: 25.4-32.8 

SO4: 2.7-3.3 

Summer-Seniors 

PM2.5: 17.8-20.5 

SO4: 2.7-3.3 

NR Units = μg/m3

: 

Winter: 

PM2.5: 6.5-15.5 

SO4: 1.7-4.2 

Summer: 

PM2.5: 11.9-21.4 

SO4: 3.6-9.0 

Turpin et al. (2007, 157062) 

Elizabeth, NJ, (and Houston, TX, and Los 

Angeles County, CA+ 

48-h avg PM2.5, Units = μg/m3: 

Elizabeth 

Child: 54.0 

Adult: 44.8 

Elizabeth: 20.1 Elizabeth: 20.4 

December 2009 A-323 
Table A-62. Summary of PM species exposure studies. 

Reference Particle Sizes Measured Component Results Primary Findings 

Adgate et al. (2007, 156196) Personal, Micro, and 

Ambient: PM2.5 - broken down 

into TE 

Ag, Al, Ca, Cd, Co, Cr, Cs, 

Cu, Fe, K, La, Mg, Mn, Na, 

Ni, Pb, S, Sb, Sc, Ti, Tl, V, Zn 

Median, units: ng/m3

: 

Outdoor, Indoor, Personal 

S 334.4 ,272.1, 351.6 

Ca 232.2,85.0, 174.1 

Al 96.3, 23.3, 58.6 

Na 33.1, 20.6, 31.9; 

Fe 12.6, 43.1, 78.6 

Mg 10.9, 16.3, 27.5 

K 3.2, 38.4, 47.5 

Ti 3.0, 0.8, 1.4 

Zn 2.7, 6.5, 9.6 

Cu 2.4, 1.5, 4.9 

NiNA -0.1, 1.8 

Pb 1.5, 2.4, 3.2 

Mn 0.6, 1.5, 2.3 

Sb 0.08, 0.21, 0.30 

Cd 0.05, 0.12, 0.14 

V 0.05, 0.12, 0.16 

La 0.02, 0.05, 0.11 

Cs 0.00, 0.00, 0.00 

Th 0.00, 0.00, 0.00 

Sc 0.00, 0.00, 0.01 

Ag 0.00, 0.07, 0.08 

Co NA 0.02, 0.07 

Cr -0.09, 1.2, 2.6 

The relationships among P, I, 

and O concentrations varied 

across TEs. Unadjusted mixedmodel results demonstrated 

that ambient monitors are more 

likely to underestimate than 

overestimate exposure to many 

of the TEs that are suspected 

to play a role in the causation 

of air pollution related health 

effects. These data also 

support the conclusion that TE 

exposures are more likely to be 

underestimated in the lower 

income and centrally located 

PHI community than in the 

comparitively higher income BC 

K community. Within the limits 

of statistical power for this 

sample size, the adjusted 

models indicated clear 

seasonal and community 

related effects that should be 

incorporated in long-term 

exposure estimates for this 

population. 

Brunekreef et al. (2005, 

090486) 

Personal, Micro & Ambient: 

PM2.5 

NO3

- Mean (SD), units = ng/m3

: 

Amsterdam: 

Personal 1389(1965) 

Indoor 1348(1843) 

outdoor 4063(4435) 

Helsinki: 

Personal 161(202) 

Indoor 267(215) 

Outdoor 1276(1181) 

In both cities, personal and 

indoor PM2.5 were lower than 

highly correlated with outdoor 

concentrations. For most 

elements, personal and indoor 

concentrations were also highly 

correlated with outdoor 

concentrations. 

Brunekreef et al. (2005, 

090486) 

Personal, Micro, and 

Ambient: PM2.5 

SO4

2–, NO3- Mean, units = μg/m3

: 

SO 2–

4 : P, I, O 

Amsterdam 4.6 4.7 5.9 

Helsinki 2.7 3.0 5.0 

NO3

-

: 

P, I, O 

Amsterdam 1.4 1.4 4.0 

Helsinki 0.2 0.3 1.3 

In both cities personal and 

indoor PM2.5 were lower than 

highly correlated with outdoor 

concentrations. For most 

elements, personal and 

indoor concentrations were 

also highly correlated with 

outdoor concentrations. 

Chillrud et al. (2004, 054799) Personal: PM2.5 

Micro: PM2.5 

Home indoor and home 

outdoor 

Ambient: Urban fixed-site and 

upwind fixed site operated for 

three consecutive 48-h 

periods each week. 

Elemental iron, manganese, 

and chromium are reported in 

this study out of 28 elements 

sampled. 

Mean of duplicate samples: 

PM2.5: 62 μg/m3

Fe: 26 μg/m3

Mn: 240 ng/m3

Cr: 84 ng/m3

Variability: 1-15% 

Personal samples had 

significantly higher 

concentration of iron, 

manganese, and chromium 

than home indoor and 

ambient samples. The ratios 

of Fe (ng/ μg of PM2.5) vs Mn 

(pg/ μg PM2.5) showed 

personal samples to be twice 

the ratio for crustal material. 

Similarly for the Cr/Mn ratio. 

The ratios and strong 

correlations between pairs of 

elements suggested steel 

dust as the source. Timeactivity data suggested 

subways as a source of the 

elevated personal metal 

levels. 

December 2009 A-324 
Reference Particle Sizes Measured Component Results Primary Findings 

Ebelt et al. (2005, 056907) Personal: PM2.5 

Micro: “ambient exposure”: 

PM2.5, PM10, PM2.5 -10; 

“non-ambient exposure:” 

PM2.5 

Ambient: PM2.5, PM10, PM2.5-10

Ambient SO4

2-, 

Ambient non-sulfate, 

Personal sulfate, 

personal ambient non-sulfate 

Mean (SD),Units μg/m3

Ambient sulfate: 2.0 (1.1), 

Ambient non-sulfate: 9.3 

(3.7), 

Personal sulfate: 1.5 (0.9), 

personal ambient non-sulfate: 

6.5 (3.0) 

Ambient exposures and (to a 

lesser extent) ambient 

concentrations were 

associated with health 

outcomes; total and 

nonambient particle 

exposures were not. 

Farmer et al. (2003, 089017) Personal: PM10

Micro: NR 

Ambient: PM10

Extractable organic material 

(EOM) 

B[a]P 

cPAHs 

Benzo[a]pyrene (B[a]P) 

Carcinogenic polycyclic 

aromatic hydrocarbons 

(cPAHs) 

Units: ng/m3

Exposed, controls: 

Prague: 

cPAHs = 12.04(11.10), 6.17 

(3.48) 

B[a]P = 1.79 (1.67), 0.84 

(0.60) 

Kosice: 

cPAHs = 21.72 (3.12), 6.39 

(1.56) 

B[a]P = 2.94 (1.44), 1.07 

(0.66) 

Sofia: 

cPAHs = 93.84 (55.0) police, 

94.74 (120.34) bus drivers, 

41.65 (33.36) 

B[a]P = 4.31 (2.6) police, 5.4 

(3.18) bus drivers, 1.96 (1.53) 

Personal exposure to B[a]P 

and to total carcinogenic 

PAHs in Prague was two fold 

higher in the exposed group 

compared to controls, in 

Kosice three fold higher, and 

in Sofia 2.5 fold higher. 

Farmer et al. (2003, 089017) Personal: PM10

Micro: NR 

Ambient: PM10

PM2.5 (not reported) 

PM10

EOM 

EOM2 

B[a]P 

c-PAHsb 

Prague-SMWinter Summer 

EOM (μg/m3

) 14.93 4.96 

EOM2 (%) 23.9 13.4 

B[a]P (μg/m3) 3.5 0.28 

c-PAHsb (μg/m3

) 24.69 2.29 

Prague-LB Winter Summer 

EOM (μg/m3

) 10.86 3.72 

EOM2 (%) 27.9 14.1 

B[a]P (μg/m3) 2.9 0.17 

c-PAHsb (μg/m3

) 20.361.32 

Košice Winter Summer 

EOM (μg/m3

) 15.3 1.67 

EOM2 (%) 26.4 6.9 

B[a]P (μg/m3) 1.37 0.15 

c-PAHsb (μg/m3

) 11.87 1.2 

Sofia Winter Summer 

EOM (μg/m3

) 24.6 3.95 

EOM2 (%) 27.37 13.3 

B[a]P (μg/m3) 4.84 0.36 

c-PAHsb (μg/m3

) 36.442.43 

Extractable organic matter 

(EOM) per PM10 was at least 

2-fold higher in winter than in 

summer, and c-PAHs over 

10-fold higher in winter than 

in summer. Personal 

exposure to B[a]P and to total 

c-PAHs in Prague ca. was 2-

fold higher in the exposed 

group compared to the 

control group, in Košice ca. 3-

fold higher, and in Sofia ca. 

2.5-fold higher. 

Gadkari et al. (2007, 156459) Personal: Respirable PM 

(RPM) 

Micro: NR 

Ambient: RPM 

 Fe, Ca, Mg, Na K, Cd, Hg, 

Ni, Cr, Zn, As, Pb, Mn and Li 

Source contributions varied 

widely among 12 sites. 

Indoor: 0-95% 

Ambient: 0-26% 

Road: 0-94% 

Soil: 0-75% 

Authors conclude that personal 

exposure to ambient RPM is 

related to local traffic and soil 

resuspension. They felt that 

indoor activities or ventilation 

determined indoor levels of 

RPM. 

December 2009 A-325 
Reference Particle Sizes Measured Component Results Primary Findings 

Geyh et al. (2005, 186949) Personal: TD, PM10, PM2.5 

Micro: NR 

Ambient: TD, PM10, PM2.5 

EC 

OC 

VOC also assessed 

Mean (SD), units = μg/m3

: 

Summary Statistics by Area 

Location 

October 2001: 

Albany and West 

EC 5.9 (NA) OC 36 (NA) 

Liberty and Greenwich 

EC 5.3 (59) OC 30 (56) 

Park Place and Greenwich 

EC 14.5 (5.4) OC 72 (26) 

Church and Dey 

EC 7.9 (3.3) OC 48 (15) 

April 2002: 

Liberty and West 

EC 4.2 (2.1) OC 26 (13) 

Barclay and Greenwich 

EC 4.0 (2.6) OC 18 (14) 

Church and Dey 

EC 4.5 (1.9) OC 27 (15) 

Middle of the Pile 

EC 6.7 (1.0) OC 40 (25) 

Comparison of recorded EC 

and OC values from October 

2001 and April 2002 with 

previous studies suggests 

that the primary source of 

exposure to EC for the WTC 

truck drivers was emissions 

from their own vehicles. 

Hanninen et al. (2004, 

056812) 

Personal: PM2.5 

Micro: NR 

Ambient: PM2.5 

PM2.5 -bound S Indoor Outdoor 

Athens 5.3 (2.0) 7.6 (5.1) 

Basel 2.6 (1.6) 3.3 (1.6) 

Helsinki 1.6 (1.3) 2.2 (1.5) 

Prague 3.1 (1.3) 4.0 (1.5) 

Associated with indoor 

concentration: wooden 

building material, city, building 

age, floor of residence (i.e. 

ground, 1st, etc.), and use of 

stove other than electric. 

Ho et al. (2004, 056804) Personal: PM2.5 

Micro: NR 

Ambient: PM2.5 

OC 

EC 

OM 

TCA 

Mean, Unit = μg/m3

Indoors: 

OM = 18.1; TCA = 22.9 

Outdoors: 

OM = 20.1; TCA = 26.5 

The major source of indoor 

EC, OC, and PM2.5 appears 

to be penetration of outdoor 

air, with a much greater 

attenuation in mechanically 

ventilated buildings. 

Jacquemin et al. (2007, 

192372) 

Personal: PM2.5 

Micro: NA 

Ambient: PM2.5 

S Mean, units = μg/m3

: 

Personal: 1.3 outdoor: 1.2 

Authors suggest that “outdoor 

measurements of absorbance 

and sulphur can be used to 

estimate both the daily 

variation and levels of 

personal exposures also in 

Southern European 

countries, especially when 

exposure to ETS has been 

taken into account. For PM2.5, 

indoor sources need to be 

carefully considered.” 

Jansen et al. (2005, 082236) Personal, Micro, and 

Ambient: PM2.5 

Estimated Elemental Carbon 

(Abs) 

Elemental composition of a 

subset of personal, indoor 

and outdoor samples 

Mean (SD), units = μg/m3

: 

Amsterdam, Helsinki 

P,O,P,O 

PM2.5 14.5, 15.7, 9.4, 11.4 

Abs 1.4, 1.6, 1.3, 1.9 

S 912.3, 1299.9, 605.3, 1435.7 

Zn 13.2, 18.3, 11.7, 18.6 

Fe 57.0, 71.3, 41.6, 79.2 

K 87.4, 70.3, 103.1 ,93.9 

Ca 72.9, 40.2, 68.5, 36.4 

Cu 5.4, 2.5, 4.3, 1.8 

Si 29.7, 13.7, 79.5, 93.9 

Cl 40.8, 72.7, 9.8, 44.2 

For most elements, personal 

and indoor concentrations 

were lower than and highly 

correlated with outdoor 

concentrations. The highest 

correlations (median r.0.9) 

were found for sulfur and 

particle absorbance (EC), 

which both represent fine 

mode particles from outdoor 

origin. Low correlations were 

observed for elements that 

represent the coarser part of 

the PM2.5 particles (Ca, Cu, 

Si, Cl). 

December 2009 A-326 
Reference Particle Sizes Measured Component Results Primary Findings 

Johannesson et al. (2007, 

156614) 

Personal, Micro, and 

Ambient: PM2.5, PM1

BS BS2.5 Mean SD 

Personal 0.65 0.47 

Exclusively smokers 0.62 

0.47 

Residential indoor 0.56 0.47 

Exclusively smokers 0.52 

0.46 

Residential outdoor 0.68 0.51 

Exclusively smokers 0.71 

0.54 

Urban background 0.63 0.37 

All measurements 0.68 0.40 

PM1/BS1 

Personal 0.55 0.20 

Residential indoor 0.54 0.45 

Exclusively smokers 0.49 

0.43 

Residential outdoor 0.66 0.51 

Exclusively smokers 0.68 

Personal exposure of PM2.5

correlated well with indoor 

levels, and the associations 

with residential outdoor and 

urban background 

concentrations were also 

acceptable. Statistically 

significantly higher personal 

exposure compared with 

residential outdoor levels of 

PM2.5 was found for 

nonsmokers. PM1 made up a 

considerable proportion 

(about 70–80%) of PM2.5. For 

BS, significantly higher levels 

were found outdoors 

compared with indoors, and 

levels were higher outdoors 

during the fall than during 

spring. There were relatively 

low correlations between 

particle mass and BS. The 

urban background station 

provided a good estimate of 

the residential outdoor 

concentrations of both PM2.5

and BS2.5 within the city. The 

air mass origin affected the 

outdoor levels of both PM2.5

and BS2.5; however, no effect 

was seen on personal 

exposure or indoor levels. 

Kim et al. (2005, 156640) Personal: PM2.5 

Micro: NR 

Ambient: PM2.5 

SO4

2-, EC, Ca2+, Mn2+, K, Na+ Mean (SD), Units = μg/m3

: 

SO4

2–: 2.7 (3.2) 

Ca2+: 0.12 (0.12) 

Mg2+: 0.02 (0.01) 

K: 0.07 (0.08) 

Na+

: 0.09 (0.20) 

EC: 0.60 (0.54) 

Traffic-related combustion, 

regional, and local crustal 

materials were found to 

contribute 19% ± 17%, 

52% ± 22%, and 10% ± 7%, 

respectively. 

Among participants that spent 

considerable time indoors, 

exposure to outdoor PM2.5

includes a greater relative 

contribution from combustion 

sources, compared with 

outdoor (ambient) PM2.5

measurements. 

Koistinen et al. (2004, 

156655) 

Personal, Micro, and 

Ambient: PM2.5 

Black smoke, SO4

2–, NO3-, 

NH4

+

, Al, Ca, Cl, Cu, K, Mg, P, 

S, Si, Zn 

% contribution to PM2.5 

Outdoor - Indoor - Work - 

Personal 

CoPM * 35, 28, 32, 33 

Secondary** 46, 36, 37, 31 

Soil 16, 27, 27, 27 

Detergents 0, 6, 2, 6 

Sea Salt 3, 2, 1, 2 

* CoPM is the difference 

between total mass and other 

identified components; i.e., 

primary combustion particles, 

nonvolatile primary and 

secondary organic particles, 

and particles from tire wear, 

water, etc. ** Secondary 

particles are the sum of 

sulfate, nitrate, and 

ammonium. 4 factors were 

identified for each exposure 

type (residential indoor, 

residential outdoor, workplace 

indoor, and personal). The 

factors contained the 

elements Al, Ca, Cl, Cu, K, 

Mg, P, S, Si, Zn, and black 

smoke. (insert in cell to left 

after consolidating PM size) 

Population exposure 

assessment of PM2.5, based 

on outdoor fixed-site 

monitoring, overestimates 

exposures to outdoor sources 

like traffic and long-range 

transport and does not 

account for the contribution of 

significant indoor sources. 

December 2009 A-327 
Reference Particle Sizes Measured Component Results Primary Findings 

Koutrakis et al. (2005, 

095800) 

Personal: PM2.5 

Micro: NR 

Ambient: PM2.5 

Elemental Carbon (EC), 

SO4

2– Mean (SD) data are provided 

for Baltimore and Boston, 

Units = μg/m3: 

EC: 

(Baltimore, Boston) 

Winter: 

Seniors: NR, 1.4 (0.9) 

Children: 2.8 (1.8), 1.6 (1.6) 

COPD: 2.0 (1.2), NR 

SO4

2–: 

(Baltimore, Boston) 

Winter: 

Seniors: 1.9 (1.1), 1.9 (1.2) 

Children: NR, 2.3 (1.7) 

COPD: 1.5 (0.8), NR 

Summer: 

Seniors: 5.7 (3.5), 2.9 (1.9) 

Ambient PM2.5 and SO4

2– are 

strong predictors of 

respective personal 

exposures. Ambient SO4

2– is 

a strong predictor of personal 

exposure to PM2.5. Because 

PM2.5 has substantial indoor 

sources and SO4

2– does not, 

the investigators concluded 

that personal exposure to 

SO4

2– accurately reflects 

exposure to ambient PM2.5

and therefore the ambient 

component of personal 

exposure to PM2.5 as well. 

Kulkarni and Patil (2003, 

156664) 

Personal: PM5

Micro: NR 

Ambient: PM5

Pb 

Ni 

Cd 

Cu 

Cr 

Fe 

Mn

Personal samples, 

Units = μg/m3: 

Mean ± SD 

Type 

Pb 

Occupational 

4.384 ± 7.766 μg/m3

Residential 

4.093 ± 5.925 μg/m3

24-h integrated 

4.205 ± 1.523 μg/m3

Cd 

Occupational 

0.201 ± 0.158 μg/m3

Residential 

0.111 ± 0.165 μg/m3

24-h integrated 

0.134 ± 0.140 μg/m3

Mn 

Occupational 

1.979 ± 7.842 μg/m3

Residential 

0.180 ± 0.261 μg/m3

24-h integrated 

1.983 ± 6.824 μg/m3

K 

Occupational 

3.473 ± 4.691 μg/m3

Residential 

4.589 ± 4.619 μg/m3

24-h integrated Check 

All listed metals were 

detected in the ambient air 

where as only Lead, 

Cadmium, Manganese, and 

Potassium were detected in 

personal exposures. Mean 

daily exposure to lead 

exceeds the Indian NAAQS 

by a factor of 4.2. However, 

ambient concentration of lead 

conforms to this standard. 

There is a rising trend in the 

personal exposures and 

ambient levels of cadmium. 

However, they are low and do 

not pose any major health 

risk as yet. Personal 

exposures to toxic metals 

exceed the corresponding 

ambient levels by a large 

factor ranging from 6.1 to 

13.2. Thus, ambient 

concentrations may 

underestimate health risk due 

to personal exposure of toxic 

metals. Outdoor exposure to 

toxic metals is greater than 

the indoor (ratios ranging 

from 2.3 to 1.1) except for 

potassium (ratio 0.77). 

However, there is no 

significant correlation 

between these two. 

December 2009 A-328 
Reference Particle Sizes Measured Component Results Primary Findings 

Lai et al. (2004, 056811) Personal, Micro, and 

Ambient: PM2.5 

Ag Cr Mn Si 

Al Cu Na Sm 

As Fe Ni Sn 

Ba Ga P Sr 

Br Ge Pb Ti 

Ca Hg Rb Tl 

Cd I S Tm 

Cl K Sb V 

Co Mg Se Zn 

 Zr 

GM (GSD), Units: ng/m3

P, RI, RO, WI, I/O 

Al 280 (7.0), 67 (7.2), 22 

(2.9), 110 (7.5), 1.4 

As 4.7 (1.6), 3.7 (1.8), 2.6 

(2.7), 6 (—) , 1.4 

Br 4.7 (2.2), 3.9 (2.0), 2.4 

(2.5), 6.2 (2.5), 1.6 

Ca 260 (2.0), 120 (2.1), 30 

(1.6), 280 (2.9), 3.3 

Cd 23 (1.4), 19 (1.8), 7 (—), 

43 (2.2), — 

Cl 400 (3.0), 270 (3.9), 220 

(5.2), 380 (3.9), 1.0 

Cu 120 (1.3), 88 (1.7), 2.3 

(2.8), 230 (2.1), 37.1 

Fe 59 (2.3) ,30 (3.8), 19 (3.5),

 85 (2.9), 1.6 

Ga 0.9 (2.1), 0.6 (2.2), 0.2 

(2.2), 2.0 (3.4), 2.4 

K 250 (2.4), 180 (2.7), 93 

(2.0), 130 (4.0),1.7 

Mg 260 (2.1), 130 (3.1),140 

(2.9), 120 (2.8), 0.7 

Mn 2.1 (2.6), 1.8 (2.4) ,2.2 

(1.5), 3.5 (3.0) ,0.8 

Na 2100 (1.6) ,1800 (1.7),

 1100 (3.2), 2700 (1.9), 1.6 

Ni 11 (2.2), 8.6 (2.5), 18 (—),

 23 (2.9) ,— 

P 110 (2.1), 70 (2.2), 27 (1.8),

 86 (2.4), 2.5 

Pb 26 (1.7), 19 (1.8), 9.4 (2.8),

 32 (2.0) ,1.9 

S 1200 (1.9),1200 (2.0), 890 

(4.8), 1.2 

Se 8.4 (1.5) ,6.8 (1.7) ,2.3 

(1.8), 16 (2.2) ,2.8 

Si 740 (3.4), 360 (2.9) ,95 

(2.2), 570 (3.8), 2.6 

Sn 35 (1.5) ,27 (1.8) ,0 (—) ,68 

(2.6), — 

Ti 6.2 (1.7) ,2.8 (2.2), 1.1 

(2.0), 6.1 (3.2) ,2.3 

V 1.8 (1.5), 1.4 (1.9), 4 (—) , — 

Zn 18 (2.4) ,15 (2.2), 13 

(2.5),23 (2.4), 0.9 

Both the indoor and outdoor 

environments have sources 

that elevated the indoor 

concentrations in a different 

extent, in turn led to higher 

personal exposures to 

various pollutants. 

Geometric mean (GM) of 

personal and home indoor 

levels of PM2.5, 14 elements, 

total VOC (TVOC) and 8 

individual compounds were 

over 20% higher than their 

GM outdoor levels. Those of 

NO2, 5 aromatic VOCs, and 5 

other elements were close to 

their GM outdoor levels. For 

PM2.5 and TVOC, personal 

exposures and residential 

indoor levels (in GM) were 

about 2 times higher among 

the tobacco-smoke exposed 

group compared to the nonsmoke exposed group, 

suggesting that smoking is an 

important determinant ofthese 

exposures. Determinants for 

CO were visualised by realtime monitoring, and the 

authors showed that the peak 

levels of personal exposure 

to CO were associated with 

smoking, cooking and 

transportation activities. 

Moderate to good 

correlations were only found 

between the personal 

exposures and residential 

indoor levels for both PM2.5

(r = 0: 60; p< 0: 001) and NO2

(r = 0: 47; p = 0: 003). 

December 2009 A-329 
Reference Particle Sizes Measured Component Results Primary Findings 

Larson et al. (2004, 098145) Personal: PM2.5 

Micro: PM2.5 outside subject’s 

residence, and inside 

residence 

Ambient: PM2.5 at Central 

outdoor site (downtown 

Seattle) 

Light absorbing carbon (LAC) 

and Al, As, Br, Ca, Cl, Cr, Cu, 

Fe, K, Mn, Ni, Pb, Si, S, Ti, V 

Personal, RI, RO, Central 

Mass 

10,500 10,250 12,693 11,970 

Al 32, 19, 21, 31 

As 1, 1, 2, 2 

LAC * 1439, 1105, 1830, 1741 

Br 3, 2, 3, 3 

Ca 72, 46, 36, 50 

Cl 248,173, 75, 78 

Cr 2, 2, 1, 2 

Cu 3, 4, 2, 3 

Fe 63, 35, 61, 95 

K 57, 54, 78, 67 

Mn 2, 2, 3, 6 

Ni 0 ,0, 1, 1 

Pb 2, 2, 5, 5 

Si 109, 65, 66, 62 

S 289, 289, 468, 492 

Ti 4, 3, 3, 6 

V 0, 1, 2, 3 

Five sources of PM2.5

identified: vegetative burning, 

mobile emissions, secondary 

sulfate, a source rich in 

chlorine, and crustal-derived 

material. The burning of 

vegetation (in homes) 

contributed more PM2.5 mass 

on avg than any other 

sources in all 

microenvironments. 

Maitre et al. (2002, 156726) Personal: PM4

Micro: NR 

Ambient: PM4

PAH, benxene-toluenexylenes (BTX), aldehydes, 

BaP 

PAHc, formaldehyde, 

acetaldehyde 

Median 

Personal Ambient 

Resp μg/m3

 124, 124 (mean) 

BaP ng/m3

 0.28, 0.14 

PAHc ng/m3

 1.19, 1.56 

PAH ng/m3

 13.14, 12.26 

Benzene μg/m3 23.5, 17 

Toluene μg/m3

 94.5, 52 

Xylene μg/m3

 74, 39 

BTX μg/m3 192, 108 

Formaldehyde μg/m3

 21, 17.5 

Acetaldehyde μg/m3

 17, 10.5 

Aldehyde μg/m3 38, 28 

The occupational exposure of 

policemen does not exceed 

any currently applicable 

occupational or medical 

exposure limits. Individual 

particulate levels should 

preferably be monitored in 

Grenoble in winter to avoid 

underestimations. 

Meng et al. (2005, 081194) Personal: PM2.5 

Micro: NA 

Ambient: NR 

EC, OC, S, Si Mean (SD), units = ng/m3: 

Indoor: 

EC: 1165.9 (2081.0) 

OC: 7725.5 (9359.3) 

S: 902.3 (602.2) 

Si: 124.0 (79.0) 

Outdoor: 

EC: 1144.1 (968.1) 

OC: 3777.7 (2520.1) 

S: 1232.3 (633.2) 

Si: 141.1 (171.3) 

Use of central-site PM2.5 as 

an exposure surrogate 

underestimates the 

bandwidth of the distribution 

of exposures to PM of 

ambient origin. 

December 2009 A-330 
Reference Particle Sizes Measured Component Results Primary Findings 

Molnár et al. (2005, 156772) Personal: PM2.5

Micro and Ambient: PM10-2.5

and PM2.5 

BS 

S 

Cl 

K 

Ca 

Mn 

Fe 

Cu 

Zn 

Br 

Rb 

Pb 

Median, unit = ng/m3

 

Wood burners 

Ref 1-sided p-value 

BS 0.97, 0.74, 0.053 

S 880, 650, 0.500 

Cl 200, 160, 0.036 

K 240, 140, 0.024 

Ca 76, 43, 0.033 

Mn 4.8, 3.5, 0.250 

Fe 64, 49, 0.139 

Cu 8.9, 2.4, 0.016 

Zn 38, 22, 0.033 

Statistically significant 

contributions of wood burning 

to personal exposure and 

indoor concentrations have 

been shown for K, Ca, and Zn. 

Increases of 66–80% were 

found for these elements, 

which seem to be good woodsmoke markers. In addition, Cl, 

Mn, Cu, Rb, Pb, and BS were 

found to be possible woodsmoke markers, though not 

always to a statistically 

significant degree for personal 

exposure and indoor 

concentrations. For some of 

these elements, subgroups of 

wood burners had clearly 

higher levels which could not 

be explained by the information 

available. 

Sulfur, one of the more typical 

elements mentioned as a 

wood-smoke marker, showed 

no relation to wood smoke in 

this study due to the large 

variations in outdoor 

concentrations from LDT air 

pollution. This was also the 

case for PM2.5 mass. Personal 

exposures and indoor levels 

correlated well among the 

subjects for all investigated 

species, and personal 

exposures were generally 

higher than indoor levels. 

Molnar et al. (2006, 156773) Personal: PM2.5 and PM1

Micro and Ambient: NR 

S 

Cl 

K 

Ca 

Ti 

V 

Mn 

Fe 

Ni 

Cu 

Zn 

Br 

Pb 

Urban background PM2.5 

Mean, median, range (ng/m3

) 

S 620, 320, 95-1900 

Cl 97, 54, 25-460 

K 55, 50, 32-130 

Ca 21, 17, 6.6-6.2 

Ti 2.1, 1.9, 1.3-3.8 

V 3.4, 2.4, 1.0-13 

Mn 1.6, 1.4, 0.67- 3.8 

Fe 36, 33, 7.1-100 

Ni 1.6, 1.2, 0.33- 5.7 

Cu 2.1, 1.4, 0.33-11 

Zn 14, 11, 2.8-38 

Br 1.7, 1.4, 0.47-44.3 

Pb 3.3, 2.1, 0.94-11 

Personal PM2.5 

Mean, median, range (μg/m3

 ) 

S –, < 470, 270-1400 

Cl 270, 170, 60-920 

K 140, 96, 39-690 

Ca 110, 80, 27-670 

Ti 11, 9.5, 3.7-27 

V 4.7, 4.0, 2.7-9.4 

Mn - - - 

Fe 68, 69, 23-150 

Ni 4.2, 2.6, 0.89-46 

Cu 10, 6.6, 1.1-81 

Zn 21, 16, 6.6-70 

Br 2.0, 1.3, 0.91-14 

Pb 2.9, 2.6, 0.92-8.3 

Personal PM1 

Mean, median, range (μg/m3)

S –, < 470, 240-1200 

Cl –, < 110, 54-160 

K 80, 82, 50-130 

Ca 32, 23, 8.4-87 

Ti 6.5, 6.3, 3.7-11 

V –, < 4.2, 2.8-8.9 

PM2.5 personal exposures were 

significantly higher than both 

outdoor and urban background 

for the elements Cl, K, Ca, Ti, 

Fe, and Cu. Personal exposure 

was also higher th

an indoor 

levels of Cl, Ca, Ti, Fe, and Br, 

but lower than outdoor Pb. 

Residential outdoor levels were 

significantly higher than the 

corresponding indoor levels for 

Br and Pb, but lower for Ti and 

Cu. The residential levels were 

also significantly higher than 

the urban background for most 

elements. 

December 2009 A-331 
Reference Particle Sizes Measured Component Results Primary Findings 

Mn - - - 

Fe 28, 25, 7.6-68 

Ni 8.2, 1.2, 0.83-58 

Cu 5.0, 4.4, 1.6-14 

Zn 15, 14, 7.6-37 

Br 1.6, 1.5, 0.83-4.4 

Pb 3.6, 2.8, 1.1-11 

Residential Outdoor PM2.5 

Mean, median, range 

S 640, 460, 190-1800 

Cl 6.3, 140, 57-840 

K 200, 78, 32-200 

Ca 82, 28, 4.6-85 

Ti 34, 5.2, 3.3-21 

V 6.3, 3.9, 2.1-14 

Mn --- 

Fe 5.5, 31, 8.8-200 

Ni 45, < 1.6, 0.65-5.5 

Cu 2.6, 1.3, 0.65-17 

Zn 22, 15, 5.5-85 

Br 2.0, >450, 0.91-51 

Pb 4.6, 2.6, 0.90-20 

Residential Outdoor PM1

S –, 1.3, 24-2000 

Cl –, < 110, 44-170 

K 76, 68, 34-170 

Ca –, < 12, 5.1-78 

Ti –, < 5.0, 2.2-9.5 

V 5.6, 4.47, 2.2-14 

Mn --- 

Fe 23, 14, 3.7-140 

Ni 3.3, 1.4, 0.73-28 

Cu –, < 1.1, 0.73-12 

Zn 15, 14, 5.2-30 

Br 1.5, 1.4, 0.78-4.3 

Pb 4.1, 1.5, 1.0-17 

Na and Cocker (2005, 

156790) 

Personal: PM2.5 

Micro: NR 

Ambient: PM2.5 

EC, OC Mean (SD), units = μg/m3 

Residential homes: EC 2.0 

(NR) 

OC 14.8 (NR) 

High school (EC): 

Weekday samples 1.1 (0.9) 

Weekend samples 1.0 (0.5) 

High school (OC): 

Weekday samples 8.8 (4.7) 

Weekend samples 7.4 (2.4) 

Indoor PM2.5 was significant 

influenced by indoor OC 

sources. 

Indoor EC sources were 

predominantly of outdoor 

origin. 

Noulett et al. (2006, 155999) Personal: PM2.5 

Micro: NR 

Ambient: PM2.5 

SO4

2- 

ABS (light absorbing carbon) 

Measurement Mean s.d. 

Ambient SO4

2- 2.72* 3.11 

Ambient ABS 1.4** 1.0 

Personal SO4

2- 1.33* 1.47 

Personal ABS 1.0** 1.7 

* Mean SO4

2- values reported 

in μg/m3

** Mean ABS values reported 

in 10-5/m-1

SO4

2- and light absorbing 

carbon concentrations had 

higher personal-ambient 

correlations and less 

variability. This indicates that 

SO4

2- and ABS were of 

outdoor origin, while PM2.5

mass was of varied indoor 

and outdoor origin. 

December 2009 A-332 
Reference Particle Sizes Measured Component Results Primary Findings 

Salma et al. (2007, 113852) Personal: PM10-2.0 and PM2.0

Micro: NA 

Ambient: NR 

30 elements (Na, Mg, Al, Si, 

P, S, Cl, K, Ca, Ti, V, Cr, Mn, 

Fe, Ni, Cu, Zn, Ga, Ge, As, 

Se, Br, Rb, Sr, Y, Zr, Nb, Mo, 

Ba, and Pb) 

Units: ng/m3

: 

PM10-2.0; PM2.0 

Mg 296 130 

Al 531 93 

Si 2.09 442 

S 978 828 

Cl 305 104 

K 318 127 

Ca 2.57 413 

Ti 47 25 

Cr 35 15 

Mn 310 148 

Fe 33.5 15.5 

Ni 29 8 

Cu 496 190 

Zn 118 50 

Br 13 DL 

Ba 145 DL 

Pb 47 21 

PM 83.6 33.0 

The concentrations observed 

in the Astoria underground 

station were clearly lower (by 

several orders of magnitude) 

than the corresponding 

workplace limits. 

Sarnat et al. (2005 RMID 

9171) (2005, 087531) 

Personal: PM2.5 

Micro: N/A 

Ambient: PM2.5 

SO4, O3, NO2, SO2 Correlations between 

personal PM2.5 and ambient 

gas 

O3 correlated in summer. 

Spearman’s R≈0.4, 

Anti-correlated in winter, 

R≈0.3-0.1. 

NOX somewhat correlated in 

summer. R≈0.3 

Winter, R≈0.2-0.4 

SO2 not well correlated in 

summer or winter. R≈0-0.1. 

CO somewhat correlated in 

summer. R≈0.1-0.3. 

Correlated in winter R≈0.2-

0.3. 

No results were significant. 

Substantial correlations 

between ambient PM2.5

concentrations and 

corresponding personal 

exposures. 

Summertime gaseous 

pollutant concentrations may 

be better surrogates of 

personal PM2.5 exposures 

(especially personal 

exposures to PM2.5 of 

ambient origin) than they are 

surrogates of personal 

exposures to the gases 

themselves. 

Sarnat et al. (2006, 089784) Personal: PM2.5 

Micro: NR 

Ambient: PM2.5 

SO4

2-

EC 

Mean (SD), units = μg/m3: 

Personal 

Ambient 

SO4

2-

Summer 

5.9 (4.2) 

7.7 (4.8) 

Fall 

4.4 (3.3) 

6.2 (4.7) 

EC 

Summer 

1.1 (0.6) 

1.1 (0.5) 

Fall 

1.2 (0.7) 

1.1 (0.7) 

High association between 

personal and ambient SO4

2–

and EC, especially for SO4

2–

for which there is no 

significant indoor source. 

Shilton et al. (2002, 049602) Personal, Micro, and 

Ambient: Respirable PM 

Respirable PM, metals (Zn, Cu, 

Mn, Al), SO4

2-, NO3-

, and Cl- Indoor Outdoor 

Zn (ng/m3) 241.1, 179.5 

Cu (ng/m3) 43.3 , 24.99 

Mn (ng/m3

) 15.6, 4.18 

Al (ng/m3

) 305.2, 52.90 

SO4

2- (ng/m3

) 4.72, 3.47 

Cl (ng/m3

) 1.08 , 0.15 

NO3

-

 (ng/m3).35, 1.08 

The indoor particulate conc 

was driven by ambient conc; 

meteorological-induced 

changes in ambient PM were 

detected indoors; 

December 2009 A-333 
Reference Particle Sizes Measured Component Results Primary Findings 

Smith et al. (2006, 156990) Personal: PM2.5 

Micro: PM2.5 

Area samplers in the offices, 

freight dock, or shop. 

Ambient: PM2.5 

Samplers were located in the 

yard upwind of the terminal. 

EC 

OC 

Work Area EC, OC, EC/TC 

Office 0.31 (3.72), 11.29 

(1.63) 

Dock 0.53 (3.24), 5.01 (1.76),

 3% (3.10) 

Yard 0.73 (2.89), 7.77 (1.65),

 9% (2.49) 

Shop 1.54 (3.52), 10.37 

(2.00), 8% (2.21) 

Non-smokers on-site: 12% 

(2.13) 

Clerk 0.09 (9.98), 15.97 

(1.31) 

Dock worker 0.76 (2.13),

 13.89 (1.45), 1% (10.19) 

Mechanic 2.00 (3.82), 16.89 

(1.64), 5% (1.96) 

Hostler 0.88 (3.04), 14.89 

(1.86), 10% (2.71) 

Non-smokers off-site 5% 

(2.09) 

Pickup/deliver driver 1.09 

(2.46), 12.40 (1.54) 

Long haul driver 1.12 (1.91),

 19.26 (2.30), 8% (2.13) 

Smokers On-Site 7% (1.82) 

Clerk 1.19 (1.70), 32.25 

(1.70), NR 

Dock worker 0.98 (1.93),

 24.02 (1.87) 

Mechanic 2.41 (2.27), 24.35 

(1.78) 

Hostler 1.74 (2.21), 43.92 

(2.03) 

Smokers off-site 

Pickup & Delivery drivers 

1.33 (3.84), 24.24 (2.14) 

Long haul drivers 1.37 

(2.40),32.81 (3.23) 

December 2009 A-334 
Reference Particle Sizes Measured Component Results Primary Findings 

Sørensen et al. (2003, 

157000) 

Personal: PM2.5 

Micro: NR 

Ambient: PM2.5 

BS Units: 10-6/m 

n Median Q25-Q75 

All 177 6.8 (5.0-13.2) 

Autumn 42 7.1 (6.5-17.2) 

Winter 46 8.2 (5.1-13.3) 

Spring 46 12.6 (5.4-10.4) 

Summer 47 8.1 (3.4-9.0) 

Personal PM2.5 exposure was 

found to be a predictor of 8-

oxodG in lymphocyte DNA. 

No other associations 

between exposure markers 

and biomarkers could be 

distinguished. ETS was not a 

predictor of any biomarker in 

the present study. The current 

study suggests that exposure 

to PM2.5 at modest levels can 

induce oxidative DNA 

damage and that the 

association to oxidative DNA 

damage was confined to the 

personal exposure, whereas 

the ambient background 

concentrations showed no 

significant association. 

For most of the biomarkers 

and external exposure 

markers, significant 

differences between the 

seasons were found. 

Similarly, season was a 

significant predictor of SBs 

and PAH adducts, with avg 

outdoor temperature as an 

additional significant 

predictor. 

Sorenson et al. (2005, 

089428) 

Personal: PM2.5 and BS 

Micro: PM2.5 and BS 

Ambient: Street monitoring 

station and roof of a campus 

building PM2.5 and BS 

BS Mean, IQR, Units = μg/m3

: 

Personal: 

Cold Season: 10.2 (5.6-14.8) 

Warm Season: 7.1 (5.5-11.4) 

Micro: 

Cold Season 

Home Indoor: 6.2 (5.5-11.4) 

Home front door: 10.8 (7.4-

16.3) 

Warm Season 

Home Indoor: 6.1 (3.7-7.6) 

Home front door: 

8.8 (5.6-11.54) 

Ambient: 

Cold Season: Street Station: 

31.6 (27.5-34.0) 

Urban Background: 

7.7 (5.9-11.0) 

Warm Season: 

Street Station: 

30.6 (24.7-36.0) 

Urban Background: 

6.8 (4.6-8.6) 

Indoor sources of PM and BS 

were shown to be greatly 

influenced by indoor sources. 

Sram et al. (2007, 192084) Personal: PM10, PM2.5 

Micro: NR 

Ambient: PM10, PM2.5 

c-PAHs, B[a]P B[a]P: 

Exposed 1.6 ng/m3

 

Control 0.8 ng/m3 

c-PAHs: 

Exposed 9.7 ng/m3 

Control 5.8 ng/m3

Ambient air exposure to cPAHs increased fluorescent 

in situ hybridization (FISH) 

cytogenetic parameters in 

non-smoking policemen 

exposed to ambient PM 

December 2009 A-335 
Reference Particle Sizes Measured Component Results Primary Findings 

Turpin et al. (2007, 157062) Personal: PM2.5 

Micro: PM2.5, in the main 

living area (not kitchen) 

Ambient: PM2.5, in the front or 

back yard 

18 volatile organics, 17 

carbonyl, PM2.5 mass and 

>23 PM2.5 species, organic 

carbon, elemental carbon, 

and PAHs 

For Los Angeles 

Carbon (μgC/m3

) 

EC 1.4 

OC 4.1 

Elements (ng/m3

) 

Ag 0.5 

Al 24.7 

As 0.5 

Ba 22.9 

Br 5.3 

Ca 80.9 

Cd 0.4 

Cl 62.0 

Co ND 

Cr 0.6 

Cu 5.5 

Fe 162.9 

Ga 0.1 

Ge 0.1 

Hg 0.1 

In 0.3 

K 74.1 

La 2.3 

Mn 2.9 

Mo 0.4 

Ni 2.0 

Pb 4.7 

Pd 0.3 

P 0.1 

Rb 0.1 

S 1022.9 

Sb 2.1 

Se 1.4 

Si 128.9 

Sn 7.9 

Sr 1.8 

Ti 10.4 

V 5.3 

Y 0.1 

Zn 16.4 

Zr 0.5 

The best estimate of the 

mean contribution of outdoor 

to indoor PM2.5 was 73% and 

the outdoor contribution to 

personal was 26%. 

Wallace and Williams (2005, 

057485) 

Personal: PM2.5 

Indoor Micro: PM2.5 

Outdoor Micro: PM2.5 

S Mean (SD), units = ng/m3

: 

Personal: 1046 (633) 

Indoor: 1098 (652) 

Outdoor: 1951 (1137) 

Generally, Finf provides a 

reliable estimate of personal 

exposure. S can be used in lieu 

of personal exposure to PM 

because it is generally derived 

from outdoors. 

Wu et al. (2006, 179950) Personal: PM2.5 

Micro: PM2.5 

Ambient: PM2.5 

LG 

EC 

OC 

Mean personal exposure 

(μg/m3

): 

LG: 0.018 (0.024) 

EC: 0.4 (0.5) 

OC: 8.5 (2.7). 

Ambient: check component 

During non-burning times: 

0.026 (0.030) 

During burning episodes: 

0.010 (0.012) 

Authors “found a significant 

between-subject variation 

between episodes and nonepisodes in both the 

Exposure during agricultural 

burning estimates and 

subjects’ activity patterns. 

This suggests that the LG 

measurements at the central 

site may not always represent 

individual exposures to 

agricultural burning smoke 

“Evidence of “Hawthorne 

Effect”: During declared 

episodes (i.e. real and sham), 

subjects spent less time 

indoors at home and more 

time in transit or indoors 

away from home than during 

non-declared episode 

periods. The differences 

remained even when limited 

to weekdays only. 

December 2009 A-336 
Reference Particle Sizes Measured Component Results Primary Findings 

Zhao et al. (2007, 156182) Personal, Micro, and Ambient: 

PM2.5 

EC, Cl, Si, NO3 Units = μg/m3

: 

Personal: 

EC: 1.64 

NO3: 0.135 

Si: 0.176 

Cl: 0.116 

Indoor: 

EC: 1.819 

NO3: 0.013 

Si: 0.051 

Cl: 0.024 

Outdoor: 

EC: 1.876 

NO3: 0.292 

Si: 0.115 

Cl: 0.013 

Four external sources and 

three internal sources were 

resolved in this study. 

Secondary NO3

-

 and motor 

vehicle exhaust were two major 

outdoor PM2.5 sources. Cooking 

was the largest contributor to 

the personal and indoor 

samples. Indoor environmental 

tobacco smoking also has an 

important impact on the 

composition of the personal 

exposure samples. 

Table A-63. Summary of personal PM exposure source apportionment studies. 

Reference Study Design Results Primary Findings 

Hopke et al. (2003, 095544) 

Source apportionment of 

personal and indoor central and 

apartment and outdoor PM2.5. 

Baltimore retirement home with 

10 elderly subjects. 

 July-Aug 1998. 

% control P, I, C, O 

External 

Secondary 

SO4

2– 46.3, 64.0, 79.0, 64.0 

Unknown 13.6, 14.5, 17.4, 14.5 

Soil 2.8, 3.1, 3.6, 3.1 

Internal 

Gypsum 0.7, 0.4, 0.0, 0.0 

Activity 36.2, 17.8, 0.0, 0.0 

Personal 

care 

 0.4, 0.3, 0.0, 0.0 

63% of personal exposure 

could be attributed to outdoor 

sources (with 46% from SO4

2-), 

and resuspension of indoor PM 

during vacuuming, cleaning, or 

other activities contributed 36% 

of personal exposure. 

Larson et al. (2004, 098145) 

Source apportionment of 

personal and residences and 

central outdoor PM2.5 around 

Seattle with 10 elderly subjects 

and 10 asthmatic children. The 

purpose of the article was to 

compare PMF2 and PMF3 

methods. 

Seattle 

Sep 2000 and May 2001 

PMF2: 

% control P, I, O 

Veg burn 28.8, 47.6, 56.7 

Mobile 0.0, 3.6, 7.5 

Fuel oil 0.0, 0.0, 6.7 

S, Mn, Fe 8.1, 0.0, 0.0 

Secondary 0.0, 34.5, 20.9 

Cl-rich 9.9, 3.6, 3.7 

Crustal 25.2, 10.7, 4.5 

Crustal 2 27.9, 0.0, 0.0 

PMF3: 

% control P, I, O 

Veg burn 41.0, 57.4, 71.3 

Mobile 7.2, 4.3, 8.2 

Secondary 19.3, 13.8, 18.0 

Crustal 32.5, 24.,5 2.5 

Results showed that vegetative 

burning was the largest 

contributor to personal 

exposure and that was related 

to outdoor combustion. Crustal 

exposures were related to 

indoor activities. 

Zhao et al. (2006, 156181) 

Source apportionment of 

personal and residential indoor 

and residential outdoor and 

central outdoor PM2.5, 

Raleigh and Chapel Hill NC with 

38 subjects. 

Summer 2000 and Spring 2001. 

% contr ol P,I, R ,O 

Motor 

vehicle 10.0, 

 9.4, 17.2, 19.4 

Soil 3.5, 3.7, 9.3, 8.5 

Secondary 

SO4

2– 15.9, 

 22.5, 59.3, 

61.9 

Secondary 

NO3

-

 

4.4 , 4.7, 7.6, 7.8 

ETS 7.0, 10.0, 0.0, 0.0 

Personal 

care and 

activity 

8.0, 19.1, 0.0, 0.0 

CU-factor 

mix with 

indoor soil 

0.4, 1.2, 0.0, 0.0 

Cooking 

52.5, 

 53.6, 0.0, 0.0 

Secondary sulfate was the 

largest ambient source and the 

largest ambient contribution to 

personal exposure. Cooking 

produced the largest 

contribution to personal and 

indoor concentrations. Note that 

sums over 100% because 

multiple sources obscured PMF 

resolution. 

December 2009 A-337 
Reference Study Design Results Primary Findings 

Meng et al. (2007, 194618) 

Source apportioned infiltration for 

personal and residential indoor 

and residential outdoor and 

central outdoor PM2.5. 

Los Angeles, Houston, and 

Elizabeth, NJ with 100 nonsmoking residences and 

residents in each city. 

In each season between summer 

1999 and spring 2001 (RIOPA). 

% contr Outdoor Indoor 

(Outdoor Origin) 

Mechanically generated 2, 17 

Primary Combustion 43, 43 

Secondary Formation* 55, 40 

*excludes nitrates 

Differential infiltration of the 

PM2.5 resulted in a reduction of 

secondary formation products 

relative to outdoors. 

Reff et al. (2007, 156045) 

Functional group distinction for 

personal and residential indoor 

and residential outdoor and 

central outdoor PM2.5, PM2.5

samples from 219 homes were 

used for this analysis. 

Los Angeles, Houston, and 

Elizabeth, NJ with 100 nonsmoking residences and 

residents in each city. 

In each season between summer 

1999 and spring 2001 (RIOPA). 

SO 2–

4 : 

R, O, I, P 

O 1.0 

I 0.54-0.76 1.0 

P 0.54-0.73 0.84-0.90 1.0 

C = O: 

R, O, I, P 

O 1.0 

I 0.12-0.61 1.0 

P -0.13-0.69 0.07-0.77 1.0 

CH: 

R, O, I, P 

O 1.0 

I -0.08-0.35 1.0 

P -0.07-0.19 0.41-0.85 1.0 

The main finding was that 

indoor and personal levels of 

CH in organic carbons were 

found to be substantially higher 

than outdoors. This reduced the 

polarity of indoor and personal 

organic carbons 

Zhao et al. (2007, 156182) 

Source apportionment of 

personal and indoor school and 

outdoor school PM2.5. 

Denver with 56 asthmatic 

children. 

Oct 2002-March 2003 and Oct 

2003-March 2004. 

% contr P I O 

Secondary 

SO4

2– 4.3 8.9 9.6 

Soil 6.6 4.2 12.4 

Secondary 

NO3- 

9.4 2.8 40.8 

Motor 

vehicle 

13.3 26.5 26.5 

Cl-based 

cleaning 

2.8 0.4 0.0 

Cooking 54.8 30.2 0.0 

ETS 9.2 2.1 0.0 

The largest personal exposure 

was from cooking (54.8%), but 

motor vehicle emissions were 

the largest outdoor contributor 

(13.3%) to personal exposure. 

Secondary nitrate comprised 

the largest outdoor source but 

accounted for only 9.4% of 

personal exposure. 

Strand et al. (2006, 089203) 

Using positive matrix factorization 

and an extrapolation method to 

estimate PM2.5 based on SO4

2–Fe 

components. 

Denver. 

Winter 1999-2000 and 

2000-2001. 

Estimation method, Mean (SD, range): 

PMF: 7.42 (1.93, 3.43-12.89) 

Extrapolation Method: 

Using SO4

2–: 6.38 (1.60, 3.20-10.97) 

Using SO4

2– and Fe: 6.50 (1.36, 3.54-10.12) 

Using SO4

2– and Fe, temperature adjusted: 7.02 

(1.48, 3.79-11.02) 

Using SO4

2– (no gamma): 8.23 (2.06, 4.12-14.14) 

Similar results were found with 

each technique. 

1 

December 2009 A-338 
Table A-64. Summary of PM infiltration studies. 

Reference Study Design Finf I/O 

Allen et al. (2003, 053578) Objective: Enhance knowledge of the 

outdoor contribution to total indoor 

and personal PM exposures. 

Methods: Continuous light scattering 

monitoring. 

Subjects: Elderly and children 

spending most of their time indoors. 

Healthy individuals, elderly with 

COPD or CHD and children with 

asthma. 44 residences measured for 

55 10-day sessions. Seattle, WA. 

PM2.5 avg- 0.65 ± 0.21 

Non-heating season- 0.79 ± 0.18 

Heating season- 0.53 ± 0.16 

Open windows (mean)- 0.69 

Closed windows (mean)- 0.58 

All days (mean)- 0.65 

Light scattering (whole peak): 0.75 ± 

0.25 

Light scattering (uncensored data): 

0.77 ± 0.24 

Sulfur concentration (slope): 0.65 ± 

0.01 

Arhami et al. (2009, 190096) Objective: To examine associations 

between size-segregated PM, their 

particle components, and gaseous 

copollutants. 

Methods: Data analyzed with linear 

mixed-effect models. 

Subjects: Four different retirement 

communities in San Gabriel Valley, 

CA and Riverside, CA. 2005-2007. 

PM2.5: 0.38-0.57 

EC: 0.64-0.82 

OC: 0.60-0.98 

N/A 

Balasubramanian et al. (2007, 

156248) 

Objective: PM monitoring and 

assessment based on analysis of 

chemical and physical characteristics of 

indoor and outdoor particles. 

Methods: Particle number and mass 

concentrations measured using realtime particle counter and low-volume 

particulate sampler. 

Subjects: 3 residential indoor and 1 

residential outdoor environments in 

Choa Chu Kang, Singapore. May 12-

May 23, 2004. 

N/A PM2.5: 0.93-1.90 

Chemical Species: 

Cl-: 0.35-0.45 

NO2

-

: 2.50-4.13 

NO3

-

: 1.41-5.41 

SO 2–

4 : 1.21-1.70 

Na+

: 0.43-0.74 

NH4

+

: 1.43-2.39 

EC: 0.75-0.96 

OC: 1.04-1.92 

Al: 1.04-1.92 

Co: 0.86-1.32 

Cr: 1.35-2.90 

Cu: 0.50-0.69 

Fe: 0.30-0.42 

Mn: 0.23-0.42 

Pb: 0.40-2.47 

Zn: 0.59-0.81 

Cd: 0.74-1.75 

Ni: 0.71-1.32 

Ti: 0.73-0.78 

V: 1.01-1.05 

Barn et al. (2008, 156252) Objective: Measure infiltration factor 

from PM2.5 from forest fires and 

determine effectiveness of HEPA 

filter. 

Methods: pDR for ambient air 

sampling. 

Subjects: Homes affected by forest 

fire or residential wood smoke. British 

Columbia, Canada. 38 homes 

sampled (valid samples: 19 winter, 13 

summer). 

PM2.5 (mean) 

Summer: 

HEPA: 0.19 ± 0.20 

Unfiltered: 0.61 ± 0.27 

Winter: 

HEPA: 0.10 ± 0.08 

Unfiltered: 0.28 ± 0.18 

Both: 

HEPA: 0.13 ± 0.14 

Unfiltered: 0.42 ± 0.27 

Mean: 

Summer: 

HEPA: 0.43 

Unfiltered: 0.77 

Winter: 

HEPA: 0.21 

Unfiltered: 0.36 

Both: 

HEPA: 0.25 

Unfiltered: 0.47 

December 2009 A-339 
Reference Study Design Finf I/O 

Baxter et al. (2007, 092726) Objective: To develop predictive 

models of residential indoor air 

pollutant concentrations for lower 

SES, urban households. Part of 

ACCESS cohort study of asthma 

etiology. 

Methods: Regression analysis; mass 

balance model; Finf from slope in 

univariate regression analyses. 

Subjects: Lower SES populations. 43 

homes, 23 homes monitored in both 

seasons, 15 in the non-heating 

season (May-Oct) only, 5 in heating 

season (Dec-Mar ) only; 2003-2005. 

PM2.5: 0.91±0.23 

EC: 0.72 ± 0.49 

Ca: 0.56 ± 0.30 

Fe: 0.38 ± 0.26 

K: 0.83 ± 0.52 

Si: 0.02 ± 0.00 

Na: 0.46 ± 0.43 

Cl: 0.40 ± 0.12 

Zn: 0.85 ± 0.28 

S: 0.95 ± 0.78 

V: 0.60 ± 0.77 

PM2.5 (mean, coefficient of variation 

(CV)): 1.14 (0.71) 

EC: 0.89 (0.64) 

Ca: 1.16 (1.90) 

Fe: 0.69 (1.40) 

K: 1.10 (0.95) 

Si: 1.04 (1.31) 

Na: 1.05 (1.84) 

Cl: 3.18 (3.79) 

Zn: 0.83 ± (1.13) 

S: 0.76 ± (0.32) 

V: 0.76 ± (0.46) 

Baxter et al. (2007, 092725) Objective: To predict residential 

indoor concentrations of trafficrelated air pollutants in lower SES 

urban households. Part of ACCESS 

cohort study of asthma etiology. 

Methods: Regression modeling, 

Bayesian variable selection I/O is 

slope from multivariate model 

Subjects: Lower statuses, urban 

households in Boston, MA. 43 sites 

among 39 households, 66 sampling 

sessions, nonheating (May-Oct) and 

heating (Dec-Mar) 2003-2005 

N/A PM2.5: 

Open Windows: 0.98 

Closed Windows: 0.64 

EC: 0.38 

Brown et al. (2008, 190894) Objective: To examine if ambient, 

home outdoor, and home indoor 

particle concentrations can be used 

as proxies of corresponding personal 

exposure. 

Methods: Associations characterized 

using univariate mixed effects models 

that included a random subject term. 

Subjects: 15 participants in Boston, 

MA in winter (Nov. 1999-Jan. 2000) 

and summer (June-July 2000). 

N/A PM2.5: 

Winter: Median: 1.2, Range: 0.8-1.8 

Summer: Median: 0.9, Range: 0.6-1.2 

EC: 

Winter: Median: 1.1, Range: 0.7-4.5 

Summer: Median- 1.0, Range: 0.9-1.3 

SO4

2–: 

Winter: Median: 0.5, Range: 0.3-0.8 

Summer: Median: 0.8, Range: 0.4-1.0 

Cao et al. (2005, 156321) Objective: To determine relationships 

and distributions of indoor and outdoor 

PM2.5, OC, and EC. To determine 

indoor/outdoor sources of indoor 

carbonaceous aerosol. 

Methods: Gravimetric analysis to 

determine PM2.5 concentrations. OC 

and EC determined by TOR following 

IMPROVE protocol. 

Subjects: 6 residences in Hong Kong 

(2 roadside, 2 urban, 2 rural). March 

6-April 18, 2004. 

N/A 20min PM2.5: 

Roadside: 0.7-4.0 

Urban: 0.9-6.7 

Rural: 0.5-1.7 

 

24h PM2.5: Roadside: 0.8-1.4 

Urban: 1.2-2.0 

Rural: 1.0-1.8 

OC (average and range): 

Roadside: 1.9 (1.1-2.3) 

Urban: 2.3 (1.5-4.0) 

Rural: 1.3 (1.2-2.2) 

EC (average and range): 

Roadside: 1.0 (0.9-1.1) 

Urban: 1.1 (0.8-1.3) 

Rural: 1.1 (0.9-1.8) 

December 2009 A-340 
Reference Study Design Finf I/O 

Cortez-Lugo et al. (2008, 156368) Objective: To determine personal 

PM2.5 and its relationship with outdoor 

and indoor PM2.5 and PM10. 

Methods: Linear regression model 

used to compare personal and indoor 

PM2.5. I/O variation studied using 

analysis of variance and predictors 

determined by generalized estimating 

equation models. I/O PM2.5 ratio 

transformed into natural logarithm. 

Subjects: 38 nonsmoking long-time 

Mexico residents with COPD. Mexico 

City, Mexico. Feb-Nov 2000. 

N/A PM2.5: 

Average: 1.2 

Range: 0.05-6.1 

Diapouli et al. (2008, 190893) Objective: To characterize the PM10f 

PM2.5, UFP concentrations at primary 

schools. To examine the relationship 

between indoor and outdoor 

concentrations. 

Methods: Chemical analysis of 

collected filters. Regressions to 

examine correlations between indoor 

and outdoor concentrations. 

Subjects: 7 primary schools with 

different characterizations of 

urbanization and traffic density in 

Athens, Greece. No ventilation 

system. Nov. 2003-Feb. 2004 and 

Oct.-Dec. 2004. 

N/A PM10: 0.54-2.46 

PM2.5- 0.67-2.77 

UFP- 0.33-0.74 

Dimitroulopoulou et al. (2006, 

090302) 

Objective: To develop a probabilistic 

indoor air model (INDAIR). 

Methods: INDAIR predicts frequency 

distributions of concentrations of up to 4 

pollutants simultaneously (NO2, CO, 

PM10, PM2.5). 3 scenarios: no source, 

gas cooking, smoking. 

Subjects: 5 UK sites- Harwell (rural), 

Birmingham East (urban 

background), Bradford (urban 

center), Bloomsbury (urban center), 

Marylebone Road (roadside). Winter 

(October 1-March 31), summer (April 

1-September 30), 1997-1999. 

N/A No source: 

PM10: 0.5-0.65; 

PM2.5: 0.6-0.7 

Gas cooking: 

PM10: 0.6-0.9 (bedroom), 1.0-2.0 

(lounge), 1.6-4.3 (kitchen); 

PM2.5: 0.74-0.9 (bedroom), 0.9-1.6 

(lounge), 1.6-2.9 (kitchen) 

Smoking: 

PM10: 0.7-1.1 (bedroom), 1.1-2.7 

(lounge), 1.1-2.5 (kitchen); 

PM2.5: 0.8-1.3 (bedroom), 1.3-2.8 

(lounge), 1.4-2.6 (kitchen) 

Fromme et al. (2008, 155147) Objective: To characterize the 

chemical and morphological 

properties of PM in classrooms and in 

corresponding outdoor air. 

Methods: PM Finf derived from sulfate 

Finf and a correction factor that 

results from division of ΒPM 

(increase of indoor PM per outdoor 

PM, linear relationship) by Βsulf

(increase of indoor sulfate per 

outdoor sulfate, linear relationship). If 

no indoor source, the sulfate Finf is 

equal to the sulfate I/O. 

Subjects: Primary school in northern 

Munich. Densely populated 

residential area 160m away from a 

very busy street. Classrooms had 21-

23 students. Sampling during 

teaching hours. Oct.-Nov. 2005. 

N/A PM10: 

SO4

2-: 0.3, 

NO -

3 : 0.1, 

Cl-: 0.6, 

Na2+: 0.9, 

NH +

4 : 0.1, 

Mg: 0.6, 

Ca2+: 1.4, 

EC: 0.7, 

OC: 1.1 

PM2.5: 

SO 2-

4 : 0.4, 

NO3

-

: 0.2, 

Cl-: 0.5, 

Na2+: 0.6, 

NH4

+

: 0.3, 

Mg: 0.5, 

Ca2+: 1.6 

December 2009 A-341 
Reference Study Design Finf I/O 

Guo et al. (2004, 156506)1 Objective: To investigate pollutant 

concentrations at air-conditioned and 

non-air-conditioned markets. To 

compare indoor air quality with the 

Hong Kong standard. 

Methods: PM10 concentrations 

measured by Hi-Vol sampler 

correlated with corresponding levels 

measured by Dust-Trak monitor. 

Subjects: 3 non-air-conditioned and 2 

air-conditioned markets in Hong 

Kong. Sept. 2001-Jan. 2002. 

N/A PM10: 

Non-air-conditioned: ~0.7, Airconditioned: ~0.98 

Hänninen et al. (2004, 056812)2 Objective: To assess indoor PM2.5 by 

origin and potential determinants. 

Methods: Part of EXPOLIS study. 

Pump and filter with gravimetric 

analysis. Univariate single and 

stepwise-multiple regression 

analyses. 

Subjects: Residential homes in 

Athens, Greece; Basle, Switzerland; 

Helsinki, Finland; Prague, Czech 

Republic. Homes by city: Athens 50, 

Basle 50, Helsinki 189, Prague 49. 

PM2.5 (mean): 

Athens- 0.70 ± 0.12 

Basle- 0.63 ± 0.15 

Helsinki- 0.59 ± 0.17 

Prague- 0.61 ± 0.14 

S (mean): 

Athens- 0.82 ± 0.14 

Basle- 0.80 ± 0.19 

Helsinki- 0.70 ± 0.20 

Prague- 0.72 ± 0.16 

PM2.5: 

Athens: ~0.84 

Basle: ~1.37 

Helsinki: ~1.30 

Prague: ~1.33 

S: 

Athens:~0.70 

Basle: ~0.80 

Helsinki: ~0.74 

Prague: ~0.77 

Ho et al. (2004, 056804)3 Objective: PM2.5, OC, and EC 

exposure assessment of occupied 

buildings located near major 

roadways under natural ventilation 

(NV) and mechanical ventilation 

(MV). 

Methods: Co-located mini-volume 

samplers and Partisol model 2000 

sampler with 2.5 micron inlet. 

IMPROVE TOR carbon analysis. 

Subjects: Occupants of MV (1 

classroom and office) and NV (3 

residences) buildings located within 

10m of major roadway; Hong Kong, 

China. Sep. 2002-Feb. 2003. 

PM2.5: 0.42 

EC: MV: 0.42, NV: 0.76 

OC: MV: 0.66, NV: 0.71 

PM2.5 (average): 0.2-1.6 

MV (average): <0.7 

NV (average): 0.6-1.6 

EC: Range: 0.5±0.1-1.1±0.4 

OC: Range: 0.6±0.2-1.5±1.0 

Hoek et al. (2008, 156554) Objective: Exposure assessmentof 

indoor/outdoor particle 

relationships.RUPIOH study. 

Methods: Sampling by condensation 

particle counters and Harvard 

impactors. Gravimetric analysis and 

reflectance. Calculations performed 

for 24h avg concentrations. Finf

estimated by linear regression 

analysis. 

Subjects: 4 European cities (Helsinki, 

Finland; Athens, Greece; Amsterdam, 

The Netherlands; Birmingham, 

England). Urban populations. >35yrs. 

Asthma or COPD. Non-smoking 

households. Work <16h/wk outside 

home. 153 homes sampled Oct. 

2002-Mar. 2004. 

Regression slope for indoor vs. central 

site outdoor: 

PM2.5: 0.30-0.51 

PM10: 0.17-0.41 

PM10-2.5: 0.01-0.17 

SO4

2-: 0.59-0.78 

Soot: 0.43-0.87 

Regression slope for indoor vs. 

residential outdoor: 

PM2.5: 0.34-0.48 

PM10: 0.26-0.44 

PM10-2.5: 0.11-0.16 

Soot: 0.63-0.84 

N/A 

December 2009 A-342 
Reference Study Design Finf I/O 

Hopke et al. (2003, 095544) Objective: To use advanced factor 

analysis models to identify and 

quantify PM sources. 1998 BPMEES 

data. 

Methods: PEM, outdoor and indoor 

sampling of unoccupied apartment in 

retirement facility. PMF used to derive 

source contributions. Multilinear 

Engine used to derive joint factors. 

Subjects: 10 non-smoking elderly 

subjects of mean age 84 who did not 

cook.Towson, MD. July 26-Aug. 22, 

1998. 

NO3

-

- SO4

2-: 0.03 

SO4

2-: 0.38 

OC: 0.77 

MV Exhaust: 0.32 

N/A 

Hystad et al. (2008, 190890) Objective: To explore the feasibility of 

modeling residential PM2.5 Finf for 

occupied residences using data 

readily available for most of North 

America. 

Methods: Finf calculated by recursive 

mass balance model where Finf is a 

function of penetration efficiency, 

particle removal rate, and air 

exchange. 

Subjects: 46 residences in Seattle, 

WA 1999-2003. 38 nonsmoking 

residences in Victoria, British 

Columbia, Canada 2006. Heating 

(Oct.-Feb.) and nonheating (MarchSept.). 

Seattle: 

Mean (all): 0.59 ± 0.21 

Mean (detached residences): 0.60 ± 

0.20 

Victoria: 

Mean (all): 0.62 ± 0.22 

Mean (detached residences): 0.59 ± 

0.22 

N/A 

Klinmalee et al. (2008, 190888) Objective: To monitor indoor and 

outdoor pollution in an university 

campus and shopping center. 

Methods: PM measured by PEM and 

quartz filters. Analyzed for mass, 

water soluble ions by ion 

chromatography, and black carbon by 

a smokestain reflectometer. I/O 

calculated for each sample pair then 

average taken. 

Subjects: University campus and 

shopping center in northern suburb of 

Bangkok, Thailand. Dec. 2005-Feb. 

2006. 

N/A PM2.5: 

University: 

Weekdays: 0.6, Weekends: 0.5 

Shopping center: 

Weekdays: 1.5, Weekends: 2.0 

BC in PM2.5: 

University: 0.9 

Shopping center: 0.67 

December 2009 A-343 
Reference Study Design Finf I/O 

Koistinen et al. (2004, 156655) Objective: To identify PM2.5 sources in 

personal exposures with principal 

component analysis of the elemental 

compositions in residential outdoor, 

indoor, and workplace indoor 

microenvironments. Part of EXPOLIS 

study. 

Methods: Principal component 

analysis to identify sources of 

microenvironmental and personal 

PM2.5 exposure. Specific mass 

contributions of sources calculated by 

source reconstruction. 

Subjects: Non-smoking, 25-55yrs. 

Helsinki, Finland. Oct. 1996-Dec. 

1997. 

N/A Median seasonal: 

PM2.5: Winter: 0.77, Spring: 1.03, Summer: 

0.95, Fall: 0.92, Total: 0.92 

Pb: 

Winter: 0.67, Spring: 0.56, Summer: 

0.86, Fall: 0.69, Total: 0.67 

S: 

Winter: 0.60, Spring: 0.63, Summer: 

0.90, Fall: 0.75, Total: 0.69 

Br: 

Winter: 0.57, Spring: 0.72, Summer: 

0.98, Fall: 0.89, Total: 0.77 

BS: 

Winter: 0.65, Spring: 0.67, Summer: 

0.91, Fall: 0.88, Total: 0.79 

Zn: 

Winter: 0.58, Spring: 0.75, Summer: 

0.66, Fall: 0.75, Total: 0.68 

Fe: 

Winter: 0.52, Spring: 0.96, Summer: 

0.90, Fall: 0.95, Total: 0.83 

K: 

Winter: 0.95, Spring: 1.05, Summer: 

1.01, Fall: 1.08, Total: 1.05 

Cl: 

Winter: 1.01, Spring: 1.24, Summer: 

1.37, Fall: 1.74, Total: 1.24 

Al: 

Winter: 1.19, Spring: 1.08, Summer: 

1.41, Fall: 2.20, Total: 1.27 

Li et al. (2003, 047845) Objective: To establish effects of 

evaporative coolers on indoor PM 

concentrations. 

Methods: Concurrent 10min avg 

indoor and outdoor concentrations 

recorded for 2 days.I/O determined 

by equation based on mass 

conversation principles. 

Subjects: 10 homes with evaporative 

coolers. El Paso, TX. June 22-Aug. 

23, 2001. 

N/A PM10: 

All: 0.60 

Cooler On: 0.57 

Cooler Off: 0.66 

PM2.5: 

 All: 0.65 

Cooler On: 0.63 

Cooler Off: 0.73 

Lunden et al. (2008, 155949) Objective: To investigate the 

physiochemical processes that 

influence the transport and fate of 

outdoor particles to the indoor 

environment. 

Methods: I/O calculated from 

measurements of aerosols collected 

on quartz filters. 

Subjects: 3-bedroom single-story 

unoccupied house in Clovis, CA. 3 

periods: Oct. 9-23, 2000; Dec. 1-19, 

2000; Jan. 12-23, 2001. 

N/A PM2.5: Oct.: 0.46 ± 0.2, Dec.: 0.39 ± 0.2, 

Jan.: 0.38 ± 0.3, All periods: 0.41 ± 0.2 

Carbon: Oct.: 0.50 ± 0.1, Dec.: 0.46 ± 

0.1, Jan.: 0.52 ± 0.2, All periods: 0.50 ± 

0.2 

OC: Oct.: 0.48 ± 0.1, Dec.: 0.44 ± 0.1, 

Jan.: 0.50 ± 0.2, All periods: 0.47 ± 0.2 

Black carbon: Oct.: 0.60 ± 0.2, Dec.: 

0.60 ± 0.2, Jan.: 0.65 ± 0.2, All periods: 

0.61 ± 0.2 

MacIntosh et al. (2009, 190887) Objective: To estimate the potential 

for residential air cleaning systems to 

mitigate exposure to fine particles of 

ambient origin. 

Methods: Multi-zone indoor air quality 

model to examine annual, 24h avg 

and diurnal concentrations of outdoor 

PM2.5 in residential indoor air. 

Subjects: Homes in Cincinnati, 

Cleveland, and Columbus, OH that 

have natural ventilation, forced air 

heating and cooling with conventional 

in-duct filtration, or forces air heating 

and cooling with high-efficiency induct air cleaning. 2005. 

N/A PM2.5 (range): 

Natural ventilation: 0.23-0.97 

Forced air – conventional filtration: 

0.13-0.94 

Forced air – high-efficiency 

electrostatic: 0.02-0.80 

December 2009 A-344 
Reference Study Design Finf I/O 

Martuzevicius et al. (2008, 190886) Objective: To determine the 

contribution of traffic-related PM to 

the indoor aerosols. 

Methods: Receptor modeling based 

on a PARAFAC model. 

Subjects: 6 houses 30-300m from a 

highway, with conventional windows, 

central HVAC, and with smoking and 

cooking allowed. Spring: Mar. 30-May 

14, 2004. Fall: Sept. 13-Oct. 22, 

2004. Cincinnati, OH. 

N/A Range- PM2.5: Spring: 0.5 ± 0.2-2.9 ± 

1.2; Fall: 0.7 ± 0.1-4.7 ± 6.9 

EC: 

Spring: 0.3 ± 0.1-2.2 ± 1.7; 

Winter: 0.6 ± 0.1-1.3 ± 0.7 

OC: 

Spring: 1.0 ± 0.7-6.9 ± 3.9; 

Winter: 1.2 ± 0.1-7.6 ± 10 

Si: 

Spring: 0.4 ± 0.1-5.1 ± 3.9; 

Winter: 0.5 ± 0.1-5.3 ± 4.5 

S: 

Spring: 0.4 ± 0.1-0.7 ± 0.1; 

Winter: 0.5 ± 0.1-0.9 ± 0.4 

Mn: 

Spring: 0.3 ± 0.2-0.8 ± 0.6; 

Winter: 0.3 ± 0.2-1.0 ± 0.2 

Fe: 

Spring: 0.3 ± 0.0-1.3 ± 0.8; 

Winter: 0.4 ± 0.1-0.9 ± 0.6 

Zn: 

Spring: 0.3 ± 0.1-0.7 ± 0.6; 

Winter: 0.6 ± 0.1-1.1 ± 0.8 

Br: 

Spring: 0.3 ± 0.1-1.0 ± 0.5; 

Winter: 0.2 ± 0.1-0.9 ± 0.6 

Pb: 

Spring: 0.3 ± 0.3-0.9 ± 0.6; 

Winter: 0.2 ± 0.2-1.9 ± 2.3 

Meng et al. (2005, 058595) Objective: Analyses of RIOPA data, 

which investigated relationships 

between indoor, outdoor, and 

personal exposure for several air 

pollutants. 

Methods: PM measured on Teflon 

filters collected by PEMs for 48h. The 

mass balance model and RCS 

statistical model used to estimate 

indoor and and personal PM 

concentrations. 

Subjects: 212 nonsmoking homes 

sampled. Houston, TX; Los Angeles 

County, CA; Elizabeth, NJ. Summer 

1999-spring 2001, all 4 seasons. 

PM2.5- 0.46 Los Angeles: 

PM2.5: Mean: 0.84, Median: 0.90; 

EC: Mean: 0.93, Median: 0.92; 

OC: Mean: 1.32, Median: 1.31 

Elizabeth: 

PM2.5: Mean: 0.99, Median: 0.86; 

EC: Mean: 1.0, Median: 0.85; 

OC: Mean: 2.4, Median: 1.8 

Houston: 

PM2.5: Mean: 1.16, Median: 1.02; 

EC: Mean: 1.0, Median: 0.71; 

OC: Mean: 2.25, Median: 2.35 

Molnár et al. (2007, 156774) Objective: To characterize and 

compare indoor and outdoor PM2.5

trace element concentrations in 

difference microenvironments related 

to children. 

Methods: Elemental concentrations 

analyzed using X-ray fluorescence 

spectroscopy. 

Subjects: 40 sampling sites (10 

classrooms in 5 schools, 10 

preschools, 20 non-smoking homes). 

3 communities in Stockholm, 

Sweden. Sampled once during spring 

and once during winter. Dec. 1, 2003-

July 1, 2004. 

PM2.5 (containing S or Pb): 0.4-0.9 S (median): 

Both seasons: 0.61 (homes), 0.53 

(schools), 0.69 (preschools); 

Winter: 0.47 (homes), 0.36 (schools), 

0.63 (preschools); 

Spring: 0.63 (homes), 0.55 (schools), 

0.90 (preschools) 

Pb (median): 

Both seasons: 0.70 (homes), 0.59 

(schools), 0.70 (preschools); 

Winter: 0.62 (homes), 0.43 (schools), 

0.63 (preschools); 

Spring: 0.70 (homes), 0.64 (schools), 

0.75 (preschools) 

December 2009 A-345 
Reference Study Design Finf I/O 

Ng et al. (2005, 155996) Objective: To estimate PM exposures 

following the September 11, 2001 

attack in NYC. 

Methods: Outdoor PM2.5 interpolated 

and used in a deterministic microenvironmental model (INTAIR) to 

simulate analytically concentrations in 

indoor micro-environments. Linear 

regression equations used. 

Subjects: Lower Manhattan residents 

divided into representative individuals 

– home-maker, office/shop-worker, 

student/child. Estimates Sept. 14-31. 

N/A Mean I/O in home simulated with 

INTAIR: 

No Source: 0.6 

Smoking: 1.9 

Cooking: 1.3 

Smoking and Cooking: 2.3 

I/O of micro-environments simulated 

by analytical and empirical methods 

(no indoor source) : 

Office/Shop: 0.4 

Classroom: 0.9: 

Transport Area: 1.9 

Store: 1.2 

Paschold et al. (2003, 156847) Objective: To identify PM sources 

inside homes with evaporative 

coolers. 

Methods: PM element composition 

analysis by ICP-MS. 

Subjects: 10 residences. El Paso, TX. 

Summer 2001. 

 N/A PM10: 

Na: 0.33, Mg: 0.43, Al: 0.50, K: 0.48, 

Ca: 0.40, Ti: 0.52, Mn: 0.48, Fe: 0.46, 

Cu: 0.74, Zn: 0.52, Ba: 0.54, Pb: 0.76

PM2.5: 

Na: 0.20, Mg: 0.29, Al: 0.34, K:0.30, 

Ca: 0.52, Ti: 0.40, Mn: 0.35, Fe: 0.30, 

Cu: 0.67, Zn: 0.34, Ba: 0.47, Pb: 0.51

Polidori et al. (2007, 156877) Objective: To investigate the 

relationships of indoor and outdoor 

PM2.5, its components, seasonal 

variations, and gaseous copollutants. 

Methods: Finf estimated by analysis of 

I/O’s and a recursive model 

technique. 

Subjects: 2 retirement facilities in Los 

Angeles, CA. July 6-Aug. 20, 2005. 

Aug. 24-Oct. 15, 2005. Oct. 19-Dec. 

10, 2005. Jan. 4-Feb. 18, 2006. 

PM2.5: 

July 6-Aug. 20: 0.71 ± 0.10; Aug. 24-

Oct. 15: 0.60 ± 0.05; Oct. 19-Dec. 10: 

0.59 ± 0.07; Jan. 4-Feb. 18: 0.45 ± 

0.06 

OC: 

July 6-Aug. 20: 0.86 ± 0.05; Aug. 24-

Oct. 15: 0.77 ± 0.09; Oct. 19-Dec. 10: 

0.82 ± 0.07; Jan. 4-Feb. 18: 0.64 ± 

0.10 

EC: 

July 6-Aug. 20: 0.73 ± 0.07; Aug. 24-

Oct. 15: 0.71 ± 0.05; Oct. 19-Dec. 10: 

0.77 ± 0.06; Jan. 4-Feb. 18: 0.64 ± 

0.10 

Only I/O’s ≤1considered 

Ramachandran et al. (2003, 195017) Objective: To examine variability in 

measurements of 24h avg and 15min 

avg PM2.5 concentrations. 

Methods: Linear regression of 

gravimetric measurements. 

Subjects: 3 urban residential 

neighborhoods in Minneaopolis-St. 

Paul, MN. 9-10 nonsmoking 

residences. Spring (April 26-June 2), 

summer (June 20-Aug. 10), fall (Sept. 

23-Nov. 20) of 1999. 

N/A 24h avg: 

Mean: 1.7, Median: 1.3, Standard 

deviation: 1.6 

15min avg: 

Mean: 2.7, Median: 1.2, Standard 

deviation: 8.7 

Rojas-Bracho et al. (2004, 054772) Objective: To examine determinants 

of personal exposure to PM2.5, PM10, 

PM2.5- 10. 

Methods: 2 sets of mixed models. 

Personal exposures modeled as 

dependent variables. Subject 

variability modeled using random 

effects. Explanatory variables and 

season modeled as fixed effects. 

Subjects: 18 COPD subjects in 

nonsmoking households. Boston, 

MA. Winters of 1996 and 1997, 

summer of 1996. 

N/A PM2.5: 

Winter: Mean: 1.58, Median: 2.11; 

Summer: Mean: 1.08, Median: 0.88 

PM10: 

Winter: Mean: 2.02, Median: 3.77; 

Summer: Mean: 1.14, Median: 1.05 

PM2.5- 10: 

Winter: Mean: 2.65, Median: 3.59; 

Summer: Mean: 1.26, Median: 1.39 

December 2009 A-346 
Reference Study Design Finf I/O 

Sarnat et al. (2006, 089166) Objective: To assess the ability of 

outdoor PM2.5 its volatile and 

nonvolatile components and particle 

sizes to infiltrate indoors. 

Methods: PM2.5 mass contributions 

estimated by the mean concentration 

ratio between each component and 

PM2.5. Indoor and outdoor particle 

concentrations relationships 

examined by Spearman correlation 

coefficient. I/O concentration ratios 

used during overnight (nonsource) 

period to estimate fraction of ambient 

particles remaining airborne indoors 

(Finf). 

Subjects: 17 occupied, nonsmoking 

Los Angeles, CA residences. July 28, 

2001-Feb. 25, 2002. 

PM2.5: 

Median: 0.48, Interquartile range: 

0.39-0.57 

BC: 

Median: 0.84, Interquartile range: 

0.70-0.96 

UFP (0.02-0.03 μm): 

Median: 0.50, Interquartile range: 

0.39-0.60 

UFP (0.08-0.3 μm): 

Median: ~0.75 

Coarse particles (5-10 μm): 

Median: <0.17 

PM2.5: 

Overnight: 0.40-0.57, Morning: 0.43-

0.74, Afternoon: 0.45-0.90, Evening: 

0.42-0.82 

BC: 

Overnight: 0.70-0.97, Morning: 0.67-

0.98, Afternoon: 0.77-1.04, Evening: 

0.70-1.01 

Stranger et al. (2008, 190884) Objective: To assess indoor air 

quality by determining indoor and 

outdoor PM2.5 mass concentrations, 

elemental composition, and gaseous 

compounds. 

Methods: PM mass concentrations 

determined gravimetrically. 

Subjects: 27 primary schools in city 

center and suburbs of Antwerp, 

Belgium. Dec. 2002 and June 2003. 

N/A PM2.5: 

Urban: Range: 0.3-6.9, Average: 1.3; 

Suburban: Range: 0.2-8.8, Average: 

2.3 

V, Ni, Zn, Pb, Br, Mn: <1 

Cl, Ca, Al, Si, K, Ti, Fe: >1 

BS: Urban: Average: Dec.- 0.7 ± 0.1, 

June- 1.1 ± 0.3; Suburban: Dec.- 0.8 ± 

0.2, June-1.0 ± 0.4 

Stranger et al. (2009, 190883) Objective: To assess indoor air 

quality in residences by quantifying 

various gaseous pollutants, and PM 

mass concentrations, elemental 

composition, and water-soluble ionic 

content. 

Methods: PM mass concentrations 

gravimetrically determined. Elemental 

bulk analysis on filters. 

Subjects: 19 residential homes in 

Antwerp, Belgium that were a subset 

of participants in the ECRHS II study. 

N/A PM1: Houses 1-15: Average: 2.0, 

Range: 0.3-9.6; Smokers: Average: 

3.9, Range: 1.2-9.7; Non-smokers: 

Average: 0.8, Range: 0.3-14 

PM2.5: Houses 1-15: Average: 1.5, 

Range: 0.4-5.4, Smokers average: 

2.5, Smokers range: 1.2-5.4, Nonsmokers average: 0.8, Non-smokers 

range: 0.4-1.3; Houses 16-19: 

Average: 2.6, Range: 0.3-3.9 

PM10: Houses 1-15: Average: 1.3, 

Range: 0.4-4.1, Smokers average: 

2.1, Smokers range: 1.1-4.1, Nonsmokers average: 0.8, Non-smokers 

range: 0.4-1.2 

Ca, Ti, V, Cr, Mn, Fe, Ni, Zn, Pb, Si, 

S, Cl: <1 

K, Cu, Br, Al: >1 

Turpin et al. (2007, 157062) Objective: To characterize and 

compare outdoor, indoor, personal 

PM2.5 exposure. Identify indoor and 

personal PM2.5 sources. Estimate 

outdoor PM2.5 effect on indoor and 

personal PM2.5. RIOPA study. 

Methods: Finf calculated in three 

ways: RCS model used to obtain 

constant Finf. Mass balance model 

shows Finf varying with AER. Robust 

regression uses major PM2.5 species 

for home-specific Finf. 

Subjects: 309 nonsmoking adults and 

118 children with no preexisting 

conditions. 219 homes sampled. 

Elizabeth NJ, Houston TX, and Los 

Angeles County CA. 

PM2.5: 

RCS model: 0.46 

Least-Trimmed Squared Regression: 

Mean: 0.69, Median: 0.70, SD: 0.23 

Mass Balance Model: ~0.08-~0.85 

Los Angeles: 

PM2.5: Mean: 0.84, Median: 0.90 

EC: Mean: 0.93, Median: 0.92 

OC: Mean: 1.32, Median: 1.31 

Elizabeth: 

PM2.5: Mean: 0.99, Median: 0.86 

EC: Mean: 1.0, Median: 0.85 

OC: Mean: 2.4, Median: 1.8 

Houston: 

PM2.5: Mean: 1.16, Median: 1.02 

EC: Mean: 1.0, Median: 0.71 

OC: Mean: 2.25, Median: 2.35 

December 2009 A-347 
Reference Study Design Finf I/O 

Wallace and Williams (2005, 057485) Objective: To estimate the 

contribution of outdoor PM2.5 to 

personal exposure in high-risk 

subpopulations. 

Methods: Longitudinal regressions of 

estimated indoor and outdoor PM2.5

for Finf. 

Subjects: 29 African-Americans with 

hypertension and 8 with implanted 

cardiac defibrillators. Measured 

7d/season, 4 seasons in 2000-2001. 

Raleigh, NC. 

Range: 0.35-0.87 PM2.5: 

Mean: 1.08 ± 1.05, Median: 0.75, 

Range: 0.24-9.48 

S: 

Mean: 0.59 ± 0.16, Median: 0.58, 

Range: 0.17-1.06 

Williams et al. (2003, 053338) Objective: To estimate ambient PM2.5

contributions to personal and indoor 

residential PM mass concentrations. 

Methods: Finf estimated from least 

squares, regression analysis, and 

mixed model slope. 

Subjects: Nonsmoking, ambulatory, ≥

50yrs. 2 cohorts: mostly Caucasian 

with implanted cardiac defibrillators in 

Chapel, NC; 30 African-Americans 

with controlled hypertension in low-tomoderate SES neighborhoods in 

Raleigh, NC. 7d/season, 4 seasons in 

2000-2001. 

Least squares estimate of indoor 

filtration factors: 

Mean: 0.42 ± 0.38, Range: -0.55 to 

1.62 

Regression analysis: 0.43 ± 0.06 

Mixed model slope: Mean- 0.45 ± 

0.21, Range- 0.05-0.94 

N/A 

Williams et al. (2008, 191201) Objective: To examine the spatial 

variability of PM2.5 and PM10-2.5 and 

their components to determine the 

suitability of conducting health 

outcome studies using a central site 

monitor in a metropolitan area having 

multiple source impacts. 

Methods: Gravimetric analysis of PM 

mass concentrations. ED-XRF 

analysis of PM elements. 

Subjects: Non-smoking, ambulatory, 

and living in detached homes and 

non-smoking households. Detroit, MI. 

PM2.5: 

Range: 0.16-6.45, Mean: 0.7 ± 0.33, 

Median: 0.70 (indicate indoor sulfur 

source when Finf>1) 

N/A 

Wilson and Brauer (2006, 088933) Objective: To provide additional 

insight into factors affecting exposure 

to airborne PM and the resultant 

health effects. 

Methods: Finf estimated by mass 

balance equation. 

Subjects: 16 nonsmoking subjects 

with COPD. 54-86yrs. Vancouver, 

British Columbia. April-Sept. 1998. 

SO4

2-: 0.72 N/A 

Wu et al. (2006, 179950) Objective: To assess personal PM2.5

exposures from ambient sources and 

agriculture burning smoke. 

Methods: Finf estimated by RCS model. 

Application of Robust regression 

algorithm. 

Subjects: 33 adult asthmatics. 18-

52yrs. Pullman, WA. Sept. 3, 2002-

Nov. 1, 2002. 

Range: 0.25-0.94 N/A 

December 2009 A-348 
Reference Study Design Finf I/O 

Yang et al. (2009, 190885) Objective: To characterize the 

concentrations of different indoor air 

pollutants. 

Methods: PM10 collected on pall flex 

membrane filer using MiniVol portable 

air samplers. Arithmetic and 

geometric means calculated for 

indoor concentrations. Differences in 

concentrations measured by KruskalWallis test. 

Subjects: 55 schools in 6 

metropolitan areas in Korea. Samples 

from a classroom, laboratory, and 

computer classroom. 3 seasons, 

July-Dec. 2004. 

N/A PM10: 

Classroom: Summer: 1.98, Autumn: 

2.25, Winter: 2.07, Total: 2.06 

Laboratory: Summer: 1.33, Autumn: 

1.32, Winter: 1.72, Total: 1.46 

Computer classroom: Summer- 0.77, 

Autumn: 1.43, Winter: 2.08, Total: 

1.43 

Zhu et al. (2005, 190081) Objective: To determine penetration 

behavior of outdoor ultrafine particles 

into indoor environments in areas 

close to freeways. 

Methods: Dynamic mass balance 

model. 

Subjects: 4 2-bedroom apartments 

within 60m from the center of the 405 

Freeway in Los Angeles, CA. Nonsmoking tenants. 2 sampling periods 

(non-cooking, non-cleaning): Oct.-

Dec. 2003 and Dec. 2003-Jan. 2004. 

N/A Highest (largest ultrafine particles70-100nm): 0.6-0.9 

Lowest (smallest ultrafine particles10-20nm): 0.1-0.4 

1

. I/O estimated from Figure 8 in study. 

2

. I/O calculated from indoor and outdoor concentrations in Table 1 in study. 

3. Finf measured by coefficient of determination, R2

. 4

. RIOPA calculated I/O’s. 

5

. I/O calculated from mean and median indoor and outdoor concentrations listed in Table 1 of study. 6

. I/O’s estimated from Figure 3 in study. 

7

. Mean and median I/O concentrations calculated from all residences in study. 

8

. Finf estimated from Figure 2 in study. 9

. Finf presented in box plot (Figure 8), however data is difficult to deduce. No numeric values reported. 

December 2009 A-349 
Table A-65. Summary of PM – copollutant exposure studies. 

Reference PM metric Copollutant metric Association between PM and copollutant Primary findings 

Fruin et al. (2008, 

097183) 

In-vehicle UFP, BC, PMbound PAH In-vehicle NOX, CO R UFP PM2.5 NO BC CO CO2

UFP 1 0.71 0.97 0.95 0.63 0.72 

PM2.5 1 0.69 0.89 0.66 0.68 

NO 1 0.91 0.78 0.85 

BC 1 0.65 0.74 

CO 1 0.94 

CO2 1 

Note that these correlations are computed from data 

presented by Fruin et al. (2008, 097183) for mean 

concentrations at different loc ations. 

Measurements of 

freeway UFP, BC, PMbound PAH, and NO 

concentrations were 

roughly one order of 

magnitude higher than 

ambient 

measurements. 

Multiple regression 

analysis suggests 

these concentrations 

were a function of truck 

density and total truck 

count. 

Schwartz et al. (2007, 

090220) 

Ambient and personal 

PM2.5 data from the 

Baltimore panel study 

Ambient and personal O3

and NO2 data from the 

Baltimore panel study. 

Median β for regressions: 

Ambient 

PM2.5 

Ambient O3 Ambient 

NO2

Personal 

PM2.5 

0.0143 -0.0016 0.0115 

Personal 

PM2.5 of 

ambient origin 

0.0183 -0.0037 0.0124 

Personal 

SO4

2– 0.0051 0.0035 0.0006 

Personal O3 0.0014 0.0010 0.0009 

Personal NO2 0.0015 0.0009 0.0010 

Results suggest that 

ambient O3 exposure 

may be related to 

personal SO4

2–

exposure but not to 

personal PM2.5

exposure on the whole. 

Ambient NO2 exposure 

was associated with 

personal PM2.5

exposure, possibly 

because both have 

traffic sources. 

Tolbert et al. (2007, 

090316) 

Ambient PM10, PM10-2.5, 

PM2.5, EC, OC, TC, SO4

2–

, water-soluble metals, 

oxygenated 

hydrocarbons 

Ambient O3, NO2, CO, 

SO2

PM10 O3 NO2 CO SO2 PMc PM2.5 

PM10 1.0 

O3 0.6 1.0 

NO2 0.5 0.4 1.0 

CO 0.5 0.3 0.7 1.0 

SO2 0.2 0.2 0.4 0.3 1.0 

PMc 0.7 0.4 0.5 0.4 0.2 1.0 

PM2.5 0.8 0.6 0.6 0.4 0.2 0.5 1.0 

SO4

2– 0.7 0.6 0.1 0.1 0.1 0.3 0.8 

EC 0.6 0.4 0.6 0.7 0.2 0.5 0.7 

OC 0.7 0.5 0.6 0.6 0.2 0.5 0.7 

TC 0.7 0.5 0.7 0.6 0.2 0.5 0.7 

Metals 0.7 0.4 0.3 0.4 0.1 0.5 0.7 

OHC 0.5 0.4 0.2 0.3 0.1 0.4 0.5 

SO4

2– EC OC TC Metals OHC 

SO4

2– 1.0 

EC 0.3 1.0 

OC 0.3 0.8 1.0 

TC 0.3 0.9 1.0 1.0 

Metals 0.7 0.5 0.5 0.5 1.0 

OHC 0.5 0.4 0.4 0.4 0.5 1.0 

Low correlations were 

seen between SO2 and 

PM constituents. 

Components were 

used in a multipollutant model to 

predict emergency 

department visits in 

Atlanta. CO was found 

to be the most 

significant predictor of 

cardiovascular disease 

visits in one-, two-, and 

three-pollutant models, 

and O3 was the most 

significant predictor of 

respiratory disease 

visits in one-, two-, and 

three-pollutant models. 

Brook et al. (2007, 

091153) 

Anbient PM10, PM10-2.5, 

PM2.5, SO4

2–, and trace 

metals in 10 Canadian 

cities. 

Ambient NO2, NO R with NO2 (min, Max) 

NO2 1.00 (1.00, 1.00) 

NO 0.67 (0.51, 0.77) 

PM2.5 0.54 (0.45, 0.71) 

PM10-2.5 0.31 (0.04, 0.50) 

PM10 0.50 (0.23, 0.70) 

SO4

2– 0.33 (0.10, 0.48) 

Fe 0.44 (0.29, 0.56) 

Zn 0.39 (0.28, 0.52) 

Ni 0.20 (0.06, 0.40) 

Mn 0.51 (0.37, 0.62) 

As 0.21 (0.07, 0.39) 

Al 0.07 (-0.17, 0.18) 

Cu 0.03 (-0.07, 0.15) 

Pb 0.28 (0.16, 0.39) 

Si 0.19 (0.00, 0.32) 

Se 0.14 (-0.04, 0.35) 

NO2 showed the 

strongest association 

with mortality, but it is 

unclear if this 

association is due to 

health effects of NO2 or 

health effects of 

copollutant PM. 

December 2009 A-350 
Reference PM metric Copollutant metric Association between PM and copollutant Primary findings 

Ito et al. (2007, 

156594) 

Ambient PM2.5 Ambient O3, NO2, SO2, 

CO 

Shown in figure format only. Authors tested 

relationship between 

meteorological 

variables and 

copollutants to 

determine if multipollutant models are 

impacted by spatial or 

temporal variation or by 

meteorological 

conditions. 

Multicollinearity varied 

by pollutant and 

season. 

Kaur et al. (2005, 

086504) 

Fixed-site and personal 

PM2.5, personal UFP 

Fixed site and personal 

CO 

Personal R: 

PM2.5 UFP CO 

PM2.5 1 0.5 0.2 

UFP 0.5 1 0.7 

CO 0.2 0.7 1 

Fairly low correlation 

was observed between 

PM2.5 and CO and 

between PM2.5 and 

UFP, stronger 

correlations between 

UFP and CO. 

Kaur et al. (2005, 

088175) 

Fixed-site and personal 

PM2.5 analyzed postsample for light 

absorbance (as indicator 

for carbonaceous 

aerosol), personal UFP 

Fixed site and personal 

CO 

Personal R: 

R PM2.5 Abs CO UFP 

PM2.5 1 0.3 -0.1 0.0 

Abs 0.3 1 0.2 0.7 

CO -0.1 0.2 1 0.1 

UFP 0.0 0.7 0.1 1 

Strongest correlation 

observed between UFP 

and absorption, which 

is reasonable given 

that much absorptive 

carbonaceous aerosol 

is in the ultrafine range.

Sørenson et al. 

(2005, 089428) 

Personal, indoor 

residential, and outdoor 

residential PM2.5 and BC 

Personal, indoor 

residential, and outdoor 

residential NO2

Personal exposure regression coefficients to: 

PM2.5 BC NO2

Bedroom 0.72 0.47 0.70 

Front door 0.46 0.61 0.60 

Background 0.29 0.03 0.56 

Personal NO2

concentration is more 

strongly influenced by 

background than PM2.5

or BC. 

Sabin et al. (2005, 

087728) 

BC, particle-bound PAH 

on a school bus. 

NO2 on a school bus. BC PB-PAH NO2

BC 1 0.94 0.49 

PB-PAH 1 0.37 

NO2 1 

Note that these correlations are computed from data 

presented by Sabin et al. for mean concentrations when the 

test bus travelled behind different vehicles. 

Less correlation was 

observed between NO2

and PM species. This 

study was aimed more 

at fuel choices and 

control technologies for 

children’s exposures on 

school buses. 

Lai et al. (2004, 

056811) 

Microenvironmental and 

personal PM2.5 and trace 

elements for personal 

exposure (P), residential 

indoor (RI), residential 

outdoor (RO), and 

workplace (WI) 

measurements. 

Microenvironmental and 

personal VOCs, NO2, and 

CO. 

R (PM2.5) P RI RO WI 

TVOC 0.21 0.21 0.41 -0.32 

NO2 -0.1 -0.02 -0.16 0.09 

CO -0.07 NR NR NR 

The EXPOLIS Oxford 

study was more 

focused on the indooroutdoor exposure 

relationship, but the 

correlation results 

showed no important 

relationships between 

the pollutants shown. 

Gomez-Perales et al. 

(2004, 054418; 2007, 

138816) 

Microenvironmental PM2.5

with SO4

2–, NO3-, EC, 

OC. 

Microenvironmental CO. Ratio of Conc PM2.5 CO Benzene 

Minibus/Bus 1.04 1.54 2.01 

 1.20 1.40 1.33 

Minibus/Metro 1.70 2.02 3.20 

 1.43 3.03 3.10 

Morning and evening 

measurements of PM2.5

were on avg higher and 

more variable than for 

benzene and CO (in 

order). Benzene and 

CO had higher and 

more variable 

concentrations for 

minibuses than for 

buses and metros, 

respectively, while 

PM2.5 concentrations 

were not substantially 

different for buses and 

minibuses. 

December 2009 A-351 
Reference PM metric Copollutant metric Association between PM and copollutant Primary findings 

Sarnat et al. (2001, 

019401) 

Fixed site and personal 

PM2.5 monitors. 

Ambient O3, NO2, SO2, 

and CO 

R PM2.5 O3 NO2 SO2 CO 

PM2.5 1 0.67 0.37 --- 0.15 

O3 -0.72 1 0.02 --- -0.06 

NO2 0.75 -0.71 1 --- 0.75 

SO2 -0.17 0.41 -0.17 1 -0.32 

CO 0.69 -0.67 0.76 -0.12 1 

Strong association 

between ambient NO2

and personal PM2.5

suggests that ambient 

gas may be a suitable 

surrogate for personal 

exposure. 

December 2009 A-352 
Annex A References 

Abu-Allaban M; Gillies JA; Gertler AW; Clayton R; Proffitt D (2007). Motor vehicle contributions to ambient PM10 and 

PM2.5 at selected urban areas in the USA. Environ Monit Assess, 132: 155-63. 098575

Adar SD; Adamkiewicz G; Gold DR; Schwartz J; Coull BA; Suh H (2007). Ambient and microenvironmental particles and 

exhaled nitric oxide before and after a group bus trip. Environ Health Perspect, 115: 507-512. 098635

Adgate JL; Mongin SJ; Pratt GC; Zhang J; Field MP; Ramachandran G; Sexton K (2007). Relationships between personal, 

indoor, and outdoor exposures to trace elements in PM(2.5). Sci Total Environ, 386: 21-32. 156196

Adgate JL; Ramachandran G; Pratt GC; Waller LA; Sexton K (2002). Spatial and temporal variability in outdoor, indoor, 

and personal PM25 exposure. Atmos Environ, 36: 3255-3265. 030676

Adgate JL; Ramachandran G; Pratt GC; Waller LA; Sexton K (2003). Longitudinal variability in outdoor, indoor, and 

personal PM25 exposure in healthy non-smoking adults. Atmos Environ, 37: 993-1002. 040341

Allen R; Larson T; Sheppard L; Wallace L; Liu L-JS (2003). Use of real-time light scattering data to estimate the 

contribution of infiltrated and indoor-generated particles to indoor air. Environ Sci Technol, 37: 3484-3492. 053578

Allen R; Wallace L; Larson T; Sheppard L; Liu L-JS (2007). Evaluation of the recursive model approach for estimating 

particulate matter infiltration efficiencies using continuous light scattering data. J Expo Sci Environ Epidemiol, 17: 

468-477. 154226

Anderson TL; Ogren JA (1998). Determining aerosol radiative properties using the TSI 3563 integrating nephelometer. 

Aerosol Sci Technol, 29: 57-69. 156213

Annesi-Maesano I; Moreau D; Caillaud D; Lavaud F; Le Moullec Y; Taytard A; Pauli G; Charpin D (2007). Residential 

proximity fine particles related to allergic sensitisation and asthma in primary school children. Respir Med, 101: 

1721-1729. 093180

Arhami M; Kuhn T; Fine PM; Delfino RJ; Sioutas C (2006). Effects of Sampling Artifacts and Operating Parameters on the 

Performance of a Semicontinuous Particulate Elemental Carbon/Organic Carbon Monitor. Environ Sci Technol, 40: 

945-954. 156224

Arhami M; Polidori A; Delfino RJ; Tjoa T; Sioutas C (2009). Associations between personal, indoor, and residential 

outdoor pollutant concentrations: implications for exposure assessment to size-fractionated particulate matter. J Air 

Waste Manag Assoc, 59: 392-404. 190096

Arnott WP; Hamasha K; Moosmüller H; Sheridan PJ; Ogren JA (2005). Towards Aerosol Light-Absorption Measurements 

with a 7-Wavelength Aethalometer: Evaluation with a Photoacoustic Instrument and 3-Wavelength Nephelometer. 

Aerosol Sci Technol, 39: 17-29. 156227

Arnott WP; Moosmuller H; Rogers CF; Jin T; Bruch R (1999). Photoacoustic spectrometer for measuring light absorption 

by aerosol; instrument description. Atmos Environ, 33: 2845-2852. 020650

Ashbaugh LL (1983). A statistical trajectory technique for determining air pollution source regions. J Air Waste Manag 

Assoc, 33: 1096-1098. 156229

Ashbaugh LL; Myrup LO; Flocchini RG (1984). A principal component analysis of sulfur concentrations in the western 

United States. Atmos Environ, 18: 783-791. 045148

Babich P; Wang PY; Allen G; Sioutas C; Koutrakis P (2000). Development and Evaluation of a Continuous Ambient 

PM2.5 Mass Monitor. Aerosol Sci Technol, 32: 309-324. 156239

Bae M-S; Schauer JJ; DeMinter JT; Turner JR; Smith D; Cary RA (2004). Validation of a semi-continuous instrument for 

elemental carbon and organic carbon using a thermal-optical method. Atmos Environ, 38: 2885-2893. 098680

Bae MS; Schauer JJ; Deminter JT; Turner JR (2004). Hourly and daily patterns of particle-phase organic and elemental 

carbon concentrations in the urban atmosphere. J Air Waste Manag Assoc, 54: 823-833. 156243

 

Note: Hyperlinks to the reference citations throughout this document will take you to the NCEA HERO database (Health and 

Environmental Research Online) at http://epa.gov/hero. HERO is a database of scientific literature used by U.S. EPA in the process of 

developing science assessments such as the Integrated Science Assessments (ISA) and the Integrated Risk Information System (IRIS) 

December 2009 A-353 
Balasubramanian R; Lee SS (2007). Characteristics of indoor aerosols in residential homes in urban locations: a case study 

in Singapore. J Air Waste Manag Assoc, 57: 981-990. 156248

Barn P; Larson T; Noullett M; Kennedy S; Copes R; Brauer M (2008). Infiltration of forest fire and residential wood 

smoke: an evaluation of air cleaner effectiveness. J Expo Sci Environ Epidemiol, 18: 503-511. 156252

Baxter LK; Clougherty JE; Laden F; Levy JI (2007). Predictors of concentrations of nitrogen dioxide, fine particulate 

matter, and particle constituents inside of lower socioeconomic status urban homes. J Expo Sci Environ Epidemiol, 

17: 433-444. 092726

Baxter LK; Clougherty JE; Paciorek CJ; Wright RJ; Levy JI (2007). Predicting residential indoor concentrations of 

nitrogen dioxide, fine particulate matter, and elemental carbon using questionnaire and geographic information 

system based data. Atmos Environ, 41: 6561-6571. 092725

Bein KJ; Zhao Y; Wexler AS; Johnston MV (2005). Speciation of size-resolved individual ultrafine particles in Pittsburgh, 

Pennsylvania. J Geophys Res, 110: D07S05. 156265

BeruBe KA; Sexton KJ; Jones TP; Moreno T; Anderson S; Richards RJ (2004). The spatial and temporal variations in 

PM10 mass from six UK homes. Sci Total Environ, 324: 41-53. 007894

Birch ME (1998). Analysis of carbonaceous aerosols: interlaboratory comparison. Analyst, 123: 851-857. 024953

Birch ME; Cary RA (1996). Elemental carbon-based method for monitoring occupational exposures to particulate diesel 

exhaust. Aerosol Sci Technol, 25: 221-241. 026004

Birch ME; Cary RA (1996). Elemental carbon-based method for occupational monitoring of particulate diesel exhaust: 

methodology and exposure issues. Analyst, 121: 1183-1190. 002352

Biswas S; Fine PM; Geller MD; Hering SV; Sioutas C (2005). Performance evaluation of a recently developed water-based 

condensation particle counter. Aerosol Sci Technol, 39: 419-427. 150694

Blanchard P; Brook JR; Brazil P (2002). Chemical characterization of the organic fraction of atmospheric aerosol at two 

sites in Ontario, Canada. J Geophys Res, 107: ICC10.1-ICC10.8 . 047598

Blanchard P; Hopper JF (1997). Concentrations and distributions of PAHs and n-alkanes in atmospheric aerosols at nonurban sites in Ontario, Canada. Presented at . 156277

Blazsó M; Janitsek S; Gelencsér A; Artaxo P; Graham B; Andreae MO (2003). Study of tropical organic aerosol by 

thermally assisted alkylation-gas chromatography mass spectrometry. J Anal Appl Pyrol, 68: 351-369. 156278

Bond TC; Anderson TL; Campbell D (1999). Calibration and Intercomparison of Filter-Based Measurements of Visible 

Light Absorption by Aerosols. Aerosol Sci Technol, 30: 582-600. 156281

Branis M; Rez cov P; Domasov M (2005). The effect of outdoor air and indoor human activity on mass concentrations of 

PM10, PM2.5, and PM1 in a classroom. Environ Res, 99: 143-149. 156290

Briggs DJ; de Hoogh K; Morris C; Gulliver J (2008). Effects of travel mode on exposures to particulate air pollution. 

Environ Int, 34: 12-22. 156294

Brook JR; Burnett RT; Dann TF; Cakmak S; Goldberg MS; Fan X; Wheeler AJ (2007). Further interpretation of the acute 

effect of nitrogen dioxide observed in Canadian time-series studies. J Expo Sci Environ Epidemiol, 17 : S36-S44. 

091153

Brown K; Sarnat J; Suh H; Coull B; Spengler J; Koutrakis P (2008). Ambient site, home outdoor and home indoor 

particulate concentrations as proxies of personal exposures. J Environ Monit, 10: 1041-1051. 190894

Brunekreef B; Janssen N; De Hartog J; Oldenwening M; Meliefste K; Hoek G; Lanki T; Timonen K; Vallius M; Pekkanen 

J; Van Grieken R (2005). Personal, indoor, and outdoor exposures to PM2.5 and its components for groups of 

cardiovascular patients in Amsterdam and Helsinki. Health Effects Institute. Montepelier, VT. 090486

Butler AJ; Andrew MS; Russell AG (2003). Daily sampling Of PM2. 5 in Atlanta: Results of the first year of the 

Assessment of Spatial Aerosol Composition. J Geophys Res, 108: 8415. 156313

Cabada JC; Rees S; Takahama S; Khlystov A; Pandis SN; Davidson CI; Robinson AL (2004). Mass size distributions and 

size resolved chemical composition of fine particulate matter at the Pittsburgh supersite. Atmos Environ, 38: 3127-

3141. 148859

December 2009 A-354 
Cao JJ; Lee SC; Chow JC; Cheng Y; Ho KF; Fung K; Liu SX; Watson JG (2005). Indoor/outdoor relationships for PM2.5 

and associated carbonaceous pollutants at residential homes in Hong Kong - case study. Indoor Air, 15: 197-204. 

156321

Chakrabarti B; Singh M; Sioutas C (2004). Development of a Near-Continuous Monitor for Measurment of the Sub-150 

nm PM Mass Concentration. Aerosol Sci Technol, 38: 239-252. 157426

Cheng M-D; Hopke PK; Zeng Y (1993). A receptor-oriented methodology for determining source regions of particulate 

sulfate observed at Dorset, Ontario. J Geophys Res, 98: 16,839-16,849. 052294

Chillrud SN; Epstein D; Ross JM; Sax SN; Pederson D; Spengler JD; Kinney PL (2004). Elevated airborne exposures of 

teenagers to manganese, chromium, and iron from steel dust and New York City's subway system. Environ Sci 

Technol, 38: 732-737. 054799

Chow JC (1995). Measurement methods to determine compliance with ambient air quality standards for suspended 

particles. J Air Waste Manag Assoc, 45: 320-382. 077012

Chow JC (2007). The application of thermal methods for determining chemical composition of carbonaceous aerosols: A 

review. J Environ Sci Health A Tox Hazard Subst Environ Eng, 42: 1521-1541. 157209

Chow JC; Chen L-WA; Lowenthal DH; Doraiswamy P; Park K; Kohl S; Trimble DL; Watson JG (2005). California 

Regional PM10/PM2.5 Air Quality Study (CRPAQS) - Initial data analysis of field program measurements. Desert 

Research Institute. Reno, NV. 156348

Chow JC; Doraiswamy P; Watson JG; Chen LW; Ho SS; Sodeman DA (2008). Advances in integrated and continuous 

measurements for particle mass and chemical composition. J Air Waste Manag Assoc, 58: 141-163. 156355

Chow JC; Watson JG; Chen LW; Chang MC; Robinson NF; Trimble D; Kohl S (2007). The IMPROVE_A temperature 

protocol for thermal/optical carbon analysis: maintaining consistency with a long-term database. J Air Waste Manag 

Assoc, 57: 1014-1023. 156354

Chow JC; Watson JG; Doraiswamy P; Chen L-WA; Sodeman DA; Ho SSH; Tropp RJ; Kohl SD; Trimble DL; Fung KK 

(2006). Climate Change - Characterization of black carbon and organic carbon air pollution emissions and 

evaluation of measurement methods, Phase I. Desert Research Institute. Reno, NV. 156350

Chow JC; Watson JG; Lowenthal DH; Chen LWA; Magliano KL (2006). Particulate carbon measurements in California's 

San Joaquin Valley. Chemosphere, 62: 337-348. 155207

Chow JC; Watson JG; Lowenthal DH; Chen LWA; Tropp RJ; Park K; Magliano KA (2006). PM25 and PM10 Mass 

Measurements in California's San Joaquin Valley. Aerosol Sci Technol, 40: 796-810. 146622

Chow JC; Watson JG; Lowenthal DH; Magliano KL (2005). Loss of PM2.5 nitrate from filter samples in central California. 

J Air Waste Manag Assoc, 55: 1158-1168. 099030

Chow JC; Watson JG; Pritchett LC; Pierson WR; Frazier CA; Purcell RG (1993). The DRI thermal/optical reflectance 

carbon analysis system: description, evaluation and applications in US air quality studies. Atmos Environ, 27: 1185-

1201. 077459

Chung A; Chang DP; Kleeman MJ; Perry KD; Cahill TA; Dutcher D; McDougall EM; Stroud K (2001). Comparison of 

real-time instruments used to monitor airborne particulate matter. J Air Waste Manag Assoc, 51: 109-120. 156357

Conner TL; Williams RWE (2004). Identification of possible sources of particulate matter in the personal cloud using 

SEM/EDX. Atmos Environ, 38: 5305-5310. 156364

Cornell SE; Jickells TD (1999). Water-soluble organic nitrogen in atmospheric aerosol: a comparison of UV and persulfate 

oxidation methods. Atmos Environ, 33: 833-840. 156367

Cortez-Lugo M; Moreno-Macias H; Holguin-Molina F; Chow JC; Watson JG; Gutierrez-Avedoy V; Mandujano F; 

Hernandez-Avila M; Romieu I (2008). Relationship between indoor, outdoor, and personal fine particle 

concentrations for individuals with COPD and predictors of indoor-outdoor ratio in Mexico city. J Expo Sci 

Environ Epidemiol, 18: 109-115. 156368

Crimmins BS; Baker JE (2006). Improved GC/MS methods for measuring hourly PAH and nitro-PAH concentrations in 

urban particulate matter. Atmos Environ, 40: 6764-6779. 097008

Crist KC; Liu B; Kim M; Deshpande SR; John K (2008). Characterization of fine particulate matter in Ohio: Indoor, 

outdoor, and personal exposures. Environ Res, 106: 62-71. 156372

December 2009 A-355 
Dams R; Rahn KA; Robbins JA; Nifong GD; Winchester JW (1970). Multi-Element Analysis of Air Pollution Particulates 

by Nondestructive Neutron Activation. Presented at Proceedings of the Second International Clean Air Congress, 

New York. 156379

Decesari S; Facchini MC; Fuzzi S; McFiggans GB; Coe H; Bower KN (2005). The water-soluble organic component of 

size-segregated aerosol, cloud water and wet depositions from Jeju Island during ACE-Asia. Atmos Environ, 39: 

211-222. 144536

Delfino RJ; Quintana PJE; Floro J; Gastanaga VM; Samimi BS; Kleinman MT; Liu L-JS; Bufalino C; Wu C-F; McLaren 

CE (2004). Association of FEV1 in asthmatic children with personal and microenvironmental exposure to airborne 

particulate matter. Environ Health Perspect, 112: 932-941. 056897

Delfino RJ; Staimer N; Gillen D; Tjoa T; Sioutas C; Fung K; George SC; Kleinman MT (2006). Personal and ambient air 

pollution is associated with increased exhaled nitric oxide in children with asthma. Environ Health Perspect, 114: 

1736-1743. 090745

Diapouli E; Chaloulakou A; Mihalopoulos N; Spyrellis N (2008). Indoor and outdoor PM mass and number concentrations 

at schools in the Athens area. Environ Monit Assess, 136: 13-20. 190893

Diapouli E; Chaloulakou A; Spyrellis N (2007). Levels of ultrafine particles in different microenvironments--implications 

to children exposure. Sci Total Environ, 388: 128-136. 156397

Dimitroulopoulou C; Ashmore MR; Hill MTR; Byrne MA; Kinnersley R (2006). INDAIR: a probabilistic model of indoor 

air pollution in UK homes. Atmos Environ, 40: 6362-6379. 090302

Dong Y; Hays MD; Dean Smith N; Kinsey JS (2004). Inverting cascade impactor data for size-resolved characterization of 

fine particulate source emissions. J Aerosol Sci, 35: 1497-1512. 156409

Drewnick F; Jayne JT; Canagaratna M; Worsnop DR; Demerjian KL (2004). Measurement of Ambient Aerosol 

Composition During the PMTACS-NY 2001 Using an Aerosol Mass Spectrometer. Part II: Chemically Speciated 

Mass Distributions. Aerosol Sci Technol, 38: 104-117. 155755

Drewnick F; Schwab J; Jayne J; Canagaratna M; Worsnop D; Demerjian K (2004). Measurement of Ambient Aerosol 

Composition During the PMTACS-NY 2001 Using an Aerosol Mass Spectrometer. Part I: Mass Concentrations. 

Aerosol Sci Technol, 38: 92-103. 155754

Drewnick F; Schwab JJ; Hogrefe O; Peters S; Husain L; Diamond D; Weber R; Demerjian KL (2003). Intercomparison and 

evaluation of four semi-continuous PM2.5 sulfate instruments. Atmos Environ, 37: 3335-3350. 099160

Eatough DJ; Eatough NL; Obeidi F; Pang Y; Modey W; Long R (2001). Continuous determination of PM2.5 mass, 

including semi-volatile species. Aerosol Sci Technol, 34: 1-8. 010303

Ebelt ST; Wilson WE; Brauer M (2005). Exposure to ambient and nonambient components of particulate matter: a 

comparison of health effects. Epidemiology, 16: 396-405. 056907

Ellis EC; Novakov T (1982). Application of thermal analysis to the characterization of organic aerosol particles. Sci Total 

Environ, 23: 227-238. 156416

Emmenegger C; Reinhardt A; Hueglin C; Zenobi R; Kalberer M (2007). Evaporative light scattering: A novel detection 

method for the quantitative analysis of humic-like substances in aerosols. Environ Sci Technol, 41: 2473-2478. 

156418

Engling G; Carrico CM; Kreidenweis SM; Collett JL; Day DE; Malm WC; Lincoln E; Min Hao W; Iinuma Y; Herrmann H 

(2006). Determination of levoglucosan in biomass combustion aerosol by high-performance anion-exchange 

chromatography with pulsed amperometric detection. Atmos Environ, 40: 299-311. 156422

Fabbri D; Prati S; Vassura I (2002). Molecular characterisation of organic material in air fine particles (PM10) using 

conventional and reactive pyrolysis-gas chromatography-mass spectrometry. J Environ Monit, 4: 210-215. 156426

Falkovich AH; Rudich Y (2001). Analysis of semivolatile organic compounds in atmospheric aerosols by direct sample 

introduction thermal desorption GC/MS. Environ Sci Technol, 35: 2326-2333. 156427

Falkovich AH; Schkolnik G; Ganor E; Rudich Y (2004). Adsorption of organic compounds pertinent to urban environments 

onto mineral dust particles. J Geophys Res, 109: D02208. 156428

December 2009 A-356 
Farmer PB; Singh R; Kaur B; Sram RJ; Binkova B; Kalina I; Popov TA; Garte S; Taioli E; Gabelova A; CebulskaWasilewska A (2003). Molecular epidemiology studies of carcinogenic environmental pollutants: effects of 

polycyclic aromatic hydrocarbons (PAHs) in environmental pollution on exogenous and oxidative DNA damage. 

Mutat Res, 544: 397-402. 089017

Fehsenfeld FC;Hastie D;Chow JC;Solomon PA (2004). Particle and gas measurements. In McMurry, P. H.Shepherd, M. 

F.Vickery, J. S. (Ed.),Particulate Matter Science for Policy Makers: A NARSTO Assessment (pp. 159-189). 

Cambridge, MA: NARSTO. 157360

Feldpausch P; Fiebig M; Fritzsche L; Petzold A (2006). Measurement of ultrafine aerosol size distributions by a 

combination of diffusion screen separators and condensation particle counters. J Aerosol Sci, 37: 577-597. 155773

Ferro AR; Kopperud RJ; Hildemann LM (2004). Source strengths for indoor human activities that resuspend particulate 

matter. Environ Sci Technol, 38: 1759-1764. 055387

Fine PM; Chakrabarti B; Krudysz M; Schauer JJ; Sioutas C (2004). Diurnal Variations of Individual Organic Compound 

Constituents of Ultrafine and Accumulation Mode Particulate Matter in the Los Angeles Basin. Environ Sci 

Technol, 38: 1296-1304. 141283

Fine PM; Jaques PA; Hering SV; Sioutas C (2003). Performance Evaluation and Use of a Continuous Monitor for 

Measuring Size-Fractionated PM 2.5 Nitrate. Aerosol Sci Technol, 37: 342 - 354. 155775

Fitz D; Chan M; Cass G; Lawson D; Ashbaugh L (1989). A multi-component size-classifying aerosol and gas sampler for 

ambient air monitoring. Presented at Presented at: 82nd annual meeting of the Air & Waste Management 

Association; June; Anaheim, CA. Pittsburgh, PA: Air & Waste Management Association; paper no. 89-140.1. 

077387

Fraser MP; Cass GR; Simoneit BRT (2003). Air quality model evaluation data for organics 6 C3-C24 organic acids. 

Environ Sci Technol, 37: 446-453. 040266

Fraser MP; Yue ZW; Buzcu B (2003). Source apportionment of fine particulate matter in Houston, TX, using organic 

molecular markers. Atmos Environ, 37: 2117-2123. 042231

Fraser MP; Yue ZW; Tropp RJ; Kohl SD; Chow JC (2002). Molecular composition of organic fine particulate matter in 

Houston, TX. Atmos Environ, 36: 5751-5758. 140741

Fromme H; Diemer J; Dietrich S; Cyrys J; Heinrich J; Lang W; Kiranoglu M; Twardella D (2008). Chemical and 

morphological properties of particulate matter (PM10, PM25) in school classrooms and outdoor air. Atmos Environ, 

42: 6597-6605. 155147

Fruin S; Westerdahl D; Sax T; Sioutas C; Fine PM (2008). Measurements and predictors of on-road ultrafine particle 

concentrations and associated pollutants in Los Angeles. Atmos Environ, 42: 207-219. 097183

Gadkari NM; Pervez S (2007). Source investigation of personal particulates in relation to identify major routes of exposure 

among urban residentials. Atmos Environ, 41: 7951-7963. 156459

Gauvin S; Reungoat P; Cassadou S; Dechenaux J; Momas I; Just J; Zmirou D (2002). Contribution of indoor and outdoor 

environments to PM2.5 personal exposure of children--VESTA study. Sci Total Environ, 297: 175-181. 034893

Geyh A; Chillrud S; Wiliams D; Herbstman J; Symons J; Rees K; Ross J; Kim S; Lim H; Turpin B; Breysse P (2005). 

Assessing truck driver exposure at the World Trade Center disaster site: personal and area monitoring for particulate 

matter and volatile organic compounds during October 2001 and April 2002. J Occup Environ Hyg, 2: 179-193. 

186949

Gómez-Perales JE; Colvile RN; Fernández-Bremauntz AA; Gutiérrez-Avedoy V; Páramo-Figueroa VH; Blanco-Jiménez S; 

Bueno-López E; Bernabé-Cabanillas R; Mandujano F; Hidalgo-Navarro M; Nieuwenhuijsen MJ (2007). Bus, 

minibus, metro inter-comparison of commuters' exposure to air pollution in Mexico City. Atmos Environ, 41: 890-

901. 138816

Gómez-Perales JE; Colvile RN; Nieuwenhuijsen MJ; Fernández-Bremauntz A; Gutiérrez-Avedoy VJ; Páramo-Figueroa 

VH; Blanco-Jiménez S; Bueno-López E; Mandujano F; Bernabé-Cabanillas R (2004). Commuters' exposure to 

PM2.5, CO, and benzene in public transport in the metropolitan area of Mexico City. Atmos Environ, 38: 1219-

1229. 054418

December 2009 A-357 
Graham B; Falkovich AH; Rudich Y; Maenhaut W; Guyon P; Andreae MO (2004). Local and regional contributions to the 

atmospheric aerosol over Tel Aviv, Israel: a case study using elemental, ionic and organic tracers. Atmos Environ, 

38: 1593-1604. 156490

Graney JR; Landis MS; Norris GA (2004). Concentrations and solubility of metals from indoor and personal exposure 

PM25 samples. Atmos Environ, 38: 237-247. 053756

Greaves RC; Barkley RM; Sievers RE (1985). Rapid sampling and analysis of volatile constituents of airborne particulate 

matter. Anal Chem, 57: 2807-2815. 156494

Greaves RC; Barkley RM; Sievers RE; Meglen RR (1987). Covariations in the concentrations of organic compounds 

associated with springtime atmospheric aerosols. Atmos Environ, 21: 2549-2561. 156495

Grosjean D; Seinfeld JH (1989). Parameterization of the formation potential of secondary organic aerosols. Atmos Environ, 

23: 1733-1747. 045643

Grover BD; Eatough NL; Eatough DJ; Chow JC; Watson JG; Ambs JL; Meyer MB; Hopke PK; Al-Horr R; Later DW; 

Wilson WE (2006). Measurement of Both Nonvolatile and Semi-Volatile Fractions of Fine Particulate Matter in 

Fresno, CA. Aerosol Sci Technol, 40: 811-826. 138080

Grover BD; Kleinman M; Eatough NL; Eatough DJ; Hopke PK; Long RW; Wilson WE; Meyer MB; Ambs JL (2005). 

Measurement of total PM25 mass (nonvolatile plus semivolatile) with the Filter Dynamic Measurement System 

tapered element oscillating microbalance monitor. J Geophys Res, 110: D07S03. 090044

Gulliver J; Briggs DJ (2004). Personal exposure to particulate air pollution in transport microenvironments. Atmos 

Environ, 38: 1-8. 053238

Gulliver J; Briggs DJ (2007). Journey-time exposure to particulate air pollution. Atmos Environ, 41: 7195-7207. 155814

Guo H; Lee SC; Chan LY (2004). Indoor air quality investigation at air-conditioned and non-air-conditioned markets in 

Hong Kong. Sci Total Environ, 323: 87-98. 156506

Hall PA; Watson AFR; Garner GV; Hall K; Smith S; Waterman D; Horsfield B (1999). An investigation of micro-scale 

sealed vessel thermal extraction-gas chromatography-mass spectrometry (MSSV-GC-MS#) and micro-scale sealed 

vessel pyrolysis-gas chromatography-mass spectrometry applied to a standard reference material of an urban dust. 

Sci Total Environ, 235: 269-276. 156512

Hamilton JF; Webb PJ; Lewis AC; Hopkins JR; Smith S; Davy P (2004). Partially oxidised organic components in urban 

aerosol using GCXGC-TOF/MS. Atmos Chem Phys, 4: 1279–1290. 156516

Hamilton JF; Webb PJ; Lewis AC; Reviejo MM (2005). Quantifying small molecules in secondary organic aerosol formed 

during the photo-oxidation of toluene with hydroxyl radicals. Atmos Environ, 39: 7263-7275. 088173

Hanninen OO; Lebret E; Ilacqua V; Katsouyanni K; Kunzli N; Sram RJ; Jantunen M (2004). Infiltration of ambient PM2.5 

and levels of indoor generated non-ETS PM2.5 in residences of four European cities. Atmos Environ, 38: 6411-

6423. 056812

Harrison D; Shik Park S; Ondov J; Buckley T; Roul Kim S; Jayanty RKM (2004). Highly time resolved fine particle nitrate 

measurements at the Baltimore Supersite. Atmos Environ, 38: 5321-5332. 136787

Hauck H; Stopper S; Puxbaum H; Kundi M; Preining O; Berner A; Gomiscek B (2004). On the equivalence of gravimetric 

PM data with TEOM and beta-attenuation measurements. J Aerosol Sci, 35: 1135-1149. 156525

Haverinen-Shaughnessy U; Toivola M; Alm S; Putus T; Nevalainen A (2007). Personal and microenvironmental 

concentrations of particles and microbial aerosol in relation to health symptoms among teachers. J Expo Sci 

Environ Epidemiol, 17: 182-190. 156526

Hays MD; Smith ND; Dong Y (2004). Nature of unresolved complex mixture in size-distributed emissions from residential 

wood combustion as measured by thermal desorption-gas chromatography-mass spectrometry. J Geophys Res, 109: 

D16S04. 156530

Hays MD; Smith ND; Kinsey J; Dong Y; Kariher P (2003). Polycyclic aromatic hydrocarbon size distributions in aerosols 

from appliances of residential wood combustion as determined by direct thermal desorption—GC/MS. J Aerosol 

Sci, 34: 1061-1084. 156529

Helmig D; Bauer A; Mueller J; Klein W (1990). Analysis of particulate organics in a forest atmosphere by 

thermodesorption GC/MS. Atmos Environ, 24: 179-184. 156536

December 2009 A-358 
Henning S; Weingartner E; Schwikowski M; Gaggeler HW; Gehrig R; Hinz KP; Trimborn A; Spengler B; Baltensperger U 

(2003). Seasonal variation of water-soluble ions of the aerosol at the high-alpine site Jungfraujoch (3580 m asl). J 

Geophys Res, 108: 4030. 156539

Henry RC (1997). History and fundamentals of multivariate air quality receptor models. Chemometr Intell Lab Syst, 37: 

37-42. 020941

Henry RC (2003). Multivariate receptor modeling by N-dimensional edge detection. Chemometr Intell Lab Syst, 65: 179-

189. 156540

Henry RC; Chang YS; Spiegelman CH (2002). Locating nearby sources of air pollution by nonparametric regression of 

atmospheric concentrations on wind direction. Atmos Environ, 36: 2237-2244. 136097

Hering S; Cass G (1999). The magnitude of bias in the measurement of PM2.5 arising from volatilization of particulate 

nitrate from Teflon filters. J Air Waste Manag Assoc, 49: 725-733. 084958

Hering S; Fine PM; Sioutas C; Jaques PA; Ambs JL; Hogrefe O; Demerjian KL (2004). Field assessment of the dynamics 

of particulate nitrate vaporization using differential TEOM® and automated nitrate monitors. Atmos Environ, 38: 

5183-5192. 155837

Hering SV; Lawson DR; Allegrini I; Febo A; Perrino C; Possanzini M; Sickles JE II; Anlauf KG; Wiebe A; Appel BR; John 

W; Ondo J; Wall S; Braman RS; Sutton R; Cass GR; Solomon PA; Eatough DJ; Eatough NL; Ellis EC; Grosjean D; 

Hicks BB; Womack JD; Horrocks J; Knapp KT; Ellestad TG; Paur RJ; Mitchell WJ; Pleasant M; Peake E; MacLean 

A; Pierson WR; Brachaczek W; Schiff HI; Mackay GI; Spicer CW; (1988). The nitric acid shootout: field 

comparison of measurement methods. Atmos Environ, 22: 1519-1539. 036012

Hering SV; Stolzenburg MR; Quant FR; Oberreit DR; Keady PB (2005). A Laminar-Flow, Water-Based Condensation 

Particle Counter (WCPC). Aerosol Sci Technol, 39: 659-672. 155838

Hermann M; Wehner B; Bischof O; Han HS; Krinke T; Liu W; Zerrath A; Wiedensohler A (2007). Particle counting 

efficiencies of new TSI condensation particle counters. J Aerosol Sci, 38: 674-682. 155840

Hidy GM; Friedlander SK (1972). The nature of the Los Angeles aerosol. Presented at Second International Clean Air 

Congress, New York. 156546

Ho KF; Cao JJ; Harrison RM; Lee SC (2004). Indoor/outdoor relationships of organic carbon (OC) and elemental carbon 

(EC) in PM2.5 in roadside environment of Hong Kong. Atmos Environ, 38: 6327-6335. 056804

Ho KF; Chow JC; Watson JG; Fung K; Lee SC; Cao JJ; Li YS (2006). Variability of organic and elemental carbon, water 

soluble organic carbon, and isotopes in Hong Kong. Atmos Chem Phys, 6: 4579-4600. 156552

Ho SSH; Yu JZ (2004). In-injection port thermal desorption and subsequent gas chromatography–mass spectrometric 

analysis of polycyclic aromatic hydrocarbons and n-alkanes in atmospheric aerosol samples. J Chromatogr A, 1059: 

121-129. 156551

Hoek G; de Hartog J; Meliefste K; ten Brink H; Katsouyanni K; Karakatsani A; Lianou M; Kotronarou A; Kavouras I; 

Pekkanen J; Vallius M; Kulmala M; Puustinen A; Thomas S; Meddings C; Ayres J; van Wijnen J; Hameri K; Kos G; 

Harrison R (2008). Indoor-outdoor relationships of particle number and mass in four European cities. Atmos 

Environ, 42: 156-169. 156554

Hogrefe O; Schwab JJ; Drewnick F; Lala GG; Peters S; Demerjian KL; Rhoads K; Felton HD; Rattigan OV; Husain L; 

Dutkiewicz VA (2004). Semicontinuous PM2.5 sulfate and nitrate measurements at an urban and a rural location in 

New York: PMTACS-NY summer 2001 and 2002 campaigns. J Air Waste Manag Assoc, 54: 1040-1060. 099003

Hopke PK; Li CL; Ciszek W; Landsberger S (1995). The use of bootstrapping to estimate conditional probability fields for 

source locations of airborne pollutants. Chemometr Intell Lab Syst, 30: 69-79. 156566

Hopke PK; Ramadan Z; Paatero P; Norris GA; Landis MS; Williams RW; Lewis CW (2003). Receptor modeling of 

ambient and personal exposure samples: 1998 Baltimore Particulate Matter Epidemiology-Exposure Study. Atmos 

Environ, 37: 3289-3302. 095544

Hystad P; Setton E; Allen R; Keller P; Brauer M (2008). Modeling residential fine particulate matter infiltration for 

exposure assessment. J Expo Sci Environ Epidemiol, 19: 570-579. 190890

Ito K; Thurston GD; Silverman RA (2007). Characterization of PM2.5, gaseous pollutants, and meteorological interactions 

in the context of time-series health effects models. J Expo Sci Environ Epidemiol, 17 Suppl 2: S45-S60. 156594

December 2009 A-359 
Jacquemin B; Cabrera L; Querol X; Bellander T; Moreno N; Peters A; Pey J; Pekkanen J; Lanki T; Sunyer J (2007). Levels 

of outdoor PM2.5, absorbance and sulphur as surrogates for personal exposures among post-myocardial infarction 

patients in Barcelona, Spain. Atmos Environ, 41: 1539-1549. 156600

Jacquemin B; Sunyer J; Forsberg B; Götschi T; Bayer-Oglesby L; Ackermann-Liebrich U; de Marco R; Heinrich J; Jarvis 

D; Torén K; Künzli N (2007). Annoyance due to air pollution in Europe. Int J Epidemiol, 10: 1-12. 192372

Jansen KL; Larson TV; Koenig JQ; Mar TF; Fields C; Stewart J; Lippmann M (2005). Associations between health effects 

and particulate matter and black carbon in subjects with respiratory disease. Environ Health Perspect, 113: 1741-

1746. 082236

Janssen NA; Lanki T; Hoek G; Vallius M; De Hartog JJ; Van Grieken R; PekkanenJ; Brunekreef B (2005). Associations 

between ambient, personal, and indoor exposure to fine particulate matter constituents in Dutch and Finnish panels 

of cardiovascular patients. Occup Environ Med, 62: 868-877. 088692

Jaques PA; Ambs JL; Grant WL; Sioutas C (2004). Field evaluation of the differential TEOM monitor for continuous PM 

2.5 mass concentrations. Aerosol Sci Technol, 38: 49-59. 155878

Jedrychowski WA; Perera FP; Pac A; Jacek R; Whyatt RM; Spengler JD; Dumyahn TS; Sochacka-Tatara E (2006). 

Variability of total exposure to PM2.5 related to indoor and outdoor pollution sources Krakow study in pregnant 

women. Sci Total Environ, 366: 47-54. 156606

Jeon SJ; Meuzelaar HLC; Sheya SAN; Lighty JS; Jarman WM; Kasteler C; Sarofim AF; Simoneit BRT (2001). 

Exploratory studies of PM10 receptor and source profiling by GC/MS and principal component analysis of 

temporally and spatially resolved ambient samples. J Air Waste Manag Assoc, 51: 766-784. 016636

Jimenez JL; Jayne JT; Shi Q; Kolb CE; Worsnop DR; Yourshaw I; Seinfeld JH; Flagan RC; Zhang X; Smith KA (2003). 

Ambient aerosol sampling using the Aerodyne Aerosol Mass Spectrometer. J Geophys Res, 108: 8425. 156611

Johannesson S; Gustafson P; Molnar P; Barregard L; Sallsten G (2007). Exposure to fine particles (PM2.5 and PM1) and 

black smoke in the general population: personal, indoor, and outdoor levels. J Expo Sci Environ Epidemiol, 17: 

613-624. 156614

John W; Wall SM; Ondo JL (1988). A new method for nitric acid and nitrate aerosol measurement using the dichotomous 

sampler. Atmos Environ, 22: 1627-1635. 045903

Kaur S; Nieuwenhuijsen M; Colvile R (2005). Personal exposure of street canyon intersection users to PM2.5, ultrafine 

particle counts and carbon monoxide in central London, UK. Atmos Environ, 39: 3629-3641. 086504

Kaur S; Nieuwenhuijsen MJ; Colvile RN (2005). Pedestrian exposure to air pollution along a major road in Central 

London, UK. Atmos Environ, 39: 7307-7320. 088175

Keeler GJ; Samson PJ (1989). Spatial representativeness of trace element ratios. Environ Sci Technol, 23: 1358-1364. 

156633

Khlystov A; Stanier CO; Takahama S; Pandis SN (2005). Water content of ambient aerosol during the Pittsburgh Air 

Quality Study. J Geophys Res, 110: D07S10. 156635

Kidwell CB; Ondov JM (2001). Development and evaluation of a prototype system for collecting sub-hourly ambient 

aerosol for chemical analysis. Aerosol Sci Technol, 35: 596-601. 017092

Kidwell CB; Ondov JM (2004). Elemental Analysis of Sub-Hourly Ambient Aerosol Collections. Aerosol Sci Technol, 38: 

205-218. 155898

Kim D; Sass-Kortsak A; Purdham JT; Dales RE; Brook JR (2005). Sources of personal exposure to fine particles in 

Toronto, Ontario, Canada. J Air Waste Manag Assoc, 55: 1134-1146. 156640

Kinsey JS; Mitchell WA; Squier WC; Linna K; King FG; Logan R; Dong Y; Thompson GJ; Clark NN (2006). Evaluation 

of methods for the determination of diesel-generated fine particulate matter: Physical characterization results. J 

Aerosol Sci, 37: 63-87. 130654

Kiss G; Varga B; Galambos I; Ganszky I (2002). Characterization of water-soluble organic matter isolated from 

atmospheric fine aerosol. J Geophys Res, 107: 8339. 156646

Klinmalee A; Srimongkol K; Kim Oanh NT (2008). Indoor air pollution levels in public buildings in Thailand and 

exposure assessment. Environ Monit Assess, 156: 581-594. 190888

December 2009 A-360 
Koenig JQ; Jansen K; Mar TF; Lumley T; Kaufman J; Trenga CA; Sullivan J; Liu LJ; Shapiro GG; Larson TV (2003). 

Measurement of offline exhaled nitric oxide in a study of community exposure to air pollution. Environ Health 

Perspect, 111: 1625-1629. 156653

Koistinen KJ; Edwards RD; Mathys P; Ruuskanen J; Kunzli N; Jantunen MJ (2004). Sources of fine particulate matter in 

personal exposures and residential indoor, residential outdoor and workplace microenvironments in the Helsinki 

phase of the EXPOLIS study. Scand J Work Environ Health, 30 Suppl 2: 36-46. 156655

Kousa A; Monn C; Rotko T; Alm S; Oblesby L; Jantunen MJ (2001). Personal exposures to NO2 in the EXPOLIS-study: 

relation to residential indoor, outdoor and workplace concentrations in Basel, Helsinki and Prague. Atmos Environ, 

35: 3405-3412. 025270

Koutrakis P; Suh HH; Sarnat JA; Brown KW; Coull BA; Schwartz J (2005). Characterization of particulate and gas 

exposures of sensitive subpopulations living in Baltimore and Boston. Health Effects Institute. Boston, MA. 131. 

http://pubs.healtheffects.org/view.php?id=91. 095800

Kulkarni MM; Patil RS (2003). Personal exposure to toxic metals in an Indian metropolitan region. J Inst Eng, 84: 23-29. 

156664

Kulmala M; Mordas G; Petäjä T; Grönholm T; Aalto PP; Vehkamäki H; Hienola AI; Herrmann E; Sipilä M; Riipinen I 

(2007). The condensation particle counter battery (CPCB): A new tool to investigate the activation properties of 

nanoparticles. J Aerosol Sci, 38: 289-304. 155911

Kulmala M; Riipinen I; Sipila M; Manninen HE; Petaja T; Junninen H; Maso MD; Mordas G; Mirme A; Vana M; Hirsikko 

A; Laakso L; Harrison RM; Hanson I; Leung C; Lehtinen KE; Kerminen VM (2007). Toward direct measurement 

of atmospheric nucleation. Science, 318: 89-92. 097838

Labban R; Veranth JM; Watson JG; Chow JC (2006). Feasibility of soil dust source apportionment by the pyrolysis-gas 

chromatography/mass spectrometry method. J Air Waste Manag Assoc, 56: 1230-1242. 156665

Lai HK; Kendall M; Ferrier H; Lindup I; Alm S; Hanninen O; Jantunen M; Mathys P; Colvile R; Ashmore MR; Cullinan P; 

Nieuwenhuijsen MJ (2004). Personal exposures and microenvironment concentrations of PM25, VOC, NO2 and 

CO in Oxford, UK. Atmos Environ, 38: 6399-6410. 056811

Lake DA; Tolocka MP; Johnston MV; Wexler AS (2003). Mass Spectrometry of Individual Particles between 50 and 750 

nm in Diameter at the Baltimore Supersite. Environ Sci Technol, 37: 3268-3274. 156669

Lake DA; Tolocka MP; Johnston MV; Wexler AS (2004). The character of single particle sulfate in Baltimore. Atmos 

Environ, 38: 5311-5320. 088411

Larson T; Gould T; Simpson C; Liu LJ; Claiborn C; Lewtas J (2004). Source apportionment of indoor, outdoor, and 

personal PM2.5 in Seattle, Washington, using positive matrix factorization. J Air Waste Manag Assoc, 54: 1175-87. 

098145

Lawson CL; Hanson RJ (1974). Solving least squares problems. Englewood Cliffs: Prentice Hall. 156673

Lee JH; Hopke PK; Holsen TM; Lee DW; AJaques P; Sioutas C; Ambs JL (2005). Performance evaluation of continuous 

PM2.5 mass concentration monitors. J Aerosol Sci, 36: 95-109. 155925

Lee JH; Hopke PK; Holsen TM; Polissar AV (2005). Evaluation of Continuous and Filter-Based Methods for Measuring 

PM 2.5 Mass Concentration. Aerosol Sci Technol, 39: 290-303. 156680

Lee JH; Hopke PK; Holsen TM; Polissar AV; Lee DW; Edgerton ES; Ondov JM; Allen G (2005). Measurements of fine 

particle mass concentrations using continuous and integrated monitors in Eastern US Cities. Aerosol Sci Technol, 

39: 261-275. 128139

Lewis CW; Norris GA; Conner TL; Henry RC (2003). Source apportionment of Phoenix PM2.5 aerosol with the Unmix 

Receptor model. J Air Waste Manag Assoc, 53: 325-338. 088413

Li W-W; Paschold H; Morales H; Chianelli J (2003). Correlations between short-term indoor and outdoor PM 

concentrations at residences with evaporative coolers. Atmos Environ, 37: 2691-2703. 047845

Li YC; Yu JZ (2005). Simultaneous Determination of Mono-and Dicarboxylic Acids, o-Oxo-carboxylic Acids, Midchain 

Ketocarboxylic Acids, and Aldehydes in Atmospheric Aerosol Samples. Environ Sci Technol, 39: 7616-7624. 

156692

December 2009 A-361 
Lim H-J; Turpin BJ; Edgerton E; Hering SV; Allen G; Maring H; Solomon P (2003). Semi-continuous aerosol carbon 

measurements: Comparison of Atlanta supersite measurements. J Geophys Res, 108: 8419. 037037

Lithgow GA; Robinson AL; Buckley SG (2004). Ambient measurements of metal-containing PM25 in an urban 

environment using laser-induced breakdown spectroscopy. Atmos Environ, 38: 3319-3328. 126616

Liu L-J; Box M; Kalman D; Kaufman J; Koenig J; Larson T; Lumley T; Sheppard L; Wallace L (2003). Exposure 

assessment of particulate matter for susceptible populations in Seattle. Environ Health Perspect, 11: 909-918. 

073841

Lunden MM; Kirchstetter TW; Thatcher TL; Hering SV; Brown NJ (2008). Factors affecting the indoor concentrations of 

carbonaceous aerosols of outdoor origin. Atmos Environ, 42: 5660-5671. 155949

Lung SC; Mao IF; Liu LJ (2007). Residents' particle exposures in six different communities in Taiwan. Sci Total Environ, 

377: 81-92. 156719

MacIntosh D; Minegishi T; Kaufman M; Baker B; Allen J; Levy J; Myatt T (2009). The benefits of whole-house in-duct air 

cleaning in reducing exposures to fine particulate matter of outdoor origin: A modeling analysis. J Expo Sci 

Environ Epidemiol, TBD: TBD. 190887

Mader BT; Yu JZ; Xu JH; Li QF; Wu WS; Flagan RC; Seinfeld JH (2004). Molecular composition of the water-soluble 

fraction of atmospheric carbonaceous aerosols collected during ACE-Asia. J Geophys Res, 109: D06206. 156724

Maitre A; Soulat JM; Masclet P; Stoklov M; Marques M; de Gaudemaris R (2002). Exposure to carcinogenic air pollutants 

among policemen working close to traffic in an urban area. Scand J Work Environ Health, 28: 402-410. 156726

Manchester-Neesvig JB; Schauer JJ; Cass GR (2003). The distribution of particle-phase organic compounds in the 

atmosphere and their use for source apportionment during the Southern California Children's Health Study. J Air 

Waste Manag Assoc, 53: 1065-79. 098102

Mar TF; Koenig JQ; Jansen K; Sullivan J; Kaufman J; Trenga CA; Siahpush SH; Liu L-JS; Neas L (2005). Fine particulate 

air pollution and cardiorespiratory effects in the elderly. Epidemiology, 16: 681-687. 087566

Martuzevicius D; Grinshpun S; Lee T; Hu S; Biswas P; Reponen T; LeMasters G (2008). Traffic-related PM2. 5 aerosol in 

residential houses located near major highways: Indoor versus outdoor concentrations. Atmos Environ, 27: 6575-

6585. 190886

Mathai CV; Watson Jr JG; Rogers CF; Chow JC; Tombach I; Zwicker JO; Cahill T; Feeney P; Eldred R (1990). 

Intercomparison of ambient aerosol samplers used in western visibility and air quality studies. Environ Sci Technol, 

24: 1090-1099. 156741

Mayol-Bracero OL; Guyon P; Graham B; Roberts G; Andreae MO; Decesari S; Facchini MC; Fuzzi S; Artaxo P (2002). 

Water-soluble organic compounds in biomass burning aerosols over Amazonia: 2. Apportionment of the chemical 

composition and importance of the polyacidic fraction. J Geophys Res, 107: 8091. 045010

Mazzoleni LR; Zielinska B; Moosmuller H (2007). Emissions of Levoglucosan, Methoxy Phenols, and Organic Acids from 

Prescribed Burns, Laboratory Combustion of Wildland Fuels, and Residential Wood Combustion. Environ Sci 

Technol, 41: 2115-2122. 098038

Meng QY; Turpin BJ; Korn L; Weisel CP; Morandi M; Colome S; Zhang J; Stock T; Spektor D; Winer A; Zhang L; Lee 

JH; Giovanetti R; Cui W; Kwon J; Alimokhtari S; Shendell D; Jones J; Farrar C; Maberti S (2005). Influence of 

ambient (outdoor) sources on residential indoor and personal PM2.5 concentrations: analyses of RIOPA data. J 

Expo Sci Environ Epidemiol, 15: 17-28. 058595

Meng QY; Turpin BJ; Lee JH; Polidori A; Weisel CP; Morandi M; Colome S; Zhang JF; Stock T; Winer A (2007). How 

does infiltration behavior modify the composition of ambient PM2.5 in indoor spaces? An analysis of RIOPA data. 

Environ Sci Tech, 41: 7315-7321. 194618

Meng QY; Turpin BJ; Polidori A; Lee JH; Weisel C; Morandi M; Colome S; Stock T; Winer A; Zhang J (2005). PM2.5 of 

ambient origin: Estimates and exposure errors relevant to PM epidemiology. Environ Sci Technol, 39: 5105-5112. 

081194

Middlebrook AM; Murphy DM; Lee S-H; Thomson DS; Prather KA; Wenzel RJ; Liu D-Y; Phares DJ; Rhoads KP; Wexler 

AS; Johnston MV; Jimenez JL; Jayne JT; Worsnop DR; Yourshaw I; Seinfeld JH; Flagan RC (2003). A comparison 

of particle mass spectrometers during the 1999 Atlanta Supersites project. J Geophys Res, 108: 8424. 042932

December 2009 A-362 
Miguel AH; Eiguren-Fernandez A; Jaques PA; Froines JR; Grant BL; Mayo PR; Sioutas C (2004). Seasonal variation of the 

particle size distribution of polycyclic aromatic hydrocarbons and of major aerosol species in Claremont, 

California. Atmos Environ, 38: 3241-3251. 123260

Mikel DK (2001). Quality assurance final report for the Southern Oxidant Study Atlanta Supersite Field Experiment 

August 3 - September 1, 1999. 156762

Molnár P; Bellander T; Sallsten G; Boman J (2007). Indoor and outdoor concentrations of PM2.5 trace elements at homes, 

preschools and schools in Stockholm, Sweden. J Environ Monit, 9: 348-357. 156774

Molnár P; Gustafson P; Johannesson S; Boman J; Barregard L; Sallsten G (2005). Domestic wood burning and PM 

sub(2.5) trace elements: Personal exposures, indoor and outdoor levels. Atmos Environ, 39: 2643-2653. 156772

Molnár P; Johannesson S; Boman J; Barregard L; Sallsten G (2006). Personal exposures and indoor, residential outdoor, 

and urban background levels of fine particle trace elements in the general population. J Environ Monit, 8: 543-551. 

156773

Na K; Cocker DR (2005). Organic and elemental carbon concentrations in fine particulate matter in residences, 

schoolrooms, and outdoor air in Mira Loma, California. Atmos Environ, 39: 3325-3333. 156790

Naumova YY; Offenberg JH; Eisenreich SJ; Meng QY; Polidori A; Turpin BJ; Weisel CP; Morandi MT; Colome SD; Stock 

TH; Winer AM; Alimokhtari S; Kwon J; Maberti S; Shendell D; Jones J; Farrar C (2003). Gas/particle distribution 

of polycyclic aromatic hydrocarbons in coupled outdoor/indoor atmospheres. Atmos Environ, 37: 703-719. 089213

Nerriere E; Zmirou-Navier D; Blanchard O; Momas I; Ladner J; Le Moullec Y; Personnaz M-B; Lameloise P; Delmas V; 

Target A; Desqueyroux H (2005). Can we use fixed ambient air monitors to estimate population long-term exposure 

to air pollutants? The case of spatial variability in the Genotox ER study. Environ Res, 97: 32-42. 089481

Neususs C; Weise D; Birmili W; Wex H; Wiedensohler A; Covert DS (2000). Size-segregated chemical, gravimetric and 

number distribution-derived mass closure of the aerosol in Sagres, Portugal during ACE-2. Tellus B Chem Phys 

Meteorol, 52: 169-184. 156804

Ng SP; Kendall M; Dimitroulopoulou C; Grossinho A; Chen LC (2005). PM2.5 exposure assessment of the population in 

Lower Manhattan area of New York City after the World Trade Center disaster. Atmos Environ, 39: 1979-1992. 

155996

NIOSH (1996). NIOSH Method 5040 Issue 1 (Interim): Elemental Carbon (diesel exhaust). National Institute for 

Occupational Safety and Health. Cincinnati, OH.http://www.cdc.gov/niosh/docs/2003-154/method-e.html. 156810

NIOSH (1999). NIOSH Method 5040 Issue 3 (Interim): Elemental Carbon (diesel exhaust). National Institute for 

Occupational Safety and Health. Cincinnati, OH.http://www.cdc.gov/niosh/docs/2003-154/pdfs/5040.pdf. 156811

Noullett M; Jackson PL; Brauer M (2006). Winter measurements of children's personal exposure and ambient fine particle 

mass, sulphate and light absorbing components in a northern community. Atmos Environ, 40: 1971-1990. 155999

Ntziachristos L; Samaras Z (2006). Combination of aerosol instrument data into reduced variables to study the consistency 

of vehicle exhaust particle measurements. Atmos Environ, 40: 6032-6042. 116722

Ogulei D; Hopke PK; Chalupa DC; Utell MJ (2006). Modeling Source Contributions to Submicron Particle Number 

Concentrations Measured in Rochester, New York. Aerosol Sci Technol, 41: 179-201. 119975

Olfert JS; Kulkarni P; Wang J (2008). Measuring aerosol size distributions with the fast integrated mobility spectrometer. J 

Aerosol Sci, 39: 940-956. 156004

Paatero P (1997). Least squares foumulation of robust non-negative factor analysis. Chemometr Intell Lab Syst, 37: 23-35. 

087001

Paatero P (1999). The Multilinear Engine: A Table-Driven, Least Squares Program for Solving Multilinear Problems, 

including the n-Way Parallel Factor Analysis Model. J Comput Graph Stat, 8: 854-888. 156835

Paatero P; Hopke PK; Song XH; Ramadan Z (2002). Understanding and controlling rotations in factor analytic models. 

Chemometr Intell Lab Syst, 60: 253-264. 156836

Pancras JP; Ondov JM; Zeisler R (2005). Multi-element electrothermal AAS determination of 11 marker elements in fine 

ambient aerosol slurry samples collected with SEAS-II. Anal Chim Acta, 538: 303-312. 098120

Pang Y; Eatough NL; Modey WK; Eatough DJ (2002). Evaluation of the RAMS continuous monitor for determination of 

PM2.5 mass including semi-volatile material in Philadelphia, PA. J Air Waste Manag Assoc, 52: 563-572. 030353

December 2009 A-363 
Parekh PP; Husain L (1981). Trace element concentrations in summer aerosols at rural sites in New York state and their 

possible sources. Atmos Environ, 15: 1717-1725. 156840

Park K; Chow JC; Watson JG; Trimble DL; Doraiswamy P; Arnott WP; Stroud KR; Bowers K; Bode R; Petzold A; Hansen 

AD (2006). Comparison of continuous and filter-based carbon measurements at the Fresno supersite. J Air Waste 

Manag Assoc, 56: 474-91. 098104

Park SS; Harrison D; Pancras JP; Ondov JM (2005). Highly Time-Resolved Organic and Elemental Carbon Measurements 

at the Baltimore Supersite in 2002. J Geophys Res, 110: D07S06. 156843

Park SS; Pancras JP; Ondov J; Poor N (2005). A new pseudodeterministic multivariate receptor model for individual source 

apportionment using highly time-resolved ambient concentration measurements. J Geophys Res, 110: D07S15. 

156844

Paschold H; Maciejewska B; Li WW; Morales H; Pingitore NE (2003). Elemental analysis of airborne particulate matter 

and cooling water in west Texas residences. Atmos Environ, 37: 2681-2690. 156847

Pekney NJ; Davidson CI; Bein KJ; Wexler AS; Johnston MV (2006). Identification of sources of atmospheric PM at the 

Pittsburgh supersite, part I: single particle analysis and filter-based positive matrix factorization. Atmos Environ, 2: 

411-423. 086115

Petäjä T; Mordas G; Manninen H; Aalto PP; Hmeri K; Kulmala M (2006). Detection efficiency of a water-based TSI 

condensation particle counter 3785. Aerosol Sci Technol, 40: 1090-1097. 156021

Peters TM; Gussman RA; Kenny LC; Vanderpool RW (2001). Evaluation of PM2.5 size selectors used in speciation 

samplers. Aerosol Sci Technol, 34: 422-429. 017108

Peters TM; Norris GA; Vanderpool RW; Gemmill DB; Wiener RW; Murdoch RW; McElroy FF; Pitchford M (2001). Field 

performance of PM2.5 federal reference method samplers. Aerosol Sci Technol, 34: 433-443. 016925

Peterson MR; Richards MH (2002). Thermal-optical-transmittance analysis for organic, elemental, carbonate, total carbon, 

and OCX2 in PM2.5 by the EPA/NIOSH method. Presented at Symposium on Air Quality Measurement Methods 

and Technology, Pittsburgh. 156861

Petzold A; Kramer H; Schonlinner M (2002). Continuous Measurement of Atmospheric Black Carbon Using a Multi-angle 

Absorption Photometer. Environ Sci Pollut Res Int, 4: 78-82. 156863

Phares DJ; Rhoads KP; Johnston MV; Wexler AS (2003). Size-resolved ultrafine particle composition analysis 2. Houston. 

J Geophys Res, 108: 8420. 156866

Pitchford ML; Chow JC; Watson JG; Moore CT; Campbell DE; Eldred RA; Vanderpool RW; Ouchida P; Hering SV; Frank 

NH (1997). Prototype PM2.5 federal reference method field studies report - An EPA staff report. 156872

Polidori A; Arhami M; Sioutas C; Delfino RJ; Allen R (2007). Indoor/outdoor relationships, trends, and carbonaceous 

content of fine particulate matter in retirement homes of the Los Angeles Basin. J Air Waste Manag Assoc, 57: 366-

379. 156877

Poore MW (2000). Oxalic acid in PM2.5 particulate matter in California. J Air Waste Manag Assoc, 50: 1874-1875. 

012839

Poore MW (2002). Levoglucosan in PM2.5 at the Fresno supersite. J Air Waste Manag Assoc, 52: 3-4. 051444

Qin X; Prather KA (2006). Impact of biomass emissions on particle chemistry during the California Regional Particulate 

Air Quality Study. Int J Mass Spectrom, 258: 142-150. 156895

Ramachandran G; Adgate JL; Pratt GC; Sexton K (2003). Characterizing indoor and outdoor 15 minute average PM2.5 

concentrations in urban neighborhoods. Aerosol Sci Technol, 37: 33-45. 195017

Rattigan OV; Hogrefe O; Felton HD; Schwab JJ; Roychowdhury UK; Husain L; Dutkiewicz VA; Demerjian KL (2006). 

Multi-year urban and rural semi-continuous PM25 sulfate and nitrate measurements in New York state: Evaluation 

and comparison with filter based measurements. Atmos Environ, 40: 192-205. 115897

Rees SL; Robinson AL; Khlystov A; Stanier CO; Pandis SNSN (2004). Mass balance closure and the Federal Reference 

Method for PM2.5 in Pittsburgh, Pennsylvania. Atmos Environ, 38: 3305-3318. 097164

Reff A; Weisel CP; Zhang J; Morandi M; Stock T; Colome S; Winer A; Turpin BJ; Offenberg JH (2007). A functional group 

characterization of organic PM2.5 exposure: Results from the RIOPA study. Atmos Environ, 41: 4585-4598. 

156045

December 2009 A-364 
Reimann C; De Caritat P (2000). Intrinsic flaws of element enrichment factors (EFs) in environmental geochemistry. 

Environ Sci Technol, 34: 5084-5091. 013269

Rinehart LR; Fujita EM; Chow JC; Magliano K; Zielinska B (2006). Spatial distribution of PM 2.5 associated organic 

compounds in central California. Atmos Environ, 40: 290-303. 115184

Rojas-Bracho L; Suh HH; Catalano PJ; Koutrakis P (2004). Personal exposures to particles and their relationships with 

personal activities for chronic obstructive pulmonary disease patients living in Boston. J Air Waste Manag Assoc, 

54: 54:207-217. 054772

Rossner P; Svecova V; Milcova A; Lnenickova Z; Solansky N; Sram RJ (2008). Seasonal variability of oxidative stress 

markers in city bus drivers - Part I. Oxidative damage to DNA. Mutat Res Fund Mol Mech Mutagen, 642: 14-20. 

156927

Rotko T; Oglesby L; Kunzli N; Carrer P; Nieuwenhuijsen MJ; Jantunen M (2002). Determinants of perceived air pollution 

annoyance and association between annoyance scores and air pollution (PM25, NO2) concentrations in the 

European EXPOLIS study. Atmos Environ, 36: 4593-4602. 037240

Rupprecht & Patashnick Company (2003). Innovative instrument for an ambient air particulate mass measurement 

standard. Rupprecht & Patashnick Co., Inc.. Albany, NY. Final Report 03-07. 157207

Russell M; Allen DT; Collins DR; Fraser MP (2004). Daily, seasonal and spatial trends in PM25 mass and composition in 

southeast Texas. Aerosol Sci Technol, 1: 14-26. 082453

Sabin LD; Kozawa K; Behrentz E; Winer AM; Fitz DR; Pankratz DV; Colome SD; Fruin SA (2005). Analysis of real-time 

variables affecting children's exposure to diesel-related pollutants during school bus commutes in Los Angeles. 

Atmos Environ, 39: 5243-5254. 087728

Sabin LD; Lim JH; Stolzenbach KD; Schiff KC (2005). Contribution of trace metals from atmospheric deposition to 

stormwater runoff in a small impervious urban catchment. Water Res, 39: 3929-3937. 088300

Salma I; Ocskay R; Chi X; Maenhaut W (2007). Sampling artefacts, concentration and chemical composition of fine watersoluble organic carbon and humic-like substances in a continental urban atmospheric environment. Atmos Environ, 

41: 4106-4118. 113852

Samson PJ (1978). Ensemble trajectory analysis of summertime sulfate concentrations in New York State. Atmos Environ, 

12: 1889-1893. 188974

Samson PJ (1980). Trajectory analysis of summertime sulfate concentrations in the northeastern United States. J Appl 

Meteorol, 19: 1382-1394. 073010

Sanderson EG; Farant JP (2004). Indoor and outdoor polycyclic aromatic hydrocarbons in residences surrounding a 

Soderberg aluminum smelter in Canada. Environ Sci Technol, 38: 5350-5356. 156942

Sarnat JA; Brown KW; Schwartz J; Coull BA; Koutrakis P (2005). Ambient gas concentrations and personal particulate 

matter exposures: implications for studying the health effects of particles. Epidemiology, 16: 385-395. 087531

Sarnat JA; Schwartz J; Catalano PJ; Suh HH (2001). Gaseous pollutants in particulate matter epidemiology: Confounders 

or surrogates? Environ Health Perspect, 109: 1053-1061. 019401

Sarnat SE; Coull BA; Ruiz PA; Koutrakis P; Suh HH (2006). The influences of ambient particle composition and size on 

particle infiltration in Los Angeles, CA residences. J Air Waste Manag Assoc, 56: 186-196. 089166

Sarnat SE; Coull BA; Schwartz J; Gold DR; Suh HH (2006). Factors affecting the association between ambient 

concentrations and personal exposures to particles and gases. Environ Health Perspect, 114: 649-654. 089784

Sarnat SE; Suh HH; Coull BA; Schwartz J; Stone PH; Gold DR (2006). Ambient particulate air pollution and cardiac 

arrhythmia in a panel of older adults in Steubenville, Ohio. Occup Environ Med, 63: 700-706. 090489

Schauer JJ; Cass GR (2000). Source apportionment of wintertime gas-phase and particle-phase air pollutants using organic 

compounds as tracers. Environ Sci Technol, 34: 1821-1832. 012225

Schauer JJ; Mader BT; Deminter JT; Heidemann G; Bae MS; Seinfeld JH; Flagan RC; Cary RA; Smith D; Huebert BJ; 

Bertram T; Howell S; Kline JT; Quinn P; Bates T; Turpin B; Lim H-J; Yu JZ; Yang H; Keywood MD (2003). ACEAsia intercomparison of a thermal-optical method for the determination of particle-phase organic and elemental 

carbon. Environ Sci Technol, 37: 993-1001. 037014

December 2009 A-365 
Schnelle-Kreis J; Sklorz M; Peters A; Cyrys J; Zimmermann R (2005). Analysis of particle-associated semi-volatile 

aromatic and aliphatic hydrocarbons in urban particulate matter on a daily basis. Atmos Environ, 39: 7702-7714. 

112944

Schwab JJ; Felton HD; Rattigan OV; Demerjian KL (2006). New York State urban and rural measurements of continuous 

PM2.5 mass by FDMS, TEOM, and BAM: Evaluations and Comparisons with the FRM . J Air Waste Manag 

Assoc, 56: 372-83. 098449

Schwab JJ; Hogrefe O; Demerjian KL; Dutkiewicz VA; Husain L; Rattigan OV; Felton HD (2006). Field and Laboratory 

Evaluation of the Thermo Electron 5020 Sulfate Particulate Analyzer. Aerosol Sci Technol, 40: 744 - 752. 098785

Schwartz J; Sarnat JA; Coull BA; Wilson WE (2007). Effects of exposure measurement error on particle matter 

epidemiology: a simulation using data from a panel study in Baltimore, MD. J Expo Sci Environ Epidemiol, 17: S2-

S10. 090220

Shalat SL; Lioy PJ; Schmeelck K; Mainelis G (2007). Improving estimation of indoor exposure to inhalable particles for 

children in the first year of life. J Air Waste Manag Assoc, 57: 934-939. 156971

Shao L; Yang S; Li H; Li W; Jones T; Sexton K; B‚ruB‚ K; Li J; Zhao H (2007). Associations between particle 

physicochemical characteristics and oxidative capacity: An indoor PM10 study in Beijing, China. Atmos Environ, 

41: 5316-5326. 156973

Shilton V; Giess P; Mitchell D; Williams C (2002). The relationships between indoor and outdoor respirable particulate 

matter: meteorology, chemistry and personal exposure. Indoor Built Environ, 11: 266-274. 049602

Sidhu S; Graham J; Striebich R (2001). Semi-volatile and particulate emissions from the combustion of alternative diesel 

fuels. Chemosphere, 42: 681-90. 155202

Slama R; Darrow L; Parker J; Woodruff TJ; Strickland M; Nieuwenhuijsen M; Glinianaia S; Hoggatt KJ; Kannan S; Hurley 

F; Kalinka J; Sram R; Brauer M; Wilhelm M; Heinrich J; Ritz B (2008). Meeting report: atmospheric pollution and 

human reproduction. Environ Health Perspect, 116: 791-798. 156985

Smith TJ; Davis ME; Reaser P; Natkin J; Hart JE; Laden F; Heff A; Garshick E (2006). Overview of particulate exposures 

in the US trucking industry. J Environ Monit, 8: 711-720. 156990

Solomon P; Baumann K; Edgerton E; Tanner R; Eatough D; Modey W; Marin H; Savoie D; Natarajan S; Meyer MB 

(2003). Comparison of integrated samplers for mass and composition during the 1999 Atlanta supersites project. J 

Geophys Res, 108: 8423. 156994

Solomon PA; Norris G; Landis M; Tolocka M (2001). Chemical analysis methods for atmospheric aerosol components. In 

Baron PA;Willeke K (Ed.),Aerosol measurement: principles, techniques, and applications (pp. 261-293). New York: 

John Wiley & Sons Ltd. 157193

Solomon PA; Sioutas C (2006). Continuous and semi-continuous methods for PM mass and composition. EM, x: 17-23. 

156995

Sram RJ; Beskid O; Rössnerova A; Rössner P; Lnenickova Z; Milcova A; Solansky I; Binkova B (2007). Environmental 

exposure to carcinogenic polycyclic aromatic hydrocarbons--the interpretation of cytogenetic analysis by FISH. 

Toxicol Lett, 172: 12-20. 192084

Stanier CO; Khlystov AY; Chan WR; Mandiro M; Pandis SN (2004). A Method for the In Situ Measurement of Fine 

Aerosol Water Content of Ambient Aerosols: The Dry-Ambient Aerosol Size Spectrometer (DAASS). Aerosol Sci 

Technol, 38: 215 - 228. 095955

Stohl A (1996). Trajectory statistics-A new method to establish source-receptor relationships of air pollutants and its 

application to the transport of particulate sulfate in Europe. Atmos Environ, 30: 579-587. 157014

Strand M; Hopke PK; Zhao W; Vedal S; Gelfand E; Rabinovitch N (2007). A study of health effect estimates using 

competing methods to model personal exposures to ambient PM2.5. J Expo Sci Environ Epidemiol, 17: 549-558. 

157018

Strand M; Vedal S; Rodes C; Dutton SJ; Gelfand EW; Rabinovitch N (2006). Estimating effects of ambient PM2.5 

exposure on health using PM2.5 component measurements and regression calibration. J Expo Sci Environ 

Epidemiol, 16: 30-38. 089203

Stranger M; Potgieter-Vermaak S; Van Grieken R (2008). Characterization of indoor air quality in primary schools in 

Antwerp, Belgium. Indoor Air, 18: 454-463. 190884

December 2009 A-366 
Stranger M; Potgieter-Vermaak S; Van Grieken R (2009). Particulate matter and gaseous pollutants in residences in 

Antwerp, Belgium. Sci Total Environ, 1182-1192: 407. 190883

Subbalakshmi Y; Patti AF; Lee GSH; Hooper MA (2000). Structural characterisation of macromolecular organic material in 

air particulate matter using Py-GC-MS and solid state 13 C-NMR. J Environ Monit, 2: 561-565. 157023

Subramanian R; Khlystov AY; Cabada JC; Robinson AL (2004). Positive and negative artifacts in particulate organic 

carbon measurements with denuded and undenuded sampler configurations. Aerosol Sci Technol, 1: 27-48. 081203

Sørensen M; Daneshvar B; Hansen M; Dragsted LO; Hertel O; Knudsen L; Loft S (2003). Personal PM2.5 exposure and 

markers of oxidative stress in blood. Environ Health Perspect, 111: 161-166. 157000

Sørensen M; Loft S; Andersen HV; Raaschou-Nielsen O; Skovgaard LT; Knudsen LE; Nielsen IV; Hertel O (2005). 

Personal exposure to PM2.5, black smoke and NO2 in Copenhagen: relationship to bedroom and outdoor 

concentrations covering seasonal variation. J Expo Sci Environ Epidemiol, 15: 413-422. 089428

Takahama S; Wittig AE; Vayenas DV; Davidson CI; Pandis SN (2004). Modeling the diurnal variation of nitrate during the 

Pittsburgh Air Quality Study. J Geophys Res, 109: D16S06. 157038

Tanaka S; Yasushi N; Sato N; Fukasawa T; Santosa SJ; Yamanaka K; Ootoshi T (1998). Rapid and simultaneous multielement analysis of atmospheric particulate matter using inductively coupled plasma mass spectrometry with laser 

ablation sample introduction. J Anal At Spectrom, 13: 135-140. 157041

Tang C-S; Chang L-T; Lee H-C; Chan C-C (2007). Effects of personal particulate matter on peak expiratory flow rate of 

asthmatic children. Sci Total Environ, 382: 43-51. 091269

Thornburg JW; Stevens CD; Williams RW; Rodes CE; Lawless PA (2004). A pilot study of the influence of residential 

HAC duty cycle on indoor air quality. Atmos Environ, 38: 1567-1577. 157052

Toivola M; Alm S; Reponen T; Kolari S; Nevalainen A (2002). Personal exposures and microenvironmental concentrations 

of particles and bioaerosols. J Environ Monit, 4: 166-174. 026571

Tolbert PE; Klein M; Peel JL; Sarnat SE; Sarnat JA (2007). Multipollutant modeling issues in a study of ambient air quality 

and emergency department visits in Atlanta. J Expo Sci Environ Epidemiol, 17: S29-S35. 090316

Tran NK; Steinberg SM; Johnson BJ (2000). Volatile aromatic hydrocarbons and dicarboxylic acid concentrations in air at 

an urban site in the Southwestern US. Atmos Environ, 34: 1845-1852. 013025

Trenga CA; Sullivan JH; Schildcrout JS; Shepherd KP; Shapiro GG; Liu LJ; Kaufman JD; Koenig JQ (2006). Effect of 

particulate air pollution on lung function in adult and pediatric subjects in a Seattle panel study. Chest, 129: 1614-

1622. 155209

Turpin BJ; Weisel CP; Morandi M; Colome S; Stock T; Eisenreich S; Buckley B (2007). Relationships of Indoor, Outdoor, 

and Personal Air (RIOPA): Part II. Analyses of concentrations of particulate matter species. 157062

Turšic J; Podkrajšek B; Grgic I; Ctyroky P; Berner A; Dusek U; Hitzenberger R (2006). Chemical composition and 

hygroscopic properties of size-segregated aerosol particles collected at the Adriatic coast of Slovenia. 

Chemosphere, 63: 1193-1202. 157063

Vallejo M; Ruiz S; Hermosillo AG; Borja-Aburto VH; Cardenas M (2006). Ambient fine particles modify heart rate 

variability in young healthy adults. J Expo Sci Environ Epidemiol, 16: 125-130. 157081

Van Roosbroeck S; Wichmann J; Janssen NAH; Hoek G; Van Wijnen JH; Lebret E; Brunekreef B (2006). Long-term 

personal exposure to traffic-related air pollution among school children, a validation study. Sci Total Environ, 368: 

565-573. 090773

Vaughn D; O'Brien T; Roberts PT; Rice J (2005). Comparison of integrated filter and semi-continuous measurements of 

PM2.5 nitrate, sulfate, and carbon aerosols in the Speciation Trends Network (STN). 157089

Veltkamp PR; Hansen KJ; Barkley RM; Sievers RE (1996). Principal component analysis of summertime organic aerosols 

at Niwot Ridge, Colorado. J Geophys Res, 101: 19,495-19,504. 081594

Venkatachari P; Zhou L; Hopke P; Schwab J; Demerjian K; Weimer S; Hogrefe O; Felton D; Rattigan O (2006). An 

intercomparison of measurement methods for carbonaceous aerosol in the ambient air in New York City. Aerosol 

Sci Technol, 40: 788-795. 105918

Vinzents PS; Moller P; Sorensen M; Knudsen LE; Herte LQ; Jensen FP; Schibye B; Loft S (2005). Personal exposure to 

ultrafine particles and oxidative DNA damage. Environ Health Perspect, 113: 1485-1490. 087482

December 2009 A-367 
Virkkula A; Ahlquist NC; Covert DS; Arnott WP; Sheridan PJ; Quinn PK; Coffman DJ (2005). Modification, Calibration 

and a Field Test of an Instrument for Measuring Light Absorption by Particles. Aerosol Sci Technol, 39: 68-83. 

157097

Voorhees KJ; Schulz WD; Kunen SM; Hendricks LJ; Currie LA; Klouda G (1991). Analysis of Insoluble Carbonaceous 

Materials from Airborne Particles Collected in Pristine Regions of Colorado. J Anal Appl Pyrol, 18: 189–205. 

157101

Wallace L; Williams R (2005). Use of personal-indoor-outdoor sulfur concentrations to estimate the infiltration factor and 

outdoor exposure factor for individual homes and persons. Environ Sci Technol, 39: 1707-1714. 057485

Wallace L; Williams R; Rea A; Croghan C (2006). Continuous weeklong measurements of personal exposures and indoor 

concentrations of fine particles for 37 health-impaired North Carolina residents for up to four seasons. Atmos 

Environ, 40: 399-414. 088211

Wan ECH; Yu JZ (2006). Determination of sugar compounds in atmospheric aerosols by liquid chromatography combined 

with positive electrospray ionization mass spectrometry. J Chromatogr A, 1107: 175-181. 157104

Wang D; Hopke PK (1989). The use of constrained least-squares to solve the chemical mass balance problem. Atmos 

Environ, 23: 2143-2150. 157105

Wang X; Fu J; Bi X; Sheng G (2006). Hospital indoor PM10/PM2.5 and associated trace elements in Guangzhou, China. 

Sci Total Environ, 366: 124-135. 157108

Ward TJ; Noonan CW; Hooper K (2007). Results of an indoor size fractionated PM school sampling program in Libby, 

Montana. Environ Monit Assess, 130: 163-171. 157112

Waterman D; Horsfield B; Leistner F; Hall K; Smith S (2000). Quantification of polycyclic aromatic hydrocarbons in the 

NIST Standard Reference Material (SRM1649A) urban dust using thermal Desorption GC/MS. Anal Chem, 72: 

3563-3567. 157116

Waterman D; Smith S; Green D; Horsfield B; Hall K (2001). The application of a thermal desorption GCMS technique for 

the organic analysis of airborne particulate matter. Adv Mass Spectrom, 14: 887-888. 157117

Watson JG; Chen LW; Chow JC; Doraiswamy P; Lowenthal DH (2008). Source apportionment: findings from the U.S. 

Supersites Program. J Air Waste Manag Assoc, 58: 265-288. 157128

Watson JG; Chow JC (2001). Ambient air sampling. In Baron PA; Willeke K (Ed.),Aerosol measurement: principles, 

techniques, and applications (pp. 821-844). New York: John Wiley & Sons Ltd. 157123

Watson JG; Chow JC (2002). Comparison and evaluation of in situ and filter carbon measurements at the Fresno Supersite. 

J Geophys Res, 107: 8341. 037873

Watson JG; Chow JC; Bowen JL; Lowenthal DH; Hering S; Ouchida P; Oslund W (2000). Air quality measurements from 

the Fresno supersite. J Air Waste Manag Assoc, 50: 1321-1334. 010354

Watson JG; Chow JC; Chen LWA (2005). Summary of organic and elemental carbon/black carbon analysis methods and 

intercomparisons. Aerosol Air Qual Res, 5: 69–102. 157125

Watson JG; Chow JC; Doraiswamy P; Chen LWA; Lowenthal DH; Trimble D; Park K (2005). Quality Assurance Final 

Report for the Fresno Supersite. 157124

Watson JG; Chow JC; Frazier CA (1999). X-ray fluorescence analysis of ambient air samples. In Landsberger, S.; 

Creatchman, M. (Ed.),Elemental analysis of airborne particles (pp. 67-96). Amsterdam, The Netherlands: Gordon 

and Breach Science Publishers. 020949

Watson JG; Chow JC; Moosmüller H; Green MC; Frank NH; Pitchford ML (1998). Guidance for using continuous 

monitors in PM2.5 monitoring networks. U.S. Environmental Protection Agency. Research Triangle Park, NC. 

EPA-454/R-98-012. http://www.epa.gov/ttn/amtic/pmpolgud.html. 198805

Watson JG; Chow JC; Shah JJ; Pace TG (1983). The effect of sampling inlets on the PM-10 and PM-15 to TSP 

concentration ratios. J Air Waste Manag Assoc, 33: 114-119. 045084

Watson JG; Cooper JA; Huntzicker JJ (1984). The effective variance weighting for least squares calculations applied to the 

mass balance receptor model. Atmos Environ, 18: 1347-1355. 045693

December 2009 A-368 
Watson JG; Lioy PJ; Mueller PK (1989). The measurement process: precision, accuracy, and validity. In Hering, S. V. 

(Ed.),Air sampling instruments for evaluation of atmospheric contaminants (pp. 51-57). Cincinnati, OH: American 

Conference of Governmental Industrial Hygienists. 046318

Watson JG; Robinson NF; Lewis CW; Coulter CT; Chow JC; Fujita EM; Lowenthal DH; Corner TL; Henry RC; Willis RD 

(1997). Chemical mass balance receptor model version 8 (CMB) users manual. Washington, D.C.: U.S. 

Environmental Protection Agency. 157121

Weber RJ; Orsini D; Daun Y; Lee Y-N; Kotz PJ; Brechtel F (2001). A particle-into-liquid collector for rapid measurement 

of aerosol bulk chemical composition. Aerosol Sci Technol, 35: 718-727. 024640

Weber RJ; Orsini D; Duan Y; Baumann K; Kiang CS; Chameides W; Lee YN; Brechtel F; Klotz P; Jongejan P (2003). 

Intercomparison of near real time monitors of PM2. 5 nitrate and sulfate at the US Environmental Protection 

Agency Atlanta Supersite. J Geophys Res, 108: 8421. 157129

Weisel CP; Zhang J; Turpin BJ; Morandi MT; Colome S; Stock TH; Spektor DM; Korn L; Winer AM; Kwon J; Meng QY; 

Zhang L; Harrington R; Liu W; Reff A; Lee JH; Alimokhtari S; Mohan K; Shendell D; Jones J; Farrar L; Maberti S; 

Fan T (2005). Relationships of Indoor, Outdoor, and Personal Air (RIOPA): Part I. Collection methods and 

descriptive analyses. Res Rep Health Eff Inst, 130: 1-107. 157131

Welthagen W; Schnelle-Kreis J; Zimmermann R (2003). Search criteria and rules for comprehensive two-dimensional gas 

chromatography-time-of-flight mass spectrometry analysis of airborne particulate matter. J Chromatogr A, 1019: 

233-249. 104056

Wenzel RJ; Liu DY; Edgerton ES; Prather KA (2003). Aerosol time-of-flight mass spectrometry during the Atlanta 

Supersite Experiment: 2. Scaling procedures. J Geophys Res, 108: 8426. 157139

Westerdahl D; Fruin S; Sax T; Fine PM; Sioutas C (2005). Mobile platform measurements of ultrafine particles and 

associated pollutant concentrations on freeways and residential streets in Los Angeles. Atmos Environ, 39: 3597-

3610. 086502

Wichmann J; Janssen NAH; Van Der Zee S; Brunekreff B (2005). Traffic-related differences in indoor and personal 

absorption coefficient measurements in Amsterdam, the Netherlands. Atmos Environ, 39: 7384-7392. 086240

Williams B; Goldstein A; Kreisberg N; Hering S (2006). An In-Situ Instrument for Speciated Organic Composition of 

Atmospheric Aerosols: Thermal Desorption Aerosol GC/MS-FID (TAG). Aerosol Sci Technol, 40: 627-638. 

156157

Williams R; Rea A; Vette A; Croghan C; Whitaker D; Stevens C; McDow S; Fortmann R; Sheldon L; Wilson H; Thornburg 

J; Phillips M; Lawless P; Rodes C; Daughtrey H (2008). The design and field implementation of the Detroit 

exposure and aerosol research study. J Expo Sci Environ Epidemiol, 19: 643-659. 191201

Williams R; Suggs J; Rea A; Sheldon L; Rodes C; Thornburg J (2003). The Research Triangle Park particulate matter panel 

study: modeling ambient source contribution to personal and residential PM mass concentrations. Atmos Environ, 

37: 5365-5378. 053338

Wilson WE; Brauer M (2006). Estimation of ambient and non-ambient components of particulate matter exposure from a 

personal monitoring panel study. J Expo Sci Environ Epidemiol, 16: 264-274. 088933

Winkler PM; Steiner G; Vrtala A; Vehkamaki H; Noppel M; Lehtinen KEJ; Reischl GP; Wagner PE; Kulmala M (2008). 

Heterogeneous Nucleation Experiments Bridging the Scale from Molecular Ion Clusters to Nanoparticles. Science, 

319: 1374-1377. 156160

Wittig AE; Takahama S; Khlystov AY; Pandis SN; Hering S; Kirby B; Davidson C (2004). Semi-continuous PM25 

inorganic composition measurements during the Pittsburgh Air Quality Study. Atmos Environ, 38: 3201-3213. 

103413

Wu C-F; Delfino RJ; Floro JN; Quintana; (2005). Exposure assessment and modeling of particulate matter for asthmatic 

children using personal nephelometers. Atmos Environ, 39: 3457-3469. 086397

Wu CF; Delfino RJ; Floro JN; Samimi BS; Quintana PJ; Kleinman MT; Liu LJ (2005). Evaluation and quality control of 

personal nephelometers in indoor, outdoor and personal environments. J Expo Sci Environ Epidemiol, 15: 99-110. 

157155

Wu CF; Jimenez J; Claiborn C; Gould T; Simpson CD; Larson T; Liu LJS (2006). Agricultural burning smoke in Eastern 

Washington: Part II. Exposure assessment. Atmos Environ, 40: 5379-5392. 179950

December 2009 A-369 
December 2009 A-370 

Xiao H-Y; Liu C-Q (2004). Chemical characteristics of water-soluble components in TSP over Guiyang, SW China, 2003. 

Atmos Environ, 38: 6297-6306. 056801

Yang H; Jian ZY; Hang Ho SS; Xu J; Wu WS; Chun HW; Wang X; Wang L (2005). The chemical composition of inorganic 

and carbonaceous materials in PM25 in Nanjing, China. Atmos Environ, 39: 3735-3749. 102388

Yang H; Li Q; Yu JZ (2003). Comparison of two methods for the determination of water-soluble organic carbon in 

atmospheric particles. Atmos Environ, 37: 865-870. 156167

Yang W; Sohn J; Kim J; Son B; Park J (2009). Indoor air quality investigation according to age of the school buildings in 

Korea. J Environ Manage, 90: 348-354. 190885

Yao X; Fang M; Chan CK; Ho KF; Lee SC (2004). Characterization of dicarboxylic acids in PM25 in Hong Kong. Atmos 

Environ, 38: 963-970. 102213

Yeh S; Small MJ (2002). Incorporating exposure models in probabilistic assessment of the risks of premature mortality 

from particulate matter. J Expo Sci Environ Epidemiol, 12: 389-403. 040077

Yip FY; Robins TG; Parker EA; Israel BA; Brakefield-Caldwell W; Keeler GJ; Dvonch JTE (2004). Personal exposures to 

particulate matter among children with asthma in Detroit, Michigan. Atmos Environ, 38: 5227-5236. 157166

Yu KN; Cheung YP; Cheung T; Henry RC (2004). Identifying the impact of large urban airports on local air quality by 

nonparametric regression. Atmos Environ, 38: 4501-4507. 101779

Yu LE; Shulman ML; Kopperud R; Hildemann LM (2005). Characterization of Organic Compounds Collected during 

Southeastern Aerosol and Visibility Study: Water-Soluble Organic Species. Environ Sci Technol, 39: 707-715. 

157167

Yue Z; Fraser M (2004). Characterization of Nonpolar Organic Fine Particulate Matter in Houston, Texas. Aerosol Sci 

Technol, 38: 60-67. 157169

Zhang J; Chameides WL; Weber R; Cass G; Orsini D; Edgerton E; Jongejan P; Slanina J (2002). An evaluation of the 

thermodynamic equilibrium assumption for fine particulate composition: Nitrate and ammonium during the 1999 

Atlanta Supersite Experiment. J Geophys Res, 108: 8414. 157181

Zhang Q; Anastasio C (2003). Free and combined amino compounds in atmospheric fine particles (PM2. 5) and fog waters 

from Northern California. Atmos Environ, 37: 2247-2258. 157182

Zhang Q; Jimenez JL; Canagaratna MR; Jayne JT; Worsnop DR (2005). Time- and size-resolved chemical composition of 

submicron particles in Pittsburgh: Implications for aerosol sources and processes. J Geophys Res, 110: 1-19. 

157185

Zhao W; Hopke PK; Norris G; Williams R; Paatero P (2006). Source apportionment and analysis on ambient and personal 

exposure samples with a combined receptor model and an adaptive blank estimation strategy. Atmos Environ, 40: 

3788-3801. 156181

Zhao W; Rabinovitch N; Hopke PK; Gelfand EW (2007). Use of an expanded receptor model for personal exposure 

analysis in schoolchildren with asthma. Atmos Environ, 41: 4084-4096. 156182

Zheng M; Cass GR; Schauer JJ; Edgerton ES (2002). Source apportionment of PM2.5 in the southeastern United States 

using solvent-extractable organic compounds as tracers. Environ Sci Technol, 36: 2361-2371. 026100

Zhou L; Hopke PK; Liu W (2004). Comparison of two trajectory based models for locating particle sources for two rural 

New York sites. Atmos Environ, 38: 1955-1963. 157190

Zhu Y; Hinds WC; Krudysz M; Kuhn T; Froines J; Sioutas C (2005). Penetration of freeway ultrafine particles into indoor 

environments. J Aerosol Sci, 36: 303-322. 190081

Zhu Y; Kuhn T; Mayo P; Hinds WC (2005). Comparison of Daytime and Nighttime Concentration Profiles and Size 

Distributions of Ultrafine Particles near a Major Highway. Environ Sci Technol, 39: 2531-2536. 157191

Zöllner I (2007). Concentrations of Particulate Matter in Schools in Southwest Germany. Inhal Toxicol, 19: 245-249. 

157192