# Annex A. A tmospheric Science

A.1. Ambient Air Particle Monitoring

A.1.1. Measurements and Analyt ical Specifications

Table A-1. Summary of integrat ed and continuous samplers incl uded in the field comparison.

Abbreviation Instrument Manuf acturer / Research Institute

INTEGRATED PARTICLE OR GAS/PARTICLE INSTRUMENTS

Dichot Dichotomous Sampler with Virtual Im pactor Andersen Instru ments (Smyrna, GA)

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

AND-246 Dichot Thermo Ander sen SA-246B Dichotomous Sample r Andersen Instruments

AND-hIVOL10 FRM Thermo Andersen GMW-1200 HiVol PM 10 FRM Sampler Andersen Instruments

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

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

DRI-SFS DRI Sequential Filter Sampler Desert Research In stitute (Reno, NV)
HEADS (or HI) Harvard EPA Annular Denuder System (or Harvard Impactor) Harvard School of P ublic Health (Boston, MA)
IMPROVE_SS b IMPROVE Speciation Sampler URG Corp. (Chapel Hill, NC)

URG-3000N b Modi fi ed IMPROVE Module C Sampler for Carbon URG Corp.

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

MASS-450 b 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 Y oung 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 Sa mpler Rupprecht & Patashnick, Co. (Albany, NY)

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

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

R&P-2025 Dichot b R&P Partisol 2025 Dichotomous Sequential Ai r Sampler Rupprecht & Patashnick, Co.

R&P-2025 FRM R&P Partisol-Plus Model 2025 PM 2.5 Sequential Samplers Rupprecht & Patashnick, Co.

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

Note: Hyperlinks to the reference citations throughout this doc ument 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 Manuf acturer / Research Institute

RAAS-100 FRM Thermo Andersen Re ference Ambient Air Sampler M odel 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-400 b Thermo Andersen RAAS Model 400 Speciation Sampler Andersen Instruments

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

SCS PM 2.5 Sequential Cyclone Sampler New York University (New York, NY)

URG-PCM b 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 (modi fi ed Tapered Element Oscillation Microbalance with diffusion denuder and Na fi on dryer) Brigham Young University

TEOM Tapered Element Oscillating Microbal ance 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 Equilibr ation System Rupprecht & Patashnick, Co.

D-TEOM Differential TEOM Rupprecht & Patashnick, Co.

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

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



### CONTINUOUS PARTICLE LIGHT SCATTERING INSTRUMENTS

Dust Trak Dust Trak nephelometer TSI Inc. (S horeview, MN)

EcoTech EcoTech Model M9003 nephelometer EcoTech Pt y Ltd., Australia (Americ an EcoTech, Warren, RI)

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

RR-M903 Radiance Research Nephelometer Model M 903 Radiance Research Inc. (Seattle, WA)



### CONTINUOUS ELEMENT INSTRUMENTS

GFAAS Graphite Furnace Atomic Abso rption Spectrometry—aerosol collection as preconcentrate slurry University of Maryland (College Park, MD)

SEAS Semicontinuous Element s 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 NO 3 -Analyzer Atmospheric Research and Analysis Inc.

R&P-8400N R&P-8400N Flash Vo latilization Continuous NO 3 - Analyzer Rupprecht & Patashnick, Co.



### CONTINUOUS SULFATE INSTRUMENTS

ADI-S Aerosol Dynamics Inc. Flash Volati lization Analyzer Aerosol Dynamics Inc.

CASM Continuous Ambient Sulfate Monito r (prototype of the TE-5020 by Thermo Electron [Franklin, MA]) Harvard School of Public Health

R&P-8400S R&P-8400S Flash Volatilization Continuous SO 4 2– Analyzer Rupprecht & Patashnick, Co.

TE-5020 Thermo Electron Model 5020 SO 4 2– Particulate Analyzer Thermo Electron Corp. (Franklin, MA)



### December 2009 A-2

### Abbreviation Instrument Manuf acturer / Research Institute

CONTINUOUS MULTI-ION INSTRUMENTS

AIM Ambient Ion Monitor Model 9000 (Cl – ,NO 2 – ,NO 3 – ,PO 4 3–

, SO 4 2– , NH 4 + ,Na + ,Mg 2+, K + ,Ca 2+ ) URG Corp.

Dionex-IC Dionex Ion Chromatograph (F – , Cl – , NO 2 – , Br –, NO 3 – , PO 4 3– , SO 4 2–

, Li + , NH 4 + ,Na + ,Mg 2+ ,K + ,Ca 2+ ) Dionex Corp.

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

, NO 3 – , SO 4 2– , NH 4 + ,Na + , Mg 2+ ,K + , Ca 2+ ) Energy Research C enter of the Nether lands (Petten, the Netherlands

PILS-IC Particle into Liquid Sampler, coupled with IC (Cl – , NO 2 – , NO 3 – , PO 4 3–

, SO 4 2– , NH 4 + ,Na+,Mg 2+ ,K + , Ca 2+ ) Georgia Institute of Technology (Atlanta, GA)

TT Texas Tech IC-based sampler (NO 3 – , SO 4 2– ) Texas Tech University (Lubbock, TX)



### CONTINUOUS CARBON INSTRUMENTS

OC and EC

ADI-C ADI Flash Volatilization Car bon Analyzer Aerosol Dynamics Inc.

RU-OGI Rutgers University/Oregon Graduate In stitute 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-T ime Carbon Aerosol Analysis Instrument Sunset Laboratory, Inc. (Tigard, OR)

BC

Aethalometer Magee Scienti fi c Co. (Berkeley, CA)

AE-16 Magee AE-16 aethalomet er (BC) Magee Scienti fi c Co.

AE-20 Magee AE-20 dual wavelength aet halometer (BC) Magee Scienti fi c Co.

AE-21 Magee AE-21 dual-wavelength aet halometer (BC) Magee Scienti fi c Co.

AE-31 Magee AE-31 seven color aet halometer (BC) Magee Scienti fi c Co.

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

MAAP Multi-Angle Absorption Photomet er, Model 5012 (BC) Thermo Scienti fi c Corp. (Franklin, MA)

PSAP Particle Soot Absorption Photometer (BC) Radiance Re search 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-C ut Monitor (0.1-2.5 μm in 3 stages) University of Californi a–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 Ori fi ce 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 Manuf acturer / Research Institute

APS-3321 TSI Model 3321 (0.5-20 μm; r eplaced 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 Part icle 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 a nd CPC Model 3010 TSI Inc.

WPS Wide-Range Particle Spectromet er (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 Spectr ometer consisting of two SMPS and One APS (0.003-10 μm) Carnegie Mellon University

LIBS Laser-Induced Breakdown Spectroscopy National Research Council, I ndustrial Materials Institute (Boucherville, Quebec, Canada)

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

RSMS-II Rapid Single Particle Mass S pectrometer -II (0.035-1.1 μm) Univer sity 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 Ca rbon Analyzer (OC, EC, Eight Carbon Fractions with re fl ectance 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 PM 2.5 

# and PM 10 

# FRM and FEM samplers.

Manufacturer a 

### Sampler Name Size Cut b 

### Description FRM or FEM c 

### Designation # FRN

BGI Inc. PQ-100 PM 10 FRM RFPS-1298-124 Vol. 63, p. 69625, 12/17/98

BGI Inc. PQ-200 PM 10 Louvered PM 10 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 ambi ent 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 PM 2.5 Identical to PM 10 sampler but uses a WINS d impactor downstream of the PM 10 inlet for PM 2.5 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 orPQ-200AVSCC PM 2.5 Same as BGI PQ200 PM 2.5 sampler but with BGI VSCC instead of WINS impactor; PQ200A is a por table audit sampler, similar in design to PQ-200, but mo re compact in nature. FEM (II) EQPM-0202-142 Vol. 67, p. 15567, 04/02/02

R&P R&P-2000 PM 10 R&P Partisol FRM Model 2000 PM 10 sampler with louvered PM 10 inlet; operates at flow rate of 16.7 L/min; 24-h integrated sampler; uses a mass flow me ter to adjust to equivalentvolumetric flow at ambient temperature and pressure; singlechannel sampler. FRM RFPS-1298-126 Vol. 63, p. 69625, 12/17/98

R&P R&P-2000 PM 2.5 FRM RFPS-0498-117 Vol. 63, p. 18911, 04/16/98

R&P R&P2000A PM 2.5 R&P Partisol FRM Model 2000 PM 2.5 sampler, identical to PM 10 sampler but uses a WINS im pactor downstream of the PM 10 inlet for PM 2.5 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 PM 10 R&P Partisol-Plus Model 2025 PM 10 sequential sampler with louvered PM 10 inlet; operates at 16.7 L/min; 24-h integrated sampler; uses a mass flow me ter to adjust to equivalent volumetric flow at ambient te mperature 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 PM 2.5 R&P Partisol-Plus Model 2025 PM 2.5 sequential sampler, identical to R&P-2025 PM 10 sampler but uses a WINS impactor downstream of the PM 10 inlet for PM 2.5 fractionation at 16.7 L/min. FRM RFPS-0498-118 Vol. 63, p. 18911, 04/16/98

R&P R&P2000- VSCC PM 2.5 Same as R&P-2000 PM 2.5 sampler but with BGI VSCC, instead of WINS impactor for PM 2.5 separation. FEM (II) EQPM-0202-143 Vol. 67, p. 15567, 04/02/02

R&P R&P2000AVSCC PM 2.5 Same as R&P-2000A PM 2.5 sampler but with BGI VSCC instead of WINS impactor for PM 2.5 separation. FEM (II) EQPM-0202-144 Vol. 67, p. 15567, 04/02/02

R&P R&P-2025- VSCC PM 2.5 Same as R&P-2025 PM 2.5 sampler but with BGI VSCC instead of WINS impactor, for PM 2.5 separation. FEM (II) EQPM-0202-145 Vol. 67, p. 15567, 04/02/02

Andersen RAAS-100 PM 10 Andersen Instruments, Inc. Model RAAS10-100 PM 10 sampler with louvered PM 10 inlet; operates at flow rate of 16.7 L/min; 24-h integrated sampler; volumetr ic 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 PM 2.5 Graseby Andersen Model RAAS2.5-100 PM 2.5 sampler, similar to RAAS-100 PM 10 with a WINS impactor for PM 2.5 separation. FRM RFPS-0598-119 Vol 63, p. 31991, 06/11/98

Andersen RAAS200A PM 10 FRM RFPS-0699-131 Vol. 64, p. 33481, 06/23/99

Andersen RAAS-200A PM 2.5 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 PM 10 Andersen Instruments, Inc. Model RAAS10-300, sequential sampler with louvered PM 10 inlet, operates at 16.7 L/min; capacity to hold eight filter-holder s for multiple day operation. FRM RFPS-0699-132 Vol. 64, p. 33481, 06/23/99

Andersen RAAS-300 PM 2.5 Graseby Andersen Model RAAS2.5-300 PM 2.5 sampler, similar to RAAS-300 PM 10 sampler with a WINS impactor for PM 2.5 separation. FRM RFPS-0598-120 Vol. 63, p. 31991, 06/11/98



### December 2009 A-5

### Manufacturer a 

### Sampler Name Size Cut b 

### Description FRM or FEM c 

### Designation # FRN

Thermo Scientific, Inc. CAPS PM 2.5 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 PM 2.5 Same as RAAS-100 PM 2.5 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 PM 2.5 Same as RAAS-200 PM 2.5 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 PM 2.5 Same as RAAS-300 PM 2.5 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 PM 2.5 Model MASS100 PM 2.5 sampler with louvered PM 10 inlet followed by WINS impactor, operates at 16.7 L/min; 24-h integrated, volumetric flow meas ured 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 PM 2.5 Model MASS300 PM 2.5 sampler with louvered PM 10 inlet followed by WINS impactor, operates at 16.7 L/mi n; 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 PM 10 Model TE-6070 PM 10 High-Volume Sampler, with TE-6001 PM 10 size selective inlet; 8” x 10” filter holder. FRM RFPS-0202-141 Vol. 67, p. 15566, 04/02/02

Met One BAM PM 10 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., F ranklin, 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 o ther 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 s harpness of the separation, and is derived by the square root of the ratio of particle diameters at penetrations of 16% andc 84%, [d 16 /d 84 ] 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 sq uare 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 specification s for filter analysis of mass, elements, ions, and carbon.

Observable Analytical Accuracy a 

### Precisionb Minimum Detectable Limit (MDL) Interferences Comparability Data Completeness

PM 2.5 mass ± 5% 4 ± 10% 4 0.04 μg/m 3 c to ~1 μg/m 3 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/m 3 g
8 PIXE: 6-360 ng/m 3

d 9 ICP/MS: 0.004-25 ng/m 3 10 0.05-11.7 ng/m 3 9,11 AAS: 0.02- 7.15 ng/m 3 12 Volatile compounds may evaporate from filters due to vacuum in XRF and PIXE. Potential contamination during extraction and incompleteextraction 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/m 3 e to 0.2 μg/m 3 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/m 3 e to 0.2 μg/m 3 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/m 3 e to 0.07 μg/m 3 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/m 3 f to 0.8 μg/m 3 d OC: Within 20 to 50%

EC: ± 20% EC: 0.03 μg/m 3 d to 0.1 μg/m 3 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/m 3 d 1,6 Subject to adsor ption (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/m 3 23

Sunset Carbon Analyzer: 0.05- 0.22 μg C/m 3 26,28

Elemental High TOC II: 0.05 μg C/m 3 29

TOA: 0.12 μg C/m 3 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/m 3 H:0.09 μg/m 3 N: 0.03 μg/m 3 S: 0.10 μg/m 3 30 Contamination during sample drying step N/A N/A

Dissolved organic nitrogen N/A ± 5-30% 31 0.001 μ g N/m 3 while inorganic nitrogen is low; ≥ 0.071 μ g N/m 3 while inorganic nitrogen is high 31 Concentration of inorganic nitrogen Good correlation between UV and persulfate oxidation methods (R 2 = 0.87) 31 N/A



### December 2009 A-7

### Observable Analytical Accuracy a 

### 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/m 3 40

2.08 ng/m 3 j 31

0.01-0.03 ng/m 3 33,41

HPLC/MS: 9-648 pg/m 3 39 GCMS: Extraction recovery interfered by sample matrix
Derivatization efficiency
IC/PAD: Overlapping peaks in chromatogram IC/PAD: Good correlation (R 2 = 0.97) with HPLC/MS; and (R 2 = 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/m 3 46

IC: 0.01-0.12 ng/m 3

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/m 3 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/m 3 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 n ot 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/m 3 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/cm 2 detection limit and 13.8 cm 2 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 cm 2 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 m olecular weight of amino acid = 150

l Based on 13.8 cm 2 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 ); 2 Watson and Chow (2001, 157123 ); 3 Watson et al. (1983, 045084 ); 4 Fehsenfeld et al. (2004, 157360 ); 5 Solomon et al. (2001, 157193 ); 6 Watson et al. (2005, 157124 ); 7 Mikel (2001,
156762 ); 8 Watson et al. (1999, 020949 ); 9 Solomon and Sioutas (2006, 156995 ); 10 Graney et al. (2004, 053756 ); 11 Tanaka et al. (1998, 157041 ); 12 Pancras et al. (2005, 098120 ); 13 John et al. (1988, 045903 );

14 Hering and Cass (1999, 084958 ); 15 Fitz et al. (1989, 077387 ); 16 Hering et al. (1988, 036012 ); 17 Solomon et al. (2003, 156994 ); 18 Cabada et al. (2004, 148859 ); 19 Fine et al. (2003, 155775 ); 20 Hogrefe et al. (2004, 099003 ); 21 Drewnick et al. (2003, 099160 ); 22 Watson et al. (2005, 157125 ); 23 Ho et al. (2006, 156552 ); 24 Decesari et al. (2005, 144536 ); 25 Mayol-Bracero et al. (2002, 045010 ); 26 Yang et al. (2003,
156167 ); 27 Turšic et al. (2006, 157063 ); 28 Mader et al.(2004, 156724 ); 29 Xiao and Liu (2004, 056801 ); 30 Kiss et al. (2002, 156646 ); 31 Cornell and Jickells (1999, 156367 ); 32 Zheng et al. (2002, 026100 );

33 Fraser et al. (2002, 140741 ); 34 Fraser et al. (2003, 042231 ) 35 Schauer er al. (2000, 012225 ); 36 Fine et al. (2004, 141283 ); 37 Yue et al. (2004, 157169 ); 38 Rinehart et al. (2006, 115184 ); 39 Wan and Yu (2006,
157104 ); 40 Poore (2000, 012839 ); 41 Fraser et al. (2003, 040266 ); 42 Engling et al. (2006, 156422 ); 43 Yu et al. (2005, 157167 ); 44 Tran et al. (2000, 013025 ); 45 Yao et al. (2004, 102213 ); 46 Li and Yu (2005,
156692 ); 47 Henning et al. (2003, 156539 ); 48 Zhang and Anastasio (2003, 157182 ); 49 Emmenegger et al. (2007, 156418 ); 50 Watson et al. (1989, 046318 ); 51 Greaves et al. (1985, 156494 ); 52 Waterman et al. (2000, 157116 ); 53 Waterman et al. (2001, 157117 ); 54 Falkovich and Rudich (2001, 156427 ); 55 Chow et al. (2007, 157209 ); 56 Miguel et al. (2004, 123260 ); 57 Crimmins and Baker (2006, 097008 ); 58 Ho and Yu (2004, 156551 ); 59 Jeon et al. (2001, 016636 ); 60 Mazzoleni et al. (2007, 098038 ); 61 Poore (2002, 051444 ); 62 Butler et al. (2003, 156313 ); 63 Chow et al. (2006, 146622 ); 64 Russell et al. (2004, 082453 ); 65 Grover et al. (2006, 138080 ); 66 Grover et al. (2005, 090044 ); 67 Schwab et al. (2006, 098449 ); 68 Hauck et al. (2004, 156525 ); 69 Jaques et al. (2004, 155878 ); 70 Rupprecht and Patashnick (2003, 157207 ); 71 Pang et al (2002, 030353 ); 72 Eatough et al. (2001, 010303 ); 73 Lee et al. (2005, 128139 ); 74 Lee et al. (2005, 156680 ); 75 Babich et al. (2000, 156239 ); 76 Lee et al. (2005, 155925 ); 77 Lee et al. (2005, 128139 ); 78 Anderson and Ogren (1998, 156213 ); 79 Chung et al. (2001, 156357 ); 80 Kidwell and Ondov (2004, 155898 ); 81 Lithgow et al. (2004, 126616 ); 82 Weber et al. (2003, 157129 ); 83 Harrison et al. (2004, 136787 ); 84 Rattigan et al. (2006, 115897 ); 85 Wittig et al. (2004, 103413 ); 86 Vaughn et al. (2005, 157089 ); 87 Chow et al. (2005, 099030 ); 88 Weber et al (2001, 024640 ) ; 89 Schwab et al. (2006, 098785 ); 90 Lim et al. (2003, 037037 );

91 Watson and Chow (2002, 037873 ); 92 Venkatachari et al. (2006, 105918 ); 93 Bae et al. (2004, 156243 ); 94 Arhami et al. (2006, 156224 ); 95 Park et al. (2005, 156843 ); 96 Bae et al. (2004, 098680 ); 97 Chow et al. (2006, 156350 ); 98 Arnott et al. (2005, 156227 ); 99 Bond et al. (1999, 156281 ); 100 Virkkula et al. (2005, 157097 ); 101 Petzold et al. (2002, 156863 ); 102 Park et al. (2006, 098104 ); 103 Arnott et al. (1999, 020650 );

104 Peters et al. (2001, 016925 ); 105 Pitchford et al. (1997, 156872 ); 106 Rees et al. (2004, 097164 ); 107 Watson et al. (2000, 010354 ); 108 Lee et al. (2005, 156680 ); 109 Hering et al. (2004, 155837 ); 110 Watson et al. (1998, 198805 ); 111 Chakrabarti et al. (2004, 157426 ); 112 Mathai et al. (1990, 156741 ); 113 Kidwell and Ondov (2001, 017092 ); 114 Stanier et al. (2004, 095955 ); 115 Khlystov et al. (2005, 156635 ); 116 Takahama et al. (2004, 157038 ); 117 Chow et al. (2005, 156348 ); 118 Zhang et al. (2002, 157181 ); 119 Subramanian et al. (2004, 081203 ); 120 Chow et al. (2006, 155207 ); 121 Birch and Cary (1996, 026004 ); 122 Birch (1998,
024953 ); 123 Birch and Cary (1996, 002352 ); 124 NIOSH (1996, 156810 ); 125 NIOSH (1999, 156811 ); 126 Chow et al. (1993, 077459 ); 127 Chow et al. (2007, 156354 ); 128 Ellis and Novakov (1982, 156416 );

129 Peterson and Richards (2002, 156861 ); 130 Schauer et al. (2003, 037014 ); 13 1Middlebrook et al. (2003, 042932 ); 132 Wenzel et al. (2003, 157139 ); 133 Jimenez et al. (2003, 156611 ); 134 Phares et al. (2003,
156866 ); 135 Qin and Prather (2006, 156895 ); 136 Zhang et al. (2005, 157185 ); 137 Bein et al. (2005, 156265 ); 138 Drewnick et al. (2004, 155754 ); 139 Drewnick et al. (2004, 155755 ); 140 Lake et al. (2003, 156669 );

141 Lake et al. (2004, 088411 )

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



### December 2009 A-8

# Table A-4. Measurement and analytical specifica tions for filter analysis of organic species.

Analytical Accuracy Pr ecision MDL Interferences Organic 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/ m 3 a 55 0.83-1.66 ng/m 3 b 38

0.033-3.85 ng/m 3 b 56

0.01-0.03 ng/m 3 33,34,37

0.76-276 pg/m 3 b 57 Fragmentatio n of labile

compounds Possible contaminants from solventsand complicated extraction procedures. Loss of volatile compoundsduring the extraction and pretreatment steps. Possible carryover from injection port. R 2 s for solvent extraction were 0.95 58

, 0.97 55 , 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/m 3 a 58

0.061-0.97 ng/m 3 a 55 0.01-0.03 ng/m 3 33,34,37 Same as PAHs Same as PAHs R 2 s 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/m 3 a 55 0.83-1.66 ng/m 3 b 38

0.01-0.03 ng/m 3 33,41

0.01 ng/m 3 37 Same as PAHs Same as PAHs R 2 s 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/m 3 a 55 0.83-1.66 ng/m 3 b 60 Same as PAHs Same as PAHs R 2 s for solvent extraction are 0.97 55 and 0.998 59

Organic acids(including nalkanoicacids, 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/m 3 a 54 0.01-0.03 ng/m 3 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 solventsand complicated extraction procedures.

Loss of volatile compoundsduring the extraction and pretreatment steps.

Possible carryover from injection port .

Low derivatization efficiency . Correlation with solvent extraction method R 2 = 0.731 59



### December 2009 A-9

### Analytical Accuracy Pr ecision MDL Interferences

Polyols and sugars,including guaiacol and substitutedguaiacols, 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/m 3 61

2.08 ng/m 3 b 38

0.01-0.03 ng/m 3 33,41 Same as organic acids Same as organic acids N/A

a Assumes 2.9 cm 2 filter used in analysis from a deposit area of 13.8 cm 2 , 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 ); 2 Watson and Chow (2001, 157123 ); 3 Watson et al. (1983, 045084 ); 4 Fehsenfeld et al. (2004, 157360 ); 5 Solomon et al. (2001, 157193 ); 6 Watson et al. (2005, 157124 ); 7 Mikel (2001,
156762 ); 8 Watson et al. (1999, 020949 ); 9 Solomon and Sioutas (2006, 156995 ); 10 Graney et al. (2004, 053756 ); 11 Tanaka et al. (1998, 157041 ); 12 Pancras et al. (2005, 098120 ); 13 John et al. (1988, 045903 );

14 Hering and Cass (1999, 084958 ); 15 Fitz et al. (1989, 077387 ); 16 Hering et al. (1988, 036012 ); 17 Solomon et al. (2003, 156994 ); 18 Cabada et al. (2004, 148859 ); 19 Fine et al. (2003, 155775 ); 20 Hogrefe et al. (2004, 099003 ); 21 Drewnick et al. (2003, 099160 ); 22 Watson et al. (2005, 157125 ); 23 Ho et al. (2006, 156552 ); 24 Decesari et al. (2005, 144536 ); 25 Mayol-Bracero et al. (2002, 045010 ); 26 Yang et al. (2003,
156167 ); 27 Turšic et al. (2006, 157063 ); 28 Mader et al.(2004, 156724 ); 29 Xiao and Liu (2004, 056801 ); 30 Kiss et al. (2002, 156646 ); 31 Cornell and Jickells (1999, 156367 ); 32 Zheng et al. (2002, 026100 );

33 Fraser et al. (2002, 140741 ); 34 Fraser et al. (2003, 042231 ) 35 Schauer er al. (2000, 012225 ); 36 Fine et al. (2004, 141283 ); 37 Yue et al. (2004, 157169 ); 38 Rinehart et al. (2006, 115184 ); 39 Wan and Yu (2006,
157104 ); 40 Poore (2000, 012839 ); 41 Fraser et al. (2003, 040266 ); 42 Engling et al. (2006, 156422 ); 43 Yu et al. (2005, 157167 ); 44 Tran et al. (2000, 013025 ); 45 Yao et al. (2004, 102213 ); 46 Li and Yu (2005,
156692 ); 47 Henning et al. (2003, 156539 ); 48 Zhang and Anastasio (2003, 157182 ); 49 Emmenegger et al. (2007, 156418 ); 50 Watson et al. (1989, 046318 ); 51 Greaves et al. (1985, 156494 ); 52 Waterman et al. (2000, 157116 ); 53 Waterman et al. (2001, 157117 ); 54 Falkovich and Rudich (2001, 156427 ); 55 Chow et al. (2007, 157209 ); 56 Miguel et al. (2004, 123260 ); 57 Crimmins and Baker (2006, 097008 ); 58 Ho and Yu (2004, 156551 ); 59 Jeon et al. (2001, 016636 ); 60 Mazzoleni et al. (2007, 098038 ); 61 Poore (2002, 051444 ); 62 Butler et al. (2003, 156313 ); 63 Chow et al. (2006, 146622 ); 64 Russell et al. (2004, 082453 ); 65 Grover et al. (2006, 138080 ); 66 Grover et al. (2005, 090044 ); 67 Schwab et al. (2006, 098449 ); 68 Hauck et al. (2004, 156525 ); 69 Jaques et al. (2004, 155878 ); 70 Rupprecht and Patashnick (2003, 157207 ); 71 Pang et al (2002, 030353 ); 72 Eatough et al. (2001, 010303 ); 73 Lee et al. (2005, 128139 ); 74 Lee et al. (2005, 156680 ); 75 Babich et al. (2000, 156239 ); 76 Lee et al. (2005, 155925 ); 77 Lee et al. (2005, 128139 ); 78 Anderson and Ogren (1998, 156213 ); 79 Chung et al. (2001, 156357 ); 80 Kidwell and Ondov (2004, 155898 ); 81 Lithgow et al. (2004, 126616 ); 82 Weber et al. (2003, 157129 ); 83 Harrison et al. (2004, 136787 ); 84 Rattigan et al. (2006, 115897 ); 85 Wittig et al. (2004, 103413 ); 86 Vaughn et al. (2005, 157089 ); 87 Chow et al. (2005, 099030 ); 88 Weber et al (2001, 024640 ) ; 89 Schwab et al. (2006, 098785 ); 90 Lim et al. (2003, 037037 );

91 Watson and Chow (2002, 037873 ); 92 Venkatachari et al. (2006, 105918 ); 93 Bae et al. (2004, 156243 ); 94 Arhami et al. (2006, 156224 ); 95 Park et al. (2005, 156843 ); 96 Bae et al. (2004, 098680 ); 97 Chow et al. (2006, 156350 ); 98 Arnott et al. (2005, 156227 ); 99 Bond et al. (1999, 156281 ); 100 Virkkula et al. (2005, 157097 ); 101 Petzold et al. (2002, 156863 ); 102 Park et al. (2006, 098104 ); 103 Arnott et al. (1999, 020650 );

104 Peters et al. (2001, 016925 ); 105 Pitchford et al. (1997, 156872 ); 106 Rees et al. (2004, 097164 ); 107 Watson et al. (2000, 010354 ); 108 Lee et al. (2005, 156680 ); 109 Hering et al. (2004, 155837 ); 110 Watson et al. (1998, 198805 ); 111 Chakrabarti et al. (2004, 157426 ); 112 Mathai et al. (1990, 156741 ); 113 Kidwell and Ondov (2001, 017092 ); 114 Stanier et al. (2004, 095955 ); 115 Khlystov et al. (2005, 156635 ); 116 Takahama et al. (2004, 157038 ); 117 Chow et al. (2005, 156348 ); 118 Zhang et al. (2002, 157181 ); 119 Subramanian et al. (2004, 081203 ); 120 Chow et al. (2006, 155207 ); 121 Birch and Cary (1996, 026004 ); 122 Birch (1998,
024953 ); 123 Birch and Cary (1996, 002352 ); 124 NIOSH (1996, 156810 ); 125 NIOSH (1999, 156811 ); 126 Chow et al. (1993, 077459 ); 127 Chow et al. (2007, 156354 ); 128 Ellis and Novakov (1982, 156416 );

129 Peterson and Richards (2002, 156861 ); 130 Schauer et al. (2003, 037014 ); 13 1Middlebrook et al. (2003, 042932 ); 132 Wenzel et al. (2003, 157139 ); 133 Jimenez et al. (2003, 156611 ); 134 Phares et al. (2003,
156866 ); 135 Qin and Prather (2006, 156895 ); 136 Zhang et al. (2005, 157185 ); 137 Bein et al. (2005, 156265 ); 138 Drewnick et al. (2004, 155754 ); 139 Drewnick et al. (2004, 155755 ); 140 Lake et al. (2003, 156669 );

141 Lake et al. (2004, 088411 )

Source: Chow et al. (2008, 156355 )



### December 2009 A-10

# Table A-5. Measurement and analytical specifica tions for continuous mass and mass surrogate instruments.

Instrument and Measurement Principle Averaging Time Analytical Accuracy a 

### Precisionb MDL Interferen ces 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/m 3 for 10-min avgc,d
± 1.5 μg/m 3 for 1-h avg c,d 0.01 μg, which is 0.06 μg/m 3 for 1-h avg c 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. TheTeflon (PTFE)-coated borosilicate glass-fiber filter thatis 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 madeduring the prior particleladen samples to obtain total PM 2.5 concentration. 1 h-24 h ± 0.75% c < 10% 65 0.01 μg, which is 0.06 μg/m 3

for 1-h avg c N/A 9 to 30% higher than FRM mass Within 10% ofmass by DTEOM, PCBOSS, RAMS and BAM 66,67 95-99% 65,68

57-65% 67

Differential Tapered ElementOscillating Microbalance (DTEOM) Similar to FDMS, but an electrostatic precipitator isused 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/m 3 for 1-h avg c 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 removegas 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/m 3 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 Accuracy a 

### Precisionb MDL Interferen ces 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/m 3 ) 75 < 5 μg/m 3 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: within2% 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 massconcentration, after subtraction of blank filter attenuation. 1 h-24 h ± 3 μg for 24-h avg concentrations < 100 μg/m 3 and 2% for 100 to 1,000 μg/m 3

± 8 μg for 1-h concentrations < 100 μg/m 3 and 8% for 100 to 1000 μg/m 3 ± 2 μg/m 3 c,h 5 μg/m 3 for 1-h avg 1 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-scatteringcoefficient. 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/m 3 c,h Nephelometer: < 1.5 Mm -1

DustTrak: ± 1 μg/m 3 for 24-h avg j 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 (R 2 >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 counter 65



### December 2009 A-12

### Instrument and Measurement Principle Averaging Time Analytical Accuracy a 

### Precisionb MDL Interferen ces 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 PM 2.5 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/m 3 , R 2 = 0.99).
j Specified as “Zero stability” by the manufacturer.
N/A: Not available.

1 Chow (1995, 077012 ); 2 Watson and Chow (2001, 157123 ); 3 Watson et al. (1983, 045084 ); 4 Fehsenfeld et al. (2004, 157360 ); 5 Solomon et al. (2001, 157193 ); 6 Watson et al. (2005, 157124 ); 7 Mikel (2001,
156762 ); 8 Watson et al. (1999, 020949 ); 9 Solomon and Sioutas (2006, 156995 ); 10 Graney et al. (2004, 053756 ); 11 Tanaka et al. (1998, 157041 ); 12 Pancras et al. (2005, 098120 ); 13 John et al. (1988, 045903 );

14 Hering and Cass (1999, 084958 ); 15 Fitz et al. (1989, 077387 ); 16 Hering et al. (1988, 036012 ); 17 Solomon et al. (2003, 156994 ); 18 Cabada et al. (2004, 148859 ); 19 Fine et al. (2003, 155775 ); 20 Hogrefe et al. (2004, 099003 ); 21 Drewnick et al. (2003, 099160 ); 22 Watson et al. (2005, 157125 ); 23 Ho et al. (2006, 156552 ); 24 Decesari et al. (2005, 144536 ); 25 Mayol-Bracero et al. (2002, 045010 ); 26 Yang et al. (2003,
156167 ); 27 Turšic et al. (2006, 157063 ); 28 Mader et al.(2004, 156724 ); 29 Xiao and Liu (2004, 056801 ); 30 Kiss et al. (2002, 156646 ); 31 Cornell and Jickells (1999, 156367 ); 32 Zheng et al. (2002, 026100 );

33 Fraser et al. (2002, 140741 ); 34 Fraser et al. (2003, 042231 ) 35 Schauer er al. (2000, 012225 ); 36 Fine et al. (2004, 141283 ); 37 Yue et al. (2004, 157169 ); 38 Rinehart et al. (2006, 115184 ); 39 Wan and Yu (2006,
157104 ); 40 Poore (2000, 012839 ); 41 Fraser et al. (2003, 040266 ); 42 Engling et al. (2006, 156422 ); 43 Yu et al. (2005, 157167 ); 44 Tran et al. (2000, 013025 ); 45 Yao et al. (2004, 102213 ); 46 Li and Yu (2005,
156692 ); 47 Henning et al. (2003, 156539 ); 48 Zhang and Anastasio (2003, 157182 ); 49 Emmenegger et al. (2007, 156418 ); 50 Watson et al. (1989, 046318 ); 51 Greaves et al. (1985, 156494 ); 52 Waterman et al. (2000, 157116 ); 53 Waterman et al. (2001, 157117 ); 54 Falkovich and Rudich (2001, 156427 ); 55 Chow et al. (2007, 157209 ); 56 Miguel et al. (2004, 123260 ); 57 Crimmins and Baker (2006, 097008 ); 58 Ho and Yu (2004, 156551 ); 59 Jeon et al. (2001, 016636 ); 60 Mazzoleni et al. (2007, 098038 ); 61 Poore (2002, 051444 ); 62 Butler et al. (2003, 156313 ); 63 Chow et al. (2006, 146622 ); 64 Russell et al. (2004, 082453 ); 65 Grover et al. (2006, 138080 ); 66 Grover et al. (2005, 090044 ); 67 Schwab et al. (2006, 098449 ); 68 Hauck et al. (2004, 156525 ); 69 Jaques et al. (2004, 155878 ); 70 Rupprecht and Patashnick (2003, 157207 ); 71 Pang et al (2002, 030353 ); 72 Eatough et al. (2001, 010303 ); 73 Lee et al. (2005, 128139 ); 74 Lee et al. (2005, 156680 ); 75 Babich et al. (2000, 156239 ); 76 Lee et al. (2005, 155925 ); 77 Lee et al. (2005, 128139 ); 78 Anderson and Ogren (1998, 156213 ); 79 Chung et al. (2001, 156357 ); 80 Kidwell and Ondov (2004, 155898 ); 81 Lithgow et al. (2004, 126616 ); 82 Weber et al. (2003, 157129 ); 83 Harrison et al. (2004, 136787 ); 84 Rattigan et al. (2006, 115897 ); 85 Wittig et al. (2004, 103413 ); 86 Vaughn et al. (2005, 157089 ); 87 Chow et al. (2005, 099030 ); 88 Weber et al (2001, 024640 ) ; 89 Schwab et al. (2006, 098785 ); 90 Lim et al. (2003, 037037 );

91 Watson and Chow (2002, 037873 ); 92 Venkatachari et al. (2006, 105918 ); 93 Bae et al. (2004, 156243 ); 94 Arhami et al. (2006, 156224 ); 95 Park et al. (2005, 156843 ); 96 Bae et al. (2004, 098680 ); 97 Chow et al. (2006, 156350 ); 98 Arnott et al. (2005, 156227 ); 99 Bond et al. (1999, 156281 ); 100 Virkkula et al. (2005, 157097 ); 101 Petzold et al. (2002, 156863 ); 102 Park et al. (2006, 098104 ); 103 Arnott et al. (1999, 020650 );

104 Peters et al. (2001, 016925 ); 105 Pitchford et al. (1997, 156872 ); 106 Rees et al. (2004, 097164 ); 107 Watson et al. (2000, 010354 ); 108 Lee et al. (2005, 156680 ); 109 Hering et al. (2004, 155837 ); 110 Watson et al. (1998, 198805 ); 111 Chakrabarti et al. (2004, 157426 ); 112 Mathai et al. (1990, 156741 ); 113 Kidwell and Ondov (2001, 017092 ); 114 Stanier et al. (2004, 095955 ); 115 Khlystov et al. (2005, 156635 ); 116 Takahama et al. (2004, 157038 ); 117 Chow et al. (2005, 156348 ); 118 Zhang et al. (2002, 157181 ); 119 Subramanian et al. (2004, 081203 ); 120 Chow et al. (2006, 155207 ); 121 Birch and Cary (1996, 026004 ); 122 Birch (1998,
024953 ); 123 Birch and Cary (1996, 002352 ); 124 NIOSH (1996, 156810 ); 125 NIOSH (1999, 156811 ); 126 Chow et al. (1993, 077459 ); 127 Chow et al. (2007, 156354 ); 128 Ellis and Novakov (1982, 156416 );

129 Peterson and Richards (2002, 156861 ); 130 Schauer et al. (2003, 037014 ); 13 1Middlebrook et al. (2003, 042932 ); 132 Wenzel et al. (2003, 157139 ); 133 Jimenez et al. (2003, 156611 ); 134 Phares et al. (2003,
156866 ); 135 Qin and Prather (2006, 156895 ); 136 Zhang et al. (2005, 157185 ); 137 Bein et al. (2005, 156265 ); 138 Drewnick et al. (2004, 155754 ); 139 Drewnick et al. (2004, 155755 ); 140 Lake et al. (2003, 156669 );

141 Lake et al. (2004, 088411 )

Source: Chow et al. (2008, 156355 )



### December 2009 A-13

# Table A-6. Measurement and analytical specifications for co ntinuous elements.

Instrument and Measurement

Principle Averaging Time Analytical Accuracya Precision MDL Interf erences 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 pg 80 Spectral interferences limit the number of elements detected simultaneously N/A N/A

Laser-Induced Breakdown Spectroscopy (LIBS)
Used for in-situ singleparticle analysis. A highpower pulsed laser is projected into particlesproducing 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 fg 81 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 analytic al specifications for continuous NO 3 

# -.

Instrument and Measurement Principle Averaging Time Analytical Accuracy a 

### 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 NO X analyzer. 10 min N/A N/A 0.1 μg/m 3 for 10-min avg 82 N/A Within 30% of filter and continuous NO 3 –

. 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 NO X 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/m 3 for 24-h avg 83,84

0.24 μg/m 3 to 0.45 μg/m 3 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 NO 3 – 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 HNO 3 and NO 2

. 30 s N/A N/A 0.5 μg/m 3 for 30-s avg 82 N/A Within 30% of filter and continuous NO 3 –

. 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/m 3 82 N/A Within 30% of filter and continuous NO 3 –

. 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 collectedalternatively 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/m 3 82 N/A Within 30% of filter and continuous NO 3 –

. 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/m 3
20,82,88 Consistent water quality is essential for good precision. Within 10% of nylon-filter NO 3 – and 37% higher than R&P-8400N 20 65-70% 20



### December 2009 A-15

### Instrument and Measurement Principle Averaging Time Analytical Accuracy a 

### 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 NO 3 – 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 dropletsare 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/m 3 for 1-h avg e 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-sizingchamber 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/m 3 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 NO 3 –

, 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 PM 2.5 total particulate NO 3 (the sum of the denuded front filter [non-volatilized NO 3 -] and HNO 3 -absorbing backup filter [volatilized NO 3 ]).

e Manufacturer specified measurement parameter
N/A: Not available.

1 Chow (1995, 077012 ); 2 Watson and Chow (2001, 157123 ); 3 Watson et al. (1983, 045084 ); 4 Fehsenfeld et al. (2004, 157360 ); 5 Solomon et al. (2001, 157193 ); 6 Watson et al. (2005, 157124 ); 7 Mikel (2001,
156762 ); 8 Watson et al. (1999, 020949 ); 9 Solomon and Sioutas (2006, 156995 ); 10 Graney et al. (2004, 053756 ); 11 Tanaka et al. (1998, 157041 ); 12 Pancras et al. (2005, 098120 ); 13 John et al. (1988, 045903 );

14 Hering and Cass (1999, 084958 ); 15 Fitz et al. (1989, 077387 ); 16 Hering et al. (1988, 036012 ); 17 Solomon et al. (2003, 156994 ); 18 Cabada et al. (2004, 148859 ); 19 Fine et al. (2003, 155775 ); 20 Hogrefe et al. (2004, 099003 ); 21 Drewnick et al. (2003, 099160 ); 22 Watson et al. (2005, 157125 ); 23 Ho et al. (2006, 156552 ); 24 Decesari et al. (2005, 144536 ); 25 Mayol-Bracero et al. (2002, 045010 ); 26 Yang et al. (2003,
156167 ); 27 Turšic et al. (2006, 157063 ); 28 Mader et al.(2004, 156724 ); 29 Xiao and Liu (2004, 056801 ); 30 Kiss et al. (2002, 156646 ); 31 Cornell and Jickells (1999, 156367 ); 32 Zheng et al. (2002, 026100 );

33 Fraser et al. (2002, 140741 ); 34 Fraser et al. (2003, 042231 ) 35 Schauer er al. (2000, 012225 ); 36 Fine et al. (2004, 141283 ); 37 Yue et al. (2004, 157169 ); 38 Rinehart et al. (2006, 115184 ); 39 Wan and Yu (2006,
157104 ); 40 Poore (2000, 012839 ); 41 Fraser et al. (2003, 040266 ); 42 Engling et al. (2006, 156422 ); 43 Yu et al. (2005, 157167 ); 44 Tran et al. (2000, 013025 ); 45 Yao et al. (2004, 102213 ); 46 Li and Yu (2005,
156692 ); 47 Henning et al. (2003, 156539 ); 48 Zhang and Anastasio (2003, 157182 ); 49 Emmenegger et al. (2007, 156418 ); 50 Watson et al. (1989, 046318 ); 51 Greaves et al. (1985, 156494 ); 52 Waterman et al. (2000, 157116 ); 53 Waterman et al. (2001, 157117 ); 54 Falkovich and Rudich (2001, 156427 ); 55 Chow et al. (2007, 157209 ); 56 Miguel et al. (2004, 123260 ); 57 Crimmins and Baker (2006, 097008 ); 58 Ho and Yu (2004, 156551 ); 59 Jeon et al. (2001, 016636 ); 60 Mazzoleni et al. (2007, 098038 ); 61 Poore (2002, 051444 ); 62 Butler et al. (2003, 156313 ); 63 Chow et al. (2006, 146622 ); 64 Russell et al. (2004, 082453 ); 65 Grover et al. (2006, 138080 ); 66 Grover et al. (2005, 090044 ); 67 Schwab et al. (2006, 098449 ); 68 Hauck et al. (2004, 156525 ); 69 Jaques et al. (2004, 155878 ); 70 Rupprecht and Patashnick (2003, 157207 ); 71 Pang et al (2002, 030353 ); 72 Eatough et al. (2001, 010303 ); 73 Lee et al. (2005, 128139 ); 74 Lee et al. (2005, 156680 ); 75 Babich et al. (2000, 156239 ); 76 Lee et al. (2005, 155925 ); 77 Lee et al. (2005, 128139 ); 78 Anderson and Ogren (1998, 156213 ); 79 Chung et al. (2001, 156357 ); 80 Kidwell and Ondov (2004, 155898 ); 81 Lithgow et al. (2004, 126616 ); 82 Weber et al. (2003, 157129 ); 83 Harrison et al. (2004, 136787 ); 84 Rattigan et al. (2006, 115897 ); 85 Wittig et al. (2004, 103413 ); 86 Vaughn et al. (2005, 157089 ); 87 Chow et al. (2005, 099030 ); 88 Weber et al (2001, 024640 ) ; 89 Schwab et al. (2006, 098785 ); 90 Lim et al. (2003, 037037 );

91 Watson and Chow (2002, 037873 ); 92 Venkatachari et al. (2006, 105918 ); 93 Bae et al. (2004, 156243 ); 94 Arhami et al. (2006, 156224 ); 95 Park et al. (2005, 156843 ); 96 Bae et al. (2004, 098680 ); 97 Chow et al. (2006, 156350 ); 98 Arnott et al. (2005, 156227 ); 99 Bond et al. (1999, 156281 ); 100 Virkkula et al. (2005, 157097 ); 101 Petzold et al. (2002, 156863 ); 102 Park et al. (2006, 098104 ); 103 Arnott et al. (1999, 020650 );

104 Peters et al. (2001, 016925 ); 105 Pitchford et al. (1997, 156872 ); 106 Rees et al. (2004, 097164 ); 107 Watson et al. (2000, 010354 ); 108 Lee et al. (2005, 156680 ); 109 Hering et al. (2004, 155837 ); 110 Watson et al. (1998, 198805 ); 111 Chakrabarti et al. (2004, 157426 ); 112 Mathai et al. (1990, 156741 ); 113 Kidwell and Ondov (2001, 017092 ); 114 Stanier et al. (2004, 095955 ); 115 Khlystov et al. (2005, 156635 ); 116 Takahama et al. (2004, 157038 ); 117 Chow et al. (2005, 156348 ); 118 Zhang et al. (2002, 157181 ); 119 Subramanian et al. (2004, 081203 ); 120 Chow et al. (2006, 155207 ); 121 Birch and Cary (1996, 026004 ); 122 Birch (1998,
024953 ); 123 Birch and Cary (1996, 002352 ); 124 NIOSH (1996, 156810 ); 125 NIOSH (1999, 156811 ); 126 Chow et al. (1993, 077459 ); 127 Chow et al. (2007, 156354 ); 128 Ellis and Novakov (1982, 156416 );

129 Peterson and Richards (2002, 156861 ); 130 Schauer et al. (2003, 037014 ); 13 1Middlebrook et al. (2003, 042932 ); 132 Wenzel et al. (2003, 157139 ); 133 Jimenez et al. (2003, 156611 ); 134 Phares et al. (2003,
156866 ); 135 Qin and Prather (2006, 156895 ); 136 Zhang et al. (2005, 157185 ); 137 Bein et al. (2005, 156265 ); 138 Drewnick et al. (2004, 155754 ); 139 Drewnick et al. (2004, 155755 ); 140 Lake et al. (2003, 156669 );

141 Lake et al. (2004, 088411 )

Source: Chow et al. (2008, 156355 )



### December 2009 A-16

# Table A-8. Measurement and analytic al specifications for continuous SO 4 2–



# .

Instrument and Measurement Principle Averaging Time Analytical Accuracy a 

### 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 SO 2 analyzer. 10 min N/A N/A 0.4 μg/m 3

82 N/A Within 15% of filter and continuous SO 4 2–

See Weber et al. 82 for details



# . 100% 7

Rupprecht and Patashni ck continuous sulfate analyzer (R&P-8400S) Particle collection by impaction followed by flash volatilization and detection of the evolved gases by a UV-fluorescence SO 2 analyzer. An activated carbon denuder at the inlet to the Nafion humidifier removes SO 2

. 10 min N/A 25% on avg < 15% at conc. >9 μg/m 3 and >30% at conc. < 2 μg/m 3 b

84 0.48 μg/m 3

85 SO 4 2– to SO 2 conversion and volatilization efficiency appears to depend on ambient composition 84 10-30% lower than filter SO 4 2–

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



### THERMAL REDUCTION INSTRUMENTS

Continuous Ambient Su lfate Monitor (CASM) Sampled air passes through a Na 2 CO 3 coated annular denuder to remove ambient SO 2 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 SO 2 . The flow then passes through a PTFE filter and into a trace-level SO 2 fluorescence analyzer. 15 min N/A N/A N/A N/A Up to 25% lower than filter SO 4 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/m 3 for 24-h avg 89

0.5 μg/m 3 for 15-min avgd SO 4 2– to SO 2 conversion efficiency depends on ambient composition 89 ~20% lower than filter SO 4 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 combinedflow from collected droplets containing dissolved aerosol components and wall steam condensate is directed to an anion IC for analysis of sulfate. Inte rfering 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 SO 4 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-wa shed 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 SO 4 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/m 3
82,88 Consistent water quality is essential for good precision. Within 30% of filter and other continuous SO 4 2– 20,21 65-70% 20,21



### December 2009 A-17

### Instrument and Measurement Principle Averaging Time Analytical Accuracy a 

### Precision MDL Interferences Comparability Data Completeness

Dionex-IC The gas-denuded air stream enters the annular channel of a concentric no zzle, 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 SO 4 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/m 3 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 partic le-sizing chamber is determined by the time-resolved mass spectrometer measur ement. 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 det ected 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 SO 4 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, R 2 >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 PM 2.5 SO 4 2–

. N/A: Not available

1 Chow (1995, 077012 ); 2 Watson and Chow (2001, 157123 ); 3 Watson et al. (1983, 045084 ); 4 Fehsenfeld et al. (2004, 157360 ); 5 Solomon et al. (2001, 157193 ); 6 Watson et al. (2005, 157124 ); 7 Mikel (2001,
156762 ); 8 Watson et al. (1999, 020949 ); 9 Solomon and Sioutas (2006, 156995 ); 10 Graney et al. (2004, 053756 ); 11 Tanaka et al. (1998, 157041 ); 12 Pancras et al. (2005, 098120 ); 13 John et al. (1988, 045903 );

14 Hering and Cass (1999, 084958 ); 15 Fitz et al. (1989, 077387 ); 16 Hering et al. (1988, 036012 ); 17 Solomon et al. (2003, 156994 ); 18 Cabada et al. (2004, 148859 ); 19 Fine et al. (2003, 155775 ); 20 Hogrefe et al. (2004, 099003 ); 21 Drewnick et al. (2003, 099160 ); 22 Watson et al. (2005, 157125 ); 23 Ho et al. (2006, 156552 ); 24 Decesari et al. (2005, 144536 ); 25 Mayol-Bracero et al. (2002, 045010 ); 26 Yang et al. (2003,
156167 ); 27 Turšic et al. (2006, 157063 ); 28 Mader et al.(2004, 156724 ); 29 Xiao and Liu (2004, 056801 ); 30 Kiss et al. (2002, 156646 ); 31 Cornell and Jickells (1999, 156367 ); 32 Zheng et al. (2002, 026100 );

33 Fraser et al. (2002, 140741 ); 34 Fraser et al. (2003, 042231 ) 35 Schauer er al. (2000, 012225 ); 36 Fine et al. (2004, 141283 ); 37 Yue et al. (2004, 157169 ); 38 Rinehart et al. (2006, 115184 ); 39 Wan and Yu (2006,
157104 ); 40 Poore (2000, 012839 ); 41 Fraser et al. (2003, 040266 ); 42 Engling et al. (2006, 156422 ); 43 Yu et al. (2005, 157167 ); 44 Tran et al. (2000, 013025 ); 45 Yao et al. (2004, 102213 ); 46 Li and Yu (2005,
156692 ); 47 Henning et al. (2003, 156539 ); 48 Zhang and Anastasio (2003, 157182 ); 49 Emmenegger et al. (2007, 156418 ); 50 Watson et al. (1989, 046318 ); 51 Greaves et al. (1985, 156494 ); 52 Waterman et al. (2000, 157116 ); 53 Waterman et al. (2001, 157117 ); 54 Falkovich and Rudich (2001, 156427 ); 55 Chow et al. (2007, 157209 ); 56 Miguel et al. (2004, 123260 ); 57 Crimmins and Baker (2006, 097008 ); 58 Ho and Yu (2004, 156551 ); 59 Jeon et al. (2001, 016636 ); 60 Mazzoleni et al. (2007, 098038 ); 61 Poore (2002, 051444 ); 62 Butler et al. (2003, 156313 ); 63 Chow et al. (2006, 146622 ); 64 Russell et al. (2004, 082453 ); 65 Grover et al. (2006, 138080 ); 66 Grover et al. (2005, 090044 ); 67 Schwab et al. (2006, 098449 ); 68 Hauck et al. (2004, 156525 ); 69 Jaques et al. (2004, 155878 ); 70 Rupprecht and Patashnick (2003, 157207 ); 71 Pang et al (2002, 030353 ); 72 Eatough et al. (2001, 010303 ); 73 Lee et al. (2005, 128139 ); 74 Lee et al. (2005, 156680 ); 75 Babich et al. (2000, 156239 ); 76 Lee et al. (2005, 155925 ); 77 Lee et al. (2005, 128139 ); 78 Anderson and Ogren (1998, 156213 ); 79 Chung et al. (2001, 156357 ); 80 Kidwell and Ondov (2004, 155898 ); 81 Lithgow et al. (2004, 126616 ); 82 Weber et al. (2003, 157129 ); 83 Harrison et al. (2004, 136787 ); 84 Rattigan et al. (2006, 115897 ); 85 Wittig et al. (2004, 103413 ); 86 Vaughn et al. (2005, 157089 ); 87 Chow et al. (2005, 099030 ); 88 Weber et al (2001, 024640 ) ; 89 Schwab et al. (2006, 098785 ); 90 Lim et al. (2003, 037037 );

91 Watson and Chow (2002, 037873 ); 92 Venkatachari et al. (2006, 105918 ); 93 Bae et al. (2004, 156243 ); 94 Arhami et al. (2006, 156224 ); 95 Park et al. (2005, 156843 ); 96 Bae et al. (2004, 098680 ); 97 Chow et al. (2006, 156350 ); 98 Arnott et al. (2005, 156227 ); 99 Bond et al. (1999, 156281 ); 100 Virkkula et al. (2005, 157097 ); 101 Petzold et al. (2002, 156863 ); 102 Park et al. (2006, 098104 ); 103 Arnott et al. (1999, 020650 );

104 Peters et al. (2001, 016925 ); 105 Pitchford et al. (1997, 156872 ); 106 Rees et al. (2004, 097164 ); 107 Watson et al. (2000, 010354 ); 108 Lee et al. (2005, 156680 ); 109 Hering et al. (2004, 155837 ); 110 Watson et al. (1998, 198805 ); 111 Chakrabarti et al. (2004, 157426 ); 112 Mathai et al. (1990, 156741 ); 113 Kidwell and Ondov (2001, 017092 ); 114 Stanier et al. (2004, 095955 ); 115 Khlystov et al. (2005, 156635 ); 116 Takahama et al. (2004, 157038 ); 117 Chow et al. (2005, 156348 ); 118 Zhang et al. (2002, 157181 ); 119 Subramanian et al. (2004, 081203 ); 120 Chow et al. (2006, 155207 ); 121 Birch and Cary (1996, 026004 ); 122 Birch (1998,
024953 ); 123 Birch and Cary (1996, 002352 ); 124 NIOSH (1996, 156810 ); 125 NIOSH (1999, 156811 ); 126 Chow et al. (1993, 077459 ); 127 Chow et al. (2007, 156354 ); 128 Ellis and Novakov (1982, 156416 );

129 Peterson and Richards (2002, 156861 ); 130 Schauer et al. (2003, 037014 ); 13 1Middlebrook et al. (2003, 042932 ); 132 Wenzel et al. (2003, 157139 ); 133 Jimenez et al. (2003, 156611 ); 134 Phares et al. (2003,
156866 ); 135 Qin and Prather (2006, 156895 ); 136 Zhang et al. (2005, 157185 ); 137 Bein et al. (2005, 156265 ); 138 Drewnick et al. (2004, 155754 ); 139 Drewnick et al. (2004, 155755 ); 140 Lake et al. (2003, 156669 );

141 Lake et al. (2004, 088411 )

Source: Chow et al. (2008, 156355 )



### December 2009 A-18

# Table A-9. Measurement and analytical specifications for i ons other than NO 3 – 

# and SO 4 2–



# .

Instrument and Measurement Principle Averaging Time Analytical Accuracy a 

### Precision MDL Interferences Comparability Data Completeness

SAMPLE DISSOLUTION FOLLOWED BY IC ANALYSIS INSTRUMENTS

NO 2 – 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/m 3 20 Consistent water quality is essential for good precision N/A N/A

NH 4 + 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/m 3

88 Consistent water quality is essential for good precision ~5% lower than all-sampler avg c 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/m 3 88 Consistent water quality is essential for good precision N/A N/A

Cl – , NO 2 – , NO 3 – , PO 4 3–, SO 4 2– , NH 4 + , 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/m 3 for 1-h avg d 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 r efer 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 integrat ed and 3 continuous samplers, although specific details are mis sing 7 . Performance evaluations at sites dominated by semi-volatile ammonium
nitrate are needed.

d Manufacturer specified measurement parameter

1 Chow (1995, 077012 ); 2 Watson and Chow (2001, 157123 ); 3 Watson et al. (1983, 045084 ); 4 Fehsenfeld et al. (2004, 157360 ); 5 Solomon et al. (2001, 157193 ); 6 Watson et al. (2005, 157124 ); 7 Mikel (2001,
156762 ); 8 Watson et al. (1999, 020949 ); 9 Solomon and Sioutas (2006, 156995 ); 10 Graney et al. (2004, 053756 ); 11 Tanaka et al. (1998, 157041 ); 12 Pancras et al. (2005, 098120 ); 13 John et al. (1988, 045903 );

14 Hering and Cass (1999, 084958 ); 15 Fitz et al. (1989, 077387 ); 16 Hering et al. (1988, 036012 ); 17 Solomon et al. (2003, 156994 ); 18 Cabada et al. (2004, 148859 ); 19 Fine et al. (2003, 155775 ); 20 Hogrefe et al. (2004, 099003 ); 21 Drewnick et al. (2003, 099160 ); 22 Watson et al. (2005, 157125 ); 23 Ho et al. (2006, 156552 ); 24 Decesari et al. (2005, 144536 ); 25 Mayol-Bracero et al. (2002, 045010 ); 26 Yang et al. (2003,
156167 ); 27 Turšic et al. (2006, 157063 ); 28 Mader et al.(2004, 156724 ); 29 Xiao and Liu (2004, 056801 ); 30 Kiss et al. (2002, 156646 ); 31 Cornell and Jickells (1999, 156367 ); 32 Zheng et al. (2002, 026100 );

33 Fraser et al. (2002, 140741 ); 34 Fraser et al. (2003, 042231 ) 35 Schauer er al. (2000, 012225 ); 36 Fine et al. (2004, 141283 ); 37 Yue et al. (2004, 157169 ); 38 Rinehart et al. (2006, 115184 ); 39 Wan and Yu (2006,
157104 ); 40 Poore (2000, 012839 ); 41 Fraser et al. (2003, 040266 ); 42 Engling et al. (2006, 156422 ); 43 Yu et al. (2005, 157167 ); 44 Tran et al. (2000, 013025 ); 45 Yao et al. (2004, 102213 ); 46 Li and Yu (2005,
156692 ); 47 Henning et al. (2003, 156539 ); 48 Zhang and Anastasio (2003, 157182 ); 49 Emmenegger et al. (2007, 156418 ); 50 Watson et al. (1989, 046318 ); 51 Greaves et al. (1985, 156494 ); 52 Waterman et al. (2000, 157116 ); 53 Waterman et al. (2001, 157117 ); 54 Falkovich and Rudich (2001, 156427 ); 55 Chow et al. (2007, 157209 ); 56 Miguel et al. (2004, 123260 ); 57 Crimmins and Baker (2006, 097008 ); 58 Ho and Yu (2004, 156551 ); 59 Jeon et al. (2001, 016636 ); 60 Mazzoleni et al. (2007, 098038 ); 61 Poore (2002, 051444 ); 62 Butler et al. (2003, 156313 ); 63 Chow et al. (2006, 146622 ); 64 Russell et al. (2004, 082453 ); 65 Grover et al. (2006, 138080 ); 66 Grover et al. (2005, 090044 ); 67 Schwab et al. (2006, 098449 ); 68 Hauck et al. (2004, 156525 ); 69 Jaques et al. (2004, 155878 ); 70 Rupprecht and Patashnick (2003, 157207 ); 71 Pang et al (2002, 030353 ); 72 Eatough et al. (2001, 010303 ); 73 Lee et al. (2005, 128139 ); 74 Lee et al. (2005, 156680 ); 75 Babich et al. (2000, 156239 ); 76 Lee et al. (2005, 155925 ); 77 Lee et al. (2005, 128139 ); 78 Anderson and Ogren (1998, 156213 ); 79 Chung et al. (2001, 156357 ); 80 Kidwell and Ondov (2004, 155898 ); 81 Lithgow et al. (2004, 126616 ); 82 Weber et al. (2003, 157129 ); 83 Harrison et al. (2004, 136787 ); 84 Rattigan et al. (2006, 115897 ); 85 Wittig et al. (2004, 103413 ); 86 Vaughn et al. (2005, 157089 ); 87 Chow et al. (2005, 099030 ); 88 Weber et al (2001, 024640 ) ; 89 Schwab et al. (2006, 098785 ); 90 Lim et al. (2003, 037037 );

91 Watson and Chow (2002, 037873 ); 92 Venkatachari et al. (2006, 105918 ); 93 Bae et al. (2004, 156243 ); 94 Arhami et al. (2006, 156224 ); 95 Park et al. (2005, 156843 ); 96 Bae et al. (2004, 098680 ); 97 Chow et al. (2006, 156350 ); 98 Arnott et al. (2005, 156227 ); 99 Bond et al. (1999, 156281 ); 100 Virkkula et al. (2005, 157097 ); 101 Petzold et al. (2002, 156863 ); 102 Park et al. (2006, 098104 ); 103 Arnott et al. (1999, 020650 );

104 Peters et al. (2001, 016925 ); 105 Pitchford et al. (1997, 156872 ); 106 Rees et al. (2004, 097164 ); 107 Watson et al. (2000, 010354 ); 108 Lee et al. (2005, 156680 ); 109 Hering et al. (2004, 155837 ); 110 Watson et al. (1998, 198805 ); 111 Chakrabarti et al. (2004, 157426 ); 112 Mathai et al. (1990, 156741 ); 113 Kidwell and Ondov (2001, 017092 ); 114 Stanier et al. (2004, 095955 ); 115 Khlystov et al. (2005, 156635 ); 116 Takahama et al. (2004, 157038 ); 117 Chow et al. (2005, 156348 ); 118 Zhang et al. (2002, 157181 ); 119 Subramanian et al. (2004, 081203 ); 120 Chow et al. (2006, 155207 ); 121 Birch and Cary (1996, 026004 ); 122 Birch (1998,
024953 ); 123 Birch and Cary (1996, 002352 ); 124 NIOSH (1996, 156810 ); 125 NIOSH (1999, 156811 ); 126 Chow et al. (1993, 077459 ); 127 Chow et al. (2007, 156354 ); 128 Ellis and Novakov (1982, 156416 );

129 Peterson and Richards (2002, 156861 ); 130 Schauer et al. (2003, 037014 ); 13 1Middlebrook et al. (2003, 042932 ); 132 Wenzel et al. (2003, 157139 ); 133 Jimenez et al. (2003, 156611 ); 134 Phares et al. (2003,
156866 ); 135 Qin and Prather (2006, 156895 ); 136 Zhang et al. (2005, 157185 ); 137 Bein et al. (2005, 156265 ); 138 Drewnick et al. (2004, 155754 ); 139 Drewnick et al. (2004, 155755 ); 140 Lake et al. (2003, 156669 );

141 Lake 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 Accuracy a 

### 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 CO 2 analyzer. OC is estimated as twice the oxidizable carbon. EC is not quantified. 10 min N/A N/A OC: 2 μg/m 3 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 CO 2 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/m 3 EC: 0.5 μg/m 3 TC: 0.5 μg/m 3 90 N/A 20 to 60% lower TC than filter TC by TOR or TOT. 91,92 56-60% 6,91

Rutgers University-O regon Graduate Institute (RU-OGI) in-situ therma l/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/m 3 EC: 0.5 μg/m 3 TC: 0.4 μg/m 3 90 N/A 8% higher OC and 20% lowerEC than R&P5400 90 86% 7

Sunset semi-continuous realtime carbon aerosol analysis instrum ent (Sunset OCEC) Particles collected on a quartz-fiber filter are subject to heating temperature ramps following the NIOSH 5040 TOT protocol and the resulting CO 2 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/m 3 (1-h avg) 95 N/A Within 7 to 25% of filter OC and EC and within 15% for TC. Wide variationdue 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 meas ured and converted to a BC mass concentration using σ abs of 14625/ λ (m 2 /g). 5 min N/A 5 to 10% d,7,97 BC e : 0.1 μg/m 3 90 Subject to multiple scatteringeffects by particle and filter matrix resulting in absorption enhancement.Empirical corrections have been proposed98 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 Accuracy a 

### 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 (b abs ). 1 min N/A 6 to 8% 99,100 BC f : 0.1 μg/m 3 90 Instrument includes an empirical correction for scatteringand loading effects 99 and adjustmentshave been proposed for thethree wavelength model 100 ~50% lowerthan 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 σ abs of 6.6 m 2 /g. 1 min N/A 12% g,101 BC h : 0.05 μg/m 3 (or b abs = 0.33 Mm -1 for 10-min avg) 0.02 μg/m 3 (or b abs = 0.13 Mm -1 for 30-min avg) 101 The instrument is designed tominimize multiple scatteringand loading effects by measuring both transmittance and reflectance and using a two-stream approximation radiative transfer model to calculate b abs

. Within 18% of filter EC by IMPROVE_TOR (R 2 = 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 (soundwave) signal which is detected by a microphone to determine babs, which is converted to BC using σ ab s = 5 m 2 /g for the 1047 nm instrument and σ abs = 10 m2/g for the 532 nm instrument. 5 s N/A N/A BC i : 0.04 μg/m 3 (or b abs = 0.4 Mm -1 for 10-min avg) at 532 nm 103 At 532 nm, absorbance by NO 2 interferes with that byparticles. Accounted by either removing NO 2 from sample line using denuders or by doing a periodic background (particle-free air) subtraction. Good correlation (R 2 >0.80), but more than 40% lower than aethalometer, MAAP and filter IMPROVE_TOR EC. Suggests need for a different σ abs

. 102 N/A



### PHOTO-IONIZATION INSTRUMENTS

Photoionization monitor for polycyclic aromatic hydrocarbons (PAS-PAH) The air stream is exposed to UV radiati on, 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/m 3 j,k N/A N/A >91% 6



### December 2009 A-21

### Instrument and Measurement Principle Averaging Time Analytical Accuracy a 

### 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 c o-located measurement precision

c Co-located precision estimates based on variation in avg ratios of replicate analysis using laboratory instrument and regressi on slopes (Slopes for OC = 1.01, EC = 0.82, TC = 0.94; R 2 = 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, σ abs) is 16.6 m2/g at 880 nm, Lim et al. (2003, 037037 ) assumed a value of 12.6 m 2 /g.

f Assuming a σ abs of 10 m 2 /g.

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

h Assuming a σ abs of 6.5 m 2 /g.

i Assuming a σ abs of 10 m 2 /g at 532 nm and 5 m 2 /g at 1047 nm.
j Specified by manufacturer as “lower threshold”; needs to be calibrated with site-specific PAH. Typically used as a relative me asure in terms of electrical output in femtoamps.

k Manufacturer specified measurement parameter
N/A: Not available.

1 Chow (1995, 077012 ); 2 Watson and Chow (2001, 157123 ); 3 Watson et al. (1983, 045084 ); 4 Fehsenfeld et al. (2004, 157360 ); 5 Solomon et al. (2001, 157193 ); 6 Watson et al. (2005, 157124 ); 7 Mikel (2001,
156762 ); 8 Watson et al. (1999, 020949 ); 9 Solomon and Sioutas (2006, 156995 ); 10 Graney et al. (2004, 053756 ); 11 Tanaka et al. (1998, 157041 ); 12 Pancras et al. (2005, 098120 ); 13 John et al. (1988, 045903 );

14 Hering and Cass (1999, 084958 ); 15 Fitz et al. (1989, 077387 ); 16 Hering et al. (1988, 036012 ); 17 Solomon et al. (2003, 156994 ); 18 Cabada et al. (2004, 148859 ); 19 Fine et al. (2003, 155775 ); 20 Hogrefe et al. (2004, 099003 ); 21 Drewnick et al. (2003, 099160 ); 22 Watson et al. (2005, 157125 ); 23 Ho et al. (2006, 156552 ); 24 Decesari et al. (2005, 144536 ); 25 Mayol-Bracero et al. (2002, 045010 ); 26 Yang et al. (2003,
156167 ); 27 Turšic et al. (2006, 157063 ); 28 Mader et al.(2004, 156724 ); 29 Xiao and Liu (2004, 056801 ); 30 Kiss et al. (2002, 156646 ); 31 Cornell and Jickells (1999, 156367 ); 32 Zheng et al. (2002, 026100 );

33 Fraser et al. (2002, 140741 ); 34 Fraser et al. (2003, 042231 ) 35 Schauer er al. (2000, 012225 ); 36 Fine et al. (2004, 141283 ); 37 Yue et al. (2004, 157169 ); 38 Rinehart et al. (2006, 115184 ); 39 Wan and Yu (2006,
157104 ); 40 Poore (2000, 012839 ); 41 Fraser et al. (2003, 040266 ); 42 Engling et al. (2006, 156422 ); 43 Yu et al. (2005, 157167 ); 44 Tran et al. (2000, 013025 ); 45 Yao et al. (2004, 102213 ); 46 Li and Yu (2005,
156692 ); 47 Henning et al. (2003, 156539 ); 48 Zhang and Anastasio (2003, 157182 ); 49 Emmenegger et al. (2007, 156418 ); 50 Watson et al. (1989, 046318 ); 51 Greaves et al. (1985, 156494 ); 52 Waterman et al. (2000, 157116 ); 53 Waterman et al. (2001, 157117 ); 54 Falkovich and Rudich (2001, 156427 ); 55 Chow et al. (2007, 157209 ); 56 Miguel et al. (2004, 123260 ); 57 Crimmins and Baker (2006, 097008 ); 58 Ho and Yu (2004, 156551 ); 59 Jeon et al. (2001, 016636 ); 60 Mazzoleni et al. (2007, 098038 ); 61 Poore (2002, 051444 ); 62 Butler et al. (2003, 156313 ); 63 Chow et al. (2006, 146622 ); 64 Russell et al. (2004, 082453 ); 65 Grover et al. (2006, 138080 ); 66 Grover et al. (2005, 090044 ); 67 Schwab et al. (2006, 098449 ); 68 Hauck et al. (2004, 156525 ); 69 Jaques et al. (2004, 155878 ); 70 Rupprecht and Patashnick (2003, 157207 ); 71 Pang et al (2002, 030353 ); 72 Eatough et al. (2001, 010303 ); 73 Lee et al. (2005, 128139 ); 74 Lee et al. (2005, 156680 ); 75 Babich et al. (2000, 156239 ); 76 Lee et al. (2005, 155925 ); 77 Lee et al. (2005, 128139 ); 78 Anderson and Ogren (1998, 156213 ); 79 Chung et al. (2001, 156357 ); 80 Kidwell and Ondov (2004, 155898 ); 81 Lithgow et al. (2004, 126616 ); 82 Weber et al. (2003, 157129 ); 83 Harrison et al. (2004, 136787 ); 84 Rattigan et al. (2006, 115897 ); 85 Wittig et al. (2004, 103413 ); 86 Vaughn et al. (2005, 157089 ); 87 Chow et al. (2005, 099030 ); 88 Weber et al (2001, 024640 ) ; 89 Schwab et al. (2006, 098785 ); 90 Lim et al. (2003, 037037 );

91 Watson and Chow (2002, 037873 ); 92 Venkatachari et al. (2006, 105918 ); 93 Bae et al. (2004, 156243 ); 94 Arhami et al. (2006, 156224 ); 95 Park et al. (2005, 156843 ); 96 Bae et al. (2004, 098680 ); 97 Chow et al. (2006, 156350 ); 98 Arnott et al. (2005, 156227 ); 99 Bond et al. (1999, 156281 ); 100 Virkkula et al. (2005, 157097 ); 101 Petzold et al. (2002, 156863 ); 102 Park et al. (2006, 098104 ); 103 Arnott et al. (1999, 020650 );

104 Peters et al. (2001, 016925 ); 105 Pitchford et al. (1997, 156872 ); 106 Rees et al. (2004, 097164 ); 107 Watson et al. (2000, 010354 ); 108 Lee et al. (2005, 156680 ); 109 Hering et al. (2004, 155837 ); 110 Watson et al. (1998, 198805 ); 111 Chakrabarti et al. (2004, 157426 ); 112 Mathai et al. (1990, 156741 ); 113 Kidwell and Ondov (2001, 017092 ); 114 Stanier et al. (2004, 095955 ); 115 Khlystov et al. (2005, 156635 ); 116 Takahama et al. (2004, 157038 ); 117 Chow et al. (2005, 156348 ); 118 Zhang et al. (2002, 157181 ); 119 Subramanian et al. (2004, 081203 ); 120 Chow et al. (2006, 155207 ); 121 Birch and Cary (1996, 026004 ); 122 Birch (1998,
024953 ); 123 Birch and Cary (1996, 002352 ); 124 NIOSH (1996, 156810 ); 125 NIOSH (1999, 156811 ); 126 Chow et al. (1993, 077459 ); 127 Chow et al. (2007, 156354 ); 128 Ellis and Novakov (1982, 156416 );

129 Peterson and Richards (2002, 156861 ); 130 Schauer et al. (2003, 037014 ); 13 1Middlebrook et al. (2003, 042932 ); 132 Wenzel et al. (2003, 157139 ); 133 Jimenez et al. (2003, 156611 ); 134 Phares et al. (2003,
156866 ); 135 Qin and Prather (2006, 156895 ); 136 Zhang et al. (2005, 157185 ); 137 Bein et al. (2005, 156265 ); 138 Drewnick et al. (2004, 155754 ); 139 Drewnick et al. (2004, 155755 ); 140 Lake et al. (2003, 156669 );

141 Lake 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 / Conf iguration 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 (R TP), 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 Type a Denuder b

RAAS2.5-100 PM 2.5 FRM 16.7 Teflon (N/A) None

RAAS2.5-300 PM 2.5 FRM 16.7 Teflon (N/A) None

RAAS2.5-200 PM 2.5 FRM 16.7 Teflon (N/A) None

R&P Partisol 2000 PM 2.5 FRM 16.7 Teflon (N/A) None

R&P Partisol-plus 2025 PM 2.5 FRM 16.7 Teflon (N/A) None

BGI PQ200 PM 2.5 FRM 16.7 Teflon (N/A) None

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

IMPROVE PM 2.5 22.8 Teflon (N/A) None

Harvard PM 2.5 Impactor 10 Teflon (N/A) None

Airmetrics battery powered PM 2.5 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-loc ated 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 < 3 σ ) were observed for other samplers in reference to the RAAS.
At Denver-Welby, 6 RAAS samplers were deployed (3 with and 3 without temperature com pensation for flow control). The units withtemperature 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 t he 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 PQ 200 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, repres entative of a mixed commercial-residential neighborhood.

Sampler Flow Rate (L/Min) Filter Type a Denuder b

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) Na 2 CO 3
R&P-2300 10 Teflon (P) None

R&P-2025 Dichot:

PM 2.5 15 Teflon (P) None

PM 10 - 2.5 1.67 Polycarbonate None Na 2 CO 3 /Citric

URG-PCM 16.7 Teflon (P) Acid

ARA-PCM 16.7 Teflon (N/A) Na 2 CO 3 /Citric acid

PC-BOSS (operated by TVA) 105 Teflon (W) CIF Solomon et al. (2003, 156994 ) 17

PM 2.5 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 werewithin ± 10% of filter RR. Avg mass measured by MASS-400, R&P2300 and R&P-2025 dichot were gr eater than filter RR butwithin ± 20%. Avg mass measured by PC-BOSS (BYU) and ARAPCM were lower than filter RR within ± 10%.
All samplers except PC-BOSS (TVA) had R 2 >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 Na 2 CO 3 denuder may have contaminated the filter in the MASS-400 sampler resulting in higher PM 2.5 values.
PC-BOSS samplers removed particles < 0.1 μm aerodynamic diameter from PM 2.5 measurements. Corrections were made using sulfate (SO 4 2– ) concentrations in the major flow or immediately after the PM 2.5 inlet, but before the flow split-up. This was insufficient to bring PC-BOSS mass close to fi lter RR. PC-BOSS was also equipped with upstream denuders ahead of the filters, which may have enhanced loss of semi-volatile co mponents, resulting in a lower mass on the filter.



### December 2009 A-23

### Site / Period / Sampler / Conf iguration Summary of Findings

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

PM 2.5 Continuous Sampler Flow Rate (L/Min) Inlet Temperature Dryer Other

TEOM 16.7 30 °C Nafion PM 2.5 Butler et al. (2003, 156313 ) 62

The sum of individual species a ccounted for ~78% of the RAAS-100 FRM PM 2.5 mass concentration.
TEOM explained ~82 to 92% of the species sum of RAAS with R 2 = 0.86.

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

PM 2.5 Sampler Flow Rate (L/Min) Filter Type a Denuder b

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

PM 2.5 Continuous Sampler Flow Rate (L/Min) Inlet Temperature Dryer Other

TEOM 16.7 30 °C Nafion PM 2.5
SES-TEOM 16.7 30 °C Nafion PM 2.5
CAMM 0.3 N/A Nafion PM 2.5

RAMS 16.7 30 °C Nafion PM 2.5 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 PM 2.5 adjusted using particle conc entrator efficiency of 0.5.

Good correlation between SES-TEOM and Radiance Research M903s (R 2 = 0.80), while medium correlation was found between CAMM and Radiance Research M903 (R 2 = 0.64) or RAMS and Radiance Research M903 (R 2 = 0.63).
CAMM = (0.75 ± 0.03) SES-TEOM + (2.51 ± 0.51); R 2 = 0.78; N = 196
RAMS = (0.85 ± 0.06) SES-TEOM + (5.34 ± 1.04); R 2 = 0.52; N = 96
RAMS = (0.91 ± 0.07) CAMM + (5.71 ± 1.20); R 2 = 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); R 2 = 0.76; N = 31
RAMS = (0.97 ± 0.22) R&P-2025 FRM + (2.39 ± 3.42); R 2 = 0.64; N = 13
SES-TEOM = (1.07 ± 0.05) R&P-2025 FRM + (-1.34 ± 0.71); R 2 = 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 Type a Denuder

MOUDI-110 30 Teflon (P,d) None

And-241 Dichot 16.7 Teflon (P None

R&P-2000 PM 2.5 FRM 16.7 Teflon (W) None

PM 2.5 Continuous Sampler Flow Rate (L/Min) Inlet Temperature Dryer Other

SES-TEOM 16.7 30 °C Nafion PM 2.5

DAASS N/A 30 °C Nafion or None PM 2.5 Cabada et al. (2004, 148859 ) 18 : Rees et al. (2004, 097164 ) 106

MOUDI PM 10 = 0.80 Dichot PM 10 , R 2 = 0.85

MOUDI PM 2.5 = 1.03 Dichot PM 2.5 , R 2 = 0.78

MOUDI PM 2.5 = 1.01 FRM PM 2.5 , R 2 = 0.78

Dichot PM 2.5 = 0.97 FRM PM 2.5 + 0.02; R 2 = 0.94
Good agreement for PM 2.5 FRM, Dichot, and MOUDI. Lower slope for PM 10 suggests loss of coarse particles in the MOUDI sampler.
Ultrafine (< 100 nm) mass (PM 0

. 10 ) measurements had high uncertainties (~30%)
Ultrafine mass by MOUDI showed no correlation with ultrafine volume (V0.10) by DAASS. Ratio of PM 0.10 /PM 2.5 mass ratio showed reasonable agreement with volume ratio (V0.10/V2.5, R 2 = 0.55, slope = 0.76). Bounce of large particles to smaller stages in MOUDI was small, since mass ratio (PM 0.10 /PM 2.5 ) did not exceed volume ratio (V0.10/V2.5). Low correlation between ultrafine mass and volume could be due to the ultraf ine mass measurement uncertainty or due to fundamental differences in the measurement methods employed by MOUDI and DAASS. Ambient conditions and characteristics of the aerosols (suc h as non-spherical shapes of fresh particles) could also in fluence these estimates.
Rees et al. (2004, 097164 ) 106

SES-TEOM PM 2.5 = 1.02 FRM PM 2.5 + 0.65; R 2 = 0.95
Volatilization did not affect SES-TEOM performance when PM 2.5 mass >20-30 μg/m 3 . 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 / Conf iguration 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 Type a Denuder

RAAS-100 PM 2.5 FRM 16.7 Teflon (P) None

RAAS-300 PM 2.5 FRM 16.7 Teflon (P) None

R&P-2000 PM 2.5 FRM 16.7 Teflon (P) None

R&P-2025 PM 2.5 FRM 16.7 Teflon (P) None

RAAS-400 PM 2.5 24 Teflon (P) None

SASS PM 2.5 6.7 Teflon (P) None

And-246 Dichot

PM 2.5 15 Teflon (P) None

PM 10 - 2.5 1.67 Teflon (P) None

DRI-SFS PM 2.5 113 Teflon (P) None

MiniVol PM 2.5 5 Teflon (P) None

MOUDI-100 30 FEPb Teflon (P) None

And-hIVOL PM 10 FRM 1130 Teflon (P) None Chow et al. (2006, 146622 ) 63

PM 2.5 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 (R 2 >0.90). e

DRI-SFS (with HNO 3 denuder) and And-246 Dichot PM 2.5 were lower (~5% and 7%, respectively , on avg) than FRM, possibly due to nitrate (NO 3 – volatilization.
Poor correlation (R 2 ) found between TEOM PM 2.5 concentrations and RAAS-100 FRM. TEOM PM 2.5 was lower than RAAS-100 FRM by 22%. Heating of TEOM inle t to 50 °C resulted in loss of semi-volatile components such as ammonium nitrate (NH 4 NO 3 ) and possibly some semi-volatile organic compounds.

TEOM PM 10 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 PM 2.5 or PM 10

. BAM PM 2.5 concentrations showed high correlation (R 2 >0.90) with the RAAS-100 and RAAS-300 FRM samplers, with slopes ranging from 0.92 to 0.97. BAM PM 2.5 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 PM 10 concentrations were 26% higher than And-hIVOL PM 10 FRM concentration on avg (R 2 >0.92).
Higher BAM measurements were attr ibuted to water absorption by hygroscopic particles. BAM PM 2.5 and PM 10 deviations were larger for concentrations < 25 μg/m 3

. Grover et al. (2006, 138080 ) 65

PC-BOSS PM 2.5 = (0.88 ± 0.04) FDMS-TEOM + (6.7 ± 4.3); R 2 = 95; n = 29

PC-BOSS PM 2.5 = (1.11 ± 0.07) D-TEOM + (7.5 ± 6.1); R 2 = 0.90; n = 29

TEOM50C PM 2.5 = (0.80 ± 0.01) TEOm 3 0C + (1.1 ± 3.1); R 2 = 0.91; n = 507

TEOm 3 0C PM 2.5 = (0.50 ± 0.01) FDMS-TEOM -(1.7 ± 6.9); R 2 = 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 bet ween FDMS-TEOM, D-TEOM, BAM, and GRIMM PM 2.5 when semi-volatile matter was dominated by NH 4 NO 3 . However, the FDMS-TEOM was higher than the other instruments during high concentration periods, associated with days with a high fraction of semi-vol atile 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 PM 2.5 and PM 10

BAM 16.7 Ambient None PM 2.5 and PM 10 Sampler Flow Rate (L/Min) Filter Type a Denuder b

PC-BOSS PM 2.5 150 Teflon (W) CIF



### December 2009 A-25

### Site / Period / Sampler / Conf iguration Summary of Findings

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

TEOM 16.7 50 °C None PM 2.5
TEOM 16.7 30 °C None PM 2.5
FDMSTEOM 16.7 30 °C Nafion PM 2.5
D-TEOM 16.7 30 °C Nafion PM 2.5
GRIMM1100 1.2 Ambient None bscat

GRIMM1100 1.2 80 °C heater, resulting in aerosol temperature Heater bscat

BAM 16.7 Ambient None PM 2.5 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.

PM 2.5 Sampler Flow Rate (L/Min) Filter Type a Denuder

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

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

TEOM 16.7 50 °C None PM 2.5

SES-TEOM 16.7 30 °C Nafion PM 2.5 Aug-Sep ‘00

CAMM 0.3 Ambient Nafion PM 2.5 Aug-Sep ‘00

RAMS 16.7 30 °C Nafion PM 2.5 TEA & CIFdenuders; 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 PM 2.5 and R&P-2025 FRM mass.
CAMM = (0.93 ± 0.03) RAMS + (3.14 ± 0.74); R 2 = 0.81
SES-TEOM = (0.92 ± 0.03) RAMS + (1.52 ± 0.77); R 2 = 0.80
SES-TEOM = (1.01 ± 0.03) CAMM + (-1.91 ± 0.79); R 2 = 0.83

Correlation of Radiance Research M903 and SESTEOM was good (R 2 = 0.95), while that of Radiance Research M903 with CAMM or RAMS was poor (R 2 ~ 0.4).
RAMS >SES-TEOM at high temperature and low RH (< 60%), suggesting loss of water and particulate NO 3 – from SES-TEOM.
CAMM = (1.02 ± 0.08) R&P-2025 + (1.62 ± 1.35); R 2 = 0.89
RAMS = (1.10 ± 0.08) R&P-2025 + (0.68 ± 1.28); R 2 = 0.89
SES-TEOM = (1.09 ± 0.07) R&P-2025 + (0.21 ± 1.27); R 2 = 0.94
Integrated mass < Continuous PM 2.5 mass. Difference possibly related to loss of SVOCs and NO 3 – from integrated sampler

LOS ANGELES SUPERSITE, CA; 9/01 to 8/02 The Los Angeles Supersite consisted of multiple sampling locati ons 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 Type a Denuder b

R&P-2025 Dichot

PM 2.5 15 Teflon (P) None

PM 10 - 2.5 16.7 N/A None

MOUDI-110 30 Teflon (P None

HEADS PM 2.5 10 Teflon (N/A) NaHCO 3

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

D-TEOM 16.7 30 °C Nafion PM 2.5
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 PM 2.5 = 0.83 MOUDI + 1.23; R 2 = 0.83 (n = 37)

Dichot PM 2.5 showed higher NO 3 – loss than MOUDI, consistent with anodized al uminum surfaces serving as efficient denuders that remove volatilized NO 3 – 2,110.

D-TEOM PM 2.5 = 1.18 MOUDI – 1.28; R 2 = 0.86 (n = 20)

Over-estimation of D-TEOM may be due to particle losses in the MOUDI.

PM 2.5 by D-TEOM during ESP-off phase (net artifact effect) tracked well with the NO 3 – concentrations.

NO 3 – vaporization from th e 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 / Conf iguration 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 measuredduring 5-min periods (ESP-on and off cycle in DTEOM) provides an esti mate of the dynamic vaporization losses.
Chakrabarti et al. (2004, 157426 ) 111

Good agreement between MOUDI PM 0.15 and NanoBAM PM 0.15 (MOUDI PM0.15 = 0.97 Nano-BAM PM 0.15 + 0.60; R 2 = 0.92; n = 24)
Nano-BAM captured peak PM 0.15 concentrations not quantified by SMPS. Po tential particle agglomeration (with result ing 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 Coas t Air Basin (SoCAB) in the northwest corner of Riverside County, 78 km downwind of the central Los Angeles metropolitan ar ea and in the middle of the remaining agricultural production area in SoCAB.
Sampler Flow Rate (L/Min) Filter Type a Denuder b

PC-BOSS PM 2.5 150 Teflon (W) CIF

R&P-2025 PM 2.5 FRM 16.7 Teflon (N/A) None

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

TEOM 16.7 50 °C None PM 2.5
FDMS-TEOM 16.7 30 °C Nafion PM 2.5
D-TEOM 16.7 30 °C Nafion PM 2.5

RAMS 16.7 30 °C Nafion PM 2.5 Denuders used

CAMM 0.3 N/A None PM 2.5
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); R 2 =0.85; n = 426; excludes 38 data points when FDMS-TEOM PM 2.5 was higher than DTEOM PM 2.5 by ~21 μg/m 3

. RAMS = (0.93 ± 0.02) FDMS-TEOM + (2.4 ± 8.2); R 2 = 0.81; n = 337
FDMS-TEOM = (0.96 ± 0. 06) PC-BOSSc onstructed mass + (-0.3 ± 3.9); R 2 = 0.90; n = 33
R&P-2025 FRM = (0.96 ± 0.06) FDMS-TEOM + (-9.3 ± 3.9); R 2 = 0.90; n = 29

The R&P-2025 FRM PM 2.5 was, on avg, ~32% lower than FDMSTEOM. Losses of NH 4 NO 3 and organics can account for the difference.
TEOM @ 50 °C PM 2.5 was consistently lower than FDMS-TEOM, DTEOM or RAMS and was, on avg, ~ 50% lower than FDMS-TEO M. This difference is due to loss of semi-volatile NO 3- 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 PM 2.5 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); R 2 = 0.84; n = 299
D-TEOM = (3.79 ± 0.10) Radiance Research M903dryed + (4.08 ± 0.84); R 2 = 0.83; n = 312
Radiance Research M903no-dryer = (1.03 ± 0.01) Radiance Research M903dryed + (0.34 ± 0.05); R 2 = 0.98; n = 513; absorbed water did not affect relationship to PM 2.5. CAMM and RAMS compared poorly (R 2 = 0 to 0.25) with D-TEOM, Radiance Research M903s and among themselves.

RAMS correlated well with D-TEOM for PM 2.5 >30 μg/m 3 due to RAMS’s efficient particle collection of larger particle sizes (historically associated with high mass loadings at this site) in the PM 2.5 size range.

D-TEOM PM 2.5 correlated well with ADI-N sized NO 3 (R 2 = 0.62) and OC by Sunset OCEC (R 2 = 0.61) suggesting that D-TEOM measured PM 2.5 mass with minimum loss of SVOCs. RAMS showed R 2 of 0.20 (NO 3 – ) to 0.30 (OC), while CAMM showed no correlation.



### December 2009 A-27

### Site / Period / Sampler / Conf iguration Summary of Findings

LINDON, UT; 01/29/03 to 02/12/03
Sampler Flow Rate (L/Min) Filter Type a Denuder b

PC-BOSS PM 2.5 150 Teflon (W) CIF

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

TEOM 16.7 30 °C None PM 2.5
FDMS-TEOM 16.7 30 °C Nafion PM 2.5

RAMS 16.7 30 °C Nafion PM 2.5 Denuder used Grover et al. (2005, 090044 ) 66

RAMS required regul ar maintenance.
RAMS = (0.92 ± 0.03) FDMS-TEOM + (1.3 ± 3.9); R 2 = 0.69; n = 332
PC-BOSS constructed mass = (0.89 ± 0.21) FDMSTEOM + (1.8 ± 2.8); R 2 = 0.66; n = 11
TEOM @ 30 °C PM 2.5 was consistently lower than FDMS-TEOM and the differ ence was consistent with concentrations SVOCs and NH 4 NO 3 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 rive r on the other. Within 0.5 km of Interstate I95 and within 30 km from downtown Philadelphia.
Sampler Flow Rate (L/Min) Filter Type a Denuder b

Harvard Impactor PM 2.5 10 Teflon (N/A) N/A
Continuous Sampler Flow Rate (L/Min) Inlet Temperature Dryer Other

SES-TEOM 16.7 35 °C Nafion PM 2.5
CAMM 0.3 N/A Nafion PM 2.5

RAMS 16.7 30 °C Nafion PM 2.5 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); R 2 = 0.95

Radiance Research M903s vs. CAMM, R 2 = 0.78

Radiance Research M903s vs. RAMS, R 2 = 0.63

Radiance Research M903s vs. SES-TEOM, R 2 = 0.72
CAMM = (0.60 ± 0.03) SES-TEOM + (2.0 ± 0.42); R 2 = 0.71; N = 185
RAMS = (0.71 ± 0.04) SES-TEOM + (2.51 ± 0.59); R 2 = 0.63; N = 185
RAMS = (0.93 ± 0.06) CAMM + (2.44 ± 0.68); R 2 = 0.55; N = 185

Both RAMS and CAMM under-measured ambient PM 2.5. CAMM = (0.70 ± 0.06) HI + (0.16 ± 0.96); R 2 = 0.87; N = 22
SES-TEOM = (1.0 ± 0.10) HI + (-0.68 ± 1.74); R 2 = 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 PM 2.5 FRM 16.7 Teflon None
Continuous Sampler Flow Rate (L/Min) Inlet Temperature Dryer Other

SES-TEOM 16.7 35 °C Nafion PM 2.5
CAMM 0.3 N/A Nafion PM 2.5

RAMS 16.7 30 °C Nafion PM 2.5 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); R 2 = 0.75, suggesting infl uence from particle-bound water.
High correlation (R 2 = 0.75) between Radiance Research M903s.
Poor correlation among the continuous instruments.

Radiance Research M903s did not follow PM 2.5 concentrations measured by other continuous instruments.
CAMM = (0.32 ± 0.07) SES-TEOM + (9.45 ± 1.61); R 2 = 0.14; N = 120 RAMS = (0.82 ± 0.10) SES-TEOM + (6.41 ± 2.09); R 2 = 0.38; N = 120
RAMS = (0.71 ± 0.12) CAMM + (11.3 ± 2.23); R 2 = 0.21; N = 120
CAMM = (0.80 ± 0.29) RAAS-100 FRM + (-0.83 ± 5.85); R 2 = 0.60; N = 7
RAMS = (1.05 ± 0.12) RAAS-100 FRM + (4.80 ± 2.60); R 2 = 0.90; N = 11
SES-TEOM = (0.86 ± 0.10) RAAS-100 FRM + (2.96 ± 1.99); R 2 = 0.90; N = 10



### December 2009 A-28

### Site / Period / Sampler / Conf iguration Summary of Findings

SEATTLE, WA; 01/28/01 to 02/21/01 Urban area near major highway and intersta te, 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; R 2 = 1.0. FILTER TYPE a DENUDER b

MASS PM 2.5 16.7 Teflon (N/A) Na 2 CO 3 Continuous Sampler Correlation of Radiance Research M903 vs. SESTEOM, R 2 = 0.80, while that of Radiance Research M903 with CAMM was R 2 = 0.84 and with RAMS was R 2 = 0.72.
CAMM = (1.07 ± 0.05) RAMS + (1.03 ± 0.55); R 2 = 0.61
SES-TEOM = (0.95 ± 0.03) RAMS + (1.24 ± 0.38); R 2 = 0.72
SES-TEOM = (0.87 ± 0.03) CAMM + (0.55 ± 0.37); R 2 = 0.74
SES-TEOM likely lost semi-v olatile organic matter.

Continuous PM 2.5 samplers were similar to filter PM 2.5 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 PM 2.5
CAMM 0.3 Ambient Nafion PM 2.5

RAMS 16.7 30 °C Nafion PM 2.5 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 Type a Denuder b

R&P-2025 PM 2.5 FRM 16.7 Teflon (N/A) None

R&P-2300 PM 2.5 16.7 Teflon (N/A) None
Continuous Sampler Flow Rate (L/Min) Inlet Temperature Dryer Other

TEOM 16.7 50 °C None PM 2.5
FDMS-TEOM 16.7 30 °C Nafion PM 2.5

BAM 16.7 “smart” heater on @ RH >44% PM 2.5 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; R 2 = 0.93; n = 244
FDMS-TEOM = (1.25 ± 0.02) FRM – (0.63 ± 0.26); R 2 = 0.95; n = 238
BAM = (1.28 ± 0.03) FRM + (1.27 ± 0.38); R 2 = 0.88; n = 320

Rural site:
FDMS-TEOM = (1.09 ± 0.02) FRM – (0.004 ± 0.18); R 2 = 0.95; n = 349

PM 2.5 FDMS-TEOM >FRM >TEOM50°C, suggesting that FRM captured a fracti on, but not al l, of the volatile components. TEOM50°C volatilizes PM 2.5 , particularly during winter.



### December 2009 A-29

### Site / Period / Sampler / Conf iguration 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 Na 2 CO 3 : Sodium carbonate; NaHCO 3 : Sodium bicarbonate CIF: Charcoal Impregnated Filter; FEP: Fluori nated 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/m 3 ; 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 ); 2 Watson and Chow (2001, 157123 ); 3 Watson et al. (1983, 045084 ); 4 Fehsenfeld et al. (2004, 157360 ); 5 Solomon et al. (2001, 157193 ); 6 Watson et al. (2005, 157124 ); 7 Mikel (2001,
156762 ); 8 Watson et al. (1999, 020949 ); 9 Solomon and Sioutas (2006, 156995 ); 10 Graney et al. (2004, 053756 ); 11 Tanaka et al. (1998, 157041 ); 12 Pancras et al. (2005, 098120 ); 13 John et al. (1988, 045903 );

14 Hering and Cass (1999, 084958 ); 15 Fitz et al. (1989, 077387 ); 16 Hering et al. (1988, 036012 ); 17 Solomon et al. (2003, 156994 ); 18 Cabada et al. (2004, 148859 ); 19 Fine et al. (2003, 155775 ); 20 Hogrefe et al. (2004, 099003 ); 21 Drewnick et al. (2003, 099160 ); 22 Watson et al. (2005, 157125 ); 23 Ho et al. (2006, 156552 ); 24 Decesari et al. (2005, 144536 ); 25 Mayol-Bracero et al. (2002, 045010 ); 26 Yang et al. (2003,
156167 ); 27 Turšic et al. (2006, 157063 ); 28 Mader et al.(2004, 156724 ); 29 Xiao and Liu (2004, 056801 ); 30 Kiss et al. (2002, 156646 ); 31 Cornell and Jickells (1999, 156367 ); 32 Zheng et al. (2002, 026100 );

33 Fraser et al. (2002, 140741 ); 34 Fraser et al. (2003, 042231 ) 35 Schauer er al. (2000, 012225 ); 36 Fine et al. (2004, 141283 ); 37 Yue et al. (2004, 157169 ); 38 Rinehart et al. (2006, 115184 ); 39 Wan and Yu (2006,
157104 ); 40 Poore (2000, 012839 ); 41 Fraser et al. (2003, 040266 ); 42 Engling et al. (2006, 156422 ); 43 Yu et al. (2005, 157167 ); 44 Tran et al. (2000, 013025 ); 45 Yao et al. (2004, 102213 ); 46 Li and Yu (2005,
156692 ); 47 Henning et al. (2003, 156539 ); 48 Zhang and Anastasio (2003, 157182 ); 49 Emmenegger et al. (2007, 156418 ); 50 Watson et al. (1989, 046318 ); 51 Greaves et al. (1985, 156494 ); 52 Waterman et al. (2000, 157116 ); 53 Waterman et al. (2001, 157117 ); 54 Falkovich and Rudich (2001, 156427 ); 55 Chow et al. (2007, 157209 ); 56 Miguel et al. (2004, 123260 ); 57 Crimmins and Baker (2006, 097008 ); 58 Ho and Yu (2004, 156551 ); 59 Jeon et al. (2001, 016636 ); 60 Mazzoleni et al. (2007, 098038 ); 61 Poore (2002, 051444 ); 62 Butler et al. (2003, 156313 ); 63 Chow et al. (2006, 146622 ); 64 Russell et al. (2004, 082453 ); 65 Grover et al. (2006, 138080 ); 66 Grover et al. (2005, 090044 ); 67 Schwab et al. (2006, 098449 ); 68 Hauck et al. (2004, 156525 ); 69 Jaques et al. (2004, 155878 ); 70 Rupprecht and Patashnick (2003, 157207 ); 71 Pang et al (2002, 030353 ); 72 Eatough et al. (2001, 010303 ); 73 Lee et al. (2005, 128139 ); 74 Lee et al. (2005, 156680 ); 75 Babich et al. (2000, 156239 ); 76 Lee et al. (2005, 155925 ); 77 Lee et al. (2005, 128139 ); 78 Anderson and Ogren (1998, 156213 ); 79 Chung et al. (2001, 156357 ); 80 Kidwell and Ondov (2004, 155898 ); 81 Lithgow et al. (2004, 126616 ); 82 Weber et al. (2003, 157129 ); 83 Harrison et al. (2004, 136787 ); 84 Rattigan et al. (2006, 115897 ); 85 Wittig et al. (2004, 103413 ); 86 Vaughn et al. (2005, 157089 ); 87 Chow et al. (2005, 099030 ); 88 Weber et al (2001, 024640 ) ; 89 Schwab et al. (2006, 098785 ); 90 Lim et al. (2003, 037037 );

91 Watson and Chow (2002, 037873 ); 92 Venkatachari et al. (2006, 105918 ); 93 Bae et al. (2004, 156243 ); 94 Arhami et al. (2006, 156224 ); 95 Park et al. (2005, 156843 ); 96 Bae et al. (2004, 098680 ); 97 Chow et al. (2006, 156350 ); 98 Arnott et al. (2005, 156227 ); 99 Bond et al. (1999, 156281 ); 100 Virkkula et al. (2005, 157097 ); 101 Petzold et al. (2002, 156863 ); 102 Park et al. (2006, 098104 ); 103 Arnott et al. (1999, 020650 );

104 Peters et al. (2001, 016925 ); 105 Pitchford et al. (1997, 156872 ); 106 Rees et al. (2004, 097164 ); 107 Watson et al. (2000, 010354 ); 108 Lee et al. (2005, 156680 ); 109 Hering et al. (2004, 155837 ); 110 Watson et al. (1998, 198805 ); 111 Chakrabarti et al. (2004, 157426 ); 112 Mathai et al. (1990, 156741 ); 113 Kidwell and Ondov (2001, 017092 ); 114 Stanier et al. (2004, 095955 ); 115 Khlystov et al. (2005, 156635 ); 116 Takahama et al. (2004, 157038 ); 117 Chow et al. (2005, 156348 ); 118 Zhang et al. (2002, 157181 ); 119 Subramanian et al. (2004, 081203 ); 120 Chow et al. (2006, 155207 ); 121 Birch and Cary (1996, 026004 ); 122 Birch (1998,
024953 ); 123 Birch and Cary (1996, 002352 ); 124 NIOSH (1996, 156810 ); 125 NIOSH (1999, 156811 ); 126 Chow et al. (1993, 077459 ); 127 Chow et al. (2007, 156354 ); 128 Ellis and Novakov (1982, 156416 );

129 Peterson and Richards (2002, 156861 ); 130 Schauer et al. (2003, 037014 ); 13 1Middlebrook et al. (2003, 042932 ); 132 Wenzel et al. (2003, 157139 ); 133 Jimenez et al. (2003, 156611 ); 134 Phares et al. (2003,
156866 ); 135 Qin and Prather (2006, 156895 ); 136 Zhang et al. (2005, 157185 ); 137 Bein et al. (2005, 156265 ); 138 Drewnick et al. (2004, 155754 ); 139 Drewnick et al. (2004, 155755 ); 140 Lake et al. (2003, 156669 );

141 Lake 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 vehi cles, coal-fired power plants and incinerators ~21 km southw est of site and regionally transported material.
Concentrated Slurry/Graphite Furnace Atomic AbsorptionSpectrometry (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 conc entrated 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 virt ual impactor major flow channel and in the condensers.
Six elements were detected simultaneously, lim ited by spectral interference and the minimum detectable limit (MDL). Twelve elemen ts (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 di ssolved 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 analytic al 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 conc entrated using a PM 2.5 inlet and a virtual impactor. The concentrated st ream 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. Ma ss 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 (u sing 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/m 3 to 30 μg/m 3 , constituting < 5% to 100% of the dry aerosol mass.
Small differences between dry and ambient c hannels of the DAASS. Number concentrations were within 5% of each other.

Additional sources of error are associated wi th temperature differ ences between measured outdoor ambient temperature and the temperat ure 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 diff erences 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 l ead to evaporation of semi-volatile components such as NH 4 NO 3 . 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 uncertain ties in estimated growth factors. Correction fact ors 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 under estimated 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 PM 2.5 

# NO 3 - 

# 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 Type a Denuder b

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 Na 2 CO 3
MASS-450 16.7 Quartz (P) None

R&P-2300 10 Nylon (P) Na 2 CO 3

VAPS 15 Polycarbonatec (front & back-up) Na 2 CO 3

URG-PCM 16.7 Teflon (P)-Cellulose-fiber (W Na 2 CO 3

ARA-PCM 16.7 Teflon (N/A)-Nylon (N/A) Na 2 CO 3 /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 Method b

ADI-N 1 Activated Carbon NO X Chemiluminescence

ARA-N 3 Potassium iodide (Kl) and dual sodium chlorite (NaClO 2 ) NO X Chemiluminescence

PILS-IC 5 Two URG annular glass denuders in series containing citric acid and CaCO 3 IC

ECN 16.7 Rotating annular wet denuder system IC

TT 5 Wet parallel plate denuder IC Solomon et al.(2003, 156994 ) 17

PM 2.5 NO 3 - 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 NO 3 - , differences in denuder design and filter types, and low concentrations (close to analytical uncertainty).
A small positive artifact (few tenths of μg/m 3 ) might be present when using Na 2
CO 3 impregnated filters, due to possible collection (and subsequent oxidation) of HONO and NO 2 on carbonate-impregnated filters. In addition, glycerol in Na 2 CO 3 coated denuders may contaminate the filters downstream.

PM 2.5 NO 3 - R&P-2000 FRM and MOUDI-100 samplers are consistently lower than other samplers.
Weber et al. (2003, 157129 ) 82

Hourly PM 2.5 NO 3 -were compared to all-sampler averages (continuous RR), similar to the approach used for integrated filt er samplers. Overall agreements were within ± 20-30% (or ± 0.2 μg/m 3 ) except for ARA-N.
Except for ARA-N, good correlations (R 2 = 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/m 3 ) near the detection limit (~0.1 μg/m 3 , except for ARA-N, which had 0.5 μg/m 3 ).

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/m 3 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; NO 3 - 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 Type a Denuder b

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/m 3 ) of NO 3 mass was lost from MOUDI samplers during summer.

MOUDI NO 3 = 0.27 CMU; R 2 = 0.40; Summer MOUDI NO 3 = 0.99 CMU; R 2 = 0.49; winter
Wittig et al. (2004, 103413 ) 85

Avg conversion efficiency to NO X (tested using NH 4 NO 3 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/m 3 ; R 2 = 0.84

Underestimation in t he R&P-8400N could be due to incomplete particle collection or incomplete conversion of various forms of NO 3 -

. 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 Type a Denuder b

DRI-SFS 113 Quartz (Pellulose Al 2 O 3
RAAS-400 24 Quartz (P)-Nylon (P) Na 2 CO 3
RAAS-400 24 Quartz (P)-Quartz (P) None

RAAS-100 FRM 16.7 Quartz (P) None
Continuous Sampler Flow Rate (L/Min) Denuder Analysis Method b

R&P-8400N 5 Activated Carbon NO X Chemiluminescence
Sampler Flow Rate (L/Min) Filter Type a Denuder b

PC-BOSS 150 Teflon (W)- Nylon (P) CIF
Continuous Sampler Flow Rate (L/Min) Denuder Analysis Method b

R&P-8400N 5 Activated Carbon NO X Chemiluminescence

Dionex-IC 5 Parallel plate wet denuder IC Chow et al. (2005, 099030 ) 87

Maximum NO 3 - volatilization was observed during summer (Jun-Aug), while the lowest volatilization was observed during winter (Dec-Feb). Seasonal avg volatilized NO 3 - in particulate NO 3 - (PNO 3 - , the sum of non-volatilized and volatilized NO 3 - ) ranged from less than 10% during winter to more than 80% during summer.

Volatilized NH 4 NO 3 accounted for 44% of actual PM 2.5 mass (i.e., measured mass plus volatilized NH 4 NO 3 ) in Fresno during summer.
Front-quartz non-volatilized NO 3 - concentrations were similar for DRISFS (0.52 ± 0.26 μg/m 3 ) and RAAS-100 FRM (0.81 ± 0.33 μg/m 3 ) for warm months (May-Sep). With preceding denuders, the DRI-SFS PNO 3 concentration (3 ± 1.9 μg/m 3 ) was much higher than the RAAS100 FRM NO 3 –

, suggesting that the FRM sampler removed gaseous nitric acid (HNO 3 ) resulting in NO 3 - volatilization. FRM Teflon-membrane filters are subject to similar NO 3 – losses.
Chow et al. (2005, 156348 ) 117



### December 2009 A-33

### SITE / PERIOD / SAMPLER / CONFIGURATION SUMMARY OF FINDINGS

High correlation (R 2 >0.90) between 24-h avg R&P-8400N NO 3 and SFS filter NO 3 – concentrations, but R&P-8400N NO 3 - was 7 to 25% lower than filter NO 3-. 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 NO 3 – by the R&P-8400N instrument.
The R&P-8400N required substantial maintenance and careful operation.
Grover et al. (2006, 138080 ) 65

Dionex-IC NO 3 = (0.71 ± 0.04) PC-BOSS NO 3 + (3.2 ± 1.1); R 2 = 0.91; n = 29
R&P-8400N = (1.10 ± 0.06) PC-BOSS NO 3 - (0.8 ± 1.8); R 2 = 0.93; n = 29
R&P-8400N = (0.55 ± 0.01) Dionex-IC + (1.4 ± 1.8); R 2 = 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 Ma ryland 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 Type a Denuder b

SASS 6.7 Nylon (N/A) MgO
Continuous Sampler Flow Rate (L/Min) Denuder Analysis Method b

R&P-8400N 5 Activated Carbon NO X 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 NO 3 – by ~33%, attributed to va riations 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 NO X (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 t han flat strips.

Reliable measurements were obtained when the instrument-outdoor temper ature 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 si te 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 Type a Denuder b

R&P-2300 10 Nylon (N/A) Na 2 CO 3 Continuous Sampler Flow Rate (L/Min) Denuder Analysis Method b

R&P-8400N 5 Activated Carbon NO X Chemiluminescence

PILS-IC 5 Na 2 CO 3 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 c aused by bigger tubing. Larger aqueous flow and inc onsistent water quality affected NO 3 – concentrations.

R&P-8400N NO 3- was lower than R&P-2300 filter NO 3 – . PILS-IC was within 5% of R&P-2300 filter NO 3- concentrations.

At the urban site, AMS was within 10% of the filter NO 3 concentration. At the rural site, AMS had a slope of 0.51 and R 2 of 0.46, compared with filter NO 3 –

.

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, with in a few kilometers away from major highways and a freight yard (experiencing signifi cant 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 Type a Denuder b

R&P-2300 10 Nylon (N/A) Na 2 CO 3
TEOM-ACCU 16.7 Zefluor None
Continuous Sampler Flow Rate (L/Min) Denuder Analysis Method b

R&P-8400N 5 Activated Carbon NO X 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 NO 3 – was systematically lower than R&P-2300 filter NO 3 over all concentration ranges, except at <1 μg/m 3

. Urban: R&P-8400N = 0.59 R&P-2300 NO 3 + 0.28; R 2 = 0.88; n = 305

Rural: R&P-8400N = 0.73 R&P-2300 NO 3 + 0.01; R 2 = 0.90; n~161; however concentrations were low with 95% of data < 1 μg/m 3.

Required weekly or biw eekly 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 Type a Denuder b Fine et al. (2003, 155775 ) 19

MOUDI = 0.68 HEADS; R 2 = 0.88

ADI-N Sized = 0.80 HEADS; R 2 = 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 Method b

ADI-N Sized 0.9 Activated Carbon NO X Chemiluminescence ADI-N Sized = 1.12 MOUDI; R 2 = 0.53

ADI-N NO 3- showed better agreement with HEADS at lower concentrations, the ADI-N deviated (biased low) from the HEADS concentrations at higher NO 3 – concentrations. Th is deviation was attributed to NO 3- vaporization, loss of NO 3 – 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 NO 3- by MOUDI compared to HEADS may be due to NO 3 – volatilization from MOUDI stages, since SO 4 2– comparisons showed MOUDI to explain 85% of HEADS SO 4 2–.

ADI-N and MOUDI showed better correlation (R 2 = 0.67) for the 1-2 μm size range NO 3 relative to other size ranges (R 2 < = 0.56). This is possibly due to NO 3 – in the form of non-volatilized sodium nitrate (NaNO 3 ) than volatilized NH 4 NO 3 in the 1- 2 μm size range. Single particle analysis also indicated this possibility of NaNO 3 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 remain ing agricultural production area in SoCAB. Grover et al. (2005, 090044 ) 66

R&P-8400N = (0.65 ± 0.07) PC-BOSS + (3.3 ± 2.4); R 2 = 0.73; n = 31
At higher concentrations (no numerical value reported), R&P-8400N NO 3 -was lower than PCBOSS NO 3-, possibly due to incomplete volatilization of NH 4 NO 3 in R&P-8400N at higher concentrations (and higher relative humidity).

At the urban site, the continuous instruments correlated well with filter NO 3 –

- measurements and among themselves (R 2 ≥ 0.89). At the rural site, R 2 ranged from 0.61 to 0.83, except for the AMS versus R&P2300 comparison, with an R 2 of 0.46.
Sampler Flow Rate (L/Min) Filter Type a Denuder b

PC-BOSS 150 Teflon (W)-Nylon (P) CIF
Continuous Sampler Flow Rate (L/Min) Denuder Analysis Method b

R&P-8400N 5 Activated Carbon NO X Chemiluminescence

R&P-8400N 5 Activated Carbon NO X Chemiluminescence

PILS-IC 5 Na 2 CO 3 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 Al 2 O 3 : Aluminum oxide; GF: Na 2 CO 3 impregnated Glass Fiber Filters; IC: Ion chromatography; MgO: Magnesium oxide; Na 2 CO 3 : Sodium carbonate; NaHCO 3 : Sodium bicarbonate NO X : Oxides of nitrogen; CIF: Charcoal Impregnated Filter; FEP: Fluorinated Ethylene Propylene copolymer; TEA: Triethanolamine; TSP: Total Suspended PM.

c Na 2 CO 3 impregnated.

d 37 mm filter.

1 Chow (1995, 077012 ); 2 Watson and Chow (2001, 157123 ); 3 Watson et al. (1983, 045084 ); 4 Fehsenfeld et al. (2004, 157360 ); 5 Solomon et al. (2001, 157193 ); 6 Watson et al. (2005, 157124 ); 7 Mikel (2001,
156762 ); 8 Watson et al. (1999, 020949 ); 9 Solomon and Sioutas (2006, 156995 ); 10 Graney et al. (2004, 053756 ); 11 Tanaka et al. (1998, 157041 ); 12 Pancras et al. (2005, 098120 ); 13 John et al. (1988, 045903 );

14 Hering and Cass (1999, 084958 ); 15 Fitz et al. (1989, 077387 ); 16 Hering et al. (1988, 036012 ); 17 Solomon et al. (2003, 156994 ); 18 Cabada et al. (2004, 148859 ); 19 Fine et al. (2003, 155775 ); 20 Hogrefe et al. (2004, 099003 ); 21 Drewnick et al. (2003, 099160 ); 22 Watson et al. (2005, 157125 ); 23 Ho et al. (2006, 156552 ); 24 Decesari et al. (2005, 144536 ); 25 Mayol-Bracero et al. (2002, 045010 ); 26 Yang et al. (2003,
156167 ); 27 Turšic et al. (2006, 157063 ); 28 Mader et al.(2004, 156724 ); 29 Xiao and Liu (2004, 056801 ); 30 Kiss et al. (2002, 156646 ); 31 Cornell and Jickells (1999, 156367 ); 32 Zheng et al. (2002, 026100 );

33 Fraser et al. (2002, 140741 ); 34 Fraser et al. (2003, 042231 ) 35 Schauer er al. (2000, 012225 ); 36 Fine et al. (2004, 141283 ); 37 Yue et al. (2004, 157169 ); 38 Rinehart et al. (2006, 115184 ); 39 Wan and Yu (2006,
157104 ); 40 Poore (2000, 012839 ); 41 Fraser et al. (2003, 040266 ); 42 Engling et al. (2006, 156422 ); 43 Yu et al. (2005, 157167 ); 44 Tran et al. (2000, 013025 ); 45 Yao et al. (2004, 102213 ); 46 Li and Yu (2005,
156692 ); 47 Henning et al. (2003, 156539 ); 48 Zhang and Anastasio (2003, 157182 ); 49 Emmenegger et al. (2007, 156418 ); 50 Watson et al. (1989, 046318 ); 51 Greaves et al. (1985, 156494 ); 52 Waterman et al. (2000, 157116 ); 53 Waterman et al. (2001, 157117 ); 54 Falkovich and Rudich (2001, 156427 ); 55 Chow et al. (2007, 157209 ); 56 Miguel et al. (2004, 123260 ); 57 Crimmins and Baker (2006, 097008 ); 58 Ho and Yu (2004, 156551 ); 59 Jeon et al. (2001, 016636 ); 60 Mazzoleni et al. (2007, 098038 ); 61 Poore (2002, 051444 ); 62 Butler et al. (2003, 156313 ); 63 Chow et al. (2006, 146622 ); 64 Russell et al. (2004, 082453 ); 65 Grover et al. (2006, 138080 ); 66 Grover et al. (2005, 090044 ); 67 Schwab et al. (2006, 098449 ); 68 Hauck et al. (2004, 156525 ); 69 Jaques et al. (2004, 155878 ); 70 Rupprecht and Patashnick (2003, 157207 ); 71 Pang et al (2002, 030353 ); 72 Eatough et al. (2001, 010303 ); 73 Lee et al. (2005, 128139 ); 74 Lee et al. (2005, 156680 ); 75 Babich et al. (2000, 156239 ); 76 Lee et al. (2005, 155925 ); 77 Lee et al. (2005, 128139 ); 78 Anderson and Ogren (1998, 156213 ); 79 Chung et al. (2001, 156357 ); 80 Kidwell and Ondov (2004, 155898 ); 81 Lithgow et al. (2004, 126616 ); 82 Weber et al. (2003, 157129 ); 83 Harrison et al. (2004, 136787 ); 84 Rattigan et al. (2006, 115897 ); 85 Wittig et al. (2004, 103413 ); 86 Vaughn et al. (2005, 157089 ); 87 Chow et al. (2005, 099030 ); 88 Weber et al (2001, 024640 ) ; 89 Schwab et al. (2006, 098785 ); 90 Lim et al. (2003, 037037 );

91 Watson and Chow (2002, 037873 ); 92 Venkatachari et al. (2006, 105918 ); 93 Bae et al. (2004, 156243 ); 94 Arhami et al. (2006, 156224 ); 95 Park et al. (2005, 156843 ); 96 Bae et al. (2004, 098680 ); 97 Chow et al. (2006, 156350 ); 98 Arnott et al. (2005, 156227 ); 99 Bond et al. (1999, 156281 ); 100 Virkkula et al. (2005, 157097 ); 101 Petzold et al. (2002, 156863 ); 102 Park et al. (2006, 098104 ); 103 Arnott et al. (1999, 020650 );

104 Peters et al. (2001, 016925 ); 105 Pitchford et al. (1997, 156872 ); 106 Rees et al. (2004, 097164 ); 107 Watson et al. (2000, 010354 ); 108 Lee et al. (2005, 156680 ); 109 Hering et al. (2004, 155837 ); 110 Watson et al. (1998, 198805 ); 111 Chakrabarti et al. (2004, 157426 ); 112 Mathai et al. (1990, 156741 ); 113 Kidwell and Ondov (2001, 017092 ); 114 Stanier et al. (2004, 095955 ); 115 Khlystov et al. (2005, 156635 ); 116 Takahama et al. (2004, 157038 ); 117 Chow et al. (2005, 156348 ); 118 Zhang et al. (2002, 157181 ); 119 Subramanian et al. (2004, 081203 ); 120 Chow et al. (2006, 155207 ); 121 Birch and Cary (1996, 026004 ); 122 Birch (1998,
024953 ); 123 Birch and Cary (1996, 002352 ); 124 NIOSH (1996, 156810 ); 125 NIOSH (1999, 156811 ); 126 Chow et al. (1993, 077459 ); 127 Chow et al. (2007, 156354 ); 128 Ellis and Novakov (1982, 156416 );

129 Peterson and Richards (2002, 156861 ); 130 Schauer et al. (2003, 037014 ); 13 1Middlebrook et al. (2003, 042932 ); 132 Wenzel et al. (2003, 157139 ); 133 Jimenez et al. (2003, 156611 ); 134 Phares et al. (2003,
156866 ); 135 Qin and Prather (2006, 156895 ); 136 Zhang et al. (2005, 157185 ); 137 Bein et al. (2005, 156265 ); 138 Drewnick et al. (2004, 155754 ); 139 Drewnick et al. (2004, 155755 ); 140 Lake et al. (2003, 156669 );

141 Lake et al. (2004, 088411 )

Source: Chow et al. (2008, 156355 )



### December 2009 A-37

# Table A-14. Summary of PM 2.5 

# SO 4 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 comme rcial-residential neighborhood.
Sampler Flow Rate (L/Min) Filter Type a Denuder b

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) Na 2 CO 3 /Citric acid

ARA-PCM 16.7 Nylon (N/A) Na 2 CO 3 /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 Method b

ADI-S 2.7 Activated Carbon SO 2 , UV Fluorescence

PILS-IC 5 Two URG annular glass denuders in series containing citric acid & CaCO 3 IC

ECN 16.7 Rotating annular wet denuder system IC

TT 5 Wet parallel plate denuder IC Solomon et al. (2003, 156994 ) 17

PM 2.5 SO 4 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 (R 2 >0.90) with daily filter RR.
PC-BOSS (TVA) had in strument 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 PM 2.5 SO 4 2– were compared to allsampler averages (continuous RR), similar to the approach used for filter samplers. Overall agreement was within 16% or 2 μg/m 3.

Good correlations (R 2 = 0.76 to 0.94) were found during the second half of the study, except for TT versus ADI.
Good correlation (R 2 = 0.84) was found between continuous and filter-based SO 4 2–

Continuous RR = (1.15 ± 0.15), Filter RR + (0.41 ± 1.73)
Variability among continuous SO 4 2– instruments (RSD = 13%) was similar to that for NO 3- 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 pa rk on the top of a hill
Sampler Flow Rate (L/Min) Filter Type a Denuder b

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 SO 4 2– 0.80 CMU; R 2 = 0.95; Summer

MOUDI SO 4 2– 0.97 CMU; R 2 = 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 Method b

R&P-8400S 5 Activated Carbon SO 2 UV Fluorescence Avg conversion efficiency to SO 2 (tested using ammonium sulfate [(NH 4 ) 2 SO 4 ] 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 (SO 4 2– ) = 0.71 CMU + 0.42 μg/m 3 ; R 2 = 0.83

Underestimation is attri buted to incomplete particle collection or incomplete conversion of various forms of SO 4 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 Type a Denuder b

MOUDI 30 Teflon (P) None

HEADS 10 Teflon (N/A) GF-GF c Carbonate Fine et al. (2003, 155775 ) 19

MOUDI explained 85% of HEADS SO 4 2– (R 2 = 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 lo cated at Whiteface mountain, 60m above sea level, in a clearing surrounded by deciduous and ev ergreen trees and no major cities within 20 km of the site.

Sampler Flow Rate (L/Min) Filter Type a Denuder b

R&P-2300 10 Nylon (N/A) Na 2 CO 3
SCS 42 Zefluor (N/A) None

TEOM-ACCU 16.7 Zefluor (N/A) None

Continuous Sampler Flow Rate (L/Min) Denuder Analysis Method b

R&P-8400S 5 Activated Carbon SO 2 UV Fluorescence

PILS-IC 5 Na 2 CO 3 and Citric acid IC

AMS 0.1 None Mass Spectrometry

CASM 5 Na 2 CO 3 and Carbon and a Nafion dryer SO 2 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 (R 2 = 0.87 to 0.94) with slopes ranging from 0.97 to 1.01. At the rural site, the variability was large (R 2 = 0.73 to 0.91) with slopes ranging from 0.76 to 1.32. SO 4 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 (R 2 = 0.97 to 1.0). ACCU underestimated SO 4 2– by ~15%.

Continuous versus 6-h SCS filter comparisons showed high R 2 (0.91 to 0.95) at the urban site. Continuous instruments consistently measured lower SO 4 2– concentrations compared to the SCS filter measurements (slopes 0.68 to 0.73)
On avg, 85% of the filter-based SO 4 2– was measured by the continuous instruments with consistent relationships. At the rural site, PILS-IC overestimated SO 4 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 (experienci ng significant truck traffic). Rural si te located at Whiteface mountain, 600m above sea level, in a clearing surrounded by deci duous and evergreen trees and no major cities within 20 km of the site. The study by Schwab et al.89 was bas ed at a rural site located at Pinnacle State Park surrounded by golf course, picnic areas and undeveloped fore st lands and no major cities within 15 km.

Integrated Sampler Flow Rate (L/Min) Filter Type a Denuder b

R&P-2300 10 Nylon (N/A) Na 2 CO 3
TEOM-ACCU 16.7 Zefluor None

Continuous Sampler Flow Rate (L/Min) Denuder Analysis Method b

R&P-8400S 5 Activated Carbon SO 2 pulsed fluorescence

TE-5020
(07/14/04 to 11/01/04) 5 Na 2 CO 3 SO 2 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 ef ficiency, flow drift, and blanks.
Calibrations used aqueous standards of (NH 4 ) 2 SO 4 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; R 2 = 0.84; n = 513

R&P-8400S = 0.74 R&P-2300 + 1.14; R 2 = 0.81; n = 322

Rural Whiteface mountain:

R&P-8400S = 0.75 TEOM-ACCU + 0.22; R 2 = 0.95; n = 207

R&P-8400S = 0.78 R&P-2300 + 0.17; R 2 = 0.85; n = 198
Required weekly or biw eekly maintenance by trained personnel
Schwab et al. (2006, 098785 ) 89

TE-5020 = 0.78 ACCU – 0.2; R 2 = 0.94
Similar studies at St. Louis, MO, show slopes near unity. This suggests that the instrument is sensitive to aeros ol composition.

Low maintenance and calibrationrequirements 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 Type a Denuder b

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

Continuous Sampler Flow Rate (L/Min) Denuder Analysis Method b

R&P-8400S 5 Activated Carbon SO 2 pulsed fluorescence

Dionex-IC 5 Parallel plate wet denuder IC Grover et al. (2006, 138080 ) 65

Dionex-IC SO 4 2– (1.03 ± 0.03) PC-BOSS SO 4 + (0.2 ± 0.3); R 2 = 0.98; n = 27

R&P-8400S SO 4 2– (0.95 ± 0.05) Dionex-IC SO 4 + (0.3 ± 0.6); R 2 = 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 Al 2 O 3 : Aluminum oxide; IC: Ion chromatography; CIF: Charcoal Impregnated Filter; FEP: Fluorinated Ethylene Propylene copolymer; MgO: Magnesium oxide; Na 2 CO 3 : Sodium carbonate; NaHCO 3 : Sodium
bicarbonate NO X : Oxides of nitrogen; SO 2 : Sulfur dioxide; TEA: Triethanolamine; TSP: Total Suspended PM; UV: Ultraviolet; XAD-4: Hydrophobic, non-polar polyaromatic re sin.

c Na 2 CO 3 impregnated.

d 37 mm filter.

1 Chow (1995, 077012 ); 2 Watson and Chow (2001, 157123 ); 3 Watson et al. (1983, 045084 ); 4 Fehsenfeld et al. (2004, 157360 ); 5 Solomon et al. (2001, 157193 ); 6 Watson et al. (2005, 157124 ); 7 Mikel (2001,
156762 ); 8 Watson et al. (1999, 020949 ); 9 Solomon and Sioutas (2006, 156995 ); 10 Graney et al. (2004, 053756 ); 11 Tanaka et al. (1998, 157041 ); 12 Pancras et al. (2005, 098120 ); 13 John et al. (1988, 045903 );

14 Hering and Cass (1999, 084958 ); 15 Fitz et al. (1989, 077387 ); 16 Hering et al. (1988, 036012 ); 17 Solomon et al. (2003, 156994 ); 18 Cabada et al. (2004, 148859 ); 19 Fine et al. (2003, 155775 ); 20 Hogrefe et al. (2004, 099003 ); 21 Drewnick et al. (2003, 099160 ); 22 Watson et al. (2005, 157125 ); 23 Ho et al. (2006, 156552 ); 24 Decesari et al. (2005, 144536 ); 25 Mayol-Bracero et al. (2002, 045010 ); 26 Yang et al. (2003,
156167 ); 27 Turšic et al. (2006, 157063 ); 28 Mader et al.(2004, 156724 ); 29 Xiao and Liu (2004, 056801 ); 30 Kiss et al. (2002, 156646 ); 31 Cornell and Jickells (1999, 156367 ); 32 Zheng et al. (2002, 026100 );

33 Fraser et al. (2002, 140741 ); 34 Fraser et al. (2003, 042231 ) 35 Schauer er al. (2000, 012225 ); 36 Fine et al. (2004, 141283 ); 37 Yue et al. (2004, 157169 ); 38 Rinehart et al. (2006, 115184 ); 39 Wan and Yu (2006,
157104 ); 40 Poore (2000, 012839 ); 41 Fraser et al. (2003, 040266 ); 42 Engling et al. (2006, 156422 ); 43 Yu et al. (2005, 157167 ); 44 Tran et al. (2000, 013025 ); 45 Yao et al. (2004, 102213 ); 46 Li and Yu (2005,
156692 ); 47 Henning et al. (2003, 156539 ); 48 Zhang and Anastasio (2003, 157182 ); 49 Emmenegger et al. (2007, 156418 ); 50 Watson et al. (1989, 046318 ); 51 Greaves et al. (1985, 156494 ); 52 Waterman et al. (2000, 157116 ); 53 Waterman et al. (2001, 157117 ); 54 Falkovich and Rudich (2001, 156427 ); 55 Chow et al. (2007, 157209 ); 56 Miguel et al. (2004, 123260 ); 57 Crimmins and Baker (2006, 097008 ); 58 Ho and Yu (2004, 156551 ); 59 Jeon et al. (2001, 016636 ); 60 Mazzoleni et al. (2007, 098038 ); 61 Poore (2002, 051444 ); 62 Butler et al. (2003, 156313 ); 63 Chow et al. (2006, 146622 ); 64 Russell et al. (2004, 082453 ); 65 Grover et al. (2006, 138080 ); 66 Grover et al. (2005, 090044 ); 67 Schwab et al. (2006, 098449 ); 68 Hauck et al. (2004, 156525 ); 69 Jaques et al. (2004, 155878 ); 70 Rupprecht and Patashnick (2003, 157207 ); 71 Pang et al (2002, 030353 ); 72 Eatough et al. (2001, 010303 ); 73 Lee et al. (2005, 128139 ); 74 Lee et al. (2005, 156680 ); 75 Babich et al. (2000, 156239 ); 76 Lee et al. (2005, 155925 ); 77 Lee et al. (2005, 128139 ); 78 Anderson and Ogren (1998, 156213 ); 79 Chung et al. (2001, 156357 ); 80 Kidwell and Ondov (2004, 155898 ); 81 Lithgow et al. (2004, 126616 ); 82 Weber et al. (2003, 157129 ); 83 Harrison et al. (2004, 136787 ); 84 Rattigan et al. (2006, 115897 ); 85 Wittig et al. (2004, 103413 ); 86 Vaughn et al. (2005, 157089 ); 87 Chow et al. (2005, 099030 ); 88 Weber et al (2001, 024640 ) ; 89 Schwab et al. (2006, 098785 ); 90 Lim et al. (2003, 037037 );

91 Watson and Chow (2002, 037873 ); 92 Venkatachari et al. (2006, 105918 ); 93 Bae et al. (2004, 156243 ); 94 Arhami et al. (2006, 156224 ); 95 Park et al. (2005, 156843 ); 96 Bae et al. (2004, 098680 ); 97 Chow et al. (2006, 156350 ); 98 Arnott et al. (2005, 156227 ); 99 Bond et al. (1999, 156281 ); 100 Virkkula et al. (2005, 157097 ); 101 Petzold et al. (2002, 156863 ); 102 Park et al. (2006, 098104 ); 103 Arnott et al. (1999, 020650 );

104 Peters et al. (2001, 016925 ); 105 Pitchford et al. (1997, 156872 ); 106 Rees et al. (2004, 097164 ); 107 Watson et al. (2000, 010354 ); 108 Lee et al. (2005, 156680 ); 109 Hering et al. (2004, 155837 ); 110 Watson et al. (1998, 198805 ); 111 Chakrabarti et al. (2004, 157426 ); 112 Mathai et al. (1990, 156741 ); 113 Kidwell and Ondov (2001, 017092 ); 114 Stanier et al. (2004, 095955 ); 115 Khlystov et al. (2005, 156635 ); 116 Takahama et al. (2004, 157038 ); 117 Chow et al. (2005, 156348 ); 118 Zhang et al. (2002, 157181 ); 119 Subramanian et al. (2004, 081203 ); 120 Chow et al. (2006, 155207 ); 121 Birch and Cary (1996, 026004 ); 122 Birch (1998,
024953 ); 123 Birch and Cary (1996, 002352 ); 124 NIOSH (1996, 156810 ); 125 NIOSH (1999, 156811 ); 126 Chow et al. (1993, 077459 ); 127 Chow et al. (2007, 156354 ); 128 Ellis and Novakov (1982, 156416 );

129 Peterson and Richards (2002, 156861 ); 130 Schauer et al. (2003, 037014 ); 13 1Middlebrook et al. (2003, 042932 ); 132 Wenzel et al. (2003, 157139 ); 133 Jimenez et al. (2003, 156611 ); 134 Phares et al. (2003,
156866 ); 135 Qin and Prather (2006, 156895 ); 136 Zhang et al. (2005, 157185 ); 137 Bein et al. (2005, 156265 ); 138 Drewnick et al. (2004, 155754 ); 139 Drewnick et al. (2004, 155755 ); 140 Lake et al. (2003, 156669 );

141 Lake et al. (2004, 088411 )

Source: Chow et al. (2008, 156355 )



# Table A-15. Summary of PM 2.5 

# 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 comme rcial-residential neighborhood.

Sampler Flow Rate (L/Min) Filter Type a Denuder b Analysis Method c

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 Al e

Continuous Sampler Flow Rate (L/Min) Denuder OC EC Comments Solomon et al. (2003, 156994 ) 17

Organic Carbon (OC);

PM 2.5 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 arti fact 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/m 3

EC:

PM 2.5 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 opt ical 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 SO 4 2– instrument w/CO 2 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% O 2 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 b abs @ 565 nm 10m 2 /g factor

AE-16 4 None b abs @ 880 nm 12.6 m 2 /g factor TC concentrations meas ured by the RU-OGI and R&P-5400 correlated reasonably well (R 2 = 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 (R 2 = 0.73), while the R&P-5400 EC was 20% higher than RU-OGI (R 2 = 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 (R 2 = 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 fa ctors 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/Pack a Denuder Analysis Method c

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 subsequent ly adsorbed on the backup quartz filter, result ed in an overestimation of the positive artifact.
Non-denuded QBQ provided a more representative estimate of the positive artifact on the non-denuded front quar tz 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/m 3 , 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 im pacted by industrial sources, and located in a mixed residential light commercial neighborhood.

Sampler Flow Rate (Lmin) Filter Type/Pack a Denuder b Analysis Method c

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/m 3

, 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 denuderbreakthrough.
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 (R 2 ) of 0.89 and 0.90, respectively.

Continuous Sunset TC in μg/m 3 = (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 (R 2 = 0.60), probably due to low EC concentrations (avg = 0.70 μg/m 3 ), close to the detection limit (0.5 μg/m 3 ).

FRESNO SUPERSITE, CA and other CRPAQS sit es; 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/Pack a Denuder b Analysis Method c

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% O 2 in He Transmittance

MAAP 16.7 None b abs @ 670 nm Transmittance 6.5 m 2 /g factor

AE-16 6.8 None b abs @ 880 nm

AE-21 6.8 None b abs @ 370, 880

nm

AE-31 6.8 None b abs @ 370, 470, 520, 590, 660, 880 and 950

nm Transmittance

14625/ λ m 2 /g factor, where λ is in nm

DRI-PA 3 None 250, 500, 650, 850°C in He

b abs @ 1047 nm Absorption, 5 m 2 /g factor

Sampler Flow Rate (Lmin) Filter Type/Pack a Denuder b Analysis Method c 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 FRMmeasured 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/m 3 (~24% of non-denuded front quartz OC) from QBT, and 1.12 ± 0.91 μg/m 3 (~17% of nondenuded front quartz OC) from QBQ. QBT >>QBQ
Results from CRPAQS showed, on avg, apositive OC artifact of 34% (of the nondenuded front quartz OC ) from QBT and 17.5% (of the non-d enuded front quartz OC) from QBQ.
Positive artifact was higher during summer than winter.
Negative artifact was, on avg, 0.61 ± 0.58 μg/m 3 (~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/m 3

. 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 (R 2 < 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 abs orption (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% O 2 & 98% He NIOSH 5040_TOT

NDIR CO 2 detector (880 nm) analyzers with the filter IMPROVE_TOR EC, gave a σ abs of 2.3, 5.5 and 10 m 2 /g, differing from the default conversion factors of 5, 6.5, and 16.6 m 2 /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); R 2 = 0.73; n = 480
Sunset TC = (0.63 ± 0.05) PC-BOSS TC + (4.1 ± 3.2); R 2 = 0.86; n = 29
R&P-5400 TC = (0.41 ± 0.02) PC-BOSS TC + (6.7 ± 1.6); R 2 = 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/Pack a Denuder b Analysis Method c

SASS 6.7 Quartz (P)- Quartz (P) None STN_TOT

Continuous Sampler Flow Rate (L/Min) Denuder b OC EC Comments

Sunset OCEC 8 Carbon 600°C, then 870°C in He 870°C in 2% O 2 in He TOT; CH4 FID detector; Denuder breakthrough ~ 0.5-1 μg C/m 3 ; 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 ~1 1.5%, 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 se ction 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/Pack a Denuder b Analysis Method c

PC-BOSS 150 Quartz (P)-CIG (S) CIF TPB (CIG heated to 450 °C in N2)

Continuous Sampler Flow Rate (L/Min) Denuder b 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); R 2 = 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/Pack a Denuder b Analysis Method c

R&P-2300 10 Quartz None STN_TOT

Continuous Sampler Flow Rate (L/Min) Denuder b 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% O 2 in He Transmittance

AE-20 N/A None b abs @ 370, 880 nm Transmittance, 14625 λ m 2 /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 PM 2.5 mass concentration yielded an intercept of 1.14 μg/m 3 , 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; R 2 = 0.86; n = 506
Sunset Optical EC = 0.62 ± 0.05 AE-20 BC; R 2 = 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); R 2 = 0.67; n = 16
Sunset OC = (0.98 ± 0.11) R&P-5400 OC - (0.47 ± 0.17); R 2 = 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); R 2 = 0.58; n = 17
Organic matter measurements by AMS showed reasonable correlation (R 2 = 0.76) with filter (R&P-2300) OC, while being poorly correlated with conti nuous OC by Sunset (R 2 = 0.32) and R&P-5400 (R 2 = 0.36)
Sunset EC = (1.21 ± 0.44) R&P-2300 EC – (0.03 ± 0.13); R 2 = 0.94; n = 16 Sunset EC = (1.35 ± 0.12) R&P-5400 EC + (0.06 ± 0.04); R 2 = 0.61; n = 327 R&P-5400 EC = (0.49 ± 0.46) R&P-2300 EC + (0.09 ± 0.26); R 2 = 0.77; n = 15 Sunset TC = (0.86 ± 0.39) R&P-2300 TC – (0.06 ± 0.69); R 2 = 0.77; n = 16 Sunset TC = (1.31 ± 0.10) R&P-5400 TC – (1.15 ± 0.15); R 2 = 0.59; n = 327 R&P-5400 TC = (0.77 ± 0.58) R&P-2300 TC + (0.35 ± 1.37); R 2 = 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 Al 2 O 3 : Aluminum oxide; IC: Ion chromatography; CIF: Charcoal Impregnated Filter; CIG: Charcoal Impregnated Glass-Fiber Filter; FEP: Fluorinated Ethylene Propylene copolymer; MgO: Magnesium oxide;
Na 2 CO 3 : Sodium carbonate; NaHCO 3 : Sodium bicarbonate NO X : Oxides of nitrogen; SO 2 : 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, EC 3, and EC4 fractions, respectively, 30, 30, 30, >120 sec respectively, in 98% He
and 2% O 2 atmosphere. OPT: Pyrolysis correction by transmittance. IMPROVE_TOR: Interagency Monitoring of Protected Visual Environments T hermal Optical Reflectance Protocol . 126 OC fractions: 120, 250, 450, 550 °C for OC1, OC2, OC3, and OC4 fractions, respective ly, 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+EC 3-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 I MPROVE_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 se c, 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% O 2 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 temperatur e 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% O 2 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% O 2 . Pyrolysis correction by transmittance.

d Custom TOT: XAD-4 impregnated quartz, analyzed in He-only atmos phere 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 ); 2 Watson and Chow (2001, 157123 ); 3 Watson et al. (1983, 045084 ); 4 Fehsenfeld et al. (2004, 157360 ); 5 Solomon et al. (2001, 157193 ); 6 Watson et al. (2005, 157124 ); 7 Mikel (2001,
156762 ); 8 Watson et al. (1999, 020949 ); 9 Solomon and Sioutas (2006, 156995 ); 10 Graney et al. (2004, 053756 ); 11 Tanaka et al. (1998, 157041 ); 12 Pancras et al. (2005, 098120 ); 13 John et al. (1988, 045903 );

14 Hering and Cass (1999, 084958 ); 15 Fitz et al. (1989, 077387 ); 16 Hering et al. (1988, 036012 ); 17 Solomon et al. (2003, 156994 ); 18 Cabada et al. (2004, 148859 ); 19 Fine et al. (2003, 155775 ); 20 Hogrefe et al. (2004, 099003 ); 21 Drewnick et al. (2003, 099160 ); 22 Watson et al. (2005, 157125 ); 23 Ho et al. (2006, 156552 ); 24 Decesari et al. (2005, 144536 ); 25 Mayol-Bracero et al. (2002, 045010 ); 26 Yang et al. (2003,
156167 ); 27 Turšic et al. (2006, 157063 ); 28 Mader et al.(2004, 156724 ); 29 Xiao and Liu (2004, 056801 ); 30 Kiss et al. (2002, 156646 ); 31 Cornell and Jickells (1999, 156367 ); 32 Zheng et al. (2002, 026100 );

33 Fraser et al. (2002, 140741 ); 34 Fraser et al. (2003, 042231 ) 35 Schauer er al. (2000, 012225 ); 36 Fine et al. (2004, 141283 ); 37 Yue et al. (2004, 157169 ); 38 Rinehart et al. (2006, 115184 ); 39 Wan and Yu (2006,
157104 ); 40 Poore (2000, 012839 ); 41 Fraser et al. (2003, 040266 ); 42 Engling et al. (2006, 156422 ); 43 Yu et al. (2005, 157167 ); 44 Tran et al. (2000, 013025 ); 45 Yao et al. (2004, 102213 ); 46 Li and Yu (2005,
156692 ); 47 Henning et al. (2003, 156539 ); 48 Zhang and Anastasio (2003, 157182 ); 49 Emmenegger et al. (2007, 156418 ); 50 Watson et al. (1989, 046318 ); 51 Greaves et al. (1985, 156494 ); 52 Waterman et al. (2000, 157116 ); 53 Waterman et al. (2001, 157117 ); 54 Falkovich and Rudich (2001, 156427 ); 55 Chow et al. (2007, 157209 ); 56 Miguel et al. (2004, 123260 ); 57 Crimmins and Baker (2006, 097008 ); 58 Ho and Yu (2004, 156551 ); 59 Jeon et al. (2001, 016636 ); 60 Mazzoleni et al. (2007, 098038 ); 61 Poore (2002, 051444 ); 62 Butler et al. (2003, 156313 ); 63 Chow et al. (2006, 146622 ); 64 Russell et al. (2004, 082453 ); 65 Grover et al. (2006, 138080 ); 66 Grover et al. (2005, 090044 ); 67 Schwab et al. (2006, 098449 ); 68 Hauck et al. (2004, 156525 ); 69 Jaques et al. (2004, 155878 ); 70 Rupprecht and Patashnick (2003, 157207 ); 71 Pang et al (2002, 030353 ); 72 Eatough et al. (2001, 010303 ); 73 Lee et al. (2005, 128139 ); 74 Lee et al. (2005, 156680 ); 75 Babich et al. (2000, 156239 ); 76 Lee et al. (2005, 155925 ); 77 Lee et al. (2005, 128139 ); 78 Anderson and Ogren (1998, 156213 ); 79 Chung et al. (2001, 156357 ); 80 Kidwell and Ondov (2004, 155898 ); 81 Lithgow et al. (2004, 126616 ); 82 Weber et al. (2003, 157129 ); 83 Harrison et al. (2004, 136787 ); 84 Rattigan et al. (2006, 115897 ); 85 Wittig et al. (2004, 103413 ); 86 Vaughn et al. (2005, 157089 ); 87 Chow et al. (2005, 099030 ); 88 Weber et al (2001, 024640 ) ; 89 Schwab et al. (2006, 098785 ); 90 Lim et al. (2003, 037037 );

91 Watson and Chow (2002, 037873 ); 92 Venkatachari et al. (2006, 105918 ); 93 Bae et al. (2004, 156243 ); 94 Arhami et al. (2006, 156224 ); 95 Park et al. (2005, 156843 ); 96 Bae et al. (2004, 098680 ); 97 Chow et al. (2006, 156350 ); 98 Arnott et al. (2005, 156227 ); 99 Bond et al. (1999, 156281 ); 100 Virkkula et al. (2005, 157097 ); 101 Petzold et al. (2002, 156863 ); 102 Park et al. (2006, 098104 ); 103 Arnott et al. (1999, 020650 );

104 Peters et al. (2001, 016925 ); 105 Pitchford et al. (1997, 156872 ); 106 Rees et al. (2004, 097164 ); 107 Watson et al. (2000, 010354 ); 108 Lee et al. (2005, 156680 ); 109 Hering et al. (2004, 155837 ); 110 Watson et al. (1998, 198805 ); 111 Chakrabarti et al. (2004, 157426 ); 112 Mathai et al. (1990, 156741 ); 113 Kidwell and Ondov (2001, 017092 ); 114 Stanier et al. (2004, 095955 ); 115 Khlystov et al. (2005, 156635 ); 116 Takahama et al. (2004, 157038 ); 117 Chow et al. (2005, 156348 ); 118 Zhang et al. (2002, 157181 ); 119 Subramanian et al. (2004, 081203 ); 120 Chow et al. (2006, 155207 ); 121 Birch and Cary (1996, 026004 ); 122 Birch (1998,
024953 ); 123 Birch and Cary (1996, 002352 ); 124 NIOSH (1996, 156810 ); 125 NIOSH (1999, 156811 ); 126 Chow et al. (1993, 077459 ); 127 Chow et al. (2007, 156354 ); 128 Ellis and Novakov (1982, 156416 );

129 Peterson and Richards (2002, 156861 ); 130 Schauer et al. (2003, 037014 ); 13 1Middlebrook et al. (2003, 042932 ); 132 Wenzel et al. (2003, 157139 ); 133 Jimenez et al. (2003, 156611 ); 134 Phares et al. (2003,
156866 ); 135 Qin and Prather (2006, 156895 ); 136 Zhang et al. (2005, 157185 ); 137 Bein et al. (2005, 156265 ); 138 Drewnick et al. (2004, 155754 ); 139 Drewnick et al. (2004, 155755 ); 140 Lake et al. (2003, 156669 );

141 Lake 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 comm ercial-residential neighborhood.

Spectrometer Inlet Characteristics (Flow Rate [L/Min]; Size Inlet; Dryer Aerodynamic Diameter, μm;

Particle Sizing Method) Volatilization/ Ionization Method a Hit Rates b Mass Spectrometer c Particle Analysis/ Classification Other

PALMS N/A PM 2.5 cyclone Nafion (17 days) / None (4 days) 0.35-2.5 Light scattering LDI,

ArF 193 nm

2x10 9 to 5x10 9 W/cm 2 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

~ 1x10 8 W/cm 2 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

1x10 8 to 2x10 8 W/cm 2 N/A Single linear TOF; Ion polarity needs to be pre-selected Peak ID/artificial neural network Pure sulfuric acid (H 2 SO 4 ), (NH 4 ) 2 SO 4 , and water (H 2 O) 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 PM 2.5 cyclone None 0.05-2.5 Aerosol TOF T~550°C/ EI N/A Quadrupole;
Mass weighted sizedistributions 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 re latively 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 PA LMS, 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/SO 4 2– , sodium/potassium sulfate, soot/hydrocarbon and mineral) were observed by all three laser instruments, they differed in the cla ssification frequencies. Differences in fr equencies that are detected and grouped are rela ted 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 corre sponded to periods of high SO 4 2– concentrations. Low hit rates in PALMS were related to a variety of factors including high SO 4 2– concentrations, differing laser fluence and laser position rela tive to particle beam. Use of a dryer in PALMS enhanced ionizat ion of particles that were difficult to ionize at high ambient RH.

The RSMS-II and ATOFMS we re less sensitive to SO 4 2– and hence may have fewer organic/SO 4 2– particles (i.e., underestimate SO 4 2– , pure sulfuric acid etc.).
The PALMS, ATOFMS and RSMS (las er 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 se mi-quantitative information.
Comparison of the ratio of NO 3 to SO 4 peaks with the results from the semi continuous instruments showed better correlation with the AMS (R 2 = 0.93) than PALMS (R 2 = 0.65 for non-dry particles to 0.70 for dry particles). While reasonable correlations between the PALMS and the composite sem i-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 SO 4 with PILS SO 4 showed good correlation (R 2 = 0.79), and the data uniformly scattered around a 1:1 line. NO 3 comparison was poor (R 2 = 0.49) because of the low signal to noise ratio at low concentrations
The continuum between particle classifica tions indicates that the particles were not adequately represent ed by non-overlapping classifications.



### December 2009 A-48ATLANTA 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 comm ercial-residential neighborhood.

HOUSTON SUPERSITE, TX; 08/23/00 to 09/18/00 Houston Regional Monitoring Site was locat ed < 1.0 km north of the Houston ship c hannel, where chemical and other industries ar e present. The site was located between a railway to the south and a chemical plant to the north. Major freew ays were located just to the north and eas t of the sampling site.

Spectrometer Inlet Characteristics (Flow Rate [L/Min]; Size Inlet; Dryer Aerodynamic Diameter, μm;

Particle Sizing Method) Volatilization/ Ionization Method a Hit Rates b Mass Spectrometer c 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 neur al network into 15 particle types
Fifteen particle type mass spectra were presented along with their size distribution, avg time of day occurrence, and wind dire ction dependence
Major classes were a K + dominant, Si/Silicon Oxide, Ca rbon, 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 Method a Hit Rates b Mass Spectrometer c 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 PM 2.5 mass concentration
Qin and Prather (2008, 156985 ) 135

Biomass burning particles reached a maximum at night and a minimu m during the day. These particles were less than 1 μm in diame ter and accounted for more than 60% of the particles detected at night.
Another particle class characte rized by high mass carbon fragments had a similar di urnal pattern. These particles were larger t han 1 μm and were interpreted as biomass particles that have undergone gas to partic le conversion of semi-volatile species followed by dissolutio n 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 Method a Hit Rates b Mass Spectrometer c

AMS 1.4 cc/s PM 2.5 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 0.03-1.1Aerodynamic 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-49ATLANTA 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 comm ercial-residential neighborhood.

Zhang et al. (2005, 157185 ) 136 ; Bein et al. (2005, 156265 ) 137

The AMS observed 75% of the SO 4 2– measured by R&P-8400S (R 2 = 0.69).
Collection efficiency (CE) of 0.5 used for SO 4 2– , NO 3 and NH 4 + and 0.7 for organics to correct mass concentrati ons for incomplete detection. Use of a constant CE irrespective of size and s hape may overestimate accumulation mode (mos tly, 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 (R 2 = 0.88) with a slope of 1.69. A 24-h avg comparison, show ed a slope of 1.45. These values are in th e typical range of 1.2 to 2.0 for OM/OC r atios.
AMS could be used along with the SMPS to estimate particle dens ity. The AMS did not always agr ee with SMPS, probably due to non -spherical particles (irregular) such as soot from fresh traffic emissi ons, whose mass may be overes timated by the SMPS.
Comparison of AMS mass with the MOUDI, showed differences for aerodynamic diameters >600 nm, probably due to the AMS transmissi on 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 preferen tially detected during the colder months and was detected fr om 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 RS MS-III class (3.3% of all detected particles) was seen to be highly dependent on wind directio n dependence and was primarily detected during August and S eptember of 2002. These particles lik ely 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 wi thin 1 km from expressways and highway s in New York City Metropolitan area.

Rural Site: Whiteface Mountain, New York, located in a clear ed area surrounded by mix of deciduous and evergreen trees, ~2 km a way 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 Method a Hit Rates b Mass Spectrometer c

AMS 0.1 PM 2.5 cyclone None 0.02-2.5 Aerosol TOF T – 700°C/ EI Quadrupole;
Mass weighted sizedistributions 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 part icle to 9% for a 2.5 μm particle, with a minimum of 0.7% for a 350 nm particle

Overall measurement uncer tainty of particle diameter was ~11%.
The AMS was reliable with proper calibration, care, and maintenanc e. Valid 10 min averages were obtained for all components mor e than 93% of the time.
The mass to charge ratios (m/z) of fragments from diff erent components may overlap (e.g., NH+, a fragment of NH 4 + and CH 3 + , a fragment of organic species, have m/z = 15) resulting in an inte rference (called as isobaric interference) Interfering signals were not used to cal culate concentrations. This loss in concentration was adjusted by applying a correct ion factor determined from laboratory studies.
Typical interferences were from fragment s of organic species, water and oxygen.
With adjustments, the SO 4 2– , NO 3 –, and ammonium concentrations measured by the AMS were consistently lowe r than that measured by other co-located instruments, probably due to in complete focusing of the (NH 4 ) 2 SO 4 and NH 4 NO 3 particles by the aerodynamic lens.

At the urban site, AMS NO 3 was within 10% of the filter NO 3 concentration. At the rural si te, it had a slope of 0.51 and R 2 of 0.46.

AMS SO 4 showed good agreement with R&P-8400S at both the rural and urban locations (R 2 = 0.89 to 0.92, slope = 0.99, n = 407 to 695) and was within 70 to 85% of filter SO 4 2– concentration.
Comparison of the total non-refractory mass measured by the AMS with the PM 2.5 TEOM mass (operated at 50°C or wi th dryer) at the urban location, showed good correlation (R 2 = 0.91) with near zero intercept (0.22 μg/m 3 ). 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 th e aerosol sample (e.g., metals, EC). The mass closure was within the estimated uncertainty of the AMS mass measurements (5-10%).



### December 2009 A-50ATLANTA 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 comm ercial-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 Method a Hit Rates b Mass Spectrometer c

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 negati ve ions decreased for smaller particles.

SO 4 + 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 relate d to specific s ource 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 ); 2 Watson and Chow (2001, 157123 ); 3 Watson et al. (1983, 045084 ); 4 Fehsenfeld et al. (2004, 157360 ); 5 Solomon et al. (2001, 157193 ); 6 Watson et al. (2005, 157124 ); 7 Mikel (2001,
156762 ); 8 Watson et al. (1999, 020949 ); 9 Solomon and Sioutas (2006, 156995 ); 10 Graney et al. (2004, 053756 ); 11 Tanaka et al. (1998, 157041 ); 12 Pancras et al. (2005, 098120 ); 13 John et al. (1988, 045903 );

14 Hering and Cass (1999, 084958 ); 15 Fitz et al. (1989, 077387 ); 16 Hering et al. (1988, 036012 ); 17 Solomon et al. (2003, 156994 ); 18 Cabada et al. (2004, 148859 ); 19 Fine et al. (2003, 155775 ); 20 Hogrefe et al. (2004, 099003 ); 21 Drewnick et al. (2003, 099160 ); 22 Watson et al. (2005, 157125 ); 23 Ho et al. (2006, 156552 ); 24 Decesari et al. (2005, 144536 ); 25 Mayol-Bracero et al. (2002, 045010 ); 26 Yang et al. (2003,
156167 ); 27 Turšic et al. (2006, 157063 ); 28 Mader et al.(2004, 156724 ); 29 Xiao and Liu (2004, 056801 ); 30 Kiss et al. (2002, 156646 ); 31 Cornell and Jickells (1999, 156367 ); 32 Zheng et al. (2002, 026100 );

33 Fraser et al. (2002, 140741 ); 34 Fraser et al. (2003, 042231 ) 35 Schauer er al. (2000, 012225 ); 36 Fine et al. (2004, 141283 ); 37 Yue et al. (2004, 157169 ); 38 Rinehart et al. (2006, 115184 ); 39 Wan and Yu (2006,
157104 ); 40 Poore (2000, 012839 ); 41 Fraser et al. (2003, 040266 ); 42 Engling et al. (2006, 156422 ); 43 Yu et al. (2005, 157167 ); 44 Tran et al. (2000, 013025 ); 45 Yao et al. (2004, 102213 ); 46 Li and Yu (2005,
156692 ); 47 Henning et al. (2003, 156539 ); 48 Zhang and Anastasio (2003, 157182 ); 49 Emmenegger et al. (2007, 156418 ); 50 Watson et al. (1989, 046318 ); 51 Greaves et al. (1985, 156494 ); 52 Waterman et al. (2000, 157116 ); 53 Waterman et al. (2001, 157117 ); 54 Falkovich and Rudich (2001, 156427 ); 55 Chow et al. (2007, 157209 ); 56 Miguel et al. (2004, 123260 ); 57 Crimmins and Baker (2006, 097008 ); 58 Ho and Yu (2004, 156551 ); 59 Jeon et al. (2001, 016636 ); 60 Mazzoleni et al. (2007, 098038 ); 61 Poore (2002, 051444 ); 62 Butler et al. (2003, 156313 ); 63 Chow et al. (2006, 146622 ); 64 Russell et al. (2004, 082453 ); 65 Grover et al. (2006, 138080 ); 66 Grover et al. (2005, 090044 ); 67 Schwab et al. (2006, 098449 ); 68 Hauck et al. (2004, 156525 ); 69 Jaques et al. (2004, 155878 ); 70 Rupprecht and Patashnick (2003, 157207 ); 71 Pang et al (2002, 030353 ); 72 Eatough et al. (2001, 010303 ); 73 Lee et al. (2005, 128139 ); 74 Lee et al. (2005, 156680 ); 75 Babich et al. (2000, 156239 ); 76 Lee et al. (2005, 155925 ); 77 Lee et al. (2005, 128139 ); 78 Anderson and Ogren (1998, 156213 ); 79 Chung et al. (2001, 156357 ); 80 Kidwell and Ondov (2004, 155898 ); 81 Lithgow et al. (2004, 126616 ); 82 Weber et al. (2003, 157129 ); 83 Harrison et al. (2004, 136787 ); 84 Rattigan et al. (2006, 115897 ); 85 Wittig et al. (2004, 103413 ); 86 Vaughn et al. (2005, 157089 ); 87 Chow et al. (2005, 099030 ); 88 Weber et al (2001, 024640 ) ; 89 Schwab et al. (2006, 098785 ); 90 Lim et al. (2003, 037037 );

91 Watson and Chow (2002, 037873 ); 92 Venkatachari et al. (2006, 105918 ); 93 Bae et al. (2004, 156243 ); 94 Arhami et al. (2006, 156224 ); 95 Park et al. (2005, 156843 ); 96 Bae et al. (2004, 098680 ); 97 Chow et al. (2006, 156350 ); 98 Arnott et al. (2005, 156227 ); 99 Bond et al. (1999, 156281 ); 100 Virkkula et al. (2005, 157097 ); 101 Petzold et al. (2002, 156863 ); 102 Park et al. (2006, 098104 ); 103 Arnott et al. (1999, 020650 );

104 Peters et al. (2001, 016925 ); 105 Pitchford et al. (1997, 156872 ); 106 Rees et al. (2004, 097164 ); 107 Watson et al. (2000, 010354 ); 108 Lee et al. (2005, 156680 ); 109 Hering et al. (2004, 155837 ); 110 Watson et al. (1998, 198805 ); 111 Chakrabarti et al. (2004, 157426 ); 112 Mathai et al. (1990, 156741 ); 113 Kidwell and Ondov (2001, 017092 ); 114 Stanier et al. (2004, 095955 ); 115 Khlystov et al. (2005, 156635 ); 116 Takahama et al. (2004, 157038 ); 117 Chow et al. (2005, 156348 ); 118 Zhang et al. (2002, 157181 ); 119 Subramanian et al. (2004, 081203 ); 120 Chow et al. (2006, 155207 ); 121 Birch and Cary (1996, 026004 ); 122 Birch (1998,
024953 ); 123 Birch and Cary (1996, 002352 ); 124 NIOSH (1996, 156810 ); 125 NIOSH (1999, 156811 ); 126 Chow et al. (1993, 077459 ); 127 Chow et al. (2007, 156354 ); 128 Ellis and Novakov (1982, 156416 );

129 Peterson and Richards (2002, 156861 ); 130 Schauer et al. (2003, 037014 ); 13 1Middlebrook et al. (2003, 042932 ); 132 Wenzel et al. (2003, 157139 ); 133 Jimenez et al. (2003, 156611 ); 134 Phares et al. (2003,
156866 ); 135 Qin and Prather (2006, 156895 ); 136 Zhang et al. (2005, 157185 ); 137 Bein et al. (2005, 156265 ); 138 Drewnick et al. (2004, 155754 ); 139 Drewnick et al. (2004, 155755 ); 140 Lake et al. (2003, 156669 );

141 Lake 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 Un it 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 fi ber fi lters 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 fi rst GC is us ed 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 fi replace 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 PM 10 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 ) PM 2.5 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 Un it 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 ) PM 0.6 and PM >0.45 collected on quartz fiber in pris tine 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 ) PM 10 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 ) PM 2.6 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 ) PM 10 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 PM 10 

# , PM 2.5 

# , and PM 10-2.5 

# Measurement

Spatial Scales PM 10 

### PM 2.5 

### PM 10-2.5

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 PM 10 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 (PM 10 ) 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 PM 10 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 PM 10 , 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 PM 10-2.5 exposure. Monitors located in populated areas that are nearly adjacent to large industrial point sources of PM 10-2.5 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 PM 10 

### PM 2.5 

### PM 10-2.5

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 PM 10 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 PM 2.5 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. PM 2.5 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 PM 2.5 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 industr ial 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 PM 10 - 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 scaleair pollution control strategies. Communityoriented PM 2.5 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. PM 2.5 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 PM 2.5 levels and may also be associated with elevated O 3 and regional haze.



### December 2009 A-55

# Table A-19. Major routine operating air monitoring networks a



### Network Lead Agency Number of Sites Initiated Measurement Parameters Location of Information and/or Data

STATE / LOCAL / FEDERAL NETWORKS

NCore b – National Core Monitoring Network EPA 75 2008 O 3 , NO/NO 2 /NO Y , SO 2 , CO, PM 2.5 / PM10-2.5 , PM 2.5 speciation, NH 3 , HNO 3 , surface meteorology c http://www.epa.gov/ttN/Amtic/monstratdoc.html

SLAMS1 – State and Local Ambient Monitoring Stations EPA ~3000 1978 O 3 , NO X /NO 2 , SO 2 , PM 2.5 /PM 10 , CO, Pb http://www.epa.gov/a ir/oaqps/qa/monprog.html

STN—PM 2.5 Speciation Trends Network EPA 300 1999 PM 2.5, PM 2.5 speciation, major Ions, metals http://www.epa.gov/t tnamti1/specgen.html

PAMS—Photochemical Assessment Monitoring Network EPA 75 1994 O 3 , NO X /NO Y , 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 PM 2.5 /PM 10 , major ions, metals, light extinction, scattering coefficient http://vista.cira.co lostate.edu/IMPROVE/

CASTNet – Clean Air Status and Trends Network EPA 80+ 1987 O 3 , SO 2 , 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 O 3 , NO X /NO/NO 2 , SO 2 , CO, surface meteorology, (plus enhanced monitoring of CO, NO, NO X , NO Y , and SO 2 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 O 3 , surface meteorology, with CASTNet-protocol filter pack (optional) sulfate, nitrate, ammonium, nitric acid, sulfur dioxide http://www2.nature.nps. gov/air/studies/port O 3 .cfm

Passive Ozone Sampler Monitoring Program NPS 43 1995 O 3 dose (weekly) http://www2.nature. nps.gov/air/Studi es/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 SO 2 , CO, O 3 , NO, NO 2 , NO X , VOCs, SVOCs, PM 10 , PM 2.5 , TSP, metals http://www.etc-cte.ec.gc.ca/NAPS/index_e.html

CAPMoN—Canadian Air and Precipitation Monitoring Network Canada 29 2002 O 3 , NO, NO 2 , NO Y , PAN, NH 3 , PM 2.5 , PM 10 and coarse fraction mass, PM 2.5 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 O 3 , NO X , CO, SO 2 , PM 10 , TSP,VOC http://www.ine.gob.mx/dgicur/calaire/indicadores.html

Mexican City Ambient Air Quality Monitoring Network Mexico 49 Late 1960s O 3 , NO X , CO, SO 2 , PM 10 , 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, PM 10 metals d , Hg http://www.epa.gov/ttN/Amtic/airtoxpg.html

State/Local Air Toxics Monitoring EPA 250+ 1987 VOCs, Carbonyls, PM 10 metals d , 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/CF M/recordisplay.cfm?deid=54811



### TRIBAL MONITORING NETWORKS

Tribal Monitoring f EPA 120+ 1995 O 3 , NO X /NO 2 , SO 2 , PM 2.5 /PM 10 , CO, Pb http://www.epa.gov/air/tr ibal/airprogs.html#ambmon



### INDUSTRY / RESEARCH NETWORKS

New Source Permit Monitoring None variable variable O 3 , NO X /NO 2 , SO 2 , PM 2.5 /PM 10 , CO, Pb Contact specific indu strial facilities

HRM Network— Houston Regional Monitoring Network None 9 1980 O 3 , NO X , PM 2.5 /PM 10 , CO, SO 2 , 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 O 3 , NO/NO 2 /NO Y , SO 2 , CO, PM 2.5 /PM 10 , PM 2.5 speciation, major Ions, NH 3 , HNO 3 , 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 O 3 , NO, NO Y , VOCs, CO, surface meteorology http://www.ncsu.edu/sos/ pubs/sos3/Stat e_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 Program g EPA ~50 U.S. cities 2002 Calculated UV radiation index http://www.epa.gov/s unwise/uvindex.html

UV Net – Ultraviolet Monitoring Program EPA 21 1995/2004 Ultraviolet solar radiation (UV-B and UV-A bands), irradiance, ozone, NO 2 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, SO 2 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 NETwork h NPS 14 1997 ozone, wet and dry deposition, visibility, surface meteorology, and ultraviolet radiation http://www.cfc.umt.edu/primenet/Assets/A nnouncements/99PReport.pdf

a Some networks listed separately may also serve as subcomponents of other larger listed networks; as a result, some double count ing 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, temperat ure, precipitation, relative humidity, solar radiation (PAMS onl y).

d PM 10 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 R espect to Population Density

Figure A-1. PM 2.5 

# monitor distribution in comparison with population density, Atlanta, GA.

December 2009 A-59

# Figure A-2. PM 10 

# monitor distribution in comparison with population density, Atlanta, GA.

December 2009 A-60

# Figure A-3. PM 2.5 

# monitor distribution in comparison with population density, Birmingham,
AL.

December 2009 A-61

# Figure A-4. PM 10 

# monitor distribution in comparison with population density, Birmingham,
AL.

December 2009 A-62

# Figure A-5. PM 2.5 

# monitor distribution in comparison with population density, Boston, MA.

December 2009 A-63

# Figure A-6. PM 10 

# monitor distribution in comparison with population density, Boston, MA.

December 2009 A-64

# Figure A-7. PM 2.5 

# monitor distribution in comparison with population density, Chicago, IL.

December 2009 A-65

# Figure A-8. PM 10 

# monitor distribution in comparison with population density, Chicago, IL.

December 2009 A-66

# Figure A-9. PM 2.5 

# monitor distribution in comparison with population density, Denver, CO.

December 2009 A-67

# Figure A-10. PM 10 

# monitor distribution in comparison with population density, Denver, CO.

December 2009 A-68

# Figure A-11. PM 2.5 

# monitor distribution in comparison with population density, Detroit, MI.

December 2009 A-69

# Figure A-12. PM 10 

# monitor distribution in comparison with population density, Detroit, MI.

December 2009 A-70

# Figure A-13. PM 2.5 

# monitor distribution in comparison with population density, Houston, TX.

December 2009 A-71

# Figure A-14. PM 10 

# monitor distribution in comparison with population density, Houston, TX.

December 2009 A-72

# Figure A-15. PM 2.5 

# monitor distribution in comparison with population density, Los Angeles, CA.

December 2009 A-73

# Figure A-16. PM 10 

# monitor distribution in comparison with population density, Los Angeles, CA.

December 2009 A-74

# Figure A-17. PM 2.5 

# monitor distribution in comparison with population density, New York, NY.

December 2009 A-75

# Figure A-18. PM 10 

# monitor distribution in comparison with population density, New York, NY.

December 2009 A-76

# Figure A-19. PM 2.5 

# monitor distribution in comparison with population density, Philadelphia,
PA.

December 2009 A-77

# Figure A-20. PM 10 

# monitor distribution in comparison with population density, Philadelphia,
PA.

December 2009 A-78

# Figure A-21. PM 2.5 

# monitor distribution in comparison with population density, Phoenix, AZ.

December 2009 A-79

# Figure A-22. PM 10 

# monitor distribution in comparison with population density, Phoenix, AZ.

December 2009 A-80

# Figure A-23. PM 2.5 

# monitor distribution in comparison with population density, Pittsburgh, PA.

December 2009 A-81

# Figure A-24. PM 10 

# monitor distribution in comparison with population density, Pittsburgh, PA.

December 2009 A-82

# Figure A-25. PM 2.5 

# monitor distribution in comparison with population density, Riverside, CA.

December 2009 A-83

# Figure A-26. PM 10 

# monitor distribution in comparison wi th population density, Riverside, CA.

December 2009 A-84

# Figure A-27. PM 2.5 

# monitor distribution in comparison with population density, Seattle, WA.

December 2009 A-85

# Figure A-28. PM 10 

# monitor distribution in comparison with population density, Seattle, WA.

December 2009 A-86

# Figure A-29. PM 2.5 

# monitor distribution in comparison with population density, St. Louis, MO.

December 2009 A-87

# Figure A-30. PM 10 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

## , PM 10-2.5


## and PM 10 

## 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). Ta bles of inter-sampler comparison statistics and
scatter plots of inter-sampler correlation vs. dist ance illustrate variability present in each area.

December 2009 A-95

# Figure A-37. PM 2.5 

# monitor distribution and major highways, Atlanta, GA.

December 2009 A-96

# Figure A-38. Box plots illustrating the seasonal distribution of 24-h avg PM 2.5 

# concentrations for Atlanta, GA.

December 2009 A-97

# Table A-20. Inter-sampler correlati on statistics for each pair of PM 2.5 

# 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-980.00 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. PM 2.5 

# inter-sampler correlations as a function of distance between monitors for Atlanta, GA.

December 2009 A-99

# Figure A-40. PM 2.5 

# monitor distribution and major highways, Birmingham, AL.

December 2009 A-100

# Figure A-41. Box plots illustrating the seasonal distribution of 24-h avg PM 2.5 

# concentrations for Birmingham, AL.

December 2009 A-101

# Table A-21. Inter-sampler correlati on statistics for each pair of PM 2.5 

# 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

. 0 0 )

351



### December 2009 A-1020.00 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. PM 2.5 

# inter-sampler correlations as a function of distance between monitors for Birmingham, AL.

December 2009 A-103

# Figure A-43. PM 2.5 

# monitor distribution and major highways, Boston, MA.

December 2009 A-104

# Figure A-44. Box plots illustrating the seasonal distribution of 24-h avg PM 2.5 

# concentrations for Boston, MA.

December 2009 A-105

# Table A-22. Inter-sampler correlati on statistics for each pair of PM 2.5 

# 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

. 0 0 0

. 8 6 (0.0, 0.00) (6.9, 0.23)

1015 330
J 1

. 0 0 ( 0

. 0

, 0

. 0 0 )

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-106Site 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 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0
Distance Between Samplers (km) 0.4 0.6 0.8

1

0Correlation



# Figure A-45. PM 2.5 

# inter-sampler correlations as a function of distance between monitors for Boston, MA.

December 2009 A-107

# Figure A-46. PM 2.5 

# 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 PM 2.5 

# concentrations for Chicago, IL.

December 2009 A-110

# Table A-23. Inter-sampler correlati on statistics for each pair of PM 2.5 

# 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

. 0 0 0

. 9 5 (0.0, 0.00) (3.8, 0.11)

176 152 O 1.00 (0.0, 0.00)

174



### December 2009 A-111P 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, 0.2 0) (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 A D 1

. 0 0 0

. 7 9 (0.0, 0.00) (7.2, 0.18)

346 332 AE 1.00



### December 2009 A-112P 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

0Correlation



# Figure A-48. PM 2.5 

# inter-sampler correlations as a function of distance between monitors for Chicago, IL.

December 2009 A-113

# Figure A-49. PM 2.5 

# monitor distribution and major highways, Denver, CO.

December 2009 A-114

# Figure A-50. Box plots illustrating the seasonal distribution of 24-h avg PM 2.5 

# concentrations for Denver, CO.

December 2009 A-115

# Table A-24. Inter-sampler correlati on statistics for each pair of PM 2.5 

# 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

. 0 0 )

334



### December 2009 A-1160 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. PM 2.5 

# inter-sampler correlations as a function of distance between monitors for Denver, CO.

December 2009 A-117

# Figure A-52. PM 2.5 

# monitor distribution and major highways, Detroit, MI.

December 2009 A-118

# Figure A-53. Box plots illustrating the seasonal distribution of 24-h avg PM 2.5 

# concentrations for Detroit, MI.

December 2009 A-119

# Table A-25. Inter-sampler correlati on statistics for each pair of PM 2.5 

# 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, 0.2 4) (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-1200 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. PM 2.5 

# inter-sampler correlations as a function of distance between monitors for Detroit, MI.

December 2009 A-121

# Figure A-55. PM 2.5 

# monitor distribution and major highways, Houston, TX.

December 2009 A-122

# Figure A-56. Box plots illustrating the seasonal distribution of 24-h avg PM 2.5 

# concentrations for Houston, TX.

Table A-26. Inter-sampler correlati on statistics for each pair of PM 2.5 

# 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-1230 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. PM 2.5 

# inter-sampler correlations as a function of distance between monitors for Houston, TX.

December 2009 A-124

# Figure A-58. PM 2.5 

# 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 PM 2.5 

# concentrations for Los Angeles, CA.

December 2009 A-126

# Table A-27. Inter-sampler correlati on statistics for each pair of PM 2.5 

# 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 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0
Distance Between Samplers (km) 0.2 0.4 0.6 0.8

1Correlation



# Figure A-60. PM 2.5 

# inter-sampler correlations as a function of distance between monitors for Los Angeles, CA.

December 2009 A-127

# Figure A-61. PM 2.5 

# 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 PM 2.5 

# concentrations for New York, NY.

December 2009 A-130

# Table A-28. Inter-sampler correlati on statistics for each pair of PM 2.5 

# 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, 0.1 6) (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

. 0 0 ( 0

. 0

, 0

. 0 0 )

357



### December 2009 A-131S 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, 0.3 3) (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) (1 1.1, 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, 0.1 8) (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-132S 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 A H 1

. 0 0 0

. 9 7 0

. 9 2 (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 A J 1

. 0 0 ( 0

. 0

, 0

. 0 0 )

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 PM 2.5 

# inter-sampler correlations as a function of distance between monitors for New York, NY.

December 2009 A-133

# Figure A-64. PM 2.5 

# monitor distribution and major highways, Philadelphia, PA.

December 2009 A-134

# Figure A-65. Box plots illustrating the seasonal distribution of 24-h avg PM 2.5 

# concentrations for Philadelphia, PA.

December 2009 A-135

# Table A-29. Inter-sampler correlati on statistics for each pair of PM 2.5 

# 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

. 0 0 0

. 9 7 ( 0

. 0

, 0

. 0 0 ) ( 3

. 5

, 0

. 1 0 )

596 447 O 1.00 (0.0, 0.00)

780



### December 2009 A-1360
0 10 20 30 40 50 60 70 80 90 10
Distance Between Samplers (km) 0.2 0.4 0.6 0.8

1

0Correlation



# Figure A-66. PM 2.5 

# inter-sampler correlations as a function of distance between monitors for Philadelphia, PA.

December 2009 A-137

# Figure A-67. PM 2.5 

# monitor distribution and major highways, Phoenix, AZ.

December 2009 A-138

# Figure A-68. Box plots illustrating the seasonal distribution of 24-h avg PM 2.5 

# concentrations for Phoenix, AZ.

December 2009 A-139

# Table A-30. Inter-sampler correlati on statistics for each pair of PM 2.5 

# 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-1400 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. PM 2.5 

# inter-sampler correlations as a function of distance between monitors for Phoenix, AZ.

December 2009 A-141

# Figure A-70. PM 2.5 

# monitor distribution and major highways, Pittsburgh, PA.

December 2009 A-142

# Figure A-71. Box plots illustrating the seasonal distribution of 24-h avg PM 2.5 

# concentrations for Pitsburgh, PA.

December 2009 A-143

# Table A-31. Inter-sampler correlati on statistics for each pair of PM 2.5 

# 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

. 0 0 ( 0

. 0

, 0

. 0 0 )

350



### December 2009 A-1440
0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0
Distance Between Samplers (km) 0.2 0.4 0.6 0.8

1

0Correlation



# Figure A-72. PM 2.5 

# inter-sampler correlations as a function of distance between monitors for Pittsburgh, PA.

December 2009 A-145

# Figure A-73. PM 2.5 

# monitor distribution and major highways, Riverside, CA.

December 2009 A-146

# Figure A-74. Box plots illustrating the seasonal distribution of 24-h avg PM 2.5 

# concentrations for Riverside, CA.

December 2009 A-147

# Table A-32. Inter-sampler correlati on statistics for each pair of PM 2.5 

# 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-1480 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. PM 2.5 

# inter-sampler correlations as a function of distance between monitors for Riverside CA.

December 2009 A-149

# Figure A-76. PM 2.5 

# monitor distribution and major highways, Seattle, WA.

December 2009 A-150

# Figure A-77. Box plots illustrating the seasonal distribution of 24-h avg PM 2.5 

# concentrations for Seattle, WA.

December 2009 A-151

# Table A-33. Inter-sampler correlati on statistics for each pair of PM 2.5 

# 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. PM 2.5 

# inter-sampler correlations as a function of distance between monitors for Seattle, WA.

December 2009 A-152

# Figure A-79. PM 2.5 

# 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 PM 2.5 

# concentrations for St. Louis, MO.

December 2009 A-154

# Table A-34. Inter-sampler correlati on statistics for each pair of PM 2.5 

# 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

. 0 0 )

1046



### December 2009 A-1550

0 10 20 30 40 50 60 70 80 90 10
Distance Between Samplers (km) 0.2 0.4 0.6 0.8

1

0Correlation



# Figure A-81 PM 2.5 

# inter-sampler correlations as a function of distance between monitors for St. Louis, MO.

December 2009 A-156

# Figure A-82. PM 10 

# monitor distribution and major highways, Atlanta, GA.

December 2009 A-157

# Figure A-83. Box plots illustrating the seasonal distribution of 24-h avg PM 10 

# concentrations for Atlanta, GA.

December 2009 A-158

# Table A-35. Inter-sampler correlati on statistics for each pair of PM 10 

# 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-1590 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. PM 10 

# inter-sampler correlations as a function of distance between monitors for Atlanta, GA.

December 2009 A-160

# Figure A-85. PM 10 

# monitor distribution and major highways, Birmingham, AL.

December 2009 A-161

# Figure A-86. Box plots illustrating the seasonal distribution of 24-h avg PM 10 

# concentrations for Birmingham, AL.

December 2009 A-162

# Table A-36. Inter-sampler correlati on statistics for each pair of PM 10 

# 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

. 0 0
( 0

. 0

, 0

. 0 0 )

1080



### December 2009 A-1630 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 PM 10 

# inter-sampler correlations as a function of distance between monitors for Birmingham, AL.

December 2009 A-164

# Figure A-88. PM 10 

# monitor distribution and major highways, Boston, MA.

December 2009 A-165

# Figure A-89. Box plots illustrating the seasonal distribution of 24-h avg PM 10 

# concentrations for Boston, MA.

Table A-37. Inter-sampler correlati on statistics for each pair of PM 10 

# 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

. 0 0 (0.0, 0.00)

168



### December 2009 A-1660 0.2 0.4 0.6 0.8

1

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0
Distance Between Samplers (km)Correlation



# Figure A-90 PM 10 

# inter-sampler correlations as a function of distance between monitors for Boston, MA.

December 2009 A-167

# Figure A-91. PM 10 

# monitor distribution and major highways, Chicago, IL.

December 2009 A-168

# Figure A-92. Box plots illustrating the seasonal distribution of 24-h avg PM 10 

# concentrations for Chicago, IL.

December 2009 A-169

# Table A-38. Inter-sampler correlati on statistics for each pair of PM 10 

# 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

. 0 0 0

. 3 1 (0.0, 0.00) (19.0, 0.29)

1051 161 O 1.00 (0.0, 0.00)

169



### December 2009 A-1700 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. PM 10 

# inter-sampler correlations as a function of distance between monitors for Chicago, IL.

December 2009 A-171

# Figure A-94. PM 10 

# monitor distribution and major highways, Denver, CO.

December 2009 A-172

# Figure A-95. Box plots illustrating the seasonal distribution of 24-h avg PM 10 

# concentrations for Denver, CO.

December 2009 A-173

# Table A-39. Inter-sampler correlati on statistics for each pair of PM 10 

# 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-1740 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. PM 10 

# inter-sampler correlations as a function of distance between monitors for Denver, CO.

December 2009 A-175

# Figure A-97. PM 10 

# monitor distribution and major highways, Detroit, MI.

December 2009 A-176

# Figure A-98. Box plots illustrating the seasonal distribution of 24-h avg PM 10 

# concentrations for Detroit, MI.

December 2009 A-177

# Table A-40. Inter-sampler correlati on statistics for each pair of PM 10 

# 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. PM 10 

# inter-sampler correlations as a function of distance between monitors for Detroit, MI.

December 2009 A-178

# Figure A-100. PM 10 

# monitor distribution and major highways, Houston, TX.

December 2009 A-179

# Figure A-101. Box plots illustrating the seasonal distribution of 24-h avg PM 10 

# concentrations for Houston, TX.

December 2009 A-180

# Table A-41. Inter-sampler correlati on statistics for each pair of PM 10 

# 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 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0
Distance Between Samplers (km)Correlation



# Figure A-102. PM 10 

# inter-sampler correlations as a function of distance between monitors for Houston, TX.

December 2009 A-182

# Figure A-103. PM 10 

# 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 PM 10 

# concentrations for Los Angeles, CA.

December 2009 A-184

# Table A-42. Inter-sampler correlati on statistics for each pair of PM 10 

# 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 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0
Distance Between Samplers (km)Correlation



# Figure A-105. PM 10 

# inter-sampler correlations as a function of distance between monitors for Los Angeles, CA.

December 2009 A-185

# Figure A-106. PM 10 

# monitor distribution and major highways, New York, NY.

December 2009 A-186concentration (ug/m3)



## 0 10 20 30 40 50 60

w in

t e r spri n g

s um

m

e r fa ll wi n t e r

s pr in

g

s um

m

er fa l l wi n t e r

s p ring

s um

m

e r fa l l



# Figure A-107. Box plots illustrating the seasonal distribution of 24-h avg PM 10 

# concentrations for New York, NY.

December 2009 A-187

# Table A-43. Inter-sampler correlati on statistics for each pair of PM 10 

# 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. PM 10 

# inter-sampler correlations as a function of distance between monitors for New York, NY.

December 2009 A-188

# Figure A-109. PM 10 

# monitor distribution and major highways, Philadelphia, PA.

December 2009 A-189

# Figure A-110. Box plots illustrating the seasonal distribution of 24-h avg PM 10 

# concentrations for Philadelphia, PA.

December 2009 A-190

# Table A-44. Inter-sampler correlati on statistics for each pair of PM 10 

# 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. PM 10 

# inter-sampler correlations as a function of distance between monitors for Philadelphia, PA.

December 2009 A-191

# Figure A-112. PM 10 

# 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 PM 10 

# concentrations for Phoenix, AZ.

December 2009 A-194

# Table A-45. Inter-sampler correlati on statistics for each pair of PM 10 

# 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-195N 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, 0.5 4) (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

. 0 0 ( 0

. 0

, 0

. 0 0 )

322



### December 2009 A-1960 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. PM 10 

# inter-sampler correlations as a function of distance between monitors for Phoenix, AZ.

December 2009 A-197

# Figure A-115. PM 10 

# monitor distribution and major highways, Pittsburgh, PA.

December 2009 A-198

# Figure A-116. Box plots illustrating the seasonal distribution of 24-h avg PM 10 

# concentrations for Pittsburgh, PA.

December 2009 A-199

# Table A-46. Inter-sampler correlati on statistics for each pair of PM 10 

# monitors reporting to AQS for Pittsburgh, PA.

S i te A B C D E F G H I A 1 0 0 0 9 3 0 9 3 0 8 0 0 9 2 0 8 9 0 9 3 0 7 9 0 8 6 (0

. 0, 0

. 00 ) (9

. 0, 0 .1 5) (8

. 0, 0 .14 ) ( 2 3

. 0, 0 .21 ) (8

. 0, 0 .12 ) ( 14. 0, 0 .1 8) (8

. 0, 0 .14 ) ( 1 6

. 0, 0 .17 ) ( 1 8

. 0, 0 .1 8) t 1 0 77 1 00 2 1 06 5 1 0 7 0 17 5 17 8 960 1 8 1 1 00 5 B 1. 00 0

. 96 0

. 80 0

. 9 1 0

. 9 2 0

. 9 7 0

. 8 1 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 )

1 0 1 9 1 00 7 1 0 12 1 63 1 66 9 11 1 69 95 4 C 1. 00 0

. 8 1 0

. 9 4 0

. 93 0

. 9 4 0 .77 0

. 8 7 (0

. 0, 0

. 00 ) ( 2 3

. 0, 0 .2 0) (6

. 0, 0 .11 ) ( 7. 0, 0 .12 ) (8

. 0, 0 .1 3) ( 21. 0, 0 .22 ) ( 1 9

. 0, 0 .17 )

1 08 3 1 0 7 5 17 3 17 6 966 17 9 1 0 1 0 D 1. 00 0 .72 0

. 66 0 .7 6 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 )

1 08 7 1 7 6 17 9 9 7 0 1 8 2 1 0 14 E 1. 00 0

. 90 0

. 90 0 .7 8 0 .77 (0

. 0, 0

. 00 ) ( 1 0

. 0, 0 .14 ) ( 1 0

. 0, 0 .17 ) ( 2 0

. 0, 0 .2 0) ( 2 0

. 0, 0 .1 9)

17 6 17 3 1 5 4 1 7 5 1 66 F 1. 00 0

. 9 4 0 .7 0 0 .74 ( 0.0

, 0.00 ) ( 7.0

, 0.12 ) ( 25.0

, 0.27 ) ( 25.0

, 0.22 )

17 9 1 5 7 1 7 8 1 68 G LEGEND Pearson R (P90, COD)

n 1. 00 0 .7 0 0

. 8 7 (0

. 0, 0

. 00 ) ( 22. 0, 0 .2 8) ( 2 0

. 0, 0 .1 9)

9 7 8 1 60 9 1 0 H 1. 00 0 .7 6 ( 0.0

, 0.00 ) ( 17.0

, 0.20 )

1 8 2 171 I 1. 00 (0

. 0, 0

. 00 )

1 0 22

J K L M N O P Q A 0 8 4 0 7 6 0 8 8 0 8 5 0 8 6 0 7 7 0 7 8 0 8 6 ( 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 )

17 6 1 0 44 1 03 3 1 05 2 1 0 74 1 66 177 1 06 1 B 0

. 93 0 .7 6 0

. 88 0

. 8 1 0

. 9 1 0 .7 6 0

. 83 0

. 88 ( 7. 0, 0 .1 6) ( 4 3

. 0, 0

. 36 ) ( 1 9

. 0, 0 .2 3) ( 2 0

. 0, 0 .2 6) ( 1 0

. 0, 0 .1 6) ( 12. 0, 0 .1 9) ( 1 8

. 0, 0 .2 8) ( 1 0

. 0, 0 .1 8) 1 6 4 986 98 2 99 4 1 0 1 6 1 5 7 1 65 1 00 3 C 0

. 90 0 .7 5 0

. 88 0

. 83 0

. 89 0 .7 8 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 1 0 4 9 1 03 9 1 05 7 1 08 0 1 6 4 1 7 5 1 06 7 D 0 .7 3 0

. 8 4 0

. 80 0 .7 8 0 .7 6 0

. 5 7 0

. 6 4 0 .74 ( 24. 0, 0 .22 ) ( 24. 0, 0 .22 ) ( 2 0

. 0, 0 .1 8) ( 2 0

. 0, 0 .2 0) ( 2 5

. 0, 0 .2 0) ( 2 8

. 0, 0 .2 6) ( 2 0

. 0, 0 .2 5) ( 2 6

. 0, 0 .21 ) 177 1 05 5 1 0 4 3 1 06 1 1 08 4 1 6 7 1 7 8 1 0 71 E 0

. 86 0

. 65 0

. 83 0

. 80 0

. 8 4 0 .77 0

. 8 4 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 1 69 1 69 172 17 6 1 6 1 172 174 F 0

. 90 0

. 5 7 0

. 8 2 0 .7 5 0

. 86 0

. 83 0

. 8 4 0

. 86 ( 7. 0, 0 .12 ) ( 41. 0, 0

. 3 4 ) ( 2 0

. 0, 0 .2 0) ( 1 9

. 0, 0 .22 ) ( 11. 0, 0 .14 ) (9

. 0, 0 .1 5) ( 1 6

. 0, 0 .22 ) (9

. 0, 0 .14 )

174 172 172 17 5 17 9 1 6 4 1 7 5 177 G 0

. 9 2 0 .7 3 0

. 8 7 0 .7 8 0

. 89 0

. 8 1 0

. 8 4 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 ) 1 56 955 938 95 2 9 7 5 14 6 1 5 7 96 7 H 0 .74 0

. 68 0 .77 0 .7 8 0 .74 0

. 60 0

. 65 0 .7 6 ( 2 3

. 0, 0 .2 6) ( 2 6

. 0, 0 .22 ) ( 1 5

. 0, 0 .1 8) ( 17. 0, 0 .1 8) ( 21. 0, 0 .22 ) ( 27. 0, 0 .2 9) ( 1 9

. 0, 0 .22 ) ( 21. 5, 0 .24 )

17 6 17 5 17 5 17 8 1 8 2 1 6 7 177 1 80 I 0 .7 9 0

. 83 0

. 8 2 0 .7 8 0

. 8 1 0

. 66 0

. 69 0 .7 8 ( 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 ) 1 66 99 2 9 7 8 998 1 0 1 9 1 58 1 6 7 1 00 9 J 1. 00 0

. 66 0 .7 9 0 .72 0

. 88 0 .7 8 0

. 86 0

. 86 (0

. 0, 0

. 00 ) ( 44. 5, 0

. 33 ) ( 1 8

. 0, 0 .2 0) ( 1 8

. 0, 0 .22 ) (8

. 0, 0 .1 3) ( 11. 0, 0 .17 ) ( 1 6

. 0, 0 .21 ) (8

. 0, 0 .1 5) 177 17 0 17 0 17 3 177 1 63 17 3 17 5 K 1. 00 0 .74 0 .7 5 0 .7 0 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 )

1 06 1 1 0 17 1 03 5 1 05 8 1 60 171 1 0 4 8 L 1. 00 0

. 8 7 0

. 85 0 .7 0 0 .74 0

. 80 (0

. 0, 0

. 00 ) ( 1 3

. 0, 0 .1 6) ( 1 6

. 0, 0 .17 ) ( 22. 0, 0 .24 ) ( 17. 0, 0 .21 ) ( 1 8

. 0, 0 .1 9)

1 05 1 1 0 2 5 1 0 4 8 1 60 171 1 03 5 M 1. 00 0 .74 0

. 6 4 0

. 6 7 0 .77 ( 0.0

, 0.00 ) ( 18.0

, 0.21 ) ( 19.0

, 0.26 ) ( 17.0

, 0.22 ) ( 18.0

, 0.19 )

1 06 9 1 06 7 1 63 174 1 05 3 N 1. 00 0 .72 0

. 86 0

. 86 (0

. 0, 0

. 00 ) ( 1 3

. 0, 0 .1 8) ( 14. 0, 0 .2 0) ( 1 0

. 0, 0 .14 )

1 09 2 1 6 7 1 7 8 1 0 7 6 O 1. 00 0 .7 5 0

. 69 LEGEND Pearson R (P90, COD)

n ( 0.0

, 0.00 ) ( 18.0

, 0.25 ) ( 14.0

, 0.19 ) 1 6 7 1 63 1 65 P 1. 00 0

. 8 4 (0

. 0, 0

. 00 ) ( 1 5

. 0, 0 .21 )

17 8 17 6 Q 1. 00 ( 0.0

, 0.00 ) 1 0 7 9



### December 2009 A-2001

0 0.2 0.4 0.6 0.8

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0
Distance Between Samplers (km)Correlation



# Figure A-117. PM 10 

# inter-sampler correlations as a function of distance between monitors for Pittsburgh, PA.

December 2009 A-201

# Figure A-118. PM 10 

# monitor distribution and major highways, Riverside, CA.

December 2009 A-202

# Figure A-119. Box plots illustrating the seasonal distribution of 24-h avg PM 10 

# concentrations for Riverside, CA.

December 2009 A-203

# Table A-47. Inter-sampler correlati on statistics for each pair of PM 10 

# 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) (5 1.0, 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-2040 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. PM 10 

# inter-sampler correlations as a function of distance between monitors for Riverside, CA.

December 2009 A-205

# Figure A-121. PM 10 

# monitor distribution and major highways, Seattle, WA.

December 2009 A-206

# Figure A-122. Box plots illustrating the seasonal distribution of 24-h avg PM 10 

# concentrations for Seattle, WA.

Table A-48. Inter-sampler correlati on statistics for each pair of PM 10 

# 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-2070 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. PM 10 

# inter-sampler correlations as a function of distance between monitors for Seattle, WA.

December 2009 A-208

# Figure A-124. PM 10 

# 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 PM 10 

# concentrations for St. Louis, MO.

December 2009 A-210

# Table A-49. Inter-sampler correlati on statistics for each pair of PM 10 

# 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-2111

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. PM 10 

# 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 PM 2.5 

# speciation data for 2005-2007 for a) annual, b) spring, c) summer, d) fall and e) winter derive d using the SANDWICH method in Atlanta, GA.

December 2009 A-213

# Figure A-128. Seasonally averaged PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# speciation data for 2005-2007 for a) annual, b) spring, c) summer, d) fall and e) winter, derive d using the SANDWICH method in Denver, CO.

December 2009 A-217

# Figure A-132. Seasonally averaged PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# speciation data for 2005-2007 for a) annual, b) spring, c) summer, d) fall and e) winter derive d using the SANDWICH method in St. Louis, MO.

December 2009 A-227

# Figure A-142. Seasonal patterns in PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 fr om all available hourly FRM-like PM 2.5 

# 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 fr om all available hourly FRM-like PM 2.5 

# 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 fr om all available hourly FRM-like PM 2.5 

# 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 fr om all available hourly FRM-like PM 2.5 

# 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 fr om all available hourly FRM-like PM 2.5 

# 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 fr om all available hourly FRM-like PM 2.5 

# 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 fr om all available hourly FRM-like PM 2.5 

# 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 PM 10 

# 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 PM 10 

# 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 PM 10 

# 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 PM 10 

# 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 PM 10 

# 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 PM 10 

# 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 PM 10 

# 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 PM 10 

# 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 PM 10 

# 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 PM 10 

# 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 PM 10 

# 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 PM 2.5 

# and co-located 24-h avg PM 10 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Atlanta, GA, stratified by season (2005- 2007). One point is included for each available monitor pair.

December 2009 A-245Spring

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 PM 2.5 

# and co-located 24-h avg PM 10 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Birmingham, AL, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-246Spring

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 PM 2.5 

# and co-located 24-h avg PM 10 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Boston, MA, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-247Spring

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 PM 2.5 

# and co-located 24-h avg PM 10 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Chicago, IL, stratified by season (2005- 2007). One point is included for each available monitor pair.

December 2009 A-248Spring

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 PM 2.5 

# and co-located 24-h avg PM 10 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Denver, CO, stratified by season (2005- 2007). One point is included for each available monitor pair.

December 2009 A-249Spring

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 PM 2.5 

# and co-located 24-h avg PM 10 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Detroit, MI, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-250Spring

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 PM 2.5 

# and co-located 24-h avg PM 10 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Houston, TX, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-251Spring

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 PM 2.5 

# and co-located 24-h avg PM 10 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Los Angeles, CA, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-252Spring

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 PM 2.5 

# and co-located 24-h avg PM 10 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for New York, NY, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-253Spring

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 PM 2.5 

# and co-located 24-h avg PM 10 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Philadelphia, PA, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-254Spring

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 PM 2.5 

# and co-located 24-h avg PM 10 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Phoenix, AZ, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-255Spring

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 PM 2.5 

# and co-located 24-h avg PM 10 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Pittsburgh, PA, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-256Spring

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 PM 2.5 

# and co-located 24-h avg PM 10 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Riverside, CA, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-257Spring

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 PM 2.5 

# and co-located 24-h avg PM 10 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for St. Louis, MO, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-258Spring

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 PM 10 

# and co-located 24-h avg PM 2.5 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Atlanta, GA, stratified by season (2005- 2007). One point is included for each available monitor pair.

December 2009 A-259Spring

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 PM 10 

# and co-located 24-h avg PM 2.5 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Birmingham, AL, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-260Spring

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 PM 10 

# and co-located 24-h avg PM 2.5 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Boston, MA, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-261Spring

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 PM 10 

# and co-located 24-h avg PM 2.5 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Chicago, IL, stratified by season (2005- 2007). One point is included for each available monitor pair.

December 2009 A-262Spring

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 PM 10 

# and co-located 24-h avg PM 2.5 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Denver, CO, stratified by season (2005- 2007). One point is included for each available monitor pair.

December 2009 A-263Spring

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 PM 10 

# and co-located 24-h avg PM 2.5 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Detroit, MI, stratified by season (2005- 2007). One point is included for each available monitor pair.

December 2009 A-264Spring

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 PM 10 

# and co-located 24-h avg PM 2.5 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Houston, TX, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-265Spring

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 PM 10 

# and co-located 24-h avg PM 2.5 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Los Angeles, CA, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-266Spring

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 PM 10 

# and co-located 24-h avg PM 2.5 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for New York, NY, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-267Spring

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 PM 10 

# and co-located 24-h avg PM 2.5 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Philadelphia, PA, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-268Spring

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 PM 10 

# and co-located 24-h avg PM 2.5 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Phoenix, AZ, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-269Spring

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 PM 10 

# and co-located 24-h avg PM 2.5 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Pittsburgh, PA, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-270Spring

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 PM 10 

# and co-located 24-h avg PM 2.5 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# for Riverside, CA, stratified by season (2005-2007). One point is included for each available monitor pair.

December 2009 A-271Spring

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 PM 10 

# and co-located 24-h avg PM 2.5 

# , PM 10-2.5 

# , SO 2 

# , NO 2 

# and CO and daily maximum 8-h avg O 3 

# 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 recep tor models used in the Supersite sour ce 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

re fl ects the historical solution in which source pro fi les 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 concentrati ons are expressed as the sum of products of species abundances in source emissions and source contributions (Equat ions A-1 or A-2). These equations are solved for the source contri bution estimates when am bient concentrations and source pro fi les are input. The single-sample effe ctive variance leas t squares is the most commonly used solution method because it incorporates uncerta inties of ambient concentrations and source pro fi les 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 pro fi les should avoid collinearity, which occurs when chemical compositions of vari ous source emissions are not sufficiently different. 121

for i = 1 to I



### Equation A-1 

# ∑

= 

# = J

j iklmn ijklmnijm iklmn 

# STF C

1



# ∑ +=

j itjtij it 

# ESFC

Equation A-2

Data Needs
CMB requires source pro fi les, which are the mass fractions of particulate or gas species in source emissions. The species and particle si ze fraction measured in source emissions should match those in ambient samples to be apportioned. Several sampling and analysis methods provide time-integrated speciation of PM 2.5 and volatile organic compounds (VOCs) for CMB. Source pro fi les are preferably obtained in the same geographical region as the ambient samples, although using source pro fi les from different regions is commonly practiced in the literatu re. The practitioner needs to decide the source pro fi les and species being included in the model, on the basis of th e conceptual model and model performance

measures. Output

Effective variance CMB determines , if converged, source contri butions to each sample in terms of PM or VOC mass. CMB also gener ates various model performance measures, including correlation R 2 , deviation Χ 2 , residue/ uncertainty ratio, and MPIN matrix that are useful for re fi ning 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 pro fi les.

Quanti fi es 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 speci fi c chemical markers.
Typically does not apportion secondary particle constituents to sources. Must be combined with pro fi le 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 recep tor models used in the Supersites so urce apportionment studies: factor analysis.

Receptor Model Description Strengths and Weaknesses

PMF

PMF x (PMF 2 and PMF 3 ) 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 PMF 2 and PMF 3 . PMF 2 solves the CMB equations (i.e., Equations A-2 and A3) using an iterative minimi zation algorithm. Source pro fi les F i j and contribution S jt are solved simultaneously. The non-negativity c onstraint is implemented in the algorithm to decrease the number of possible solutions (local mi nimums) in the PMF analyses, because both source pro fi le and contribution should not contain negative values. There is rotational ambiguity in all two-way factor analyses (i.e.,F jt and S jt matrices may be rotated and still fi t 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 val ues in the source matrix F j t .F key can further bind individual elements in F jt t o zero On the basis of a similar algorit hm, PMF3 solves a three-way problem.

PMFx and UNMIX estimate F ij and S jt by minimizing:



### Equation A-3

Where the weighing factor, σ it, represents the magnitude of Eit, PMFx limits solutions of Equation A-2 to non-negative F ij and S jt

. 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 so lution. Species commonly used in PMF are also those in CMB. Weight ing factors associated with each measurement need to be assigned before analysis. The prac titioner also needs to decide the number of factors, FPEAK, and Fkey in the model.
Output

PMFx reports all the elements in F ij and S jt matrices (PMF 2). It also calculates model performance measures such as deviation Χ 2 and standard deviation of each matrix element. The practitioner needs to interpret th e results linking them to source pro fi les 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 pro fi les 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 pro fi les or existing pro fi les to verify the representativeness of calculated factor pro fi les and uncertainties of factor contributions.
Relies on many parameters/initial conditions adjustable to model input; sensitive to the preset parameters.

ME2 125

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 fi tted by sums of products of unknown (and known) factor elements. The fi rst part of the algorithm interprets instructions from the user and generates a table that speci fi es the model. The second part solves the model us ing an iterative minimization appr oach. Additional constraints could be programmed into the model to reduc e the ambiguity in source apportionment. These constraints may include known source pro fi les 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 fl exible. 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 stru cture, the input, and the output.
Output
ME2 calculates and reports all unk nown variables in the model. Strengths

Software available.
Can handle user-speci fi ed 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 num ber of dimensions in the space to the number of factors that produce the data, followed by an unique “edge detection” technique to identify “edges” de fi ned by the data points in the space of reduced dimension (e.g., Figures 1 and 3). The number of factors is estimat ed by the NUMFACT algorithm in advance 127 , which reports the R 2 and signal-to-noise (S/N) ra tio associated with the fi rst N principle components (PCs) in the data matrix. The number of factor s 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 signi fi es missing or small contribution from one or more factors. Therefore, UNMIX searches all the edges and us es them to calculate the vert ices of the simplex, which are then converted back to source compositi on and contributions. Geometrical concepts of self-modeling curve resolution are used to ensur e that the results obey (to within error) nonnegativity constraints on source co mpositions 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 so lution. Species commonly used in UNMIX are also those in CMB. The meas urement precision is not required. The practitioner needs to specify the number of factors on th e basis of the NUMFACT results.
Output

UNMIX determines all the el ements in the factor (F ij ) and contribution ( S jt ) matrices. It also calculates the uncertainty associated with the factor elements and model performance measures including: (1) R 2 , (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., R 2 , S/N ratio).

Weaknesses
Requires large (>100) ambient datasets.

Need to assume or predetermine number of retained factors.

Does not make ex plicit 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 pro fi les to evaluate the solutions.



### December 2009 A-275

### Receptor Model Description Strengths and Weaknesses

PDRM 97

PDRM was developed under the Supersites Program and requires MatLab or equivalent software to perform the calculation. Principle

PDRM estimates contributions fr om selected stationary sources fo r a receptor site using high time-resolution measurements and meteorologica l data. In PDRM, Equation A-2 is modi fi ed to:



### Equation A-4

where ER i,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 fi t minimizing the objective function, FUN:



### Equation A-5

Because the number of solutions for a product of unknowns is in fi nite, additional constraints are set up for ( Χ /Q,t on the basis of the G aussian plume model, thus:



### Equations A-6 & A-7

Equations A-6 and A-7 limit the solution of E quation 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 ( σ y and σ z), and stack height (h).
Output

PDRM determines emission rates and contributions from each point source considered in the model at the same time reso lution as the measurement. Strengths
Explicitly include meteorological information and stack con fi guration 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 fi t.
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 signi fi cantly 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

PLS 128 Principle Strengths
PLS examines the relationships betwee n 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 th an 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) variable s, respectively. Equation A- 2 is modi fi ed 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 iterat ive 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 s ource or source categories.

Data Needs
Typical applications of PLS require bot h chemical speciated and size-segregated measurements. The practitioner needs to decide the number of la tent variables on the basis of the correlation of resulting T and U matrices.
Output

PLS calculates latent variables, which are co mmon factors best explaining the predictor and response variables, and the residues from fi tting. 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.

Dif

fi cult 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 recep tor models used in the Supersites so urce 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 s pecies and other sources. Using EFs-, concentration of the ith pollutant at a receptor si te at time t (i.e., Ci,t) can be expressed as:



### Equation A-12

where the enrichment factor EF i,pj is the ratio of emission rate of the pollutant of interest ( 

## F ij ) and tracer species (F pj ) from source j. C pj,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. EF i,pj is usually unknown but may be estimated from source pro fi les, edges of a two-way scatter plot or the ratio of C i,t to C pj,t for a particular period when it is believed that a single s ource is dominant. In cases where Z i , t is a constant, EF i,pj may be derived from MLR.

Data Needs

The minimum data needs include concentrati ons of all primar y 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 speci fi c 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 identi fi cation than for quanti fi cation.

NNLS 131,132

The MatLab Optimization Toolbox provides a function “lsqnonneg” for performing the NNLS calculation. Principle
NNLS also solves the EF equation (Equat ion 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 in to the algorithm, i.e.:



### Equation A-13

Utilizing orthogonal decomposition, a NNLS problem can be reduced to the more familiar least-distance programming and so lved by a set of iterative subroutines developed and tested by Lawson and Hans on.131 In a more general sense, NNLS linearly relates a response variable to a set of independent variables with only non-negative coef fi cients.

Data Needs
When applied to EF or MLR problems, NNL S requires the concentration of target (response) and tracer (independent) species.
Output
NNLS generates non-negative regression coef fi cients for an EF/MLR problem and these coef fi cients can be related to the source contributions. Strengths
Implemented by many statistical software packages.
Generate only non-negative EFs or regression coef fi cients.
Do not require source measurements.
Possible to include meteorological or other (besides chem istry) data into the model.

Weaknesses
Require a large (>100) set of ambient measurements.
Semiquantitative method, not speci fi c. 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 esti mating the SOA production rate using the emission inventories of primary precur sor VOCs. FAC is actually a sourceoriented modeling technique but it does not take into account all the atmospheric processes. FAC is de fi ned as the fraction of SOA that would result from the reactions of a particular VOC:



### Equation A-14

where [VOC i ] 0 is the emission rate of VOC i and [SOA] is the fo rmation rate of SOA. Equation A-14 can be viewed as an extension of Equation -12 but concentrations are replaced with emissi on rates and EFs are replaced with FACs. FAC and the fraction of VOC reacted u nder typical ambient conditions have been developed for a large number of hydrocarbons >C 6 1 11



## . The most signi fi cant 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 in terest. The knowledge of O 3 and radiation intensity is also helpful for slight modi fi cations 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 in fl uence of aerosolconcentration 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.

Dif

fi cult 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 mo dels used in the Supersites so urce 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 giv en wind direction will exceed a predetermined threshold criterion (e.g., upper 25th percentile of th e fractional contribution from the source of interest). The calculati on of CPF uses source contributions (i.e., O 3 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 anal ysis because of the uncertain wind direction. CPF is then determined by:



### Equation A-15

where m Δθ is the number of occurrenc es in the direction sector θ → θ + Δθ that exceeds the speci fi ed threshold, and n Δθ is the total number of wind occurr ences 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 s ource contributions repr esent 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 conc entration measurements at a receptor site.

Data Needs
CPF requires the time series of s ource contributions at a receptor site, which is usually determined by CMB or factor analysis met hods 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 veri fi cation for the source identi fi cation made by factor analysis method.
Easy to implement.

Weaknesses

Criterion for the threshold is subjective.

Absolute source contribution (or fractional contribution) may be in fl uenced 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 (a veraged) source contribution as a function of wind direction following:



### Equation A-16

where Wi is the wind direction for the ith sa mple and Si is the c ontribution from a speci fi c 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 impor tant 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-speci fi c spatial distribution of source s. NPR also estimates the con fi dence intervals of S( θ ) based on the asymptotic normal distributio n 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 func tion of wind direction and the

con fi dence level associated with it. Strengths

Infer the direction of sources or factors relative to the receptor site.

Provide veri fi cation for the source identi fi cation 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 in fl uenced 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 calculat ed 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 samp le i, TSA obtains one or more trajectories and calculates their total residence time in the jth directional sector ( τ i,j , i.e., the total number of 1-h trajectory end points that fall into the sector). T he pollutant concentration or source contribution in the sample, S i , is then linearly apportioned into each directional sector according to τ i,j and averaged over all samples to produce the direct ional dependent pollutant concentration/source contribution for the per iod of interest:



### Equation A-18

where N is the number of samples. Compared wi th CPF and NPR, TSA c onsiders the entire air mass history rather than just the wind direction at the receptor.

Data Needs
TSA requires the time series of po llutant concentration or source c ontribution at the receptor site, and back trajectories initiated over the site durin g 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 pollut ant concentration or source contribution as a func tion of wind direction based on back trajectory calculations. Strengths

Infer the direction of sources or factors relative to the sampling site.

Provide veri fi cation for the source identi fi cation 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 ef

fi ciency, dispersion, and deposition.

Dif

fi cult 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 t he statistical analysis on a group of trajectories to retrieve useful patterns regarding the spatial distri bution of sources. Uncert ainties associated with individual trajectory calculations largely cancel out for a suf fi cient 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 fi rst calculated for each sample at the re ceptor 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 gr id cell is calculated for all ba ck trajectories and for a subset of trajectories corresponding to “high” pollutant concentration or source contri bution at the site. PSCF in cell (i,j) is then de fi ned 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 cont ributes to high pollutant concentrations or source contribution at the downwi nd 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 veri fi cation for the source identi fi cation 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 ef

fi ciency, dispersion, and deposition.

Dif

fi cult 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 fi eld” for each trajectory ignoring th e effects of chemical reactions and deposition. Back trajectories are fi rst 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 upwind 144,145 , thus:



### Equation A-20

where (X,Y) is the coordi nate of the grid center, a is the di spersion speed, and x’(t’) and x’ (t’) represent the trajectory. The probability fi eld, Q, for a given trajectory is then integrated over the upwind period, τ , to produce a two-dimensional “ natural” (nonweighted) transport fi eld:



### Equation A-21

After the transport fi eld for each trajectory is established, they are we ighted by the corresponding pollutant concentration or source contribution at the receptor site and summed to yield the overall SQTBA fi eld. 117

Data Needs
SQTBA requires the time series of pollutant concent ration or source contri bution at the receptor site, and back trajectories initia ted over the site during the samp ling period. Trajectories should be calculated with 1to 3-h segment to reduce t he uncertainty from in terpolation (if needed).
Output
SQTBA put more weight on trajectories associ ated higher pollutant concentration or source contribution and therefore the resulting fi eld 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 fi eld is unclear.

Dif

fi cult 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, RT WC requires back trajectories calculated for each sample at the receptor site, and a study domain cont aining the receptor site divided into an array of grid cells. RTWC assumes that no major polluta nt sources are located along “clean” (associated with low pollutant concentrations) trajectories and that “polluted” trajectories picked up emissions along their paths. In practice, RT WC distributes pollutant concentra tions at the rec eptor to upwind grid cells along the back trajectories according to the trajectory residence times in those cells. 117,146



### Equation A-22

where S k is the pollutant concentrati on or source contribution det ermined upon the arrival of trajectory k and S i,k is the redistributed pollutant concentra tion or source contribution for cell i upwind. RTWC is known for the problem of “tailing effe ct,” i.e., spurious s ource areas can be identi fi ed 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 fi eld and source strength).
Physical meaning of the RTWC fi eld is unclear.

Dif

fi cult 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 % U ncertainty 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 1.041 1 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 0.01 12 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 NO 3  ̄ 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 SO 4  ̄ 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 CO 3 –
Organic carbon II O C 2

Organic carbon III O C 3

Organic carbon IV O C 4

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 O C 1

EC II EC2

Sulfur dioxide SO 2 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 NO 3 – 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 SO 4 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 CO 3 – 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 0.001 1 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 SO 2
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. PM 2.5 

# receptor model results ( μ g/m 3 

# )

Sampling Site Measured PM 2.5 

### Concentration Vegetative Burning Road Dust, Soil (NH 4 

### ) 2 

### SO 4 

### NH 4 

### NO 3 

### 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. PM 10 

# 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, 1995 2 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 co llected between 8:00 and 9:00 a.m. on the mo rnings before and after each trip. In the h ours surrounding these samples, group-level measur ements of particle concentrations also were collected using several continuous instruments installed on two portable carts. These carts were fi rst positioned in a central location inside the participants’ l iving facilities 24-h before each trip. The carts remained at the facilitie s until it was time for the trips, at which point they fol lowed the participants from the health testing room , onto the bus, to the group activity, and to lunch. After the trip home aboard the bu s, the carts were returned to the central location in the living fa cility where they remained until the conclusion of the health t esting on the following morning. Continuous m easurements of ambient particles and gase s also were collected from a central monitoring station in East St. Louis, Illinois. Two portable ca rts containing continuous air pollution monitors were used to measure group-level micro-environm ental exposures to traffic related pollutants, including PM 2.5 , BC, and size-specific particle counts. PM 2.5 concentrations were measured continuously using a DustTrak aerosol monitor model 8520 with a Nafion diffusion dryer. Integrated samples of PM 2.5 mass also were collected using a Harvard Impa ctor for daily calibration of the trip and facility. Periods Continuous BC concentrations were me asured using a portable aethalometer with a 2.5- μ m 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 PM 2.5 Ambient Size PM 2.5 , PM 10 Component(s) BC, pollen and mold also assessed Primary Findings PM 2.5 exposures resulted in increas ed levels of FeNO in elderly adults, suggesti ve of increased airway inflammation. These associations were best assessed by mi croenvironmental exposure measurements duri ng periods of high personal particle exposures. In pre- trip samples, both microenvironment al and ambient exposures to PM 2.5 were positively associated with FeNO. For example, an interquartile increase of 4 μg/m 3 in the daily microenvironmental PM 2.5 concentration was associated with a 13% [95% CI: 2-24) increase in FeNO. After the trip s, however, FeNO concentrations were associated predominantly with microenvironmental exposures , with significant associations for conc entrations measured throughout the whole day. Associations with exposur es during the trip also were strong and statistically significant with a 24% (95% CI: 15-34) increase in FeNO predicted per interquar tile increase of 9 μg/m 3 in PM 2.5 . Although pre-trip findings were generally robust and the post-trip findings were generally robust, the post-trip findings we re sensitive to several influential days.



## Adgate et al. (2002, 030676 )

Study Design Comparison of outdoor, indoor and personal PM 2.5 in three communities. Period April-June, June-Au gust, September-November, 1999 Location Battle Creek, East St. Paul, and Phillips, Minnesota, constituting t he 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 impact ors (PEM) in a foam-insulated bag with shoulder strap with the inlet mounted on the front. Personal Size PM 2.5 Microenvironment Size PM 2.5 Ambient Size PM 2.5 Component(s) NR Primary Findings The relative level of concentrations report in other studies was duplicated. Outd oor < indoor < personal. On days with paired samples (n = 29), outdoor concentrations were significantly lower (mean difference 2.9 μg/m 3 , 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 k erosene heaters (Leaderer et al., 199 3; Ferro et al., 2004). Personal Method Personal monitoring was c onducted for two consecutive days, and was c onducted so that the two 24-h averages ma tched 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 impact or environmental monitoring inlets and air sampling pumps. To obtain I measurements, monitors were placed inside each resi dence in a room where the part icipants 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 tw o consecutive 24-h periods, followed by a day to change filters. Gravimetric O PM 2.5 concentrations were obtained using a federal reference method sampler. Personal Size PM 2.5 Microenvironment Size PM 2.5 Ambient Size PM 2.5 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 concentra tions varied across trace element s (TE). Unadjusted mixed-model results demonstrated that O monitors are more likely to underestimate than ov erestimate exposur e to many of the TEs that are suspected to play a role in the causation of air pollution related health effe cts. 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 powe r for this sample size, the adjusted models indicated clear seasonal and community related effects that should be incorpor ated in long-term exposure es timates 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; fa ll: 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 gravimetri c PM concentrations were collected using PM 2.5 inertial impactor environmental monitoring inlets and air sampling pumps. Monitors were placed inside each part icipant’s residence in the room where he/she reported spending the majority of their waking hours to obtain I measurements. Pa rticipants 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 PM 2.5 concentrations were obtained using a federal refe rence method sampler and EPA site requirements for ambient sampling. Gravometric sample s were collected near the approximate geographic center of each neighborhood, and monitors ran from midnight to midni ght 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 PM 2.5 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 PM 2.5 measurements. A sensitiv ity 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 PM 2.5 over time. These results suggest that the studies showing relative ly strong longitudinal correlation coefficients between P and O PM 2.5 for individuals sensitive to air pollution health effects do not necessarily predict exposure to PM 2.5 in the general population.



## Allen et al. (2003, 053578 )

Study Design Use of continuous light scattering data to sepa rate indoor PM into indoor- and outdoor-generated components to en hance knowledge of the outdoor contribution to total indoor and pers onal 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 sub jects, 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 Suggest ed (not identified) Personal Method NR. Indoor and outdoor sampling conducted Personal Size NR Microenvironment Size PM 2.5



### December 2009 A-292Ambient Size PM 2.5 Component(s) S Primary Findings A recursive mass balance model can be successfu lly used to attribute indoor PM to its outdoor and indoor comp onents and to estimate an avg Penetration, air exchange rate, deposition rate, and NH 4+ 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 PM 2.5 was measured using a 10-l/min single-stage Harvard Im pactor (HI) with 37-mm Teflon filters. The relationship between particle mass concentration and light scatte ring coefficient (bsp) was also measured on a continuous basis indoors and outdoors using nephelometers (model 902 and 903). Personal Size NR Microenvironment Size PM 2.5 Ambient Size PM 2.5 Component(s) S (measured by XRF) Primary Findings The authors showed that RM can reliably estimate F inf . Simulation results suggest that the RM F inf

estimates are minimally impacted by measurement error. In addi tion, 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 F inf . Individual homes vary in their infiltration efficiencie s, 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 me thods, such as RM or S, instea d 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; Clerm ont-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 PM 2.5 was monitored simultaneously in both schoolyards (proximity leve l) 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 PM 2.5 Component(s) NR Primary Findings Results show an increased risk for EIB and flexur al 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 (PM 2.5 concentrations exceeding 10 μg/m 3 ). Population based findings are also consistent with experimental data t hat have demonstrated that inhalation of traffic-related air pollutants either individually or in comb ination, can enhance the immune re sponses 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 fl at 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 ident ified tobacco smoking, cooking, househol d cleaning and general resident movements. Personal Method NR Personal Size NR Microenvironment Size PM 2.5 Ambient Size PM 2.5 Primary Findings I/O suggest that chemicals such as Cl - , Na + , Al, Co, Cu, Fe, Mn, Ti, V, Zn, and EC we re derived from the mi gration of outdoor particles (I/O <1 or ~1).



### December 2009 A-293

## Barn et al. (2008, 156252 )

Study Design Measure indoor F inf

of PM 2.5 from forest fires/wood smoke, effectiveness of high-efficiency part iculate air (HEPA) filter air cleaners in reducing indoor PM 2.5 , and to analyze the home determinants of F inf

and air cleaner effectiveness (ACE). Period 2004-2005 (summer 2004 and 2005, winter 2004) Location British Columbia, Canada Population Homes affected by either fo rest 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 PM 2.5 Microenvironment Size NR Ambient Size Outdoor home PM 2.5 Component(s) NR Primary Findings Use of HEPA filter ai r cleaners can dramatically reduce indoor PM 2.5 concentrations. Number of windows and season predict F inf (p< 0.001).



## Baxter et al. (2007, 092726 )

Study Design Part of a prospective bi rth cohort study performed by the Asthma C oalition for Community, Environment, and Social Stress (ACCESS) Period 2003-2005. Non-heating season: May to Oc tober; Heating season: December to March Location Boston (urban) Population Lower socio-economic status (SES) households Age Groups NR Indoor Source NR Personal Method PM 2.5 samples were collected with Harvard personal environmental monitors (PEM). NO concent rations were measured using Yanagisawa passive filter badges. Personal Size NR Microenvironment Size PM 2.5 Ambient Size PM 2.5 Component(s) EC Primary Findings The authors’ r egression models indicated that PM 2.5 was influenced less by local traffic but had significant indoor sources, while EC was associated with local traffic and NO 2 was associated with both traffic and i ndoor sources. However, local traffic was found to be a larger contributor to indoor NO 2 where traffic density is high and windows are opened, whereas indoor sources are a larger contributor when traffic density is low or wi ndows are closed. Similarly, tr affic contributed up to 0.2 μ g/m 3 to indoor EC for homes with open windows, with an insignificant contribution for homes wh ere 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 NO 2 and EC, respectively. However, results from the Bayesian approach also suggested a high degree of uncertainty in se lecting 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 ai r 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 st udy 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, openi ng windows, time spent cooking, us e of candles or air freshener, clea ning activities, air conditioner use. Personal Method NR Personal Size NR Microenvironment Size PM 2.5 Ambient Size NR Component(s) EC (m-1 x 10 -5 ); Ca (ng/m 3 ); Fe (ng/m 3 ); K (ng/m 3 ); Si (ng/m 3 ); Na (ng/m 3 ); CI (ng/m 3 ); Zn (ng/m 3 ); S (ng/m 3 ); V (ng/m 3 ) Copollutant(s) NO 2 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 contri buted to indoor pollutants.



### December 2009 A-294

## BéruBé et al. (2004, 007894 )

Study Design 6 homes in Wales and Cornwall were monitored f our 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 PM 10 Ambient Size NR Component(s) NR Primary Findings There are greater masses of PM 10 indoors, and that the composition of the indoor PM 10 is controlled by outdoor sources and to a lesser extent by indoor anthropogenic acti vities, except in the pr esence of tobacco smokers. The indoor and outdoor PM 10 collected was characterized as being a heter ogeneous mixture of particles (soot, fibers, sea salt, smelter, gypsum, pollen and fungal spores).



## Branis et al. (2005, 156290 )

Study Design Human exposure assessm ent 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 identif ied as a source of indoor fine particles (PM 1.0 and PM 2.5 ) Personal Method Harvard impactors (H I) with membrane Teflon filters Personal Size PM 1 , PM 2.5 , PM 10 Microenvironment Size PM 1 , PM 2.5 , PM 10 Ambient Size PM 10 Component(s) NR Primary Findings Presence of people is an im portant source of coarse particles indoor s; 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 shoul der strap or backpack with GK2.05 cyclone samplers 4 L/min. Personal Size PM 2.5 Microenvironment Size PM 2.5 Ambient Size PM 2. 5 Component(s) SO 4 2- Primary Findings In both cities, personal and indoor PM 2.5 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 tr aveling the subway Age Groups 14-18 yr Indoor Source No Personal Method Sampling packs carried by subjects Personal Size PM 2.5 Microenvironment Size PM 2.5 (home indoor and home outdoor) Ambient Size PM 2.5 . 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 conc entration of Fe, Mn, and Cr than home indoor and ambient sample s. The ratios of Fe (ng/μg of PM 2.5 ) vs Mn (pg/μg PM 2.5 ) showed personal samples to be twice the ra tio 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 pa rt 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 PM 2.5 Personal Size PM 2.5 Microenvironment Size NR Ambient Size NR Primary Finding(s) A greater va riety of particles was observed in the personal samples compared to the fixed-location apartmen t 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 sp rays used, boiler use and location, animals , mold, tobacco smoking, windows closed Personal Method Personal pumps with 37-mm Te flon filters, flow rate 4 l/min in a bag with shoulder strap. The impactor was nea r the breathing

zone Personal Size PM 2.5 Microenvironment Size PM 2.5 , PM 10 Ambient Size PM 2.5 , PM 10 Component(s) NR Primary Findings Indoor PM 2.5 concentrations explained 40% of the variability of personal exposure. Th e 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, PM 2.5 Personal Size Indoor school; Filter, PM 2.5 Microenvironment Size Outdoor school; Filter, PM 2.5 Ambient Size PM 2.5 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 μ m Microenvironment Size PM 10 and PM 2.5 ; measured immediately outside the house a nd in the living room of the home. Ambient Size PM 10 Copollutant(s) O 3 and NO 2 measured at central site Primary Findings Percent predicted FEV 1 was inversely associated with personal exposure to fi ne particles. Also with indoor, outdoor and central site gravimetric PM 2.5 , PM 10 , and with hourly TEOM PM 10

.



### December 2009 A-296

## Delfino et al. (2006, 090745 )

Study Design Cohort. Measur ed daily expired NO (FeNO) Period Aug-Dec 2003 Location Riverside and Whittier, California Population Children with asthma exacerbations in previous 12 mont hs, non-smokers, non-smoking households Age Groups 9-18 yr Indoor Source No Personal Method Wore a backpack during waking hours for PM 2.5 , EC and OC, NO 2 , temperature, and relative humid ity. Exhaled air collected in Mylar bags to analyze for NO. Personal Size 24-h PM 2.5 ; 1-h max PM 2.5 ; 8-h max PM 2.5 ; 24-h NO 2 Microenvironment Size NR Ambient Size 24-h PM 2.5 ; 24-h PM 10 ; 8-h max O 3 ; 8-h max NO 2 ; 24-h NO 2 ; 8-h max CO Component(s) 24-h PM 2.5 EC; 24-h PM 2.5 OC Primary Findings The strongest positive asso ciations were between FeNO and 2-day aver age pollutant concentrations. Per IQR inc reases 1.1 ppb FeNO/24 μg/m 3 personal PM 2.5 .; 0.7 ppb FeNO/0.6 μg/m 3 personal EC; 1.6 ppb FeNO / 17 ppb personal NO 2 Ambient PM 2.5 and personal and ambient EC were significant only when subj ects were taking inhaled corticosteroids. Subjects taking both inhaled steroids and antileukotrienes had no significant associations. Distributed lag models showed personal PM 2.5 in the preceding 5 h was associated with FeNO.



## Diapouli et al. (2007, 156397 )

Study Design Exposure assessment. Sampli ng 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. Prima ry schools indoor measurements were primar ily conducted inside classrooms, at table height. However, at three of the schools, rooms of different uses were selected. These incl uded 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 almo st 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 At hens area are exposed to significant PM c oncentration levels, both indoors and outdoors. Vehicular em issions 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 vari ation, both outside the schools and the resi dence, supports the close relation of UFPs levels with traffic density. Indoor concentrations within schools exhibited variabili ty during the school day only when there w ere significant changes in room occupancy. 24-h variation of indoor concentrations at the residence were well correlated with the outdoor concentration (R 2 = 0.89).



## Diapouli et al. (2008, 190893 )

Study Design Indoor, outdoor air monitoring of PM. To determi ne children exposure in school environment. To evaluate relationship between indoor and outdoor levels. Period Athens, Greece Location Primary schools Population NR Indoor Source Indoor PM 1 , PM 2.5 , PM 10 , presence of children and activities of childr en in classrooms, infiltrated vehicular exhaust Personal Method Harvard PEM, Teflon filter s Dust Trak Condensation particle counter Personal Size NR Microenvironment Size PM 1 , PM 2.5 , PM 10 Ambient Size PM 1 , PM 2. , PM 10 5 Component(s) NO 3 - , SO 4 2– Primary Findings High levels of PM 10 and PM 2.5 measured indoors and outdoors. PM 10 more variable spatially than PM 2.5 . I/O ratio for PM 10 and PM 2.5 close to 1 at almost all sites. Ratio of PM 1 smaller than 1 in all cases. Vehicular traffic presumed to be the main source of PM 1 . Indoor PM 2.5 and PM 10 levels dependent on the amount of activity in classroom and outdoor levels. Indoor SO 4 2– concentrations strongly asso ciated with outdoor levels. Result suggests that SO 4 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 PM 2.5 Microenvironment Size PM 2.5 , PM 10 , PM 10-2.5 Ambient Size PM 2. , PM 10 , PM 10-2.5 5 Component(s) SO 4 2- Primary Findings Ambient exposur es and (to a lesser extent) ambien t concentrations were associat ed with health outcomes. Total and nonambient particle ex posures were not.



## Farmer et al. (2003, 089017 )

Study Design Case control molecular epidemiology studies of carcinogenic environmental pollu tants, particularly PAHs Period 12 months Location Prague, Czech Republic (2 sites); Košice, Slovak republic; Sofia, Bulgaria Population Policeman and busdrivers usua lly 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 Devi ces; Blood and Urine Samples; Stationary Versatile Air Pollution Samplers (VAPS) Personal Size PM 10 Microenvironment Size NR Ambient Size PM 10 ; PM 2.5 (not reported) Component(s) Extractable organic matter (EOM), B[a]P, c-PAHs Primary Findings EOM per PM 10 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-t ime particle counters and integr ated filter samplers (Met-One Model 237B) were used to measur e 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 acti vities. The SIM was attached to a six foot step-ladder with the intake at breathin g height. The SAM was located under a two-sided roofed shed in the ba ckyard of the home with the filter samplers supported by a metal stand and the real-time particle counters sitting on a table. Personal Size PM 5 Microenvironment Size PM 2.5 ; PM 5 Ambient Size PM 2.5 ; PM 5 Component(s) NR Primary Findings The results of this st udy indicate that house dust resuspended from a range of human activities increases per sonal PM concentrations and this resuspension effect significantly contribut es to the personal cloud. The results of this study also suggest that normal human activiti es that resuspend house dust may contribute si gnificantly to the strong correlations found between personal exposure and indoor PM conc entrations in previous studies. The PEM/SIM ratios for human activity presented in this paper are also in the r ange 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 residen tial 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 respirabl e 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 conc luded that “(1) indoor activities and poor ventila tion 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 individu als has much impact on relationship investi gations of their personal RP M with their respective indoor and ambient-outdoor RPM levels; as reported in earlier reports and (4) residential indoors, local road-traf fic and soil-borne RPMs are the dominating routes of personal expos ure 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, Grenobl e, Toulouse, France Population Children aged 8-14 yr Indoor Source ETS from mother, rodents at home. Personal Method SKC pump 4 Lpm with PM 2.5 inlet and 37 mm, 2 micron Teflon filter Personal Size PM 2.5 Microenvironment Size NR Ambient Size PM 10 Component(s) NR Primary Findings The final model explains 36% of the between subjects variance in PM 2.5 exposure, with ETS contribut ing 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 severa l analytical methods to measur e the total as well as soluble amounts of metals with PM 2.5 collected from indoor and PM samples. (X-ray fluorescence and instrumental neutron activation analysis) Location Retirement facility in Towson, Maryland Population Retirement facility with subj ects 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 PM 2.5 Microenvironment Size PM 2.5 Ambient Size NR Component(s) 42 elements were analyzed for in the PM 2.5 samples collected from personal and well as indoor samples Primary Findings Most of the extractable components of the me tals were in a water-soluble form suggesting a high potential for bioavailability of elements from respiratory exposure to PM 2.5 . 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-Shaughness y 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/m 3 ); Total bacteria (cells/m 3 ); Viable fungi MEA (CFU/m 3 ); Viable fungi DG18 (CFU/m 3 ); Viable bacteria (CFU/m 3 ) Primary Findings The recall period of 7 da ys provided the most reliable data for heal th effect assessment. Both personal expos ure and concentrations of pollutants at home were more frequentl y associated with health sympt oms 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 ve rsus 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 μ m inlet. All samples on 47 mm Whatman quartz microfiber f ilters, weighed on an electronic microbalance. Analyzed for OC and EC using DRI Model 2001 Thermal/Optical Carbon Analyzer. Personal Size PM 2.5 Microenvironment Size NR Ambient Size PM 2.5 Component(s) OC, EC, OM, TC Primary Findings The major s ource of indoor EC, OC, and PM 2.5 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, characte rizing indoor/outdoor particle relationships Period October 2002-March 2004 Location 4 European cities Amster dam, Athens, Birmi ngham, Helsinki Population Urban populations Age Groups NR Indoor Source Smoking, candl e burning, cooking/frying Personal Method No personal ex posure assessment was conducted Personal Size NR Microenvironment Size PM 10 , PM 2.5 , PM 10 - 2.5 , Ultrafine (UFP) Ambient Size PM 10 , PM 2.5 , PM 10 - 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 PM 2.5 , soot, or SO 4 2-

, probably related to greater losses during infiltration due to smaller particle size. Infiltration factors for UFP and PM 2.5 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 nitrat e, secondary sulfate, OC, and motor vehicle exhaust Personal Method Inertial impactor PEM in the breathing zone of the subjects Personal Size PM 2.5 Microenvironment Size PM 2.5 Ambient Size PM 2.5 Component(s) SO 4 2- Primary Findings Personal exposures were influenced by a combination of indoor and ou tdoor factors. Indoor factors included gypsum, personal grooming products, and an unknown indoor source. Outdoor factor included SO 4 2- , soil, and an unknown factor. Outdoor factors accounted for 63% of personal expos ure, 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 PM 2.5 absorbance and sulfur among postmyocardial infarction patients Period January 2004-June 2004 Location Barcelona, Spain Population Survivors of a myocar dial 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 PM 2.5 Microenvironment Size NA Ambient Size PM 2.5 Component(s) S Primary Findings Ambient measurem ents of light extinction and S can be used as surrogates to personal PM 2.5 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 Ne therlands; Helsinki, Finland Population Elderly Cardiovascular Patients Age Groups 50-84 yr Indoor Source No Personal Method Personal PM 2.5 GK2.05; cyclones; indoor & outdoor Harvard Impactors; Reflectance EEL 43 reflectometers; Elemental Composition Tracor Spectrace 5000 ED-XRF system Personal Size PM 2.5 Microenvironment Size PM 2.5 Ambient Size PM 2.5 Component(s) Estimated EC, elemental composition of a subset of personal, indoor and outdoor samples Primary Findings For most elements, pers onal and indoor; concentrations were lower than and highly correlated with outdoor con centrations. The highest correlations (median r = 0.9) were found for sulfur and particle absorbance (EC), whic h both represent fine; mode particles from outdoor origin. Low correlations were observed for elements that represent the coarser part of the PM 2.5 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 PM 2.5 Microenvironment Size NR Ambient Size PM 10 Component(s) NR Primary Findings The contribution of the background ambient PM 10 level was a very strong determinant of the total personal exposure to PM 2.5 , 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 moni toring equipment. Personal monito ring of PM2.5 and PM 1 was carried out simultaneously with parallel measurements of PM 2.5 and PM 1 indoors in living rooms and outside the house on a balcony, porch, etc. In addition, urban background PM 2.5 levels were measured. Personal monitoring was performed in two ways. The 20 randomly selected s ubjects carried personal monitoring equipment for PM 2.5 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 PM 2.5 cyclone and one PM 1 cyclone. On the second occasion, duplicate monitors for PM 2.5 were used. For personal and residential monitoring, the BGI Personal Sa mpling Pump was used together with the GK2.05 cyclone for PM 2.5 sampling and the Triplex cyclone SCC1.062 for PM 1 sampling. The personal sampling pump was placed in a small



### December 2009 A-301shoulder bag and the cyclone attached to the shoulder strap near the subject’s breathing zone. The personal monitoring equipment was carried by the subject duri ng awake time. During the night, it was placed in the living room. For indoor monitoring in living rooms, cyclones (PM 2.5 and PM 1 ) were placed at about 1.5 m above the floor. The same setup was used for residential outdoor monitoring. The urban backg round monitor was placed on top of a roof somewhat south of the city center but not near any major highway. Personal Size PM 2.5 ; PM 1 Microenvironment Size PM 2.5 ; PM 1 Ambient Size PM 2.5 ; PM 1 Component(s) BS Primary Findings Personal exposure of PM 2.5 correlated well with indoor levels, and the a ssociations with residential outdoor and urban background concentrations were also accept able. Statistically significantly higher personal exposure compared with residential outdoor levels of PM 2.5 was found for nonsmokers. PM 1 made up a considerable proportion (about 70–80%) of PM 2.5 . 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 betw een particle mass and BS. The urban background station provided a good estimate of the residential outdoor concentrations of both PM 2.5 and BS2.5 within the city. The air mass origin affected the outdoor levels of both PM 2.5 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 ex posures between modes of transport, routes, timing Period April 28-May 23, 2003 Location Street canyon inters ection in Central London, UK Population Users of an urban street canyon intersection Age Groups NR Indoor Source NR Personal Method PM 2.5 measured usnig high-flow grav imetric personal samplers (PM 2.5 ) operating at a flow rate of 16 l/min carried in a backpack with sampling head positioned in personal breathing zone. UF P measured using TSI P-TRAK pa rticle counters in which isopropyl alcohol condens es to form droplets that can be easily counted by a photodetector as they pass through a laser beam. Personal Size PM 2.5 , UFP (0.02-1.0um) Microenvironment Size PM 2.5 , UFP (0.02-1.0um) Ambient Size PM 2.5 Component(s) NR Primary Findings Personal exposures to PM 2.5 while walking were significantly lower then while ri ding in a car or taxi, likely a function of greater distance to roadside. No si gnificant differences in PM 2.5 were observed between exposures on the high traffic road compared with the backroad. Personal exposure levels we re lowest during midday measurements for PM 2.5 and highest in the early evening. Personal exposures to ul trafine particles were lowest while walking and highest while riding the bus. Exposures to ultrafine particles were also signific antly higher on the high traffic road and during morning measurements. Exposure to ultrafine particles were highest in the morn ing, likely the result of peak traffic de nsity in the morning. Exposure assessment also revealed that the background and curbside moni toring stations were not representative of the personal exposure of individuals to PM 2.5 and CO at and around a str eet 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 PM 2.5 gravimetric filter measur ement, UFP (0.02-1 μm ) P-TRAK device, reflectance re flectometer measurement of PM 2.5 filter Personal Size PM 2.5 , UFP (0.02-1 μm) Microenvironment Size NR Ambient Size PM 2.5 , UFP (0.02-1 μm) Component(s) Absorbance of PM 2.5 filter Primary Findings PM 2.5 pedestrian exposure was well correlated with and above backgr ound fixed-site monitoring le vels. PM pedestrian exposure was influenced by proximity to curbsi de 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 heatin g fuel (68%); Oil heating fuel (7%) Personal Method Rupprecht and Patashni ck ChemPass Personal Sampling System Personal Size PM 2.5 Microenvironment Size NR Ambient Size PM 2.5 Component(s) NR Primary Findings Personal PM 2.5 exposures were higher than out door ambient levels. Personal PM 2.5 exposures levels were correlated with ambient levels, mean r = 0.58



## Koistinen et al. (2004, 156655 )

Study Design Representat ive 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 cleaner s, sea salt, combustion sources Personal Method Integrated 24-h filter sample Personal Size PM 2.5 Microenvironment Size PM 2.5 Ambient Size PM 2.5 Component(s) BS Primary Findings Populati on exposure assessment of PM 2.5 , based on outdoor fixed-site monitoring, ov erestimates 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, Swit zerland; 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 PM 2.5 Microenvironment Size PM 2.5 Ambient Size PM 2.5 , PM 10 Component(s) NR Primary Findings Throughout the study, t he highest correlations were those between personal exposures and indoor concentration s, which suggests that indoor sources were import ant. 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 PM 2.5 ; were collected using a specially designed mult ipollutant sampler (Demokritou et al. 2001). PM 2.5 was collected using personal environmental monitors (PEMs) and 37-mm; Teflon filters (T eflo, Gelman Sciences, Ann Arbor MI). Personal Size PM 2.5 Microenvironment Size NR Ambient Size PM 2.5 Component(s) EC, SO 4 2– Primary Findings Ambient PM 2.5 and SO 4 2– are strong predictors of respecti ve personal exposures. Ambient SO 4 2– is a strong predictor of personal exposure to PM 2.5 . Because PM 2.5 has substantial indoor sources and SO 4 2– does not, the investigators; concluded that personal exposure to SO 4 2– accurately reflects exposure to ambient PM 2.5 and therefore the ambient component of personal exposure to PM 2.5 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 PM 2.5 Microenvironment Size PM 2.5 Ambient Size PM 2.5 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 sour ces, and indoor levels exceeded ambient lev els for PM 2.5 as well as for VOCs and eight other compounds. Correlation between personal and indoor PM 2.5 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 PM 2.5 Microenvironment Size PM 2.5 outside subject’s residence, and inside residence Ambient Size PM 2.5 at Central outdoor si te (downtown Seattle) Component(s) Light absorbing ca rbon (LAC) and trace elements Primary Findings Five sources of PM 2.5 identified vegetative burning, mobile emissions, secondar y sulfate, a source rich in chlorine, and crustalderived material. The burning of vegetation (in homes) contributed more PM 2.5 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 PM 10 and PM 2.5 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 PM 2.5 and PM 10 ; indoor and outdoor; tapered element oscillating microbal ance (TEOM) instruments. 2 days were monitored for PM 2.5 , and 2 for PM 10

. Personal Size NR Microenvironment Size NR Primary Findings Evaporative coolers were found to act as PM filters, creating indoor concentrations approximately 40% of outd oor PM 10 and 35% of outdoor PM 2.5 , 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 pers on was below 65, but his/her age was not specified) Personal Method Harvard Personal Environmental Monitor for PM 2.5 (HPEM 2.5 ) Personal Size PM 2.5 , PM 10 Microenvironment Size PM 2.5 , PM 10 Ambient Size PM 2.5 , PM 10 Primary Findings Average personal PM 2.5 exposure was similar to ambient PM 2.5 concentrations but much higher than average indoor concentrations. Personal, indoor, and outdoor PM 2.5 and PM 10 , as well as the ratio PM2.5/PM 10, were all significantly higher during the winter. Personal PM 2.5 and PM 10 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 Ta ichung, and 2 in Kaohsiung. Sites are industrial, commercial, resi dential and mixed. Age Groups 18 to >70 Indoor Source Being in kitc hen, park, major boulevard, stadi um, 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 PM 10 Microenvironment Size PM 10 Ambient Size PM 10 Component(s) None Primary Findings Outdoor rather than indoor levels contributed significantly to personal exposure. Important factors include t ime 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 ac tual 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 sampli ng bag near the breathing zone. Pu mp, battery, and motion sensor were on the hip or back. Personal Size PM 2.5 Microenvironment Size PM 2.5 Ambient Size PM 2.5 Component(s) EC, OC, S, Si Primary Findings Use of central-site PM 2.5 as an exposure surrogat e 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 i ndoor home & outdoor exposure assessment Period May-October (hot); No vember-April (cool); (1999-2001) Location Los Angeles County, CA; Elizabeth, NJ; Houston, TX Population Non-smoking homes Indoor Source Combustion (primary); at mospheric (secondary); sulfate, organics, nitrates; mechanically (abrasion) generated. Personal Method Filter (not specified) Personal Size NR Microenvironment Size Indoor home.; PM 2.5 Ambient Size PM 2.5 , outdoor home Component(s) Organic and elemental carbon; 24 elements (metals). Primary Findings The median contribution of ambient sources to indoor PM 2.5 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 asse ssment 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 PM 10-2.5 from PM 2.5 Personal Size PM 2.5 Microenvironment Size PM 10 - 2.5 , PM 2.5 Ambient Size PM 10 - 2.5 , PM 2.5 Component(s) BS, S, Cl, K, Ca, Mn, Fe, Cu, Zn, Br, Rb, Pb Primary Findings Wood burning made statisti cally significant contributi ons 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 thei r 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 liv ing in urban settings Age Groups 20 subjects 20-50 yr randomly selected fr om the population and 10 from departmental colleagues. Indoor Source NR Personal Method Integrated filter samples with cyclones for PM 2.5 and PM 1 cut points Personal Size PM 2.5 and PM 1 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 si te ambient concentrations, and personal ex posure to Cl, Ca, Ti, Fe, and Br were also signi ficantly higher than in door 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 (element al carbon) concentrations primar ily of outside origin; Indoor PM 2.5 significantly influenced by indoor OC (organic carbon) sources, including indoor smoking. Personal Method Integrated filter samples for PM 2.5 Personal Size PM 2.5 Microenvironment Size NR Ambient Size PM 2.5 Component(s) EC, OC Primary Findings Indoor PM 2.5 was significant influenced by indoor OC sources. Indoor EC sources we re predominantly of outdoor origin.



### December 2009 A-306

## Naumova et al. (2003, 089213 )

Study Design RIOPA Study-PAH partitioning indoor and outdoor po llutants to evaluate the hypothesis that outdoor air pollution contributed strongly to indoor air pollution. Period July 1999-June 2000 Location Los Angeles, CA, H ouston, TX, Elizabeth, NJ Population Houses Age Groups NR Indoor Source NR Personal Method Modified MSP Samplers, 37 mm quartz filter Personal Size PM 2.5 Microenvironment Size PM 2.5 Ambient Size PM 2.5 Component(s) OC, EC Primary Findings Both EC and OC were associated with gas/part icle 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 highl y exposed to traffic emissions, one influence d by local industrial sources, and a background urban environment. Industrial sources of pollu tion were present in each city. Age Groups 6-13 yr and 20-71 yr. All non-smok ers and not exposed to environmental toba cco smoke or industrial air pollution. Indoor Source NR Personal Method Rucksack with Harvard ChemPass Personal Size PM 2.5 , PM 10 Microenvironment Size NR Ambient Size PM 2.5 , PM 10 Copollutant(s) NO 2 Primary Findings The difference between ambient air concent rations and average total exposur e is pollutant specific. PM 2.5 and PM 10 concentrations underestimate popula tion exposures across almost all cities, season, and age groups, but the opposite is true for NO 2

.



## 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 PM 2.5 Harvard Personal Environment Monitors (HPEM 2.5 ) Personal Size PM 2.5 Microenvironment Size NR Ambient Size PM 2. 5 Component(s) SO 4 2- , ABS (light absorbing carbon) Primary Findings Thermal inversions were associ ated with personal exposures as well as ambient PM 2.5 concentrations and likely caused observed spatial variability. However, ambient sampling locations were correlated in time. Similar observations were made for SO 4 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, c ooking, transport in motor vehicl es, low-effort home activities, moderate-effort home activities , activities in public places, and resting or sleeping. Personal Method PEM Personal Size PM 2.5 , PM 10 , and PM 10-2.5 Microenvironment Size PM 2.5 , PM 10 , & PM10-2.5 Ambient Size NR Component(s) NR Primary Findings During both seasons, pers onal exposures were higher t han indoor or outdoor means, except during the winter wh en indoor concentrations were higher t han 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, Swit zerland; Milan, Italy; Oxford, UK; Prague, Czech Republic; Helsinki, Finland Population Adults Age Groups 25-55 yr Indoor Source NR Personal Method Integrated 48-h PM 2.5 filter samples Personal Size PM 2.5 Microenvironment Size PM 2.5 Ambient Size PM 2.5 Copollutant(s) NO 2 Primary Findings Personal PM 2.5 and NO 2 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 nei ghborhoods around aluminum smelting plant Age Groups NR Indoor Source NR Personal Method Indoor quartz filter sample Personal Size PM 2.5 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 majo r 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 c ould provide useful es timates 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), kitc hen fans, air conditioner/heating usage, number of occupants, nearby roadways Personal Method NR Personal Size NR Microenvironment Size PM 2.5 , Particle number Ambient Size PM 2.5 Component(s) BC (nonv olatile component); NO 3 (volatile component) Primary Findings Infiltration rate for PM 2.5 was intermediate, while BC was highest and NO 3 lowest. Infiltration rate varied with particle size, air exchange rate, outdoor NO 3 . PM 2.5 infiltration was lowest for volatile components. Outdoor volatile PM 2.5 components may be less representative of indoor exposure to volatile PM 2.5 of ambient origin. Outdoor nonvolatile PM 2.5 components may be more representative of indoor ex posure to nonvolatile PM 2.5 of ambient origin.



## Sarnat et al. (2006, 090489 )

Study Design Personal and ambient exposure assessment Period June 14-August 18 (summe r); Sep 24-Dec 15 (fall), 2000 Location Steubenville, OH Population Non-smoking, older adults Age Groups NR Personal Method Integrated fi lter gravimetric measurement Personal Size PM 2.5 Microenvironment Size NR Ambient Size PM 2.5 Component(s) SO 4 2- ; EC Primary Findings 24-h ambient measurements are more representative of personal particle exposure than gases, and ventilation i s an important exposure modifier.



## Sarnat et al. (2005, 087531 )

Study Design Time-series epidemiologic study Period Summer 1999 and winter 2000 Location Boston, MA. Comparis ons to a previous study in Baltimore are also made. Population School children and seniors Age Groups NR Indoor Source PM 2.5 Personal Method NR Personal Size PM 2.5 Microenvironment Size NR Ambient Size PM 2.5 Component(s) SO 4 , Copollutant(s) O 3 , NO 2 , SO 2 Primary Findings Substantial correlations between ambient PM 2.5 concentrations and corresponding pers onal exposures. Summertime gaseous pollutant concentrations may be better surrogates of personal PM 2.5 exposures (especially personal exposures to PM 2.5 of ambient origin) than they ar e surrogates of personal exposur es to the gases themselves.



## Shalat et al. (2007, 156971 )

Study Design Indoor home exposure asse ssment; 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 he ight 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 ar e exposed to more inhalable PM and TSP because PM becomes res uspended 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 PM 10 measured with integrated filter samples Personal Size PM 10 Microenvironment Size PM 10 Ambient Size PM 10 Component(s) NR Primary Findings Plasmid scission assay, coupled with the image analysis, can be used to evaluate the relationship between par ticle 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 Monda ys to Fridays between 9/19/00 to 5/01/01 Location Wolverhampton city center, University of Wolverhampton, UK Population NR Indoor Source Mn,Al, NO 3 , 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) NO 3 - , metals (Zn, Cu, Mn, Al), SO 4 2- , Cl - Primary Findings The indoor particulate c oncentration was driven by am bient concentration, and me teorological-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 fix ed-site ambient monitoring (multiple methods) Personal Size NR Microenvironment Size NR Ambient Size PM 2.5 Component(s) NR Primary Findings Using modeled or extrapolated personal ambient PM exposure results in a deattenuation of decrements in FEV 1 associated with PM exposure, relative to use of fixed- site ambient monitoring PM levels. Asso ciations between FEV1 decrements and the various estimation procedures (modeling and ex trapolation) 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, GR IMM Labortechnik Ltd., Germany Personal Size PM 10 , PM 2.5 , PM 1 , PM 10-2.5 , PM 2.5-1 Microenvironment Size NR Ambient Size PM 10 , PM 2.5 , PM 10-2.5- Component(s) NR Primary Findings Results of linear mixed-effect model analysis suggested that personal PM data was more suitable for the asses sment 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 PM 10 from a carpet and cooking Personal Method Harvard impactor s and PEMs, MIE pdr1000 nepholometer Personal Size PM 2.5 , PM 10 Microenvironment Size NR Ambient Size PM 2.5 , PM 10 Component(s) NR Primary Findings The association of duty cycle with indoor-outdoor (I/O) ratio was confounded by the short time span of ventil ation 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 bacte ria, viable fungi, viable bacteria Primary Findings Personal BS exposure corre lated with both home and work BS exposures . BS concentrations explained best the va riation 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 (Har vard Personal Environmental Monitor for PM 2.5 ) Personal Size PM 2.5 Microenvironment Size PM 2.5 Ambient Size PM 10 - 2.5 , PM 2.5 for residential outdoor, PM 2.5 for central site Component(s) NR Primary Findings FEV 1 decrements associated with 1-day lagged central site PM 2.5 in adult subjects with CO PD. Associations between PM and lung function decrements were significant only in asthmati c children not receiving anti-inflammatory medication.



## Turpin et al. (2007, 157062 )

Study Design RIOPA Study 24-h integrated indoor, ou tdoor, 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 pa ck, and motion

sensor Personal Size PM 2.5 Microenvironment Size PM 2.5 , in the main living area (not kitchen) Ambient Size PM 2.5 , in the front or back yard Component(s) 18 volatile organics, 17 carbonyl, PM 2.5 mass and >23 PM 2.5 species, organic carbon, elemental carbon, and PAHs Primary Findings The best estimate of the mean contribution of outdoor to indoor PM 2.5 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 y oung, non-smoking adults Age Groups Mean age 27 yr Indoor Source NR Personal Method pDR nephelometric method Personal Size PM 2.5 Microenvironment Size NR Ambient Size NR Component(s) NR Primary Findings The descriptive analysis showed that overall outdoor median concentration of PM 2.5 was higher than the indoor concentration. In the indoor microenvironment, the highest concentrations occurred in the subway fo llowed by the school, and the lowest was at home. The outdoor microenvironment with the highest conc entrations was the public trans portation (bus), while the automobile had the lowest. It was found that PM 2.5 concentration levels had a circadi an-like behavior probably related to an increase in the population daily activities during the morning hours, which decreas e in the evening, especially at indoor microenvironments. The Center city area was found to have the highest concentrations of PM 2.5 .; Multivariate analysis corroborated that PM 2.5 concentrations are mainly deter mined 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 gr avimetric measuremen t. Light absorbance. Personal Size PM 2.5 Microenvironment Size NR Ambient Size PM 2.5 Component(s) Absorbance Primary Findings Children living near busy roads had 35% higher personal exposure to ‘soot’ than ch ildren living in urban back ground 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 PM 10 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 pe rsons with elevated risk fr om exposure to particles. Age Groups NR Indoor Source NR Personal Method PEM PM 2.5 monitor Personal Size PM 2.5 Microenvironment Size Indoors PM 2.5 Ambient Size Outdoors near residence PM 2.5 PM 2.5 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 exposur e 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 im planted defibrillators were monit ored for 7 consecutive days in 4 seasons. Period 2000-2001 Location North Carolina Population Health-compr omised adults, non-smokers Age Groups Adults Indoor Source Cooking, cleaning, personal care, smoking Personal Method PEM Personal Size PM 2.5 Microenvironment Size PM 2.5 ; Indoor and outdoor Ambient Size NR Component(s) NR Primary Findings Use of continuous particle measuring instruments allowed more precise identification of sources, frequency an d magnitude of short-term peaks, and more accurate ca lculation 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 ex posure assessment was conducted. Personal Size NR Microenvironment Size PM 10 , PM 2.5 Ambient Size PM 10 , PM 2.5 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 PM 2.5 and PM 10 suggest that they came from similar emi ssion sources. Outdoor infiltration could lead to direct transportation of PM indoors. Human activities and ventilation types co uld also influence indoor PM. levels.



## Ward et al. (2007, 157112 )

Study Design Indoor air sampling to determine size fr actionated 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 conc entrations were much higher inside the elementary schools, with particle size frac tion (>2.5, 0.5- 1.0, 0.25-0.5, and < 0.25 mm) c oncentrations between 2 and 5 times higher when compared to the middle school. The 1.0-2.5 mm fraction had the largest diff erence 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 conc entrations 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, rec ent renovations (< 1 yr), type of home (single, multiple fam ily), attached gar age, carpet indoo rs, local pollution

sources. Personal Method PEM on a harness with inlet near breathing zone. Personal Size PM 2.5 Microenvironment Size PM 2.5 Ambient Size PM 2.5 Component(s) NR Primary Findings Personal PM 2.5 was significantly higher than indoor and outdoor PM 2.5 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 y r), schoolchildren (10-12 yr) Indoor Source NR Personal Method Personal impactor Personal Size PM 10 Microenvironment Size PM 10 Ambient Size PM 10 Component(s) Absorbance c oefficient measurements Primary Findings Found tentative support for using type of road as a proxy for indoor and personal exposur e 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 PM 2.5 Microenvironment Size PM 2.5 ; PM 10 ; PM 10 - 2.5 Ambient Size PM 2.5 ; PM 10 ; PM 10 - 2.5 Component(s) NR Primary Findings When comparing cohorts, there was no statistically significant difference between PM 2.5 exposure. Little spatial variability was observed regarding PM 2.5 concentrations; this was obser ved to a lesser extent for PM 10 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 integr ated filter gravimetric measur ement; TEOM outdoor ambient Personal Size PM 2.5 Microenvironment Size NR Ambient Size NR Component(s) SO 4 2- Primary Findings It was observed that ambient PM 2.5 exposure, estimated with the SO 4 2- method, accounted for 71% of measured ambient concentration and 44% of measured total personal exposure. No correlati on 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 H ealth, Boston, MA)\ Personal Size PM2.5 Microenvironment Size PM2.5 Ambient Size PM2.5 Component(s) Levoglucosan (LG); Element al Carbon (EC); Organic Carbon (OC) Primary Findings The authors observed si gnificant variability between subjects fo r burning and nonburning episodes. The autho rs postulated that activity patterns contribute to this variability and that central-site measuremen ts of LG might not be a good surrogate for biomass combustion smoke exposurefor th is 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 PM 2.5 Microenvironment Size PM 2.5 Ambient Size PM 2.5 Component(s) NR Primary Findings Personal exposure was higher than those at fixed sites. Subjects re ceived only 45.0% of their exposure indoor s at, although they spent more than 60% of t heir time there. In contrast, 29.2% of their exposure was rece ived 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 a ssessment of AM E and IES models Period 1997 (364 days) spring March-May, summer June-A ugust, Fall September-November, winter December-February Location Los Angeles County, CA Population General populat ion; ETS and non-ETS Homes Age Groups NR Indoor Source Indoor Cooking, ETS, Other sources and unexplained particulates th at maybe generated with engaging in various ac tivities Personal Method NR Personal Size PM 10 PM 2.5 Microenvironment Size NR Ambient Size PM 10 PM 2.5 Component(s) NR Primary Findings Adjusting from outdoor concentrations to personal ex posures and correcting dose-re sponse bias produce nearly equal results. Roughly the same premature mortalities associat ed with short-term exposure to both ambient PM 2.5 and PM 10 are predicted by both models



## Yip et al. (2004, 157166 )

Study Design A panel study with repeated m easures with personal & home monitoring for 8 2-week Periods. Children were stratif ied 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 PM 10 Microenvironment Size PM 10 ; indoor at home & indoor at school Ambient Size PM 10 Component(s) NR Primary Findings Personal PM concentrati ons were significantly correlated with home environment (r = 0.38 to 0.70), with the s trongest relationships in home with non-smokers.



### December 2009 A-315

## Zhao et al. (2006, 156181 )

Study Design Aerosol source apportionm ent under four environments (personal, resi dential indoor, residential outdoor and ambie nt) to evaluate the relationship between differ ent 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 resp iratory ailments most likely. Age Groups NR Indoor Source 4 main sources to resident ial indoor PM Cu-factor mixed with indoor soil, secondary sulfate, Personal care and a ctivity, ETS and its mixture Personal Method PEM and HI Personal Size NR Microenvironment Size NR Ambient Size NR Component(s) SO 4 2- , OC, EC, and trace elements Primary Findings Secondary SO 4 2- and vehicle emissions were significant contribut ors of personal PM exposur e and residential indoor PM concentrations.



## Zhao et al. (2007, 156182 )

Study Design Comprehensive analysis of the sources of PM 15 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 ur ban-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 identifi ed as primary internal sources. Personal Method PEM Personal Size PM 2.5 Microenvironment Size PM 2.5 Ambient Size PM 2.5 Component(s) EC, Cl, Si, NO 3 Primary Findings Four external sources and three internal sources were resolved in this study. Secondary nitrate and motor veh icle were two major outdoor PM 2.5 sources. Cooking was the largest contributor to the personal and indoor samples. Indoor environmental tobacco smoking also has an important impact on t he composition of the per sonal exposure samples.



## Zhu et al. (2005, 157191 )

Study Design 4 apartments near the freeway were monitored at 2 times for 6 consecut ive days, 24 h per day. Subjects did not en ter the bedrooms where the samplers were, no c ooking, cleaning, children, or pets. Period Oct. 2003-Dec. 2003 and Dec. 2003-Jan. 2004 Location Los Angles, CA Population Urban Populati ons 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 di stributions 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 da ta 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-Wuer ttemberg, Germany Population School children Age Groups NR Personal Method NR Personal Size NR Microenvironment Size They onl y reported concentrations for PM 2.5 . PM ranging in size from 0.02 to >20 μ m were collected and analyzed but only PM 2.5 concentration were reported. Ambient Size They only r eported concentrations for PM 2.5 . PM ranging in size from 0.02 to >20 μ m were collected and analyzed but only PM 2.5 concentration were reported. Primary Findings The impaction of PM was strongly influenced by specif ic weather conditions. Time resolution of measurements i n classrooms showed variation in particle concentrati on depending on the type of building and indoor activities. E'[Concent rations of very small particles indoors and in ambient ai r measured by condensation part icle 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 CP C 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 peri od is 3-5 s in comparison with the 1 min scanning time of the DMA.
Hering et al. (2005, 155838 ) CPC (water) Water-based CP C 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 μ m DE TEOM, SMPS, CPC, DustTrak, EBAM, ELPI, integrated filter samples TEOM compared best with gravimet ric filter among mass concentration analyzers, ELPI and SMPS compar able for differential number distribution but ELPI not useful fo r 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-insol uble nucleation-mode particles.

Ntziachristos and Samaras (2006, 116722 ) 7 nm-1 μ m Automobile exhaust 5 instruments used simultaneously to reduce uncertainty: Tefl on-coated filter downstream of constant volume sampling, ELPI with thermodenuder, CPC, SMPS, diffusion charger Use of four reduced variables comb ining output from all instruments (ratio of particle number concent ration 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 invers ion technique designed to correct for particle residence time in t he FIMS, which operates at 0.1 s resolution.
Petäjä et al. (2006, 156021 ) CPC (water) Water-based CP C performance evaluation.

Winkler et al. (2008, 156160 ) 1.5-4 nm Tungsten ox ide CPC (n-Propanol) Authors remove excess charge on particles with ion trap to detect particles down to ~ 1 nm (by elimi nating electrostati c 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), PM 10 , PM 2.5 , and PM 1 (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: PM 1 -PM 10 ( μ g/m 3 ), UFP (p cm -3 ) Avg Car Exposure: PM 10 5.87 (3.09) PM 2. 3.01 (1.10) 5 PM 1 1.82 (1.10) UFP 21639 (14379)

Avg Walking Exposure: PM 10 27.56 (13.16) PM 2. 6.59 (3.12) 5 PM 1 3.37 (3.40) UFP 30334 (17245) In-car concentrations of PM 2.5 , PM 1 , 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 weremeasured 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/cm 3 ): 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/cm 3 ) 13-43 PM ( μ g/m 3 ) 7.9-45

2.5 BC ( μ g/m 3 ) 0.74-3.3

Freeway range of medians: UFP (1000p/cm 3 ) 47-190 PM ( μ g/m 3 ) 25-110

2.5 BC ( μ g/m 3 ) 2.4-13 Measurements of freeway UFP, BC, PM-bound PAH, and NO x 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 ) PM 2.5 (personal filter pump), CO (T15 electrochemical cell), and benzene (canister) were measured on transit routes, and PM 2.5 filters were analyzed for mass, OC/EC, SO 4 2– , NO 3 -, and trace metals.
Study period: 3-h morning and evening rush hour May-June 2002 Bus

Minibus

Metro PM 2.5 ( μ g/m 3 ): Bus 68 Minibus 71 Metro 61 Generally, PM 2.5 concentration was higher in the morning than evening rush hour, but variability was higher for minibuses than ot her modes of transport. Wind speed was found to be associated with PM 2.5 concentration on minibuses.



### December 2009 A-318

### Reference Study Design Mode of Transport Exposures Primary Findings

Gomez-Perales et al. (2007, 138816 ) PM 2.5 (personal filter pump), CO (T15 electrochemical cell), and benzene (canister) were measured on transit routes, and PM 2.5 filters were analyzed for mass, OC/EC, SO 4 2– , NO 3 -, and trace metals.
Study period: 3-h morning and evening rush hour Jan-March 2003 Bus

Minibus

Metro Units: PM 2.5 mass ( μ g/m 3 ), components (% of mass) Bus: PM 2.5 20-58 (NH 4 O 3 5-8 (NH 4 ) 2 SO 4 10-18 OC 17-39 EC 8-20 Crustal 15-18 Non-crustal 2-3 Unknown 6-24

Minibus: PM 2.5 25-55 (NH 4 O 3 4-13 (NH 4 ) 2 SO 4 7-22 OC 22-37 EC 9-19 Crustal 12-13 Non-crustal 3-3 Unknown 4-26

Metro: PM 2.5 24-41 (NH4O 3 5-8 (NH4)2SO 4 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 PM 2.5 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 transpor t. Measured concentrations were higher in the morning than in the evening rush hour peri ods. Maximum historical wind speeds (1995-2003) appeared to be inversely associated with measured concentration.

Gulliver and Briggs (2004, 053238 ) PM 10 , PM 2.5 , and PM 1 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: μ g/m 3 Walk, Car, Background PM 10 38.2, 43.2, 26.6 PM 2. 15.1, 15.5 5 PM 1 7.1, 7.0 In-car PM 10 concentrations were elevated compared with walking and background. PM 2.5 and PM 1 concentrations were comparable for walking and background. Periods of elevated PM 2.5 compared with PM 10 generally corresponded to times when SO 4 2– levels were also high.

Gulliver and Briggs (2007, 155814 ) TSP, PM 10 , PM 2.5 , and PM 1 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: μ g/m 3 Walk, Car, Background

TSP-PM 10 19.1 (19.8) 18.2 (18.0) 4.9 (5.1)

PM 10 - 2.5 22.1 (22.8) 15.1 (14.2) 10.0 (9.0)

PM 2.5 -1 10.9 (10.4) 8.3 (8.4) 7.6 (7.1)

PM 1 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 PM 1 . Peak exposures during walking we re significantly higher than peak in-car exposures.

Rossner et al. (2008, 156927 ) Measured PM 2.5 exposure of 50 city bus drivers and 50 controls in Prague, Czech Republic using personal samplers (type not specified) and VOCs using passive samplers. PM 2.5 filters analyzed for cPAHs. Focus of study is oxidative stress biomarkers in drivers.
Study period: winter 2005, summer 2006, winter 2006. Bus Units: ng/m 3

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 2005for 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 we re significantly higher in bus drivers than cont rols 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 ( μ g/m 3 ) PAH (ng/m 3 )

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 st udies with no indoor source during 2002-2008.

Reference / Location Personal Micro Ambient

SOUTHWEST Delfino et al. (2004, 056897 ) Alpine, California Method: pDR, Units = μ g/m 3 Last 2-h PM 2. 34.4 (33.7) 5 Diurnal PM 55.7 (31.6) 2.5 Nocturnal PM 22.3 (13.6) 2.5 1-h max PM 2.5 151.0 (120.3) 4-h max PM 2.5 87.5 (55.3) 8-h max PM 67.6 (39.0) 2.5 24-h PM 2.5 37.9 (19.9) Method: HI, Units = μ g/m 3 Indoor 24-h PM 10 30.3 (11.9) Indoor 24-h PM 12.1 (5.4) 2.5 Outdoor 24-h PM 10 25.9 (10.4) Outdoor 24-h PM 2.5 11.0 (5.4) Method: TEOM, Units = μ g/m 3 Diurnal PM 35.1 (11.3) 10 Nocturnal PM 23.3 (8.4) 10 1-h max PM 10 54.4 (13.8) 4-h max PM 10 44.5 (12.4) 8-h max PM 39.8 (11.2) 10 24-h PM 10 23.6 (9.1) 24-h PM 2.5 10.3 (5.6) Delfino et al. (2006, 090745 ) Riverside and Whittier, California Method: PEM, Units = μ g/m 3 Riverside: n 13 24-h PM 2.5 32.78 (21.84) 1-h max PM 2.5 97.94 (70.29) 8-h max PM 2.5 47.21 (30.0)

Whittier: n 32 24-h PM 2.5 36.2 (21.84) 1-h max PM 2.5 93.63 (75.19) 8-h max PM 2.5 51.75 (36.88) Method: FRM, Units = μ g/m 3 Riverside: 24-h PM 2.5 36.63 (23.46) 24-h PM 10 70.82 (29.36)

Whittier: 24-h PM 2.5 18.0 (12.14) 24-h PM 10 35.73 (16.6)

Turpin et al. (2007, 157062 ) Los Angeles County, CA (and Elizabeth, NJ, Houston, TX) Method: PEM, Units = μ g/m 3 Avg of 48-h PM 2.5 Child 40.2 Adult 29.2 Method: HI, Units = μ g/m 3 Avg of 48-h PM 2.5 : 16.2 Method: HI, Units = μ g/m 3 Avg of 48-h PM 2.5 : 19.2

Wu et al. (2005, 157155 ) Alpine, CA Method: pDR, Units = μ g/m 3 n 11 Avg of 24-h PM 2.5 11.4 (7.8) Method: pDR, Units = μ g/m 3 n 14 Avg of 24-h PM 2.5 5.6 (2.9)

Method: HI n 14 Avg of 24-h PM 2.5 9.8 (2.5) Method: pDR, Units = μ g/m 3 n 8 Avg of 24-h PM 2.5 14.0 (11.4)

Method: HI n 8 Avg of 24-h PM 2.5 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 = μ g/m 3 Indoor home: PM 10 11.93 PM 2.5 7.29

Outdoor home: PM 10 13.47 PM 2.5 10.47 Method: HI, Units = μ g/m 3 PM 10 18.0 PM 2.5 14.0

Koenig et al. (2003, 156653 ) Seattle, WA 13.4 ± 3.2 μg/m 3 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/m 3 ) between October 1999 and May 2001 by study group.

Group Mean ± SD Personal PM 2.5 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/m 3 ) between October 1999 and May 2001 by study group. Group Mean ± SD Indoor PM 2.5 COPD 8.5 ± 5.1 Healthy 7.4 ± 4.8 Asthmatic 9.2 ± 6.0 CHD 9.5 ± 6.8 PM 10 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/m 3 ) between October 1999 and May 2001 by study group. Location Pollutant Group Mean ± SD Outdoor PM 2.5 COPD 9.2 ± 5.1 Healthy 9.0 ± 4.6 Asthmatic 11.3 ± 6.4 CHD 12.7 ± 7.9 PM 10 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 = μ g/m 3 PM 2.5 : Healthy: 9.3 (8.4) CVD: 10.8 (8.4) COPD: 10.5 (7.2) Method: HI, Units = μ g/m 3 PM 2.5 : Healthy: 7.4 (4.8) CVD: 9.5 (6.8) COPD: 8.5 (5.1)

PM 10 : Healthy: 12.7 (7.8) CVD: 16.2 (11.3) COPD: 14.1 (6.6) Method: HI, Units = μ g/m 3 PM 2.5 : Healthy: 9.0 (4.6) CVD: 12.7 (7.9) COPD: 9.2 (5.1)

PM 10 : 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 = μ g/m 3 Median PM 2.5 Child 11.3 Adult 8.5 Method: HI, Units = μ g/m 3 Median PM 2.5 Child 7.5 Adult 7.6 Method: HI, Units = μ g/m 3 Residential Outdoor Median PM 2.5 Child 9.6 Adult 8.6 Residential Outdoor Median PMcoarse Child 4.7 Adult 5.0 Residential Outdoor Median PM 2.5 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 = μ g/m 3 (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. PM 2.5 , Units = μ g/m 3

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) PM 2.5 , Units = μ g/m 3

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) PM 2.5 , Units = μ g/m 3

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 PM 2.5 , Units = μ g/m 3 Athens (rural): 17.61 (17.81) Koebel (urban): 14.59 (13.05) New Albany (suburb): 13.93 (12.25) PM 2.5 , Units = μ g/m 3 Indoor Athens (rural): 17.20 (13.56) Koebel (urban): 14.98 (12.30) New Albany (suburb): 16.52 (13.53) PM 2.5 , Units = μ g/m 3 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): PM 2.5 , Units = μ g/m 3

Summer n = 169 mean (SD) = 19.9 (9.4)

Fall mean (SD) = 20.1 (11.6) Mean (SD): PM 2.5 , Units = μ g/m 3

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 = μ g/m 3 PM 2.5 pers = 23.0 (16.4) PM 2.5 pers/PM 2.5 out = 1.31 (0.99) Units = μ g/m 3 PM 2.5 in = 19.4 (16.5) PM 2.5 in/PM 2.5 out = 1.08 (1.05) Units = μ g/m 3 PM 2.5 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/m 3 ) across all subjects, residences, seasons, and cohorts

Variable N Geo mean Mean RSD(a) Personal PM 2.5 (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/m 3 ) across all subjects, residences, seasons, and cohorts

Variable N Geo mean Mean RSD(a) Indoor PM 2.5 (c) 761, 15.3, 19.1, 80.1 Outdoor PM 2.5 (c) 761, 17.5, 19.3, 43.7 Indoor PM 10 (b) 761, 23.2, 27.7, 70.6 Outdoor PM 10 (b) 761, 27.5, 30.4, 46.4 Indoor PM 10-2.5 (d) 761, 6.3, 8.6, 111.8 Outdoor PM 10-2.5 (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 PM 10 monitor and co-located HI PM 2.5 mass concentrations. Pooled PM mass concentrations (μg/m 3 ) across all subjects, residences, seasons, and cohorts

Variable N Geo mean Mean RSD(a) Ambient PM 2.5 (c) 746, 17.3, 19.2, 44.9 Ambient PM 10 (b) 752, 27.9, 31.4, 51.5 Ambient PM 10-2.5 (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 PM 10 monitor and co-located HI PM 2.5 mass concentrations.



### December 2009 A-322

### Reference / Location Personal Micro Ambient

NORTHEAST Koutrakis et al. (2005, 095800 ) Baltimore, MD Boston, MA PM 2.5 , Units = μ g/m 3 :
(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, NRSO 4 :
(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 PM 2.5 , Units = μ g/m 3 :
(Baltimore, Boston) Winter: All: 20.1 (9.4), 11.6 (6.8)

summer: Seniors: 25.2 (1 1.5), 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, NRSO 4 :
(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 = μ g/m 3 :

Winter-Children: PM 2.5 : 17.4-25.8 SO 4 : 1.6-3.3

Winter-Seniors: PM 2.5 : 10.8-16.2 SO 4 : 1.6-2.6

Summer-Children PM 2.5 : 25.4-32.8 SO 4 : 2.7-3.3

Summer-Seniors PM 2.5 : 17.8-20.5 SO 4 : 2.7-3.3 NR Units = μ g/m 3 :

Winter: PM 2.5 : 6.5-15.5 SO 4 : 1.7-4.2

Summer: PM 2.5 : 11.9-21.4 SO 4 : 3.6-9.0

Turpin et al. (2007, 157062 ) Elizabeth, NJ, (and Houston, TX, and Los Angeles County, CA+ 48-h avg PM 2.5 , Units = μ g/m 3 : 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: PM 2.5 - 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/m 3 :
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 variedacross 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 NO 3 - Mean (SD), units = ng/m 3 :

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 PM 2.5 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: PM 2.5 SO 4 2– , NO 3 - Mean, units = μ g/m 3 : SO 2– :

4 P, I, O Amsterdam 4.6 4.7 5.9 Helsinki 2.7 3.0 5.0

NO 3 - : P, I, O Amsterdam 1.4 1.4 4.0 Helsinki 0.2 0.3 1.3 In both cities personal and indoor PM 2.5 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: PM 2.5
Micro: PM 2.5 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: PM 2.5 : 62 μ g/m 3 Fe: 26 μ g/m 3 Mn: 240 ng/m 3 Cr: 84 ng/m 3 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 PM 2.5 ) vs Mn (pg/ μg PM 2.5 ) 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 steeldust 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: PM 2.5 Micro: “ambient exposure”: PM 2.5 , PM 10 , PM 2.5 -10 ; “non-ambient exposure:” PM 2.5
Ambient: PM 2.5 , PM 10 , PM 2.5-10 Ambient SO 4 2-

, Ambient non-sulfate, Personal sulfate, personal ambient non-sulfate Mean (SD),Units μ g/m 3 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: PM 10
Micro: NR

Ambient: PM 10 Extractable organic material (EOM) B[a]P cPAHs Benzo[a]pyrene (B[a]P) Carcinogenic polycyclic aromatic hydrocarbons (cPAHs) Units: ng/m 3

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: PM 10
Micro: NR

Ambient: PM 10 PM 2.5 (not reported) PM 10 EOM EOM2 B[a]P c-PAHsb Prague-SMWinter Summer EOM (μg/m 3 ) 14.93 4.96 EOM2 (%) 23.9 13.4 B[a]P (μg/m 3 ) 3.5 0.28 c-PAHsb (μg/m 3 ) 24.69 2.29

Prague-LB Winter Summer EOM (μg/m 3 ) 10.86 3.72 EOM2 (%) 27.9 14.1 B[a]P (μg/m 3 ) 2.9 0.17 c-PAHsb (μg/m 3 ) 20.36 1.32

Košice Winter Summer EOM (μg/m 3 ) 15.3 1.67 EOM2 (%) 26.4 6.9 B[a]P (μg/m 3 ) 1.37 0.15 c-PAHsb (μg/m 3 ) 11.87 1.2

Sofia Winter Summer EOM (μg/m 3 ) 24.6 3.95 EOM2 (%) 27.37 13.3 B[a]P (μg/m 3 ) 4.84 0.36 c-PAHsb (μg/m 3 ) 36.44 2.43 Extractable organic matter (EOM) per PM 10 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, PM 10 , PM 2.5
Micro: NR

Ambient: TD, PM 10 , PM 2.5 EC OC VOC also assessed Mean (SD), units = μ g/m 3 : 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: PM 2.5
Micro: NR

Ambient: PM 2.5 PM 2.5 -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: PM 2.5
Micro: NR

Ambient: PM 2.5 OC EC OM TCA Mean, Unit = μ g/m 3

Indoors: OM = 18.1; TCA = 22.9

Outdoors: OM = 20.1; TCA = 26.5 The major source of indoor EC, OC, and PM 2.5 appears to be penetration of outdoor air, with a much greater attenuation in mechanically ventilated buildings.

Jacquemin et al. (2007, 192372 ) Personal: PM 2.5
Micro: NA

Ambient: PM 2.5 S Mean, units = μ g/m 3 :

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 PM 2.5 , indoor sources need to be carefully considered.”
Jansen et al. (2005, 082236 ) Personal, Micro, and Ambient: PM 2.5 Estimated Elemental Carbon (Abs) Elemental composition of a subset of personal, indoor and outdoor samples Mean (SD), units = μ g/m 3 : Amsterdam, Helsinki P,O,P,O PM 2.5 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 PM 2.5 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: PM 2.5 , PM 1 BS BS 2.5 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

PM 1 /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 PM 2.5 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 PM 2.5 was found for nonsmokers. PM 1 made up a considerable proportion (about 70–80%) of PM 2.5 . 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 PM 2.5 and BS 2.5 within the city. The air mass origin affected the outdoor levels of both PM 2.5 and BS 2.5 ; however, no effect was seen on personal exposure or indoor levels.

Kim et al. (2005, 156640 ) Personal: PM 2.5
Micro: NR

Ambient: PM 2.5 SO 4 2- , EC, Ca 2+ , Mn 2+ , K, Na + Mean (SD), Units = μ g/m 3 :

SO 4 2– : 2.7 (3.2)

Ca 2+ : 0.12 (0.12)
Mg 2+ : 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 PM 2.5 includes a greater relative contribution from combustion sources, compared with outdoor (ambient) PM 2.5 measurements.
Koistinen et al. (2004, 156655 ) Personal, Micro, and Ambient: PM 2.5 Black smoke, SO 4 2– , NO 3 -, NH 4 + , Al, Ca, Cl, Cu, K, Mg, P, S, Si, Zn % contribution to PM 2.5 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 PM 2.5 , 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: PM 2.5
Micro: NR

Ambient: PM 2.5 Elemental Carbon (EC), SO 4 2– Mean (SD) data are provided for Baltimore and Boston, Units = μ g/m 3 :

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

SO 4 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 PM 2.5 and SO 4 2– are strong predictors of respective personal exposures. Ambient SO 4 2– is a strong predictor of personal exposure to PM 2.5 . Because PM 2.5 has substantial indoor sources and SO 4 2– does not, the investigators concluded that personal exposure to SO 4 2– accurately reflects exposure to ambient PM 2.5 and therefore the ambient component of personal exposure to PM 2.5 as well.

Kulkarni and Patil (2003, 156664 ) Personal: PM 5
Micro: NR

Ambient: PM 5 Pb

Ni

Cd

Cu

Cr

Fe

Mn Personal samples, Units = μ g/m 3 : Mean ± SD Type

Pb Occupational 4.384 ± 7.766 μg/m 3 Residential 4.093 ± 5.925 μg/m 3 24-h integrated 4.205 ± 1.523 μg/m 3

Cd Occupational 0.201 ± 0.158 μg/m 3 Residential 0.111 ± 0.165 μg/m 3 24-h integrated 0.134 ± 0.140 μg/m 3

Mn Occupational 1.979 ± 7.842 μg/m 3 Residential 0.180 ± 0.261 μg/m 3 24-h integrated 1.983 ± 6.824 μg/m 3

K Occupational 3.473 ± 4.691 μg/m 3 Residential 4.589 ± 4.619 μg/m 3 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: PM 2.5 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/m 3

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 PM 2.5 , 14 elements, total VOC (TVOC) and 8 individual compounds were over 20% higher than their GM outdoor levels. Those of NO 2 , 5 aromatic VOCs, and 5 other elements were close to their GM outdoor levels. For PM 2.5 and TVOC, personal exposures and residential indoor levels (in GM) were about 2 times higher among the tobacco-smoke exposedgroup compared to the nonsmoke exposed group, suggesting that smoking is an important determinant ofthese exposures. Determinants forCO 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 PM 2.5 (r = 0: 60; p< 0: 001) and NO 2 (r = 0: 47; p = 0: 003).



### December 2009 A-329

### Reference Particle Sizes Measured Component Results Primary Findings

Larson et al. (2004, 098145 ) Personal: PM 2.5
Micro: PM 2.5 outside subject’s residence, and inside residence

Ambient: PM 2.5 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 PM 2.5 identified: vegetative burning, mobile emissions, secondary sulfate, a source rich in chlorine, and crustal-derived material. The burning of vegetation (in homes) contributed more PM 2.5 mass on avg than any other sources in all microenvironments.

Maitre et al. (2002, 156726 ) Personal: PM 4
Micro: NR

Ambient: PM 4 PAH, benxene-toluenexylenes (BTX), aldehydes, BaP PAHc, formaldehyde, acetaldehyde Median Personal Ambient Resp μ g/m 3 124, 124 (mean) BaP ng/m 3 0.28, 0.14 PAHc ng/m 3 1.19, 1.56 PAH ng/m 3 13.14, 12.26

Benzene μ g/m 3 23.5, 17 Toluene μ g/m 3 94.5, 52 Xylene μ g/m 3 74, 39 BTX μ g/m 3 192, 108 Formaldehyde μ g/m 3 21, 17.5 Acetaldehyde μ g/m 3 17, 10.5 Aldehyde μ g/m 3 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: PM 2.5
Micro: NA

Ambient: NR EC, OC, S, Si Mean (SD), units = ng/m 3 :

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 PM 2.5 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: PM 2.5
Micro and Ambient: PM 10 - 2.5 and PM 2.5 BS S Cl K Ca Mn Fe Cu Zn Br Rb Pb Median, unit = ng/m 3

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 werefound 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 PM 2.5 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: PM 2.5 and PM 1
Micro and Ambient: NR S

Cl

K

Ca

Ti

V

Mn

Fe

Ni

Cu

Zn

Br

Pb Urban background PM 2.5 Mean, median, range (ng/m 3 ) 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 PM 2.5 Mean, median, range ( μ g/m 3 ) 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 PM 1 Mean, median, range ( μ g/m 3 )

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 PM 2.5 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 t h 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 PM 2.5 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 PM 1 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: PM 2.5
Micro: NR

Ambient: PM 2.5 EC, OC Mean (SD), units = μ g/m 3

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 PM 2.5 was significant influenced by indoor OC

sources. Indoor EC sources were predominantly of outdoor origin.

Noulett et al. (2006, 155999 ) Personal: PM 2.5
Micro: NR

Ambient: PM 2.5 SO4 2- ABS (light absorbing carbon) Measurement Mean s.d.

Ambient SO4 2- 2.72* 3.11

Ambient ABS 1.4** 1.0

Personal SO 4 2- 1.33* 1.47 Personal ABS 1.0** 1.7

* Mean SO 4 2- values reported in μg/m 3 ** Mean ABS values reported in 10-5/m -1 SO 4 2- and light absorbing carbon concentrations had higher personal-ambient correlations and less variability. This indicates that SO 4 2- and ABS were of outdoor origin, while PM 2.5 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: PM 10 - 2.0 and PM 2.0
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/m 3 :

PM 10 - 2.0 ; PM 2.0 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: PM 2.5
Micro: N/A

Ambient: PM 2.5 SO 4 , O 3 , NO 2 , SO 2 Correlations between personal PM 2.5 and ambient gas

O 3 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

SO 2 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 PM 2.5 concentrations and corresponding personal exposures. Summertime gaseous pollutant concentrations may be better surrogates of personal PM 2.5 exposures (especially personal exposures to PM 2.5 of ambient origin) than they are surrogates of personal exposures to the gases themselves.

Sarnat et al. (2006, 089784 ) Personal: PM 2.5
Micro: NR

Ambient: PM 2.5 SO 4 2- EC Mean (SD), units = μ g/m 3 : Personal Ambient

SO 4 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 SO 4 2– and EC, especially for SO 4 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), SO 4 2- , NO 3 - , and Cl - Indoor Outdoor
Zn (ng/m 3 ) 241.1, 179.5 Cu (ng/m 3 ) 43.3 , 24.99 Mn (ng/m 3 ) 15.6, 4.18 Al (ng/m 3 ) 305.2, 52.90 SO 4 2- (ng/m 3 ) 4.72, 3.47 Cl (ng/m 3 ) 1.08 , 0.15 NO 3 - (ng/m 3 ).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: PM 2.5
Micro: PM 2.5 Area samplers in the offices, freight dock, or shop.

Ambient: PM 2.5 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: PM 2.5
Micro: NR

Ambient: PM 2.5 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 PM 2.5 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 PM 2.5 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: PM 2.5 and BS

Micro: PM 2.5 and BS
Ambient: Street monitoring station and roof of a campus building PM 2.5 and BS BS Mean, IQR, Units = μ g/m 3 :

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: PM 10 , PM 2.5
Micro: NR

Ambient: PM 10 , PM 2.5 c-PAHs, B[a]P B[a]P: Exposed 1.6 ng/m 3 Control 0.8 ng/m 3

c-PAHs: Exposed 9.7 ng/m 3 Control 5.8 ng/m 3 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: PM 2.5
Micro: PM 2.5 , in the main living area (not kitchen)

Ambient: PM 2.5 , in the front or back yard 18 volatile organics, 17 carbonyl, PM 2.5 mass and >23 PM 2.5 species, organic carbon, elemental carbon, and PAHs For Los Angeles
Carbon (μgC/m 3 ) EC 1.4 OC 4.1 Elements (ng/m 3 )
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 PM 2.5 was 73% and the outdoor contribution to personal was 26%.

Wallace and Williams (2005, 057485 ) Personal: PM 2.5
Indoor Micro: PM 2.5
Outdoor Micro: PM 2.5 S Mean (SD), units = ng/m 3 : Personal: 1046 (633)
Indoor: 1098 (652)
Outdoor: 1951 (1137) Generally, F inf 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: PM 2.5
Micro: PM 2.5
Ambient: PM 2.5 LG EC

OC Mean personal exposure (μg/m 3 ):
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 variationbetween 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: PM 2.5 EC, Cl, Si, NO 3 Units = μ g/m 3 :

Personal: EC: 1.64 NO 3 : 0.135 Si: 0.176 Cl: 0.116

Indoor: EC: 1.819 NO 3 : 0.013 Si: 0.051 Cl: 0.024

Outdoor: EC: 1.876 NO 3 : 0.292 Si: 0.115 Cl: 0.013 Four external sources and three internal sources were resolved in this study. Secondary NO 3 - and motor vehicle exhaust were two major outdoor PM 2.5 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 exp osure 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

SO 4 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 SO 4 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 PM 2.5 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 PM 2.5 , 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

SO 4 2– 15.9, 22.5, 59.3, 61.9 Secondary

NO 3 - 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, andElizabeth, 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 PM 2.5 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 PM 2.5 , PM 2.5 samples from 219 homes were used for this analysis.
Los Angeles, Houston, andElizabeth, 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 PM 2.5

. Denver with 56 asthmatic children.

Oct 2002-March 2003 and Oct 2003-March 2004. % contr P I O

Secondary

SO 4 2– 4.3 8.9 9.6

Soil 6.6 4.2 12.4 Secondary

NO 3 - 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 PM 2.5 based on SO 4 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 SO 4 2– : 6.38 (1.60, 3.20-10.97) Using SO 4 2– and Fe: 6.50 (1.36, 3.54-10.12) Using SO 4 2– and Fe, temperature adjusted: 7.02 (1.48, 3.79-11.02) Using SO 4 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 F inf 

### 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. PM 2.5 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-segre gated 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. PM 2.5 : 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 massconcentrations 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 PM : 0.93-1.90 2.5 Chemical Species: Cl - : 0.35-0.45 NO 2 - : 2.50-4.13 NO 3 - : 1.41-5.41 SO 2– : 1.21-1.70

4 Na + : 0.43-0.74 NH 4 + : 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 PM 2.5 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). PM 2.5 (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 F inf 

### 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; F inf 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. PM 2.5 : 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 PM 2.5 (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 residentialindoor 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 PM 2.5 : 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: Associatio ns 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 PM 2.5 : 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

SO 4 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 PM 2.5 , OC, and EC. To determine indoor/outdoor sources of indoor carbonaceous aerosol.
Methods: Gravimetric analysis to determine PM 2.5 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 PM : 2.5 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 F inf 

### I/O

Cortez-Lugo et al. (2008, 156368 ) Objective: To determine personal PM 2.5 and its relationship with outdoor and indoor PM 2.5 and PM 10

. Methods: Linear regression model used to compare personal and indoor PM 2.5 . I/O variation studied using analysis of variance and predictors determined by generalized estimating equation models. I/O PM 2.5 ratio transformed into nat ural logarithm.
Subjects: 38 nonsmoking long-time Mexico residents with COPD. Mexico City, Mexico. Feb-Nov 2000. N/A PM 2.5 : Average: 1.2
Range: 0.05-6.1

Diapouli et al. (2008, 190893 ) Objective: To characterize the PM 10f PM 2.5 , UFP concentrati ons 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 charac terizations of urbanization and traffic density in Athens, Greece. No ventilation system. Nov. 2003-Feb. 2004 and Oct.-Dec. 2004. N/A PM 10 : 0.54-2.46

PM 2.5 - 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 (NO 2 , CO, PM 10 , PM 2.5 ). 3 scenarios: no source, gas cooking, smoking.
Subjects: 5 UK sites- Harwell (rural), Birmingham East (urban background), Bradford (urban center), Bloomsbur y (urban center), Marylebone Road (roadside). Winter (October 1-March 31), summer (April 1-September 30), 1997-1999. N/A No source: PM 10 : 0.5-0.65; PM 2.5 : 0.6-0.7

Gas cooking: PM 10 : 0.6-0.9 (bedroom), 1.0-2.0 (lounge), 1.6-4.3 (kitchen); PM 2.5 : 0.74-0.9 (bedroom), 0.9-1.6 (lounge), 1.6-2.9 (kitchen)

Smoking: PM 10 : 0.7-1.1 (bedroom), 1.1-2.7 (lounge), 1.1-2.5 (kitchen); PM 2.5 : 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 F inf derived from sulfate Finf and a correction factor that results from division of Β PM (increase of indoor PM per outdoor PM, linear relationship) by Β sulf (increase of indoor sulfate per outdoor sulfate, linea r relationship). If no indoor source, the sulfate F inf 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 PM 10 : SO 4 2- : 0.3, NO - : 0.1,

3 Cl - : 0.6, Na 2+ : 0.9, NH + : 0.1,

4 Mg: 0.6, Ca 2+ : 1.4, EC: 0.7, OC: 1.1 PM :

2.5 SO 2- : 0.4,

4 NO 3 - : 0.2, Cl - : 0.5, Na 2+ : 0.6, NH 4 + : 0.3, Mg: 0.5, Ca 2+ : 1.6



### December 2009 A-341

### Reference Study Design F inf 

### 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: PM 10 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 PM 10 :
Non-air-conditioned: ~0.7, Airconditioned: ~0.98

Hänninen et al. (2004, 056812 )2 Objective: To assess indoor PM 2.5 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; Basl e, Switzerland; Helsinki, Finland; Prague, Czech Republic. Homes by city: Athens 50, Basle 50, Helsinki 189, Prague 49. PM 2.5 (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 PM 2.5 : 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: PM 2.5 , 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. PM 2.5 : 0.42 EC: MV: 0.42, NV: 0.76 OC: MV: 0.66, NV: 0.71 PM 2.5 (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: Ex posure assessmentof indoor/outdoor particle relationships.RUPIOH study.
Methods: Sampling by condensation particle counters and Harvard impactors. Grav imetric analysis and reflectance. Calculations performed for 24h avg concentrations. F inf estimated by linear regression analysis.
Subjects: 4 European cities (Helsinki, Finland; Athens, Gr eece; Amsterdam, The Netherlands; Birmingham, England). Urban popul ations. >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:

PM 2.5 : 0.30-0.51

PM 10 : 0.17-0.41

PM 10 - 2.5 : 0.01-0.17

SO 4 2- : 0.59-0.78

Soot: 0.43-0.87
Regression slope for indoor vs. residential outdoor:

PM 2.5 : 0.34-0.48

PM 10 : 0.26-0.44

PM 10 - 2.5 : 0.11-0.16

Soot: 0.63-0.84 N/A



### December 2009 A-342

### Reference Study Design F inf 

### 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. NO 3 - - SO 4 2- : 0.03

SO 4 2- : 0.38

OC: 0.77

MV Exhaust: 0.32 N/A

Hystad et al. (2008, 190890 ) Objective: To explor e the feasibility of modeling residential PM 2.5 F inf

for occupied residences using data readily available for most of North America.

Methods: F inf

calculated by recursive mass balance model where F inf

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 PM 2.5 : University:
Weekdays: 0.6, Weekends: 0.5
Shopping center:
Weekdays: 1.5, Weekends: 2.0

BC in PM 2.5 : University: 0.9
Shopping center: 0.67



### December 2009 A-343

### Reference Study Design F inf 

### I/O

Koistinen et al. (2004, 156655 ) Objective: To identify PM 2.5 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 PM 2.5 exposure. Specific mass contributions of s ources calculated by source reconstruction.
Subjects: Non-sm oking, 25-55yrs. Helsinki, Finland. Oct. 1996-Dec. 1997. N/A Median seasonal:
PM 2.5 : 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 PM 10 :

All: 0.60

Cooler On: 0.57

Cooler Off: 0.66

PM 2.5 :

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 PM 2.5 : 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 PM 2.5 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 heatingand cooling with high-efficiency induct air cleaning. 2005. N/A PM 2.5 (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 F inf 

### 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- PM 2.5 : 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. PM 2.5 - 0.46 Los Angeles: PM 2.5 : Mean: 0.84, Median: 0.90; EC: Mean: 0.93, Median: 0.92; OC: Mean: 1.32, Median: 1.31 Elizabeth: PM : Mean: 0.99, Median: 0.86;

2.5 EC: Mean: 1.0, Median: 0.85; OC: Mean: 2.4, Median: 1.8 Houston: PM 2.5 : 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 PM 2.5 trace element c oncentrations 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. PM 2.5 (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 F inf 

### I/O

Ng et al. (2005, 155996 ) Objective: To estimate PM exposures following the September 11, 2001 attack in NYC.

Methods: Outdoor PM 2.5 interpolatedand 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 PM 10 : 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

PM 2.5 : 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 PM 2.5 , its components, seasonal variations, and gaseous copollutants.

Methods: F inf 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, 20 05. Oct. 19-Dec. 10, 2005. Jan. 4-Feb. 18, 2006. PM 2.5 : 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 PM 2.5 concentrations.
Methods: Linear regression of gravimetric m easurements.
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 PM 2.5 , PM 10 , PM 2.5- 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 PM 2.5 :
Winter: Mean: 1.58, Median: 2.11; Summer: Mean: 1.08, Median: 0.88

PM 10 :
Winter: Mean: 2.02, Median: 3.77; Summer: Mean: 1.14, Median: 1.05

PM 2.5- 10 :
Winter: Mean: 2.65, Median: 3.59; Summer: Mean: 1.26, Median: 1.39



### December 2009 A-346

### Reference Study Design F inf 

### I/O

Sarnat et al. (2006, 089166 ) Objective: To assess the ability of outdoor PM 2.5 its volatile and nonvolatile components and particle sizes to infiltrate indoors.

Methods: PM 2.5 mass contributions estimated by the mean concentration ratio between each component and PM 2.5 . Indoor and outdoor particle concentrations relationships examined by Spearman correlation coefficient. I/O c oncentration ratios used during overnight (nonsource) period to estimate fraction of ambient particles remaining airborne indoors (F inf ).
Subjects: 17 occupied, nonsmoking Los Angeles, CA re sidences. July 28, 2001-Feb. 25, 2002. PM 2.5 : Median: 0.48, Interquartile range: 0.39-0.57

BC:
Median: 0.84, Interquartile range: 0.70-0.96
UFP (0.02-0.03 μ m):
Median: 0.50, Interquartile range: 0.39-0.60
UFP (0.08-0.3 μ m):

Median: ~0.75
Coarse particles (5-10 μ m):

Median: <0.17 PM 2.5 : 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 PM 2.5 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 PM 2.5 : 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 PM 1 : 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

PM 2.5 : 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

PM 10 : 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 PM 2.5 exposure. Identify indoor and personal PM 2.5 sources. Estimate outdoor PM 2.5 effect on indoor and personal PM 2.5 . RIOPA study.

Methods: F inf

calculated in three ways: RCS model used to obtain constant F inf . Mass balance model shows F inf

varying with AER. Robust regression uses major PM 2.5 species for home-specific F inf

. Subjects: 309 nonsmoking adults and 118 children with no preexisting conditions. 219 homes sampled. Elizabeth NJ, Hous ton TX, and Los Angeles County CA. PM 2.5 :

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: PM : Mean: 0.84, Median: 0.90

2.5 EC: Mean: 0.93, Median: 0.92 OC: Mean: 1.32, Median: 1.31

Elizabeth: PM 2.5 : Mean: 0.99, Median: 0.86 EC: Mean: 1.0, Median: 0.85 OC: Mean: 2.4, Median: 1.8

Houston: PM 2.5 : 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 F inf 

### I/O

Wallace and Williams (2005, 057485 ) Objective: To estimate the contribution of outdoor PM 2.5 to personal exposure in high-risk subpopulations.
Methods: Longitudinal regressions of estimated indoor and outdoor PM 2.5 for F inf

. 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 PM 2.5 :
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 PM 2.5 contributions to personal and indoor residential PM mass concentrations.

Methods: F inf estimated from least squares, regression analysis, and mixed model slope.
Subjects: Nonsmoking, ambulatory, ≥ 50yrs. 2 cohorts: mo stly Caucasian with implanted cardiac defibrillators in Chapel, NC; 30 African-Americanswith controlled hypertension in low-tomoderate SES neighborhoods in Raleigh, NC. 7d/season, 4 seasons in 2000-2001. Least squares esti mate 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 PM 2.5 and PM 10 - 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. PM 2.5 : Range: 0.16-6.45, Mean: 0.7 ± 0.33, Median: 0.70 (indicate indoor sulfur source when F inf >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: F inf

estimated by mass balance equation.
Subjects: 16 nonsmoking subjects with COPD. 54-86yrs. Vancouver, British Columbia. April-Sept. 1998. SO 4 2- : 0.72 N/A

Wu et al. (2006, 179950 ) Objective: To assess personal PM 2.5 exposures from am bient sources and agriculture burning smoke.

Methods: F inf 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 F inf 

### I/O

Yang et al. (2009, 190885 ) Objective: To characterize the concentrations of different indoor air pollutants.

Methods: PM 10 collected on pall flex membrane filer using MiniVol portable air samplers. Arithmetic and geometric means calculated for indoor concentrations. Differences inconcentrations 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 PM 10 :
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, Wint er: 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. F

inf measured by coefficient of determination, R 2

. 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

. F

inf estimated from Figure 2 in study. 9

. F

inf 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 NO X , CO R UFP PM 2.5 NO BC CO CO 2 UFP 1 0.71 0.97 0.95 0.63 0.72 PM 2.5 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 CO 2 1 Note that these correlations are computed from data presented by Fruin et al. (2008, 097183 ) for mean concentrations at different loc ations. Measurements offreeway 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 PM 2.5 data from the Baltimore panel study Ambient and personal O 3 and NO 2 data from the Baltimore panel study. Median β for regressions:

Ambient PM 2.5 Ambient O 3 Ambient NO 2 Personal PM 2.5 0.0143 -0.0016 0.0115

Personal PM 2.5 of ambient origin 0.0183 -0.0037 0.0124

Personal SO 4 2– 0.0051 0.0035 0.0006

Personal O 3 0.0014 0.0010 0.0009 Personal NO2 0.0015 0.0009 0.0010 Results suggest that ambient O 3 exposure may be related to personal SO 4 2– exposure but not to personal PM 2.5 exposure on the whole. Ambient NO 2 exposure was associated with personal PM 2.5 exposure, possibly because both have traffic sources.
Tolbert et al. (2007, 090316 ) Ambient PM 10 , PM 10 - 2.5 , PM 2.5 , EC, OC, TC, SO 4 2– , water-soluble metals, oxygenated hydrocarbons Ambient O 3 , NO 2 , CO, SO 2 PM 10 O 3 NO 2 CO SO 2 PMc PM 2.5 PM 10 1.0 O 3 0.6 1.0 NO 2 0.5 0.4 1.0 CO 0.5 0.3 0.7 1.0 SO 2 0.2 0.2 0.4 0.3 1.0 PMc 0.7 0.4 0.5 0.4 0.2 1.0 PM 2.5 0.8 0.6 0.6 0.4 0.2 0.5 1.0 SO 4 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 SO 4 2– EC OC TC Metals OHC SO 4 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 SO 2 and PM constituents. Components wereused 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 O 3 was the most significant predictor of respiratory disease visits in one-, two-, and three-pollutant models.
Brook et al. (2007, 091153 ) Anbient PM 10 , PM 10 - 2.5 , PM 2.5 , SO 4 2– , and trace metals in 10 Canadian cities. Ambient NO 2 , NO R with NO 2 (min, Max) NO 2 1.00 (1.00, 1.00) NO 0.67 (0.51, 0.77) PM 2.5 0.54 (0.45, 0.71) PM 10 - 2.5 0.31 (0.04, 0.50) PM 10 0.50 (0.23, 0.70) SO 4 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) NO 2 showed the strongest association with mortality, but it is unclear if this association is due to health effects of NO 2 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 PM 2.5 Ambient O 3 , NO 2 , SO 2 , CO Shown in figure format only. Authors tested relationship between meteorological variables and copollutants todetermine 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 PM 2.5 , personal UFP Fixed site and personal CO Personal R: PM 2.5 UFP CO PM 1 0.5 0.2

2.5 UFP 0.5 1 0.7 CO 0.2 0.7 1 Fairly low correlation was observed between PM 2.5 and CO and between PM 2.5 and UFP, stronger correlations between UFP and CO.
Kaur et al. (2005, 088175 ) Fixed-site and personal PM 2.5 analyzed postsample for light absorbance (as indicator for carbonaceous aerosol), personal UFP Fixed site and personal CO Personal R: R PM 2.5 Abs CO UFP PM 2.5 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 PM 2.5 and BC Personal, indoor residential, and outdoor residential NO 2 Personal exposure regression coefficients to:

PM 2.5 BC NO 2 Bedroom 0.72 0.47 0.70 Front door 0.46 0.61 0.60 Background 0.29 0.03 0.56 Personal NO 2 concentration is more strongly influenced by background than PM 2.5 or BC.
Sabin et al. (2005, 087728 ) BC, particle-bound PAH on a school bus. NO 2 on a school bus. BC PB-PAH NO 2 BC 1 0.94 0.49 PB-PAH 1 0.37

NO 2 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 NO 2 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 PM 2.5 and trace elements for personal exposure (P), residential indoor (RI), residential outdoor (RO), and workplace (WI) measurements. Microenvironmental and personal VOCs, NO 2 , and CO. R (PM 2.5 ) P RI RO WI TVOC 0.21 0.21 0.41 -0.32 NO 2 -0.1 -0.02 -0.16 0.09 CO -0.07 NR NR NR The EXPOLIS Oxford study was morefocused 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 PM 2.5 with SO 4 2– , NO 3 -, EC, OC. Microenvironmental CO. Ratio of Conc PM 2.5 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 PM 2.5 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 PM 2.5 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 PM 2.5 monitors. Ambient O 3 , NO 2 , SO 2 , and CO R PM 2.5 O 3 NO 2 SO 2 CO PM 2.5 1 0.67 0.37 --- 0.15 O 3 -0.72 1 0.02 --- -0.06 NO 2 0.75 -0.71 1 --- 0.75 SO 2 -0.17 0.41 -0.17 1 -0.32 CO 0.69 -0.67 0.76 -0.12 1 Strong association between ambient NO 2 and personal PM 2.5 suggests that ambient gas may be a suitable surrogate for personal exposure.



### December 2009 A-352

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Note: Hyperlinks to the reference citations throughout this doc ument 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

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### 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,
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### 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 environm ental pollution on exogenous and oxidative DNA damage. Mutat Res, 544: 397-402. 089017

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