Pilot Study on Coarse PM Monitoring 
This document contains blank pages to accommodate two-sided printing. 
EPA-454/R-15-001 

February 2015 

Pilot Study on Coarse PM Monitoring 

Prepared by: 

Jay R. Turner 

Department of Energy, Environmental and Chemical Engineering 

Washington University at St. Louis 

1 Brookings Drive 

St. Louis, MO 63130 

Steven G. Brown 

Hilary A. Minor 

Sonoma Technology, Inc. 

1455 N. McDowell Blvd., Suite D 

Petaluma, CA 94954-6503 

Prepared for: 

Joann Rice 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Contract No. EP-D-09-097

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 27711
EPA’s Coarse PM Pilot Study Acknowledgments 

ii

Acknowledgments 

RTI performed laboratory analyses, overseen by James Flanagan with contributions by 

Karin Foarde and Frank Weber, and provided logistical support on instrumentation and 

maintenance, led by Jeff Nichol. Washington University in St. Louis students and field operators 

conducted the measurements in St. Louis. Maricopa County staff, led by Ben Davis, conducted 

the measurements in Phoenix. At Washington University, Varun Yadav led the field operations 

and Li Du led the analysis of St. Louis and Phoenix TEOM data. At Sonoma Technology, Inc., 

Adam Pasch and Theresa O’Brien assisted in data handling and quality control, Paul Roberts 

was the Principal Investigator, and Mary Jo Teplitz, Ron Teplitz, and Marina Michaels provided 

editorial support. 

Disclosure: The St. Louis field operations were funded by EPA and were conducted by 

students and staff of the Jay Turner group at Washington University in St. Louis. Jay Turner 

was also a consultant to Sonoma Technology, Inc., on this project. 
EPA’s Coarse PM Pilot Study Table of Contents 

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Table of Contents 

Section Page

Acknowledgments .......................................................................................................................ii

List of Figures ............................................................................................................................. v

List of Tables ........................................................................................................................... viii

Executive Summary: Key Findings and Recommendations...................................................ES-1

1. Introduction .................................................................................................................... 1-1

1.1 Study Objectives................................................................................................... 1-2

1.2 Study Design ........................................................................................................ 1-3

1.3 Technical Approach .............................................................................................. 1-6

1.3.1 Instrumentation ......................................................................................... 1-7

1.3.2 Sample Analysis ....................................................................................... 1-7

1.3.3 Nomenclature and Dichot Sampling Concentration Equations .................. 1-7

1.4 Field Operations Overview ................................................................................... 1-9

1.4.1 Methods .................................................................................................... 1-9

1.4.2 Field Operations Summary ....................................................................... 1-9

1.5 Guide to This Report .......................................................................................... 1-11

2. Gravimetric Mass and Composition Overview ................................................................ 2-1

2.1 Gravimetric Mass Comparisons Between Dichot and FRM Samplers ................... 2-1

2.2 Distribution of Species Between Fine and Coarse Mode ...................................... 2-4

2.3 Collocated Precision of Dichot Measurements ...................................................... 2-5

3. Analysis of Elements ..................................................................................................... 3-1

3.1 Concentrations by Site and Size Fraction ............................................................. 3-1

3.2 Concentrations of Elements: Comparison of Dichot and FRM .............................. 3-2

3.3 Attenuation of X-Ray Intensity for Light Elements ................................................. 3-6

3.4 XRF versus ICP-MS Measurements and Implications ........................................ 3-10

3.5 Variation in Crustal Composition ......................................................................... 3-16

4. Analysis of Carbon ......................................................................................................... 4-1

4.1 Total Carbon Comparisons Between Dichot and FRM Samplers .......................... 4-1

4.2 Thermal Fraction Analysis (OC/EC Split) .............................................................. 4-3

4.3 Carbonate Concentrations .................................................................................... 4-4

4.4 Carbon Artifacts .................................................................................................... 4-7

4.4.1 OC Trip Blanks and Field Blanks .............................................................. 4-8

4.4.2 OC Mass Loadings on the Backup Filters ................................................. 4-8

4.4.3 Comparison of OC Blanks, Front Filters, and Backup Filters .................. 4-10

4.4.4 Summary of Carbon Artifacts .................................................................. 4-13

4.5 Biological Data ................................................................................................... 4-13

5. Analysis of Ions.............................................................................................................. 5-1

5.1 Approaches to Measuring Ions ............................................................................. 5-1

5.2 Nitrate Concentrations on Teflon and Backup Nylon Filter .................................... 5-2

5.3 Nitrate Partitioning Between Fine and Coarse Modes ........................................... 5-2

5.4 Ammonium Balance and Implications for Ammonium Measurements ................... 5-3

5.5 Comparison of Concentrations With and Without Denuders ................................. 5-7

5.6 Comparison of Ion and Corresponding Element Concentrations ........................... 5-9
EPA’s Coarse PM Pilot Study Table of Contents 

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Section Page

6. Mass Balance ................................................................................................................ 6-1

6.1 Results for Dichotomous Samplers ....................................................................... 6-1

6.2 Mass Balance Implications ................................................................................... 6-5

7. Comparison of FDMS TEOM and Filter Coarse PM Measurements ............................... 7-1

7.1 Results ................................................................................................................. 7-2

7.2 FDMS TEOM-to-Dichot Summary ........................................................................ 7-9

8. References .................................................................................................................... 8-1

Appendix A: Summary Statistics ............................................................................................. A-1 

Appendix B: Summary Ratios of Collocated Dichot Measurements ......................................... B-1 

Appendix C: Summary of Dichot-to-FRM Comparisons ........................................................... C-1 

Appendix D: Quartz Fiber Filter Carbon Blanks ....................................................................... D-1 

Appendix E: Nitrate Correlations with Other Species .............................................................. E-1 
EPA’s Coarse PM Pilot Study List of Figures

v

List of Figures 

Figure Page

1-1. Site maps for W 43rd Ave, Phoenix monitoring site.. .....................................................1-4

1-2. Site maps for 13th and Tudor, East St. Louis monitoring site.. .......................................1-5

1-3. Four-month sampling schedule for collocated dichots. .................................................1-6

2-1. Dichot versus FRM mass concentrations for St. Louis and Phoenix for PM2.5, PM10-

2.5, and PM10.. ...............................................................................................................2-2

2-2. Stacked bar plots of average composition for (a) PMc at Phoenix; (b) PMf at 

Phoenix; (c) PMc at St. Louis; and (d) PMf at St. Louis.. ...............................................2-4

2-3. Average FRM PM10/PM2.5 ratio (log scale) by species for each site. .............................2-5

2-4. Collocated dichot versus dichot plots for (a) gravimetric mass at Phoenix; 

(b) gravimetric mass at St. Louis; (c) Si at Phoenix; (d) Si at St. Louis..........................2-6

2-5. Scatterplot matrices of the observed differences, , in the 

collocated concentrations at Phoenix: (a) PMc and (b) PMf. ........................................ 2-10

2-6. Scatterplot vector of the observed differences, , across the fine 

and coarse PM size fractions in the collocated concentrations at Phoenix. ................. 2-11

2-7. Average ratio of collocated dichot measurements (log scale) by site, species, and 

size, using only data above detection limits for both measurements. .......................... 2-11

3-1. Percentage of samples above detection limit for dichot PMc by site. .............................3-2

3-2. Scatter plots of PMf and PMc concentrations from dichot and FRM at Phoenix and 

St. Louis for S, Zn, Ca, and Fe. ....................................................................................3-5

3-3. Cumulative distribution of gravimetric mass concentrations for all dichot sampler 

minor flow channel (PMc) samples that were analyzed by XRF and the six samples 

with mass loadings reported by RTI both with and without attenuation correction.. .......3-8

3-4. Attenuation factors for dichot minor flow channel samples, PM10 FRM samples, 

and dichot major flow channel samples. .......................................................................3-9

3-5. Estimated crustal PMc mass concentrations with and without corrections for selfattenuation applied to the dichot sampler data. ........................................................... 3-10

3-6. PHX dichot PMc aluminum concentrations by XRF (A = 0.51) and ICP-MS. ............... 3-11

3-7. PHX dichot PMc aluminum concentrations by ICP-MS with blank correction (mean 

laboratory blank of 1.092 μg/m3 from Table 3-6) and assuming 100% recovery, 

and by XRF with (a) XRF attenuation factor of unity; and (b) best-fit attenuation 

factor to reconcile the XRF and ICP-MS data. ............................................................ 3-15

)/ln(  CCx

,, AxBx

)/ln(  CCx

,, AxBx
EPA’s Coarse PM Pilot Study List of Figures

vi

Figure Page

3-8. Simple visualization of major crustal elements: fraction of the sum of Si, Fe, and 

Ca by element for primary dichot PMc at (a) Phoenix (N = 51) and (b) St. Louis 

(N = 31). ..................................................................................................................... 3-17

4-1. Dichot versus FRM total carbon concentrations for St. Louis and Phoenix for PM2.5, 

PM10-2.5, and PM10.........................................................................................................4-2

4-2. Carbonaceous PM average concentration values at Phoenix (N = 30) and St. 

Louis (N = 22). ..............................................................................................................4-3

4-3. Distribution of organic carbon average concentration values at Phoenix (N = 30) 

and St. Louis (N = 22).. .................................................................................................4-4

4-4. PMc carbonate from (a) collocated dichots and (b) mean collocated dichot versus 

FRM-by-difference. .......................................................................................................4-5

4-5. Relationship between dichot PMc carbonate and dichot PMc calcium expressed as 

molar concentrations in (a) Phoenix and (b) St. Louis. ..................................................4-6

4-6. OC mass loadings on backup filters: (a) PM2.5 FRM versus PM10 FRM; (b) dichot 

major flow versus PM2.5 FRM; and (c) dichot minor flow versus dichot major flow. .......4-9

4-7. OC mass loading distributions (μg/filter) for the dichotomous samples. ...................... 4-11

4-8. Dichotomous sampler OC mass loadings (μg/filter) for paired front and back filters: 

(a) major flow and (b) minor flow. ............................................................................... 4-12

4-9. OC concentrations for collocated dichot samplers: (a) PM2.5 and (b) PMc ................... 4-13

4-10. Box-whisker plots by site of (a) glucan (ng/m3) and (b) endotoxin (EU/m3). ................ 4-15

5-1. Scatter plots for dichotomous sampler nitrate showing (a) the partitioning of nitrate 

between the fine and coarse PM fractions and (b) total fine nitrate and the nitrate 

on the front filter. ..........................................................................................................5-3

5-2. Measured ammonium versus calculated ammonium scatter plots at Phoenix and 

St. Louis on Teflon and nylon (right four plots). .............................................................5-5

5-3. Collocated dichot (dichot with denuder versus dichot without denuder) scatter plots 

for (a) NH4, (b) NO3, (c) SO4, and (d) Ca, Fe, and Si at Phoenix. .................................5-8 

5-4 Comparison of ion and element concentrations (μg/m3) at St. Louis for K and K+, 

Na and Na+, and S and SO4

2- via dichot and FRM samplers for PMc and PMf. ........... 5-10

5-5. Comparison of ion and element concentrations (μg/m3) at Phoenix for K and K+, 

Na and Na+, and S and SO4

2- via dichot and FRM samplers for PMc and PMf. ........... 5-11

6-1. Mass balance closure between Teflon filter gravimetric mass and the chemical 

speciation data for dichot samples. ...............................................................................6-2
EPA’s Coarse PM Pilot Study List of Figures

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Figure Page

6-2. Reconstructed mass versus gravimetric mass for the dichotomous sampler Teflon 

filters: (a) PMf and (b) PMc for Phoenix and St. Louis. ..................................................6-3

6-3. RMS residuals between reconstructed mass and gravimetric mass as a function of 

the assumed OM/OC ratio: (a) Phoenix and (b) St. Louis. ............................................6-4

7-1. East St. Louis and Phoenix 24-hour average dichot TEOM total and nonvolatile 

PM mass concentrations versus the 24-hour integrated PM mass concentrations 

from filter-based samplers with gravimetric analysis on the Teflon filters: fine PM 

and coarse PM. ............................................................................................................7-3

7-2. Daily-average dichot TEOM time series for fine PM and coarse PM at (a) East St. 

Louis, 9/2010-2/2011; and (b) Phoenix, 7/2010-2/2011. ...............................................7-4

7-3. Daily-average dichot TEOM time series for fine PM and coarse PM at East St. 

Louis, 9/2010-2/2011, and Phoenix, 7/2010-2/2011......................................................7-5

7-4. PM2.5 diurnal profiles for East St. Louis, 9/2010 to 2/2011 and Phoenix, 7/2010 to 

2/2011: (a) total TEOM mass; (b) nonvolatile TEOM mass; and (c) volatile TEOM 

mass.............................................................................................................................7-6

7-5. PM10-2.5 diurnal profiles for East St. Louis, 9/2010 to 2/2011 and Phoenix, 7/2010 to 

2/2011: (a) total TEOM mass and (b) volatile TEOM mass. ..........................................7-7

7-6. PM10-2.5 weekday and weekend diurnal profiles for East St. Louis, 9/2010 to 2/2011 

and Phoenix, 7/2010 to 2/2011. ....................................................................................7-8

7-7. Diurnal profiles for the difference in weekday and weekend hourly median PM10-2.5

concentrations for East St. Louis, 9/2010 to 2/2011 and Phoenix, 7/2010 to 2/2011 ....7-8
EPA’s Coarse PM Pilot Study List of Tables 

viii

List of Tables 

Table Page

1-1. Definitions of the different types of PM referenced in this document. ............................1-7

1-2. Sample completeness by site and sampler type for operations from June 1, 2010, 

through May 31, 2011, excluding sampling days for biological aerosols analysis. ...... 1-10

2-1. Gravimetric mass comparisons between the dichot and FRM samplers. ..................... 2-3

2-2. Measures of agreement between the collocated dichot sampler data for PMc

measured at Phoenix. ...................................................................................................2-7

2-3. Measures of agreement between the collocated dichot sampler data for PMf

measured at Phoenix. ...................................................................................................2-7

2-4. Measures of agreement between the collocated dichot sampler data for PMc

measured at St. Louis. ..................................................................................................2-8

2-5. Measures of agreement between the collocated dichot sampler data for PMf

measured at St. Louis.. .................................................................................................2-8

3-1. Measures of agreement between the collocated FRM and dichot data for PMc

measured at Phoenix. ...................................................................................................3-3

3-2. Measures of agreement between the collocated FRM and dichot data for PMf

measured at Phoenix. ...................................................................................................3-3

3-3. Measures of agreement between the collocated FRM and dichot data for PMc

measured at St. Louis. .................................................................................................3-4

3-4. Measures of agreement between the collocated FRM and dichot data for PMf

measured at St. Louis. ..................................................................................................3-4

3-5. Average attenuation factors (A) back-calculated by RTI for a subset of samples. .........3-8

3-6. ICP-MS analysis of laboratory blanks with the membrane separated from the 

support ring (with adhesive) and of field blanks. ......................................................... 3-12

3-7. Recoveries for urban particle matter NIST standard reference material for the 

sample digestion protocol and ICP-MS analysis used in this study ............................. 3-13

3-8. Attenuation factors for dichot PMc aluminum at PHX. ................................................. 3-15

4-1. Mean mass loadings on the field blanks and dichot backup filters. ............................. 4-12

4-2. Biomarker field and trip blank data summary. ............................................................. 4-14

4-3. Biomarker data summary for blank-corrected data. .................................................... 4-14
EPA’s Coarse PM Pilot Study List of Tables 

ix

Table Page

5-1. Summary of nitrate concentrations via dichot on Teflon, nylon, and total (Teflon + 

nylon), plus fraction of total nitrate on Teflon filter. ........................................................5-2

6-1. Measures of agreement between reconstructed and gravimetric mass for Phoenix 

and St. Louis, using OM = 1.6 × OC and no correction for OC artifacts. .......................6-3

EPA’s Coarse PM Pilot Study Executive Summary 

ES-1

Executive Summary: Key Findings and Recommendations 

The U.S. Environmental Protection Agency (EPA) conducted this field study in 2010 and 

2011 to evaluate the challenges in sampling and analyzing coarse aerosol, the precision of 

coarse PM (PMc) mass species measurements using dichotomous (dichot) samplers, and mass 

balance of PMc. The study database is publicly available through the EPA Air Quality System 

(AQS) to EPA personnel, atmospheric scientists, and others concerned with the science of PM 

air pollution, related health effects, and human exposure to the coarse PM fraction of particulate 

matter. Additional samplers—including paired PM10 and PM2.5 Federal Reference Method (FRM) 

samplers to calculate PM10-2.5 mass and species concentrations by the difference method, and 

semi-continuous monitors—were operated to further characterize coarse PM and aid in the 

interpretation of any differences between dichot data and difference method data. The results of 

this study may be used to establish routine field operating procedures and laboratory standard 

operating procedures (SOPs) for use in PMc speciation monitoring. 

ES-1. Primary Objectives 

The primary objectives of the coarse PM pilot speciation study were to: 

1. develop the target species analyte list for routine speciation monitoring (what species 

need to be measured); 

2. evaluate and define appropriate analysis methods for routine speciation monitoring and 

the necessary SOPs; 

3. evaluate the field performance of the dichot samplers for routine speciation monitoring 

(e.g., comparing gravimetric mass and speciation to the FRM by difference data and 

assessing dichot collocated precision); 

4. learn about sampling and operational issues regarding the use of dichots; and 

5. evaluate data from the study to inform several issues related to coarse PM speciation 

measurements. 

ES-2. Study Methods 

The coarse PM pilot speciation study included one year of 1-in-3 day sampling at sites in 

Phoenix (Arizona) and East St. Louis (Illinois), from June 2010 through May 2011. At each site, 

two Thermo 2025D sequential dichot samplers, one Thermo 2025 sequential PM10 FRM 

sampler, one Thermo 2025 sequential PM2.5 FRM sampler, and one Thermo 1405-DF Filter 

Dynamics Measurement Systems (FDMS) dichotomous Tapered Element Oscillating 

Microbalance (TEOM) monitor were used to make routine measurements. Samples were 

collected for laboratory analysis using Teflon®/nylon (T/N) and quartz/quartz (Q/Q) filter 

sandwiches. 

Analytical methods adopted from the PM2.5 Chemical Speciation Network (CSN) were 

used to characterize fine and coarse particle speciation for about half of the sampling events. 

The rest were archived for further study if needed. The analytical methods included gravimetric 

mass, elements by x-ray fluorescence (XRF), ions by ion chromatography (IC) from the Teflon 
EPA’s Coarse PM Pilot Study Executive Summary 

ES-2

filter, and organic carbon (OC) and elemental carbon (EC) by thermal-optical analysis (TOA) 

from the quartz filter. Subsets of samples were analyzed for elements by inductively coupled 

plasma mass spectrometry (ICP-MS) and were analyzed for carbonate by TOA with sample 

acidification. Dichot PMc and PMfine (PMf) concentrations were adjusted to correct for 10% PMf

intrusion into the PMc channel; results in this report incorporate this correction. Coarse PM was 

thus measured directly both via the dichot (PMc) and via the difference between FRM PM10 and 

PM2.5 measurements (i.e., PM10-2.5). 

ES-3. Key Findings 

The key study findings were as follows. 

Sample completeness. The sample collection completeness objective of 80% was met 

for three of the four 2025D sequential dichot samplers. Sample collection completeness 

exceeded 90% for the two sequential dichot samplers at Phoenix and was 66% and 87% for the 

two sequential dichot samplers in St. Louis. Valid samples were collected from both sequential 

samplers on 92% of days at Phoenix, but only 52% of days in St. Louis. A major hardware 

failure required one St. Louis dichot sampler to be returned to the manufacturer, leading to low 

data completeness at that site. The most common field operations issues were filter exchange 

errors and pump failures in the sequential samplers. 

Dichot versus FRM by difference. PMc constituents measured on the Teflon filters 

(gravimetric mass, elements, and ions) were biased low for the dichot method compared to the 

FRM by difference method. In Phoenix, PMc mass from the dichot was, on average, about 20% 

lower than the FRM difference method mass (dichot-on-FRM slope = 0.67-0.71, intercept = 

2.2-2.7 g/m3 depending on the dichot sampler); in St. Louis, the dichot PMc mass was 10% to 

25% lower (dichot-on-FRM slope = 0.83-0.96, intercept statistically indistinguishable from zero 

[95% confidence level], depending on the dichot sampler). In contrast, PMc total carbon and 

carbonate measured on the quartz filters showed no bias between the two methods, though the 

relationship for total carbon exhibited more scatter. The bias for constituents measured on the 

Teflon filters is attributed to particle losses from the dichot minor flow channel Teflon filter, which 

contains all of the coarse particles and 10% of the fine particles. Losses may occur during the 

automated filter exchange in the sequential dichot sampler, during handling, during shipping to 

the analytical laboratory, or during any combination of these events. Coarse particles collected 

on quartz filters are much less prone to losses because the particles are more deeply 

embedded into the filter matrix. Dichot Federal Equivalent Method (FEM) samplers with a 

modified shuttle mechanism and firmware to minimize particle loss due to filter exchange are 

now available, but were not available from the manufacturer for this study. After the dichots 

were modified to be FEM compliant, a follow-up study conducted at Research Triangle Park 

(North Carolina) by RTI and EPA resulted in better agreement between the dichot PMc

gravimetric mass and the FRM by difference (PM10-2.5) gravimetric mass, with a dichot-on-FRM 

regression slope of 1.05 and an intercept statistically indistinguishable from zero (95% 

confidence level). Biases between the dichot method and the FRM by difference method 

prevented an evaluation of the potential measurement bias from mixing of PMc and PMf species 

components on the PM10 filter. 
EPA’s Coarse PM Pilot Study Executive Summary 

ES-3

Dichot precision. Dichot collocated precision for PMc gravimetric mass and major soil 

constituents (aluminum [Al], calcium [Ca], iron [Fe], silicon [Si], and titanium [Ti]) was in the 8% 

to 15% range. In contrast, the collocated precision of organic carbon, which was about 15% of 

the mass at both sites, was 19% in Phoenix and 34% in St. Louis. The OC data in St. Louis are 

less precise in part from having lower concentrations. Dichot collocated precision for PMf

gravimetric mass was 10% in Phoenix―consistent with the precision for PMf major crustal 

species―and 2% in St. Louis. 

Acid gas denuders. Ambient nitric acid can adsorb onto filters and cause a positive 

artifact for PM nitrate measurements. Sampling conducted in the summertime with collocated 

samplers, with and without acid gas denuders, showed insignificant differences in PMf and PMc

nitrate. It appears the sampler inlets can efficiently remove nitric acid and suppress a nitrate 

measurement bias. 

Organic Carbon. OC mass loadings on the dichot PMc channel backup filters were 

statistically indistinguishable from the trip blanks and field blanks OC mass loadings. This is 

consistent with very little volatile OC in the PMc size fraction. 

Carbonate fraction. Carbonate (CO3) was measured from the dichot PMc quartz filters 

on 69 selected sampling events (43 in Phoenix, 26 in St. Louis). Carbonate was also measured 

on the dichot PMf quartz filter for 15 of these sampling events. PMf carbonate was below the 

3-sigma minimum detection limit (MDL) of 0.52 μgC/m3 for all samples. However, PMc

carbonate was consistently detected with mean concentrations of approximately 1.2 μg/m3 and 

75th percentile concentrations of approximately 1.6 μg/m3 in both Phoenix and St. Louis. The 

mean carbonate concentrations correspond to 6% and 12% of the PMc mass in Phoenix and St. 

Louis, respectively. PMc carbonate was highly correlated with PMc calcium at both sites. 

Assuming all carbonate is present as calcium carbonate, about half of the PMc calcium in 

Phoenix and two-thirds of the PMc calcium in St. Louis can be explained as being calcium 

carbonate. 

Biomarker concentrations. Biomarkers (proteins, (1,3)-β-D-glucans, and endotoxin) 

were measured from Teflon filters in the dichot coarse particle channel for 54 sampling events 

(28 in Phoenix and 26 in St. Louis). These samples were collected from February through May 

2011. In both Phoenix and St. Louis, median PMc glucan concentration was approximately 

0.2 ng/m3, and protein concentration was about 0.08 μg/m3. However, relatively high blank 

corrections caused large uncertainties in the proteins data. PMc endotoxin concentrations were 

suspect in Phoenix because of dramatic differences in concentrations between analysis 

batches, although the batches correspond to adjacent but not overlapping time periods. Median 

endotoxin concentration was 0.07 EU/m3 St. Louis. 

Mass closure via dichot. Closure between the gravimetric mass and sum-of-species 

mass was evaluated for the dichot Teflon filters. The analysis ignored OC artifacts and assumed 

that EC and OC loadings on the quartz filters were representative of EC and OC loadings on the 

Teflon filters (it is possible that carbonaceous particulate matter is also lost from the dichot PMc

channel filters as reported above for mass, elements, and ions). The analysis also assumed that 

the equation commonly used to estimate PMf crustal mass concentration from the major crustal 

elements (Al, Ca, Fe, Si, and Ti) is valid for estimating PMc crustal mass concentrations. 
EPA’s Coarse PM Pilot Study Executive Summary 

ES-4

Assuming an organic-matter-(OM)-to-OC ratio of 1.6, both PMf and PMc mass concentrations 

reconstructed from the speciation data at Phoenix were, on average, about 13% higher than the 

gravimetric mass. For the St. Louis data, the reconstructed mass was 6% lower for PMf and 1% 

higher for PMc compared to the gravimetric mass. Additional analyses were performed using the 

OM/OC ratio as an adjustable parameter to obtain best-fit mass balance closure. For PMf, the 

best-fit OM/OC ratios were about 1.2 in Phoenix and 1.8 in St. Louis; these ratios are consistent 

with estimates reported in the literature, e.g., Simon et al. (2011). For PMc, the best-fit OM/OC 

ratio for St. Louis was about 1.5, but subject to large uncertainty; for Phoenix, the ratio was 0.6, 

which is physically unrealistic (the ratio cannot be less than unity). This finding that the PMc

reconstructed mass is biased high, especially in Phoenix, suggests systematic errors in the 

estimation methodology, such as improper multipliers for estimating crustal PMc from elemental 

concentrations, or corrections for X-ray attenuation during XRF analysis of light elements (e.g., 

Al, Si, Ca) that are too large. Two additional confounders are the assumption that EC and OC 

are not lost from the Teflon filter (accounting for such losses may improve mass closure, though 

it may lead to an overestimate of mass collected on the Teflon filter), and the exclusion of 

carbonate from the reconstructed mass calculation (accounting for carbonate would increase 

the reconstructed mass concentrations and thus lead to even larger overestimation of the 

gravimetric mass). 

XRF measurements. Corrections for X-ray attenuation during XRF analysis (selfattenuation) were evaluated by analyzing Teflon filters from 18 sampling events (10 in Phoenix, 

8 in St. Louis) using both XRF and ICP-MS. Dichot PMf and PMc channel filters were analyzed 

for all 18 events, and PM10 and PM2.5 FRM filters were analyzed for 10 of the events. For light 

elements associated with crustal material (Al, Ca), the coarse particle concentrations by blankcorrected ICP-MS were greater than the concentrations by XRF. This pattern does suggest that 

the corrections for self-attenuation for these constituents are too large. However, quantitative 

comparisons were confounded by large ICP-MS blank values for elements such as Al and Ca, 

which are present in the membrane filter support ring, the adhesive, and the ink used to stamp 

the filter ID number. Smaller corrections for self-attenuation will yield lower PMc concentrations 

for these elements and a lower estimate for the crustal PMc mass concentration. 

Dichot FDMS TEOM measurements. Hourly PM2.5 and PM10-2.5 concentrations from the 

Thermo 1405-DF FDMS TEOM instruments revealed appreciable volatile PM2.5 mass, but 

volatile PM10-2.5 mass was too small to be reliably distinguished from measurement error. This is 

consistent with expectations that ammonium nitrate and particle-phase semivolatile organic 

compounds tend to be in the fine size fraction. 

ES-4. Recommendations 

The recommendations presented here are based on experiences from the one-year pilot 

study with sampling in Phoenix and St. Louis. There are limitations when basing 

recommendations on the operations and data for only two sites, and care should be taken to 

adapt the recommendations as appropriate for other environmental settings. 

Sequential dichot sampling is an attractive approach to particle collection for PMc

measurement. The dichot sampler segregates fine and coarse particles onto separate filters, 
EPA’s Coarse PM Pilot Study Executive Summary 

ES-5

thus minimizing the potential for measurement artifacts from the mixing of these particles. With 

sequential sampling, the instrument can be programmed for multiple sampling events, reducing 

the manpower burden for field operations. Care must be taken to maintain the setpoint fine and 

coarse channel flowrates to achieve the design PM2.5 cutpoint and to appropriately correct the 

coarse channel data for fine particle intrusion. 

On the basis of this study, paired dichotomous samplers are recommended, with one 

sampler collecting particles onto Teflon filters and the other sampler collecting particles onto 

Q/Q filters (see note below). If high concentrations of coarse particle nitrate are expected, e.g., 

at sites where atmospheric processes have converted sea salt into sodium nitrate, a T/N filter 

and Q/Q filter combination should also be used during the first year of operations, with analysis 

of ions on the T and N filters to assess coarse particle nitrate concentrations. In environments 

similar to St. Louis or Phoenix, a denuder does not appear to be necessary; in environments 

where there may be significant nitric acid that could absorb onto the Teflon filter and be 

quantified as aerosol nitrate, it may be useful to conduct a series of test days to determine 

whether a denuder is needed as part of routine sampling. Specific recommendations and 

caveats regarding field operations and chemical analyses are discussed below. 

Post pilot study note: since the completion of this pilot study, EPA has determined that 

backup quartz filters are not necessary for OC artifact correction; therefore, the 

recommendation for a paired dichot with Q/Q filters is revised to recommend a paired dichot 

with a Q filter only. 

Sampling and Field Operations 

At both sites, dichot PMc constituents measured on Teflon filters (gravimetric mass, XRF 

elements, and ions) were biased low compared to PM10-2.5 data collected under the FRM by 

difference method. In contrast, such bias was not observed for PMc constituents measured on 

quartz filters in the dichot PMc channel (carbon). It is likely that coarse particles become 

dislodged from the dichot PMc channel Teflon filter during the automated filter exchange, but it is 

also possible that the particles are dislodged during shipping from the field sites to the analytical 

laboratory or during filter handling. Although a shipping protocol recommended by the EPA’s 

Office of Research and Development (ORD) was used to minimize particle loss, this study did 

not conclusively determine which mechanism was responsible (filter exchange or shipping) for 

particle loss. 

Hardware failures were more frequent than anticipated, especially in St. Louis, with the 

most common problem being errors during the automated filter exchanges and pump failures, 

together accounting for 8% of dichot sampling events being invalid. Although the Thermo 2025D 

sequential dichotomous sampler has since been designated a FEM for PMc, the 2025D 

samplers used for this study were not FEM-compliant. The aforementioned issues with particle 

losses and field robustness of the sampler may be specific to the non-FEM version of the 

2025D, and users should ensure they are using FEM-compliant samplers. Given that a 

complete speciation sample requires valid data be collected by two independently operating 

samplers, it may be necessary to maintain an inventory of backup hardware to minimize 

sampler downtimes. 
EPA’s Coarse PM Pilot Study Executive Summary 

ES-6

Low-volume (16.7 liter per minute [LPM]) dichot samplers were used in this study. 

Detectability and precision were deemed adequate for the constituents of primary interest. 

Standard operating procedures (SOPs) for the analytical methods used were included in 

the pilot study’s Quality Assurance Project Plan (QAPP). These SOPs are appropriate for use in 

PMc speciation measurement except for the analysis of elements by XRF (where attenuation 

correction factors would need to be revised) and ICP-MS (where a stronger digestion method is 

required for the crustal species). 

Sample Analyses 

Based on experiences in Phoenix and St. Louis, the following baseline measurements 

are recommended for PMc speciation. 

1. Gravimetric mass concentrations using Teflon filters and following the PM2.5 CSN 

method. Analysis must be performed on both the PMf and PMc channel filters. This study 

used the filter handling and shipping protocols developed by the EPA’s ORD and are 

presumed to be adequate for mass and chemical speciation. However, these protocols 

should be verified by conducting a specific study to assess potential filter handling and 

shipping effects on PMc once a routine network is operational. 

2. Elemental mass concentrations by XRF using Teflon filters and following the PM2.5 CSN 

method. Analysis must be performed on both the PMf and PMc channel filters. At both 

sites, coarse PM mass was dominated by crustal material, so it is important to quantify 

the major crustal constituents. However, the corrections for self-attenuation applied to 

XRF results for light elements such as Al, Ca, and Si in PMc appear to be too high. 

Examination of PMc mass balance closure and comparisons of PMc constituents 

measured by XRF and ICP-MS suggest that the corrections for self-attenuation are 

necessary, but that the current corrections overestimate the actual concentrations. 

Additional work is needed to establish corrections for use with PMc data. The best-fit 

corrections determined in this study are subject to confounders that may bias the 

estimates. Numerous factors that influence the corrections, such as the particle size 

distribution, should be taken into consideration to generate robust corrections. The 

comparison should be made using a larger data set, with samples collected at sites that 

have high crustal loadings, and ideally including coarse PM from different sources, such 

as desert dust and agricultural dust. 

3. Elemental and organic carbon concentrations by TOA using quartz filters and following 

the PM2.5 CSN method with the Interagency Monitoring of Protected Visual 

Environments (IMPROVE) analysis protocol IMPROVE_A. Both the PMf and PMc

channel filters must be analyzed. The IMPROVE_A protocol is recommended because it 

would be consistent with the PM2.5 CSN network. Also, the maximum temperature during 

analysis by the IMPROVE_A protocol is below the decomposition temperature for 

calcium carbonate. However, carbonate might decompose at lower temperatures 

because of matrix interactions among particle constituents, and more work should be 

done to evaluate whether carbonate, which was observed in PMc at both sites, interferes 

with the measurement of EC and OC. 
EPA’s Coarse PM Pilot Study Executive Summary 

ES-7

4. Carbonate mass concentration by TOA with acidification and using quartz filters. 

Carbonate was measured for a subset of the collected samples. PMf carbonate 

concentrations were all below the 3-sigma minimum detection limit and did not contribute 

to PMf mass. However, PMc carbonate concentrations were, on average, about 6% of 

the PMc gravimetric mass at Phoenix and 12% of the PMc gravimetric mass at St. Louis. 

PMf carbonate is expected to be low at virtually all sites; therefore, the measurement 

should be performed on the PMc channel filter only, because the correction for fine 

particle carbonate will be negligible. While carbonate concentrations were similar at 

Phoenix and St. Louis despite the dramatically different environments, it is possible that 

PMc carbonate might be negligible at some sites and could be dropped from the analysis 

plan for such sites after a period of sampling that demonstrates persistently low 

carbonate concentrations. 

In addition to the above baseline measurements, the following analyses are 

recommended depending on site-specific conditions. 

1. Anion species mass concentrations by water extraction and IC using Teflon and nylon 

filters and following the PM2.5 CSN analysis method. PMc sulfate and nitrate 

concentrations were persistently low at both Phoenix and St. Louis. XRF measurement 

of total sulfur (S) includes sulfate, and for PMc speciation it is likely unnecessary to 

discriminate the sulfate contribution to total sulfur. In contrast to Phoenix and St. Louis, 

some locations—particularly sites near coastlines—may have significant concentrations 

of PMc nitrate, which should be measured on the Teflon filter. PMc nitrate is expected to 

be nonvolatile, and in the absence of fine particle ammonium nitrate, it is possible to 

analyze only the Teflon filter. However, in some locations with PMc nitrate, there may be 

considerable fine particle ammonium nitrate—in such cases, it will be necessary to also 

measure nitrate on a nylon filter placed immediately downstream of the PMf Teflon filter 

to properly correct the PMc data for fine particle intrusion in the dichot PMc channel. In 

locations where there is abundant nitric acid, a denuder may also be necessary to 

ensure that nitric acid is not quantified as aerosol nitrate. As part of a site-specific 

assessment of the abundance of coarse particle nitrate, collocated samplers with and 

without a denuder should be run for a limited period to assess the need for a denuder as 

part of the site’s routine operations. 

2. Cation species mass concentrations by water extraction and IC using Teflon filters and 

following the PM2.5 CSN analysis method. PMc ammonium concentrations were very low 

in Phoenix and St. Louis and are expected to be low at most locations. PMc sodium 

concentrations were higher than PMf sodium concentrations and were present 

predominantly as the monovalent cation (Na+). PMc potassium concentrations were 

similar to PMf potassium concentrations, and were present predominantly in forms other 

than the monovalent cation (K+). The limited utility from measuring PMc ammonium, ionic 

sodium, and ionic potassium does not justify the additional cost for routine operations at 

most sites. 
EPA’s Coarse PM Pilot Study Executive Summary 

ES-8

The following analyses may be considered for special cases, but are not warranted as 

routine measurements for PMc speciation. 

 Scanning electron microscopy (SEM) can provide substantial insights into particle 

sources by classifying particle shape and/or composition. However, Teflon and quartz 

filters cannot be used for quantitative analysis by SEM. An additional sampler would be 

needed to collect particles onto a suitable substrate, such as polycarbonate membrane 

filters. The extra sampling requirement and analytical costs relegate SEM to special 

studies rather than routine measurements. 

 Biological material—both intact and fragmented—can be a significant contributor to PMc. 

Biomarker concentrations for glucans (an indicator for spores) and proteins can provide 

insights into spatial and temporal patterns, but to be most valuable to PMc speciation, 

multipliers are needed to convert the biomarker concentrations to mass concentrations 

of the corresponding biologic material (i.e., mass biologic material per mass of 

biomarker). This issue and the extra analytical costs relegate biomarkers to special 

studies rather than routine measurements. 

 For many elements, ICP-MS provides better sensitivity than XRF. However, this study 

demonstrates that detectability and precision using XRF are adequate for the primary 

elements of interest. ICP-MS may be attractive for special cases where higher-quality 

trace elements data are desired or to confirm that appropriate corrections for selfattenuation are being used for XRF analysis. EPA’s current PM2.5 ICP-MS SOP would 

need to be optimized for the specific elements targeted for ICP-MS analysis of PMc

elements. For example, the ICP-MS analyses conducted for this project required the use 

of microwave and mixed acid (nitric, hydrochloric, and hydrofluoric) digestion process 

because crustal elements were the primary target of the analysis. 
EPA’s Coarse PM Pilot Study Introduction 

1-1 

1. Introduction 

In 1997, the U.S. Environmental Protection Agency (EPA) promulgated revisions to the 

National Ambient Air Quality Standards (NAAQS) for particulate matter (PM) and added a 

standard for fine particulate matter (PM2.5). In 2006, the EPA issued a final monitoring rule for 

thoracic coarse particles. Coarse particles have aerodynamic diameters between 2.5 μm and 

10 μm; here coarse PM is referred to as PMc, if measured from dichot samplers, or termed 

PM10-2.5 if from FRM by difference method. The promulgated monitoring requirements specified 

the placement of coarse PM speciation samplers at National Core (NCore) monitoring sites. In 

2013, the requirement for coarse PM speciation at NCore was revoked because of technical 

issues related to the development of appropriate monitoring methods. Sample collection 

procedures and analysis methods for coarse PM speciation measurements were explored as 

part of the small-scale pilot monitoring study presented here. 

In 2009, the Clean Air Scientific Advisory Committee (CASAC) Ambient Air Monitoring 

and Methods (AAMM) Subcommittee provided input on sampling and analysis issues for coarse 

PM speciation. For coarse PM speciation, the CASAC AAMM strongly recommended the use of 

dichotomous samplers (dichots), where coarse particles are directly sampled, rather than 

Federal Reference Method (FRM) samplers, where coarse PM is derived from a difference of 

PM10 and PM2.5 measurements, i.e., PM10-2.5. 

To address concerns of the EPA and CASAC AAMM, a small-scale pilot monitoring 

study was deployed, the results of which are presented in this report. This pilot study is 

important from several perspectives. 

One reason why this study is important is the need to assess whether chemical and 

physical characterization of coarse PM differ when the values are determined using the PM10

minus PM2.5 method (termed PM10-2.5 in this report) as compared with characterization of the 

PMc fraction derived from the dichotomous sampler. Dichots directly sample the coarse 

particles, with 10% of the fine particles drawn through the inlet also present in the sample 

stream. There was concern that “mixing” of the PMc fraction with the PM2.5 fraction on a filter 

from the PM10 sampler (in the difference method) could lead to changes in aerosol composition 

that are different from the changes that occur on the coarse particle filter in the dichot (which 

contains only 10% of the PM2.5 mass). 

A second important reason for conducting the pilot study was to assess the robustness 

of commercial samplers and the training and skills required of the field operator and supporting 

laboratory to produce quality data with a high percentage of data capture. 

A third reason for the pilot study was to compile a database of coarse PM chemical and 

physical information, supplemented by information from measurements not normally made in 

the PM2.5 Chemical Speciation Network (CSN) (e.g., protein content, metals determination by 

inductively coupled plasma mass spectrometry [ICP-MS], organics speciation by gas 

chromatography–mass spectrometry [GC-MS]) and by information derived from collocated 

instruments, including a dichotomous tapered element oscillating microbalance (TEOM) monitor 

for hourly PM10-2.5 and PM2.5 volatile and nonvolatile mass measurements. 
EPA’s Coarse PM Pilot Study Introduction 

1-2 

1.1 Study Objectives 

The primary pilot study objectives were to develop the target species analyte list for 

routine speciation monitoring (what species need to be measured); evaluate and define analysis 

methods and the necessary SOPs; evaluate the appropriateness of using a dichot sampler; 

learn about sampling and operational issues regarding the use of dichots; and evaluate data 

from the study to inform other issues (e.g., closure between gravimetric mass and sum-ofspecies mass). The coarse PM measurement system includes media preparation, media 

shipping, sample handling, routine sampling operations, and laboratory analyses. A list of 

species and appropriate measurements needed to reasonably characterize PMc using the lowvolume dichot measurement system is recommended. 

The main objectives of the study were as follows: 

 Objective 1: Develop the target species analyte list for routine speciation 

monitoring. This objective was addressed by starting with the speciate analyte list for 

the PM2.5 Chemical Speciation Network and supplementing with additional 

measurements such as PMc carbonate.

 Objective 2: Evaluate and define analysis methods for routine speciation 

monitoring and the necessary SOPs. Again, the PM2.5 Chemical Speciation Network 

was used as a starting point with supplemental measurements added to evaluate the 

conventional methods. 

 Objective 3: Evaluate the field performance of the dichot samplers for routine 

speciation monitoring. To meet project objectives, the PMc dichot and associated 

comparison samplers and monitors were operated for one year to provide sufficient 

comparison data over a range of atmospheric and seasonal conditions. This information 

was needed for the major components of the PMc aerosol, including elements, ions, and 

carbon. The needed information was obtained from collocated measurements, trip 

blanks, and field blanks. Primary and collocated dichot samplers were used to collect 

eight collocated samples of each substrate type per sampling season (with three 

sampling seasons per year). In order to accomplish this objective, both dichots were run 

with a Teflon/nylon filter pair for eight events per season, and with quartz filters for eight 

events per season. Both trip and field blank filters were collected, and four times each 

season, field blanks were collected that mimicked a sampling event (but with no air 

pulled through the sampler). 

 Objective 4: Learn about sampling and operational issues regarding the use of 

dichots. Again, the PMc dichot and associated comparison samplers and monitors were 

operated for one year to provide information on sampling and operational issues. 

 Objective 5: Evaluate data from the study to inform several issues related to 

coarse PM speciation measurements. Data analyses were conducted to inform 

sampling measurement performance including precision, comparability, and 

representativeness. Mass balance closure was examined to identify potential issues in 

the speciation measurements. 
EPA’s Coarse PM Pilot Study Introduction 

1-3 

1.2 Study Design 

For this pilot study, two monitoring sites were chosen representing different 

environmental concentrations and aerosol mixes to operate for nominally one year (May 2010 to 

May 2011). The pilot study included sites in Phoenix, Arizona (abbreviated as PHX in tables and 

figures), and East St. Louis, Illinois (abbreviated as STL in tables and figures). 

At both sites coarse PM was likely to be dominated by crustal elements, but as Phoenix 

is in the arid Southwest, concentrations were likely to be higher there. Primarily, two methods 

were used to collect coarse PM samples for analysis: 

 dichotomous samplers to directly measure PMfine (PMf) and PMc, and 

 paired PM10 and PM2.5 FRM samplers to determine PM10-2.5 (difference method). 

All samples were collected for 24 hours from midnight to midnight local time. Laboratory 

analysis methods consistent with PM2.5 CSN processes (i.e., gravimetric mass, ions by ion 

chromatography [IC], elements by XRF, carbon by thermal-optical analysis) were used to 

analyze about 50% of the filter samples. The remaining samples were archived for future 

analyses. 

Sampling commenced in May 2010, with four weeks of nearly daily sampling to refine 

the field operations and provide a data set for preliminary evaluation of certain sampling 

configurations (e.g., whether the presence of a denuder affected the PMc mass measurements). 

Sampling was conducted on a one-in-three day schedule from June 1, 2010, through May 31, 

2011, using various sampling configurations to address the technical objectives of the project. 

For details, see the PM10-2.5 Speciation Pilot Monitoring Quality Assurance Project Plan 

(abbreviated as QAPP and approved in 2010). Additional sampling was conducted periodically 

during the study to provide samples for biological content analyses. Under contract EP-D-08-

047, RTI International personnel and subcontractors who regularly serve the EPA/Office of Air 

Quality Planning and Standards (OAQPS) PM2.5 CSN provided support for integrated sampler 

installation and operation, necessary training, initial equipment audits and flow checks. Filter 

preparation and laboratory sample processing and analyses were provided under contract EPD-09-010. Under EPA contract EP-D-09-097, Sonoma Technology, Inc. (STI) personnel and Dr. 

Jay Turner (Washington University, St. Louis) analyzed the data. 

The Phoenix site (Figure 1-1) is at 43rd Avenue and Broadway Road in Phoenix, Arizona 

(AQS ID 04-013-4009). The Maricopa County Air Quality Department in Phoenix managed the 

day-to-day operations. 

The East St. Louis, Illinois, coarse PM speciation pilot site (Figure 1-2) is the PM 

Supersite location used previously for PM research (AQS ID 17-163-9010). The St. LouisMidwest Supersite is located at 13th Street and Tudor Avenue in East St. Louis, Illinois, which is 

about 3 km east of the City of St. Louis, Missouri, central business district. The Air Quality 

Laboratory at Washington University in St. Louis managed the day-to-day operations. The 

physical footprint managed by Washington University is immediately adjacent to the East St. 

Louis compliance monitoring site operated by the Illinois EPA (AQS ID 17-163-0010). 
EPA’s Coarse PM Pilot Study Introduction 

1-4 

Figure 1-1. Site maps for W 43rd Ave, Phoenix monitoring site. Concentric circles in the 

bottom map are 500 m and 1,000 m radii from the site. 
EPA’s Coarse PM Pilot Study Introduction 

1-5 

Figure 1-2. Site maps for 13th and Tudor, East St. Louis monitoring site. Concentric 

circles in the bottom map are 500 m and 1,000 m radii from the site. 
EPA’s Coarse PM Pilot Study Introduction 

1-6 

1.3 Technical Approach 

Each dichot had two channels, one for coarse PM and one for fine PM. To collect data 

for the calculation of mass balance, at a minimum, a Teflon filter (for ions and elements) and a 

quartz fiber filter (for carbon) had to be collected for each size fraction; this means data from two 

collocated dichots were needed to achieve mass balance on any given day. To understand 

collocated dichot precision, two dichots were run with the same filter media, i.e., either Teflon or 

quartz fiber. In addition, the sampling schedule was harmonious with the field work already 

occurring on site, which was typically one-in-three day sampling. Filter blanks were also 

collected at a regular interval. 

To best achieve these goals, a six-day cycle was implemented that alternated between 

collocated, mass balance, and field blank collection days for a four-month period, as shown in 

Figure 1-3. This cycle was repeated twice more to complete a year of sampling. This sampling 

approach resulted in filters for mass balance every sixth day, while the other every-sixth-day 

pattern resulted in a series of collocated filters or field blanks. In addition, PM2.5 and PM10 FRM 

samples were also collected on Teflon filters in parallel with the dichot measurements. Thus, at 

each site there were two dichots plus collocated PM2.5 and PM10 FRM samplers. In addition, a 

Thermo 1405-DF Filter Dynamics Measurement Systems (FDMS) dichotomous TEOM – which 

is a Federal Equivalent Method (FEM) for PM2.5 but not PM10-2.5 – was operated to obtain hourly 

PM2.5 and PM10-2.5 data. 

Figure 1-3. Four-month sampling schedule for collocated dichots. In Column 1, green 

indicates a “mass balance” day with Teflon (T) filters and nylon (N) backup filters on one 

dichot and quartz fiber (Q) filters on the other dichot. Field blanks were collected four 

times in this cycle, as indicated by blue in Column 4. Gold indicates the days when 

collocated Teflon/nylon filters were collected, and yellow indicates the days when 

collocated quartz fiber filters were collected. 
EPA’s Coarse PM Pilot Study Introduction 

1-7 

1.3.1 Instrumentation 

The following equipment was installed and operated at each of the two sites: 

 Two (2) sequential dichotomous samplers (Thermo 2025D); 

 Two (2) sequential Thermo 2025 FRM samplers of the same make and model, one for 

PM10 and the other for PM2.5 (for the PM10-2.5 “difference method” measurements); 

 One (1) dichotomous semi-continuous mass monitor (Thermo 1405-DF FDMS TEOM); 

and

 One (1) eight-stage (0.18, 0.32, 0.56, 1.0, 1.8, 2.5, 5.6, and 10 μm) MOUDI impactor 

(MSP Corporation). 

While the Thermo 2025D sequential dichotomous samplers were designated as an FEM 

prior to this study, the samplers provided by Thermo were older models that were not FEMcompliant. Thus, it will be important to obtain additional field operations experience with the 

FEM-compliant version of the sampler. 

1.3.2 Sample Analysis 

Routine sample handling and analysis protocols followed the Standard Operating 

Procedures (SOP) used by RTI for the PM2.5 CSN. Details on sample handling and analysis are 

provided in the Pilot Study QAPP (U.S. EPA, 2010). The following Whatman® 47 mm filters 

(GE Healthcare, Pittsburgh, PA) were used: Teflon membrane (Part Number 7592-304); nylon 

membrane (Part Number 7410-004), and quartz fiber (Part Number 1851-047). Teflon filters 

were analyzed for mass by gravimetric weighing, for elements by XRF, and for water-soluble 

ions by IC. The backup nylon filters were analyzed for ions by IC. Quartz fiber filters were 

analyzed for carbon using the IMPROVE_A thermal-optical analysis (TOA) protocol currently 

used in the CSN network. Mass and speciation data from these measurements have been 

uploaded to the EPA Air Quality System (AQS) database. Additional filters were selected for 

analysis of carbonate, biologicals, metals by ICP-MS, microscopy, and speciated organics. 

1.3.3 Nomenclature and Dichot Sampling Concentration Equations 

Table 1-1 defines the different types of particulate matter referenced in this document. 

PM2.5 from the FRM is termed PMfine (PMf) in some cases when the distinction is unambiguous. 

Table 1-1. Definitions of the different types of PM referenced in this document.

Abbreviation Description

PM10-2.5

Coarse PM by difference method

FRM-measured PM10 minus FRM-measured PM2.5

PM10 FRM-measured PM10

PM2.5 FRM-measured PM2.5

PMf Fine PM measured by a dichotomous sampler

PMc Coarse PM measured by a dichotomous sampler
EPA’s Coarse PM Pilot Study Introduction 

1-8 

The dichot samplers used in this study include a 16.7 liters-per-minute (LPM) standard 

PM10 inlet followed by a virtual impactor that splits the sample stream into major (15.0 LPM) and 

minor (1.67 LPM) flows. The major flow includes only fine particles, whereas the minor flow 

includes all of the coarse particles and 10% of the fine particles. PMf mass concentrations are 

calculated directly from the major flow, and PMc mass concentrations are calculated by 

correcting the minor flow for fine particle intrusion. For a nonvolatile species j collected on the 

front filter of the two-filter (routine/backup) sandwiches, the governing equations for species 

mass concentration are 

j, front major

,

major

minor

, j, front minor j, front major

total major

j, front minor minor

,

total total

1

j f

j c

j f

m

PM

V

V

PM m m

V V

m V

PM

V V



 

   

 

 

(Eq. 1-1)

where PMj,k is the mass concentration of species j in size fraction k (f = fine, c = coarse), mj,p is 

the species j mass loading on the front filter in the specified flow channel p (major or minor); and 

Vp is the total air volume sampled by flow channel p (major, minor, or total = major + minor). For 

operation with the setpoint flow rates, Vminor/Vtotal = 0.10; thus, the correction to the coarse 

particle concentration for fine particle intrusion is 10% of the fine particle concentration (Dzubay 

and Stevens, 1975). 

For species such as nitrate that are collected on both the front (Teflon) filter and back 

(nylon) filter, the governing equations are 

   

j, front major j, back major

,

major

minor

, j, front minor j, back minor j, front major j, back major

total major

j, back minor minor j, back major

,

total total major

'

1

'

j f

j c

j c

m m

PM

V

V

PM m m m m

V V

m m V

PM

V V V





 

     

 



  





   

(Eq. 1-2)

where the prime species mass concentration includes mass on both the front and back filters. 
EPA’s Coarse PM Pilot Study Introduction 

1-9 

1.4 Field Operations Overview 

1.4.1 Methods 

Field operations methods are detailed in the Pilot Study QAPP (U.S. EPA, 2010). All 

work performed and data collected at the Pilot Monitoring Program site locations were based on 

the following quality objectives: 

 Multiple samplers were installed at each pilot site with inlets as close to the same height 

as possible and within a 1- to 4-meter separation from each other. 

 All routine field sampling information (start time, end time, average flow rate, 

temperature and pressure data, meteorological conditions, etc.) and verification quality 

assurance (QA) checks were recorded on hard-copy field data forms prepared for use 

with each sampler.

 All integrated sampler field data were verified and placed in the RTI database and 

periodically provided to EPA/STI. These data were also posted to the EPA AQS 

database. The TEOM mass monitor data was submitted to STI via the AirNow-Tech 

website. RTI was the central repository for all integrated sampler data (including raw 

data) and related field information.

 All sampler parameters (flow rate, ambient and filter temperature, and barometric 

pressure) were verified against NIST-traceable standards prior to beginning and at the 

completion of the study, or after any sampler maintenance. 

 For this project, the target completeness objective (completeness being the percentage 

of valid data compared to the total expected data) for all species and measurements was 

80% of all scheduled measurements. In addition to individual measurement 

completeness, the program completeness (sampling events with all attempted 

measurements having valid data) was tracked, because program completeness dictates 

the robustness of the data set across the entire measurement strategy. 

The PM10-2.5 Pilot Study QAPP details audit procedures and routine operations, as well 

as sample handling and laboratory procedures. 

1.4.2 Field Operations Summary 

Table 1-2 summarizes the study sample collection completeness for June 1, 2010, 

through May 31, 2011, excluding the extra sampling days programmed to collect samples for 

biological content analysis. At each site, the two dichotomous samplers were distinguished by 

the designations “A” and “B,” or primary (P) and collocated (C). Sampler hardware failures were 

the most common reasons for invalid samples. Sampling completeness for the Phoenix 

operations was above 90% for all samplers, and each sampler met the 80% completeness 

target. For St. Louis, the sampling completeness was much lower because of issues with the 

2025D (dichotomous) samplers, and one sampler failed to meet the 80% completeness target. 

The low sample collection completeness in St. Louis was particularly problematic for this 

methods evaluation study, which places high value on the number of days with valid sample 

collection for all four samplers (only 44% of all sampling days for St. Louis). 
EPA’s Coarse PM Pilot Study Introduction 

1-10

Table 1-2. Sample completeness by site and sampler type for operations from June 1, 

2010, through May 31, 2011, excluding sampling days for biological aerosols analysis. 

About half of valid samples were archived by RTI rather than analyzed. The total number 

of possible sample days is 122. 

Sample Type

Phoenix St. Louis

Valid 

Sample 

Days

% of Total 

Possible 

Sample Days

Valid 

Sample 

Days

% of Total 

Possible 

Sample Days

PM2.5 FRM (2025) 115 94% 101 83%

PM10 FRM (2025) 114 93% 112 92%

Dichot A (2025D) 113 93% 80 66%

Dichot B (2025D) 120 98% 106 87%

Both Dichots 112 92% 71 58%

All Samplersa 105 86% 54 44%

a Sampling events with all four samplers (PM2.5 and PM10 FRMs, Dichots A and B) having 

valid sample collection.

At both sites, there were sampler issues in the beginning of the study. These issues 

were most often due to issues with the filter exchange mechanism. Overall, there were fewer 

valid dichot measurements in St. Louis than in Phoenix. 

At Phoenix, one sample from Dichot B and four samples from Dichot A were lost 

because of issues with the filter exchange mechanism. In addition, Dichot A sampling events 

were lost because of a short run time (one), a flow rate problem (one), and operator error (two); 

one sample from each dichot was lost because of non-operational events (e.g., filter 

mishandling). 

At St. Louis, for Dichot A, 28 samples were not collected because an electronic board 

failure required the sampler to be returned to the manufacturer for repairs, and there was a 

delay in receiving a functioning replacement. Other samples were lost or not collected because 

of filter exchange errors (five samples), operator error (four samples, two due to a denuder 

installed for quartz/quartz [Q/Q] sampling), pump failure (four), and laboratory technician error 

(one). Once the original Dichot A was replaced in mid-January, data completeness for the 

remaining 45 sampling events was 96%, indicating that the majority of lost samples were due to 

the malfunctioning unit. Dichot B samples were lost because of a pump failure that was initially 

misdiagnosed as a filter exchange mechanism error (six), a second/replacement pump failure 

(six), filter exchange errors (three), and operator error (one). One problem with the pump 

failures is that they often initially manifest as a filter exchange error when the actuator line 

pressure drops too low to properly advance the filter shuttle mechanism. PM2.5 FRM sampling 

issues were predominantly limited to filter exchange errors. 

While the Thermo 2025D sequential dichotomous air sampler was designated as a FEM 

prior to this study, FEM-compliant samplers were not available and therefore, not provided by 

Thermo. Given the sampler delivery lead time and time constraints, only the two dichotomous 

and two FRM samplers sent to the Phoenix site were verified operational at RTI prior to 
EPA’s Coarse PM Pilot Study Introduction 

1-11

deployment. These four samplers were evaluated for flow rate, leaks, temperature, barometric 

pressure, and filter exchange. Double quartz (Q/Q) filter cassettes required modification in 

thickness to reduce filter cassette jamming. Samplers deployed in St. Louis were also newly 

purchased but were not verified operational at RTI; minimal sampler testing was performed at 

St. Louis due to time constraints, likely contributing to equipment problems during the study. 

1.5 Guide to This Report 

Section 1 provides an overview of the pilot study and field operations. 

Section 2 compares dichot and FRM samplers for gravimetric mass in order to gauge 

precision and bias. In this section, to put analyses presented in later sections in context, the 

overall composition of the fine and coarse aerosol at each site is discussed. The order in which 

the results of the study are presented here builds up to the question of mass balance closure. 

As crustal elements are the largest contributor to coarse aerosol mass, Section 3 

provides details on the collocated precision of these measurements, a discussion of potential 

biases regarding the correction in for X-ray attenuation in XRF measurements, and a description 

of how the XRF measurements compared to ICP-MS measurements. 

Section 4 presents details on carbonaceous aerosol, OC/EC splits, and the influence of 

biological material on OC. 

Section 5 examines ions, including an analysis of nitrate loss on Teflon filters and an 

assessment of the usefulness of ion measurements as part of a long-term monitoring network. 

Building on the results in Sections 2 through 5, Section 6 then presents mass balance 

results for dichot measurements, i.e., an examination of how well the measured species 

reconstruct the measured gravimetric mass, including exploration of “best fit” OM/OC ratios to 

achieve mass closure. 

Section 7 provides additional collocated measurement comparisons using hourly FDMS 

TEOM data, and examines volatile versus non-volatile coarse PM. 

The appendices provide supporting information as follows. 

 Appendix A provides tables of summary statistics (concentrations and minimum 

detection limits [MDLs] by species and size fraction) for the samplers in St. Louis and 

Phoenix. 

 Appendix B is a table of the summary ratios of collocated dichot measurements. 

 Appendix C is a table summarizing the dichot-to-FRM comparisons. 

 Appendix D provides information on quartz fiber filter carbon blanks. 

 Appendix E is a table showing nitrate correlations with other species. 

EPA’s Coarse PM Pilot Study Gravimetric Mass and Composition Overview 

2-1 

2. Gravimetric Mass and Composition Overview 

PMc constituents measured on the Teflon filters (gravimetric mass, elements, and ions) 

were biased low for the dichot compared to the FRM by difference method (Section 2.1). In 

Phoenix, PMc mass from the dichot was on average about 20% lower than the FRM difference 

method mass, and in St. Louis, the dichot PMc mass was 10% to 25% lower, depending on the 

dichot sampler. These findings are consistent with those found for pre-FEM 2025D sequential 

dichots evaluated during a multi-site evaluation of candidate methodologies for PM10-2.5 (U.S. 

EPA, 2011). In contrast, PMc total carbon measured on the quartz filters showed no bias 

between the two methods, though the relationship exhibited more scatter. The bias for 

constituents measured on the Teflon filters is attributed to particle losses from the dichot minor 

flow channel Teflon filter, which contains all of the coarse particles and only 10% of the fine 

particles. On average, PMc was predominantly composed of crustal oxides at both sites, with 

15% of the mass attributed to OC and less than 5% of the mass from other species, such as 

sulfate and nitrate (Section 2.2). As shown in Section 2.3, dichot collocated precision for PMc

constituents was typically in the 8% to 15% range for species with high rates of detectability. An 

exception was organic carbon, which had a collocated precision of 19% in Phoenix and 34% in 

St. Louis. The relatively less precise OC results in St. Louis arise in part from lower OC 

concentrations than in Phoenix. Organic carbon is further discussed in Section 4. 

2.1 Gravimetric Mass Comparisons Between Dichot and FRM 

Samplers 

Figure 2-1 shows scatter plots of the gravimetric mass measured by the dichotomous 

samplers at Phoenix and St. Louis compared to the FRM measurements for PM2.5, PM10-2.5, and 

PM10. Using the FRM samplers, the PM10-2.5 concentration is not directly measured, but instead 

is calculated from the difference between PM10 and PM2.5 measurements. 

Similarly, dichot PMc is not directly measured, but instead must be corrected for fine PM 

particle intrusion into the dichot minor flow channel; dichot PM10 is not directly measured, but is 

rather the sum of the PMf and PMc measurements. At each site, there were two dichotomous 

samplers, labeled “primary” and “collocated.” Summary statistics for gravimetric mass 

comparisons are presented in Table 2-1. For PMf gravimetric mass, excellent agreement was 

observed at St. Louis for both the primary and collocated dichot samplers compared to the PM2.5

FRM (Figure 2-1a). The mean value of the daily dichot-to-FRM mass ratio was 1.01 for both 

dichots, with mean absolute relative differences of 4% to 5%. In contrast, Figure 2-1d shows 

that at Phoenix, the dichot PMf gravimetric mass was biased low with respect to the FRM, with 

mean ratios of 0.91 for both dichots. The mean absolute relative differences of 11% to 13% 

were worse than observed in St. Louis. Samples at high concentrations and outside the ±20% 

envelope (i.e., below the lower dashed line in Figure 2-1d) all correspond to samples collected 

in February and early March 2011. Dichot PMc agreed reasonably well with FRM10-2.5 on these 

days, so it is the dichot PMf measurement that is suspect. The reason for the discrepancies on 

these sample days is not known. 
EPA’s Coarse PM Pilot Study Gravimetric Mass and Composition Overview 

2-2 

Figure 2-1. Dichot versus FRM mass concentrations for St. Louis (top row) and Phoenix 

(bottom row) for PM2.5 (left), PM10-2.5 (center), and PM10 (right). Triangles are data from 

the primary dichot sampler and circles are data from the collocated dichot sampler. 

Diagonal lines are 1:1 (solid) and 20% of 1:1 (dashed). 

Dichot PMc gravimetric mass was consistently biased low compared to FRM10-2.5 for both 

dichots at both sites (Figures 2-1b, e). At St. Louis, the dichot-to-FRM mean ratio for the primary 

dichot was 0.90 while for the collocated dichot it was only 0.76. At Phoenix, the two dichots 

show quite similar performances, with dichot-to-FRM mean ratios of approximately 0.82. 

Overall, the mean absolute relative differences of 12-26% across the sites and four samplers is 

driven more by systematic bias than random measurement error, as demonstrated by 

regression slopes significantly different from unity. 

Dichot PM10 gravimetric mass comparison metrics1 (Table 2-1) fall between the PMf and 

PMc results. For St. Louis, the PM10 values are near the midpoint of PMf and PMc values, which 

 

1

 These metrics include a reduced major axis (RMA) regression, which is a type of orthogonal regression. Ordinary 

least squares (OLS) regression minimizes the sum of square differences between the reported and predicted 

y-values (i.e. the vertical distance between the best-fit line and measured values) and assumes the x-values are 

exact. In contrast, RMA minimizes the sum of square differences between the reported and the predicted values 

based on the distance perpendicular to the best-fit line and the measured values. Thus, RMA takes into consideration 

uncertainty in both the x- and y-values. 
EPA’s Coarse PM Pilot Study Gravimetric Mass and Composition Overview 

2-3 

reflects the similar contributions from fine and coarse PM mass to PM10 mass. For Phoenix, the 

PM10 values are close to the PMc values, since PM10 in Phoenix is generally dominated by 

coarse PM. The dichot-to-FRM bias observed for PM10 demonstrates that the PMf and PMc

biases do not arise from poor cutpoint performance of the dichot virtual impactor. There must be 

other sources of significant bias, such as functional differences in the standard PM10 inlet 

performance (e.g., from differences in the cleaning protocol and frequency), particle losses 

during filter exchanges within the dichot sequential samplers, or particle losses from the filters 

during shipping and handling. 

Table 2-1. Gravimetric mass comparisons between the dichot and FRM samplers. 

P = primary dichot and C = collocated dichot. Mean and median absolute relative 

differences were calculated using the FRM as the reference sampler. Mean and median 

ratios have been calculated for dichot-to-FRM comparisons. Regression statistics are 

from reduced major axis (RMA) regressions of dichot mass on FRM mass. 

Total Samples

PHX-P PHX-C STL-P STL-C

80 55 45 40

PMf (PM2.5)

Mean Absolute Relative Difference

(Median Absolute Relative Diff.) 11% (9%) 13% (7%) 4% (2%) 5% (4%)

Mean Ratio (Median Ratio) 0.91 (0.92) 0.91 (0.94) 1.01 (1.01) 1.01 (1.00)

Regression Slope, μg/m3 0.91  0.07 0.65  0.08 1.00  0.03 0.97  0.05

Regression Intercept -0.1  0.7 2.0  0.8 0.1  0.4 0.3  0.5

r

2 0.893 0.798 0.987 0.978

PMc (PM10-2.5)

Mean Absolute Relative Difference

(Median Absolute Relative Diff.) 19% (19%) 20% (19%) 12% (10%) 26% (27%)

Mean Ratio (Median Ratio) 0.83 (0.83) 0.81 (0.81) 0.90 (0.92) 0.76 (0.74)

Regression Slope, μg/m3 0.71  0.07 0.67  0.10 0.96  0.05 0.83  0.08

Regression Intercept 2.2  1.9 2.7  2.5 -0.6  0.6 -0.6  1.1

r

2 0.787 0.719 0.971 0.904

PM10

Mean Absolute Relative Difference

(Median Absolute Relative Diff.) 17% (16%) 18% (17%) 5% (4%) 13% (12%)

Mean Ratio (Median Ratio) 0.84 (0.84) 0.82 (0.83) 0.96 (0.96) 0.87 (0.88)

Regression Slope, μg/m3 0.79  0.07 0.71  0.08 0.97  0.04 0.91  0.06

Regression Intercept 1.1  2.5 3.2  2.7 -0.3  0.9 -0.7  1.3

r

2 0.845 0.839 0.983 0.960
EPA’s Coarse PM Pilot Study Gravimetric Mass and Composition Overview 

2-4 

2.2 Distribution of Species Between Fine and Coarse Mode 

Next, the average composition was examined at Phoenix and St. Louis for PMc and PMf, 

shown in Figure 2-2. At this stage, the objective was not to determine mass balance closure but 

rather to examine which classes of species dominate each size fraction. The PMf composition 

was similar to the composition observed with longer-term measurements at both sites, where 

Phoenix PMf is predominantly crustal oxides and carbonaceous aerosol, while St. Louis is 

mostly ammonium sulfate and carbonaceous aerosol. For PMc, both sites were dominated by 

crustal oxides; OC was about 15% of the mass, and other species, including sulfate and nitrate, 

made up less than 5% of the average mass. Appendix A shows summaries of average 

concentration and MDL, the fraction of samples above MDL and 3×MDL, and the average 

uncertainty greater than MDL by species, site, and size (fine or coarse) for species analyzed 

from the Teflon and quartz filters. 

Figure 2-2. Stacked bar plots of average composition for (a) PMc at Phoenix; (b) PMf at 

Phoenix; (c) PMc at St. Louis; and (d) PMf at St. Louis. All concentrations are in g/m3. 
EPA’s Coarse PM Pilot Study Gravimetric Mass and Composition Overview 

2-5

As seen in Figure 2-3 and detailed in Appendix A, the PM10/PM2.5 ratio from FRM 

measurements is typically much greater than unity, with values greater than two representing an 

enhancement in coarse PM species abundance relative to fine PM. Ammonium, sulfate (both by 

IC and by XRF sulfur), bromine, and rubidium are almost exclusively in the fine fraction. 

Because the PM10-2.5 concentration will be the difference between two very similar values, 

measurement of these species in PM10-2.5 using the FRM-by-difference method can result in 

large relative uncertainties. However, from a mass balance perspective, these species 

contribute little to PM10-2.5 mass. EC, OC, soluble potassium (by IC), lead, and zinc are, on 

average, nearly evenly distributed between the fine and coarse fractions. The remaining 

elements tend to be more abundant in the coarse fraction, with the species commonly 

associated with crustal material (e.g., aluminum [Al], calcium [Ca], magnesium [Mg], and silicon 

[Si]) enhanced in PM10-2.5 by more than a factor of five (i.e., a PM10/PM2.5 ratio greater than ten). 

Based on these coarse PM concentration and relative abundance patterns, the analysis 

of measurement precision focuses primarily on crustal species and secondarily on OC. 

Figure 2-3. Average FRM PM10/PM2.5 ratio (log scale) by species for each site. 

2.3 Collocated Precision of Dichot Measurements 

A major component of the sampling plan was to periodically run the collocated dichots 

using the same filter type, making it possible to evaluate dichot measurement precision. 

Figure 2-4 shows collocated dichot sampler comparisons for gravimetric mass and silicon 

including all data. As demonstrated later in this section, silicon collocated performance is 

representative of most crustal species. For these two parameters, the agreement appears good 

for both sites and for both particle sizes. Gravimetric mass data for PMc at Phoenix does exhibit 

relatively more scatter at high concentrations. Measures of agreement for PMc and PMf at both 

sites are reported in Tables 2-2 through 2-5 for gravimetric mass, elements used in the soil 

8

1

2

4

6

8

10

2 Average of FRM PM10/PM2.5 Ratio

EC_TOR

OC_TOR

Al_XRF

As_XRF

Ba_XRF

Br_XRF

Ca_XRF

Cl_XRF

Co_XRF

Cr_XRF

Cu_XRF

Fe_XRF

K_IC

K_XRF

Mg_XRFMn_XRF

Na_IC

Na_XRF

NH4_IC

Ni_XRF

NO3_IC

Pb_XRF

Rb_XRF

S_XRF

Si_XRF

SO4_IC

Sr_XRF

Ti_XRF

V_XRF

Zn_XRF

Mass_Grav

 STL PHX
EPA’s Coarse PM Pilot Study Gravimetric Mass and Composition Overview 

2-6

equation, and OC. (In those tables, OC-TOR is organic carbon measured by thermal-optical 

reflectance.) The gravimetric mass data sets were conditioned to include only those samples 

with XRF elements also reported. Linear regressions of Dichot B concentrations on Dichot A 

concentrations were performed using reduced major axis regression, which assumes a constant 

measurement uncertainty for the two samplers. For most slopes, the 95% confidence intervals 

include unity, and for most intercepts, the 95% confidence intervals include zero. 

Figure 2-4. Collocated dichot versus dichot plots for (a) gravimetric mass at Phoenix; 

(b) gravimetric mass at St. Louis; (c) Si at Phoenix; (d) Si at St. Louis. The solid lines are 

1:1 lines and the dashed lines in panels (a) and (b) are the reduced major axis 

regressions for the PMc data; the regression coefficients are reported in Tables 2-2 

through 2-5. 
EPA’s Coarse PM Pilot Study Gravimetric Mass and Composition Overview 

2-7

Table 2-2. Measures of agreement between the collocated dichot sampler data for PMc

measured at Phoenix. Slopes and intercepts are reported for reduced major axis 

regressions of Dichot B on Dichot A. 

PHX PMc

(N=22)

Mean

(μg/m3)

Min, Max 

(μg/m3)

Slope 

+/- 95% 

C.I.

Intercept 

+/- 95% 

C.I. 

(μg/m3)

r

2

B/A

Ratio: 

Mean 

(Median)

Collocate

d 

Precision 

(μg/m3)

Grav. 

mass 

18.87 8.09, 

37.88 0.91 ± 0.13 2.2 ± 2.6 0.91 0.97 (0.95) 1.86 (10%)

Al 1.02 0.41, 2.57 1.19 ± 0.17 -0.22 ± 0.19 0.90 0.95 (0.94) 0.12 (12%)

Ca 1.19 0.44, 2.81 1.14 ± 0.17 -0.19 ± 0.22 0.90 0.96 (0.95) 0.15 (12%)

Fe 0.81 0.30, 1.78 1.04 ± 0.18 -0.06 ± 0.16 0.87 0.96 (0.97) 0.11 (13%)

Si 3.15 1.45, 8.09 1.22 ± 0.18 -0.77 ± 0.61 0.90 0.96 (0.95) 0.37 (12%)

Ti 0.061 0.024, 

0.14 1.21 ± 0.21 -0.01 ± 0.01 0.86 0.97 (0.94) 0.01 (15%)

OC-TOR

(N=14) 2.29 0.91, 4.01 1.04 ± 0.38 0.30 ± 0.84 0.67 1.23 (1.26) 0.43 (19%)

Table 2-3. Measures of agreement between the collocated dichot sampler data for PMf

measured at Phoenix. Slopes and intercepts are reported for reduced major axis 

regressions of Dichot B on Dichot A. 

PHX PMf

(N=22)

Mean

(μg/m3)

Min, Max 

(μg/m3)

Slope 

+/- 95% C.I.

Intercept 

+/- 95% C.I. 

(μg/m3)

r

2

B/A

Ratio: 

Mean 

(Median)

Collocated 

Precision 

(μg/m3)

Grav. mass 8.10 3.24, 18.61 1.01 ± 0.21 -0.56 ± 1.94 0.8 1.09 

(1.02) 1.35 (17%)

Al 0.14 0.03, 0.43 1.12 ± 0.11 -0.0005 ± 

0.02 0.96 1.13 (1.07) 0.02 (15%)

Ca 0.17 0.04, 0.46 1.17 ± 0.15 -0.003 ± 0.03 0.92 1.14 

(1.11) 0.03 (17%)

Fe 0.19 0.04, 0.53 1.01 ± 0.11 0.01 ± 0.02 0.95 1.08 

(1.07) 0.02 (11%)

Si 0.43 0.10, 1.26 1.15 ± 0.12 -0.004 ± 0.06 0.95 1.13 

(1.12) 0.07 (16%)

Ti 0.008 0.002, 

0.025 1.33 ± 0.27 -0.0007 ± 0.002 0.81 1.26 (1.20) 0.002 (29%)

OC-TOR

(N=14) 2.11 0.58, 3.75 0.91 ± 0.21 0.18 ± 0.49 0.87 1.05 (1.01) 0.25 (12%)
EPA’s Coarse PM Pilot Study Gravimetric Mass and Composition Overview 

2-8

Table 2-4. Measures of agreement between the collocated dichot sampler data for PMc

measured at St. Louis. Slopes and intercepts are reported for reduced major axis 

regressions of Dichot B on Dichot A. 

STL PMc

(N=11)

Mean

(μg/m3)

Min, Max 

(μg/m3)

Slope 

+/- 95% 

C.I.

Intercept 

+/- 95% 

C.I. 

(μg/m3)

r

2

B/A

Ratio: 

Mean 

(Median)

Collocate

d 

Precision 

(μg/m3)

Grav. 

mass 

9.77 1.33, 

21.31 1.07 ± 0.09 0.67 ± 0.95 0.99 0.84 (0.89) 1.08 (11%)

Al 0.27 0.01, 0.73 1.04 ± 0.11 -0.05 ± 0.04 0.98 0.81 (0.83) 0.03 (11%)

Ca 1.14 0.13, 3.64 0.98 ± 0.09 -0.08 ± 0.14 0.99 0.90 (0.92) 0.12 (10%)

Fe 0.29 0.08, 0.83 1.00 ± 0.02 -0.03 ± 0.03 0.99 0.88 (0.91) 0.03 (9%)

Si 0.83 0.10, 1.79 1.03 ± 0.10 -0.09 ± 0.10 0.98 0.90 (0.91) 0.07 (8%)

Ti 0.012

0.000, 

0.031 1.05 ± 0.17 -0.002 ± 0.003 0.95 0.86 (0.87) 0.002 (13%)

OC-TOR

(N=14) 1.71 0.49, 4.53 0.74 ± 0.38 0.25 ± 0.80 0.40 0.93 (1.00) 0.59 (34%)

Table 2-5. Measures of agreement between the collocated dichot sampler data for PMf

measured at St. Louis. Slopes and intercepts are reported for reduced major axis 

regressions of Dichot B on Dichot A. 

STL PMf

(N=11)

Mean

(μg/m3)

Min, Max 

(μg/m3)

Slope 

+/- 95% 

C.I.

Intercept 

+/- 95% 

C.I. 

(μg/m3)

r

2

B/A

Ratio: 

Mean 

(Median)

Collocate

d 

Precision 

(μg/m3)

Grav. 

mass 

11.21 6.33, 17.64 0.99 ± 0.09 0.12 ± 1.04 0.99 1.00 

(1.02) 0.27 (2%)

Al 0.05 0.01, 0.27 1.04 ± 0.09 -0.002 ± 

0.008 0.99 1.04 (1.03) 0.01 (13%)

Ca 0.09 0.01, 0.20 1.17 ± 0.35 0.001 ± 

0.03 0.85 1.23 (1.15) 0.02 (20%)

Fe 0.08 0.03, 0.19 1.10 ± 0.23 -0.0001 ± 

0.02 0.93 1.11 (1.04) 0.01 (15%)

Si 0.11 0.01, 0.53 0.99 ± 0.10 0.01 ± 0.02 0.98 1.18 

(1.15) 0.01 (12%)

Ti 0.003 0.000, 

0.019 1.10 ± 0.36 0.001 ± 0.002 0.81 1.72 (1.15)

0.002 

(55%)

OC-TOR

(N=14) 2.59 0.82, 5.30 1.07 ± 0.20 -0.51 ± 0.60 0.92 0.87 (0.90) 0.33 (13%)
EPA’s Coarse PM Pilot Study Gravimetric Mass and Composition Overview 

2-9

For PMc at Phoenix (Table 2-2) the mean ratio of 0.97 for gravimetric mass 

demonstrates that there is little mass bias between the samplers. Mean ratios for the crustal 

species are consistently in the range 0.95–0.97, which shows that, on average, Dichot B 

concentrations are a few percent lower than Dichot A concentrations for these species. In 

contrast, the mean ratio is 1.23 for OC, which shows that, on average, Dichot B concentrations 

are 23% higher than Dichot A concentrations. Collocated precision, defined as the root mean 

square (RMS) of the Dichot A-to-Dichot B concentration differences divided by the square root 

of 2, is in the range 12% to 19% for the PMc components, with the highest value observed for 

OC. For PMf at Phoenix (Table 2-3) the mean ratio of 1.09 for gravimetric mass demonstrates 

some bias, with Dichot B reading higher than Dichot A. This pattern is observed for all of the 

major crustal species with mean ratios ranging from 1.08 (Fe) to 1.26 (Ti). Similar to PMc, the 

PMf collocated precision estimates are in the range of 10% to 20% for each of the reported 

species. 

Collocated precision for the St. Louis data was, with a few exceptions, similar to the 

Phoenix data. Precision was poorer in St. Louis for PMc OC-TOR and PMf titanium, and better in 

St. Louis for PMf gravimetric mass. 

Measurement error for the collocated dichot data collected at Phoenix was further 

examined by calculating the concentration differences between samplers (expressed as the 

natural logarithm of the concentration ratio) for all species. Figure 2-5 shows scatterplots for the 

concentration differences between dichot samplers for the elements used in the soil equation, 

XRF sulfur, and gravimetric mass. For PMc (Figure 2-5a), all of these species combinations 

show strong correlation. Interpreting the concentration difference between dichot samplers to be 

measurement error, these strong correlations mean that these species share the same 

dominant source of measurement error (Hyslop and White, 2011), and this measurement error 

leads to the small but consistent deviation in the mean ratios from unity for these species shown 

in Table 2-2. One possible source of such measurement error could be functional differences 

between the dichots in performance of the PM10 size-selective inlets. 
EPA’s Coarse PM Pilot Study Gravimetric Mass and Composition Overview 

2-10

Figure 2-5. Scatterplot matrices of the observed differences, , in the 

collocated concentrations at Phoenix: (a) PMc and (b) PMf. 

For comparison, Figure 2-5b shows scatterplots for the observed concentration 

differences for PMf from the collocated dichots at Phoenix. Ca, Fe, Si, and to some extent sulfur 

(S) show strong correlation. Correlations for Al and Ti with the other species are weaker; this 

weaker correlation might arise from the relatively greater contribution of analytical errors to the 

overall measurement errors for these species, which are at relatively low concentrations in the 

PMf size range. Another potential source of measurement error is differences in the cutpoint 

curves between virtual impactors in the two dichots. This error could explain the Dichot 

B-to-Dichot A ratios for gravimetric mass and crustal species being less than one for PMc and 

greater than one for PMf (Tables 2-2 and 2-3). In this case, for a given species, the 

concentration differences between dichots should be anti-correlated for the PMc and PMf size 

ranges. 

Figure 2-6 shows the observed differences for the major soil species. In each case, the 

correlation is very weak, and thus, if there are differences in virtual impactor performance 

between the two dichot samplers, the effect on concentration is masked by other measurement 

errors. 

)/ln(  CCx

,, AxBx
EPA’s Coarse PM Pilot Study Gravimetric Mass and Composition Overview 

2-11

Figure 2-6. Scatterplot vector of the observed differences, , across 

the fine and coarse PM size fractions in the collocated concentrations at Phoenix. 

The average and median concentration ratios between collocated dichots by species, 

site, and size (fine or coarse) are summarized in Appendix B. Figure 2-7 shows the average 

Dichot B-to-Dichot A ratio by species for both sites and size fraction, using only data above the 

MDL. For most species, the average ratio is near unity. One noteworthy exception is 

ammonium, which has low abundance in PMc and has high uncertainty from the correction for 

fine particle intrusion into the dichot minor flow channel. 

Figure 2-7. Average ratio of collocated dichot measurements (log scale) by site, species, 

and size, using only data above the MDL for both measurements. 

)/ln(  CCx

,, AxBx

5

6

7

8

9

1

2 Average Dichot/Dichot Ratio EC_TOR EC_TOR EC_TOT EC_TOT OC_TOR OC_TOR OC_TOT OC_TOT TC_TOA TC_TOA Ag Ag Al Al As As Ba Ba Br Br Ca Ca Cd Cd Ce Ce Cl Cl Co Co Cr Cr Cs Cs Cu Cu Fe Fe In In K_IC K_IC

K K Mass Mass Mg Mg Mn Mn Na_IC Na_IC Na Na NH4_IC NH4_IC Ni Ni NO3_IC NO3_ICP P Pb Pb Rb RbS S Sb Sb Se Se Si Si Sn Sn SO4_IC SO4_IC Sr Sr Ti TiV V Zn Zn Zr Zr

 Phoenix, PMc

 Phoenix, PMf

 STL, PMc

 STL, PMf

EPA’s Coarse PM Pilot Study Analysis of Elements 

3-1

3. Analysis of Elements 

Species that were typically above the MDL in the PM2.5 CSN were generally above the 

MDL in PMc (Section 3.1), including the crustal species, OC and EC, sulfate, nitrate, and metals 

such as barium (Ba), chlorine (Cl), copper (Cu), potassium (K), manganese (Mn), sodium (Na),

and zinc (Zn). 

Unlike PM2.5, ammonium was above the MDL only 60% of the time in St. Louis and 50% 

of the time in Phoenix, reflective of the low ammonium concentrations in PMc. Agreement of 

individual species’ PMc concentrations between dichot and FRM measurements (Section 3.2) 

were generally similar to those seen for gravimetric mass (Section 2.1). 

At St. Louis, dichot and FRM measurements compared fairly well for both fine and 

coarse fractions. At Phoenix, there was a consistent bias for PMc, with FRM concentrations 

higher than dichot concentrations for most species, especially the crustal elements. Given that 

crustal oxides make up the majority of the PMc mass, and that FRM PM10-2.5 measurements of 

these species were consistently higher than dichot PMc measurements, the sensitivity of these 

species’ measurements to the assumptions in the XRF method was examined (Section 3.3). 

Attenuation factors are negligible for PM2.5 measurements; however, attenuation factors 

must be applied for PMc measurements. The attenuation factors used in this study for PMc were 

further investigated for a subset of samples. Results from ICP-MS analysis were compared to 

XRF results for a subset of samples (Section 3.4), which further indicated that the coarse 

particle concentrations measured by blank-corrected ICP-MS were greater than those 

measured by XRF. This pattern does suggest that the XRF attenuation corrections for these 

constituents are too large, although more is needed to verify these preliminary findings. 

Lastly, Section 3.5 shows that there was little daily variation in the crustal composition, 

though the composition was somewhat different at the two monitoring sites. 

3.1 Concentrations by Site and Size Fraction 

Similar to CSN measurements, many species that were analyzed were often below 

detection. Twenty species were above the MDL more than 80% of the time at both sites.

Appendix A summarizes average concentration and MDL, fraction of samples above MDL and 

3×MDL, and the average uncertainty by species, site, and size (fine or coarse) for species 

analyzed from the Teflon and quartz filters. Elements were analyzed by XRF, and ions were 

analyzed by IC. Potassium and sodium were analyzed by both methods. Figure 3-1 shows the 

fraction of samples above the MDL for dichot PMc. Species were detected at similar rates at 

both sites, with a few exceptions: the detection rates for cobalt (Co), chromium (Cr), rubidium 

(Rb), and strontium (Sr) were higher at Phoenix. This is consistent with the higher PMc crustal 

concentrations in Phoenix compared to St. Louis, but might also reflect differences in species 

abundance in the crustal material at these sites.
EPA’s Coarse PM Pilot Study Analysis of Elements 

3-2

Figure 3-1. Percentage of samples above detection limit for dichot PMc by site. 

3.2 Concentrations of Elements: Comparison of Dichot and FRM 

Trends in elements between dichot and FRM measurements were generally similar to 

those seen for gravimetric mass (Section 2.1). Tables 3-1 through 3-4 summarize, and

Figure 3-2 shows, comparisons of dichot to FRM concentrations for four species (Ca, Fe, S, 

and Zn). The gravimetric mass data sets used for the tables were conditioned to include only 

those samples with XRF elements also reported. Sulfur is predominantly found in the fine 

fraction, zinc is present at relatively low concentrations, and calcium and iron are crustal 

components present at relatively high concentrations. At St. Louis, dichot and FRM 

measurements compared fairly well for both fine and coarse fractions. At Phoenix, there was a 

consistent bias for coarse PM with FRM concentrations higher than dichot concentrations. In 

particular, crustal elements (Al, Ca, Fe, Si) all had a similar bias, and in general, the bias was 

evident in all species that were above detection more than 75% of the time. Caution must be 

used when interpreting the collocated precision values reported in Tables 3-1 through 3-4 

because in cases with large bias, the precision estimates are driven by the bias and not by 

random measurement error. Appendix C summarizes dichot-to-FRM ratios of average and 

median concentration by species, site, and size (fine or coarse) for species analyzed from the 

Teflon and quartz filters. 

100

80

60

40

20

0 % of samples > MDL, dichot PMc

Ag Ag Al Al As As Ba Ba Br Br Ca Ca

Cd CdCe Ce

Cl Cl

Co Co

Cr Cr

Cs Cs

Cu Cu Fe Fe In In K_IC K_IC

K_XRF K_XRF

Mg Mg Mn Mn Na_IC Na_IC

Na_XRF Na_XRF NH4 NH4 NI NI NO3 NO3

P P Pb Pb Rb RbS S

Sb SbSe Se

Si Si

Sn Sn

SO4 SO4

Sr Sr Ti Ti

V V Zn Zn Zr Zr EC_TOT EC_TOT

OC_TOT OC_TOT

 St Louis

 Phoenix
EPA’s Coarse PM Pilot Study Analysis of Elements 

3-3

Table 3-1. Measures of agreement between the collocated FRM and dichot data for PMc

measured at Phoenix. Slopes and intercepts are reported for reduced major axis 

regressions of dichot on FRM. 

PHX PMc

(N=35)

FRM 

Mean

(μg/m3)

FRM

Min, Max 

(μg/m3)

Slope 

+/- 95% 

C.I.

Intercept 

+/- 95% 

C.I. 

(μg/m3)

r

2

D/F 

Ratio: 

Mean 

(Median)

Collocate

d 

Precision 

(μg/m3)

Grav. mass 22.7 9.6, 54.9 0.64 ± 0.10 3.6 ± 2.5 0.8

0

0.83 

(0.85) 5.1 (25%)

S 0.12 0.04, 0.28 1.11 ± 0.27 0.00 ± 0.04 0.5

3

1.17 

(1.08) 0.03 (24%)

Zn 0.06 0.01, 0.57 0.46 ± 0.04 0.01 ± 0.00 0.9

4

0.83 

(0.77) 0.04 (82%)

Ca 1.43 0.53, 4.06 0.65 ± 0.13 0.22 ± 0.22 0.6

8

0.86 

(0.87) 0.40 (31%)

Fe 0.99 0.35, 2.34 0.71 ± 0.12 0.10 ± 0.13 0.7

9

0.85 

(0.85) 0.23 (26%)

Si 4.23 1.59, 11.26 0.65 ± 0.14 0.65 ± 0.65 0.6

4

0.85 

(0.87) 1.10 (29%)

Table 3-2. Measures of agreement between the collocated FRM and dichot data for PMf

measured at Phoenix. Slopes and intercepts are reported for reduced major axis 

regressions of dichot on FRM. 

PHX PMf

(N=35)

FRM 

Mean

(μg/m3)

FRM

Min, Max 

(μg/m3)

Slope 

+/- 95% C.I.

Intercept +/-

95% C.I. 

(μg/m3)

r

2

D/F Ratio: 

Mean 

(Median)

Collocated 

Precision 

(μg/m3)

Grav. mass 8.4 3.5, 19.6 0.98 ± 0.08 -0.5 ± 0.77 0.94 0.92 (0.93) 0.8 (10%)

S 0.31 0.09, 1.02 1.03 ± 0.04 -0.01 ± 0.01 0.99 0.99 (1.01) 0.01 (3%)

Zn 0.015 0.003, 0.047 1.00 + 0.10 0.000 + 0.002 0.91 1.00 (0.95) 0.002 (14%)

Ca 0.18 0.04, 0.63 0.85 ± 0.10 0.00 ± 0.02 0.88 0.88 (0.87) 0.04 (24%)

Fe 0.24 0.04, 0.67 0.92 ± 0.12 -0.01 ± 0.03 0.86 0.89 (0.90) 0.05 (22%)

Si 0.42 0.10, 1.74 0.91 ± 0.10 -0.01 ± 0.05 0.90 0.91 (0.89) 0.08 (20%)
EPA’s Coarse PM Pilot Study Analysis of Elements 

3-4

Table 3-3. Measures of agreement between the collocated FRM and dichot data for PMc

measured at St. Louis. Slopes and intercepts are reported for reduced major axis 

regressions of dichot on FRM. 

STL PMc

(N=18)

FRM 

Mean

(μg/m3)

FRM 

Min, Max 

(μg/m3)

Slope 

+/- 95% C.I.

Intercept +/-

95% C.I. 

(μg/m3)

r

2

D/F Ratio: 

Mean 

(Median)

Collocated 

Precision 

(μg/m3)

Grav. mass 12.3 3.1, 24 0.99 ± 0.08 -0.9 ± 1.1 0.98 0.90 (0.93) 1.0 (8%)

S 0.10 -0.13, 0.24 0.42 ± 0.19 0.07 ± 0.03 0.28 -0.75 (0.84) 0.06 (57%)

Zn 0.025 0.004, 0.136 0.96 ± 0.08 -0.001 ± 0.003 0.98 1.01 (0.99) 0.004 (17%)

Ca 1.51 0.15, 3.92 0.95 ± 0.08 0.02 ± 0.15 0.97 0.97 (0.98) 0.13 (9%)

Fe 0.39 0.1, 0.98 0.84 ± 0.08 0.03 ± 0.04 0.97 0.94 (0.9) 0.05 (13%)

Si 1.14 0.18, 2.75 0.88 ± 0.08 0.07 ± 0.12 0.97 0.96 (0.96) 0.12 (11%)

Table 3-4. Measures of agreement between the collocated FRM and dichot data for PMf

measured at St. Louis. Slopes and intercepts are reported for reduced major axis 

regressions of dichot on FRM. 

STL PMf

(N=18)

FRM 

Mean

(μg/m3)

FRM

Min, Max 

(μg/m3)

Slope 

+/- 95% 

C.I.

Intercept +/-

95% C.I. 

(μg/m3)

r

2

D/F Ratio: 

Mean 

(Median)

Collocated 

Precision 

(μg/m3)

Grav. mass 12.0 6.3, 18.1 1.01 ± 0.09 0.0 ± 1.1 0.97 1.01 (1) 0.4 (3%)

S 0.88 0.44, 1.36 0.99 + 0.11 0.00 + 0.10 0.95 1.00 (1.01) 0.03 (3%)

Zn 0.019 0.003, 0.067 0.98 + 0.13 0.000 + 0.003 0.930 1.00 (0.95) 0.003 (16%)

Ca 0.09 0.02, 0.20 1.13 + 0.21 -0.01 + 0.02 0.87 1.08 (1.00) 0.02 (22%)

Fe 0.09 0.02, 0.24 1.07 + 0.13 0.00 + 0.01 0.95 1.05 (1.00) 0.01 (11%)

Si 0.11 0.02, 0.54 0.96 + 0.08 0.00 + 0.01 0.98 1.04 (0.98) 0.01 (9%)
EPA’s Coarse PM Pilot Study Analysis of Elements 

3-5

S, PHX S, STL 

Zn, PHX Zn, STL 

Figure 3-2. Scatter plots of PMf and PMc concentrations from dichot (y-axis) and FRM (xaxis) at Phoenix (left column) and St. Louis (right column) for S, Zn, Ca, and Fe. Solid 

lines are the 1:1 lines and dashed lines indicate the +/-20% difference between 

measurements. (Figure continued on next page.) 

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Dichot Concentration (μg/m3

)

FRM Concentration (μg/m3)

PMc

PMf

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Dichot Concentration (μg/m3

)

FRM Concentration (μg/m3)

PMc

PMf

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.00 0.10 0.20 0.30 0.40 0.50 0.60

Dichot Cconcentration (μg/m3

)

FRM Concentration (μg/m3)

PMc

PMf
EPA’s Coarse PM Pilot Study Analysis of Elements 

3-6

Ca, PHX Ca, STL 

Fe, PHX Fe, STL

Figure 3-2 (continued). Scatter plots of PMf and PMc concentrations from dichot (y-axis) 

and FRM (x-axis) at Phoenix (left column) and St. Louis (right column) for S, Zn, Ca, and 

Fe. Solid lines are the 1:1 lines and dashed lines indicate the ± 20% difference between 

measurements. 

3.3 Attenuation of X-Ray Intensity for Light Elements 

Measurement error can arise from both sampling errors and analytical errors. A 

potentially significant source of analytical error for PMc elements is X-ray attenuation during 

XRF analysis (self-attenuation). This error is not captured in the collocated dichot comparisons 

and is only indirectly captured in the dichot-to-FRM comparisons through the dependence of the 

attenuation effect on the size distribution of collected particles. 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

Dichot Concentration (μg/m3

)

FRM Concentration (μg/m3)

PMc

PMf

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

Dichot Concentration (μg/m3

)

FRM Concentration (μg/m3)

PMc

PMf

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Dichot Concentration (μg/m3

)

FRM Concentration (μg/m3)

PMc

PMf

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Dichot Concentration (μg/m3

)

FRM Concentration (μg/m3)

PMc

PMf
EPA’s Coarse PM Pilot Study Analysis of Elements 

3-7

RTI applied attenuation factors (Ai), calculated using proprietary software, to estimate 

attenuation-corrected mass loadings (mi) from the XRF instrument-reported mass loadings 

(mno corr,i):

i

no icorr

i

A

m

m

,

 (Eq. 3-1)

Attenuation factors are in the range Ai  1, and they vary with both element and PM size 

range (PMf, PMc, PM10, and PM2.5). For Ai = 1, there is no attenuation correction, while for Ai < 1, 

the attenuation-corrected mass loading is higher than the instrument-reported mass loading. 

These adjustments are made to the “raw” mass loadings on the filter reported by the XRF. RTI 

provided Ai for a subset of PM10 FRM samples and dichot major flow channel (PMf filter) and 

minor flow channel (PMc filter) samples. PM2.5 FRM attenuation factors are identical to the 

dichot PMf attenuation factors. For PM2.5 FRM, PM10 FRM, and dichot PMf samples, the 

attenuation factor linearly propagates through the calculation of ambient concentrations. For 

dichot PMc, however, the dichot minor flow attenuation factors do not linearly propagate through 

the calculation of ambient calculations because the dichot minor flow channel mass loadings are 

corrected for fine particle intrusion. 

Attenuation factors are specific to each sample (Gutknecht et al., 2010) and are 

automatically generated and applied by the signal processing software. RTI provided the 

uncorrected mass loadings for a subset of samples and back-calculated the sample-specific 

attenuation factors. The results are summarized in Table 3-5. Figure 3-3 demonstrates that 

samples selected for the dichot minor flow channel were well distributed over the observed 

range of gravimetric mass loadings. Attenuation factors were unity for all elements with atomic 

numbers (Z) greater than 20. For each element and sample type, the standard deviation of the 

sample-specific attenuation factors was relatively small, so the arithmetic mean attenuation 

factor is deemed representative of all samples. Figure 3-4 shows the mean attenuation factors 

stratified by element and sample type. 

Because of the physics of self-attenuation, coarse particles are affected more than finer 

particles. Since PM10 includes both PMc and PMf, the attenuation factors for PM10 should be 

intermediate between those of PMc and PM2.5. Thus, for any element, attenuation factors occur 

in the order APMc < APM10 < (APM2.5 = APMf). When comparing attenuation results for PMc with 

the dichot vs. the FRM (i.e., PM10-PM2.5 vs. PMc [dichot]), the intrusion of PMf into the PMc

deposit must be taken into account. 
EPA’s Coarse PM Pilot Study Analysis of Elements 

3-8

Table 3-5. Average attenuation factors (A) back-calculated by RTI for a subset of 

samples.

Element Z

Dichot Major Flow 

Channel (PMf), N=13

Dichot Minor Flow 

Channel (PMc), N=6

PM10 FRM,

N=9

N > 

MDL A

N > 

MDL A

N > 

MDL A

Na 11 12 0.96 ± 0.02 6 0.44 ± 0.00 8 0.51 ± 0.00

Mg 12 11 0.97 ± 0.01 6 0.46 ± 0.00 7 0.54 ± 0.00

Al 13 12 0.98 ± 0.01 6 0.51 ± 0.00 7 0.58 ± 0.00

Si 14 12 0.99 ± 0.01 6 0.57 ± 0.00 8 0.61 ± 0.00

P (phosphorus) 15 3 0.99 ± 0.00 5 0.70 ± 0.00 7 0.84 ± 0.00

S 16 12 0.99 ± 0.00 6 0.85 ± 0.00 8 0.96 ± 0.01

Cl 17 12 0.99 ± 0.00 6 0.79 ± 0.00 7 0.85 ± 0.00

K 19 13 1.00 ± 0.00 6 0.87 ± 0.00 8 0.90 ± 0.00

Ca 20 12 1.00 ± 0.00 6 0.86 ± 0.00 8 0.88 ± 0.00

-- >20 12 1.00 ± 0.00 6 1.00 ± 0.00 7 1.00 ± 0.00

Figure 3-3. Cumulative distribution of gravimetric mass concentrations for all dichot 

sampler minor flow channel (PMc) samples that were analyzed by XRF (open circles) and 

the six samples with mass loadings reported by RTI both with and without attenuation 

correction (closed circles). The sample shown at the 99th percentile was the sample with 

the highest mass loading. 
EPA’s Coarse PM Pilot Study Analysis of Elements 

3-9

Figure 3-4. Attenuation factors for dichot minor flow channel samples (Coarse), PM10

FRM samples (PM10), and dichot major flow channel samples (Fine). 

In addition to the elemental concentrations reported for elements with Z  20, because 

Al, Si, and Ca are typically included in the crustal material estimation and mass closure 

calculations, the corrections for self-attenuation affect the total PM concentration assigned to 

crustal material. For example, throughout this study, crustal PM (also called “resuspended soil 

oxides”) has been estimated using the standard formula applied to IMPROVE network data, 

which includes terms for Al, Ca, Fe, Si, and Ti (see Section 6). Assuming the mean attenuation 

factors reported in Table 3-5 are applicable to all samples, Figure 3-5 shows the impact of the 

applied attenuation factors on the crustal PMc mass concentration estimates. Because the 

attenuation factors are unity for Fe and Ti, attenuation corrections affect only Al, Si, and Ca. 

Attenuation-corrected crustal PMc mass concentrations are approximately 50% greater than the 

uncorrected mass concentrations. Corrections for self-attenuation have been applied throughout 

this study; in the absence of these corrections, the estimated crustal PMc mass concentrations 

would be considerably lower. 
EPA’s Coarse PM Pilot Study Analysis of Elements 

3-10

Figure 3-5. Estimated crustal PMc mass concentrations with (y-axis) and without (x-axis) 

corrections for self-attenuation applied to the dichot sampler data. 

3.4 XRF versus ICP-MS Measurements and Implications 

To assess the adequacy of the attenuation factors being applied to the RTI XRF results, 

filters from 18 sampling events (10 from Phoenix, 8 from St. Louis) were selected for elemental 

analysis at RTI using both XRF and ICP-MS. ICP-MS was chosen as an independent “referee” 

method because of its sensitivity and immunity to the attenuation effect that is seen in XRF. The 

10 sets of filters from Phoenix exhibited a broader range of crustal species concentrations than 

those from St. Louis. Samples were analyzed by XRF and then digested for analysis by ICP-MS

using a microwave-assisted, mixed acid extraction method2

 to get the more difficult metals into 

solution (e.g., Al, Si, Mg, Fe, and Cr). 

Figure 3-6 shows the dichot PMc data for Al with A = 0.51 for XRF and no adjustments 

to the ICP-MS data. The oblique solid line represents 1:1 agreement between x and y. The 

sample within the square was an outlier for many elements and has been excluded from the 

remainder of the analysis. Al concentrations by ICP-MS are biased high, and a reduced major 

axis (RMA) regression3 of ICP-MS concentration on XRF concentration has intercept 

 

2

 Microwave-assisted digestion was performed on the filters using 2.5 mL nitric acid, 2.5 mL hydrochloric acid, and 

0.5 mL hydrofluoric acid. (This digestion matrix is more aggressive than the 8% HCl/3% HNO3 matrix recommended 

for analysis of PM2.5 samples on Teflon filters (RTI, 2010).) A two-step digestion protocol was conducted using a 

CEM MARS5 unit, with the first step a 15-minute ramp to 100C at 400W, followed by a 20-minute ramp to 200C at 

1600W. The pressure limit was 800 psi for both steps. Deionized water was used to bring the contents to a final 

volume of 50 mL. Analysis was conducted using a Thermo X-Series II ICP-MS with a collision cell and cell gas 10% 

hydrogen and 90% helium. 

3

 Reduced major axis regression is an orthogonal regression method. Smith (2009) discusses the use of ordinary 

least squares and reduced major axis regressions. 
EPA’s Coarse PM Pilot Study Analysis of Elements 

3-11

0.88  0.27 μg/m3, which is statistically greater than zero at the 95% confidence level. This 

intercept value implies background contamination for the ICP-MS measurement (as described 

below). The slope of 0.75  0.16 is less than unity and implies that the attenuation factor applied 

to the XRF data is too small (i.e., the data are overcorrected). 

Figure 3-6. PHX dichot PMc aluminum concentrations by XRF (A = 0.51) and ICP-MS. 

Four field blanks (whole 47 mm Teflon filters), extracted and analyzed by ICP-MS along 

with the ambient samples, revealed high background levels for several elements, including Al. 

The mean and median Al field blank effective concentrations (assuming 24 m3 air volume 

sampled) were equal to 1.4 and 1.2 μg/m3, respectively. These concentration values are 

consistent with the intercept in Figure 3-6 (indeed, are modestly higher than the intercept of 

0.88  0.27 μg/m3) and suggested the presence of significant filter contamination for certain 

elements. Given that high blank levels were observed for ICP-MS but not XRF, our hypothesis 

was that certain elements are present in the filter outside the XRF beam zone, with the likely 

sources being the adhesive that attaches the support ring to the membrane filter, or the ink used 

to stamp the filter ID number onto the support ring. 

RTI subsequently analyzed by ICP-MS seven clean filters with the support ring first 

removed from the membrane; these components (membrane versus support ring) were 

separately digested and analyzed. Table 3-6 shows the effective concentration values for the 

membrane, support ring, and also the four field blanks. The support ring has elevated 

concentrations for several elements, including Al. Another possible confounder when comparing 

ICP-MS to XRF data is the recovery of elements from the sample during the digestion step that 

precedes analysis by ICP-MS. 
EPA’s Coarse PM Pilot Study Analysis of Elements 

3-12

Table 3-6. ICP-MS analysis of laboratory blanks with the membrane separated from the 

support ring (with adhesive) and of field blanks. For each cell, the top row is the median 

and the bottom row is the mean and standard deviation. Values are concentrations 

reported in μg/m3 for an effective air volume sampled of 24 m3 (16.7 LPM for 24 hours). 

Element

Laboratory Blanks (N = 7)

Field Blanks (N = 4)

Membrane Support Ring

Na 0.003

0.003 ± 0.001

0.294

0.271 ± 0.041

0.418

0.493 ± 0.159

Mg 0.003

0.003 ± 0.001

0.428

0.419 ± 0.026

0.378

0.379 ± 0.116

Al 0.004

0.004 ± 0.004

1.152

1.088 ± 0.111

1.163

1.406 ± 0.525

Si -0.018

0.004 ± 0.081

1.271

1.414 ± 0.590

1.660

2.647 ± 2.176

P

-0.002

-0.004 ± 0.006

0.251

0.242 ± 0.018

0.246

0.253 ± 0.018

S

-0.011

-0.011 ± 0.005

0.030

0.032 ± 0.004

0.091

0.140 ± 0.134

K

0.004

0.006 ± 0.005

0.009

0.009 ± 0.002

0.028

0.127 ± 0.214

Ca -0.021

-0.017 ± 0.009

0.229

0.210 ± 0.040

0.334

0.711 ± 0.812

Fe 0.003

0.009 ± 0.016

0.007

0.011 ± 0.008

0.049

0.246 ± 0.422

Ti -0.001

0.000 ± 0.001

0.013

0.016 ± 0.008

0.012

0.012 ± 0.002

Zn -0.001

-0.001 ± 0.001

0.027

0.034 ± 0.024

0.025

0.026 ± 0.022

Analysis of a Standard Reference Material (SRM) is commonly used to characterize 

recoveries. RTI analyzed five samples (each nominally 10 mg) of NIST SRM 1648a, which is an 

urban particulate matter reference material. One sample had anomalous recoveries for several 

elements and was excluded from the data analysis. Table 3-7 summarizes the results. 

Recoveries were about 90% for several elements of interest to this study, such as Al and Ca. 
EPA’s Coarse PM Pilot Study Analysis of Elements 

3-13

Table 3-7. Recoveries for urban particle matter NIST standard reference material for the 

sample digestion protocol and ICP-MS analysis used in this study. S.E. is the standard 

error of the mean. 

Element

SRM 1648a Recovery (%)

(N = 4)

a

Median Mean  S.E.

Na 90 89 ± 1

Mg 91 90 ± 2

Al 94 91 ± 4

S 88 89 ± 1

K 91 90 ± 2

Ca 89 89 ± 2

Ti 69 71 ± 5

V (vanadium) 79 96 ± 27

Cr 73 72 ± 5

Mn 81 82 ± 3

Fe 91 92 ± 1

Co 81 81 ± 2

Ni (nickel) 103 100 ± 4

Cu 83 83 ± 2

Zn 119 113 ± 8

As (arsenic) 103 102 ± 2

Se (selenium) 123 116 ± 8

Sr 96 95 ± 2

Ag (silver) 110 108 ± 9

Cd (cadmium) 111 107 ± 5

Sb (antimony) 95 91 ± 7

Pb (lead) 86 86 ± 2

a Excludes one run that exhibited low recoveries for Al, Mg, K, Zn,

and Se, and high recoveries for V and As.

In light of these results, the following approach was taken to evaluate the XRF 

attenuation factors. For a given element analyzed by both XRF and ICP-MS: 

1. For the dichot minor flow channel filters (i.e., PMc filters), use the XRF attenuation 

factors Ai reported in Table 3-5 and Equation 3-1 to back-calculate the XRF mass 

loadings without correction for self-attenuation, mno corr,i. 

2. Estimate a revised attenuation factor Ai′ and use Equation 3-1 to calculate revised mass 

loadings mi′. Calculate XRF PMc elemental concentrations, PMc′, using the revised mass 

loadings for the PMc filter data. Attenuation factors for the dichot minor flow channel 

filters (i.e., PMf filters) are close to unity (Table 3-5) and were not revised. Thus, no 
EPA’s Coarse PM Pilot Study Analysis of Elements 

3-14

adjustments were made to the PMf concentrations that are used to correct the PMc

concentrations for fine particle intrusion into the dichot PMc filter channel. 

3. Blank-correct the ICP-MS mass loadings data using the laboratory blanks results (i.e., 

the mass loadings data underlying Table 3-6). Mean loadings from the support ring and 

membrane were summed and subtracted from the dichot PMc and PMf filter mass 

loadings. 

4. Calculate ICP-MS PMc elemental concentrations using the blank-corrected dichot PMc

and PMf filter mass loadings. 

5. Calculate the square difference between the PMc concentration values for blankcorrected ICP-MS data and XRF data with revised attenuation factors (PMc′). 

6. Calculate the sum of squared differences (SOSD) over the nine samples and iterate on 

the revised attenuation factor Ai′ to determine the best-fit Ai′ that minimizes the SOSD. 

7. Repeat the above steps, adding to Step 5 an adjustment for the ICP-MS recoveries 

using the mean recoveries data reported in Table 3-7. 

Figure 3-7 shows the PMc aluminum concentrations after blank-correcting the ICP-MS 

data using the mean laboratory blank value in Table 3-6 (support ring plus membrane) and 

using no adjustment for XRF attenuation (A = 1, left panel) and the best-fit XRF attenuation 

factor (A = 0.73, right panel). For Al, the best-fit attenuation factor was 0.73, assuming 100% 

recovery for the digestion and ICP-MS analysis. This attenuation factor is 43% higher than the 

value of 0.51 used by RTI (Table 3-5) and would decrease the RTI-reported PMc aluminum 

concentrations by about 30%. If the Al recovery of 91% (Table 3-7) is included in the analysis, 

the best-fit attenuation factor is 0.63 and decreases the RTI-reported PMc aluminum 

concentrations by about 20%. 

Table 3-8 shows the best-fit attenuation factors for the light elements. Best-fit 

attenuation factors (assuming 100% recovery for the ICP-MS analysis) are in good agreement 

with the RTI-reported values for Na and S, about 5% higher for Ca, and 20% to 45% higher for 

(in increasing order) K, P, Si, and Al. The best-fit attenuation factor for K is greater than unity; 

this suggests that another source of error is present; such as poor digestion recovery or 

calibration bias for XRF or ICP-MS. Silicon has a very low coefficient of linear correlation 

(r2 = 0.07) and high RMS error (RMSE; 78%), which makes the results for silicon suspect. The 

best-fit attenuation factor for Mg is about 20% lower than the RTI-reported value. When 

recoveries for ICP-MS analysis are included, the best-fit attenuation factors are as much as 

20% lower than the factors that are estimated assuming 100% recovery, but are still higher than 

the RTI-reported factors for Al, K, and Ca (recovery data are not available for Si and P). 
EPA’s Coarse PM Pilot Study Analysis of Elements 

3-15

Al - dichot PMc - PHX

XRF attenuation factor = 1

blank-corrected ICP-MS

concentration by XRF, g/m3

0 1 2 3 4 concentration by ICP-MS, g/m

3

0

1

2

3

4

(a)

Al - dichot PMc - PHX

XRF attenuation factor = 0.73

blank-corrected ICP-MS

concentration by XRF, g/m3

0 1 2 3 4 concentration by ICP-MS, g/m

3

0

1

2

3

4

(b)

Figure 3-7. PHX dichot PMc aluminum concentrations by ICP-MS with blank correction 

(mean laboratory blank of 1.092 μg/m3 from Table 3-6) and assuming 100% recovery, 

and by XRF with (a) XRF attenuation factor of unity; and (b) best-fit attenuation factor to 

reconcile the XRF and ICP-MS data. 

Table 3-8. Attenuation factors for dichot PMc aluminum at PHX. Values in parenthesis for 

the best-fit attenuation factor with 100% recovery are the ranges for SOSD at 110% of 

the minimum. RMSE is divided by the mean concentration. Elements in bold appear in 

the equation used to estimate crustal PM. 

Element Z

Dichot PMc Filter 

Attenuation Factor

Metrics for best-fit A

with 100% recovery

A (RTI) A (best-fit, 

100% recovery)

A (best-fit, 

recovery adj.) r

2 RMSE 

(%)

Na 11 0.44 0.44 (0.40-0.47) 0.37 0.81 34

Mg 12 0.46 0.39 (0.36-0.41) 0.31 0.88 24

Al 13 0.51 0.73 (0.70-0.75) 0.63 0.95 16

Si 14 0.57 0.78 (0.63-1.01) -- 0.07 78

P 15 0.70 0.90 (0.81-1.00) -- 0.79 48

S 16 0.85 0.87 (0.85-0.90) 0.87 0.96 16

Cl 17 0.79 NR NR NR NR

K 19 0.87 1.06 (1.04-1.08) 0.96 0.98 9

Ca 20 0.86 0.91 (0.87-0.95) 0.91 0.95 16

NR = analyte not resolved by ICP-MS

-- = no recovery data
EPA’s Coarse PM Pilot Study Analysis of Elements 

3-16

The response surface for the minimization (SOSD versus A) was examined by 

calculating the attenuation factors for 110% of the minimum SOSD. The attenuation factor 

ranges, shown in Table 3-8 in parenthesis after the best-fit A assuming 100% recovery, have 

relatively narrow bounds except for P and Si. Attenuation factors were also calculated by 

excluding each data point one at a time (“leave one out” analysis) and minimizing the SOSD. In 

this case, the range of attenuation factors for Al was 0.70 to 0.75. 

These preliminary results suggest that the RTI-reported dichot PMc filter attenuation 

factors for some crustal elements (e.g., Al, Ca, and possibly Si) are perhaps too low and thus 

the attenuation-corrected concentrations overestimate the true concentrations. The software 

used by RTI to apply attenuation factors should be examined to tabulate the assumptions being 

made about particle size distribution and particle composition. These assumptions could be 

adjusted within reasonable ranges to assess whether a better fit between XRF and ICP-MS can 

be achieved. The comparison between XRF and ICP-MS should be repeated for a larger data 

set, with samples collected at sites that have high crustal loadings, and ideally including coarse 

PM from different sources, such as desert dust and agricultural dust. XRF should be performed 

on each filter, and then the membrane should be separated from the support ring prior to 

digesting the particle-laden membrane for ICP-MS analysis. This approach will dramatically 

decrease the blank levels and thus reduce the uncertainty introduced by the blank correction. 

Samples suitable for particle size analysis, e.g., by electron microscopy, should be collected in 

parallel so the assumptions used to develop the attenuation factors can be evaluated. 

3.5 Variation in Crustal Composition 

The defining feature of the PMc elemental data is the high concentrations of species 

associated with crustal material. Since crustal species are the dominant component of PMc

mass, the variation of crustal composition between sites and over the course of the study was 

examined. Seasonal variation of crustal composition could be expected if there was influence of 

dust from long-range transport (e.g., Asian dust event) rather than locally generated dust, and if 

the mix of regional and local dust varied across seasons. Figure 3-8 shows a simple 

visualization of the crustal composition, comparing the fraction of Si, Fe, and Ca to the sum of 

these elements. At both sites, there is little temporal variability in the ratio of these species. 

However, Si accounts for nearly twice the fraction of crustal mass measured at Phoenix 

compared to St. Louis, the latter having more mass from Ca. This finding is consistent with likely 

different soil composition at the two sites, with St. Louis having karst topography, which could 

lead to higher abundance of calcium in the crustal PM profile. 
EPA’s Coarse PM Pilot Study Analysis of Elements 

3-17

(a) Phoenix

(b) St. Louis

Figure 3-8. Simple visualization of major crustal elements: fraction of the sum of Ti, Si, 

Fe, Ca, and Al by element for primary dichot PMc at (a) Phoenix (N = 51) and (b) St. 

Louis (N = 31). 

EPA’s Coarse PM Pilot Study Analysis of Carbon

4-1

4. Analysis of Carbon 

After crustal species, carbon was the second largest contributor to PMc mass, on 

average about 15% (see Section 2). Unlike crustal species, there is only marginal agreement 

between dichot PMc and FRM PM10-2.5 carbon measurements; most points were beyond a ±20% 

difference (Section 4.1), though without the bias towards the FRM that was seen with crustal 

species and gravimetric mass. 

Analysis of OC and EC by thermal fraction indicate that the composition of OC and EC is 

different between Phoenix and St. Louis, though there is little difference at each site between 

the fine and coarse OC and EC composition by thermal fraction (Section 4.2). 

Carbonate was measured for a subset of samples (Section 4.3). PMf carbonate was 

below the detection limit for all samples analyzed. In contrast, PMc carbonate was on average 

6% of PMc gravimetric mass at Phoenix and 12% of PMc gravimetric mass at St. Louis. 

Carbonate carbon was on average 9% of PMc total carbon (TC) at Phoenix and 14% of PMc TC 

at St. Louis. Thus, at these two sites, carbonate significantly contributes to gravimetric mass 

and total carbon. 

Unlike fine PM, OC mass loadings on backup filters for the dichot minor flow (used to 

calculate PMc concentrations) are statistically indistinguishable from the trip and field blanks 

(Section 4.4). This finding, together with other analyses presented in this section, lead to the 

conclusion that there is very little volatile OC in the coarse PM size range, and thus a low 

capacity for negative artifacts. Since coarse OC may comprise biological material, 54 samples 

for biological analysis were collected between February and May 2011 (see Section 4.5). 

Concentrations of biomarkers (proteins, (1,3)-β-D-glucans, and endotoxin) were relatively low 

compared to the OC, with a median PMc glucan concentration of about 0.2 ng/m3, protein 

concentrations of about 0.08 μg/m3 in both Phoenix and St. Louis, and endotoxin concentrations 

of 0.017 EU/m3 at St. Louis; endotoxin values at Phoenix were well outside typical variability and 

were suspect. 

4.1 Total Carbon Comparisons Between Dichot and FRM Samplers 

Figure 4-1 shows a comparison of total carbon (TC; sum of OC and EC) as measured in 

each size fraction by dichot and FRM. Most of the measurements for PM2.5 at both sites are 

within 20% (as indicated by dashed lines on the plot), and are well correlated (r2 varying from 

0.91 to 0.96). PM10 measurements similarly have a high correlation (greater than 0.90), and 

most measurements are within 20%. However, the dichot PMc and FRM PM10-2.5 measurements 

have lower correlations than either PM2.5 or PM10 measurements, with more points beyond 

±20% difference. At each site, each of the dichots yields different correlation statistics when 

compared with the FRM measurements (i.e., correlation of dichot to FRM varies between 0.64 

and 0.84 at St. Louis and between 0.67 and 0.84 at Phoenix, depending on which dichot is 

compared to the FRM). However, the striking feature is that PMc total carbon does not exhibit 

the dichot-to-FRM bias that is prevalent for PMc gravimetric mass and the crustal species 

(Figure 2-1 and Tables 3-1 and 3-3). 
4-2

Figure 4-1. Dichot versus FRM total carbon concentrations for St. Louis (top row) and Phoenix (bottom row) for PM2.5 (left), 

PM10-2.5 (center), and PM10 (right). Triangles are data from the primary dichot sampler and circles are data from the collocated 

dichot sampler. Diagonal lines are 1:1 (solid) and 20% of 1:1 (dashed). 
EPA’s Coarse PM Pilot Study Analysis of Carbon

4-3

4.2 Thermal Fraction Analysis (OC/EC Split) 

In Section 3.6, the variation in PMc crustal composition between sites was examined. In 

this section, the variation in carbon fractions across sites and size fractions is examined. 

Figure 4-2 shows the carbonaceous PM average concentration values by site, size fraction, and 

carbon subfraction from the dichotomous samples. These data have not been adjusted for 

carbon artifacts. The average coarse PM total carbon concentration (height of the stacked bar) 

is nearly equal to the fine concentration in Phoenix and is less than the fine concentration in 

St. Louis. For both fine and coarse PM, the EC and OC concentrations are higher in St. Louis 

than in Phoenix. 

Figure 4-3 shows the relative distribution of the organic carbon subfractions. For each 

size range, the distributions at Phoenix and St. Louis are quite similar. The fine PM has 

relatively higher OC1 and OC2 concentrations, whereas the coarse PM has relatively higher 

OC3 and OC4 concentrations. This suggests differences in the composition of organic carbon 

between the fine and coarse fractions at both sites. 

PM Carbon (no artifact correction)

fine coarse fine' coarse'

average concentration, ug/m

3

0

1

2

3

4

5

OC1 

OC2 

OC3 

OC4 

PCR 

EC1* 

EC2 

PHX STL

Figure 4-2. Carbonaceous PM average concentration values at Phoenix (N = 30) and St. 

Louis (N = 22). No artifact correction has been applied to these data. Concentration 

values are stratified by the IMPROVE protocol carbon subfractions, with PCR = pyrolytic 

carbon by the reflectance method and EC1* = EC1 – PCR. The average EC3 

concentration was zero for all cases and is not shown. 
EPA’s Coarse PM Pilot Study Analysis of Carbon

4-4

Figure 4-3. Distribution of organic carbon average concentration values at Phoenix 

(N = 30) and St. Louis (N = 22). No artifact correction has been applied to these data. 

Concentration values are stratified by the IMPROVE protocol carbon subfractions, with 

PCR = pyrolytic carbon by the reflectance method. 

4.3 Carbonate Concentrations 

Carbon can be present in ambient particulate matter as carbonate (CO3

2

). Assuming that 

carbonate decomposes at temperatures greater than 800C, carbonate should not confound 

measurement of EC and OC used during TOA temperature protocols with maximum 

temperatures below 800C (Chow and Watson, 2002). The maximum temperature for the 

IMPROVE_A protocol is 800C. Carbonate in geological samples is most commonly present as 

calcium carbonate; its presence will bias low the estimate of crustal-derived PM using 

conventional soil estimation equations, which assume that calcium is present as calcium oxide 

(CaO). 

The Desert Research Institute (DRI) used a TOA protocol with sample acidification to 

analyze selected quartz fiber filter samples for carbonate. Initially, dichot filters from 15 sampling 

events (six from Phoenix and nine from St. Louis) were selected, which reflected a range of 

Si/Ca ratios, based on the hypothesis that differences in Si/Ca ratio may be indicative of 

differences in PMc composition, and thus the amount of calcium carbonate (CaCO3) in a given 

sample. For PMf, only one sample exceeded the reported carbonate uncertainty value of 

0.17 μgC/m3—a Phoenix sample with PMf carbonate concentration of 0.18 μg C/m3.

4, 5

 In 

 

4

 Carbonate concentrations are reported as carbonate carbon, i.e., μgC/m3

. 

5

 The 3 MDL reported by DRI, based on a standard set of laboratory blanks, is 0.93 μgC/m3, which corresponds to 

0.51 μgC/m3 for PMf and 0.46 μgC/m3 for PMc. The error structure has the form 

2 2 Unci CV Ci MDL 3)/(+)×(=

, where 

Unci is the uncertainty for sample i, CV is the coefficient of variance from replicate analyses, and Ci is the sample 

concentration. Thus, at low concentrations, the uncertainty is MDL/3. PM Carbon (no artifact correction)

PHX STL PHX' STL'

percentage of total OC

0

20

40

60

80

100

OC1 

OC2 

OC3 

OC4 

PCR 

fine coarse
EPA’s Coarse PM Pilot Study Analysis of Carbon

4-5

contrast, for this relatively small set of samples, the dichot PMc carbonate was on average 

0.43 μg C/m3 for Phoenix and 0.36 μg C/m3 for St. Louis. Dichot PMc carbonate corrections for 

fine particle intrusion into the minor flow channel were at most 5%, so no additional dichot PMf

filter samples were analyzed.6

Subsequently, 12 collocated quartz filters sampling events were selected (six from each 

site) with carbonate analysis on both dichot PMc filters; PM10 and PM2.5 FRM filters were 

analyzed for six of these samples. Dichot PMc filters from an additional 42 mass balance 

protocol sampling events were analyzed (31 from Phoenix and 11 from St. Louis). 

Figure 4-4a shows the PMc carbonate data for collocated dichot samples. The 

collocated precision was statistically indistinguishable between the sites, with a pooled 

collocated precision of 0.053 μg C/m3 (22% relative precision). This precision is much better 

than the laboratory-reported uncertainty of 0.15 μg C/m3 for low carbonate concentrations, and 

suggests that the reported MDL of 0.46 μg C/m3 is conservative. Figure 4-4b compares the PMc

carbonate from the dichots (mean of the collocated values) to FRM by difference (i.e., PM10-2.5) 

for the six samples for which collocated dichot and FRM filters were analyzed for carbonate. 

The precision of 0.072 μg C/m3 (23% relative precision) is only slightly degraded compared to 

the collocated dichots. The ratio of means is 1.00, which demonstrates no bias between the 

dichot and FRM carbonate measurements, similar to results for total carbon; this result is in 

sharp contrast to gravimetric mass and species measurements from the Teflon filters, which 

showed higher loadings for FRM PM10-2.5 than for dichot PMc. 

Figure 4-4. PMc carbonate from (a) collocated dichots and (b) mean collocated dichot 

versus FRM-by-difference. 

 

6

 For the remainder of this section, dichot PMc carbonate data do not include a correction for fine particle carbonate. 
EPA’s Coarse PM Pilot Study Analysis of Carbon

4-6

PMc carbonate from the mass balance protocol sampling events was compared to PMc

mass, carbon (TC, EC-TOR, and OC-TOR), major crustal species (Al, Ca, Fe, Si) and S. 

Correlation coefficients (r) were statistically significant at the 95% confidence level (CL) for all 

comparisons except EC in Phoenix and OC and S in St. Louis. However, at both sites, the 

carbonate was most highly correlated with PMc Ca (r = 0.85 for Phoenix, r = 0.97 for St. Louis); 

in Phoenix, none of the other comparisons exceeded r = 0.7. 

Figure 4-5 shows the relationship between PMc carbonate and PMc calcium on a molar 

basis; data shown in Figure 4-5 include 37 samples from Phoenix and 20 samples from St. 

Louis. Carbonate can on average explain two-thirds of the PMc calcium, and there are virtually 

no samples with a carbonate-to-calcium molar ratio greater than one. For the subset of these 

sampling events with FRM PM10-2.5 data available, PMc carbonate can explain 67% of the 

PM10-2.5 Ca in St. Louis (N = 12), but only 52% of the PM10-2.5 calcium in Phoenix (N = 24). With 

the majority of calcium in CaCO3 rather than CaO form, using the typical equation for calculating 

crustal mass (see Section 6) likely underestimates the contribution from crustal material. 

However, this result should be qualified by the differences in particle losses between the dichot 

Teflon and quartz filters used to measure calcium and carbonate, respectively, since a larger 

correction of fine particle intrusion is used for calcium than for carbonate. 

The grand mean PMc carbonate concentrations were 0.23 μg C/m3 for Phoenix (N = 43) 

and 0.27 μg C/m3 for St. Louis (N = 26). The molecular weight of carbonate is five times as 

great as the molecular weight of carbon. For those sampling events with PMc mass available, 

the carbonate mass was, on average, 6% of the PMc gravimetric mass in Phoenix (range 0% to 

20%, N = 37) and 12% of the PMc gravimetric mass in St. Louis (range 0% to 21%, N = 20). 

Figure 4-5. Relationship between dichot PMc carbonate and dichot PMc calcium 

expressed as molar concentrations in (a) Phoenix and (b) St. Louis. 
EPA’s Coarse PM Pilot Study Analysis of Carbon

4-7

Dichot PMc carbonate mass was on average 5% of the FRM PM10-2.5 mass in Phoenix (N = 24) 

and 11% of the FRM PM10-2.5 mass in St. Louis (N = 12). 

These results demonstrate that carbonate can be a major contributor to PMc mass. For 

these data, PMf carbonate was negligible, and measurement of carbonate on the dichot minor 

flow channel filter is adequate to characterize PMc carbonate; i.e., no correction for fine particle 

intrusion appears necessary. While pure calcium carbonate decomposes at temperatures above 

800C, analysis conditions and matrix effects with other PM constituents might cause the 

carbonate carbon to decompose at lower temperatures (Chow and Watson, 2002). Thus, given 

the relatively large ratio of PMc carbon present as carbonate to PMc carbon present as EC and 

OC (averages of 9% in Phoenix and 14% in St. Louis), more work is needed to evaluate 

whether carbonate evolves during TOA by the IMPROVE_A protocol and thereby biases high 

the EC, OC, or both. 

4.4 Carbon Artifacts 

Filter-based sampling to quantify organic carbon in ambient particulate matter is prone to 

measurement artifacts. Positive artifacts can arise from the adsorption of vapor onto the sample 

(including the quartz filter), while negative artifacts can arise from the volatilization of collected 

particulate matter. The two major PM2.5 monitoring networks deployed in the United States take 

different approaches to addressing these artifacts. 

In the past, IMPROVE network OC data were adjusted on the assumption that the OC 

measured on backup quartz filters at a subset of sites is representative of the positive artifact at 

all sites in the network. Adjustments were derived and applied on a monthly basis. Although 

field blanks, trip blanks, and backup quartz filter blanks are collected by the CSN, the OC data 

currently reported for the CSN network are not adjusted for artifacts; the trip blanks, field blanks, 

and backup filter data are available to the user through EPA’s AQS data base. A workgroup of 

EPA and IMPROVE technical experts was convened to explore OC artifact corrections in both 

the CSN and IMPROVE monitoring networks. The initial recommendation based on a series of 

exploratory analyses is to use monthly network-wide quartz filter field blanks for adjustment. 

Field blank filters are collected at all CSN sites and housed in the sampler for the entire duration 

of the sampling event. The field blanks are less variable over time and space, decrease the 

additive artifact, and do not over-correct by including the multiplicative factor. At the finalization 

of this report, the IMPROVE network has begun implementing these recommendations and the 

CSN plans to begin January 2015. 

This section summarizes an analysis of carbon artifacts from this study. All samples 

collected for carbon analysis used a Q/Q filter sandwich. A subset of samples was analyzed for 

chemical speciation with carbon analysis performed at the DRI using the IMPROVE_A thermaloptical analysis protocol. This analysis focuses on organic carbon measured by optical 

reflectance (OC-TOR). 
EPA’s Coarse PM Pilot Study Analysis of Carbon

4-8

4.4.1 OC Trip Blanks and Field Blanks 

OC loadings on trip blanks and field blanks provide a context for characterizing and 

interpreting OC on the front and backup filters. The blanks data were examined and the key 

results are summarized in this section, with additional details provided in Appendix D. Seven 

sets of trip blanks were analyzed by OC-TOR. Each set included two to four filters per site. The 

May 2010 data were excluded from the analysis because of data quality issues (discussed in 

Appendix D); after this date, there was evidence that the trip blanks data could be pooled across 

all samples and sites. The pooled trip blanks data have mean and median OC mass loadings of 

5.4  5.7 and 4.2 μg/filter, respectively (N = 44). These mass loadings are the same as the 

estimated IMPROVE_A analysis MDL for OC of 5.3 μg/filter.7

The same statistical analyses were performed for the seven sets of field blanks that 

were collected at the same times as the trip blanks. Each set included four to twelve filters per 

site, with the maximum case being front and back filters in the PM2.5 FRM, PM10 FRM, and both 

channels of both dichot samplers. While in principle the sampler type and filter position (front 

versus back) could be treated as additional factors, their effects were deemed inconsequential, 

and samples pooled across these factors were treated as pseudo-replicates. As described in 

Appendix D, the site had a statistically insignificant effect, whereas the date had a statistically 

significant effect on OC mass loadings, even after removing the May 2010 data. The pooled 

field blanks data have mean and median OC mass loadings of 6.6  5.5 and 5.2 μg/filter, 

respectively (N = 130). 

Our interpretation of the blanks data concludes that (1) in May, which was a one-month 

intensive to shake down the field operations prior to the start of routine sampling in June, there 

were anomalously high trip blank and field blank mass loadings; and (2) for subsequent months, 

the trips blanks mass loadings were statistically indistinguishable, while the field blanks mass 

loadings decreased as the study progressed. The temporal behavior confounds the use of the 

95th percentile OC mass loading as a robust estimate of the lower quantifiable limit (LQL) but it 

can be used as a conservative estimate. Excluding the May 2010 samples, the 95th percentile 

trip blank and field blanks mass loadings are each 19 μg/filter. The May 2010 measurements 

are not included in the results presented in the remainder of this section. 

4.4.2 OC Mass Loadings on the Backup Filters 

Backup filter OC mass loadings can arise from contamination and vapor adsorption 

during sampling. Adsorption can be from “native” vapor (semivolatile OC entering the sampler in 

the vapor phase) or semivolatile OC volatilized (desorbed) from particles on the front filter as a 

result of the filter face velocity and pressure drop across the filter during sampling. PM2.5 and 

PM10 backup filters are exposed to the same flow rate (16.7 LPM) with incrementally more 

particulate matter OC on the PM10 front filter than on the PM2.5 front filter. Figure 4-6 shows the 

OC mass loading on the back filter for these two samplers. For both Phoenix and St. Louis, the 

data are scattered about the 1:1 line. The median PM2.5/PM10 OC ratios for the backup filters are 

 

7

 Carbon analysis MDL values for this study were estimated by assuming the same MDL as the CSN network when 

expressed as g/cm2 filter area. Reported CSN network MDL values are 1.5 g/filter for OC and 0.42 g/filter for EC 

and were scaled using filter diameters of 25 mm for the CSN network and 47 mm for this study. 
EPA’s Coarse PM Pilot Study Analysis of Carbon

4-9

nearly equal to unity (1.02 and 1.10 for Phoenix and St. Louis, respectively). Nearly all OC mass 

loadings are greater than the 95th percentile trip blank, and several OC mass loadings are 

greater than the 95th percentile field blank. Based on these lines of evidence, there is no 

compelling evidence for coarse particle volatilization. In particular, the PM2.5 and PM10 FRM 

backup filters have nearly identical OC mass loading, which means that coarse particle 

volatilization from the PM10 front filter is likely negligible. 

Figure 4-6. OC mass loadings on backup filters: (a) PM2.5 FRM versus PM10 FRM; 

(b) dichot major flow versus PM2.5 FRM; and (c) dichot minor flow versus dichot major 

flow. Data from May 2010 are excluded. 

Filters in the dichot major flow channel are exposed to 90% of the flow rate and 90% of 

the fine particles sampled by the PM2.5 FRM. Figure 4-6b shows the OC mass loading on the 

backup filters for these two samplers. Data are widely scattered, with median dichot/PM2.5 OC 

ratios of 0.81 for Phoenix and 0.99 for St. Louis. There appears to be, on average, modestly 

(a)

PM10 FRM backup filter OC, ug/filter

0 10 20 30 40

PM2.5 FRM backup filter OC, ug/filter

0

10

20

30

40

PHX

STL

(b)

PM2.5 FRM backup filter OC, ug/filter

0 10 20 30 40

dichot major flow backup filter OC, ug/filter

0

10

20

30

40

PHX

STL

(c)

dichot major flow backup filter OC, ug/filter

0 10 20 30 40

dichot minor flow backup filter OC, ug/filter

0

10

20

30

40

PHX

STL
EPA’s Coarse PM Pilot Study Analysis of Carbon

4-10

less OC mass on the dichot major flow channel backup filters compared to the PM2.5 FRM 

backup filters. This pattern could arise from less adsorption of native vapor at the lower flow rate 

of the dichot major flow channel (the flow rate is 10% lower than the PM2.5 FRM flow rate), or 

less volatilization of fine PM from the dichot front filter compared to the PM2.5 FRM front filter 

(the dichot major flow channel collects only 90% of the fine PM entering the sampler), or both. 

More data are needed to determine whether the suggested patterns are real. 

Figure 4-6c shows the OC mass loadings on backup filters for the dichot minor flow 

(used to measure coarse PM) against the dichot major flow (used to measure fine PM). There is 

virtually always more OC on the backup filter for the major flow compared to the minor flow. The 

excess at St. Louis is greater than the excess at Phoenix, with median major/minor OC ratios of 

1.8 for Phoenix and 2.6 for St. Louis. Again, it is not clear whether the trend occurs from higher 

adsorption of native vapor at the nine-fold higher flow rate for the major flow, or from particle 

volatilization from the fine particles which are distributed 90% to the major flow and 10% to the 

minor flow, or both. 

4.4.3 Comparison of OC Blanks, Front Filters, and Backup Filters 

Enhanced OC on the major flow backup filter compared to the minor flow backup filter is 

consistent with measurements conducted at each site using a dichotomous TEOM with FDMS 

modules (Section 7). For mid-to-late summer through mid-winter, the TEOM data exhibited 

FDMS volatile component concentrations for the major flow that were 3.3 times (Phoenix) to 

4.3 times (St. Louis) higher than measured for the minor flows. For both sites, the major-tominor flow TEOM-derived volatile OC enhancement is approximately 1.7 times greater than 

major-to-minor flow OC enhancement on the dichot sampler backup filters. The FDMS volatility 

component is an upper bound on the semivolatile losses from the integrated sampler filter, and 

this analysis suggests that fine PM on the integrated sampler front filter could be volatilizing with 

at least a portion of the evolved OC adsorbing onto the backup filter. For the data collected over 

the mid-to-late summer through mid-winter period, there was relatively little nitrate, so the 

semivolatile fine PM is assumed to be dominated by OC. 

Figure 4-7 shows box-whisker plots for OC mass loadings on trip and field blanks, the 

dichot major and minor flow backup filters, and dichot major and minor flow front filters. The 

minor flow backup filter mass loadings are statistically indistinguishable from the trip and field 

blanks loadings (95% confidence level), and the backup filter loading is more than 7% of the 

corresponding front filter loading for 25% of the samples. Major flow backup filter mass loadings 

are statistically higher than the trip and field blanks loadings (95% confidence level), and the 

backup filter loading is more than 17% of the corresponding front filter loading for 25% of the 

samples. These trends reaffirm that, at least for these two sites, backup filter measurements 

have more OC in fine PM, but might not bring added value to characterizing OC in coarse PM. 

Figure 4-8 shows the OC mass loadings relationships for the paired front and back 

filters of the major and minor flows. Solid and dashed horizontal lines are the 95th percentile trip 

blanks and field blanks OC mass loadings, respectively. As previously discussed, the field 

blanks mass loadings decreased as the study progressed, and the 95th percentile value was not 

a good representation of the LQL. Thus, the trip blanks 95th percentile mass loading was used 

as an LQL proxy. Figure 4-8a shows that most of the major flow backup filter OC mass loadings 
EPA’s Coarse PM Pilot Study Analysis of Carbon

4-11

are above the trip blanks-based LQL. Front and backup filter mass loadings are weakly 

correlated (0.29). In contrast, a recent analysis of the URG-3000N sampler data for Missouri 

PM2.5 CSN sites shows a stronger positive correlation between the front and backup filter mass 

loadings (not shown). OC mass loadings on the front and backup filter might positively correlate 

if semivolatile OC vapor concentrations increase with increasing front filter OC particulate matter 

loading, or if more particulate matter OC volatilization occurs at increasing front filter OC 

particulate matter loading, or both. Front and back filter mass loadings for the minor flow are 

uncorrelated (-0.07), consistent with the backup filter mass loadings being indistinguishable 

from the trip and field blanks. 

Figure 4-7. OC mass loading distributions (μg/filter) for the dichotomous samples. The 

interior solid blank line is the median and the interior dashed red line is the arithmetic 

mean. Whiskers are 10th/90th percentiles, and closed circles are 5th/95th percentiles. 

Values in parentheses above each box are the number of samples in the respective 

distribution. Data from May 2010 are excluded. Mass loadings were averaged for days 

with collocated carbon sampling. 

trip blanks

field blanks

major backminor back

major frontminor front

OC mass loading, ug/filter

0

20

40

60

80

100

120

(28) (82) (47) (46) (65) (65)
EPA’s Coarse PM Pilot Study Analysis of Carbon

4-12

Figure 4-8. Dichotomous sampler OC mass loadings (μg/filter) for paired front and back 

filters: (a) major flow and (b) minor flow. The horizontal lines are the 95th percentile trip 

blanks (solid) and field blanks (dashed). 

As shown in Table 4-1, mean OC mass loadings on the dichot minor flow backup filters 

are very similar to the mean field blank mass loadings. In contrast, mean OC mass loadings on 

the dichot major flow backup filters are higher than the site-specific mean field blanks by 1.7 

times for Phoenix and 2.5 times for St. Louis. 

Table 4-1. Mean mass loadings on the field blanks and dichot backup filters. 

Mass loadings are in μg/filter. 

Mean Mass Loading Item Phoenix St. Louis

Field blanks 8.1 6.0

Dichot minor flow backup filters 7.8 5.7

Dichot major flow backup filters 13.6 15.1

Figure 4-9 shows OC concentration scatter plots for collocated dichot samplers at each 

site, excluding the May 2010 data. Collocated precision with no artifact correction is 0.44 μg/m3

(14% of the average OC) for dichot PM2.5 and 0.61 μg/m3 (23% of the average OC) for dichot 

PMc. Future work could include examining the sensitivity of collocated precision to different 

forms of the artifact correction. Backup filters are collected with every example, so it would be 

possible to evaluate whether the collocated precision improves or degrades when using samplespecific artifact corrections versus a single-valued correction derived from an ensemble of 

backup filters or field blanks. In principle, it would be possible to examine whether the collocated 

precision can be explained by the variation in the field blanks, but this is confounded by the 

apparent changes in field blank OC mass loadings as the study has progressed. For dichot PMc, 

there are additional considerations. The minor flow collocated precision could be calculated and 

compared to the PMc collocated precision to determine the influence from applying the 

correction for fine particle intrusion. However, as demonstrated in Figure 4-9b, analysis of the 

dichot PMc collocated OC data is confounded by an apparent bias between the samplers; such 

bias was also observed for crustal species. 

front filter OC, ug/filter

0 20 40 60 80 100 120 140 160

back filter OC, ug/filter

0

10

20

30

40

50

(a) major flow

front filter OC, ug/filter

0 20 40 60 80 100 120 140 160

back filter OC, ug/filter

0

10

20

30

40

50

(b) minor flow
EPA’s Coarse PM Pilot Study Analysis of Carbon

4-13

Figure 4-9. OC concentrations for collocated dichot samplers: (a) PM2.5 and (b) PMc. 

Data from May 2010 are excluded. 

4.4.4 Summary of Carbon Artifacts 

Based on TOA analysis of samples, the following conclusions are drawn. First, as 

described in Appendix D, OC mass loadings for the field blanks decreased as the study 

progressed, and the most recent mass loadings were comparable to the trip blanks. Second, 

OC mass loadings on backup filters for the dichot minor flow are statistically indistinguishable 

from the trip and field blanks. This finding, together with other analyses presented in this 

section, lead to the conclusion that there is very little volatile OC in the coarse PM size range, 

and thus a low capacity for negative artifacts. In contrast, fine PM exhibits enhanced OC mass 

loadings on the backup filters. The study design cannot distinguish whether these higher 

loadings are from adsorption of native vapors or adsorption of OC that volatilized PM on the 

front filter. 

4.5 Biological Data 

In addition to the routine one-in-three day sampling, additional dichot samples were 

collected between routine sampling days and analyzed for biomarkers. The data set includes 22 

sampling events in Phoenix and 19 sampling events in St. Louis over the period from February 

to May 2011. Four field blanks and two trip blanks were also taken. RTI analyzed Teflon filters 

from the dichot minor flow channel (i.e., coarse particles with no correction for fine particle 

intrusion) for gravimetric mass, (1,3)-β-D-glucans, endotoxins, and proteins. RTI used water to 

extract the samples prior to using the assays specific to each biomarker. 

 The (1-3)-β-D-glucans are found in the cell walls of many types of fungi. While this 

constituent is not unique to fungi, it is commonly used as a proxy to evaluate 

spatiotemporal patterns in fungal concentrations. The (1-3)-β-D-glucans were measured 

using Glucatell®, a commercially available assay. 

(a)

primary dichot (B) PM2.5 OC, ug/m3

0 2 4 6 8 collocated dichot (A) PM2.5 OC, ug/m

3

0

2

4

6

8

PHX 

STL 

(b)

primary dichot (B) PM10-2.5 OC, ug/m3

0 2 4 6 8 collocated dichot (A) PM10-2.5 OC, ug/m

3

0

2

4

6

8

PHX 

STL 
EPA’s Coarse PM Pilot Study Analysis of Carbon

4-14

 Endotoxin was measured by the sample reaction with Pyrochrome®, a commercially 

available Limulus Amebocyte Lysate (LAL) assay. 

 Protein, an indirect marker of total biological load, was measured using the Molecular 

Probes® NanoOrange® Protein Quantitation Kit. Seven of these sampling events 

included the collection of collocated samples (four at Phoenix, three at St. Louis). For 

these seven sample pairs, the collocated precision was 9% for dichot minor flow channel 

mass concentration (PMc not corrected for fine particle intrusion). 

RTI analyzed samples in two batches, with the field blanks and trip blanks data 

corresponding to each batch (Table 4-2) used to blank-correct the data. Blank values were 

converted to concentration units using the target air volume sampled of 24 m3. Field blanks and 

trip blanks were statistically indistinguishable at the 95% confidence level. Samples with 

negative biomarker concentration after blank correction were deemed to be below the 

operational detection limit. Table 4-3 summarizes the glucan and endotoxin data by batch and 

also pooled across the two batches. Figure 4-10 shows the concentration distributions for these 

data. Protein was excluded from these summaries for reasons described below. 

Table 4-2. Biomarker field and trip blank data summary. Each batch included two field 

blanks and one trip blank. Field blanks and trip blanks were statistically indistinguishable 

at the 95% confidence level. Concentration units were calculated using a total air volume 

sampled of 24 m3. ND = not detected.

Analysis Batch

(Sample Dates)

Mean  1

Glucans,

ng/m3

Endotoxin,

EU/m3

Protein,

μg/m3

Batch 1 (2/3/11 to 3/18/11) 0.0056  0.0003 ND 0.14  0.02 

Batch 2 (3/23/11 to 5/20/11) 0.0071  0.0019 ND 0.07  0.05

Pooled 0.0063  0.0015 ND 0.10  0.05

Table 4-3. Biomarker data summary for blank-corrected data. Values are the geometric 

mean (GM), with the geometric standard deviation (GSD) in parentheses.

Analysis Batch

Phoenix St. Louis

N

Glucans,

ng/m3

Endotoxin,

EU/m3 N

Glucans,

ng/m3

Endotoxin,

EU/m3

Batch 1 (2/3/11 to 

3/18/11) 10 0.18 (1.3) 2.8 (2.1) 10 0.17 (1.5) 0.04 (2.6)a

Batch 2 (3/23/11 

to 5/20/11) 12 0.16 (2.5) 0.30 (2.3) 9 0.21 (1.8) 0.13 (2.8)

Pooled 22 0.17 (2.0) 0.81 (4.0) 19 0.19 (1.6) 0.07 (3.1)a

a Endotoxin was not detected in one sample at STL. A concentration value of 0.01 EU/m3 was imputed, which 

corresponds to one-half of the lowest observed concentration.
EPA’s Coarse PM Pilot Study Analysis of Carbon

4-15

Figure 4-10. Box-whisker plots by site of (a) glucan (ng/m3) and (b) endotoxin (EU/m3). 

Endotoxin data at Phoenix are stratified by analysis batches (PHX-1, February 3 to March 

18, 2011; and PHX-2, March 23 to May 20, 2011). 

For protein, the collocated precision on blank-corrected data was 0.07 μg/m3 (70%) for 

the four sample pairs that had both samples above the operational detection limit. The protein 

data have high uncertainty because the blanks correction is relatively large. Only six samples 

(two in Phoenix, four in St. Louis) had raw protein loadings more than three times the pooled 

blank level, and at both sites only about 40% of the raw protein loadings were more than two 

times the pooled blank level. Geometric means and standard deviations cannot be reliably 

computed because the results will be very sensitive to the method used to impute values that 

are negative (i.e., smaller than the blank correction). The median blank-corrected protein 

concentrations are 0.18 μg/m3 for Phoenix and 0.07 μg/m3 for St. Louis; these concentrations 

are close to the blank values (Table 4-2). Given that the protein concentration values are 

subject to large uncertainties, further interpretation of these data is not warranted. 

Glucans had good detectability, with concentration values for all but one sample at least 

ten times higher than the blank correction. Collocated precision on glucan blank-corrected data 

was 0.04 ng/m3 (19%). Geometric mean glucan mass concentrations were 0.17 ng/m3 at 

Phoenix and 0.19 ng/m3 at St. Louis. 

Endotoxin was not detected in the blanks samples. Collocated precision for endotoxin 

was 0.51 EU/m3 (99%), including all seven sample pairs, and 0.19 EU/m3 (48%) when excluding 

the March 2, 2011, sample at Phoenix. The geometric mean endotoxin level was ten times 

higher for Phoenix than for St. Louis, and also exhibited more variability for Phoenix. Samples 

were analyzed in two batches, with batch-specific geometric mean endotoxin values for Phoenix 

of 2.8 EU/m3 for the first batch (February to mid-March) and 0.3 EU/m3 for the second batch 

(late March to May). While the between-batch variability in endotoxin was large, monthly median 

PM10 endotoxin concentrations for Fresno, California, reported by Tager et al. (2010) exhibited 

similar range and variability. Thus, while the Phoenix endotoxin levels exhibit a large stepchange precisely coinciding with the batches, it is not clear whether this change is real or from 

measurement error. While there is not clear evidence that the data are invalid, caution should 

be used when interpreting results for Phoenix endotoxin concentrations. 

EPA’s Coarse PM Pilot Study Analysis of Ions 

5-1

5. Analysis of Ions 

This section describes analyses focused on concentrations of ions. Coarse-mode ion 

concentrations, including nitrate and sulfate, were very low at both sites throughout the 

monitoring period, and much lower than fine-mode concentrations (Section 2 and Section 5.3). 

Coarse-mode nitrate appears to be non-volatile compared to fine-mode nitrate, since the 

majority of the coarse-mode nitrate was retained on the Teflon filter (Section 5.1). Ammonium 

predictions based on anion concentrations were much higher than observed, indicating the 

coarse-mode nitrate is predominantly paired with cations other than ammonium (Section 5.4), 

though there was no consistent correlation of PMc nitrate with other ions (Appendix E). 

Concentrations of both fine- and coarse-mode nitrate were similar with and without a denuder to 

remove nitric acid in the sampling stream (Section 5.5). Coarse-mode XRF sulfur predominantly 

consisted of sulfate, and coarse-mode XRF sodium predominantly consisted of sodium ion, so 

the ion measurements are largely redundant. In contrast, most of the potassium was nonionic 

(Section 5.6). Given these findings, laboratory analysis of PMc ions as part of a routine 

monitoring network does not appear to justify the necessary costs, except in environments, such 

as coastal areas, where coarse-mode nitrate is expected to be significant. 

5.1 Approaches to Measuring Ions 

Ambient PM2.5 ions are routinely measured in both the CSN and IMPROVE networks. In 

both cases, there is a devoted sample collection channel to ions, with a nylon filter and an 

upstream acid gas denuder to minimize positive artifacts from the uptake of such gases by the 

nylon filter. The filter samples are extracted in water and analyzed by IC. The ion measurements 

serve three purposes: 

1. They are the only measurement of nitrate (both networks) and ammonium (CSN only), 

which can be significant contributors to ambient PM2.5 burdens. 

2. Together with the element concentration measurements from the Teflon filter, they allow 

for a quality check on the sampling and laboratory analyses. 

3. The fraction of a given element that is present in ionic form can provide insights into the 

relevant sources. 

In contrast to the CSN and IMPROVE networks, which feature a dedicated sampling 

channel for ion measurements using a nylon filter, to reduce the total number of samplers 

needed for this study, ions were measured using a nylon-behind-Teflon filter sandwich. Ion 

analysis was performed on both the Teflon and nylon filters with the ions from nonvolatile salts 

(e.g., ammonium sulfate) to be found only on the Teflon filters, and the ions from semivolatile 

salts (e.g., ammonium nitrate) to be found on both the Teflon and nylon filters. The nylon filter is 

used specifically to measure nitrate and ammonium from the volatilization of ammonium nitrate 

from the Teflon filter, although other ions might be present in the nylon filter from chemistry that 

takes places on the Teflon filter that displaces ions. In cases where semivolatile forms of nitrate 

are low, the nylon filter sampling and analysis might be unwarranted. The Teflon and nylon 

filters are in series in the same sampling channel, so although the ion and elemental 

measurements (e.g., XRF sulfur and IC sulfate) do not provide a quality check on the sampling, 

they do provide a quality check on the laboratory analyses. 
EPA’s Coarse PM Pilot Study Analysis of Ions 

5-2

5.2 Nitrate Concentrations on Teflon and Backup Nylon Filter 

As described in Section 2, PMc nitrate concentrations were generally low. Some forms of 

nitrate can be lost or volatilized off of Teflon filters, so backup nylon filters were used to quantify 

the amount of nitrate volatilized off of the Teflon filter. Table 5-1 summarizes average 

concentrations at each site, size fraction of nitrate by filter, and the average fraction of nitrate on 

the Teflon filter. Most of the nitrate is retained on the PMc Teflon filter (71% to 77% on average) 

while half or less of the nitrate is retained on the PMf Teflon filter (40% to 53%). This is 

consistent with coarse-mode nitrate consisting of salts that are less volatile than fine-mode 

nitrate, which is predominantly ammonium nitrate. 

If only Teflon filters are used in routine sampling, with no backup nylon filters, then finemode nitrate would be severely under-measured (Ashbaugh and Eldred, 2004), though the 

majority of the coarse-mode nitrate will be captured. Nitrate is a small fraction of coarse mass in 

these two locations, so the effect of not having backup nylon filter measurements on coarse 

mass balance would be negligible at these and similar sites. Thus, the decision on whether to 

use nylon backup filters and analyze them by IC will be dictated by the extent to which the PMc

speciation sampling network will also be used for PM2.5 speciation. 

Table 5-1. Summary of nitrate concentrations via dichot on Teflon, nylon, and total 

(Teflon + nylon), plus fraction of total nitrate on Teflon filter. 

Site Size 

Fraction

Avg. Nylon 

NO3 (μg/m3)

Avg. Teflon 

NO3 (μg/m3)

Avg. Total 

NO3 (μg/m3)

Avg. % of NO3

on Teflon

PHX PMc 0.13 0.42 0.54 77

PHX PMf 0.44 0.50 0.94 53

STL PMc 0.13 0.43 0.56 71

STL PMf 0.54 1.01 1.54 39

5.3 Nitrate Partitioning Between Fine and Coarse Modes 

Figure 5-1 shows the partitioning of PM10 nitrate between the fine and coarse fractions 

as measured by the dichotomous samplers (Figure 5-1a) and PMf nitrate partitioning to the front 

Teflon filter compared to the Teflon/nylon filters (Figure 5-1b). This analysis assumes negligible 

loss of nitrate during XRF analysis; however, results from an EPA study on PM2.5 speciation 

indicated that the vacuum applied during XRF can reduce the amount of nitrate on a Teflon filter 

by as much as 40% (U.S.EPA, 2001). If nitrate measurements need to be made using a dichot 

then the issue of nitrate loss from theTeflon filter due to XRF would need to be addressed. The 

data set includes all Dichot A nitrate data and Dichot B nitrate data for days on which no Dichot 

A nitrate data were collected. Figure 5-1(a) shows that some samples with PMf nitrate below 

about 1 μg/m3

 have more PMc nitrate than PMf nitrate. However, PMc nitrate is not elevated on 

days of high PMf nitrate concentrations. Deviations below the 1:1 (diagonal) line in Figure 5-1(b) 

represent PMf nitrate volatilization loss from the front filter that is captured by the back filter. At 

low nitrate concentrations, PMf nitrate volatilization losses are generally lower in Phoenix 
EPA’s Coarse PM Pilot Study Analysis of Ions 

5-3

compared to St. Louis (this observation is not discernible in Figure 5-1b). This difference 

between the two sites might reflect a counter-ion other than ammonium for nitrate in many of the 

Phoenix PMf samples, or the differential influences of environmental conditions on nitrate 

volatilization. For PMf nitrate greater than about 2.5 μg/m3, the mean Teflon filter nitrate loss is 

0.7  0.3 μg/m3(N = 12) at St. Louis, and 1.5  1.3 μg/m3(N = 6) at Phoenix,8

 reflecting greater 

and more variable absolute loss of nitrate from the Teflon filter at Phoenix compared to St. Louis 

for high nitrate concentrations. 

Figure 5-1. Scatter plots for dichotomous sampler nitrate showing (a) the partitioning of 

nitrate between the fine and coarse PM fractions and (b) total fine nitrate and the nitrate 

on the front (Teflon) filter. Phoenix is represented by gray triangles and St. Louis by open 

circles. 

5.4 Ammonium Balance and Implications for Ammonium 

Measurements 

To understand whether sulfate (SO4) and nitrate (NO3) are associated with ammonium 

(NH4), or whether they are associated with other cations, such as sodium, it is useful to compare 

measured ammonium to the amount of ammonium expected based on sulfate and nitrate 

concentrations. 

Figure 5-2 shows measured versus predicted ammonium for Teflon and backup nylon 

dichot and FRM filters at each site for fine and coarse aerosol. Full neutralization of sulfate and 

nitrate was assumed for predicted ammonium: 

Predicted ammonium based on NH4NO3 and (NH4

)2SO4 = 0.29(NO3

−) + 0.38(SO4

=)

 

8

 Excluding the Phoenix value at (5.1, 1.3) in Figure 5-1b, for PMf nitrate greater than 2.5 μg/m3, the mean Teflon filter 

nitrate loss is 1.1  0.7 μg/m3 (N = 5). 
EPA’s Coarse PM Pilot Study Analysis of Ions 

5-4

For PMf on the Teflon filter, whether dichot or FRM, at both sites, the predicted and 

measured ammonium concentrations are highly correlated with quantitative agreement in 

St. Louis and overprediction of the measured ammonium in Phoenix. The latter overprediction 

may indicate that the sulfate aerosol is not fully neutralized by ammonium, and so ammonium is 

not the sole counter-ion for sulfate and nitrate. For PMc on the Teflon filter, ammonium is grossly 

overpredicted, again indicating that the sulfate and/or nitrate are associated with nonammonium cations. At both sites, the PMc ammonium concentrations are low (only one value 

greater than 0.1 μg/m3). 

For PMf on the nylon filter, predicted and measured ammonium are highly correlated; in 

this case, there is quantitative agreement at Phoenix and overprediction for St. Louis. The 

reason for this overprediction is not known. For PMc on the nylon filter, ammonium is 

underpredicted for the FRM in St. Louis and overpredicted for all other samplers at both sites. At 

both sites, the PMc ammonium concentrations are low. 

The low PMc ammonium concentrations at both sites on the Teflon and nylon filter 

indicate that PMc ammonium measurements may not be warranted. 
EPA’s Coarse PM Pilot Study Analysis of Ions 

5-5

Figure 5-2. Measured ammonium versus predicted ammonium scatter plots at Phoenix 

(a–d) and St. Louis (e–h) on Teflon and nylon. Coarse particles are represented by blue 

diamonds and fine particles by red squares. Diagonal lines show the 1:1 relationship. 

(Figure continued on next page.) 
EPA’s Coarse PM Pilot Study Analysis of Ions 

5-6

Figure 5-2 (continued). Measured ammonium versus predicted ammonium scatter plots 

at Phoenix (a–d) and St. Louis (e–h) on Teflon and nylon. Coarse particles are 

represented by blue diamonds and fine particles by red squares. Diagonal lines show the 

1:1 relationship. 
EPA’s Coarse PM Pilot Study Analysis of Ions 

5-7

5.5 Comparison of Concentrations With and Without Denuders 

To test the influence of using a denuder on ammonium and nitrate concentrations, on a 

subset of days, one dichot was run with a denuder and the other dichot was run without a 

denuder. Multi-channel annular denuders coated with magnesium oxide were placed in the 

sampler down tubes. The collocated sampler data were used to examine whether nitric acid is 

adsorbing onto the Teflon filter and then being quantified as aerosol nitrate, since a denuder 

strips out gaseous nitric acid. As shown in Figure 5-3, concentrations for both fine and coarse 

mode ammonium and nitrate were essentially no different whether or not a denuder was used. 

For comparison, sulfate and crustal species also showed no difference between measurements 

with and without a denuder, as expected. Thus for dichot measurements at locations similar to 

Phoenix and St. Louis, a denuder is likely not needed. If some other sites are deemed to 

warrant denuders based on a denuder/no denuder comparison study, the good quantitative 

agreement for PMc crustal species demonstrates that the denuder used in this study can be 

used with negligible particle losses. 
EPA’s Coarse PM Pilot Study Analysis of Ions 

5-8

(a) ammonium (b) nitrate

(c) sulfate (d) crustal species (Ca+Fe+Si)

Figure 5-3. Collocated dichot (dichot with denuder versus dichot without denuder) scatter 

plots for (a) NH4, (b) NO3, (c) SO4, and (d) Ca, Fe, and Si at Phoenix. Coarse particles 

are represented by blue diamonds and fine particles by red squares. Diagonal lines show 

the 1:1 relationship. 
EPA’s Coarse PM Pilot Study Analysis of Ions 

5-9

5.6 Comparison of Ion and Corresponding Element Concentrations 

Figures 5-4 and 5-5 display the correlation between total elemental concentration 

(analyzed by XRF) and ion concentration (analyzed by IC) for certain elements measured on 

Teflon filters from Dichot A and B and the FRM samplers. If an ion is well-correlated with the 

corresponding element, then the element could feasibly be used to estimate ion concentrations 

without having to conduct IC analysis for ions. At both sites, PMf potassium was primarily ionic, 

while PMc potassium was primarily nonionic. Sodium was primarily ionic for both size fractions 

and both sites. Sulfate ion (reported as sulfur in the figures) accounted for most of the sulfur at 

both sites for both coarse and fine fractions. Thus, based on the data from these two sites, the 

added value from the measurement of ions is the ability to measure nitrate and to resolve PMc

potassium ion compared to elemental potassium. Given the limited added value, at least for 

sites with low PMc nitrate concentrations, and the superior detection limits for XRF compared to 

IC for most of the reported ions, the added cost for ion analysis is not justified except possibly in 

cases with high PMc nitrate. 
EPA’s Coarse PM Pilot Study Analysis of Ions 

5-10

Figure 5-4. Comparison of ion and element concentrations (μg/m3) at St. Louis for K and 

K+, Na and Na+, and S and SO4

2- via dichot and FRM samplers for PMc and PMf. 
EPA’s Coarse PM Pilot Study Analysis of Ions 

5-11

Figure 5-5. Comparison of ion and element concentrations (μg/m3) at Phoenix for K and 

K+, Na and Na+, and S and SO4

2- via dichot and FRM samplers for PMc and PMf.

EPA’s Coarse PM Pilot Study Mass Balance 

6-1

6. Mass Balance 

Mass balance closure reflects the ability to reconstruct the observed Teflon filter 

gravimetric mass from the chemical species data. It is an uncertain process because not all 

species are quantified (e.g., retained water), and some of the chemical species data are derived 

from parallel samples that have different measurement artifacts (e.g., carbon data from a quartz 

filter). The objective of this analysis is to account for the mass on the Teflon filter rather than to 

make a best estimate of the species concentrations in the atmosphere. Thus, the data were not 

corrected for nitrate volatilization measured as the nitrate taken up by the nylon backup filter. 

The approach also assumes that the fraction of fine-particle nitrate loss from the dichot major 

flow Teflon filter is the same as the fraction of fine-particle nitrate loss from the dichot minor flow 

Teflon filter. 

The organic carbon data have not corrected for OC artifacts, and a sensitivity study 

showed that including an estimate of the artifact correction did not change the key findings. To 

account for other elements in the organic aerosol, OM was assumed to be OC multiplied by 1.6 

(i.e., [OM] = [OC]  1.6). An OC multiplier of 1.6 was chosen because it is the midpoint of the 

commonly used range of 1.4 to 1.8, but, as discussed in the next section, sensitivity studies 

were performed using various OC multipliers. Other species included in the reconstructed mass 

include sulfate ion, nitrate ion, major inorganic cations (ammonium [NH4+], sodium [Na+], and 

potassium [K+]), Cl (as measured by XRF on the Teflon filter), EC, soil oxides (estimated as 

2.20[Al] + 2.49[Si] + 1.63[Ca] + 2.42[Fe] + 1.94[Ti]),9

 and other metals.10 The species group 

“other metals” was included to initially generate a reconstructed mass estimate that would be 

conservatively high. It was deemed inconsequential to the mass balance reconstruction and, 

given that such metals are factored to some extent into the above soil equation, this species 

group could have been omitted from the mass reconstruction. As described in Section 3, RTI 

applied XRF attenuation factors to the low atomic number elements (Z  20). 

6.1 Results for Dichotomous Samplers 

Figure 6-1 shows the species distributions and mass balance closure for each sample. 

Soil oxides dominate PMc, while organic matter accounts for 10% to 20% of the PMc mass. 

Figure 6-2 compares the reconstructed mass and gravimetric mass for PMf and PMc with 

statistical metrics summarized in Table 6-1. Ordinary least squares (OLS) regression, which 

assumes there is no uncertainty in the x-axis values, was used for this analysis. This should be 

a robust approach because the uncertainty in gravimetric mass is much smaller than the 

uncertainty in the reconstructed mass. 

 

9 This soil-oxides estimate is the standard formula applied to IMPROVE network data 

(http://vista.cira.colostate.edu/improve/tools/aertypeeqs.htm).

10 Other metals=(silver [Ag] + arsenic [As] + barium [Ba] + bromine [Br] + cadmium [Cd] + cerium [Ce] + cobalt [Co] + 

chromium [Cr] + cesium [Cs] + copper [Cu] + indium [In] + magnesium [Mg] + manganese [Mn] + nickel [Ni] + 

phosphorus [P] + lead [Pb] + rubidium [Rb] + antimony [Sb] + selenium [Se] + tin [Sn] + strontium [Sr] + vanadium [V] 

+ zinc [Zn] + zirconium [Zr]). 
6-2

Figure 6-1. Mass balance closure between Teflon filter gravimetric mass and the chemical speciation data for dichot samples. All 

species were measured on the Teflon filter except EC and OC, which were measured on the quartz filter from a collocated dichot 

sampler. Organic matter was estimated using OM = 1.6 × OC, and the OC was not corrected for measurement artifacts. 
EPA’s Coarse PM Pilot Study Mass Balance 

6-3 

Figure 6-2. Reconstructed mass versus gravimetric mass for the dichotomous sampler 

Teflon filters: (a) PMf and (b) PMc for Phoenix and St. Louis. Organic matter was 

estimated using OM = 1.6 × OC, and the OC data were not corrected for measurement 

artifacts. 

Table 6-1. Measures of agreement between reconstructed and gravimetric mass for 

Phoenix and St. Louis, using OM = 1.6 × OC and no correction for OC artifacts. 

Site and Size N OLS Regression, μg/m3 (a)r

2 Ratio of 

Means(b)

PHX PMf 37 [recon] = 0.92 [grav] + 1.6 0.96 1.14

PHX PMc 37 [recon] = 1.18 [grav] – 1.0 0.94 1.13

STL PMf 21 [recon] = 0.90 [grav] + 0.5 0.92 0.94

STL PMc 21 [recon] = 0.87 [grav] + 1.3 0.92 1.01

a OLS regression of reconstructed mass on gravimetric mass.

b The PMc ratio of means is 1.09 for PHX PMc and 0.96 for STL PMc when applying a multiplier of 0.82 to 

the OM to correct for the presumed carbon losses from the Teflon filter. This multiplier is the dichot-toFRM mean ratio for gravimetric mass (Table 2-1). The PMc ratios of means further decrease to 1.07 

and 0.93, respectively, when also neglecting K+ and other metals, assuming these species are 

present as crustal material and are included in the standard formula for soil oxides.

Several factors that may influence the quality of mass balance closure include, but are 

not limited to, the assumed OM/OC ratio, OC measurement artifact corrections, the assumed 

oxide forms of the crustal elements, and XRF attenuation factors applied to light elements. As 

detailed in Section 3.5, the latter is particularly relevant for PMc. In this section, the attenuation 

factors developed and applied by RTI were used for all data. An alternative form for the soil 

oxides equation that excludes Al and increases the Si multiplier from 2.49 to 3.48 (Simon et al., 

2011) yields soil oxides contributions that are, on average, 5% higher for PMc.

PMf gravimetric mass, g/m3

0 5 10 15 20 25

PMf reconstructed mass, g/m

3

0

5

10

15

20

25

PHX

STL

PMc gravimetric mass, g/m3

0 5 10 15 20 25 30 35 40

PMc reconstructed mass, g/m

3

0

5

10

15

20

25

30

35

40

PHX

STL

(a) (b)
EPA’s Coarse PM Pilot Study Mass Balance 

6-4 

For Phoenix data using OM/OC = 1.6 and no OC artifact correction, the reconstructed 

mass consistently overestimates the gravimetric mass for both PMf and PMc, with an average 

difference of approximately 13% for both size ranges (9% for PMc after applying a correction for 

loss of carbonaceous matter from the Teflon filter as described in a footnote to Table 2-1). The 

OM/OC ratio that best closes mass balance (i.e., the minimum difference between 

reconstructed and gravimetric mass, which are shown as root-mean-square (RMS) residuals in 

Figure 6-3) is 1.2 to 1.3 for PMf. This ratio is consistent with the value of 1.25 reported by 

Simon et al. (2011) for Phoenix IMPROVE PM2.5 data. The best-fit OM/OC ratio for PMc is 0.6 

(off the scale range in Figure 6-3) with the same ratio obtained when applying a multiplier of 

0.82 to the carbon data to account for losses from the Teflon filter. The PMc ratio of 0.6 is 

implausibly low (OM/OC must be greater than or equal to unity), and in this case there must be 

other factors driving the observed bias. 

For St. Louis data using OM/OC = 1.6 and no OC artifact correction, the reconstructed 

mass underestimates the gravimetric mass by 6% for PMf, and the agreement is within 1% for 

PMc. The OM/OC ratio that yields the best agreement for PMf is 1.8; this ratio is consistent with 

the value of 1.81 reported by Bae et al. (2006) for PM2.5 data collected at the East St. Louis site. 

For PMc, shown as the open circles in Figure 6-3b, the best-fit OM/OC ratio is 1.4 to 1.6 with a 

ratio of 1.8 obtained when applying a multiplier of 0.82 to the carbon data (to account for losses 

from the Teflon filter). However, the RMS residual varies by only 0.3 μg/m3 over the range of 

OM/OC ratios between one and two. This flat response surface means the best-estimate 

OM/OC ratio of 1.4 to 1.6 for St. Louis PMc is highly uncertain. 

Figure 6-3. RMS residuals between reconstructed mass and gravimetric mass as a 

function of the assumed OM/OC ratio: (a) Phoenix and (b) St. Louis. 

OM/OC

1.0 1.2 1.4 1.6 1.8 2.0

root mean square residual, g/m

3

0

1

2

3

4

5

PHX - PMf

PHX - PMc

OM/OC

1.0 1.2 1.4 1.6 1.8 2.0

root mean square residual, g/m

3

0

1

2

3

4

5

STL - PMf

STL - PMc

(a) (b)
EPA’s Coarse PM Pilot Study Mass Balance 

6-5 

The sensitivity of mass balance closure to OC artifact correction was examined by 

assuming a negligible negative artifact and assuming the grand mean field blank mass loading. 

(Appendix D is a representative measure of the positive artifact for both the dichot major and 

minor flow channel samples). Artifact-corrected OC ambient mass concentrations for PMf and 

PMc were calculated after subtracting the filter- and trip-blank pooled mean OC loading of 

6.6 μg/filter from the major and minor flow channels. The best-fit OM/OC ratios were 1.4 for PMf

at Phoenix and 2.1 for St. Louis. For PMc, the best-fit OM/OC ratios were 0.5 at Phoenix and 1.6 

at St. Louis. Once again, the ratio for PMc at Phoenix is implausible and the response surface 

for the RMS residual at St. Louis is very flat, which means the ratio is highly uncertain. 

6.2 Mass Balance Implications 

Mass balance closure for PMc can be influenced by several factors, including but not 

limited to OC artifact correction, the assumed OM/OC ratio, the assumed soil oxides 

composition, and XRF attenuation factors used for light elements. If all else is equal, a 

correction applied for a negative OC artifact decreases the reconstructed mass and requires a 

higher OM/OC ratio to achieve closure compared with the case of no artifact correction. 

For PMc at St. Louis, plausible OM/OC ratios were estimated when including and 

excluding a correction for positive artifacts. However, the estimated OM/OC ratios are highly 

uncertain. 

For PMc at Phoenix, implausible OM/OC ratios were estimated for no OC artifact 

correction and also for when a correction was applied for positive artifacts using field blanks 

data. In both cases, the reconstructed mass is greater than the gravimetric mass for OM/OC 

ratios larger than approximately 0.5. This finding demonstrates that the reconstructed mass is 

being systematically overestimated, with possible explanations being bias in the XRF 

attenuation factors applied to Al, Si, and Ca; or bias in the multipliers used to account for the 

oxide forms of the crustal elements; or both. Mass balance closure alone cannot be used to 

obtain meaningful best-fit attenuation factors and oxides multipliers. Furthermore, the best-fit 

values depend on assumed OC artifact correction and OM/OC ratio. 

EPA’s Coarse PM Pilot Study Comparison of FDMS TEOM and Filter Coarse PM Measurements 

7-1 

7. Comparison of FDMS TEOM and 

Filter Coarse PM Measurements 

In addition to the dichot and FRM measurements, each site also had a Thermo 1405-DF 

dichotomous TEOM with FDMS®. The Thermo 1405-DF dichotomous sampler with FDMS is an 

approved FEM for PM2.5. The 1405-DF measures fine (PM2.5) and coarse (PM10-2.5) particulate 

matter mass concentrations at six-minute time resolution. Its deployment in this study provides 

insights into the climatology of coarse PM by placing the 24-hour integrated chemical speciation 

samples in a broader context with daily, high-time resolved, coverage of PM mass, and also by

providing the ability to examine diurnal behavior. 

The dichot TEOM includes a 16.7 LPM standard PM10 inlet followed by a virtual impactor 

that splits the sample stream into major (15.0 LPM) and minor (1.67 LPM) flows. A portion of the 

major flow stream and the entire minor flow stream pass through Nafion dryers; subsequently, 

the entrained particles are deposited onto TEOM filters for mass determination. 

The major flow includes only fine particles, whereas the minor flow includes all of the 

coarse particles and 10% of the fine particles; therefore, PM2.5 mass concentrations are 

calculated directly from the major flow, and PM10-2.5 mass concentrations are calculated by 

correcting the minor flow for fine-particle intrusion. In both cases, the size-segregated mass is 

further partitioned into volatile (reference) and nonvolatile (base) components by the dualchannel FDMS (Meyer et al., 2002). This partitioning is operationally defined by the 

measurement method. PM2.5 FDMS volatility component concentrations are the same as the 

major flow volatility component concentrations. It is often not appreciated that the instrument 

reports FDMS volatility component (base and reference) concentrations for the minor flow rather 

than for PM10-2.5. The base and reference flows contain 10% of the fine particles; therefore, the 

user must apply Equation 7-1 to calculate the PM10-2.5 volatile and nonvolatile mass 

concentrations from the instrument-reported data. 

minor

10 2.5 minor major

total

( ) ( ) ( ) QPM k PM k PM k

Q

  

 

(Eq. 7-1)

where PM is the mass concentration, Q is the volumetric flow rate, and k is the FDMS volatility 

component, i.e., volatile (reference) or nonvolatile (base). 

Previous work has linked the volatile component of fine PM, as measured by the FDMS 

TEOM, to semivolatile particulate matter, including ammonium nitrate and semivolatile organic 

compounds (Grover et al., 2005; Faveza et al., 2007). The project team’s immediate objective, 

as described below, was to use the dichot TEOM data to examine the temporal behavior of the 

volatile and nonvolatile components of both fine and coarse PM at the two contrasting sites. 

Dichot TEOMs were deployed at the Phoenix and St. Louis sites, with data reporting 

starting in July 2010 and September 2010, respectively. Data were collected on six-minute 

cycles and converted to hourly averages. Raw (unvalidated) hourly fine and coarse PM mass 

concentrations were automatically uploaded to AirNow-Tech (www.airnowtech.org). Data for 

mass and FDMS volatility components were periodically provided by the Maricopa County Air 
EPA’s Coarse PM Pilot Study Comparison of FDMS TEOM and Filter Coarse PM Measurements 

7-2 

Quality Department (for Phoenix) and the Air Quality Laboratory at Washington University (for 

St. Louis) to the data analysis team for further validation and interpretation. 

The data were censored to remove records with unstable base and reference readings 

(e.g., large fluctuations within the hour). In the case of Phoenix, this resulted in the removal of 

18 days (about 10% of the total data set), including the six days for which the highest 

concentrations were reported. The removal of these data does not significantly affect the 

analyses presented in this report, which focuses on central tendencies of the data sets. 

Non-volatile/volatile data at Phoenix after February 2011 were unstable and showed 

unusually high noise, and so are not included in analyses here; data at St. Louis after 

replacement of a Nafion dryer in mid-March exhibited noise as well, and are not included here. 

7.1 Results 

Figure 7-1 compares daily-average dichot TEOM total and nonvolatile mass 

concentrations to 24-hour integrated gravimetric mass concentrations using the sequential 

dichot sampler (St. Louis) and paired PM2.5 and PM10 FRM samplers (Phoenix) with Teflon 

filters. The Phoenix dichot TEOM data are compared to the paired FRM samplers, rather than 

the dichot sampler, because the latter suffered from significant particle losses at Phoenix. For 

PM2.5 at St. Louis (Figure 7-1a), the filter-based gravimetric mass concentrations are bounded 

by the TEOM-measured total and nonvolatile mass concentrations. This pattern is consistent 

with the Teflon filter retaining only a portion of the volatile ambient particulate matter due to 

partial loss of species such as ammonium nitrate and semivolatile organic matter. For PM10-2.5 at 

St. Louis (Figure 7-1b), there is also very good agreement between the dichot TEOM and Teflon 

filter data, especially at low concentrations. The volatile component of PM10-2.5 is relatively small. 

For PM2.5 (Figure 7-1c), there is good agreement at low concentrations, with the Teflon filter 

mass closely tracking the dichot TEOM nonvolatile mass. The FRM filter sampler retains 

virtually none of the volatile mass, with the nonvolatile mass in nearly quantitative agreement 

with the Teflon filter gravimetric mass data. At high concentrations, the dichot TEOM PM2.5

mass is biased high compared to the FRM PM2.5 mass. This bias is also observed at all 

concentrations for the PM10-2.5 data (Figure 7-1d). For all samples, the Phoenix PM10-2.5 is 

essentially nonvolatile. 

The minor flow volatile mass was typically 1 μg/m3 or smaller. PM10-2.5 volatile mass 

calculated using Equation 7-1 was about 50% of the minor flow volatile mass and the 

propagation of uncertainties through Equation 7-1 suggests that the PM10-2.5 volatile mass 

concentration for such cases is highly uncertain. PM chemical composition data are available for 

many of these days, but the dichot TEOM PM10-2.5 volatile component mass concentrations are 

too small and too uncertain to support meaningful comparisons to the speciation data. The 

remainder of this report focuses on time series analysis of the data collected to date. The 

project team’s primary interest is the PM10-2.5 trends, but PM2.5 trends are also shown for 

comparison. 
EPA’s Coarse PM Pilot Study Comparison of FDMS TEOM and Filter Coarse PM Measurements 

7-3 

Figure 7-1. East St. Louis (left) and Phoenix (right) 24-hour average dichot TEOM total 

(red markers) and nonvolatile (black markers) PM mass concentrations versus the 24-hour 

integrated PM mass concentrations from filter-based samplers with gravimetric analysis on 

the Teflon filters: fine PM (top) and coarse PM (bottom). Lines connecting markers 

indicate corresponding data pairs. The diagonal line is the 1:1 relationship. 

Figure 7-2 shows the time series for daily-average PM10 mass concentrations, stratified 

by fine and coarse PM contributions and presented as “stacked” graphs, at St. Louis 

(Figure 7-2a) and Phoenix (Figure 7-2b). At St. Louis, the fine PM mass is typically greater than 

the coarse PM mass (median PM10-2.5/PM2.5 ratio = 0.67), but there are periods with high coarse 

PM concentrations, such as mid-October. In contrast, at Phoenix, the coarse PM mass is much 

greater than the fine PM mass (median PM10-2.5/PM2.5 ratio = 2.8), but there are periods with 

relatively high fine particle mass, such as late January and early February. Overall, PM10

concentrations are much higher at Phoenix compared to St. Louis (note the twofold difference in 

the concentration scale ranges). 
EPA’s Coarse PM Pilot Study Comparison of FDMS TEOM and Filter Coarse PM Measurements 

7-4 

Figure 7-2. Daily-average dichot TEOM time series for fine PM (yellow) and coarse PM 

(black) at (a) East St. Louis, 9/2010-2/2011; and (b) Phoenix, 7/2010-2/2011. Note the 

concentration scale range differences (60 g/m3 for STL, 120 g/m3 for PHX). Asterisks 

below the x-axis in the PHX plot denote periods with three or more successive days of 

data removed. 

Figure 7-3 shows the fine PM and coarse PM mass concentrations further stratified by 

FDMS volatility components. PM2.5 volatile mass is appreciable at both St. Louis and Phoenix 

(Figure 7-3a,c), with median PM2.5 volatile/total mass ratios of 0.30 and 0.21, respectively. In 

contrast, the PM10-2.5 volatile mass at both sites (Figure 7-3b,d) is very small, with median PM2.5

volatile/total mass ratios of 0.05 at St. Louis and 0.01 at Phoenix. The PM10-2.5 mass 

concentrations for Phoenix are twice as high as those for St. Louis, as noted by the twofold 

scale range for Phoenix compared to St. Louis. 
EPA’s Coarse PM Pilot Study Comparison of FDMS TEOM and Filter Coarse PM Measurements 

7-5 

Figure 7-3. Daily-average dichot TEOM time series for fine PM (top) and coarse PM 

(bottom) at East St. Louis, 9/2010-2/2011 (left), and Phoenix, 7/2010-2/2011 (right). Daily 

average mass concentrations are partitioned into FDMS volatile (red) and nonvolatile 

(black) components. Note the concentration scale range is twice as high for Phoenix 

PM10-2.5 (panel d). Asterisks below the x-axis in the Phoenix plots denote periods with 

three or more successive days of data removed.

Figure 7-4 shows diurnal profiles for PM2.5 total (Figure 7-4a), nonvolatile (Figure 7-4b), 

and volatile (Figure 7-4c) mass at St. Louis and Phoenix. At St. Louis, there is a shallow midday 

minimum in both the total and nonvolatile mass that is consistent with dilution by daytime growth 

in the mixing layer depth. In contrast to the nonvolatile component, the volatile component 

exhibits a midday maximum. The data collected includes relatively low nitrate, so the volatile 

component is likely semivolatile organic aerosol. The midday maximum in the diurnal profile is 

consistent with the production of semivolatile organic aerosol by secondary processes in the 

atmosphere (Faveza et al., 2007). At Phoenix, total and nonvolatile mass increases in the early 

morning before rush hour, reaching a maximum at 0800 to 0900 local standard time (LST), 

followed by a deep midday minimum. Like St. Louis, the volatile component at Phoenix exhibits 

a midday maximum consistent with the secondary production of semivolatile organic aerosol. 
EPA’s Coarse PM Pilot Study Comparison of FDMS TEOM and Filter Coarse PM Measurements 

7-6 

Figure 7-4. PM2.5 diurnal profiles for East St. Louis, 9/2010 to 2/2011 (left) and Phoenix, 

7/2010 to 2/2011 (right): (a) total TEOM mass; (b) nonvolatile TEOM mass; and (c) 

volatile TEOM mass. Whiskers are 10th and 90th percentiles, boxes are 25th and 75th

percentiles, and the interior black and red lines are the median and arithmetic mean, 

respectively. 

Figure 7-5 shows diurnal profiles for PM10-2.5 total mass (Figure 7-5a) and volatile mass 

(Figure 7-5b) at St. Louis and Phoenix. Because of the extremely low concentrations, the 

volatile component concentrations have high uncertainty and therefore are not interpreted. 

Because of the very low volatile mass in the coarse aerosol (Figure 7-5b), nonvolatile 

mass concentrations closely track the total mass; therefore, profiles are not shown for the 

nonvolatile mass concentrations. The PM10-2.5 total mass diurnal profile at St. Louis exhibits a 

broad midday maximum, which is in contrast to the broad midday minimum exhibited for PM2.5

total mass. The Phoenix PM10-2.5 total mass diurnal profile closely tracks the PM2.5 profile with an 

early morning maximum and a deep midday minimum. 
EPA’s Coarse PM Pilot Study Comparison of FDMS TEOM and Filter Coarse PM Measurements 

7-7 

Figure 7-5. PM10-2.5 diurnal profiles for East St. Louis, 9/2010 to 2/2011 (left) and 

Phoenix, 7/2010 to 2/2011 (right): (a) total TEOM mass and (b) volatile TEOM mass. 

Whiskers are 10th and 90th percentiles, boxes are 25th and 75th percentiles, and the 

interior black and red lines are the median and arithmetic mean, respectively. 

To further examine these patterns, Figure 7-6 shows PM10-2.5 total mass diurnal profiles 

for weekdays (Figure 7-6a,b) and weekends (Figure 7-6c,d). For St. Louis, the broad midday 

maximum is observed on weekdays but not weekends. This suggests that the dominant driver 

for the elevated midday PM10-2.5 concentrations at St. Louis is related to anthropogenic activities 

rather than windblown dust. For Phoenix, the early morning maximum is observed on weekdays 

but not weekends. This suggests that the elevated early morning PM10-2.5 concentrations at 

Phoenix also arise from anthropogenic activities rather than windblown dust. 

To further demonstrate the weekday excess PM10-2.5 at both sites, Figure 7-7 shows 

diurnal profiles for the difference between the median weekday mass and median weekend 

mass. The St. Louis weekday excess mass (green bars) is about 5 μg/m3 from approximately 

0700 to approximately 1900 LST. The weekday excess mass at Phoenix (red bars) is more than 

5 μg/m3 at 0500 LST, reaches a maximum of approximately 25 μg/m3 at 0700 LST, and decays 

throughout the remainder of the morning hours. The weekday excess is about 4 μg/m3 during 

the afternoon, and there is a local maximum at 1900 LST. Weekday versus weekday chemical 

speciation data should be examined for further insights into the origins of the weekday excess. 
EPA’s Coarse PM Pilot Study Comparison of FDMS TEOM and Filter Coarse PM Measurements 

7-8 

Figure 7-6. PM10-2.5 weekday (top) and weekend (bottom) diurnal profiles for East St. 

Louis, 9/2010 to 2/2011 (left) and Phoenix, 7/2010 to 2/2011 (right). Whiskers are 10th

and 90th percentiles, boxes are 25th and 75th percentiles, and the interior black and red 

lines are the median and arithmetic mean, respectively. 

Figure 7-7. Diurnal profiles for the difference in weekday and weekend hourly median 

PM10-2.5 concentrations for East St. Louis, 9/2010 to 2/2011 (green) and Phoenix, 7/2010 

to 2/2011 (red). 
EPA’s Coarse PM Pilot Study Comparison of FDMS TEOM and Filter Coarse PM Measurements 

7-9 

7.2 FDMS TEOM-to-Dichot Summary 

For the seven to eight months of dichot TEOM data collected in this study, the 

FDMS-defined volatile component of PM2.5 at both St. Louis and Phoenix exhibits a midday 

maximum consistent with the production of semivolatile organic aerosol by secondary 

processes. The governing equation is presented in Equation 7-1 for calculating the FDMSdefined PM10-2.5 volatility components from the minor and major flow data. At both sites, the 

PM10-2.5 volatile component was relatively small. PM10-2.5 total and nonvolatile mass is elevated 

on weekdays compared to weekends at both sites, albeit with different diurnal patterns for the 

weekday excess mass. Anthropogenic activities are the likely driver for this excess; more work 

exploring speciation data is needed to determine the precise nature of the contributing activities. 

 

EPA’s Coarse PM Pilot Study References

8-1 

8. References 

Ashbaugh L.L. and Eldred R.A. (2004) Loss of particle nitrate from Teflon sampling filters: 

Effects on measured gravimetric mass in California and in the IMPROVE network. J. Air 

Waste Manage., 54(1), 93-104, Jan. 

Bae M.S., Schauer J.J., and Turner J.R. (2006) Estimation of the monthly average ratios of 

organic mass to organic carbon for fine particulate matter at an urban site. Aerosol 

Science & Technology, 40, 1123-1139, September. 

Chow J.C. and Watson J.G. (2002) PM2.5 carbonate concentrations at regionally representative 

Interagency Monitoring of Protected Visual Environment sites. Journal of Geophysical 

Research-Atmospheres, 107(D21), Sep-Oct. 

Dzubay T.G. and Stevens R.K. (1975) Ambient air analysis with dichotomous sampler and X-ray 

fluorescence spectrometer. Environ. Sci. Technol., 9(7), July. Available at 

http://pubs.acs.org/doi/abs/10.1021/es60105a011. 

Faveza O., Cachiera H., Sciarea J., and Moullec Y.L. (2007) Characterization and contribution 

to PM2.5 of semi-volatile aerosols in Paris (France). Atmos. Environ., 41(36), 7969-7976. 

Available at http://www.sciencedirect.com/science/article/pii/S1352231007008126. 

Grover B.D., Kleinman M., Eatough N.L., Eatough D.J., Hopke P.K., Long R.W., Wilson W.E., 

Meyer M.B., and Ambs J.L. (2005) Measurement of total PM2.5 mass (nonvolatile plus 

semivolatile) with the Filter Dynamic Measurement System tapered element oscillating 

microbalance monitor. J. Geophys. Res., 110(D07S03), doi: 10.1029/2004JD004995. 

Available at http://www.agu.org/pubs/crossref/2005/2004JD004995.shtml. 

Gutknecht W., Flanagan J., McWilliams A., Jayanty R.K., Kellogg R., Rice J., Duda P., and 

Sarver R.H. (2010) Harmonization of uncertainties of X-ray fluorescence data for PM2.5

air filter analysis. J. Air Waste Manage., 60(2), 184-194, (PubMed PMID: 20222531). 

Available at http://www.ncbi.nlm.nih.gov/pubmed/20222531. 

Hyslop N.P. and White W.H. (2011) Identifying sources of uncertainty from the inter-species 

covariance of measurement errors. Environ. Sci. Technol., 45(9), 4030-4037, doi: 

10.1021/es102605x. Available at http://pubs.acs.org/doi/abs/10.1021/es102605x. 

Lee T., Yu X-Y, Ayres B., Kreidenweis S.M., Malm W.C., and Collet J.L. (2008) Observations of 

fine and coarse particle nitrate at several rural locations in the United States. Atmos. 

Environ., 42, 2720-2732. 

Meyer M.B., Patashnick H., and Ambs J.L. (2002) Ongoing development of a continuous 

reference standard particulate matter mass monitor for ambient air. In Proceedings from 

the Symposium on Air Quality Measurement Methods and Technology; A&WMA: 

Pittsburgh, PA. Paper No. 53. 

Simon H., Bhave P.V., Swall J.L., Frank N.H., and Malm W.C. (2011) Determining the spatial 

and seasonal variability in OM/OC ratios across the US using multiple regression. 

Atmospheric Chemistry and Physics, 11, 2933-2949, doi: 10.5194/acp-11-2933-2011. 

Available at http://www.atmos-chem-phys.net/11/2933/2011/acp-11-2933-2011.html. 

Smith R.J. (2009) Use and misuse of the reduced major axis for line-fitting. American Journal of 

Physical Anthropology, 140, 476-486. 
EPA’s Coarse PM Pilot Study References

8-2 

Tager I.B., Lurmann F.W., Haight T., Alcorn S., Penfold B., and Hammond S.K. (2010) 

Temporal and spatial patterns of ambient endotoxin concentrations in Fresno, California. 

Environ. Health Persp., 118(10), 1490–1496, doi: 10.1289/ehp.0901602. 

U.S. EPA (2001) Evaluation of PM2.5 chemical speciation samplers for use in the EPA national 

PM2.5 chemical speciation network. EPA-454/R-01-005, Office of Air Quality Planning 

and Standards, Research Triangle Park, NC 27711. 

U.S.EPA (2010) PM10-2.5 speciation pilot monitoring program, sample analysis, and data 

reporting: Quality Assurance Project Plan (QAPP). U.S. Environmental Protection 

Agency, March 2010. Available at http://epa.gov/ttn/amtic/pm10pilot.html. 

U.S. EPA (2011) Field evaluation of the PM2.5 federal reference method and development of a 

reference method method to measure and chemically speciate ambient coarse-mode 

aerosols. EPA/600/R-11/099, Office of Research and Development, National Exposure 

Research Laboratory, Reseach Triangle Park, NC 27711.
EPA’s Coarse PM Pilot Study Appendix A

A-1 

Appendix A: Summary Statistics 

Table A-1. Summary of concentrations and MDLs by species and size fraction (PMf or 

PMc) at St. Louis for primary dichot, on Teflon and quartz filters; all species except OC 

and EC are from Teflon filter measurements. Two species, K and Na, were analyzed via 

XRF and IC; the latter analysis results are indicated by “_IC.”

Species Size

Avg

Conc 

(μg/m3)

Avg

MDL

(μg/m3)

N

N >

MDL

N >

3MDL

% >

MDL

% >

3MD

L

Avg 

Uncertainty

(μg/m3)

Ag_XRF PMc 0.0001 0.0145 31 0 0 0 0 N/A

Al_XRF PMc 0.2779 0.0098 31 30 29 97 94 0.0397

As_XRF PMc 0.0001 0.0010 31 0 0 0 0 N/A

Ba_XRF PMc 0.0142 0.0033 31 28 18 90 58 0.0025

Br_XRF PMc 0.0011 0.0009 31 12 3 39 10 0.0006

Ca_XRF PMc 1.1732 0.0017 31 31 31 100 100 0.1024

Cd_XRF PMc 0.0003 0.0089 31 0 0 0 0 N/A

Ce_XRF PMc 0.0000 0.0023 31 0 0 0 0 N/A

Cl_XRF PMc 0.2613 0.0019 31 31 31 100 100 0.0208

Co_XRF PMc 0.0008 0.0006 31 20 2 65 6 0.0005

Cr_XRF PMc 0.0017 0.0009 31 19 5 61 16 0.0005

Cs_XRF PMc 0.0004 0.0050 31 2 0 6 0 0.0029

Cu_XRF PMc 0.0050 0.0007 31 31 26 100 84 0.0006

Fe_XRF PMc 0.3109 0.0007 31 31 31 100 100 0.0227

In_XRF PMc 0.0004 0.0129 31 0 0 0 0 N/A

K_IC PMc 0.0376 0.0058 36 33 30 92 83 0.0040

K_XRF PMc 0.0921 0.0016 31 31 31 100 100 0.0082

Mass_Grav PMc 9.4453 0.3022 36 36 36 100 100 0.5661

Mg_XRF PMc 0.0740 0.0039 31 30 29 97 94 0.0139

Mn_XRF PMc 0.0071 0.0007 31 30 28 97 90 0.0007

Na_IC PMc 0.1974 0.0117 36 35 26 97 72 0.0337

Na_XRF PMc 0.2935 0.0113 31 29 24 94 77 0.0632

NH4_IC PMc 0.0083 0.0069 36 22 10 61 28 0.0250

Ni_XRF PMc 0.0003 0.0005 31 6 0 19 0 0.0002

NO3_IC PMc 0.4334 0.0028 36 36 36 100 100 0.0484

P_XRF PMc 0.0342 0.0037 31 22 22 71 71 0.0070

Pb_XRF PMc 0.0060 0.0018 31 15 5 48 16 0.0018

Rb_XRF PMc 0.0002 0.0010 31 1 0 3 0 0.0004

S_XRF PMc 0.1094 0.0026 31 31 31 100 100 0.0243

Sb_XRF PMc 0.0020 0.0206 31 1 0 3 0 0.0189

Se_XRF PMc 0.0001 0.0010 31 0 0 0 0 N/A
EPA’s Coarse PM Pilot Study Appendix A

A-2 

Species Size

Avg

Conc 

(μg/m3)

Avg

MDL

(μg/m3)

N

N >

MDL

N >

3MDL

% >

MDL

% >

3MD

L

Avg 

Uncertainty

(μg/m3)

Si_XRF PMc 0.8686 0.0048 31 31 31 100 100 0.1180

Sn_XRF PMc 0.0016 0.0146 31 0 0 0 0 N/A

SO4_IC PMc 0.1959 0.0038 36 36 36 100 100 0.0590

Sr_XRF PMc 0.0024 0.0014 31 21 4 68 13 0.0007

Ti_XRF PMc 0.0118 0.0017 31 28 23 90 74 0.0016

V_XRF PMc 0.0004 0.0012 31 4 0 13 0 0.0008

Zn_XRF PMc 0.0663 0.0009 31 31 28 100 90 0.0051

Zr_XRF PMc 0.0022 0.0087 31 3 0 10 0 0.0048

Ag_XRF PMf 0.0002 0.0162 31 0 0 0 0 N/A

Al_XRF PMf 0.0406 0.0108 31 25 12 81 39 0.0073

As_XRF PMf 0.0005 0.0011 31 6 1 19 3 0.0009

Ba_XRF PMf 0.0029 0.0036 31 7 1 23 3 0.0024

Br_XRF PMf 0.0044 0.0010 31 30 19 97 61 0.0007

Ca_XRF PMf 0.0788 0.0019 31 31 31 100 100 0.0057

Cd_XRF PMf 0.0002 0.0099 31 0 0 0 0 N/A

Ce_XRF PMf 0.0000 0.0025 31 0 0 0 0 N/A

Cl_XRF PMf 0.0232 0.0021 31 31 27 100 87 0.0022

Co_XRF PMf 0.0004 0.0006 31 6 0 19 0 0.0004

Cr_XRF PMf 0.0010 0.0010 31 9 2 29 6 0.0005

Cs_XRF PMf 0.0007 0.0045 31 2 0 6 0 0.0028

Cu_XRF PMf 0.0046 0.0008 31 27 20 87 65 0.0006

Fe_XRF PMf 0.0831 0.0008 31 31 31 100 100 0.0060

In_XRF PMf 0.0007 0.0144 31 0 0 0 0 N/A

K_IC PMf 0.0577 0.0065 36 36 35 100 97 0.0045

K_XRF PMf 0.0626 0.0018 31 31 31 100 100 0.0045

Mass_Grav PMf 10.7419 0.3342 36 36 36 100 100 0.5517

Mg_XRF PMf 0.0068 0.0044 31 17 5 55 16 0.0025

Mn_XRF PMf 0.0025 0.0008 31 26 12 84 39 0.0004

Na_IC PMf 0.0505 0.0129 36 33 17 92 47 0.0252

Na_XRF PMf 0.0593 0.0119 31 30 17 97 55 0.0088

NH4_IC PMf 1.2044 0.0076 36 36 36 100 100 0.0853

Ni_XRF PMf 0.0002 0.0006 31 2 0 6 0 0.0003

NO3_IC PMf 1.0070 0.0031 36 36 36 100 100 0.0720

P_XRF PMf 0.0001 0.0040 31 0 0 0 0 N/A

Pb_XRF PMf 0.0070 0.0020 31 22 7 71 23 0.0016

Rb_XRF PMf 0.0001 0.0012 31 0 0 0 0 N/A
EPA’s Coarse PM Pilot Study Appendix A

A-3 

Species Size

Avg

Conc 

(μg/m3)

Avg

MDL

(μg/m3)

N

N >

MDL

N >

3MDL

% >

MDL

% >

3MD

L

Avg 

Uncertainty

(μg/m3)

S_XRF PMf 0.8575 0.0028 31 31 31 100 100 0.0608

Sb_XRF PMf 0.0031 0.0228 31 0 0 0 0 N/A

Se_XRF PMf 0.0006 0.0011 31 5 0 16 0 0.0004

Si_XRF PMf 0.1005 0.0054 31 31 29 100 94 0.0092

Sn_XRF PMf 0.0008 0.0162 31 0 0 0 0 N/A

SO4_IC PMf 2.4379 0.0042 36 36 36 100 100 0.1734

Sr_XRF PMf 0.0004 0.0016 31 0 0 0 0 N/A

Ti_XRF PMf 0.0026 0.0019 31 12 3 39 10 0.0011

V_XRF PMf 0.0009 0.0013 31 9 1 29 3 0.0006

Zn_XRF PMf 0.0308 0.0010 31 31 31 100 100 0.0023

Zr_XRF PMf 0.0004 0.0097 31 0 0 0 0 N/A

EC_TOT PMc 0.3576 0.0640 41 29 25 71 61 N/A

OC_TOT PMc 2.0415 0.0640 41 40 40 98 98 N/A

EC_TOT PMf 0.5671 0.0640 41 40 37 98 90 N/A

OC_TOT PMf 3.1828 0.0640 41 41 41 100 100 N/A
EPA’s Coarse PM Pilot Study Appendix A

A-4 

Table A-2. Summary of concentrations and MDLs by species and size fraction (PMf or 

PMc) at St. Louis for FRM sampler, on Teflon and quartz filters; all species except OC 

and EC are from Teflon filter measurements. Two species, K and Na, were analyzed via 

XRF and IC; the latter analysis results are indicated by “_IC.” 

Species Size

Avg

Conc 

(μg/m3)

Avg

MDL

(μg/m3)

N

N >

MDL

N >

3MDL

% >

MDL

% >

3MD

L

Avg 

Uncertainty

(μg/m3)

Ag_XRF PMc 0.0002 0.0136 38 0 0 0 0 N/A

Al_XRF PMc 0.3543 0.0094 38 38 38 100 100 0.0473

As_XRF PMc 0.0003 0.0010 38 6 2 16 5 0.0011

Ba_XRF PMc 0.0239 0.0034 38 37 32 97 84 0.0039

Br_XRF PMc 0.0019 0.0009 38 16 7 42 18 0.0013

Ca_XRF PMc 1.5866 0.0019 38 38 38 100 100 0.1361

Cd_XRF PMc 0.0007 0.0087 38 1 0 3 0 0.0083

Ce_XRF PMc 0.0024 0.0025 38 4 4 11 11 0.0035

Cl_XRF PMc 0.1622 0.0021 38 38 38 100 100 0.0138

Co_XRF PMc 0.0008 0.0006 38 25 3 66 8 0.0007

Cr_XRF PMc 0.0015 0.0009 38 25 7 66 18 0.0006

Cs_XRF PMc -0.0001 0.0041 38 1 0 3 0 0.0042

Cu_XRF PMc 0.0071 0.0007 38 38 36 100 95 0.0011

Fe_XRF PMc 0.4297 0.0007 38 38 38 100 100 0.0380

In_XRF PMc 0.0001 0.0123 38 0 0 0 0 N/A

K_IC PMc 0.0391 0.0058 38 38 31 100 82 0.0091

K_XRF PMc 0.1274 0.0019 38 38 38 100 100 0.0164

Mass_Grav PMc 13.4626 0.3026 38 38 38 100 100 1.3528

Mg_XRF PMc 0.0908 0.0042 38 36 35 95 92 0.0162

Mn_XRF PMc 0.0104 0.0008 38 38 37 100 97 0.0012

Na_IC PMc 0.1445 0.0117 38 35 23 92 61 0.0376

Na_XRF PMc 0.1781 0.0116 38 37 26 97 68 0.0420

NH4_IC PMc -0.0399 0.0069 38 10 5 26 13 0.1082

Ni_XRF PMc 0.0003 0.0005 38 14 0 37 0 0.0003

NO3_IC PMc 0.5952 0.0028 38 38 38 100 100 0.1253

P_XRF PMc 0.0526 0.0040 38 36 34 95 89 0.0067

Pb_XRF PMc 0.0048 0.0017 38 24 10 63 26 0.0026

Rb_XRF PMc 0.0002 0.0010 38 2 0 5 0 0.0006

S_XRF PMc 0.0661 0.0027 38 31 31 82 82 0.0787

Sb_XRF PMc 0.0000 0.0201 38 1 0 3 0 0.0201

Se_XRF PMc 0.0002 0.0009 38 3 0 8 0 0.0006

Si_XRF PMc 1.2516 0.0049 38 38 38 100 100 0.1676

Sn_XRF PMc 0.0007 0.0140 38 0 0 0 0 N/A
EPA’s Coarse PM Pilot Study Appendix A

A-5 

Species Size

Avg

Conc 

(μg/m3)

Avg

MDL

(μg/m3)

N

N >

MDL

N >

3MDL

% >

MDL

% >

3MD

L

Avg 

Uncertainty

(μg/m3)

SO4_IC PMc 0.2262 0.0037 38 38 38 100 100 0.2505

Sr_XRF PMc 0.0031 0.0013 38 33 10 87 26 0.0014

Ti_XRF PMc 0.0179 0.0018 38 38 36 100 95 0.0022

V_XRF PMc 0.0013 0.0012 38 9 4 24 11 0.0012

Zn_XRF PMc 0.0326 0.0009 38 38 35 100 92 0.0043

Zr_XRF PMc 0.0017 0.0080 38 3 0 8 0 0.0070

Ag_XRF PMf 0.0001 0.0137 40 0 0 0 0 N/A

Al_XRF PMf 0.0387 0.0094 40 38 18 95 45 0.0064

As_XRF PMf 0.0006 0.0010 40 12 2 30 5 0.0008

Ba_XRF PMf 0.0042 0.0033 40 17 3 43 8 0.0023

Br_XRF PMf 0.0047 0.0009 40 40 31 100 78 0.0007

Ca_XRF PMf 0.0971 0.0018 40 40 40 100 100 0.0070

Cd_XRF PMf 0.0005 0.0087 40 1 0 3 0 0.0071

Ce_XRF PMf 0.0001 0.0024 40 0 0 0 0 N/A

Cl_XRF PMf 0.0167 0.0020 40 39 29 98 73 0.0017

Co_XRF PMf 0.0004 0.0006 40 9 0 23 0 0.0003

Cr_XRF PMf 0.0008 0.0009 40 12 2 30 5 0.0004

Cs_XRF PMf 0.0004 0.0040 40 0 0 0 0 N/A

Cu_XRF PMf 0.0052 0.0007 40 38 28 95 70 0.0005

Fe_XRF PMf 0.0956 0.0007 40 40 40 100 100 0.0068

In_XRF PMf 0.0009 0.0124 40 0 0 0 0 N/A

K_IC PMf 0.0632 0.0059 40 39 39 98 98 0.0050

K_XRF PMf 0.0717 0.0017 40 40 40 100 100 0.0052

Mass_Grav PMf 10.9770 0.3025 40 40 40 100 100 0.5600

Mg_XRF PMf 0.0088 0.0040 40 26 14 65 35 0.0024

Mn_XRF PMf 0.0029 0.0008 40 39 21 98 53 0.0004

Na_IC PMf 0.0433 0.0116 40 38 19 95 48 0.0207

Na_XRF PMf 0.0507 0.0110 40 40 24 100 60 0.0077

NH4_IC PMf 1.1790 0.0070 40 40 40 100 100 0.0836

Ni_XRF PMf 0.0002 0.0005 40 3 0 8 0 0.0002

NO3_IC PMf 0.8343 0.0028 40 40 40 100 100 0.0597

P_XRF PMf 0.0003 0.0039 40 1 0 3 0 0.0025

Pb_XRF PMf 0.0076 0.0018 40 35 17 88 43 0.0015

Rb_XRF PMf 0.0001 0.0010 40 1 0 3 0 0.0005

S_XRF PMf 0.8726 0.0027 40 40 40 100 100 0.0619

Sb_XRF PMf 0.0044 0.0202 40 2 0 5 0 0.0182

Se_XRF PMf 0.0006 0.0009 40 9 0 23 0 0.0004
EPA’s Coarse PM Pilot Study Appendix A

A-6 

Species Size

Avg

Conc 

(μg/m3)

Avg

MDL

(μg/m3)

N

N >

MDL

N >

3MDL

% >

MDL

% >

3MD

L

Avg 

Uncertainty

(μg/m3)

Si_XRF PMf 0.1107 0.0047 40 40 40 100 100 0.0099

Sn_XRF PMf 0.0001 0.0140 40 0 0 0 0 N/A

SO4_IC PMf 2.4086 0.0036 40 40 40 100 100 0.1712

Sr_XRF PMf 0.0004 0.0013 40 2 0 5 0 0.0006

Ti_XRF PMf 0.0025 0.0017 40 19 2 48 5 0.0010

V_XRF PMf 0.0009 0.0012 40 10 2 25 5 0.0006

Zn_XRF PMf 0.0215 0.0009 40 40 40 100 100 0.0016

Zr_XRF PMf 0.0004 0.0081 40 0 0 0 0 N/A

EC_TOT PMc 0.3834 0.0640 26 24 20 92 77 N/A

OC_TOT PMc 1.8947 0.0640 26 24 24 92 92 N/A

EC_TOT PMf 0.5907 0.0640 29 29 27 100 93 N/A

OC_TOT PMf 3.3402 0.0640 29 29 29 100 100 N/A
EPA’s Coarse PM Pilot Study Appendix A

A-7 

Table A-3. Summary of concentrations and MDLs by species and size fraction (PMf or 

PMc) at Phoenix for primary dichot, on Teflon and quartz filters; all species except OC 

and EC are from Teflon filter measurements. Two species, K and Na, were analyzed via 

XRF and IC; the latter analysis results are indicated by “_IC.” 

Species Size

Avg

Conc 

(μg/m3)

Avg

MDL

(μg/m3

)

N

N>

MDL

N >

3MDL

% >

MDL

% >

3MD

L

Avg 

Uncertainty

(μg/m3)

Ag_XRF PMc 0.0001 0.0138 51 0 0 0 0 N/A

Al_XRF PMc 1.1182 0.0094 51 51 51 100 100 0.1420

As_XRF PMc 0.0003 0.0010 51 5 0 10 0 0.0007

Ba_XRF PMc 0.0281 0.0033 51 51 47 100 92 0.0037

Br_XRF PMc 0.0007 0.0009 51 22 1 43 2 0.0005

Ca_XRF PMc 1.1447 0.0019 51 51 51 100 100 0.0982

Cd_XRF PMc 0.0004 0.0087 51 0 0 0 0 N/A

Ce_XRF PMc 0.0010 0.0024 51 5 1 10 2 0.0024

Cl_XRF PMc 0.2680 0.0021 51 51 51 100 100 0.0216

Co_XRF PMc 0.0018 0.0006 51 44 27 86 53 0.0007

Cr_XRF PMc 0.0027 0.0009 51 47 21 92 41 0.0005

Cs_XRF PMc 0.0009 0.0043 51 7 0 14 0 0.0038

Cu_XRF PMc 0.0151 0.0007 51 51 51 100 100 0.0013

Fe_XRF PMc 0.7905 0.0007 51 51 51 100 100 0.0575

In_XRF PMc 0.0004 0.0124 51 0 0 0 0 N/A

K_IC PMc 0.0732 0.0057 61 61 61 100 100 0.0064

K_XRF PMc 0.3853 0.0018 51 51 51 100 100 0.0321

Mass_Grav PMc 18.7492 0.3013 61 61 61 100 100 0.9885

Mg_XRF PMc 0.1221 0.0040 51 51 51 100 100 0.0211

Mn_XRF PMc 0.0156 0.0008 51 51 51 100 100 0.0013

Na_IC PMc 0.2472 0.0120 61 61 61 100 100 0.0349

Na_XRF PMc 0.2731 0.0113 51 51 51 100 100 0.0547

NH4_IC PMc 0.0039 0.0068 61 29 6 48 10 0.0076

Ni_XRF PMc 0.0010 0.0005 51 40 10 78 20 0.0003

NO3_IC PMc 0.4154 0.0028 61 61 61 100 100 0.0368

P_XRF PMc 0.0414 0.0039 51 51 46 100 90 0.0063

Pb_XRF PMc 0.0026 0.0017 51 26 3 51 6 0.0013

Rb_XRF PMc 0.0012 0.0010 51 29 1 57 2 0.0005

S_XRF PMc 0.1274 0.0027 51 51 51 100 100 0.0151

Sb_XRF PMc 0.0036 0.0202 51 4 0 8 0 0.0187

Se_XRF PMc 0.0001 0.0009 51 0 0 0 0 N/A

Si_XRF PMc 3.3396 0.0048 51 51 51 100 100 0.4380
EPA’s Coarse PM Pilot Study Appendix A

A-8 

Species Size

Avg

Conc 

(μg/m3)

Avg

MDL

(μg/m3

)

N

N>

MDL

N >

3MDL

% >

MDL

% >

3MD

L

Avg 

Uncertainty

(μg/m3)

Sn_XRF PMc 0.0016 0.0140 51 0 0 0 0 N/A

SO4_IC PMc 0.2836 0.0036 61 61 61 100 100 0.0324

Sr_XRF PMc 0.0068 0.0013 51 47 39 92 76 0.0011

Ti_XRF PMc 0.0575 0.0018 51 51 51 100 100 0.0045

V_XRF PMc 0.0009 0.0012 51 12 2 24 4 0.0010

Zn_XRF PMc 0.0354 0.0009 51 51 51 100 100 0.0029

Zr_XRF PMc 0.0026 0.0081 51 3 0 6 0 0.0056

Ag_XRF PMf 0.0002 0.0153 51 0 0 0 0 N/A

Al_XRF PMf 0.1286 0.0105 51 51 51 100 100 0.0139

As_XRF PMf 0.0006 0.0011 51 12 1 24 2 0.0008

Ba_XRF PMf 0.0064 0.0037 51 29 11 57 22 0.0026

Br_XRF PMf 0.0034 0.0010 51 51 32 100 63 0.0006

Ca_XRF PMf 0.1543 0.0021 51 51 51 100 100 0.0114

Cd_XRF PMf 0.0007 0.0097 51 0 0 0 0 N/A

Ce_XRF PMf 0.0000 0.0027 51 0 0 0 0 N/A

Cl_XRF PMf 0.0792 0.0022 51 51 43 100 84 0.0060

Co_XRF PMf 0.0006 0.0006 51 20 1 39 2 0.0005

Cr_XRF PMf 0.0015 0.0010 51 29 4 57 8 0.0005

Cs_XRF PMf 0.0010 0.0043 51 5 0 10 0 0.0029

Cu_XRF PMf 0.0060 0.0008 51 51 43 100 84 0.0006

Fe_XRF PMf 0.1990 0.0008 51 51 51 100 100 0.0141

In_XRF PMf 0.0001 0.0138 51 0 0 0 0 N/A

K_IC PMf 0.0801 0.0063 61 57 56 93 92 0.0063

K_XRF PMf 0.1336 0.0019 51 51 51 100 100 0.0097

Mass_Grav PMf 7.6092 0.3325 61 61 61 100 100 0.3985

Mg_XRF PMf 0.0264 0.0044 51 46 37 90 73 0.0041

Mn_XRF PMf 0.0051 0.0008 51 49 43 96 84 0.0006

Na_IC PMf 0.1084 0.0133 61 61 51 100 84 0.0264

Na_XRF PMf 0.1013 0.0120 51 51 42 100 82 0.0130

NH4_IC PMf 0.3586 0.0075 61 61 61 100 100 0.0254

Ni_XRF PMf 0.0007 0.0006 51 20 3 39 6 0.0003

NO3_IC PMf 0.5000 0.0031 61 61 61 100 100 0.0361

P_XRF PMf 0.0005 0.0043 51 2 0 4 0 0.0032

Pb_XRF PMf 0.0041 0.0019 51 26 9 51 18 0.0015

Rb_XRF PMf 0.0002 0.0012 51 1 0 2 0 0.0006

S_XRF PMf 0.3052 0.0030 51 51 51 100 100 0.0221
EPA’s Coarse PM Pilot Study Appendix A

A-9 

Species Size

Avg

Conc 

(μg/m3)

Avg

MDL

(μg/m3

)

N

N>

MDL

N >

3MDL

% >

MDL

% >

3MD

L

Avg 

Uncertainty

(μg/m3)

Sb_XRF PMf 0.0036 0.0224 51 2 0 4 0 0.0206

Se_XRF PMf 0.0002 0.0010 51 2 0 4 0 0.0004

Si_XRF PMf 0.3880 0.0053 51 51 51 100 100 0.0363

Sn_XRF PMf 0.0013 0.0155 51 0 0 0 0 N/A

SO4_IC PMf 0.8465 0.0040 61 61 61 100 100 0.0608

Sr_XRF PMf 0.0012 0.0015 51 13 1 25 2 0.0010

Ti_XRF PMf 0.0083 0.0020 51 51 31 100 61 0.0013

V_XRF PMf 0.0004 0.0013 51 5 0 10 0 0.0007

Zn_XRF PMf 0.0227 0.0009 51 51 51 100 100 0.0017

Zr_XRF PMf 0.0009 0.0090 51 0 0 0 0 N/A

EC_TOT PMc 0.1919 0.0640 48 36 21 75 44 N/A

OC_TOT PMc 2.3576 0.0640 48 48 48 100 100 N/A

EC_TOT PMf 0.7180 0.0640 48 47 45 98 94 N/A

OC_TOT PMf 2.4693 0.0640 48 48 48 100 100 N/A
EPA’s Coarse PM Pilot Study Appendix A

A-10

Table A-4. Summary of concentrations and MDLs by species and size fraction (PMf or 

PMc) at Phoenix for FRM sampler, on Teflon and quartz filters; all species except OC and 

EC are from Teflon filter measurements. Two species, K and Na, were analyzed via XRF 

and IC; the latter analysis results are indicated by “_IC.” 

Species Size

Avg

Conc

(μg/m3)

Avg

MDL

(μg/m3)

N

N >

MDL

N >

3MDL

% >

MDL

% >

3MD

L

Avg 

Uncertainty

(μg/m3)

Ag_XRF PMc -0.0001 0.0136 44 0 0 0 0 N/A

Al_XRF PMc 1.3564 0.0094 44 44 44 100 100 0.1738

As_XRF PMc 0.0005 0.0010 44 13 1 30 2 0.0011

Ba_XRF PMc 0.0369 0.0034 44 44 43 100 98 0.0056

Br_XRF PMc 0.0012 0.0009 44 28 2 64 5 0.0009

Ca_XRF PMc 1.4398 0.0020 44 44 44 100 100 0.1339

Cd_XRF PMc 0.0005 0.0087 44 0 0 0 0 N/A

Ce_XRF PMc 0.0013 0.0025 44 4 2 9 5 0.0029

Cl_XRF PMc 0.2782 0.0022 44 44 44 100 100 0.0279

Co_XRF PMc 0.0019 0.0006 44 36 23 82 52 0.0010

Cr_XRF PMc 0.0030 0.0009 44 37 22 84 50 0.0008

Cs_XRF PMc -0.0005 0.0042 44 4 1 9 2 0.0049

Cu_XRF PMc 0.0186 0.0007 44 44 44 100 100 0.0021

Fe_XRF PMc 0.9827 0.0007 44 44 44 100 100 0.0900

In_XRF PMc 0.0004 0.0123 44 0 0 0 0 N/A

K_IC PMc 0.0773 0.0059 44 44 43 100 98 0.0144

K_XRF PMc 0.4816 0.0020 44 44 44 100 100 0.0502

Mass_Grav PMc 22.6664 0.3032 44 44 44 100 100 1.6672

Mg_XRF PMc 0.1480 0.0043 44 44 44 100 100 0.0259

Mn_XRF PMc 0.0194 0.0008 44 44 44 100 100 0.0021

Na_IC PMc 0.2424 0.0116 44 44 44 100 100 0.0475

Na_XRF PMc 0.3140 0.0121 44 44 44 100 100 0.0695

NH4_IC PMc 0.0030 0.0070 44 21 14 48 32 0.0314

Ni_XRF PMc 0.0013 0.0005 44 37 16 84 36 0.0004

NO3_IC PMc 0.4946 0.0028 44 44 44 100 100 0.1030

P_XRF PMc 0.0593 0.0040 44 43 42 98 95 0.0071

Pb_XRF PMc 0.0036 0.0018 44 33 6 75 14 0.0021

Rb_XRF PMc 0.0017 0.0010 44 33 6 75 14 0.0007

S_XRF PMc 0.1212 0.0028 44 44 44 100 100 0.0375

Sb_XRF PMc 0.0003 0.0202 44 0 0 0 0 N/A

Se_XRF PMc 0.0001 0.0010 44 1 0 2 0 0.0006

Si_XRF PMc 4.3101 0.0050 44 44 44 100 100 0.5878

Sn_XRF PMc -0.0014 0.0140 44 0 0 0 0 N/A
EPA’s Coarse PM Pilot Study Appendix A

A-11 

Species Size

Avg

Conc

(μg/m3)

Avg

MDL

(μg/m3)

N

N >

MDL

N >

3MDL

% >

MDL

% >

3MD

L

Avg 

Uncertainty

(μg/m3)

SO4_IC PMc 0.3097 0.0037 44 44 44 100 100 0.1026

Sr_XRF PMc 0.0092 0.0013 44 42 40 95 91 0.0016

Ti_XRF PMc 0.0728 0.0018 44 44 44 100 100 0.0065

V_XRF PMc 0.0014 0.0012 44 16 3 36 7 0.0013

Zn_XRF PMc 0.0530 0.0009 44 44 44 100 100 0.0053

Zr_XRF PMc 0.0032 0.0080 44 6 2 14 5 0.0067

Ag_XRF PMf 0.0003 0.0136 45 0 0 0 0 N/A

Al_XRF PMf 0.1526 0.0094 45 45 45 100 100 0.0144

As_XRF PMf 0.0005 0.0010 45 9 1 20 2 0.0008

Ba_XRF PMf 0.0106 0.0033 45 31 22 69 49 0.0028

Br_XRF PMf 0.0032 0.0009 45 44 25 98 56 0.0006

Ca_XRF PMf 0.2033 0.0018 45 45 45 100 100 0.0145

Cd_XRF PMf 0.0005 0.0087 45 1 0 2 0 0.0080

Ce_XRF PMf 0.0000 0.0024 45 0 0 0 0 N/A

Cl_XRF PMf 0.0827 0.0020 45 45 43 100 96 0.0061

Co_XRF PMf 0.0009 0.0006 45 29 3 64 7 0.0005

Cr_XRF PMf 0.0018 0.0009 45 29 10 64 22 0.0005

Cs_XRF PMf 0.0016 0.0041 45 9 0 20 0 0.0028

Cu_XRF PMf 0.0077 0.0007 45 45 43 100 96 0.0007

Fe_XRF PMf 0.2564 0.0007 45 45 45 100 100 0.0182

In_XRF PMf 0.0008 0.0123 45 0 0 0 0 N/A

K_IC PMf 0.0920 0.0059 45 43 42 96 93 0.0072

K_XRF PMf 0.1478 0.0017 45 45 45 100 100 0.0105

Mass_Grav PMf 9.0100 0.3024 45 45 45 100 100 0.4649

Mg_XRF PMf 0.0255 0.0040 45 43 35 96 78 0.0033

Mn_XRF PMf 0.0063 0.0008 45 44 42 98 93 0.0006

Na_IC PMf 0.1092 0.0116 45 45 42 100 93 0.0243

Na_XRF PMf 0.0962 0.0110 45 45 39 100 87 0.0110

NH4_IC PMf 0.4103 0.0069 45 45 45 100 100 0.0292

Ni_XRF PMf 0.0009 0.0005 45 23 5 51 11 0.0003

NO3_IC PMf 0.7160 0.0028 45 45 45 100 100 0.0513

P_XRF PMf 0.0017 0.0039 45 5 2 11 4 0.0032

Pb_XRF PMf 0.0047 0.0018 45 23 10 51 22 0.0015

Rb_XRF PMf 0.0002 0.0010 45 2 0 4 0 0.0005

S_XRF PMf 0.3032 0.0027 45 45 45 100 100 0.0216

Sb_XRF PMf 0.0021 0.0201 45 1 0 2 0 0.0156

Se_XRF PMf 0.0002 0.0009 45 1 0 2 0 0.0005
EPA’s Coarse PM Pilot Study Appendix A

A-12

Species Size

Avg

Conc

(μg/m3)

Avg

MDL

(μg/m3)

N

N >

MDL

N >

3MDL

% >

MDL

% >

3MD

L

Avg 

Uncertainty

(μg/m3)

Si_XRF PMf 0.4651 0.0047 45 45 45 100 100 0.0388

Sn_XRF PMf 0.0017 0.0140 45 0 0 0 0 N/A

SO4_IC PMf 0.8495 0.0037 45 45 45 100 100 0.0609

Sr_XRF PMf 0.0017 0.0013 45 24 4 53 9 0.0007

Ti_XRF PMf 0.0117 0.0017 45 45 38 100 84 0.0014

V_XRF PMf 0.0004 0.0012 45 4 0 9 0 0.0007

Zn_XRF PMf 0.0173 0.0009 45 45 45 100 100 0.0013

Zr_XRF PMf 0.0013 0.0080 45 1 0 2 0 0.0047

EC_TOT PMc 0.2818 0.0640 29 24 17 83 59 N/A

OC_TOT PMc 2.1730 0.0640 29 28 27 97 93 N/A

EC_TOT PMf 0.6420 0.0640 30 28 25 93 83 N/A

OC_TOT PMf 2.3259 0.0640 30 30 30 100 100 N/A
EPA’s Coarse PM Pilot Study Appendix A

A-13

Table A-5. Summary of concentrations by species and size fraction (PM10 or PM2.5) at St. 

Louis for FRM sampler, on Teflon and quartz filters; all species except OC and EC are 

from Teflon filter measurements. Two species, K and Na, were analyzed via XRF and IC; 

the latter analysis results are indicated by “_IC.” 

Species Avg PM2.5

(μg/m3)

Avg PM10

(μg/m3)

Avg (PM10/PM25) 

(μg/m3)

Avg PM10-2.5

(μg/m3)

EC_TOR 0.7776 1.244 1.8529 0.4664

OC_TOR 3.114 4.9257 1.7234 1.8117

Ag_XRF 0.0001 0.0003 0.6316 0.0002

Al_XRF 0.0389 0.3931 15.7212 0.3543

As_XRF 0.0006 0.0009 2.865 0.0003

Ba_XRF 0.0036 0.0275 8.5088 0.0239

Br_XRF 0.0047 0.0066 1.342 0.0019

Ca_XRF 0.0979 1.6845 17.6537 1.5866

Cd_XRF 0.0005 0.0012 0.7959 0.0007

Ce_XRF 0.0001 0.0025 10.0014 0.0024

Cl_XRF 0.0173 0.1795 9.2252 0.1622

Co_XRF 0.0003 0.0012 3.6365 0.0008

Cr_XRF 0.0008 0.0023 14.3764 0.0015

Cs_XRF 0.0004 0.0004 2.1755 -0.0001

Cu_XRF 0.0052 0.0123 2.6465 0.0071

Fe_XRF 0.0977 0.5273 5.7266 0.4297

In_XRF 0.0009 0.0011 0.8065 0.0001

K_IC 0.064 0.1031 1.7057 0.0391

K_XRF 0.0723 0.1997 2.9407 0.1274

Mass_Grav 10.9587 24.4213 2.3083 13.4626

Mg_XRF 0.0087 0.0995 24.7169 0.0908

Mn_XRF 0.003 0.0133 5.1863 0.0104

Na_IC 0.0444 0.1889 3.8968 0.1445

Na_XRF 0.0509 0.229 4.2644 0.1781

NH4_IC 1.1763 1.1363 0.9748 -0.0399

Ni_XRF 0.0002 0.0005 3.0465 0.0003

NO3_IC 0.8753 1.4705 5.5111 0.5952

P_XRF 0.0003 0.0529 360.3697 0.0526

Pb_XRF 0.0077 0.0125 1.713 0.0048

Rb_XRF 0.0001 0.0004 1.1014 0.0002

S_XRF 0.859 0.9252 1.1038 0.0661

Sb_XRF 0.0043 0.0043 1.7227 0

Se_XRF 0.0006 0.0008 1.4583 0.0002

Si_XRF 0.1107 1.3622 14.1381 1.2516
EPA’s Coarse PM Pilot Study Appendix A

A-14

Species Avg PM2.5

(μg/m3)

Avg PM10

(μg/m3)

Avg (PM10/PM25) 

(μg/m3)

Avg PM10-2.5

(μg/m3)

Sn_XRF 0.0001 0.0007 1.4 0.0007

SO4_IC 2.3774 2.6035 1.1025 0.2262

Sr_XRF 0.0004 0.0034 11.1222 0.0031

Ti_XRF 0.0025 0.0204 10.9788 0.0179

V_XRF 0.0009 0.0023 2.9389 0.0013

Zn_XRF 0.0222 0.0548 2.1138 0.0326

Zr_XRF 0.0004 0.0021 0.8464 0.0017
EPA’s Coarse PM Pilot Study Appendix A

A-15

Table A-6. Summary of concentrations by species and size fraction (PM10 or PM2.5) at 

Phoenix for FRM sampler, on Teflon and quartz filters; all species except OC and EC are 

from Teflon filter measurements. Two species, K and Na, were analyzed via XRF and IC; 

the latter analysis results are indicated by “_IC.” 

Species Avg PM2.5

(μg/m3)

Avg PM10

(μg/m3)

Avg

(PM10/PM25) 

(μg/m3)

Avg PM10-

2.5 (μg/m3

)

EC_TOR 0.705 0.9455 1.5653 0.2405

OC_TOR 2.2175 4.4318 2.0787 2.2143

Ag_XRF 0.0003 0.0002 0 -0.0001

Al_XRF 0.1548 1.5112 12.3484 1.3564

As_XRF 0.0004 0.0009 2.3656 0.0005

Ba_XRF 0.0105 0.0475 6.8446 0.0369

Br_XRF 0.0032 0.0044 1.5075 0.0012

Ca_XRF 0.2067 1.6465 9.2552 1.4398

Cd_XRF 0.0005 0.001 1.2469 0.0005

Ce_XRF 0 0.0014 0 0.0013

Cl_XRF 0.0841 0.3623 7.2231 0.2782

Co_XRF 0.0009 0.0028 9.68 0.0019

Cr_XRF 0.0019 0.0048 5.2488 0.003

Cs_XRF 0.0016 0.0011 1.5787 -0.0005

Cu_XRF 0.0078 0.0263 3.7401 0.0185

Fe_XRF 0.2592 1.2418 5.5261 0.9827

In_XRF 0.0009 0.0012 1.0127 0.0004

K_IC 0.0927 0.17 2.4991 0.0773

K_XRF 0.1492 0.6308 5.6951 0.4816

Mass_Grav 9.0859 31.7523 3.9565 22.6664

Mg_XRF 0.026 0.174 12.627 0.148

Mn_XRF 0.0063 0.0258 5.1943 0.0194

Na_IC 0.1105 0.3529 3.5033 0.2424

Na_XRF 0.0974 0.4115 4.8287 0.314

NH4_IC 0.4099 0.4128 1.0612 0.003

Ni_XRF 0.0009 0.0022 4.3738 0.0013

NO3_IC 0.7237 1.2184 2.8001 0.4946

P_XRF 0.0017 0.061 29.5247 0.0593

Pb_XRF 0.0048 0.0084 3.6215 0.0036

Rb_XRF 0.0002 0.002 6.657 0.0017

S_XRF 0.3023 0.4234 1.5213 0.1212

Sb_XRF 0.0021 0.0025 2.4151 0.0003

Se_XRF 0.0002 0.0002 1.5455 0
EPA’s Coarse PM Pilot Study Appendix A

A-16

Species Avg PM2.5

(μg/m3)

Avg PM10

(μg/m3)

Avg

(PM10/PM25) 

(μg/m3)

Avg PM10-

2.5 (μg/m3

)

Si_XRF 0.4725 4.7826 11.8616 4.3101

Sn_XRF 0.0018 0.0004 0.018 -0.0014

SO4_IC 0.8447 1.1544 1.4591 0.3097

Sr_XRF 0.0017 0.0109 8.7813 0.0092

Ti_XRF 0.0118 0.0846 8.6226 0.0728

V_XRF 0.0004 0.0018 4.2143 0.0014

Zn_XRF 0.0175 0.0705 5.1821 0.053

Zr_XRF 0.0013 0.0045 2.8155 0.0032
EPA’s Coarse PM Pilot Study Appendix B 

B-1 

Appendix B: Summary Ratios of 

Collocated Dichot Measurements 

Table B-1. Dichot B-to-Dichot A concentration ratios were calculated for each sample 

and the means and medians of these distributions by species and size fraction (PMf or 

PMc) at Phoenix and St. Louis on Teflon and quartz filters are reported below; all species 

except OC and EC are from Teflon filter measurements. Two species, K and Na, were 

analyzed via XRF and IC; the latter analysis results are indicated by “_IC.” Species that 

have at least 25% of samples above MDL are indicated in the last two columns. 

Site ID Size Species N > 

MDL

Avg

(DichotB/

DichotA)

Med

(DichotB/

DichotA)

%>MDL 

(Dichot

A)

%>MDL 

(Dichot

B)

PHX PMc EC_TOT 5 0.95 0.68 68 75

PHX PMc OC_TOT 14 1.22 1.20 100 100

PHX PMc Al_XRF 22 0.95 0.94 100 100

PHX PMc As_XRF 1 0.50 0.50

PHX PMc Ba_XRF 22 0.94 0.93 100 100

PHX PMc Br_XRF 3 0.92 0.95 43

PHX PMc Ca_XRF 22 0.96 0.95 100 100

PHX PMc Ce_XRF 2 1.03 1.03

PHX PMc Cl_XRF 22 0.94 0.93 100 100

PHX PMc Co_XRF 18 1.18 0.86 86 78

PHX PMc Cr_XRF 19 0.99 1.00 92 88

PHX PMc Cs_XRF 1 1.34 1.34

PHX PMc Cu_XRF 22 0.95 0.95 100 100

PHX PMc Fe_XRF 22 0.96 0.97 100 100

PHX PMc K_IC 31 0.98 0.98 100 100

PHX PMc K_XRF 22 0.96 0.96 100 100

PHX PMc Mass_Grav 31 0.97 0.95 100 100

PHX PMc Mg_XRF 22 0.97 1.01 100 100

PHX PMc Mn_XRF 22 0.96 0.97 100 100

PHX PMc Na_IC 31 1.01 0.95 100 100

PHX PMc Na_XRF 22 0.92 0.86 100 100

PHX PMc NH4_IC 6 1.05 1.11 48 32

PHX PMc Ni_XRF 14 0.81 0.83 78 75

PHX PMc NO3_IC 31 0.96 0.95 100 100

PHX PMc P_XRF 20 0.91 0.93 100 91

PHX PMc Pb_XRF 8 1.11 1.08 51 59

PHX PMc Rb_XRF 8 1.14 1.11 57 53

PHX PMc S_XRF 22 0.96 0.95 100 100

PHX PMc Si_XRF 22 0.96 0.96 100 100
EPA’s Coarse PM Pilot Study Appendix B 

B-2 

Site ID Size Species N > 

MDL

Avg

(DichotB/

DichotA)

Med

(DichotB/

DichotA)

%>MDL 

(Dichot

A)

%>MDL 

(Dichot

B)

PHX PMc SO4_IC 31 0.99 0.94 100 100

PHX PMc Sr_XRF 20 0.94 0.94 92 91

PHX PMc Ti_XRF 22 0.97 0.95 100 100

PHX PMc V_XRF 3 1.04 1.11 28

PHX PMc Zn_XRF 22 0.98 0.98 100 100

PHX PMf Na_IC 3 1.21 1.28

PHX PMf NH4_IC 19 0.98 0.99 56 62

PHX PMf NO3_IC 31 0.96 0.98 100 100

PHX PMf SO4_IC 27 1.07 0.98 87 90

PHX PMf EC_TOT 13 1.18 1.07 93 98

PHX PMf OC_TOT 14 1.04 1.01 100 100

PHX PMf Al_XRF 22 1.13 1.08 100 97

PHX PMf As_XRF 2 0.89 0.89

PHX PMf Ba_XRF 11 1.03 0.85 57 62

PHX PMf Br_XRF 21 1.07 1.07 100 94

PHX PMf Ca_XRF 22 1.14 1.11 100 100

PHX PMf Cl_XRF 22 1.11 1.09 100 97

PHX PMf Co_XRF 6 0.88 0.81 39 44

PHX PMf Cr_XRF 9 0.89 0.81 57 50

PHX PMf Cs_XRF 1 1.25 1.25

PHX PMf Cu_XRF 22 1.08 1.04 100 97

PHX PMf Fe_XRF 22 1.08 1.07 100 100

PHX PMf K_IC 28 1.00 1.00 93 93

PHX PMf K_XRF 22 1.07 1.07 100 100

PHX PMf Mass_Grav 31 1.06 1.01 100 100

PHX PMf Mg_XRF 18 1.15 1.04 90 88

PHX PMf Mn_XRF 21 1.11 1.13 96 94

PHX PMf Na_IC 31 1.04 1.02 100 100

PHX PMf Na_XRF 22 1.13 1.13 100 100

PHX PMf NH4_IC 31 0.99 0.98 100 100

PHX PMf Ni_XRF 8 1.13 1.13 39 38

PHX PMf NO3_IC 31 1.05 1.03 100 100

PHX PMf Pb_XRF 9 1.21 1.13 51 56

PHX PMf S_XRF 22 1.00 1.00 100 100

PHX PMf Si_XRF 22 1.12 1.12 100 100

PHX PMf SO4_IC 31 1.00 0.99 100 100

PHX PMf Sr_XRF 5 1.30 1.17 25 47
EPA’s Coarse PM Pilot Study Appendix B 

B-3 

Site ID Size Species N > 

MDL

Avg

(DichotB/

DichotA)

Med

(DichotB/

DichotA)

%>MDL 

(Dichot

A)

%>MDL 

(Dichot

B)

PHX PMf Ti_XRF 21 1.29 1.23 100 94

PHX PMf Zn_XRF 22 1.08 1.05 100 100

STL PMc EC_TOT 7 0.77 0.62 88 71

STL PMc OC_TOT 13 0.97 0.90 100 98

STL PMc Al_XRF 11 0.81 0.83 97 100

STL PMc Ba_XRF 11 0.93 0.94 90 93

STL PMc Br_XRF 4 1.19 1.15 39 36

STL PMc Ca_XRF 11 0.90 0.92 100 100

STL PMc Cl_XRF 11 0.84 0.85 100 100

STL PMc Co_XRF 4 1.27 0.98 65 61

STL PMc Cr_XRF 4 1.06 1.05 61 68

STL PMc Cu_XRF 11 0.85 0.86 100 100

STL PMc Fe_XRF 11 0.88 0.91 100 100

STL PMc K_IC 8 0.89 0.93 92 90

STL PMc K_XRF 11 0.88 0.87 100 100

STL PMc Mass_Grav 11 0.84 0.89 100 100

STL PMc Mg_XRF 11 0.78 0.88 97 100

STL PMc Mn_XRF 10 0.90 0.89 97 100

STL PMc Na_IC 11 0.91 0.87 97 97

STL PMc Na_XRF 10 0.92 0.81 94 82

STL PMc NH4_IC 3 0.54 0.63 61 45

STL PMc Ni_XRF 1 1.10 1.10

STL PMc NO3_IC 11 0.97 0.94 100 100

STL PMc P_XRF 8 0.83 0.85 71 82

STL PMc Pb_XRF 5 0.88 0.92 48 54

STL PMc S_XRF 11 0.92 0.86 100 100

STL PMc Si_XRF 11 0.90 0.91 100 100

STL PMc SO4_IC 11 0.86 0.83 100 100

STL PMc Sr_XRF 6 0.83 0.82 68 64

STL PMc Ti_XRF 10 0.86 0.87 90 100

STL PMc V_XRF 1 1.10 1.10

STL PMc Zn_XRF 11 0.92 0.88 100 100

STL PMf Na_IC 1 1.08 1.08

STL PMf NH4_IC 9 1.10 1.09 83 87

STL PMf NO3_IC 11 1.02 1.01 100 100

STL PMf SO4_IC 11 1.27 1.08 100 97

STL PMf EC_TOT 13 0.82 0.80 100 98
EPA’s Coarse PM Pilot Study Appendix B 

B-4 

Site ID Size Species N > 

MDL

Avg

(DichotB/

DichotA)

Med

(DichotB/

DichotA)

%>MDL 

(Dichot

A)

%>MDL 

(Dichot

B)

STL PMf OC_TOT 13 0.87 0.89 100 100

STL PMf Al_XRF 8 1.14 1.08 81 82

STL PMf As_XRF 1 1.90 1.90 29

STL PMf Ba_XRF 2 1.20 1.20 39

STL PMf Br_XRF 10 1.00 1.08 97 100

STL PMf Ca_XRF 11 1.23 1.15 100 100

STL PMf Cl_XRF 11 0.91 0.94 100 96

STL PMf Cr_XRF 2 0.44 0.45 29 39

STL PMf Cs_XRF 1 1.28 1.28

STL PMf Cu_XRF 10 1.51 1.30 87 96

STL PMf Fe_XRF 11 1.11 1.04 100 100

STL PMf K_IC 11 0.98 0.99 100 97

STL PMf K_XRF 11 1.00 1.00 100 100

STL PMf Mass_Grav 11 1.00 1.02 100 100

STL PMf Mg_XRF 4 0.89 0.79 55 64

STL PMf Mn_XRF 7 1.05 1.02 84 86

STL PMf Na_IC 10 1.06 1.07 92 93

STL PMf Na_XRF 10 1.24 1.21 97 100

STL PMf NH4_IC 11 0.96 0.96 100 100

STL PMf NO3_IC 11 0.93 0.96 100 100

STL PMf Pb_XRF 6 0.96 0.89 71 82

STL PMf S_XRF 11 0.96 0.97 100 100

STL PMf Si_XRF 11 1.18 1.15 100 100

STL PMf SO4_IC 11 0.98 0.98 100 100

STL PMf Ti_XRF 3 1.40 1.10 39 50

STL PMf V_XRF 1 0.74 0.74 29

STL PMf Zn_XRF 11 1.15 1.03 100 100
EPA’s Coarse PM Pilot Study Appendix C 

C-1 

Appendix C: Summary of Dichot-to-FRM Comparisons 

Table C-1. Dichot-to-FRM concentration ratios were calculated for each sample and the 

means and medians of these distributions by species and size fraction (PMf or PMc) at 

Phoenix and St. Louis on Teflon and quartz filters are reported below; all species except 

OC and EC are from Teflon filter measurements. Two species, K and Na, were analyzed 

via XRF and IC; the latter analysis results are indicated by “_IC.” Species that have at 

least 25% of samples above MDL are indicated in the last two columns. Elements were 

analyzed by XRF, and ions were analyzed by IC. Potassium and sodium were analyzed 

by both methods. 

Sampler_ID Size Species N > 

MDL

Avg

(Dichot/

FRM)

Med

(Dichot/

FRM)

% > MDL 

(FRM) 

% > MDL 

(Dichot)

PHX-Dichot_A PMc EC_TOT 13 0.92 0.86 83 68

PHX-Dichot_A PMc OC_TOT 20 1.13 0.98 97 100

PHX-Dichot_A PMc Al_XRF 35 0.90 0.93 100 100

PHX-Dichot_A PMc As_XRF 2 0.86 0.87 30

PHX-Dichot_A PMc Ba_XRF 35 0.85 0.80 100 100

PHX-Dichot_A PMc Br_XRF 8 0.74 0.63 64 43

PHX-Dichot_A PMc Ca_XRF 35 0.86 0.87 100 100

PHX-Dichot_A PMc Ce_XRF 3 0.78 0.86

PHX-Dichot_A PMc Cl_XRF 35 0.92 0.94 100 100

PHX-Dichot_A PMc Co_XRF 28 1.06 0.93 82 86

PHX-Dichot_A PMc Cr_XRF 29 0.99 0.96 84 92

PHX-Dichot_A PMc Cs_XRF 2 0.80 0.81

PHX-Dichot_A PMc Cu_XRF 35 0.99 0.93 100 100

PHX-Dichot_A PMc Fe_XRF 35 0.85 0.85 100 100

PHX-Dichot_A PMc K_IC 35 1.01 0.86 100 100

PHX-Dichot_A PMc K_XRF 35 0.83 0.82 100 100

PHX-Dichot_A PMc Mass_Grav 35 0.83 0.85 100 100

PHX-Dichot_A PMc Mg_XRF 35 0.91 0.90 100 100

PHX-Dichot_A PMc Mn_XRF 35 0.84 0.84 100 100

PHX-Dichot_A PMc Na_IC 35 0.90 0.93 100 100

PHX-Dichot_A PMc Na_XRF 35 0.87 0.89 100 100

PHX-Dichot_A PMc NH4_IC 11 0.56 0.59 48 48

PHX-Dichot_A PMc Ni_XRF 24 0.85 0.82 84 78

PHX-Dichot_A PMc NO3_IC 35 0.83 0.83 100 100

PHX-Dichot_A PMc P_XRF 34 0.68 0.66 98 100

PHX-Dichot_A PMc Pb_XRF 18 0.84 0.71 75 51

PHX-Dichot_A PMc Rb_XRF 15 0.75 0.69 75 57
EPA’s Coarse PM Pilot Study Appendix C 

C-2 

Sampler_ID Size Species N > 

MDL

Avg

(Dichot/

FRM)

Med

(Dichot/

FRM)

% > MDL 

(FRM) 

% > MDL 

(Dichot)

PHX-Dichot_A PMc S_XRF 35 1.17 1.08 100 100

PHX-Dichot_A PMc Si_XRF 35 0.85 0.87 100 100

PHX-Dichot_A PMc SO4_IC 35 0.93 0.93 100 100

PHX-Dichot_A PMc Sr_XRF 33 0.76 0.77 95 92

PHX-Dichot_A PMc Ti_XRF 35 0.85 0.86 100 100

PHX-Dichot_A PMc V_XRF 7 0.92 0.71 36

PHX-Dichot_A PMc Zn_XRF 35 0.83 0.77 100 100

PHX-Dichot_A PMc Zr_XRF 1 1.47 1.47

PHX-Dichot_A PMf Na_IC 1 0.98 0.98

PHX-Dichot_A PMf NH4_IC 22 1.00 0.99 60 56

PHX-Dichot_A PMf NO3_IC 36 1.03 1.03 100 100

PHX-Dichot_A PMf SO4_IC 22 1.49 1.36 73 87

PHX-Dichot_A PMf EC_TOT 18 1.06 0.96 93 93

PHX-Dichot_A PMf OC_TOT 21 1.03 0.99 100 100

PHX-Dichot_A PMf Al_XRF 36 0.94 0.93 100 100

PHX-Dichot_A PMf As_XRF 2 0.90 0.90

PHX-Dichot_A PMf Ba_XRF 18 0.88 0.98 69 57

PHX-Dichot_A PMf Br_XRF 35 1.07 1.09 98 100

PHX-Dichot_A PMf Ca_XRF 36 0.89 0.89 100 100

PHX-Dichot_A PMf Cl_XRF 36 0.94 0.87 100 100

PHX-Dichot_A PMf Co_XRF 12 0.99 0.83 64 39

PHX-Dichot_A PMf Cr_XRF 18 0.87 0.90 64 57

PHX-Dichot_A PMf Cs_XRF 1 0.93 0.93

PHX-Dichot_A PMf Cu_XRF 36 0.87 0.85 100 100

PHX-Dichot_A PMf Fe_XRF 36 0.89 0.91 100 100

PHX-Dichot_A PMf K_IC 34 1.00 1.00 96 93

PHX-Dichot_A PMf K_XRF 36 0.93 0.94 100 100

PHX-Dichot_A PMf Mass_Grav 36 0.92 0.94 100 100

PHX-Dichot_A PMf Mg_XRF 30 1.48 0.98 96 90

PHX-Dichot_A PMf Mn_XRF 34 0.91 0.94 98 96

PHX-Dichot_A PMf Na_IC 36 0.89 0.85 100 100

PHX-Dichot_A PMf Na_XRF 36 0.97 0.88 100 100

PHX-Dichot_A PMf NH4_IC 36 0.97 0.99 100 100

PHX-Dichot_A PMf Ni_XRF 12 0.92 0.88 51 39

PHX-Dichot_A PMf NO3_IC 36 0.94 0.93 100 100
EPA’s Coarse PM Pilot Study Appendix C 

C-3 

Sampler_ID Size Species N > 

MDL

Avg

(Dichot/

FRM)

Med

(Dichot/

FRM)

% > MDL 

(FRM) 

% > MDL 

(Dichot)

PHX-Dichot_A PMf P_XRF 1 0.79 0.79

PHX-Dichot_A PMf Pb_XRF 14 0.91 0.87 51 51

PHX-Dichot_A PMf Rb_XRF 1 1.27 1.27

PHX-Dichot_A PMf S_XRF 36 1.00 1.02 100 100

PHX-Dichot_A PMf Si_XRF 36 0.92 0.89 100 100

PHX-Dichot_A PMf SO4_IC 36 0.97 0.99 100 100

PHX-Dichot_A PMf Sr_XRF 8 1.12 1.09 53 25

PHX-Dichot_A PMf Ti_XRF 36 0.84 0.81 100 100

PHX-Dichot_A PMf V_XRF 2 1.43 1.43

PHX-Dichot_A PMf Zn_XRF 36 1.01 0.97 100 100

PHX-Dichot_B PMc EC_TOT 11 1.18 0.63 83 75

PHX-Dichot_B PMc OC_TOT 22 1.40 1.13 97 100

PHX-Dichot_B PMc Al_XRF 25 0.84 0.87 100 100

PHX-Dichot_B PMc As_XRF 4 0.96 0.95 30

PHX-Dichot_B PMc Ba_XRF 25 0.79 0.76 100 100

PHX-Dichot_B PMc Br_XRF 4 0.84 0.74 64

PHX-Dichot_B PMc Ca_XRF 25 0.81 0.82 100 100

PHX-Dichot_B PMc Ce_XRF 3 0.70 0.64

PHX-Dichot_B PMc Cl_XRF 25 0.89 0.88 100 100

PHX-Dichot_B PMc Co_XRF 17 1.07 0.84 82 78

PHX-Dichot_B PMc Cr_XRF 18 0.81 0.84 84 88

PHX-Dichot_B PMc Cs_XRF 1 0.83 0.83

PHX-Dichot_B PMc Cu_XRF 25 0.90 0.89 100 100

PHX-Dichot_B PMc Fe_XRF 25 0.80 0.81 100 100

PHX-Dichot_B PMc K_IC 25 1.02 0.92 100 100

PHX-Dichot_B PMc K_XRF 25 0.79 0.78 100 100

PHX-Dichot_B PMc Mass_Grav 25 0.81 0.83 100 100

PHX-Dichot_B PMc Mg_XRF 25 0.87 0.93 100 100

PHX-Dichot_B PMc Mn_XRF 25 0.81 0.82 100 100

PHX-Dichot_B PMc Na_IC 25 0.88 0.89 100 100

PHX-Dichot_B PMc Na_XRF 25 0.76 0.85 100 100

PHX-Dichot_B PMc NH4_IC 7 0.44 0.42 48 32

PHX-Dichot_B PMc Ni_XRF 16 0.75 0.71 84 75

PHX-Dichot_B PMc NO3_IC 25 0.80 0.76 100 100

PHX-Dichot_B PMc P_XRF 23 0.65 0.63 98 91
EPA’s Coarse PM Pilot Study Appendix C 

C-4 

Sampler_ID Size Species N > 

MDL

Avg

(Dichot/

FRM)

Med

(Dichot/

FRM)

% > MDL 

(FRM) 

% > MDL 

(Dichot)

PHX-Dichot_B PMc Pb_XRF 11 0.71 0.54 75 59

PHX-Dichot_B PMc Rb_XRF 12 0.87 0.87 75 53

PHX-Dichot_B PMc S_XRF 25 1.11 0.96 100 100

PHX-Dichot_B PMc Si_XRF 25 0.79 0.82 100 100

PHX-Dichot_B PMc SO4_IC 25 0.89 0.90 100 100

PHX-Dichot_B PMc Sr_XRF 23 0.84 0.81 95 91

PHX-Dichot_B PMc Ti_XRF 25 0.79 0.80 100 100

PHX-Dichot_B PMc V_XRF 5 0.78 0.64 36 28

PHX-Dichot_B PMc Zn_XRF 25 0.80 0.80 100 100

PHX-Dichot_B PMf NH4_IC 14 1.01 1.05 60 62

PHX-Dichot_B PMf NO3_IC 25 1.04 1.04 100 100

PHX-Dichot_B PMf SO4_IC 18 1.32 1.14 73 90

PHX-Dichot_B PMf EC_TOT 20 1.14 1.05 93 98

PHX-Dichot_B PMf OC_TOT 22 1.05 1.03 100 100

PHX-Dichot_B PMf Al_XRF 25 0.99 0.94 100 97

PHX-Dichot_B PMf As_XRF 3 1.44 0.82

PHX-Dichot_B PMf Ba_XRF 13 0.73 0.66 69 62

PHX-Dichot_B PMf Br_XRF 24 1.09 1.04 98 94

PHX-Dichot_B PMf Ca_XRF 25 0.89 0.91 100 100

PHX-Dichot_B PMf Cl_XRF 25 0.93 0.89 100 97

PHX-Dichot_B PMf Co_XRF 9 1.01 1.03 64 44

PHX-Dichot_B PMf Cr_XRF 11 0.90 0.72 64 50

PHX-Dichot_B PMf Cs_XRF 2 0.84 0.84

PHX-Dichot_B PMf Cu_XRF 25 0.82 0.82 100 97

PHX-Dichot_B PMf Fe_XRF 25 0.88 0.89 100 100

PHX-Dichot_B PMf K_IC 23 0.99 0.98 96 93

PHX-Dichot_B PMf K_XRF 25 0.91 0.93 100 100

PHX-Dichot_B PMf Mass_Grav 25 0.92 0.95 100 100

PHX-Dichot_B PMf Mg_XRF 22 1.07 0.96 96 88

PHX-Dichot_B PMf Mn_XRF 24 0.86 0.86 98 94

PHX-Dichot_B PMf Na_IC 25 0.91 0.87 100 100

PHX-Dichot_B PMf Na_XRF 25 1.08 0.95 100 100

PHX-Dichot_B PMf NH4_IC 25 0.94 0.97 100 100

PHX-Dichot_B PMf Ni_XRF 9 0.85 0.76 51 38

PHX-Dichot_B PMf NO3_IC 25 0.91 0.89 100 100
EPA’s Coarse PM Pilot Study Appendix C 

C-5 

Sampler_ID Size Species N > 

MDL

Avg

(Dichot/

FRM)

Med

(Dichot/

FRM)

% > MDL 

(FRM) 

% > MDL 

(Dichot)

PHX-Dichot_B PMf Pb_XRF 12 1.00 0.94 51 56

PHX-Dichot_B PMf Rb_XRF 1 1.25 1.25

PHX-Dichot_B PMf S_XRF 25 0.96 0.95 100 100

PHX-Dichot_B PMf Se_XRF 1 1.09 1.09

PHX-Dichot_B PMf Si_XRF 25 0.92 0.92 100 100

PHX-Dichot_B PMf SO4_IC 25 0.95 0.97 100 100

PHX-Dichot_B PMf Sr_XRF 9 1.00 1.06 53 47

PHX-Dichot_B PMf Ti_XRF 24 0.91 0.86 100 94

PHX-Dichot_B PMf Zn_XRF 25 1.03 0.97 100 100

STL-Dichot_A PMc EC_TOT 10 0.91 0.56 92 88

STL-Dichot_A PMc OC_TOT 11 1.08 1.04 92 100

STL-Dichot_A PMc Al_XRF 18 0.99 0.98 100 97

STL-Dichot_A PMc Ba_XRF 16 0.85 0.76 97 90

STL-Dichot_A PMc Br_XRF 5 0.77 0.86 42 39

STL-Dichot_A PMc Ca_XRF 18 0.97 0.96 100 100

STL-Dichot_A PMc Cl_XRF 18 1.04 1.03 100 100

STL-Dichot_A PMc Co_XRF 10 0.97 0.99 66 65

STL-Dichot_A PMc Cr_XRF 9 1.38 1.14 66 61

STL-Dichot_A PMc Cu_XRF 18 1.05 0.99 100 100

STL-Dichot_A PMc Fe_XRF 18 0.94 0.90 100 100

STL-Dichot_A PMc K_IC 18 1.46 1.15 100 92

STL-Dichot_A PMc K_XRF 18 0.90 0.88 100 100

STL-Dichot_A PMc Mass_Grav 18 0.90 0.93 100 100

STL-Dichot_A PMc Mg_XRF 16 1.04 0.97 95 97

STL-Dichot_A PMc Mn_XRF 18 0.96 0.91 100 97

STL-Dichot_A PMc Na_IC 17 0.99 0.94 92 97

STL-Dichot_A PMc Na_XRF 16 0.94 0.88 97 94

STL-Dichot_A PMc NH4_IC 2 0.16 0.16 26 61

STL-Dichot_A PMc Ni_XRF 1 0.95 0.95 37

STL-Dichot_A PMc NO3_IC 18 0.82 0.86 100 100

STL-Dichot_A PMc P_XRF 14 0.86 0.91 95 71

STL-Dichot_A PMc Pb_XRF 6 1.23 0.93 63 48

STL-Dichot_A PMc S_XRF 15 1.12 0.89 82 100

STL-Dichot_A PMc Si_XRF 18 0.96 0.96 100 100

STL-Dichot_A PMc SO4_IC 18 0.77 0.74 100 100
EPA’s Coarse PM Pilot Study Appendix C 

C-6 

Sampler_ID Size Species N > 

MDL

Avg

(Dichot/

FRM)

Med

(Dichot/

FRM)

% > MDL 

(FRM) 

% > MDL 

(Dichot)

STL-Dichot_A PMc Sr_XRF 13 1.01 0.87 87 68

STL-Dichot_A PMc Ti_XRF 17 0.95 0.93 100 90

STL-Dichot_A PMc V_XRF 2 0.78 0.79

STL-Dichot_A PMc Zn_XRF 18 1.01 0.99 100 100

STL-Dichot_A PMc Zr_XRF 2 1.00 1.01

STL-Dichot_A PMf Na_IC 2 1.15 1.16

STL-Dichot_A PMf NH4_IC 14 1.05 0.96 85 83

STL-Dichot_A PMf NO3_IC 19 1.06 1.02 100 100

STL-Dichot_A PMf SO4_IC 18 1.96 1.36 88 100

STL-Dichot_A PMf EC_TOT 16 1.17 1.16 100 100

STL-Dichot_A PMf OC_TOT 16 1.11 1.11 100 100

STL-Dichot_A PMf Al_XRF 14 1.15 1.12 95 81

STL-Dichot_A PMf Ba_XRF 5 1.05 1.06 42

STL-Dichot_A PMf Br_XRF 18 0.98 1.01 100 97

STL-Dichot_A PMf Ca_XRF 19 1.07 0.99 100 100

STL-Dichot_A PMf Cl_XRF 18 1.29 1.02 98 100

STL-Dichot_A PMf Co_XRF 1 0.89 0.89

STL-Dichot_A PMf Cr_XRF 3 1.05 1.20 30 29

STL-Dichot_A PMf Cu_XRF 15 1.16 1.21 95 87

STL-Dichot_A PMf Fe_XRF 19 1.05 1.02 100 100

STL-Dichot_A PMf K_IC 19 1.04 1.01 98 100

STL-Dichot_A PMf K_XRF 19 1.00 1.02 100 100

STL-Dichot_A PMf Mass_Grav 19 1.01 1.00 100 100

STL-Dichot_A PMf Mg_XRF 7 1.38 1.02 65 55

STL-Dichot_A PMf Mn_XRF 15 1.04 1.01 98 84

STL-Dichot_A PMf Na_IC 18 1.01 0.99 95 92

STL-Dichot_A PMf Na_XRF 19 0.88 0.83 100 97

STL-Dichot_A PMf NH4_IC 19 0.98 0.98 100 100

STL-Dichot_A PMf Ni_XRF 1 1.98 1.98

STL-Dichot_A PMf NO3_IC 19 1.01 1.00 100 100

STL-Dichot_A PMf Pb_XRF 13 1.06 1.12 88 71

STL-Dichot_A PMf S_XRF 19 0.99 1.01 100 100

STL-Dichot_A PMf Se_XRF 2 1.21 1.21

STL-Dichot_A PMf Si_XRF 19 1.02 0.98 100 100

STL-Dichot_A PMf SO4_IC 19 1.01 1.00 100 100
EPA’s Coarse PM Pilot Study Appendix C 

C-7 

Sampler_ID Size Species N > 

MDL

Avg

(Dichot/

FRM)

Med

(Dichot/

FRM)

% > MDL 

(FRM) 

% > MDL 

(Dichot)

STL-Dichot_A PMf Ti_XRF 3 0.80 0.82 48 39

STL-Dichot_A PMf V_XRF 5 0.98 0.93 29

STL-Dichot_A PMf Zn_XRF 19 0.99 0.94 100 100

STL-Dichot_B PMc EC_TOT 10 0.99 0.92 92 71

STL-Dichot_B PMc OC_TOT 15 1.11 1.10 92 98

STL-Dichot_B PMc Al_XRF 21 0.86 0.84 100 100

STL-Dichot_B PMc Ba_XRF 19 0.84 0.71 97 93

STL-Dichot_B PMc Br_XRF 4 1.49 1.15 42 36

STL-Dichot_B PMc Ca_XRF 21 0.88 0.90 100 100

STL-Dichot_B PMc Ce_XRF 2 0.69 0.69

STL-Dichot_B PMc Cl_XRF 21 1.10 0.90 100 100

STL-Dichot_B PMc Co_XRF 7 1.45 1.77 66 61

STL-Dichot_B PMc Cr_XRF 8 1.17 1.12 66 68

STL-Dichot_B PMc Cu_XRF 21 0.81 0.86 100 100

STL-Dichot_B PMc Fe_XRF 21 0.85 0.82 100 100

STL-Dichot_B PMc K_IC 20 1.08 0.93 100 90

STL-Dichot_B PMc K_XRF 21 0.84 0.83 100 100

STL-Dichot_B PMc Mass_Grav 21 0.80 0.79 100 100

STL-Dichot_B PMc Mg_XRF 21 0.93 0.85 95 100

STL-Dichot_B PMc Mn_XRF 21 0.85 0.83 100 100

STL-Dichot_B PMc Na_IC 20 0.90 0.86 92 97

STL-Dichot_B PMc Na_XRF 16 0.76 0.75 97 82

STL-Dichot_B PMc NH4_IC 3 1.33 0.97 26 45

STL-Dichot_B PMc Ni_XRF 3 1.10 1.10 37

STL-Dichot_B PMc NO3_IC 21 0.69 0.80 100 100

STL-Dichot_B PMc P_XRF 18 0.77 0.80 95 82

STL-Dichot_B PMc Pb_XRF 11 0.96 0.86 63 54

STL-Dichot_B PMc S_XRF 17 1.75 1.17 82 100

STL-Dichot_B PMc Si_XRF 21 0.86 0.85 100 100

STL-Dichot_B PMc SO4_IC 21 0.73 0.69 100 100

STL-Dichot_B PMc Sr_XRF 13 0.79 0.74 87 64

STL-Dichot_B PMc Ti_XRF 21 0.85 0.82 100 100

STL-Dichot_B PMc V_XRF 2 0.57 0.57

STL-Dichot_B PMc Zn_XRF 21 0.97 0.90 100 100

STL-Dichot_B PMf Na_IC 2 1.06 1.07
EPA’s Coarse PM Pilot Study Appendix C 

C-8 

Sampler_ID Size Species N > 

MDL

Avg

(Dichot/

FRM)

Med

(Dichot/

FRM)

% > MDL 

(FRM) 

% > MDL 

(Dichot)

STL-Dichot_B PMf NH4_IC 18 1.00 0.97 85 87

STL-Dichot_B PMf NO3_IC 21 1.05 1.02 100 100

STL-Dichot_B PMf SO4_IC 18 2.33 1.79 88 97

STL-Dichot_B PMf EC_TOT 19 0.96 0.98 100 98

STL-Dichot_B PMf OC_TOT 19 0.98 1.05 100 100

STL-Dichot_B PMf Al_XRF 17 1.34 1.11 95 82

STL-Dichot_B PMf As_XRF 5 1.37 1.53 30 29

STL-Dichot_B PMf Ba_XRF 8 1.40 1.46 42 39

STL-Dichot_B PMf Br_XRF 21 0.93 0.94 100 100

STL-Dichot_B PMf Ca_XRF 21 1.17 1.17 100 100

STL-Dichot_B PMf Cl_XRF 20 1.04 1.03 98 96

STL-Dichot_B PMf Co_XRF 3 1.41 1.32

STL-Dichot_B PMf Cr_XRF 3 1.04 0.86 30 39

STL-Dichot_B PMf Cu_XRF 20 1.26 1.02 95 96

STL-Dichot_B PMf Fe_XRF 21 1.08 1.09 100 100

STL-Dichot_B PMf K_IC 20 1.02 1.03 98 97

STL-Dichot_B PMf K_XRF 21 0.99 0.99 100 100

STL-Dichot_B PMf Mass_Grav 21 1.01 1.01 100 100

STL-Dichot_B PMf Mg_XRF 13 1.02 0.90 65 64

STL-Dichot_B PMf Mn_XRF 18 1.17 1.15 98 86

STL-Dichot_B PMf Na_IC 20 1.06 1.02 95 93

STL-Dichot_B PMf Na_XRF 21 0.99 0.94 100 100

STL-Dichot_B PMf NH4_IC 21 0.96 0.96 100 100

STL-Dichot_B PMf NO3_IC 21 1.18 1.08 100 100

STL-Dichot_B PMf Pb_XRF 15 0.96 0.83 88 82

STL-Dichot_B PMf S_XRF 21 0.97 0.96 100 100

STL-Dichot_B PMf Se_XRF 1 0.91 0.91

STL-Dichot_B PMf Si_XRF 21 1.14 1.12 100 100

STL-Dichot_B PMf SO4_IC 21 0.99 0.99 100 100

STL-Dichot_B PMf Ti_XRF 7 0.94 0.90 48 50

STL-Dichot_B PMf V_XRF 3 1.07 0.81

STL-Dichot_B PMf Zn_XRF 21 1.06 1.00 100 100
EPA’s Coarse PM Pilot Study Appendix D 

D-1 

Appendix D: Quartz Fiber Filter Carbon Blanks 

Seven sets of trip blanks and field blanks (collected in May, July, September, and 

November 2010, and January, April, and May 2011) were analyzed by thermal optical 

reflectance (TOR) using the IMPROVE_A thermal-optical analysis (TOA) method. Each set 

included two to four trip blank filters and four to twelve field blank filters per site. OC loadings on 

these trip blanks and field blanks filters provide a context for characterizing and interpreting OC 

on the front and backup filters. The OC minimum detection limit (MDL) for the IMPROVE_A 

method is 2.03 μg/filter. As summarized in this section, the trip blanks and field blanks data can 

be used to estimate the lower quantifiable limits (LQL) for particulate matter OC. LQL is a metric 

that complements the MDL estimate and aids in data interpretation. 

Figure D-1 shows the mean and standard deviation of OC mass loadings for each trip 

blank and field blank collection event at each site. For the trip blanks (Figure D-1a), the OC 

mass loadings were highest for the first collection event (May 2010) and relatively low and 

consistent during the remainder of the study. Exceptions were the April and May 2011 samples 

for St. Louis, for which the trip blank mass loadings exhibited relatively high mean values. At St. 

Louis a single sample (mass loading 34 μg/filter) was responsible for the high mean value in 

April 2011; by excluding this sample, the April 2011 mean loading decreased from 10 μg/filter to 

2 μg/filter. In contrast, the May 2011 mean loading of 10 μg/filter at St. Louis included two high 

samples (16 and 12 μg/filter), and two low samples (6 and 5 μg/filter). 

The field blanks (Figure D-1b) initially decreased with each event, exhibited nearly 

constant values from November 2010 to April 2011, and increased in May 2011. 

Figure D-1. Mean and standard deviation of OC (a) trip blanks data and (b) field blanks 

data. Data are stratified by site and by the seven collection events between May 2010 

and May 2011. 
EPA’s Coarse PM Pilot Study Appendix D 

D-2 

Statistical analyses were performed separately for the trip blanks and field blanks data 

sets. In each case, unbalanced two-way analysis of variance (ANOVA) was performed on logtransformed OC with site and date as factors. All hypothesis testing was conducted for the 95% 

confidence level. For the trip blank data, site had an insignificant effect (p = 0.62), whereas date 

had a significant effect (p < 0.01) on the OC mass loadings. Trip blank mass loading values 

measured in May 2010, the first month of the study, were relatively high compared to 

subsequent months. ANOVA with the May 2010 data excluded resulted in insignificant effects 

for both the site and date (p = 0.51 and p = 0.18, respectively). Pooling all the trip blanks data 

(but excluding the May 2010 samples) yields mean and median OC mass loadings of 5.4 ± 5.7 

μg/filter and 4.2 μg/filter, respectively (N = 44). 

The same statistical analyses were performed for the seven sets of field blanks, which 

were collected at the same times as the trip blanks. Each set included four to twelve filters per 

site, with the maximum case being front and back filters in the PM2.5 FRM, PM10 FRM, and both 

channels of both dichot samplers. While in principle the sampler type and filter position (front 

versus back) could be treated as additional factors, their effects were deemed insignificant, and 

samples pooled across these factors were treated as pseudo-replicates. Site had an 

insignificant effect (p = 0.28), whereas date had a significant effect (p < 0.01). After removing 

the May 2010 data, site still had an insignificant effect (p = 0.21) and date a significant effect 

(p < 0.01) on the OC mass loadings. Pooling all the field blanks data (but excluding the May 

2010 samples) yields mean and median OC mass loadings of 6.6 ± 5.0 and 5.2 μg/filter, 

respectively (N = 130). 

The trip and field blanks ANOVA analyses demonstrated statistically insignificant 

dependencies on site. Therefore, the Phoenix and St. Louis data were pooled. Figure D-2

shows the mean and standard deviation of OC mass loadings for each trip and field blanks 

collection event. ANOVA on the site pooled data yielded significant effects (p < 0.01) for both 

date and blanks type (trip versus field). This result was obtained both including and excluding 

the May 2010 samples. 

Figure D-2. Mean and standard deviation of OC trip and field blanks data stratified by 

date and combined across the Phoenix and St. Louis sites. 
EPA’s Coarse PM Pilot Study Appendix D 

D-3 

In summary, one year of data is too short a time series to conclusively interpret the trip 

and field blank trends. ANOVA analysis suggests that date has an insignificant effect on the 

values. However, the high trip blank values observed in the first month of the study (May 2010) 

may be the driver for the high field blank values observed during that collection event. For 

subsequent months, it is not clear whether the field blanks exhibit an intrinsic seasonal behavior 

or are significantly influenced by the trip blanks. Large differences between trip and field blank 

values in July 2010 suggest some decoupling, but the higher field blank values towards the end 

of the study coincided with higher trip blank values. 

The possibility of temporal behavior in blank loadings confounds using the 95th percentile 

OC mass loading as a robust estimate of the LQL, but it can be used as a conservative 

estimate. The 95th percentile trip blank and field blanks mass loadings are each 19 g/filter for 

all samples and 15 μg/filter excluding the May 2010 samples. 

Appendix E: Nitrate Correlations with Other Species 

Nitrate (NO3) can be difficult to accurately measure, since much of it can be volatilized 

off of the Teflon filter. Lee et al., (2008) found that it was not uncommon at rural locations to find 

a significant fraction of particulate NO3 present in forms other than ammonium nitrate. In order 

to explore whether nitrate was associated with species other than ammonium, correlations 

between PMf and PMc nitrate and the other measured species were evaluated. 

Tables E-1 and E-2 display the correlation between NO3 (Table E-1 for Teflon filter NO3

and Table E-2 for Teflon/nylon filter NO3) and a subset of species that are primarily above 

minimum detection limit (MDL) for the primary dichotomous sampler (Dichot A). No consistently 

high correlations were observed between NO3 and the other parameters at either St. Louis or 

Phoenix, except for ammonium in the PMf fraction. Moderate correlations (around 0.6) were 

found for some elements in St. Louis for the PMc fraction. Visual inspection of scatter plot 

matrices showed that there were no outliers biasing the correlation coefficients low, and 

confirmed the correlation coefficients reported in Tables E-1 and E-2.
Table E-1. Correlation between Teflon filter NO3 and a subset of species that are 

primarily above MDL for the primary dichotomous sampler (A) in PHX and STL. 

Correlations are colored from high (red) to low (green). 

Correlation 

with Species PHX NO3 PMc PHX NO3 PMf STL NO3 PMc STL NO3 PMf

SO4 0.50 -0.15 0.56 0.13

K_IC 0.25 0.27 0.32 0.21

Na_IC 0.29 -0.12 0.34 -0.07

NH4 0.33 0.84 0.22 0.80

Al 0.50 -0.15 0.59 -0.24

Ba 0.40 0.37 0.46 0.15

Br 0.22 0.35 0.11 0.31

Ca 0.44 -0.11 0.56 -0.12

Cl 0.15 0.21 0.13 0.49

Co 0.18 -0.09 0.52 -0.02

Cr 0.38 0.05 0.15 0.10

Cu -0.11 0.54 0.25 0.11

Fe 0.33 0.02 0.54 0.05

K 0.47 0.23 0.60 -0.19

Mg 0.53 -0.10 0.65 -0.36

Mn 0.41 0.10 0.47 0.27

Na 0.29 -0.12 0.38 -0.13

Ni 0.21 -0.01 0.15 -0.26

P 0.42 -0.07 0.40 -0.11

Pb -0.02 0.12 0.12 -0.04

Rb 0.31 -0.02 0.15 -0.18

S 0.54 -0.13 0.58 0.12

Si 0.48 -0.15 0.66 -0.33

Sr 0.24 0.47 0.56 0.08

Ti 0.37 -0.06 0.60 -0.23

Zn -0.14 0.02 0.08 -0.05

Gravimetric 

Mass 0.36 0.69 0.64 0.60
Table E-2. Correlation between Teflon/nylon filter NO3 and a subset of species that are 

primarily above MDL for the primary dichotomous sampler (A) in PHX and STL. 

Correlations are colored from high (red) to low (green). 

Correlation 

with Species PHX NO3 PMc PHX NO3 PMf STL NO3 PMc STL NO3 PMf

SO4 0.37 -0.24 0.55 0.15

K_IC 0.14 0.22 0.31 0.25

Na_IC 0.10 -0.26 0.30 -0.07

NH4 0.25 0.91 0.22 0.82

Al 0.37 -0.18 0.60 -0.25

Ba 0.36 0.35 0.49 0.17

Br 0.13 0.34 0.13 0.41

Ca 0.36 -0.14 0.57 -0.10

Cl -0.01 0.07 0.09 0.51

Co 0.10 -0.07 0.53 0.01

Cr 0.32 0.10 0.17 0.10

Cu -0.12 0.47 0.28 0.13

Fe 0.26 0.05 0.56 0.04

K 0.35 0.18 0.60 -0.14

Mg 0.40 -0.21 0.65 -0.35

Mn 0.34 0.18 0.48 0.26

Na 0.14 -0.26 0.34 -0.09

Ni 0.15 -0.03 0.16 -0.26

P 0.31 -0.06 0.41 -0.12

Pb -0.06 0.10 0.11 0.03

Rb 0.26 -0.06 0.14 -0.16

S 0.43 -0.22 0.63 0.12

Si 0.36 -0.15 0.67 -0.34

Sr 0.16 0.37 0.57 0.08

Ti 0.30 -0.07 0.61 -0.26

Zn -0.14 -0.01 0.08 0.03

Gravimetric 

Mass 0.29 0.63 0.64 0.62
United States

Environmental Protection

Agency

Office of Air Quality Planning and Standards

Air Quality Assessment Division

Research Triangle Park, NC

Publication No. EPA-454/R-15-001

February 2015