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Evaluation of the Thermo Scientific Model 2025 Sequential
Dichotomous Sampler for the Collection of Fine (<2.5 µµµµm) and Coarse
(2.5 – 10 µµµµm) Particulate Matter in the Alberta Oil Sands Region
(AOSR) for Trace Element Determination
Final Pilot Feasibility Report
Prepared For
Wood Buffalo Environmental Association
#100 – 330 Thickwood Blvd.
Fort McMurray, Alberta T9K 1Y1
April 11, 2012
Matthew S. Landis, Integrated Atmospheric Solutions, LLC, Raleigh, NC, USA
Eric Edgerton, Atmospheric Research & Analysis Inc., Cary, NC, USA
Joseph Graney, SUNY Binghamton, Binghamton, NY, USA
2
Table of Contents
1. INTRODUCTION .................................................................................................................. 3
2. METHODS ............................................................................................................................. 4
2.1. Sequential Dichotomous Samper Filter Collection ............................................................................... 4
2.2. X-ray Fluorescence Analysis................................................................................................................... 8
2.3. Filter Sample Extraction for Multi-Elements Quantification.............................................................. 8
2.4. Multi-Element ICPMS Analysis ............................................................................................................. 8
2.5. Stable Lead Isotope Analysis .................................................................................................................. 9
2.6. Statistical Analysis ................................................................................................................................... 9
3. RESULTS AND DISCUSSION ............................................................................................. 9
3.1. 2010 PM Mass Concentrations ............................................................................................................... 9
3.2. 2011 PM Mass Concentrations ..............................................................................................................14
3.3. ED-XRF Analysis ...................................................................................................................................18
3.4. DRC-ICPMS Analysis ............................................................................................................................18
3.5. Stable Lead Isotope Analysis .................................................................................................................22
4. CONCLUSIONS ................................................................................................................... 26
5. RECOMMENDATIONS ..................................................................................................... 26
5.1. Dichotomous Sampler Field Operation ................................................................................................26
5.2. Dichotomous Sampler Sample Analysis ...............................................................................................26
5.3. Addition of PM Carbon Measurements ...............................................................................................27
5.4. Expansion of the Dichotomous Sampler Network ...............................................................................27
6. RESPONSE TO INITIAL REVIEW COMMENTS ......................................................... 28
6.1. Housing the Sampler Inside the Monitoring Station ...........................................................................28
6.2. Acceptance of Dichotomous Sampler....................................................................................................28
6.3. Movement of Dichotomous Sampler .....................................................................................................29
6.4. Sampling by Difference versus Dichotomous Sampler........................................................................30
7. PATH FORWARD ............................................................................................................... 30
7.1. Evaluation of Data Completeness for the Dichotomous Sampler versus Existing FRMs ................30
7.2. Comparison of Dichotomous Sampler and Existing FRM Mass Data ...............................................31
7.3. Comparison of Dichotomous Sampler and Existing FRM Metals Data ............................................34
8. ACKNOWLEDGEMENTS ................................................................................................. 35
9. REFERENCES ..................................................................................................................... 35
3
1. Introduction
The Wood Buffalo Environmental Association (WBEA) is a multi-stakeholder, community-based,
not-for-profit association located in Fort McMurray, Alberta, Canada. The WBEA airshed covers
70,000 sq km in northeastern Alberta and includes the Athabasca Oil Sands Region (AOSR).
WBEA measures air quality within the airshed at 15 monitoring sites, from Fort Chipewyan in the
north to Anzac in the south. There are both natural (forest fires) and anthropogenic pollution
emission sources in the AOSR. The anthropogenic air pollution sources are mainly composed of oil
sand mining, oil extraction facilities, oil refining, tailings ponds, and transportation sources. Air
quality management decisions require information on the sources contributing to air pollution to
develop effective air pollution control strategies. Assessing the local and regional scale
contributions of air pollution sources on particulate matter (PM) air quality in the AOSR is currently
limited due to an insufficient amount of PM speciation data required for a comprehensive source
apportionment analysis.
The regulation and monitoring of PM in ambient air focuses on aerosols that can be inhaled into the
respiratory system (e.g., aerodynamic diameter <10 µm (PM10)). Researchers generally recognize
that these aerosols may cause adverse health effects. Atmospheric aerosols commonly occur in two
distinct size modes as shown in Figure 1: the fine (<2.5 µm) mode and the coarse (2.5-10.0 µm)
mode (Seinfeld and Pandis, 2006). The fine or accumulation mode (also termed respirable
particulate matter) is typically attributed to direct emissions from anthropogenic sources or growth
of particles from the gas phase and subsequent agglomeration, while the coarse mode is primarily
made of mechanically abraded or ground particles. Particles that have grown from the gas phase
(either due to condensation, transformation, or combustion) occur initially as very fine nuclei (~
0.05 µm). These particles tend to grow rapidly to accumulation mode particles around 0.5 µm,
which are relatively stable in the air. Because of their initially gaseous origin, particle sizes in this
range include inorganic ions such as sulfate, nitrate, ammonia, combustion-form carbon, organic
aerosols, metals, and other combustion products. Coarse PM, on the other hand, is produced mainly
by mechanical forces such as crushing and abrasion. Therefore, coarse particles typically consist of
finely divided minerals such as oxides of aluminum, silicon, iron, calcium, and potassium. Coarse
particles of soil or dust mostly result from entrainment by wind or from other mechanical action.
Since the size of these particles is normally greater than 2.5 µm, their retention time in the
atmosphere and transport scales are generally substantially shorter than PM2.5.
4
Figure 1. Particulate Matter Size Mode Distribution.
This report describes a pilot study aimed at evaluating the operation of an automated dichotomous
sampler for the measurement of fine and coarse mode PM and the suitability of the collected filter
samples for trace element quantification for source apportionment modeling. Source apportionment
is the estimation of the contributions to the airborne pollutant concentrations that arise from the
emissions of natural and anthropogenic sources (Hopke, 2009). Statistical data analysis tools called
“receptor models” are applied to evaluate the variance structure of a monitoring data set at a
specific location (receptor site) to reduce its dimensionality and elicit information on the sources of
air pollution. The models utilize unique tracer species combinations from different source types to
quantify the relative importance of sources on observed pollutant concentrations.
The main objectives of this pilot feasibility study are to:
a. Evaluate the operation of the ThermoScientific Model 2025 Sequential Dichotomous PM
Sampler in the AOSR.
b. Investigate appropriate extraction and inorganic analysis methods for dichotomous sampler
Teflon filters.
c. Evaluate if 24-hour sample collected fine and coarse mode mass in the AOSR is sufficient
for quantification of a sufficient number of relevant tracer species for application in routine
PM air quality monitoring and for use in source apportionment analysis.
2. Methods
2.1. Sequential Dichotomous Sampler Filter Collection
A ThermoScientific (Franklin, MA) model 2025 sequential dichotomous particulate matter (PM)
sampler (a U.S. EPA designated Federal Equivalent Method for PM2.5) was installed in the AMS-1
site shelter at Fort McKay in late 2009 (Figure 2). After several test runs the sampler began
continuous operation collecting daily samples on February 22, 2010. The sampler uses a PM10
impactor inlet (Figure 3) to remove particles with an aerodynamic diameter greater than 10 µm, and
an internal virtual impactor
5
Figure 2. AMS-1 Fort McKay Installation of Sequential Dichotomous Sampler.
Figure 3. AMS-1 Fort McKay Sequential Dichotomous Sampler Inlet.
6
to separate the coarse (2.5 - 10.0 µm) and fine aerosols (<2.5 µm) onto separate filters (Loo and
Cork, 1998; Figure 4). The virtual impactor accelerates incoming PM10 aerosols using a jet to
impart sufficient momentum that they resist the lateral sheer of the Q2 major flow and traverse into
the receiving jet and are captured onto the PMcoarse filter.
The virtual impactor results in the collection of all coarse mode particles from the Q0 total flow and
the fine mode particles in the Q1 minor flow (Figure 4) on the coarse filter. As a result, the fine
mode and corrected coarse mode concentrations (mass and trace elements) are adjusted for this
artifact using equation 1 and 2, respectively.
= (1)
= ∗ !"# $ (2)
Where: CFine= Concentration PM2.5 (µg m-3
)
CCoarse = Concentration PMCoarse (µg m-3
)
VFine= Volume of Air Through PM2.5 Filter (m-3
)
VCoarse = Volume of Air Through PMCoarse Filter (m-3
)
VTotal = Volume of Air Through Sampler (m-3
)
MFine= Mass on Fine Filter (µg)
MCoarse = Mass on Coarse Filter (µg)
Measurement Technologies Laboratories (MTL; Minneapolis, MN) 47 mm Teflon membrane filters
with Teflon support rings were procured, pre-weighed, installed into filter cassettes/magazines,
shipped to WBEA, received from WBEA, and post-weighed by Atmospheric Research & Analysis,
Inc (ARA) at its laboratory in Morrisville, NC. Each filter magazine was loaded with 15 filter
cassettes, enough for two weeks of unattended sampling and a field blank. The standard
ThermoScientific stainless steel filter support screens were replaced with custom cross-linked
Teflon-coated support screens in the filter cassettes to avoid potential trace level metals
contamination of the filters. Filters were pre- and post-weighed by ARA in a Class 1000 clean
environment using a Mettler Toledo (Columbus, OH) Model UMX2 micro-balance fitted with an
MTL Model AH225-6 robotic auto-handler (Figure 5). The ARA clean environment maintains
temperature (± 0.1 ºC) and relative humidity (± 2 %) within strict tolerances to ensure consistent
results. The MTL AH225 system performs five replicate weighings of each filter and automatically
minimizes electrostatic effects by utilizing a static discharge Po α-particle emission and Faraday
pan. The balance is zeroed before and after each filter weighing and 1-NIST-traceable Class A
weight and 2 unexposed reference filters are weighed every 6 hours. 3-sigma uncertainties for the
NIST-traceable weight and reference filters are typically 1.0 microgram and 1.6 microgram,
respectively, or <0.1 µg m-3
for a 24-hour sample. ARA performs zero and buoyancy corrections
and reports the mean ± standard deviation weight of each filter.
WBEA contracted site operation personnel received the filter magazines shipped by ARA,
exchanged filters, downloaded sampler data files, maintained the sampler, and reshipped sampled
filters back to ARA. The dichotomous sampler files containing the filter, interval and input data
7
Figure 4. Schematic of ThermoScientific Dichotomous Sampler Virtual Impactor.
Where: Qo = 16.7 liters min-1
(Total Flow); Q1 = 1.7 liters min-1
(Minor Flow); Q2 = 15.0 liters min-1
(Major Flow)
Figure 5. Picture of ARA’s MTL Model AH225-6 Robotic Weighing System.
8
were also e-mailed to ARA. These data files contain information for each sample filter such as date
sampled, filter ID, cassette ID, sample volume, flows, temperatures, and quality assurance flags.
2.2. X-ray Fluorescence Analysis
Energy-dispersive X-ray fluorescence analysis (EDXRF) is commonly used as a nondestructive
analytical method for quantifying elemental content in ambient PM samples (Solomon et al., 2001).
EDXRF involves excitation of the constituent atoms in a sample with a nearly bi-chromatic X-ray
beam from a secondary target. As the atoms relax back to the ground state, they emit X-rays whose
energies are characteristic of the element. The fluorescent X-rays impinge on a detector and the
resulting spectrum has an energy profile that is directly related to the elements and their
concentrations in the sample. The spectra are subsequently processed. Multiple linear regression
analysis is used to de-convolute the pulse height spectrum into its background and constituent
element peaks by least-squares fitting of stored elemental thin-film library spectra and lot specific
MTL filter backgrounds. Subsequent processing performs attenuation and interference corrections
and converts raw data to reportable information. Calibrations are performed empirically with thin-
film standards, and verified routinely via analysis of NIST SRM 2783 (Air Particulate Matter on
Filter Media). WBEA dichotomous filters were analyzed using ARA’s PANalytical Epsilon 5
EDXRF spectrometer.
2.3. Filter Sample Extraction for Multi-Element Quantification
Dichotomous filters were digested by ARA personnel with a CEM Corporation (Matthews, NC)
Mars Express microwave digestion system in a cocktail of ultra-pure H2O2, HF and HNO3 with
heating to180ºC for 40 minutes. After cooling, ASTM Type II ultrapure (Resistivity ≥ 18.2
MΩ⋅cm) water was added to each vessel to bring the extraction sample up to a final volume of 25
ml. A standard reference material (SRM) was also digested in triplicate: U.S. National Institute of
Standards and Technology (NIST) 1648a (urban particulate matter).
2.4. Multi-Element ICPMS Analysis
Inductively Coupled Plasma Mass Spectroscopy (ICPMS) has broad acceptance as a method for
determining a wide range of elements in atmospheric PM due to technological advances that
continue to improve sensitivity and reduce interferences (Grohse, 1999). In ICPMS, the filter
sample extract is aspirated through a nebulizer and injected as a droplet aerosol into an argon radio
frequency plasma. In the plasma, the bombardment of the droplets by free electrons causes removal
of the solvent, breakdown of molecules to atoms, and ionization of the atoms to give them a charge
so they can be identified by mass spectroscopy. The ions are extracted from the plasma through a
differentially pumped vacuum interface using a series of electrostatic ion lenses that repel negative
ions and direct positive ions into a quadrupole mass spectrometer. The ions are then sorted
according to their mass-to-charge ratio and individual ions are detected by a counting electron
multiplier. ICPMS provides detection limits on the order of 1-1000 parts per trillion for
approximately 65 elements with a linear dynamic range in excess of eight orders of magnitude
(Solomon et al., 2001).
Sample extracts were analyzed using ARA’s Perkin Elmer (Waltham, MA) Sciex DRCII dynamic
reaction cell ICPMS (DRC-ICPMS) and quantified for 42 elements. The DRC method was
optimized with O2 reagent gas for AsO and NH3 reagent gas for Se quantification, respectively.
Two independent reference solutions and one SRM (NIST 1643e, trace elements in water) were
analyzed to confirm the DRC-ICPMS calibration and six digestion spikes (0.1-1.0 ppb) were
analyzed to assess matrix interferences and estimate method detection limits.
9
2.5. Stable Lead Isotope Analysis
A portion of each extract from the twelve samples that were run on the DRC-ICPMS were
subsequently analyzed for lead (Pb) isotope ratios using a High Resolution Magnetic Sector Field
ICPMS (HR-ICPMS) at U.S. EPA in Research Triangle Park, NC. The samples represented three
high mass and three low mass sample collection periods for the coarse and fine material. Four of
the twelve samples were coarse and fine samples collected on the same dates (two sets of sample
pairs).
Lead has four major isotopes, 204, 206, 207, and 208. 208
Pb is formed from the radioactive decay of 232
Th, 207
Pb from 235
U, and 206
Pb from 238
U. 204
Pb is referred to as common lead; it has no
radioactive parent, and is much less abundant than the other isotopes. The uranium and thorium
parents have differing decay rates resulting in predictable changes in lead isotope ratios. Plotting
results as 208
Pb/206
Pb isotope ratios (y-axis) versus 207
Pb/206
Pb isotope ratios (x-axis) often yields
a linear array (or a triangular field). Individual data points on such plots typically reflect the age at
which lead was incorporated into the host rock (ore, coal, sediments, oil, or oil sands for example).
The Pb ratios of the parent material are preserved during the subsequent process(es) that emitted the
lead into the environment. On 208
Pb/206
Pb versus 207
Pb/206
Pb plots, older lead will be found in the
upper right quadrant of the diagram, and younger lead in the lower left quadrant. The decrease in 207
Pb/206
Pb ratios from old to young lead reflects the difference in decay rate of the parent 235
U and 238
U isotopes. The difference in 208
Pb/206
Pb can reflect differences in the amount of parent 232
Th
and 238
U. More thorogenic source materials generate higher 208
Pb/206
Pb ratios. Plotting elemental
concentrations on the y-axis versus either 207
Pb/206
Pb or 208
Pb/206
Pb isotope ratios on the x-axis
can result in clusters of data points that are related to the source of the lead in the sample of interest.
2.6. Statistical Analysis
All data management and statistical analysis was conducted using SAS (Cary, NC) v9.2.
3. Results and Discussion
This final report covers the results of the sequential dichotomous sampler operation at the WBEA
AMS-1 Fort McKay site from February 22, 2010 to July 25, 2011, during which ARA processed
399 dichotomous filter sample pairs. This amount represents all the data that ARA has finalized
and provided to date for this period. To evaluate the potential for trace element quantification of the
dichotomous study filters, a subset of samples were selected for EDXRF and DRC-ICPMS analysis.
One hundred seven (107) fine/coarse sample pairs from March 2010 through July 2011 were
selected for EDXRF analysis. Thirty five (35) fine/coarse sample pairs from the EDXRF subset
were then selected for DRC-ICPMS analysis representing a distribution of high and low mass
sample periods. The results are presented and discussed below.
3.1. 2010 PM Mass Concentrations
From February 22, 2010 to December 24, 2010 ARA received 237 daily dichotomous filter sample
pairs from the 306 possible sampling days during the period, resulting in a data completeness of
77%. Reasons for the instrument not sampling include: (i) filter exchange error putting instrument
in “wait” mode until the site operator clears error and restarts; (ii) site operator not loading new
filter magazines before instrument runs out of filters in the supply magazine; and (iii) operator error.
The dichotomous sampler data files indicate that there were typically several days between filter
changes where the sampler ran out of filters and entered “stop” mode before the new filter
magazines were loaded and the sampler restarted. Following QA/QC data screening, there were
10
208 valid daily dichotomous filter sampling pairs. All but one of the 29 dichotomous filter sample
pairs that were screened out were invalidated due to total sample runs times that were less than 23
hours (resulting from power or sampler problems).
The 2010 PM2.5 and PMcoarse results are summarized below in Table 1. The average 24-hour PM2.5
mass concentration at AMS-1 Fort McKay was 5.7 ± 5.0 µg m-3 (mean ± standard deviation) and the
average 24-hour PMcoarse mass concentration was 7.3 ± 6.0 µg m-3
in 2010. Relative frequency
histograms of the fine and coarse PM data are presented in Figure 6a-b. Box plots depicting the
measures of central tendency and variance of the fine and coarse PM distributions are presented in
Figure 7. All 416 valid dichotomous filters received were above the ARA weighing detection limit
(3σ) of 0.12 µg m-3
.
Table 1. Summary Statistics of Valid AMS-1 PM (µµµµg m-3
) from Feb –Dec 2010 (n=208).
Mean Median Std. Dev. Min 25% Q1 75% Q3 Max
PM2.5 5.7 4.8 5.0 0.4 3.0 7.3 52.3
PMCoarse 7.3 5.6 6.0 0.3 2.8 9.8 30.2
Figure 6a. Relative Frequency Histogram of 2010 AMS-1 PM2.5 Concentrations.
Fine PM (µg m-3
)
0 10 20 30 40 50
Count
0
20
40
60
80
100
120
11
12
Figure 6b. Relative Frequency Histogram of 2010 AMS-1 PMcoarse Concentrations.
Coarse PM (µg m-3)
0 5 10 15 20 25 30
Count
0
10
20
30
40
50
13
Figure 7. Box Plots of 2010 AMS-1 PM Mass Concentrations.
Fine PM Coarse PM
PM
(µ
g m
-3)
0
10
20
30
40
50
60
90th Percentile
75th Percentile
25th Percentile
10th Percentile
Median
PM data from July 2010, presented in Figure 8, show a four-day PM2.5 mass excursion resulting in
the year’s highest concentration value of 52.3 µg m-3
, while the PMcoarse mass does not reflect a
similar dramatic increase. These data could represent a local impact from forest fires that could be
confirmed by investigating black carbon and/or potassium concentrations on the collected
dichotomous sample filters.
14
Figure 8. July 2010 PM2.5 and PMcoarse Mass Concentrations at AMS-1.
3.2. 2011 PM Mass Concentrations
From January 9, 2011 to July 25, 2011 ARA received 162 daily dichotomous filter sample pairs
from the 193 possible sampling days during the period, resulting in a data completeness of 84%.
Improved data completeness in 2011 was achieved by the operators as they addressed most of the
operation and troubleshooting issues that were identified. The only remaining significant source of
missed potential samples during this period was the site operator not loading new filter magazines
before instrument runs out of filters in the supply magazine. The dichotomous sampler data files
indicate that there were typically 1-5 days (3 ± 1) between filter changes where the sampler ran out
of filters and entered “stop” mode before the new filter magazines were loaded and the sampler
restarted. Following QA/QC data screening, there were 151 valid daily dichotomous filter sampling
pairs. All 11 of the dichotomous filter sample pairs that were screened out were invalidated due to
total sample run times that were less than 23 hours.
Seven of the eleven samples that ran less than 23 hours were associated with significant forest fire
impacted samples collected between May 18, 2011 and June 15, 2011. When the loading on the
filter reaches a point where the flow is reduced to below 90% of the programmed set point of either
channel, the sampler will shut down to (i) prevent damage to the system and (ii) to prevent the PM10
and PM2.5 cut points from significantly changing. When the sampler reaches the start time for the
next sample the filters are exchanged and the sampler starts again until those filters are also
overloaded. While samples that ran less than 23 hours are considered not valid indicators of the
daily concentrations, the concentrations are real and representative of that period of time when they
ran. The run time associated with these samples ranged from 4.3 – 15.8 hours (8.5 ± 5). The PM2.5
0
20
40
60
80
100
0
10
20
30
40
50
60
7/2/2010 7/5/2010 7/8/2010 7/11/2010 7/14/2010 7/17/2010 7/20/2010 7/23/2010 7/26/2010 7/29/2010
PM2.5 PMcoarse %Fine
15
and PMcoarse results associated with the forest fire impacted samples that ran less than 23 hours are
summarized below in Table 3.
Table 3. Summary Statistics of Invalid AMS-1 PM (µµµµg m-3
) from May 18 – June 15, 2011
During Forest Fire Impact (n=7).
Mean Median Std. Dev. Min 25% Q1 75% Q3 Max
PM2.5 351.7 423.8 157.3 152.0 175.9 479.5 522.9
PMcoarse 44.3 40.4 11.0 36.9 38.5 44.5 68.5
The 2011 valid PM2.5 and PMcoarse results are summarized below in Table 4. The average 24-hour
PM2.5 mass concentration at AMS-1 Fort McKay was 8.9 ± 19.7 µg m-3 (mean ± standard deviation)
and the average 24-hour PMcoarse mass concentration was 6.6 ± 5.6 µg m-3
in 2011. Relative
frequency histograms of the fine and coarse PM data are presented in Figure 9a-b. Box plots
depicting the measures of central tendency and variance of the fine and coarse PM distributions are
presented in Figure 10. Of the 302 valid dichotomous filters received by ARA for weighing all but
one coarse filter (0.11 µg m-3
) were above the weighing detection limit (3σ) of 0.12 µg m-3
.
Table 4. Summary Statistics of Valid AMS-1 PM (µµµµg m-3
) from Jan – Jul 2011 (n=151).
Mean Median Std. Dev. Min 25% Q1 75% Q3 Max
PM2.5 8.9 4.8 19.7 0.8 3.2 7.4 170.5
PMcoarse 6.6 4.5 5.6 0.1 2.4 9.4 25.1
The 2011 PM2.5 results were skewed higher (8.9 ± 19.7) versus the 2010 results (5.7 ± 5.0 µg m-3)
due to forest fire impacted samples collected at the site. Three high concentration forest fire
samples in particular collected at the site (May 29, May 30, and June 8) were significant outliers
143.4, 170.5, and 98.0, respectively (Figure 9a and Figure 10). Eleven other samples around this
same time period were invalidated as discussed above and summarized in Table 4 due to sample
filter overloading. If all eleven (11) forest fire impacted samples that ran less than 23 hours were
included in the analysis, they would have had an enormous impact on the 2011 summary statistics
(Table 5).
Table 5. Summary Statistics of All AMS-1 PM (µµµµg m-3
) from Jan – Jul 2011 (n=162).
Mean Median Std. Dev. Min 25% Q1 75% Q3 Max
PM2.5 23.7 5.0 78.6 0.8 3.3 8.0 522.9
PMcoarse 8.4 5.1 9.7 0.1 2.7 10.4 68.5
16
Figure 9a. Relative Frequency Histogram of 2011 AMS-1 PM2.5 Concentrations.
Fine PM (µg m-3)
0 20 40 60 80 100 120 140 160 180
Count
0
20
40
60
80
100
120
140
17
Figure 9b. Relative Frequency Histogram of 2011 AMS-1 PMcoarse Concentrations.
Coarse PM (µg m-3)
0 5 10 15 20 25 30
Co
un
t
0
10
20
30
40
50
18
Figure 10. Box Plots of 2011 AMS-1 PM Mass Concentrations (NOTE: Axis Break).
Fine PM Coarse PM
PM
(µ
g m
-3)
0
10
20
30
40
50
100
150
200
90th
Percentile
75th
Percentile
25th
Percentile10
th Percentile
Median
3.3. ED-XRF Analysis
Method detection limits (MDLs) were calculated by ARA for the major, minor, and trace elements
that their PANalytical Epsilon 5 instrument was calibrated to quantify, and they are presented in
Table 6. In general, it was found that the EDXRF instrument (i) was suitable for Al, Si, S, K, Ca,
Fe, and Mn, (ii) was potentially suitable for Na, Mg, Ti, and Zn, and (iii) was unsuitable for Cu, As,
Se, Pb, Ni, V, and Cd in the WBEA dichotomous filter samples from AMS-1. While the EDXRF
instrument did a nice job on sulfur and crustal elements, its inability to adequately detect the
anthropogenic tracers for the source types in the AOSR (e.g., V, Ni, Se, and Pb) would preclude it
from providing adequate data useful for routine PM air quality monitoring as well as for source
apportionment analysis.
3.4. DRC-ICPMS Analysis
Lower detection limits were calculated by ARA for the major, minor, and trace elements that their
Perkin Elmer Sciex DRCII instrument was calibrated to quantify, and are presented in Table 6. In
general, the DRC-ICPMS instrument was capable of providing excellent detection for most
elements of interest, particularly the crustal (Al, Fe, Ca, Si, La, Ce, Sm) and anthropogenic (V, Ni,
Se, and Pb) tracers for the sources in the AOSR. Results for the platinum group elements (Pt and
Pd) were generally at or below instrument detection limits. Larger sample volumes or sample pre-
concentration would be necessary to consistently quantify these elements. DRC-ICPMS analysis
results for the thirty five (35) filter pairs are presented in Table 7 (PM2.5) and Table 8 (PMcoarse).
19
Results show that virtually all elements of interest are detectable, even at the lowest ambient
concentrations.
Table 6. ARA DRC-ICPMS and XRF Method Detection Limits (ng m-3
).
Element DRC-ICPMS Isotope DRC-ICPMS MDL XRF MDL
Li 7 0.0366
Be 9 0.0025
Na 23 2.4210 5.62
Mg 26 1.4244 -
Al 27 5.2355 5.66
Si 28 51.6464 3.96
K 39 1.8733 0.93
Ca 44 23.8882 2.51
Ti 49 0.2247 1.62
V 51 0.0094 -
Cr 53 0.1853 -
Mn 55 0.1520 3.11
Fe 56 29.4447 3.45
Ni 62 0.1434 -
Cu 65 0.7259 1.02
Zn 68 0.4420 1.32
Se 78 0.0160 3.91
Rb 85 0.0036 -
Sr 88 0.0233 -
AsO 91 0.0067 1.89
Nb 93 0.0102 -
Mo 98 0.0193 -
Pd 108 0.0052 -
Cd 114 0.0020 -
Sn 118 0.0234 -
Sb 123 0.0178 -
Cs 133 0.0009 -
Ba 137 0.1403 18.06
La 139 0.0009 -
Ce 140 0.0017 -
Nd 143 0.0009 -
Ta 181 0.0001 -
W 182 0.0058 -
Pt 195 0.0003 -
Pb 208 0.1742 3.61
Th 232 0.0003 -
U 238 0.0007 -
20
Table 7. DRC-ICPMS Analysis Results of 35 PM2.5 samples at AMS-1 (ng m-3
).
Element Isotope n (>MDL) Mean Stdev Min Max
Li 7 29 0.0765 0.0577 0.0023 0.2165
Be 9 22 0.0020 0.0019 0.0001 0.0061
Na 23 32 16.90 39.27 0.7433 217.36
Mg 26 34 19.36 20.28 0.6049 104.74
Al 27 33 54.24 48.88 6.89 178.9
Si 28 30 128.35 112.80 6.999 392.8
K 39 34 171.02 315.37 2.697 1064.6
Ca 44 29 63.07 56.51 6.15 291.9
Ti 49 33 1.71 2.24 0.01 9.65
V 51 33 0.208 0.171 0.004 0.796
Cr 53 24 0.596 0.730 0.013 2.504
Mn 55 34 2.494 3.268 0.027 17.252
Fe 56 33 57.898 49.368 0.469 184.732
Ni 62 25 2.168 9.861 0.007 49.496
Cu 65 22 1.717 2.977 0.017 14.083
Zn 68 31 17.015 30.103 0.037 105.793
Se 78 33 0.199 0.480 0.012 2.770
Rb 85 34 0.454 0.759 0.004 3.042
Sr 88 33 0.352 0.291 0.007 1.295
As 91 34 0.271 0.396 0.002 1.486
Nb 93 22 0.007 0.007 0.001 0.027
Mo 98 32 0.546 0.055 0.001 0.210
Pd 108 17 0.023 0.049 0.001 0.196
Cd 114 34 0.552 1.587 0.001 7.824
Sn 118 33 0.116 0.147 0.007 0.734
Sb 123 30 0.050 0.065 0.001 0.290
Cs 133 34 0.012 0.014 0.001 0.053
Ba 137 33 0.910 0.898 0.041 4.557
La 139 34 0.036 0.031 0.002 0.117
Ce 140 34 0.069 0.060 0.005 0.230
Nd 143 34 0.028 0.025 0.001 0.094
Ta 181 30 0.0005 0.0005 0.0003 0.0021
W 182 28 0.0093 0.0088 0.0004 0.0365
Pt 195 17 0.0006 0.0009 0.0001 0.0038
Pb 208 32 0.762 0.826 0.020 3.389
Th 232 34 0.010 0.010 0.0001 0.0365
U 238 32 0.0031 0.0035 0.0001 0.0141
21
Table 8. DRC-ICPMS Analysis Results of 35 PMcoarse samples at AMS-1 (ng m-3
).
Element Isotope n (>MDL) Mean Stdev Min Max
Li 7 35 0.351 0.339 0.001 1.325
Be 9 34 0.0122 0.0114 0.0002 0.0436
Na 23 32 25.52 24.52 1.03 104.73
Mg 26 35 86.96 80.61 2.25 324.09
Al 27 35 375.40 366.45 14.72 1445.69
Si 28 35 761.12 735.65 2.04 2854.59
K 39 35 112.10 101.25 13.51 438.92
Ca 44 35 450.86 450.77 5.96 2054.31
Ti 49 35 8.89 8.30 0.28 34.27
V 51 35 0.878 0.760 0.003 2.557
Cr 53 27 0.919 2.026 0.014 10.706
Mn 55 34 9.23 13.04 0.05 71.51
Fe 56 35 329.93 316.80 1.77 1393.09
Ni 62 29 0.896 2.306 0.034 12.700
Cu 65 21 1.377 1.677 0.008 5.444
Zn 68 33 2.765 3.192 0.068 13.284
Se 78 35 0.123 0.162 0.002 0.899
Rb 85 35 0.575 0.536 0.031 2.226
Sr 88 35 1.544 1.425 0.006 6.221
As 91 35 0.084 0.078 0.002 0.316
Nb 93 30 0.038 0.036 0.004 0.146
Mo 98 28 0.080 0.091 0.002 0.433
Pd 108 20 0.0342 0.1064 0.0003 0.4841
Cd 114 34 0.0084 0.0176 0.0001 0.1031
Sn 118 29 0.0721 0.1565 0.0006 0.8501
Sb 123 28 0.0396 0.0583 0.0001 0.2165
Cs 133 35 0.0280 0.0271 0.0004 0.0944
Ba 137 35 3.775 3.280 0.024 16.358
La 139 36 0.2189 0.2065 0.0014 0.8569
Ce 140 36 0.4114 0.3852 0.0008 1.5645
Nd 143 35 0.1810 0.1686 0.0009 0.6661
Ta 181 35 0.0046 0.0099 0.0001 0.0554
W 182 32 0.0499 0.0518 0.0008 0.1906
Pt 195 21 0.0013 0.0027 0.0001 0.0122
Pb 208 28 0.2237 0.2781 0.0011 1.4190
Th 232 34 0.0595 0.0547 0.0031 0.2219
U 238 34 0.0149 0.0138 0.0007 0.0572
22
3.5. Stable Lead Isotope Analysis
Forty-two elements were quantified using the DRC-ICPMS. For purposes of this pilot feasibility
study, the results for elements that are representative tracer species for sources in the AOSR will be
included on the data plots. The elements chosen were Aluminum (Al), Calcium (Ca), Uranium (U),
Vanadium (V), Lead (Pb) and Zinc (Zn).
On a 208
Pb/206
Pb versus 207
Pb/206
Pb plot from the twelve (12) dichotomous samples analyzed in
this pilot feasibility study, a good range in isotopic ratios was found, indicating the potential for Pb
isotopes to assist in source type identification in the AOSR. This potential is exemplified when
either mass or element concentrations are combined on the same plot with Pb isotope ratios. On
such plots, several fields of data points (clusters) were found. The clusters likely correspond to
differences in source materials. A total of three or four fields of data clusters are found on Figures
11 and 12.
Figure 11. Relationship between PM Mass and 207
Pb/206
Pb.
0.0
5.0
10.0
15.0
20.0
0.8300 0.8400 0.8500 0.8600 0.8700 0.8800
207 Pb / 206 Pb
Ma
ss (
ug
/m3
)
Coarse Mass
Fine Mass
23
Figure 12. Relationship between 208
Pb/206
Pb and 207
Pb/206
Pb.
High mass concentration in the coarse fraction samples corresponds best to samples with elevated
Al, Ca, U and V concentrations (Figure 13). The lowest 207
Pb/206
Pb ratios are found in these high
mass concentration samples, clustering near a 207
Pb/206
Pb ratio of 0.835. High mass concentrations
found in the fine fraction samples corresponded best to elevated Zn and Pb concentrations. The 207
Pb/206
Pb ratios in these samples clustered near values of 0.862. The two other sets of data
clusters include samples with low concentrations for most elements, but differences in Pb isotope
ratios (0.860 versus 0.870) and Zn concentrations are still apparent.
2.0600
2.0700
2.0800
2.0900
2.1000
2.1100
2.1200
0.8300 0.8400 0.8500 0.8600 0.8700 0.8800
207 Pb / 206 Pb
20
8 P
b /
20
6 P
b
Coarse
Fine
24
Figure 13(a-f). Relationship between Trace Elements and 207
Pb/206
Pb.
Some indication of the source of the PM collected by the dichotomous sampler can be elucidated by
coupling the Pb isotope ratios to AOSR source type samples that had previously been analyzed for
Pb isotope ratios (Joe, need reference here). Three groups of source type samples were analyzed that
tend to cluster into distinct fields in isotope space. The lowest 207
Pb/206
Pb and 208
Pb/206
Pb ratios are
found in processed oil sand samples (e.g., fly ash and coke samples from the oil upgrading
facilities), the mid range 207
Pb/206
Pb and 208
Pb/206
Pb ratios are found in the raw oil sands, and the
highest 207
Pb/206
Pb and 208
Pb/206
Pb ratios were found in the tailings sand (raw oil sand from which
the oil had been extracted).
When comparing the dichotomous samples to the source samples, it appears that high mass, coarse
fraction samples are most similar to the raw oil sands signature in Pb isotope space (Figure 14).
This suggests the dichotomous samples captured fugitive dust emissions from mining operations on
the high mass concentration days. The signature of the Pb isotopes from the other dichotomous
samples (both coarse and fine fractions) clusters into fields that are closest to the signature of the
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
0.040
0.045
0.8300 0.8400 0.8500 0.8600 0.8700 0.8800
207 Pb / 206 Pb
Ura
niu
m (
ng/m
3)
U Coarse
U Fine
0.000
0.500
1.000
1.500
2.000
2.500
0.8300 0.8400 0.8500 0.8600 0.8700 0.8800
207 Pb / 206 Pb
Lead
(n
g/m
3)
Pb Coarse
Pb Fine
0
200
400
600
800
1000
1200
1400
0.8300 0.8400 0.8500 0.8600 0.8700 0.8800
207 Pb / 206 Pb
Alu
min
um
(n
g/m
3)
Al Coarse
Al Fine
0.000
2.000
4.000
6.000
8.000
10.000
0.8300 0.8400 0.8500 0.8600 0.8700 0.8800
207 Pb / 206 Pb
Zin
c (n
g/m
3)
Zn Coarse
Zn Fine
0
200
400
600
800
1000
1200
1400
1600
0.8300 0.8400 0.8500 0.8600 0.8700 0.8800
207 Pb / 206 Pb
Calc
ium
(n
g/m
3)
Ca Coarse
Ca Fine
0.000
0.500
1.000
1.500
2.000
2.500
0.8300 0.8400 0.8500 0.8600 0.8700 0.8800
207 Pb / 206 Pb
Van
ad
ium
(n
g/m
3)
V Coarse
V Fine
(a) (b)
(c) (d)
(e) (f)
25
tailings sand. The tailings sand, however, might also be expected to have a similar signature to that
of background soils away from the mining sites. At this time, the source of materials with high
concentrations of Zn and Pb in the fine mass fraction has not been identified. In addition, none of
the dichotomous samples captured a significant amount of material that corresponds to the
processed oil sand Pb isotope signatures.
Figure 14. Relationship between Dichotomous Sample and Source Sample Pb Isotopes.
1.960
1.980
2.000
2.020
2.040
2.060
2.080
2.100
2.120
2.140
0.7800 0.8000 0.8200 0.8400 0.8600 0.8800 0.9000207 Pb / 206 Pb
20
8 P
b /
20
6 P
b
Processed
Oil Sand
Tailings Sand
Dichot Coarse
Dichot Fine
26
4. Conclusions
• The ThermoScientific Model 2025 Sequential Dichotomous PM Sampler operated well in
the AOSR. Data completeness (77%) suffered mainly due to site operation procedures
rather than mechanical problems with the sampler. Recommendations for increased sample
collection completeness are presented in Section 5.1 of this report.
• The PM mass concentration data provided by ARA’s robotic weighing system was robust,
with all but one (99.8%) of the filters being above their weighing detection limit (3σ) of
0.12 µg m-3
.
• EDXRF analysis of the dichotomous sampler filters provided reliable trace element data for
sulfur and some other mainly crustal elements (Al, Si, K, Ca, Fe, Mn). These data are
amenable for use in evaluating regional sulfate, windblown dust, and extraction efficiency of
subsequent DRC-ICPMS analysis, but not for providing data for adequate source type
identification from routine PM air quality monitoring or adequate data for a robust statistical
receptor modeling effort.
• DRC-ICPMS analysis of the dichotomous sampler filters provided reliable data for a
comprehensive list of trace elements. The resulting data (even from low mass samples) are
amenable for routine PM air quality monitoring and a robust statistical receptor modeling
effort.
• HR-ICPMS stable Pb isotope analysis appears to have great potential for use as a source
apportionment tool in conjunction with a statistical receptor modeling effort.
5. Recommendations
5.1. Dichotomous Sampler Field Operation
Review of the Dichotomous Sampler Data files revealed that during the 2010 and 2011 sampling
years, the site technicians responsible for the operation of the sampler allowed it to run out of filters
at the end of each 2 week filter change cycle. This means the sampler ran out of filters and
automatically shut down on a routine basis, resulting in overall data completeness of only about
80%. Typically, 2-4 days later new filters were installed and the sampler was restarted. It is
recommended that a new sampling SOP be implemented that directs the site operators to load new
filter magazines while the last filter in the previous magazine is running. This procedural change
will minimize missed sample days, avoid confusion at the ARA lab concerning the status of the
instrument, and result in regular filter shipment and instrument maintenance scheduling. Field
operations and data management could also be tightened by routine (daily) automated polling of
data from the dichotomous sampler. This would allow near real-time detection of sample exchange
failures and equipment malfunctions by WBEA personnel and would allow better filter tracking by
ARA personnel.
5.2. Dichotomous Sampler Sample Analysis
It is recommended that an analysis plan be developed for archived dichotomous filter samples
collected to date using DRC-ICPMS (multi-element) and HR-ICPMS (Pb stable isotopes), with a
sufficient number of samples analyzed to satisfy the requirements of contemporary statistical
receptor models such as Positive Matrix Factorization (PMF) and Unmix that require a relatively
large data set (n>100) to provide feasible analytical solutions. A one in three day analysis plan
27
would provide analytical results for approximately 122 filter pairs. Annual receptor modeling
results will provide (i) a temporal record of significant sources impacting AMS-1 allowing WBEA
to track the relative strengths of source types in the AOSR as oil production increases, and (ii) the
data to evaluate the efficacy of adopted abatement measures.
5.3. Addition of PM Carbon Measurements
It is recommended that instrumentation to measure organic carbon (OC) and black carbon (BC) be
deployed to supplement the dichotomous sampler measurements. Particulate carbon is a major
component of ambient PM and can account for 25-50% of PM mass (Solomon et al., 2001; Landis
et al., 2001). Black carbon is emitted primarily from anthropogenic sources, such as diesel vehicle
exhaust, due to incomplete combustion. Organic carbon is emitted from anthropogenic sources,
biogenic sources and wildfires; and is formed in the atmosphere from gaseous precursors
(secondary aerosols). The addition of OC and BC measurements at AMS-1, in conjunction with the
dichotomous sampler and the URG AIM 9000 sampler, will provide data for (i) a comprehensive
PM mass reconstruction to fully elucidate the chemistry of aerosols in the AOSR, and (ii) an
integrated source apportionment analysis into the source types contributing to all the major PM
components in the OASR.
OC/BC measurements can be made using automated continuous instruments such as the Sunset
Instruments (Portland, OR) Model 4 analyzer or from integrated filter-based collection. FRM/FEM
samplers (16.7 LPM) configured with quartz filters can provide sufficient mass to make reliable
OC/BC measurements. The use of high volume PM2.5 samplers such as the Tisch (Village of
Cleves, OH) model TE-1202 (113 LPM) or model TE-1000 (226 LPM) are typically used for the
collection of samples for subsequent speciation of primary and secondary organic species,
respectively. The speciation of OC would provide important information on (i) the sources of
primary organic aerosol emissions, (ii) the importance of atmospheric chemistry leading to the
formation of secondary aerosols, and (iii) the overall impact of OC on PM concentrations in the
AOSR. The nature of the raw oil sands and the resulting on-site upgrading activities may result in
unique emission profiles that may prove extremely beneficial in elucidating the relationship
between oil production activities and PM concentrations in the AOSR. Unique tracers for the
AOSR activities might include napthenic acids and unique tracers for forest fires might include
levoglucosan.
5.4. Expansion of the Dichotomous Sampler Network
It is recommended that a plan be developed for the expansion of the dichotomous monitoring
network to include (i) a regional background site, (ii) a significant source impacted site, and (iii) a
downwind site. The addition of a background site will allow quantification of the AOSR local
source enhancement to ambient PM. The addition of a source impacted site would assist in the
elucidation of the geographic location of significant PM air pollution sources in the AOSR by
enabling the use of hybrid receptor models like quantitative transport bias analysis (QTBA). The
QTBA model can incorporate meteorological and multiple site monitoring data to generate spatial
probability fields of source areas (Keeler and Samson, 1989). The downwind site will provide
information on transport scales for fine and coarse PM and will be instrumental in constraining
atmospheric dispersion and transport models.
As part of the network expansion, it is also recommended that an ongoing one in three day analysis
plan be developed for the inorganic speciation of PM2.5 and PMcoarse. Over time, this recommended
level of effort will generate a unique time series of PM2.5 and PMcoarse mass and trace element data,
as well as generate an invaluable inventory of PM2.5 and PMcoarse samples for use in special studies
28
(e.g., Pb isotope analysis, investigation of forest fire impacts, extreme event analysis). Collection of
daily samples will provide the ability to investigate transient events that could otherwise be missed
on a one in three day sampling plan and provide additional filters for analysis to make up for
maintenance or repair related sampler down time.
6. Response to Initial Review Comments
6.1. Housing the Sampler Inside the Monitoring Station
Question: The current sampler is housed inside the station, while typical applications are for
outside installations. Are there any concerns around certain compounds being lost due to being in a
cold ambient environment and then sitting on a filter for several days at room temperature? How
much does the internal/external sampling conditions factor into the final result?
Answer: In the winter, there may be a potential for the loss of some semi-volatile compounds.
However, all reference methods for the weighing of filters call for the equilibration of the filters for
a minimum of 24 hours at 20-23ºC and 30-40% relative humidity. Therefore, we do not think there
would be an additional significant loss of sample mass over and above that expected to be lost
during the filter equilibration and weighing process.
The inorganic species targeted by DRC-ICPMS analysis for the proposed source apportionment
analysis are non-volatile and would not be significantly impacted by sampling inside the shelter. If
the reports recommendation to initiate carbon PM measurements is implemented, a total PM mass
reconstruction could be performed and any loss of PM mass could be quantified. If additional
dichotomous samplers are procured for deployment, we suggest a short collocated sampling
experiment during the winter with one sampler inside and one sampler outside. This test would
provide a sound basis for future sampler placement. Because of the negative impact of cold
temperatures on the sampler seals, pumps, and filter exchange mechanisms it is preferable that the
samplers are located inside the shelter.
6.2. Acceptance of Dichotomous Sampler
Question: Would the wider audience of monitoring stakeholders accept the use of these samplers
based upon this report and in comparison with other sampling methods? Based on the sample
location recommendation, are 3 stations enough to represent the airshed for PM data?
Answer: The ThermoScientific model 2025 sequential dichotomous PM sampler is a U.S. EPA
designated Federal Equivalent Method for PM2.5 that has been evaluated against both manual and
sequential FRM samplers and found to be equivalent (Poor et al., 2002; Chen et al., 2011). In
addition, dichotomous samplers have seen broad application in both the international PM
monitoring and research communities (Dzubay and Stevens, 1975; McFarland et al., 1978; Stevens,
et al., 1980; O'Conner and Jaklevic, 1981; Loo and Cork, 1988; Chan et al., 1997; Wei et. al, 1999;
Cabada et al., 2004; Kim et al., 2005), including the Canadian National Air Pollution Surveillance
(NAPS) Network (Brook et al., 1996) and the seminal work linking PM and health effects such as
the Harvard Six Cities Study (Dockery et al., 1993). The science is clear and we anticipate no
problems with the wider audience of monitoring stakeholders accepting the use of dichotomous
samplers.
29
Recommendation 5.4 suggests the addition of three (3) dichotomous sampling sites to the existing
site AMS #1 at Fort McKay making a total of four (4) sites. PM2.5 concentrations should be well
characterized with a long term four sampler network in the AOSR, given the dispersion and mixing
characteristics of fine mode aerosols. PMcoarse concentration can vary significantly over short
spatial and temporal scales. This proposed network design (collection and analysis) will provide
very important data, and should be adequate to address the goals identified above including (i) to
quantify the local source enhancement, (ii) to elucidate transport scales of PM, (iii) to constrain
atmospheric dispersion and transport models, and (iv) to identify the geographic location of
significant PM air pollution source types in the AOSR.
6.3. Movement of Dichotomous Sampler
Question: Why wouldn’t the current sampler be moved to another location, rather than install
additional samplers? This would be consistent with WBEA’s typical approach for non-compliance
sampling.
Answer: If the only goal of the dichotomous sampling strategy is to quantify PM mass for
regulatory compliance, moving a single sampler around the network may be satisfactory. If the goal
is to understand the dynamics of PM emission, transport, and atmospheric deposition; then
simultaneous sampling at multiple locations is required. Spatial dynamics, statistical association,
and variance/covariance structure between network sites is critical to addressing the research goals
identified above.
30
6.4. Sampling by Difference versus Dichotomous Sampler
Question: While this report seems to show that the dichotomous sampler works, is it really any
better than sampling for PM2.5 fine separate from PMcoarse? Various industry approvals require
sampling of PM2.5 and PM10 and this is completed through the separate filter measurements.
Answer: There are three main reasons why we feel the dichotomous sampler is a superior approach
to sampling for PM2.5 and PM10 separately, and calculating PMcoarse by difference:
(1) The chemistry of fine mode (acidic) and coarse mode (basic/alkaline) aerosols are very
different. Collecting them together on a common filter allows for surface chemistry to occur
and can lead to increased gas phase artifact formation and aerosol decomposition. By design,
the dichotomous sampler collects all the PMcoarse aerosols from the total sampler flow of 16.7
LPM on a filter with an actual 1.67 LPM flow rate (pre-concentration). This means there is an
order of magnitude less volume of air flowing through the filter leading to (i) much less chance
of gas phase artifact formation and (ii) lower face velocity/pressure drop which leads to less
aerosol decomposition.
(2) There is always an associated uncertainty with analytical chemistry results. In many cases the
signal to noise ratio is a major determiner of lower limits of detection and overall uncertainty.
For anthropogenic species that are present predominantly in the fine mode aerosols, the PM2.5
correction may be larger than the total PMcoarse concentration, leading to higher overall
uncertainties in the analytical results and hindering the subsequent hypothesis testing and
statistical model applications for the data.
(3) Logistically, running one sampler per site instead of two reduces the time and effort required
for the operation, maintenance, data collection, and QA/QC procedures.
7. Path Forward
7.1. Evaluation of Data Completeness for the Dichotomous Sampler versus Existing FRMs
The dichotomous study team did not have knowledge of the existence of the FRM samplers at
AMS-1 prior to receipt of the review comments on the initial report, and therefore did not have a
comparison between the samplers in mind during the filter selection for chemical analysis. WBEA
subsequently provided the study team a 2010 and a 2011 data file containing a total of 44 PM2.5 and
107 PM10 FRM sample results from Fort McKay. We did not receive a schedule of planned FRM
sampling days to compare to the actual filters collected and successfully analyzed, so overall
completeness could not be assessed. However, we ran the samples in the data set through the
QA/QC screening program that was developed for the dichotomous data set and was able to
calculate the percentage of reported filter collections that passed QA/QC and compare these results
to the dichotomous sampler in Table 9. The QA/QC screening parameters are (i) a valid daily
sample must have a run time of 24 ± 1 hour, and (ii) the PM mass must be greater than 0 µg m-3
.
The reasons for the dichotomous samples not passing QA/QC are discussed in sections 3.1 and 3.2,
including significant forest fire impacted samples causing excessive loading on the filters and
reaching a point where the sampler automatically shuts down to protect hardware components.
31
Table 9. Collected Filters that Pass QA/QC from FRM and Dichotomous Samplers.
Sampler Total Samples Total Passed QA/QC Percentage Passed
FRM PM2.5 44 20 45%
FRM PM10 107 46 43%
Dichot PM2.5 399 360 90%
Dichot PM10 399 360 90%
7.2. Comparison of Dichotomous Sampler and Existing FRM Mass Data
There is very limited overlap in the FRM and dichotomous sampler data sets. The PM2.5 FRM
sampler began operation on February 2, 2011 and the dichotomous sampler ended operation on July
25, 2011. During the overlap time frame, nineteen (18) PM2.5 FRM samples were contained in the
data set provided by WBEA, of those samples eight (7) passed QA/QC criteria (February 2, 8, 20,
26; March 4, 10, 16). The dichotomous sampler was not run on February 8 or March 10, 2011,
leaving five (5) valid observations for comparison. Figure 15 depicts the relationship between the
PM2.5 FRM and the dichotomous PM2.5. The dichotomous sampler error bars are the weighing
uncertainty provided by ARA. The slope of the linear regression line is 0.74 and the coefficient of
determination is 0.99. On average, the two samplers provided highly correlated results with the
FRM sampler measuring 26% higher PM2.5 mass.
32
Figure 15. PM2.5 Relationship between FRM and Dichotomous Samplers at AMS #1.
WBEA FRM (µg m-3)
0 2 4 6 8 10 12 14 16
WB
EA
Dic
ho
t F
ine
(µg m
-3)
0
2
4
6
8
10
12
14
16
Dichot = 0.7406 * FRM + 0.8856
r2 = 0.9942
Data from the WBEA continuous TEOM PM2.5 monitor at Fort McKay were also provided to the
study team. The relationship between the dichotomous sampler and the TEOM for the five
common sampling days between the FRM and dichotomous sampler are presented in Figure 16.
The slope of the linear regression line is 1.39 and the coefficient of determination is 0.997. On
average, the two samplers provided highly correlated results with the dichotomous sampler
measuring 39% higher PM2.5 mass. During this small sample comparison window in the winter of
2011, all three sampling methods were highly correlated with mass concentrations reported by the
samplers FRM>dichot>TEOM. All the valid 2011 TEOM and dichotomous sampler PM2.5 mass
data was also compared and is presented in Figure 17. When evaluating the longer data period
(January – July, 2011) including winter and summer seasons, the instruments compared very well
with a slope of 1 and a coefficient of determination of 0.95.
33
Figure 16. PM2.5 Relationship between TEOM and Dichotomous Sampler at AMS #1.
WBEA TEOM (µg m-3)
0 2 4 6 8 10 12
WB
EA
Dic
ho
t ( µ
g m
-3)
0
2
4
6
8
10
12
Dichot = 1.393 * TEOM - 0.009
r2 = 0.997
34
Figure 17. PM2.5 Relationship between TEOM and Dichotomous Sampler at AMS #1
(January 2011 – July 2011; n=149).
WBEA TEOM (µg m-3
)
0 25 50 75 100 125 150 175
WB
EA
Dic
hot
(µg
m-3
)
0
25
50
75
100
125
150
175
Dichot = 1.00 * TEOM + 0.63
r2 = 0.952
In all of 2010 & 2011 there are eight (8) days with valid PM2.5 and PM10 FRM filters (July 26, 2011;
August 7, 2011; August 13, 2011; August 19, 2011; September 30, 2011; October 6, 2011; October
12, 2011; October 30, 2011). Since the dichotomous sampler study ended on July 25, 2011, there
are no days in common for which to base a comparison.
7.3. Comparison of Dichotomous Sampler and Existing FRM Metals Data
The dichotomous sampler filters for the five (5) days in common with the PM2.5 FRM are currently
being extracted in preparation for analysis by DRC-ICPMS by ARA. An addendum to this report
detailing this comparison will be submitted shortly.
35
8. Acknowledgements
The pilot sequential dichotomous sampler PM research study described in this report was funded by
WBEA. We thank Kevin Percy (WBEA), Allan Legge (Biosphere Solutions), and Robert K.
Stevens for their support and insight. The content and opinions expressed by the authors in this
report are their own and do not necessarily reflect the views of the Wood Buffalo Environmental
Association (WBEA) or of the WBEA membership.
9. References
Brook, J.R.; Dann, T.F.; Burnett, R.T. 1997. The relationship among TSP, PM10, PM2.5, and
inorganic constituents of atmospheric particulate matter at multiple Canadian locations. Journal of
the Air and Waste Management Association, 47, 2-19.
Chan, Y.C.; Simpson, R.W.; McTainsh, G.H.; Vowles, P.D.; Cohen, D.D.; Bailey, G.M. 1997.
Characterization of chemical species in PM2.5 and PM10 aerosols in Brisbane, Australia. Atmos
Environ, 31, 3773-3785.
Chen, F.L.; Vanderpool, R.; Williams, R.; Dimmick, F.; Grover, B.D.; Long, R.; Murdocj, R. 2011.
Field Evaluation of portable and central site PM samplers emphasizing additive and differential
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