Carbonaceous aerosol is a major contributor to the total aerosol load and
being monitored by diverse measurement approaches. Here, 10 years
(2005–2015) of continuous carbonaceous aerosol measurements collected at the
Centre of Atmospheric Research Experiments (CARE) in Egbert, Ontario, Canada,
on quartz-fiber filters by three independent networks (Interagency
Monitoring of Protected Visual Environments, IMPROVE; Canadian Air and
Precipitation Monitoring Network, CAPMoN; and Canadian Aerosol Baseline
Measurement, CABM) were compared. Specifically, the study evaluated how
differences in sample collection and analysis affected the concentrations of
total carbon (TC), organic carbon (OC), and elemental carbon (EC). Results
show that different carbonaceous fractions measured by various networks were
consistent and comparable in general among the three networks over the 10-year period, even with different sampling systems/frequencies, analytical
protocols, and artifact corrections. The CAPMoN TC, OC, and EC obtained from
the DRI
model 2001 thermal–optical carbon analyzer following the IMPROVE-TOR
protocol (denoted as DRI-TOR) method were lower than those determined from the
IMPROVE_A TOR method by 17 %, 14 %, and 18 %,
respectively. When using transmittance for charring correction, the
corresponding carbonaceous fractions obtained from the Sunset-TOT were lower
by as much as 30 %, 15 %, and 75 %, respectively. In comparison, the
CABM TC, OC, and EC obtained from a thermal method, EnCan-Total-900 (ECT9), were higher than
the corresponding fractions from IMPROVE_A TOR by 20 %–30 %,
0 %–15 %, and 60 %–80 %, respectively. Ambient OC and EC concentrations were
found to increase when ambient temperature exceeded 10 ∘C. These
increased ambient concentrations of OC during summer were possibly
attributed to secondary organic aerosol (SOA) formation and forest fire
emissions, while elevated EC concentrations were potentially influenced by
forest fire emissions and increased vehicle emissions. Results also show
that the pyrolyzed organic carbon (POC) obtained from the ECT9 protocol could provide additional information on SOA although more
research is still needed.
Introduction
Carbonaceous aerosols, including elemental carbon (EC), which is often
referred to as black carbon (BC) and organic carbon (OC), make up a large
fraction of the atmospheric fine particulate matter (PM) mass (Heintzenberg,
1989). Atmospheric OC and BC particles that are emitted directly into the
atmosphere have both natural (e.g., biomass burning or forest fires) and
anthropogenic (e.g., internal combustion engines) sources. A significant
amount of the particulate OC is also formed in the atmosphere through
oxidation and condensation of volatile organic compounds (e.g., isoprene and
terpenes), which are emitted directly from vegetation. BC is a by-product of
incomplete combustion of hydrocarbon fuels, generated mainly from fossil
fuel combustion and biomass burning. Atmospheric particles have direct and
indirect influences on climate, visibility, air quality, and ecosystems and
adverse human health effects (Bond et al., 2013; Japar et al., 1986; Lesins et
al., 2002; Watson, 2002). Atmospheric BC absorbs solar radiation while OC
primarily scatters it (Schulz et al., 2006). However, BC and OC co-exist in
atmospheric particles and the net radiative forcing of the aerosol particles
depends on the particle size, composition, and mixing state of the
particles, while all of these variables also change as aerosol particles age
(Fuller et al., 1999; Lesins et al., 2002).
Black carbon is a generic term in the literature and it is often
interchanged with other terms such as EC, soot, refractory BC, light-absorbing carbon, or equivalent BC (Petzold et al., 2013). Although BC is
highly relevant to climate research, there is no universally agreed upon and
clearly defined terminology concerning the metrics of carbonaceous aerosol.
The use of different terminology is linked to the different methodologies
used to measure different physical or chemical properties of BC. The
scientific community generally accepts that BC particles
possess the following properties: (1) strongly absorb in the visual
spectrum with an inverse wavelength (λ) dependence (i.e., λ-1) (Bond and Bergstrom 2006), (2) refractory in nature with a
vaporization temperature near 4000 K (Schwarz et al., 2006), (3) insoluble
in water and common organic solvents (Fung, 1990), (4) fractal-like
aggregates of small carbon spherules (Kittelson, 1998), (5) contain a
large fraction of graphite-like sp2-bonded carbon atoms (Bond et al.,
2013; Petzold et al., 2013), and (6) chemical inertness in the atmosphere
(Bond et al., 2013). In this article, the recommendation from Petzold et al. (2013) is adopted as the definition of BC whenever the context of climate
effects impacted by strong light-absorption carbonaceous substance is
mentioned. EC is referred to as the carbon mass determined from the thermal
evolution analysis (TEA) or thermal–optical analysis (TOA) of carbonaceous
materials at the highest temperature set point (e.g., >550∘C) under an oxygenated environment. It is also assumed that
ambient EC and BC concentration time series correlate with each other.
TOA and TEA have been applied in many long-term monitoring networks with
various protocols to quantify OC and EC concentrations from aerosol deposits
on quartz-fiber filters (Birch and Cary, 1996; Cachier et al., 1989; Cavalli
et al., 2010; Chow et al., 1993; Huang et al., 2006; Huntzicker et al.,
1982) due to the simplicity in filter sample collection and the analytical
procedures. TOA and TEA provide a direct measurement of the carbon mass in
the collected PM mass. One of the limitations of TOA and TEA is the need for
sufficient sampling time to accumulate enough mass for precise measurements
(i.e., ensuring a high signal-to-noise ratio), which constrains the temporal
resolution of such samples. In addition, EC and OC are defined differently
in different protocols and could affect the absolute mass values measured.
Generally, OC is quantified under a pure helium (He) atmosphere at a low
heating temperature whereas EC is quantified under an oxygen (O2)/He
atmosphere at high temperatures. Estimates of total carbon (TC = OC + EC)
derived from different TOA and TEA methods are generally consistent, whereby
the differences in OC and EC estimates could vary from 20 % to 90 %, and
often larger differences are found for EC, owing to its smaller contribution
to TC (Cavalli et al., 2010; Chow et al., 1993, 2001, 2005; Countess, 1990;
Watson et al., 2005; Hand et al., 2012).
During thermal analysis, some of the OC chars to form pyrolyzed organic
carbon (POC) when heated in the inert He atmosphere, darkening the filter
(Chow et al., 2004; Watson et al., 2005). When O2 is added, POC combusts
to EC, resulting in an overestimation of EC of the filter. The formation of
POC depends on the nature of the organic materials; amount of the
oxygenated compounds in the collected particles; rate, duration, and
temperature of the heating; and the supply of O2 in the carrier gas
(Cachier et al., 1989; Chan et al., 2010; Han et al., 2007; Yang and Yu,
2002). POC in TOA is estimated by monitoring reflectance and/or
transmittance of a 633–650 nm laser beam, which is termed thermal–optical
reflectance (TOR) or thermal–optical transmittance (TOT),
respectively. When the reflected or transmitted laser signal returns to its
initial intensity at the start of the analysis (i.e., at OC / EC split point),
it is assumed that artifact POC has left the sample and the remaining carbon
belongs to EC. The carbon mass before the split point is defined as OC
whereas that after the split point is defined as EC. POC is defined as the
mass determined between the time when O2 is introduced and the OC / EC
split point. Different from TOA, the TEA used in this study applies a
different approach for POC determination (see below).
Specifications for the filter sampling systems and
analytical instruments/methods used by the three networks.
IMPROVECAPMoN CABMData coverage period2005–20152005–20072008–20152005–2015Analytical instrumentDRISunsetDRISunsetThermal/thermal–optical protocolIMPROVE_AIMPROVEIMPROVEECT9Pyrolyzed organic carbon detectionReflect.Transmit.Reflect. & transmit.Retention timeParticle size selection methodCycloneImpactor platesImpactor platesCycloneParticle size cutoff diameter (nm)2.52.52.52.5Sampling flow rate (L min-1)22.810.010.016.7Filter media model2500QAT-UP2500QAT-UP2500QAT-UP2500QAT-UPQuartz filter diameter (mm)25474747Filter deposition exposure area (cm2)3.5310.7510.7513.85Filter face velocity (cm s-1)107.6515.5015.5020.09Sampling frequencyDaily every 3 dDaily every 3 dDaily every 3 dIntegrated weeklyDaily sampled air volume (L d-1)31 68014 40014 40024 048Air volume per sample (m3)31.6814.414.4168.3Positive artifact correctionYesYesYesNoFilter blank correctionYesNoNoYesNumber of 24 h sample1228254907–Number of weekly sample–––476Number of monthly averaged sample1242893117
Quartz-fiber filters adsorb organic vapours (Chow et al., 2009; Turpin et
al., 1994; Viana et al., 2006; Watson et al., 2010), resulting in non-PM
contributions to OC and charring enhancement within the filter. These vapours
are adsorbed passively when the filter is exposed to air and more so as air
is drawn through the filter during PM sampling. Sampling at low filter face
velocities for long periods of time could lead to more adsorption (McDow and
Huntzicker, 1990), while using high filter face velocities for longer sample
durations may result in evaporation of semi-volatile compounds as negative
artifacts (Khalek, 2008; Sutter et al., 2010; Yang et al., 2011). The
positive OC artifact from adsorption usually exceeds the negative
evaporation artifact, especially at low temperatures, resulting in OC
overestimation (Watson et al., 2009; WMO, 2016). This can be corrected by
subtracting the OC concentration from field blanks or backup filters located
downstream of a Teflon-membrane or quartz-fiber filter (Chow et al., 2010;
Watson et al., 2005, 2010).
Previous studies further suggested that TOT could over-estimate the POC mass
more than TOR, resulting in higher POC (and lower EC) because of the
charring of the adsorbed organic vapours within the filter (Chow et al., 2004;
Countess, 1990). Since only a portion (0.5–1.5 cm2) of the filter is
analyzed, inhomogeneous PM deposits add to measurement uncertainty when OC
and EC are normalized to the entire filter deposit area. Deposits that are
light or too dark can cause unstable laser signals that affect the OC / EC
split (Watson et al., 2005).
The short lifetime of atmospheric aerosols (in days to weeks) and the
different chemical and microphysical processing that occur in the atmosphere
result in high spatial and temporal variations in aerosol properties. To
facilitate the determination of the trends in emission changes and
evaluation of the effectiveness of emission mitigation policies (Chen et al., 2012), consistent long-term atmospheric measurements are required, including
aerosol carbon fractions. The emission sources of OC and EC at regional
and global scales are often constrained through the use of atmospheric
transport models in conjunction with long-term OC and EC measurements (Collaud
Coen et al., 2013; Huang et al., 2018). Usually an integration of data sets
from different networks is necessary for sufficient spatial coverage. The
objective of this study is to conduct an inter-comparison study for
evaluating the comparability and consistency of 10-year co-located
carbonaceous aerosol measurements at Egbert made by three North American
networks (Interagency Monitoring of Protected Visual Environments, Canadian
Air and Precipitation Monitoring Network, and Canadian Aerosol Baseline
Measurement), all of which use different sampling instruments, frequencies,
durations, analytical methods, and artifact corrections. This
inter-comparison study is also expected to provide some
suggestions/recommendations for improving the compatibility and consistency
of long-term measurements.
Sampling and measurementsSampling site
The sampling station is the Center for Atmospheric Research Experiments
(CARE) located near Egbert, Ontario (44∘12′ N,
79∘48′ W, 251 m a.s.l.), Canada. This station is
owned and operated by Environment and Climate Change Canada (ECCC), and is
located 70 km NNW of the city of Toronto. There are no major local
anthropogenic sources within about 10 km of the site. Air that reaches this
site from southern Ontario and the northeastern United States typically
carries urban or anthropogenic combustion pollutants that were emitted
within last 2 d (Rupakheti et al., 2005; Chan and Mozurkewich, 2007; Chan
et al., 2010). Air from the north generally contains biogenic emissions and
is often accompanied by SOA during summer (Chan et al.,
2010; Slowik et al., 2010). Table 1 compares the instrument and analytical
specifications among the three networks.
The Interagency Monitoring of Protected Visual Environment
Network
IMPROVE, established in 1987, includes regional-scale monitoring
stations for detecting visibility trends, understanding long-term trends,
and evaluating atmospheric processes (Malm, 1989; Malm et al., 1994; Yu et
al., 2004). IMPROVE operates about 150 sites and provides long-term records
of PM10 and PM2.5 (particles with aerodynamic diameter less than
10 and 2.5 µm, respectively) mass as well as PM2.5 composition,
including anions (i.e., chloride, nitrate, and sulfate), and carbon (OC and
EC). IMPROVE 24 h samples at Egbert were acquired once every third day
from 2005 to 2015. The sampling period was from 08:00 to 08:00 LST (local
standard time) except for 16 August 2006 through 24 October 2008
(from 00:00 to 00:00 LST). Module C of the IMPROVE sampler uses a modified
air-industrial hygiene laboratory (AIHL) cyclone with a 2.5 µm cut
point at a flow rate of 22.8 L min-1. PM samples were
collected onto a 25 mm diameter quartz-fiber filter (Tissue quartz, Pall
Life Sciences, Ann Arbor, MI, USA), which were pre-fired at 900 ∘C
for 4 h. Once sampled, filters were stored in a freezer until they were
ready to be analyzed in the DRI laboratory in Reno. All samples were
analyzed by the IMPROVE_A thermal–optical reflectance
protocol (Fig. S1a in the Supplement) (Chow et al., 2007) as
shown in Table S1 (Supplement). The IMPROVE data (denoted as IMPROVE_A TOR) were obtained from the Cooperative Institute for Research in the Atmosphere (CIRA) of the Federal Land Manager Environmental Database (FED), Colorado State University, Fort Collins, CO http://views.cira.colostate.edu/fed/ (last access: 13 August 2019) (Malm et al., 1994).
The Canadian Air and Precipitation Monitoring Network
CAPMoN was established in 1983 to understand the source impacts of acid-rain-related pollutants from long-range transport to the Canadian soil and
atmosphere. The network operates 30 regionally representatives sites (as of
2015) across Canada with most located in Ontario and Quebec. Measurements
include PM, trace gases, mercury (in both air and precipitation),
tropospheric ozone, and multiple inorganic ions in air and precipitation. In
addition, a few sites include carbon (OC and EC) measurements
(https://www.canada.ca/en/environment-climate-change/services/air-pollution/monitoring-networks-data/canadian-air-precipitation.html, last access: 13 August 2019).
The 24 h samples (08:00 to 08:00 LST) were acquired every third day
from 2005 to 2015 using the modified Rupprecht and Patashnick (R&P) model
2300 PM2.5 speciation sampler with ChemComb cartridges and PM2.5
impactor plates with impactor foam to direct particles onto a 47 mm diameter
tissue quartz-fiber filter (Thermo Scientific, Waltham, MA, USA) operated at
10 L min-1. Samples were taken on the same date when the IMPROVE samples were
collected. A second parallel cartridge was configured with a 47 mm front
Teflon-membrane filter and a quartz-fiber backup filter to estimate vapour
adsorption artifact. All quartz-fiber filters were pre-fired at either
800 or 900 ∘C for over 2 h and cooled at
105 ∘C overnight and stored in a freezer (-15∘C) before
loading onto sample cartridges. The loaded cartridges were shipped from
the CAPMoN Toronto laboratory to the Egbert site at ambient temperature.
After sample collection, filter cartridges were shipped back to the
laboratory at ambient temperature where the sampled filters were stored in a
freezer until they are ready to be analyzed in the CAPMoN Toronto
laboratory.
Carbon was determined using the Sunset laboratory-based carbon analyzer
(Sunset Laboratory Inc., OR, USA; http://www.sunlab.com/, last access: 13 August 2019) following the
IMPROVE-TOT protocol from 2005 to 2007 (denoted as Sunset-TOT), then by DRI
model 2001 thermal–optical carbon analyzer following the IMPROVE-TOR
protocol (denoted as DRI-TOR) from 2008 to 2015 (Chow et al., 1993). As
shown in Table S1, the temperature settings for the IMPROVE protocol (i.e.,
DRI-TOR) for CAPMoN samples are lower than those of the IMPROVE_A
TOR protocol for IMPROVE samples by 20 to 40 ∘C
(Fig. S1b). Overall, Chow et al. (2007) found that the small difference in
the temperature ramp between these protocols results in correlated but
different OC, EC, and TC mass.
The Canadian Aerosol Baseline Measurement Network
The Climate Chemistry Measurements and Research (CCMR) section in the
Climate Research Division of ECCC has operated the Canadian Aerosol Baseline
Measurement (CABM) network since 2005 to acquire data relevant to climate
change
(https://www.canada.ca/en/environment-climate-change/services/climate-change/science-research-data/greenhouse-gases-aerosols-monitoring/canadian-aerosol-baseline-measurement-program.html, last access: 13 August 2019).
The CABM network includes six sites (as of 2016) for aerosol chemical,
physical, and optical measurements that cover ecosystems at coastal,
interior urban–rural areas, boreal forests, and the Arctic. Measurements are
intended to elucidate influences from various emission sources on regional
background air, including biogenic emissions, biomass burning, and
anthropogenic contributions from industrial/urban areas.
The CABM filter pack system uses a PM2.5 stainless steel cyclone
(URG-2000-30EHS) operated at 16.7 L min-1 for sampling from 2006 to 2015 with
an operator manually changing the 47 mm quartz-fiber filter on a weekly
basis. All quartz-fiber filters were pre-fired at 900 ∘C overnight
prior to being sampled. Once sampled, filters were shipped cold and then stored
in a freezer until they were ready to be analyzed in the CCMR laboratory in
Toronto. A TEA method, EnCan-Total-900 (ECT9), developed by Huang et al. (2006) and refined later (Chan et al., 2010), was used to analyze the OC,
POC, and EC on the quartz-fiber filters using a Sunset laboratory-based
carbon analyzer. The ECT9 protocol was developed to permit stable carbon
isotope (13C) analysis of the OC and EC masses without causing isotope
fractionation, as demonstrated by Huang et al. (2006). This method
first heats the filter at 550 and 870 ∘C for 600 s
each in the He atmosphere to determine OC and POC (including carbonate
carbon; CC), respectively, and then combusts the sample at 900 ∘C
under 2 % O2 and 98 % He atmosphere for 420 s to determine EC
(Fig. S1c and Table S1). The ECT9 POC definition (released as CO2 at
870 ∘C) includes the charred OC and some calcium carbonate
(CaCO3) that decomposes at 830 ∘C, as well as any refractory
OC that is not combusted at 550 ∘C. Chan et al. (2010) found that
POC determined by ECT9 was proportional to the oxygenated compounds (e.g.,
aged aerosol from atmospheric photochemical reaction) and possibly
humic-like materials. Consistent with the IMPROVE_A TOR
protocol (Chow et al., 2007), OC is defined as the sum of OC and POC, as CC
is usually negligible in PM2.5.
CABM sites are also equipped with particle soot absorption photometers (PSAPs;
Radiance Research, Seattle, WA, USA) that continuously monitor aerosol light
absorption at 1 min time resolution, as changes in the amount of light
transmitted through a quartz-fiber filter. Assuming the mass absorption
coefficient (MAC) for aerosol is constant at Egbert, the 1 min PSAP
absorption measurements are linearly proportional to the BC or EC
concentrations. In this study, 5 years of PSAP data (2010–2015) collected
at Egbert were used to assess the impact of different sampling duration on
the derived monthly average EC values.
Differences in sampling and analysis among networks
Depending on the sharpness (i.e., slope) of the inlet sampling effectiveness
curve (Watson et al., 1983), different size-selective inlets may introduce
measurement uncertainties. CAPMoN uses impactors whereas CABM and IMPROVE
use cyclones. An impactor may have larger pressure drops across the inlet that
might enhance semi-volatile PM evaporation. Larger solid particles might
bounce off when in contact with the impactor and be re-entrained in the
PM2.5 samples if the impactor is overloaded (Flagan and Seinfeld, 1998;
Hinds, 1999). Atmospheric mass size distributions typically peak at about 10 µm with a minimum near 2.5 µm; therefore, the difference in
mass collected with different impactors or cyclones among the three networks
is not expected to be large (Watson and Chow, 2011). Analyzing OC and EC
content by TEA or TOA is also subject to a number of artifacts, including
adsorption of volatile organic compound (VOC) gases by a quartz-fiber filter,
leading to positive artifacts, and evaporation of particles, leading to
negative artifacts (Malm et al., 2011).
The small filter disc (25 mm diameter) and high flow rate (22.8 L min-1) in
the IMPROVE sampler result in a 5- to 7-fold higher filter face velocity
(i.e., 107.7 cm s-1) than that for the CAPMoN and CABM samplers (16–20 cm s-1).
McDow and Huntzicker (1990) assert that higher filter face velocity may
reduce sampling artifacts. However, very high face velocity (>100 cm s-1) may enhance OC volatilization (Khalek, 2008).
Both IMPROVE and CAPMoN correct for vapour adsorption, while the CABM
network does not. For CAPMoN measurements, the organic artifact derived from
each 24 h backup quartz filter was subtracted from the corresponding OC
measurement. For IMPROVE measurements (up until 2015), the monthly median OC
value obtained from the backup quartz filters from 13 sites (not including
Egbert) was subtracted from all samples collected in the corresponding
month. Monthly averaged OC values were then derived from the 24 h
artifact corrected measurements.
Multiple studies show that using the same TOA protocol on both DRI and
Sunset carbon analyzers can produce comparable TC concentrations (Chow et
al., 2005; Watson et al., 2005). However, large differences in EC are found
between the reflectance and transmittance POC correction (Chow et al., 2004, 2005; Watson et al., 2005). Difference in OC and EC definitions among
different TOA and TEA protocols introduces measurement uncertainties. Among
the TOA methods, how POC is determined from the laser signals at different
temperatures in the inert He atmosphere introduces uncertainties. Large
uncertainties in laser transmittance were found for lightly and
heavily loaded samples (Birch and Cary, 1996). For the CABM samples, the POC
determined at 870 ∘C by ECT9 represents different OC properties
and does not equal the charred OC obtained by Sunset-TOT, DRI-TOR, or
IMPROVE_A TOR.
Both IMPROVE and CAPMoN data sets are 24 h
measurements made once every third day collected on the same date while the CABM data are weekly
integrated samples. A comparison between the integrated weekly samples and
24 h samples has already been performed by Yang et al. (2011) and therefore
will not be repeated here. Based on 2 years of Egbert measurements
(2005–2007), Yang et al. (2011) suggested that integrated weekly samples
might experience reduced vapour adsorption but increased losses of
semi-volatile organics leading to lower OC measurements. Weekly EC values
were higher than those from 24 h samples, which were attributed to the
higher analytical uncertainties for the lower loadings on the 24 h samples
(Yang et al., 2011).
(a) Real-time particle soot absorption photometer (PSAP)
measurements averaged to match the corresponding sampling frequencies used
in different networks. (b) Monthly PSAP measurements derived from (a). (c) Comparison of the different sets of measurements from (b) with the 1:1 line
shown in red.
A total of 5 years (2010–2015) of real-time (1 min average) PSAP particle light
absorption measurements (at 567 nm) were used here as a proxy common EC data
set to assess the effect of different sample duration on monthly average EC
concentrations. First, the 1 min PSAP data were averaged to 24 h
samples taken once every 3 d and integrated weekly samples, and the
comparison of the two data sets is shown in Fig. 1a. The results
demonstrate that both data sets capture the variations adequately. Monthly
averages derived from the two sets of measurements show highly correlated
results (r=0.78; Fig. 1b) and a slope of 0.96 (Fig. 1c). Assuming the
variations in light absorption can represent the variations in EC, these
results suggest that monthly averaged EC based on integrated weekly sampling
is about 4 % lower than the monthly averaged EC based on 24 h sampling.
Results and discussionsNIST urban dust standard comparison
The National Institute of Standards and Technology (NIST) Urban Dust
Standard Reference Material (SRM) 8785 air particulate matter on filter
media is intended primarily for use to evaluate analytical methods used to
characterize the carbon composition of atmospheric fine PM (Cavanagh and
Watters, 2005; Klouda et al., 2005). These samples were produced by
resuspension of the original SRM 1649a urban dust sample, followed by
collection of the fine fraction (PM2.5) on quartz-fiber filters (Klouda
et al., 2005; May and Trahey, 2001). Past studies on SRM 1649a and SRM 8785
have shown consistent composition and both samples were supplied with
certified values for OC and EC (Currie et al., 2002; Klouda et al., 2005).
The consistency between the ECT9 and the IMPROVE_A TOR
analytical methods was assessed by analyzing NIST SRM 8785 filters. Four SRM
8785 filters with mass loading of 624–2262 µg were analyzed following
the ECT9 method by the ECCC laboratory and the IMPROVE_A TOR
protocol by the DRI laboratory during 2009–2010.
Comparison of the TC, OC, and EC measurements of the NIST
SRM samples reported by the ECCC and DRI groups during the inter-comparison
study (ICP) conducted between 2009 and 2010. “Reported” represents the
published value in the NIST SRM certificate (Cavanagh and Watters, 2005).
Error bars represent uncertainties covering the 95 % confidence interval. In (d), the ECT9 value (in green) represents the calculated EC / TC ratio
determined based on stable carbon isotope measurement obtained from the SRM
1649a sample (Currie et al., 2002).
The values in the SRM 8785 certificate were reported in grams of OC or EC
per gram of PM mass, which are average mass ratios based on analysis of a
small number of randomly selected samples. Figure 2a–c show that
measurements by IMPROVE_A TOR protocol were within
uncertainties of the certificate values. Ratios measured with ECT9 were
greater, but not significantly different from the certificate values. When
fitting the ECT9 measurements to the IMPROVE_A TOR
measurements using a linear regression (Fig. 3a–c), good correlations
(r=0.9–0.99) were observed with 21 %–25 % higher values by the ECT9
method than IMPROVE_A TOR.
Comparison of (a) TC, (b) OC, and (c) EC concentrations
obtained from the same NIST SRM 8785 filters reported by ECCC following the
TEA (ECT9) method and by DRI following the IMPROVE_A TOR
protocol during the inter-comparison study in 2009/2010.
The parameter EC / TC, calculated based on the reported certificate values,
was compared with the average EC / TC values determined from the
inter-comparison study (ICP) by the DRI group (using IMPROVE_A TOR) and the ECCC group (using ECT9) (Fig. 2d). These results show that
EC / TC reported by both analytical methods was statistically the same as the
certificate value.
Finally, the EC / TC value was further verified by analyzing SRM 1649a samples
with the ECT9 method. The combusted CO2 from OC, EC, and TC was
analyzed for the isotope ratios (i.e., 14C/12C) expressed as a
fraction of modern carbon (i.e., FMi is the ratio of 14C/12C
in the sample i, relative to a modern carbon standard) for individual mass
fractions (i.e., FMTC, FMOC, and FMEC). Using isotopic
mass balance, the EC / TC ratio can be derived from Eq. (1):
FMTC=FMOC×1-ECTC+FMEC×ECTC.
The 14C/12C ratios were determined using the off-line combustion method at
the Keck carbon cycle accelerator mass spectrometry (KCCAMS) facility at the
University of California Irvine. A FMTC value of 0.512 was obtained,
which is close to certificate values that range from 0.505 to 0.61 (Currie
et al., 2002). Average measured values of FMOC and FMEC for the
SRM 1649a via ECT9 were 0.634 (n=3) and 0.349 (n=3), respectively. This
yields an EC / TC ratio of 0.425, which is comparable to the ECT9 value of
0.44, and close to the reported certificate value of 0.49 and the
IMPROVE_A TOR value of 0.47 (Fig. 2d), reconfirming a good
separation of OC from EC using the ECT9 method. This analysis also confirms
the consistency between the IMPROVE_A TOR and ECT9 methods.
Monthly averaged CAPMoN (a) OC, (b) EC, and (c) POC mass
concentration time series with and without vapour adsorption correction. Note
that the y axes in (a), (b), and (c) are on a different scale.
Vapour adsorption corrections
Figure 4 shows the monthly averaged carbon concentration time series with
and without the artifact correction for CAPMoN samples over the period from
2005 to 2015. Vapour adsorption contributes to a large amount of the measured
OC (Fig. 4a), but a negligibly amount to EC (Fig. 4b) and POC after 2008
(Fig. 4c). The median vapour adsorption artifact was 0.79 µg m-3
from 2008 to 2015 for DRI-TOR, representing about 50.9 % of the
uncorrected OC, compared to 0.92 µg m-3 (43.3 % of uncorrected
OC) using the Sunset-TOT before 2008 (Fig. S2). Linear least-square regressions between corrected and uncorrected carbon in Fig. 5
show a slope of 0.52 for OC and 0.56 for TC with good correlations
(r=0.93–0.94). Sunset-TOT measurements acquired prior to 2008 are mostly
scattered around the regression line, with higher concentrations. On
average, about 48 % of the uncorrected OC (0.84 µg m-3) can be
attributed to vapour adsorption. The low filter face velocity (15.5 cm s-1) in
CAPMoN samples could be one of the contributing factors.
Relationship between the monthly averaged CAPMoN vapour
adsorption corrected and uncorrected measurements for (a) TC, (b) OC, (c) EC, and (d) POC. Black solid markers represent the TOR measurements
(2008–2015) analyzed by the DRI analyzer (i.e., DRI-TOR). Red open markers
represent the TOT measurements before 2008 analyzed by the Sunset analyzer
(i.e., Sunset-TOT). The red line represents the best-fitted linear
regression of all the DRI-TOR measurements through the origin. All the
corresponding statistics (i.e., best-fitted slope, correlation coefficient,
total number of measurement points) are included in the legend.
Figure 5c indicates that artifact-corrected EC concentrations are 7.8 %
(0.02 µg m-3) lower than the uncorrected values. The artifact
magnitude is close to the detection limit of 0.022 µg m-3 (0.197 µg m-3) and within analytical uncertainties (Chow et al., 1993).
Some Sunset-TOT EC measurements are scattered from the regression line,
indicating a more accurate and consistent adsorption correction for DRI-TOR
(Fig. 5b). Although not expected to impact EC concentration, vapour
adsorption directly affects POC correction and thus influences EC mass
determination.
Figure 5d shows that 4.3 % (0.01 µg m-3) of POC was caused by
vapour adsorption using the DRI-TOR protocol. For Sunset-TOT, however, up to
21.1 % (0.17 µg m-3) of the POC was detected on the backup
filter. Note that POC is part of OC and is a charring correction in the
DRI-TOR and Sunset-TOT protocols. Results show that filter transmittance is
influenced by both surface and within-filter charring and EC from different
sources has been observed to have different filter penetration depths (Chen
et al., 2004; Chow et al., 2004). Based on the available information from
this study, an optical correction by reflectance appears to be more
appropriate and give more consistent results when POC concentration is
relatively large compared to EC (Chen et al., 2004). Regardless, the
absolute POC and EC concentrations were much lower than OC and the
adsorption correction on TC is mostly attributed to the OC artifact.
Since the IMPROVE aerosol samples were acquired at a higher filter face
velocity (107.7 cm s-1), it is expected that the magnitude of the vapour
adsorption correction would be smaller for the IMPROVE samples. This is
supported by the observations from Watson et al. (2009) at six anchor
IMPROVE sites (i.e., Mount Rainier National Park, Yosemite National Park,
Hance Camp at Grand Canyon National Park, Chiricahua National Monument,
Shenandoah National Park, and Okefenokee National Wildlife Refuge),
suggesting that vapour adsorption obtained from backup quartz filters
represented about 23 % of the uncorrected OC values. Filter fibers are
saturated over a long sampling interval (Khalek, 2008; Watson et al., 2009);
thus, artifacts for the CABM samples are expected to be relatively lower.
Monthly averaged (a) TC, (b) OC, (c) EC, and (d) POC
concentration time series obtained from three different networks at Egbert.
CAPMoN measurements before 2008 were obtained using the Sunset-TOT method (in
green) while measurements starting in 2008 were obtained using the DRI-TOR method
(in orange).
Regression results (slope, correlation coefficient, and
total number of points) obtained when fitting various CABM (ECT9) and CAPMoN
(Sunset-TOT & DRI-TOR) carbonaceous mass concentration time series
against IMPROVE (IMPROVE_A TOR) measurements.
IMPROVE_A TOR and ECT9 measurements cover the period from
2005 to 2015. Sunset-TOT and DRI-TOR measurements cover the periods for
2005–2008 and 2008–2015, respectively. Regression 1 indicates the
best-fitted slope through the origin. Regression 2 is the best-fitted slope
with intercept (in brackets).
Regression 1Regression 2RNSunset-TOT TC vs. IMPROVE_A TOR TC0.888±0.0330.713±0.112 (0.301±0.186)0.7828Sunset-TOT OC vs. IMPROVE_A TOR OC0.967±0.0410.873±0.135 (0.125±0.170)0.7928Sunset-TOT EC vs. IMPROVE_A TOR EC0.639±0.0420.233±0.130 (0.171±0.053)0.3328Sunset-TOT POC vs. IMPROVE_A TOR POC1.769±0.0911.776±0.351 (-0.003±0.127)0.7028DRI-TOR TC vs. IMPROVE_A TOR TC0.832±0.0150.946±0.044 (-0.164±0.059)0.9193DRI-TOR OC vs. IMPROVE_A TOR OC0.835±0.0170.934±0.046 (-0.116±0.050)0.9093DRI-TOR EC vs. IMPROVE_A TOR EC0.818±0.0190.929±0.072 (-0.032±0.020)0.8193DRI-TOR POC vs. IMPROVE_A TOR POC0.986±0.0281.230±0.080 (-0.073±0.023)0.8593ECT9 TC vs. IMPROVE_A TOR TC1.304±0.0221.197±0.065 (0.164±0.093)0.88107ECT9 OC vs. IMPROVE_A TOR OC1.149±0.0211.004±0.056 (0.179±0.064)0.87107ECT9 EC vs. IMPROVE_A TOR EC1.834±0.0461.661±0.149 (0.056±0.046)0.74107ECT9 POC vs. IMPROVE_A TOR POC0.998±0.0310.615±0.082 (0.124±0.025)0.59107Comparison among IMPROVE, CAPMoN, and CABM measurements
Figure 6 shows the temporal variations in the monthly averaged
IMPROVE_A TOR, CAPMoN Sunset-TOT, DRI-TOR, and CABM ECT9
measurements. Also included in the figure are the monthly averaged
temperature and the wind direction and speed (expressed in wind barbs). It
is evident that better correlations of TC, EC, and OC were found between the
protocols that use same POC correction method (DRI-TOR and
IMPROVE_A TOR) than between Sunset-TOT (which uses
transmittance for POC correction) and IMPROVE_A TOR (Table 2). In particular correlation of EC between Sunset-TOT and
IMPROVE_A TOR was poor.
Comparison of the monthly averaged carbonaceous mass
concentrations from the DRI-TOR (red circles and orange triangles) and ECT9
(black squares) protocols against the IMPROVE_A TOR protocol. The
different straight lines represent the linear regression best fitted line
through the origin (i.e., Regression 1). The fitted parameters for all
corresponding data sets with (Regression 2) and without (Regression 1) the
y intercept are summarized in Table 2.
Comparisons of the monthly averaged carbonaceous measurements among
different networks are summarized in Fig. 7. When fitting the monthly
averaged DRI-TOR and Sunset-TOT measurements to IMPROVE_A TOR
measurements using a linear regression fit through the origin,
Regression 1 typically yields less than unity slopes (0.64–0.97; Table 2),
suggesting that the carbonaceous masses reported by CAPMoN were in general
lower than those reported by IMPROVE. Fitting the measurements allowing an intercept, Regression 2 typically yields least-square slopes close to unity
(>0.92) with small intercepts.
Figure shows the relationship of averaged (a) TC, (b) OC,
and (c) EC concentrations from all networks as a function of ambient
temperature. Each data point represents the average value of all network
measurements within a 3 ∘C temperature range. Uncertainties are
standard deviations of the measurements. The red curve represents the
best-fitted sigmoid function. Figure 8d shows the seasonality of ECT9 POC
compared to the average OC and EC seasonality. The black solid curve represents
the best-fitted sigmoid function on all ECT9 POC measurements.
The effect of using transmittance or reflectance for POC determination is
apparent. The SunsetTOT POC correction is larger because transmittance
is affected by the charred OC within the filter. This is consistent with the
larger regression slopes in POC (Regression 1: 1.8) between the Sunset-TOT and
IMPROVE_A TOR protocols than the slope in POC (1.0) between
the DRI-TOR and IMPROVE_A TOR protocols.
The ECT9 versus IMPROVE_A TOR via Regression 1 slopes are
equal to or greater than unity, ranging from 1.0 to 1.8 (Table 2). Linear
regression with intercept (i.e., Regression 2) yields lower slopes
(0.6–1.7) with positive intercepts (0.06–0.18 µg m-3),
signifying higher TC and EC concentrations for ECT9 samples. Higher
intercepts (0.12-0.18 µg m-3) for TC, OC, and POC are consistent
with ECT9 measurements uncorrected for vapour adsorption. However, the
systematically higher TC, OC, and EC by 21 %–25 % via ECT9 relative to those
via IMPROVE_A TOR in SRM 8785 could not be simply attributed
to the uncorrected vapour adsorption.
Specifically, ECT9 OC concentrations are 15 % higher than the
IMPROVE_A TOR measurements (Table 2) with good correlation
(r=0.87; Table S2). The ECT9 method yielded 66 %–83 % higher EC than
IMPROVE_A TOR, with moderate correlation (r=0.74).
Differences in combustion temperatures for OC / EC split determination could
contribute to these discrepancies. Heating under an oxidative environment at
a constant temperature of 900 ∘C in the ECT9 protocol could
combust more highly refractory carbon than the IMPROVE_A TOR
protocol, which only heats progressively from 580 to 840 ∘C. Another minor factor could include inhomogeneous deposition
of mass loading on the filter spot. When plotted on different scales, Fig. S3 shows that the two EC data sets track well, capturing both long-term
trends and seasonal variations.
A slope approaching unity (1.00) was obtained when fitting the ECT9 POC to
IMPROVE_A TOR POC through the origin (Fig. 7d). Refitting
the data allowing an intercept leads to a slope of 0.62 with a y intercept
(0.12; Table 2), comparable in magnitude to the vapour adsorption artifact.
The correlation coefficient between ECT9 POC and IMPROVE_A TOR
POC is low (r=0.46; Table S3). However, correlation between
IMPROVE_A TOR POC and IMPROVE_A TOR OC is much
higher (r=0.91), and even to a lesser extent between
IMPROVE_A TOR POC and IMPROVE_A TOR EC
(r=0.71). In comparison, ECT9 POC has weak correlation with ECT9 OC
(r=0.65) and ECT9 EC (r=0.37). These observations show that the POC
definition in ECT9 is not dominated by charred OC correction and likely
includes the characterization of other oxygenated organic materials as
observed in Chan et al. (2010). Additional research is needed to verify if
ECT9 POC is proportional to SOA formation.
Seasonality in carbon concentration and possible origination
Figure 6 shows elevated carbon during summer, consistent with the
observations from Yang et al. (2011) and Healy et al. (2017). A sigmoid
function was applied here to characterize the relationship between ambient
carbon concentration and ambient temperature. The sigmoid function has a
characteristic “S” shape and represents an integral of a Gaussian
function. Relationships between carbon concentrations and ambient
temperatures are illustrated in Fig. S5. Apparent increases in OC and TC
concentrations are found when ambient temperatures exceed about 10 ∘C, a phenomenon not as apparent in EC. EC from the week-long
CABM samples is more scattered.
The TC, OC, and EC from all measurements are averaged and shown in Fig. 8
with the following best-fitted sigmoid functions.
2TC=1.053+3.5581+exp23.081-T3.7603OC=0.780+1.8381+exp20.089-T2.9784EC=0.239+1.4461+exp34.776-T8.404
Equations (2)–(4) show that lower limits of the observed TC, OC, and EC
concentrations are 1.05, 0.78, and 0.24 µg m-3, with the midpoint
of the maximum growth curve occurring at about 23, 20, and 35 ∘C, respectively. The predicted maximum
concentrations for TC, OC, and EC are 4.61, 2.62, and 1.69 µg m-3,
respectively.
Preliminary analysis based on simple wind roses and a Lagrangian particle
dispersion transport model (FLEXible PARTicle dispersion model) (Stohl et
al., 2005) was conducted (see the Supplement). Results from the analysis
appear to suggest that human activities (e.g., local transportation,
residential heating, and industrial activities), biogenic emissions (e.g.,
monoterpenes) from the boreal forest, SOA formation, biomass burning, and
transboundary transport could contribute to the variations in OC and EC at
Egbert in a complicated way (Ding et al., 2014; Chan et al., 2010; Leaitch
et al., 2011; Passonen et al., 2013; Tunved et al., 2006; Lavoué et al., 2000; Healy et al., 2017), which requires additional research to confirm. At
Egbert, increasing ambient temperature from 10 to 20 ∘C leads to higher OC concentrations from 0.84 to 1.61 µg m-3 (91.7 % increase) and EC concentration from 0.31 to
0.45 µg m-3 (45.2 % increase). The temperature dependency of OC
and EC suggests a potential climate feedback mechanism consistent with the
observations from Leaitch at al. (2011) and Passonen et al. (2013).
Chan et al. (2010) showed that ECT9 POC possesses a positive relationship
with oxygenated organics and aged aerosol particles. The seasonality in ECT9
POC is compared with the average OC and EC seasonality observed at Egbert
(Fig. 8d). Interestingly, the ECT9 POC concentration does not show a
gradual exponential shape of function as for OC and EC. Instead, it shows a
small but obvious two-step function when plotted against ambient
temperature. The ECT9 POC temperature-dependent results (Fig. 8d) suggest
constant sources of background emissions of possible oxygenated organic
compounds that are independent of the measured OC, with additional
secondary organic compound (SOA) formation at higher temperatures (e.g.,
>15∘C). Future studies are needed to verify this.
Summary of the inter-comparison study
A total of 10 years of OC and EC measurements at Egbert were obtained from three
independent networks (IMPROVE, CAPMoN, CABM) and observable differences in
carbon concentrations were attributed to different sampling methods,
analytical protocols, sampling time, and filter artifact corrections. Vapour
adsorption did not affect EC values but contributed 20 %–50 % of the
measured OC, depending on the sampling filter face velocity. The higher TC
and OC concentration of the CABM measurements by 20 %–30 % and 15 %,
respectively, compared to the IMPROVE measurements could be partially due to
the absence of vapour adsorption correction. These results are consistent
with other inter-comparison studies before data adjustments (Hand et al.,
2012). The differences in analytical protocol also play a role in causing
higher carbon values, supported by the higher TC, OC, and EC values from the
SRM8785 analysis obtained by the ECT9 method compared to those by the
IMPROVE_A TOR method. Pyrolyzed OC (POC) from ECT9 is shown
to be more than a charring correction and more research is needed to develop
its relationship with SOA.
Important observations from the inter-comparison study are as follows. (1) CAPMoN
DRI-TOR TC, OC, and EC are 5 %–17 %, 7 %–16 %, and 7 %–18 % lower than the
corresponding masses from IMPROVE_A TOR. (2) CAPMoN
Sunset-TOT TC, OC, and EC are lower than the IMPROVE_A TOR
values by up to 30 %, 15 %, and 75 %. (3) CABM TC, OC, and EC by ECT9
are higher than the IMPROVE_A TOR values by 20 %–30 %,
0 %–15 %, and 60 %–80 %, respectively.
Carbon concentrations observed from all three networks exhibited a
non-linear positive dependency with ambient temperature, which can be
characterized by a sigmoid function. Although further research is needed,
preliminary observations suggested that increased anthropogenic activities,
urban emissions, SOA formation, forest fire emissions, and long-range
transport could have an impact on the observed OC and EC at Egbert. The
increase in OC concentration with temperature is consistent with the climate
feedback mechanisms reported from various studies. The different
characteristic temperature dependency of the ECT9 POC suggests the need for
future investigation, which could provide additional insights into SOA
formation from acquired carbonaceous measurements.
Suggestions going forward
Long-term measurements play important roles for detecting the trends in
atmospheric compositions, constraining their emission changes, and allowing for
assessment of the effectiveness of emission mitigation policies at regional
scales (WMO, 2016, 2003), provided that the measurements are consistent and
comparable across different networks. Recognizing the absence of a
universally accepted carbonaceous standard, long-term inter-comparison
studies become challenging and even more important. Echoing the recommendations
from the World Meteorological Organization (WMO) guidelines and
recommendations for long-term aerosol measurements (WMO, 2016, 2003), this
study illustrates the importance of measurement consistency (e.g., sampling
method–procedures, analytical instrument–method–protocols and data
processing, quality assurance and quality control protocols) within a
network over a long period of time. As indicated in the guidelines, regular
inter-comparison of filter samples should be encouraged. These activities
include analyzing exchanged common filter samples and co-located filter
samples. In addition, there is a need to develop proper reference materials
for assessing comparability and consistency and incorporating the use of
such a reference as part of the inter-comparison effort.
Data availability
The original IMPROVE raw data can be obtained from the Cooperative Institute for Research in the Atmosphere (CIRA) of the Federal Land Manager Environmental Database (FED), Colorado State University, Fort Collins, CO http://views.cira.colostate.edu/fed/ (last access: 13 August 2019). The original CAPMoN raw data can be provided upon request through ec.rcepa-capmon.ec@canada.ca. The original CABM raw data can be provided upon request through lin.huang@canada.ca. Monthly averaged measurements from all networks used in this study are summarized and available in Excel format as part of the Supplement of this paper.
Nomenclature
AIHLAir-industrial hygiene laboratoryAMSAccelerator mass spectrometryBCBlack carbonCABMCanadian Aerosol Baseline MeasurementCAPMoNCanadian Air and Precipitation Monitoring NetworkCARECenter for Atmospheric Research ExperimentCCMRClimate Chemistry Measurements and ResearchDRIDesert Research InstituteDRI-TORCAPMoN measurements using IMPROVE on DRI analyzer with TOR correctionECElemental carbonECCCEnvironment and Climate Change CanadaECT9EnCan-Total-900 protocolFIDFlame ionization detectorFLEXPARTFLEXible PARTicle dispersion modelICPInter-comparison studyIMPROVEInteragency Monitoring Protected Visual EnvironmentsIMPROVE_A TORIMPROVE_A TOR protocol on DRI analyzerKCCAMSKeck carbon cycle accelerator mass spectrometryMACMass absorption coefficientNISTNational Institute of Standard and TechnologyOCOrganic carbonPMParticulate matterPOCPyrolyzed organic carbonPSAPParticle soot absorption photometerSOASecondary organic aerosolSRMStandard Reference MaterialSunset-TOTIMPROVE TOT protocol on Sunset analyzerTCTotal carbonTEAThermal evolution analysisTOAThermal–optical analysisTORThermal–optical reflectanceTOTThermal–optical transmittanceUCIUniversity of California IrvineWMOWorld Meteorological Organization
The supplement related to this article is available online at: https://doi.org/10.5194/amt-12-4543-2019-supplement.
Author contributions
TWC and LH designed the study and wrote the paper, with contributions from KB, JCC, XLW, JGW, CIC, GMS, and KJ. KB was responsible for CAPMoN’s data measurements. JCC, JGW, and XLW were responsible for IMPROVE data. LH was responsible for CABM data with technical assistance from WZ and DE. CIC, GMS, and LH were responsible for 14C measurements. SS was responsible for the PSAP data. TWC conducted the data processing. All authors commented on the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
Authors would like to acknowledge Elton Chan and Douglas Chan of ECCC for providing the FLEXPART model results and providing technical advice. The authors acknowledge Environment and Climate Change Canada's Canadian Aerosol Baseline Measurement, and Canadian Air and Precipitation Monitoring Networks and the United States Interagency Monitoring of Protected Visual Environments for the provision of their elemental and organic carbon mass measurement data. IMPROVE measurements were obtained directly from the IMPROVE website (http://vista.cira.colostate.edu/IMPROVE/Data/QA_QC/Advisory.htm, last access: 13 August 2019). IMPROVE is a collaborative association of state, tribal, and federal agencies and international partners. The U.S. Environmental Protection Agency is the primary funding source, with contracting and research support from the National Park Service. IMPROVE carbon analysis was provided by the Desert Research Institute under the contract number P16PC00229. Funding of this study was initiated by the Climate Change Technology and Innovation Initiative (CCTI) program, operated through Natural Resources Canada (NRCan), and supported by the Clean Air Regulatory Agenda (CARA) initiative and ECCC internal federal funding.
Review statement
This paper was edited by Pierre Herckes and reviewed by two anonymous referees.
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