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Aerosol and Air Quality Research, x: 1–15, xxxx
Copyright © Taiwan Association for Aerosol Research
ISSN: 1680-8584 print / 2071-1409 online
doi: 10.4209/aaqr.2015.02.0106
Optical Calibration and Equivalence of a Multiwavelength Thermal/Optical
Carbon Analyzer
Judith C. Chow1,2,3*, Xiaoliang Wang1,3, Benjamin J. Sumlin1, Steven B. Gronstal1,
L.-W. Antony Chen1,4, Dana L. Trimble1, Steven D. Kohl1, Sierra R. Mayorga1, Gustavo Riggio1,
Patrick R. Hurbain1, Megan Johnson1, Ralf Zimmermann5, John G. Watson1,2,3
1
Division of Atmospheric Sciences, Desert Research Institute, Reno, Nevada 89512, USA
The State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of
Sciences, Xi’an, Shaanxi, 710075, China
3
Graduate Faculty, University of Nevada, Reno, Nevada 89503, USA
4
Department of Environmental and Occupational Health, University of Nevada, Las Vegas, Nevada 89154, USA
5
Joint Mass Spectrometry Centre, Chair of Analytical Chemistry, Institute of Chemistry, University of Rostock, Rostock,
Germany
2
ABSTRACT
Organic and elemental carbon (OC and EC) are operationally-defined by the measurement process, so long-term trends
may be interrupted with instrumentation changes. A modification to the U.S. IMPROVE carbon analysis protocol and
hardware is examined that replaces the 633 nm laser light used for OC charring adjustments with seven wavelengths
ranging from 405 to 980 nm, including one at 635 nm. Reflectance (R) and Transmittance (T) values for each wavelength
are made traceable to primary standards through transfer standards consisting of a range of aerosol deposits on filter media
similar to that of the analyzed samples. R and T values are assigned to these filters using a UV/VIS spectrometer calibrated
with these standards. Using ambient and source (e.g., diesel exhaust, flaming biomass, and smoldering biomass) samples,
it is demonstrated that R and T calibration is independent of the sample type. Total carbon (TC), OC, and EC comparisons
with the earlier hardware design for urban- and non-urban samples demonstrate equivalence, within precisions derived from
replicate analyses, for the 633 nm and 635 nm wavelengths. Several uses of the additional multiwavelength information
are identified, including: 1) ground-truthing of multi-spectral remote sensors; 2) improving estimates of the Earth’s
radiation balance; 3) associating specific organic compounds with their light absorption properties; and 4) appropriating
sources of black and brown carbon.
Keywords: Brown carbon; Multiwavelength; Aerosol light absorption; Angstrom Absorption Exponent.
INTRODUCTION
Organic and elemental carbon (OC and EC, respectively;
EC is sometimes termed black carbon [BC]) are important
components of suspended particulate matter (PM), especially
in the respirable fraction with aerodynamic diameters less
than 2.5 micrometers (PM2.5 ). Excessive OC and EC
concentrations can adversely affect human health, visibility,
climate, materials, and biotic systems (Chow and Watson,
2011; U.S.EPA, 2012; Eklund et al., 2014; Grahame et al.,
2014). Brown carbon (BrC) (Andreae and Gelencser, 2006;
*
Corresponding author.
Tel.: 775-674-7050; Fax: 775-674-7009
E-mail address: judith.chow@dri.edu
Moosmüller et al., 2011), which absorbs light more efficiently
at shorter visible wavelengths (400 to 700 nm) than BC, has
become an important research topic because it is an indicator
of biomass burning and secondary organic aerosol (SOA)
formation and differs from BC in its effects on radiative
transfer. The nature of BrC is especially important in Asian
countries where source emissions differ from those of North
America and Europe (Betha and Balasubramanian, 2014;
Chuesaard et al., 2014; Gargava et al., 2014; Pervez et al.,
2015). More information on the light absorption properties
of ambient aerosols would be useful, especially if it could
be acquired as part of the ongoing chemical speciation
networks.
OC and EC have been measured using more than 20
variations of the evolved gas analysis (EGA) method (Watson
et al., 2005). Ambient air and source emission samples are
collected onto a heat-resistant filter and a portion of each
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Chow et al., Aerosol and Air Quality Research, x: 1–15, xxxx
filter is subjected to various temperatures and atmospheres.
PM carbon in the sample volatilizes and combusts to carboncontaining gases that are oxidized to carbon dioxide (CO2).
The evolved CO2 may be either directly determined by a nondispersive infrared (NDIR) detector or reduced to methane
(CH4) and quantified by a flame ionization detector (FID). A
charring adjustment (Huntzicker et al., 1982) based on light
reflected from or transmitted through the filter was introduced
to separate OC and EC. Different EGA methods have been
found to produce equivalent values for total carbon (TC =
OC + EC) but dissimilar values for OC and EC (Watson et
al., 2005 and references therein).
The Interagency Monitoring of PROtected Visual
Environments (IMPROVE, 2015) thermal/optical carbon
analysis protocol (Chow et al., 1993a, 2007, 2011) has
been consistently applied at the non-urban IMPROVE and
urban Chemical Speciation Network (CSN, U.S.EPA, 2015)
sites since 1986 and 2005/2006, respectively, thereby
allowing long-term trends to be assessed (Murphy et al.,
2011; Chen et al., 2012). Consistent trends are needed to
develop emission control strategies in urban areas and track
reasonable progress toward natural visibility conditions by
CY 2065 at U.S. national parks and wilderness areas, as
required by the Clean Air Visibility Rule (U.S.EPA, 1999;
Watson, 2002; U.S.EPA, 2005). Fig. 1 shows these trends at a
representative site from the IMPROVE network, indicating
that emission reduction measures are having a positive
effect on lowering ambient concentrations. In addition to the
IMPROVE and CSN networks, the IMPROVE protocol has
been adopted for other long-term PM chemical speciation
networks in the U.S. (Hansen et al., 2006), Canada (DabekZlotorzynska et al., 2011), and China (Huang et al., 2012;
Zhang et al., 2012), as well as for shorter-term ambient and
source characterization studies in these and other parts of the
world. Since OC and EC are operationally defined by the
measurement protocol, changes in equipment and procedures
may adversely affect the ability to track trends, as noted in
Fig. 1. This is the case for many environmental measurements,
and it presents a problem as newer, and possibly better,
measurement technologies supplant older ones.
With recent laser diode technology, it is possible to add a
multiwavelength light absorption capability to the IMPROVE
thermal/optical protocol. To be quantitative, the reflected
and transmitted light must be calibrated against and traceable
to available standards, and the OC and EC fractions must
be equivalent to those obtained with earlier instrumentation.
The objectives of this study are to: 1) describe
multiwavelength changes to the IMPROVE carbon analysis;
2) develop a traceable calibration procedure for quantifying
multiwavelength light absorption along with OC, EC, and
thermal carbon fractions; and 3) demonstrate equivalence
between the multiwavelength and single wavelength systems.
CHANGES TO CARBON ANALYZERS AND
PROCEDURES
Since CY 2005, OC and EC measurements for the
IMPROVE network have been made with the DRI Model
2001 thermal/optical carbon analyzer (Atmoslytics,
Fig. 1. Trends in annual average organic carbon (OC) and elemental carbon (EC) concentrations at the Shenandoah National Park IMPROVE site. The Model 2001
thermal/optical carbon analyzer replaced the DRI/OGC carbon analyzers beginning in 2005, and a small increase in EC and a small decrease in OC may have been
attributed in part to that change. However, this feature was not consistent at all of the IMPROVE sites that underwent the same change (Chen et al., 2012). This
illustrates some of the difficulties in changing instrumentation and methods in assessing trends. Data were obtained from CIRA (2014). 2
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3
a)
b)
Fig. 2. Example of IMPROVE_A thermograms for samples from: a) Model 2001; and b) Model 2015 for the same sample
from the IMPROVE network. Reflectance (R) and transmittance (T) are normalized to their initial values and carbon is
expressed as the fraction of total carbon (TC) on the sample. The 633 nm or 635 nm laser beams monitor darkening of the
sample by R and T, which indicates OC charring to EC during the analysis. Carbon that evolves after R returns to its initial
value is classified as EC in the aerosol deposit. EC by transmittance (ECT) is usually less than EC by reflectance (ECR)
owing to charring of organic vapors adsorbed within the quartz-fiber filter (Chow et al., 2004; Watson et al., 2009, Chow
et al., 2010). The two thermograms are nearly identical, except that the Model 2015 shows greater sensitivity in the R
detection of OC charring, as evidenced by low point in R before O2 is added to the analysis atmosphere. Temperaturedefined fractions are: OC1–OC4 (OC evolved from the 0.5 cm2 circular filter punch in a pure He [> 99.999%] atmosphere
at 140, 280, 480, and 580°C, respectively), and EC1–EC3 (EC evolved from the filter punch in a 98% He/2% O2
atmosphere at 580, 740, and 840°C, respectively). The analysis temperature stays constant until each fraction is fully
evolved, and total analysis times are longer for more heavily loaded samples. Carbon evolving after O2 is added until R
and T achieve their original values are termed optical pyrolyzed carbon by reflectance (OPRC) and optical pyrolyzed
carbon by transmittance (OPTC), respectively. These have negative values when R or T achieve their initial values before
O2 is added.
Calabasas, CA) following the IMPROVE_A thermal/optical
protocol (Chow et al., 2007, 2011) described in Fig. 2.
Model 2001 replaced the earlier, and more specialized,
DRI/OGC analyzers that had been used since 1986 after
extensive testing and comparison to demonstrate equivalence
(Chow et al., 2007) of OC and EC, as well as the thermallyevolved carbon fractions. The thermal fractions have been
found useful for source apportionment (Watson et al., 1994;
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4
Maykut et al., 2003; Kim et al., 2004; Kim and Hopke,
2004a, b; Begum et al., 2005; Cao et al., 2005; Chen et al.,
2007; Chow et al., 2009) and for understanding OC
adsorption artifacts on quartz-fiber filters (Watson et al.,
2009; Chow et al., 2010). The fractions can be summed in
different ways to simulate and compare with other thermal
and optical OC/EC protocols (Watson et al., 2005).
Although hundreds of thousands of samples have been
reliably analyzed by the Model 2001 in more than 50
laboratories worldwide over the past decade, technology
has advanced to the point that a re-design is needed. The
cost of pure inert helium (He) gas has skyrocketed, and its
use needs to be reduced. NDIR detectors can now quantify
CO2 at low levels, obviating the need for reduction to CH4
for FID detection with its associated need for a hydrogen
(H2) source. Customized circuit boards can be replaced with
off-the-shelf programmable integrated circuits, and a small
computer can control the mechanical, optical, and data
acquisition functions. Most importantly, small, compact,
and inexpensive diode lasers with various wavelengths can
replace the larger and more costly helium-neon (He-Ne) light
source, thereby providing more information on PM optical
properties and their changes throughout the analysis.
Fig. 3 summarizes the carbon analyzer modifications. To
ensure compatibility, the sample presentation, calibration,
and measurement procedures remain the same, except for
the minor change in the red light wavelength from 633 to
635 nm, as illustrated in Fig. 4. The analysis procedures,
calibration methods, quality control, and quality assurance
measures are detailed in the supplemental information, with
some enhancements over those reported by Chow et al.
(2011).
Reflectance
Detector
UV-VIS-NIR
Light Source
(λ=405-980 nm)
Symbols:
Optical
Fibers
MFC
Mass Flow Controller
MFM
Mass Flow Meter
3-Way Solenoid Valve
Manual Ball Valve
Filter
Thermocouple
Oven
Filter Loading
Push Rod
6-Port
Injection
Valve
Filter Holder
Oxidation Oven
(MnO2)
C→CO2
NDIR CO2
Detector
MFM
Loop
Transmittance
Detector
Vent
Soda Lime
CO2 Scrubber
10% O2 in He (OC Stage)
100% He (EC Stage)
Calibration
Gas
MFC
MFC
Compressed
Air
MFC
10% O2 in He
100% He
5% CH4 in He
MFC
Makeup Air
DRI Model 2001 Carbon Analyzer
From
Oven
Oxidation
Reactor
C→CO2
Methanator
CO2→CH4
FID
Carrier/Reaction
Gases
Fig. 3. DRI Model 2015 multiwavelength carbon analyzer configuration compared to Model 2001. Major changes are: 1)
the 633 nm He-Ne laser is replaced with 405, 445, 532, 635, 780, 808, and 980 nm diode lasers that are sequentially
modulated to separate their signals; 2) the bifurcated fiber optic bundle is replaced with eight single fibers to convey the
diode laser beams perpendicular to the sample surface; 3) the quartz-glass sample oven is modified to eliminate the
methanator and to automatically replenish the manganese dioxide (MnO2) oxidation catalyst, thereby minimizing its
replacement; 4) manually-adjusted rotameters are replaced with more precise mass flow controllers (MFCs); 5) the
nichrome oven heater is powered by 24V DC instead of 110 V AC to minimize high frequency fluctuations in glow
intensity; 6) customized circuit boards are replaced with generic and programmable integrated circuits; 7) a high sensitivity
non-dispersive infrared (NDIR) detector for CO2 replaces the flame ionization detector (FID) for CH4, eliminating the
need for ultrapure hydrogen (H2) and the heated reduction catalyst to convert CO2 to CH4; and 8) software is written in
LABVIEW to control analysis and acquire data.
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a)
5
b)
He/Ne Laser
633 nm
Reflectance
Photodiode
Detector
Optical
Chopper
Wheel
Bifurcated Bundled
Optical Fibers
Sample
Oven
Quartz
Light
Pipes
Sample
Filter
Transmittance
Photodiode
Detector
Modulated
Diode Lasers
405
445
532 635
Reflectance
Photodiode
Detector
780 808 980 nm
8-furcated Single
Optical Fibers
Sample
Oven
Quartz
Light
Pipes
Sample
Filter
Transmittance
Photodiode
Detector
Fig. 4. Optical configurations for: a) Model 2001; and b) Model 2015. Mechanical modulation of the 633 nm helium-neon
(He-Ne) laser is replaced by sequential pulse modulation of seven diode lasers. The aerosol deposit faces the incident
radiation in both cases. The single optical fiber in the Model 2015 provides more efficient light transmission than the
optical fiber bundle in the Model 2001.
The 633 nm reflectance (R) and transmittance (T) allowed
light absorption properties for each sample to be estimated
by taking the ratio of the initial to the final R and T after
the light absorbing carbon was removed, leaving a filter
remnant that was usually white, like the unexposed filter.
The R measure of filter darkening, similar to that of British
Smoke (Heal and Quincey, 2012), was used to further
demonstrate a consistent EC trend when the Model 2001
replaced the earlier DRI/OGC analyzers (Chen et al., 2012).
To obtain the most information from multiple-wavelengths,
and to ensure comparability among different analyzers, a
more absolute measure of R and T is desired. This is
accomplished by using transfer standards composed of
increasing aerosol deposits on quartz-fiber filters identical
to those used for routine sampling. Absolute R and T of the
filters in percentage (%) of light reflected from or transmitted
through each filter are measured for a range of wavelengths
with a UV/VIS spectrometer (Lambda 35, Perkin Elmer,
Waltham, MA) equipped with an integrating sphere to
fully capture scattered light. For T measurements, the
spectrometer output is set to 100% transmittance when the
beam is unobstructed and to 0% transmittance when the
incident beam is blocked; linearity of the response to
intermediate transmittances is verified with neutral density
filters (Travis et al., 2000; Upstone, 2002). T for each
transfer standard is quantified by illuminating the deposit
side of each filter located in front of the integrating-sphere
and detector (see Supplemental Fig. S-3) and scanning from
200 to 1100 nm (1-nm resolution) for 300 seconds. R is
standardized with a Spectralon diffusive reflectance standard
(Labsphere, 2000). Spectralon is a proprietary Teflon-based
material used to calibrate satellite remote sensors (Georgiev
and Butler, 2007), and it is the same material that coats the
spectrometer’s integrating sphere. R is 100% with the
white Spectralon standard inserted into the outlet of the
integrating sphere and 0% when it is removed, allowing the
incident light to pass through the sphere. NIST-traceable
Spectralon standards of known reflectance are used to
evaluate linearity of the spectrometer response.
Chen et al. (2015) performed R and T optical calibrations
using sections of quartz-fiber filters with different EC
levels from the Fresno Supersite in central California
(Watson et al., 2000; Park et al., 2006; Chow et al., 2009),
assuming that all aerosols are equally useful for use as
transfer standards as long as there is a range of darkening in
the deposits. The basis for this assumption is that the R and
T measurements by a carbon analyzer linearly correspond to
absolute filter R and T, respectively, regardless of the
sample type and loading. To evaluate this assumption, and
to develop a more repeatable process for creating transfer
standards, quartz-fiber filter samples with increased loadings
were prepared from laboratory-generated aerosols using a
diesel generator, flaming dry pine needles, smoldering damp
pine cones, and smoldering Florida peat. Other potential
transfer standards might be obtained from graphite electric
arc soot (Evans et al., 2003), resuspended carbon black or
graphite powders (Medalia and Richards, 1972; Jäger et al.,
1999), or a lean-burning gas torch (Sheridan et al., 2005;
Chow et al., 2009; Moore et al., 2014), but these are
dominated by BC and do not include a major BrC component.
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Diesel exhaust was generated by an Onan Cummins
12.5 kW diesel generator with an accumulated 2,500 hours
operating on commercial diesel fuel (< 15 ppmw sulfur
content) under idling conditions. Biomass samples were
combusted in a Pineridge Genuine Woodstove. Dry Sierra
Nevada pine needles (recovered from the forest floor) were
ignited with a butane lighter and sampled only during the
flaming phase. Sierra Nevada pine cones were wetted with
water and subjected to a temperature-adjustable electric heater
to attain flameless smoldering prior to sampling. Florida peat
was also ignited by the electric heater to a smoldering phase.
Samples were drawn from the plume of each source
through a Bendix 240 PM2.5 cyclone at a total flow rate of
113 L/min into a conical mixing chamber (Chow et al.,
1993b). The cyclone inlet was placed at ~0.5–1.5 m from
the emission point to allow for cooling and dilution with
ambient air and to obtain a wide range of deposit opacities.
Eight pre-fired 47 mm quartz-fiber filters and two Teflonmembrane filters were mounted to the 12-port manifold
with Savillex fluorinated ethylene propylene (FEP) Teflon
filter holders. Flow rates through the filters ranged from 1
to 32 L/min, in a logarithmic sequence, to obtain increasing
deposits on each filter. A DustTrak DRX (Wang et al., 2009)
monitored particle concentrations in real time to adjust sample
durations, which ranged from ~5 min for diesel exhaust to 15
min for peat smoldering. This yielded aerosol deposits
ranging from ~0.05‒1.5 mg/filter. Filters were weighed before
and after sampling to determine the amount of aerosol
collected, but this is not a necessary feature of the transfer
standard. Fig. 5 shows examples of the transfer standards.
The diesel and flaming standards are similar in appearance
to many ambient samples, with shades changing from light
gray to deep black. The smoldering standards have a
yellowish-brown appearance that is distinct from the
others. A set of absolute R(λ) and T(λ) were read from the
UV/VIS spectrometer for each Model 2015 wavelength
(i.e., 405, 445, 532, 635, 780, 808, and 980 nm) that
characterize each transfer standard, as shown in Figs. S-4
to S-7. The calibration spectra in Fig. 6 show that R and T
are lower at the shorter wavelengths for all of the samples.
R and T also decrease as the sample loadings increase.
However, the smoldering samples show small changes with
loading at the longer wavelengths compared to the shorter
wavelengths, making them less useful than the blacker
standards for covering a large range of wavelengths and
filter loadings. R has a larger range than T, especially at the
shorter wavelengths, on a linear scale. The T signal is both
scattered and absorbed on the aerosol deposit and within the
filter, whereas the R signal is affected mostly by the surface
of the filter and its deposit, thereby experiencing less
attenuation.
Each transfer standard was placed into the Model 2015
sample boat and inserted into the analysis zone to measure
instrument-specific R and T for one or two minutes to obtain
an average photodiode detector response. The detector output
is linear over several decades, and it responds to different
wavelengths at both high and low R and T values, so the
outputs are scaled such that they can be viewed on a
similar basis. Fig. 7 illustrates the resulting calibration
curves for R(λ) and T(λ) derived from this practice. These
appear to be independent of the type and loading of sample,
confirming the hypothesis of Chen et al. (2015). Ambient
samples with different shadings may still be preferable to
laboratory-generated samples as transfer standards, since
their filter material is more likely to be consistent from
sample to sample.
Fig.5. Transfer standards with varying amounts of deposit. The leftmost samples are blanks. Fresno Supersite samples
were obtained from large 406 cm2 quartz-fiber filter deposits using a high-volume sampler equipped with a PM2.5 inlet
during 2003 and 2004. The remaining samples were generated in the laboratory from simultaneous sampling at different
flow rates to obtain a range of different filter loadings.
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Diesel Reflectance
7
Diesel Transmittance
15
90
80
12
Diesel1
Diesel2
60
Diesel3
50
Diesel4
Diesel5
40
Diesel6
30
Diesel7
20
Diesel8
Diesel1
Transmittance (%)
Reflectance (%)
70
Diesel2
Diesel3
9
Diesel4
Diesel5
6
Diesel6
Diesel7
Diesel8
3
10
0
0
200
400
600
800
200
1000
400
600
800
1000
Wavelength (nm)
Wavelength (nm)
Smoldering Peat Reflectance
Smoldering Peat Transmittance
15
90
80
12
Peat1
Peat2
60
Peat3
50
Peat4
Peat5
40
Peat6
Peat7
30
Peat8
20
Transmittance (%)
Reflectance (%)
70
Peat1
Peat2
9
Peat3
Peat4
6
Peat5
Peat6
Peat7
3
Peat8
10
0
0
200
400
600
800
1000
200
Wavelength (nm)
400
600
800
1000
Wavelength (nm)
Fig. 6. Optical calibration of diesel and smoldering peat transfer standards determined with a calibrated UV/VIS
spectrometer. Sample loadings increase for higher-numbered samples. Vertical lines designate the seven wavelengths in
Model 2015.
Based on the calibration curves, absolute R and T in %
can be inferred from carbon analyzer photodiode signals.
Atmospheric absorption as a function of wavelength, babs(λ),
for the integrated filter deposit can then be estimated from
attenuations (ATN) in either the absolute filter R or T,
defined by:
 R    final
ATN R     ln 
 R  
initial





 T    final
ATN T     ln 
 T  
initial





(1)
(2)
where R(λ)/T(λ)initial and R(λ)/T(λ)final are the time-averaged
(typically 10 seconds) initial and final filter reflectance/
transmittances at each wavelength, respectively. The R(λ)final
and T(λ)final may differ from those of a true blank filter due
to refractory residues after the thermal analysis, and these rare
cases are flagged when observed after analysis is complete.
Nonetheless, in the first-order approximation (Hansen et
al., 1984; ISO, 1993; Lindberg et al., 1999; Quincey, 2007),
babs(λ) of the thermally liberated fraction including OC and
EC can be expressed as:
 A 
babs     ATN R     
 / 100
 2V 
(3)
 A
babs      ATN T       / 100
V 
(4)
where A is the area (cm2) of the surface deposit and V is
the sample volume (m3). These yield babs(λ) in the commonly
used unit of inverse megameter (Mm–1) (Richards, 1984;
Watson, 2002). The comparison of babs(λ) by R and T will
be discussed in a companion paper. Though highly correlated,
they differ substantially in magnitude. More precise
estimations of babs(λ) should consider multiple scattering
and loading effects of the particle-filter matrix, leading to
modifications to Eqs. (3) and (4) (Arnott et al., 2005; Quincey
et al., 2011; Heal and Quincey, 2012; Chen et al., 2015).
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Reflectance Calibration
Transmittance Calibration
Fig. 7. Model 2015 R and T calibration curves using transfer standards. The laser signals are multiples of millivolt
photodetector outputs normalized to a common scale for all wavelengths. Example calibrations for 633 nm in the Model
2001 (see Supplemental Fig. S-8) show similar linearity.
EQUIVALENCE OF OC, EC, AND CARBON
FRACTIONS
The conversion from the DRI/OGC to Model 2001
analysis in CY2005 involved a major change in the optical
configuration to accommodate simultaneous R and T
measurements. The only change from the Model 2001 to
multiwavelength measurements is in the laser wavelength
from 633 to 635 nm, which is believed to be imperceptible
for the quantification of OC and EC (see examples in Fig. 2).
The small diameter of the 25 mm IMPROVE filter limits the
number of comparisons that can be performed on a single
sample. Collocated samples are taken at several IMPROVE
sites for research purposes, and 73 of these were used in an
initial comparison between the Model 2015 and Model 2001.
As a contrast to non-urban IMPROVE samples, portions of
67 large (406 cm2) PM2.5 samples from the urban Fresno
Supersite (Watson et al., 2000) were also tested in replicate.
Results for the different carbon fractions are summarized
in Table 1, with scatterplots shown in Figs. S-9 to S-12.
The slopes are within three standard errors (± 3σ) of unity
for the TC, OC, and EC, including OC and EC by reflectance
and transmittance optical adjustment (i.e., OCR, ECR,
OCT, and ECT), with high correlations. Precision bars on
the scatterplots show that the 1:1 line is within the precision
estimated from replicate analyses. It has been found in
previous samples that much of the variability comes from
inhomogeneity of the deposit rather than variability of the
carbon detection.
The thermal carbon fractions are not as similar in these
replicates with slopes differing from unity by more than a
few standard errors. Similar variability was found by
Chow et al. (2007), and this variability is typical of that
among replicates from different carbon analyzers. The
inter-instrumental variability was attributed to temperature
calibration, which could deviate up to ±1% from the
specified temperatures (e.g., ~15°C difference at the EC2
[740°C] for any two carbon analyzers, see Chow et al.,
2005), as well as to trace oxygen levels in the oven (varying
by a factor of two, see Chow et al., 2007). As a result, the
error bars for carbon fractions are larger than those for TC,
OC, and EC, with most of them still including the 1:1 line
(see Figs. S-11 and S-12). In addition, OC1 is semi-volatile
and is often found to be poorly reproduced even in replicates
analyzed on the same instrument. Fig. S-12 shows low
temperature OC1 (140°C) to be the most variable, especially
for the low-concentration IMPROVE samples. High
temperature EC2 for Fresno samples (Fig. S-11) also showed
a lower correlation and higher slope than that for IMPROVE,
even though most of the values are within ± 1σ of the 1:1
line supporting a typical inter-instrumental variability. Fig. 8
contrasts replicate analyses for two Model 2001 analyzers
with replicates from the Model 2015, showing comparable
reproducibility.
POTENTIAL USES OF MULTIWAVELENGTH R
AND T DATA
Multiwavelength aethalometers (Park et al., 2006) and
multi-spectral remote sensors (Hoff and Christopher, 2009)
have shown the value of aerosol absorption data, especially
related to radiative transfer and aerosol climate forcing.
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Table 1. Comparison measures for replicate measurements of carbon fractions among Model 2015 and Model 2001 units
from 67 Fresno Supersite samples and 73 IMPROVE network samples.
Average Model 2015
Average Model 2001
(µg/cm2)
(µg/cm2)
TC Fresno
1.01 ± 0.01
0.99
23.76
24.18
TC IMPROVE
0.97 ± 0.01
0.96
8.04
7.83
OCR Fresno
1.03 ± 0.01
0.99
18.68
19.30
OCR IMPROVE
0.97 ± 0.01
0.95
6.88
6.72
OCT Fresno
1.00 ± 0.01
0.99
20.98
21.23
OCT IMPROVE
0.97 ± 0.01
0.96
7.36
7.20
ECR Fresno
0.95 ± 0.02
0.97
5.08
4.88
ECR IMPROVE
0.98 ± 0.03
0.94
1.16
1.11
ECT Fresno
1.08 ± 0.02
0.96
2.78
2.96
ECT IMPROVE
0.95 ± 0.03
0.89
0.69
0.63
OC1 Fresno
0.75 ± 0.04
0.94
2.90
1.92
OC1 IMPROVE
0.67 ± 0.09
0.26
0.24
0.19
OC2 Fresno
1.10 ± 0.02
0.93
4.14
4.59
OC2 IMPROVE
0.97 ± 0.02
0.93
1.57
1.52
OC3 Fresno
1.14 ± 0.02
0.94
5.99
6.72
OC3 IMPROVE
0.91 ± 0.08
0.94
4.01
3.71
OC4 Fresno
1.29 ± 0.04
0.93
3.18
4.02
OC4 IMPROVE
1.05 ± 0.04
0.86
1.25
1.30
EC1 Fresno
0.85 ± 0.01
0.97
7.01
6.17
EC1 IMPROVE
0.94 ± 0.02
0.96
2.34
2.27
EC2 Fresno
1.32 ± 0.05
0.58
0.55
0.73
EC2 IMPROVE
1.05 ± 0.09
0.73
0.49
0.54
a
Deming (1943) regression with zero intercept and standard error (σ). This regression minimizes the perpendicular
distances between the slope line and the data points, yielding the same slope when the x and y axes are interchanged. This
is the correct approach when there are errors in both variables. Carbon fractions are described in Fig. 2 caption.
Carbon Fraction
Slope ± 1σa
R2
The carbon measurement enhancement described here is
complementary to, rather than a replacement for, these
other methods. Since extensive thermal/optical analysis is
being done for both research and compliance monitoring,
multiwavelength spectral measurements as well as the
charring adjustment provide added value to the data sets.
Raw and calibrated R(λ) and T(λ) before and after analysis
(i.e., initial and final values, respectively) will be reported
routinely, while those during analysis can be retrieved from
digitally-archived thermograms when needed. Additional
data reporting includes OCR, ECR, OCT, and ECT for
each of the seven wavelengths.
Chen et al. (2015) demonstrate that based on spectral
attenuation by transmittance (ATNT), contributions of BC
and BrC to light absorption as well as Angstrom Absorption
Exponent (AAE) of BrC can be derived from a sample prior
to thermal/optical analysis. The loading effect was addressed
using diesel exhaust samples. The same can be done for
attenuation by reflectance (ATNR) to achieve consistent BC
and BrC measurements. During analysis, however, charring
occurs to increase ATNR and ATNT, as demonstrated in
Fig. 9. Charring lowers the AAE on the filter, consistent
with an optical property similar to natural BC. AAE based
on ATNR approaches unity at the end of OC4 as all BrC
has been removed from the filter or converted to char.
AAE based on ATNT is higher than unity, however, likely
due to charring within the filter. Optical adjustment based
on R should not depend on wavelength if BrC does not
exist, and therefore the difference in R split points between
BrC sensitive and insensitive wavelengths (e.g., 445 and
808 nm, respectively) may reflect the BrC content in the
sample. This can be verified by comparing with BrC derived
from ATNR or ATNT prior to thermal/optical analysis. Both
approaches for estimating BrC should be further investigated
with standards of known BC/BrC mixtures, such as mixtures
of diesel soot and aerosol humic acids.
For BC-dominated samples, such as diesel exhaust, initial
tests show little variation in OC and EC fractions across
wavelengths. However, for smoldering samples, such as
those illustrated in Fig. 9, the OC and EC fractions,
particularly OCR and ECR, differ depending on wavelength.
A larger number of ambient and source samples needs to
be analyzed to identify relevant patterns. Several potential
data uses include:
● Identifying light absorbing compounds: More specific
organic compound detectors have been interfaced to
thermal/optical analyzers (Grabowsky et al., 2011; Diab
et al., 2015), and these can be associated with their
spectral and thermal evolution properties.
● Separating artifact OC from aerosol OC: Organic
vapors adsorbed by the quartz-fiber filter affect the OC
measurement and are the major cause of differences
between ECR and ECT (Chow et al., 2004; Watson et
al., 2009; Chow et al., 2010). Multiwavelenth R and T
measurements offer opportunities to constrain radiative
transfer models through the filter to separate charred
0
0
10
15
1:1
20
20
30
2
TC Model 2001 (µg/cm )
10
1:1
40
TC Model 2001‐Original (µg/cm2)
5
Slope = 0.97 ± 0.01
R2 = 0.96
0
5
10
15
Slope = 1.01 ± 0.01
R2 = 0.99
0
4
8
12
16
20
0
0
5
10
15
20
5
10
15
1:1
20
8
12
2
16
1:1
OCR Model 2001 (µg/cm )
4
Slope = 0.97 ± 0.01
R2 = 0.95
20
OCR Model 2001‐Original (µg/cm2)
0
Slope = 1.02 ± 0.02
R2 = 0.98
0
2
4
6
8
10
ECR Model 2001‐Replicates (µg/cm2)
0
0
1
2
3
4
5
1
2
3
4
1:1
4
6
2
8
ECR Model 2001 (µg/cm )
2
1:1
5
10
ECR Model 2001‐Original (µg/cm2)
Slope = 0.98 ± 0.03
R2 = 0.94
0
Slope = 0.98 ± 0.02
R2 = 0.98
Fig. 8. Replicate analyses for Model 2001 vs. another Model 2001 unit (top row) and a Model 2015 vs. a Model 2001 (bottom row), showing similar comparability.
Slopes use a Deming regression with zero intercept. Since only three 0.5 cm2 punches can be obtained from the 25 mm diameter IMPROVE filter samples, comparisons
among instruments for the same samples are limited. OCR and ECR are OC and EC by reflectance, respectively.
0
10
20
30
40
TC Model 2001‐Replicates (µg/cm2)
TC Model 2015 (µg/cm2)
OCR Model 2001‐Replicates (µg/cm2)
OCR Model 2015 (µg/cm2)
10
ECR Model 2015 (µg/cm2)
20
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Chow et al., Aerosol and Air Quality Research, x: 1–15, xxxx
11
3000
8
AAE445/808nm
6
2500
4
2000
ATNR,445nm
2
ATNR,808 nm
0
1500
‐2
Temperature
‐4
1000
500
‐6
Carbon
‐8
0
1000
2000
0
3000
Temperature (°C) & Carbon ( ng/cm2) Attenuation, AAE
(445/808 nm)
a)
Analysis Time (sec)
8
3000
AAE445/808nm
6
2500
4
ATNT,445nm
2
2000
ATNT,808nm
0
1500
‐2
Temperature
‐4
1000
500
‐6
Carbon
‐8
0
1000
2000
0
3000
Temperature (°C) & Carbon ( ng/cm2) Attenuation, AAE
(445/808 nm)
b)
Analysis Time (sec)
Fig. 9. Thermograms of IMPROVE_A analysis of a smoldering peat sample; the two attenuations are: a) attenuation by
reflectance (ATNR); and b) attenuation by transmittance (ATNT) at 445 and 808 nm as defined by Eqs. (1) and (2) are
shown along with the Angstrom Absorption Exponent (AAE) calculated from the two wavelengths. Red dots indicate the
R or T split points for OC and EC. The two R split points between the two wavelengths are more diverse than the two T
split points.
carbonaceous material within the filter from that of the
aerosol deposit (Chen et al., 2004; Petzold et al., 2005).
● Ground-truthing remotely-sensed BrC: Algorithms are
being developed to estimate BrC from space- and landbased remote sensors (Wang et al., 2013), but these need
to be verified with surface measurements. A growing
database of spatially and temporally dispersed data at
regional monitors would provide a means of verifying
and validating these algorithms and determining their
limitations.
● Improving radiation transfer estimates: It is widely
assumed that aerosol absorption follows a λ–AAE
relationship. This is a good approximation for samples
dominated by BC, where AAE = ~1. However, for many
aerosols, such as those from smoldering combustion,
this simple relationship is not followed and AAE varies
with wavelength (Moosmüller et al., 2012). Interpolations
between the seven wavelengths can be used to infer more
complex functions of wavelength for different sources, a
fraction of which can be verified by the more detailed
spectrometric analyses outlined above. These could be
included as part of emissions inputs to global radiative
transfer models (Chakrabarty et al., 2013).
● Appropriating sources of BC and BrC: The simplest
approach, outlined by Sandradewi et al. (2008a, b),
extrapolates the BC absorption using an AAE = 1
assumption, then attributes the remaining absorption at
shorter wavelengths to the BrC associated with smoldering
ARTICLE IN PRESS
Chow et al., Aerosol and Air Quality Research, x: 1–15, xxxx
12
combustion or SOA. Absorptions are translated to BC
and BrC with mass absorption efficiencies derived from
source emission measurements (i.e., part of the source
profile). A more complex, and potentially accurate, source
apportionment approach would look at carbon associated
with each of the seven wavelengths in source samples
and use these in a receptor-oriented model along with
other source indicators (Watson et al., 2008).
ACKNOWLEDGEMENTS
This work was supported, in part, by the U.S. National
Science Foundation (CHE 1214163) and National Park
Service
IMPROVE
Carbon
Analysis
Contract
(C2350000894). The authors wish to thank Miss Iris Saltus
of the Desert Research Institute for her help in assembling
and editing the manuscript. The conclusions are those of
the authors and do not necessarily reflect the views of the
sponsoring agencies.
SUPPLEMENTARY MATERIALS
Supplementary data associated with this article can be
found in the online version at http://www.aaqr.org.
REFERENCES
Andreae, M.O. and Gelencser, A. (2006). Black Carbon or
Brown Carbon? The Nature of Light-Absorbing
Carbonaceous Aerosols. Atmos. Chem. Phys. 6: 3131–
3148.
Arnott, W.P., Hamasha, K., Moosmüller, H., Sheridan, P.J.
and Ogren, J.A. (2005). Towards Aerosol LightAbsorption Measurements with a 7-Wavelength
Aethalometer: Evaluation with a Photoacoustic Instrument
and 3-Wavelength Nephelometer. Aerosol Sci. Technol.
39: 17–29.
Begum, B.A., Hopke, P.K. and Zhao, W.X. (2005). Source
Identification of Fine Particles in Washington, DC, by
Expanded Factor Analysis Modeling. Environ. Sci.
Technol. 39: 1129–1137.
Betha, R. and Balasubramanian, R. (2014). PM2.5 Emissions
From Hand-Held Sparklers: Chemical Characterization
and Health Risk Assessment. Aerosol Air Qual. Res. 14:
1477–1486.
Cao, J.J., Wu, F., Chow, J.C., Lee, S.C., Li, Y., Chen, S.W.,
An, Z.S., Fung, K.K., Watson, J.G., Zhu, C.S. and Liu,
S.X. (2005). Characterization and Source Apportionment
of Atmospheric Organic and Elemental Carbon During
Fall and Winter of 2003 in Xi'an, China. Atmos. Chem.
Phys. 5: 3127–3137.
Chakrabarty, R.K., Arnold, I.J., Francisco, D.M., Hatchett,
B., Hosseinpour, F., Loria, M., Pokharel, A. and Woody,
B.M. (2013). Black and Brown Carbon Fractal Aggregates
From Combustion of Two Fuels Widely Used in Asian
Rituals. J. Quant. Spectrosc. Radiat. Transfer 122: 25–
30.
Chen, L.-W.A., Chow, J.C., Watson, J.G., Moosmüller, H.
and Arnott, W.P. (2004). Modeling Reflectance and
Transmittance of Quartz-fiber Filter Samples Containing
Elemental Carbon Particles: Implications for
Thermal/Optical Analysis. J. Aerosol Sci. 35: 765–780.
Chen, L.-W.A., Watson, J.G., Chow, J.C. and Magliano,
K.L. (2007). Quantifying PM2.5 Source Contributions
for the San Joaquin Valley with Multivariate Receptor
Models. Environ. Sci. Technol. 41: 2818–2826.
Chen, L.-W.A., Chow, J.C., Watson, J.G. and Schichtel, B.A.
(2012). Consistency of Long-Term Elemental Carbon
Trends from Thermal and Optical Measurements in the
IMPROVE Network. Atmos. Meas. Tech. 5: 2329–2338.
Chen, L.-W.A., Chow, J.C., Wang, X.L., Robles, J.A.,
Sumlin, B.J., Lowenthal, D.H. and Watson, J.G. (2015).
Multi-Wavelength Optical Measurement to Enhance
Thermal/Optical Analysis for Carbonaceous Aerosol.
Atmos. Meas. Tech. 8: 451–461.
Chow, J.C., Watson, J.G., Pritchett, L.C., Pierson, W.R.,
Frazier, C.A. and Purcell, R.G. (1993a). The DRI
Thermal/Optical Reflectance Carbon Analysis System:
Description, Evaluation and Applications in U.S. Air
Quality Studies. Atmos. Environ. 27A: 1185–1201.
Chow, J.C., Watson, J.G., Bowen, J.L., Frazier, C.A.,
Gertler, A.W., Fung, K.K., Landis, D. and Ashbaugh,
L.L. (1993b). A Sampling System for Reactive Species
in the Western United States. In Sampling and Analysis
of Airborne Pollutants, Winegar, E.D. and Keith, L.H.
(Eds.), Lewis Publishers, Ann Arbor, MI, p. 209–228.
Chow, J.C., Watson, J.G., Chen, L.-W.A., Arnott, W.P.,
Moosmüller, H. and Fung, K.K. (2004). Equivalence of
Elemental Carbon by Thermal/Optical Reflectance and
Transmittance with Different Temperature Protocols.
Environ. Sci. Technol. 38: 4414–4422.
Chow, J.C., Watson, J.G., Chen, L.-W.A., Paredes-Miranda,
G., Chang, M.-C.O., Trimble, D.L., Fung, K.K., Zhang,
H. and Yu, J.Z. (2005). Refining Temperature Measures
in Thermal/Optical Carbon Analysis. Atmos. Chem. Phys.
5: 2961–2972.
Chow, J.C., Watson, J.G., Chen, L.-W.A., Chang, M.C.O., Robinson, N.F., Trimble, D.L. and Kohl, S.D.
(2007). The IMPROVE_A Temperature Protocol for
Thermal/Optical
Carbon
Analysis:
Maintaining
Consistency with a Long-Term Database. J. Air Waste
Manage. Assoc. 57: 1014–1023.
Chow, J.C., Watson, J.G., Doraiswamy, P., Chen, L.-W.A.,
Sodeman, D.A., Lowenthal, D.H., Park, K., Arnott, W.P.
and Motallebi, N. (2009). Aerosol Light Absorption, Black
Carbon, and Elemental Carbon at the Fresno Supersite,
California. Atmos. Res. 93: 874–887.
Chow, J.C., Watson, J.G., Chen, L.-W.A., Rice, J. and Frank,
N.H. (2010). Quantification of PM2.5 Organic Carbon
Sampling Artifacts in US Networks. Atmos. Chem.
Phys. 10: 5223–5239.
Chow, J.C. and Watson, J.G. (2011). Air Quality
Management of Multiple Pollutants and Multiple Effects.
Air Qual. Clim. Change 45: 26–32.
Chow, J.C., Watson, J.G., Robles, J., Wang, X.L., Chen,
L.-W.A., Trimble, D.L., Kohl, S.D., Tropp, R.J. and
Fung, K.K. (2011). Quality Assurance and Quality Control
for Thermal/Optical Analysis of Aerosol Samples for
ARTICLE IN PRESS
Chow et al., Aerosol and Air Quality Research, x: 1–15, xxxx
Organic and Elemental Carbon. Anal. Bioanal. Chem.
401: 3141–3152.
Chuesaard, T., Chetiyanukornkul, T., Kameda, T., Hayakawa,
K. and Toriba, A. (2014). Influence of Biomass Burning
on the Levels of Atmospheric Polycyclic Aromatic
Hydrocarbons and Their Nitro Derivatives in Chiang
Mai, Thailand. Aerosol Air Qual. Res. 14: 1247–1257.
CIRA (2014). Federal Land Manager Environmental
Database (FED). Prepared by Colorado State University,
Fort Collins, CO.
Dabek-Zlotorzynska, E., Dann, T.F., Martinelango, P.K.,
Celo, V., Brook, J.R., Mathieu, D., Ding, L.Y. and Austin,
C.C. (2011). Canadian National Air Pollution Surveillance
(NAPS) PM2.5 Speciation Program: Methodology and
PM2.5 Chemical Composition for the Years 2003-2008.
Atmos. Environ. 45: 673–686.
Deming, W.E. (1943). Statistical Adjustment of Data.,
John Wiley and Sons (Dover Publications edition, 1985),
New York, NY.
Diab, J., Streibel, T., Cavalli, F., Lee.S.C., Saathoff, H.,
Mamakos, T., Chow, J.C., Chen, L.-W.A., Watson, J.G.,
Sippula, O. and Zimmermann, R. (2015). Hyphenation
of a EC/OC Thermal-Optical Carbon Analyzer to Photo
Ionization Time-of-Flight Mass Spectrometry: A New
Off-Line Aerosol Mass Spectrometric Approach for
Characterization of Primary and Secondary Particulate
Matter. Atmos. Meas. Tech. Discuss. 8: 269–308.
Eklund, A.G., Chow, J.C., Greenbaum, D.S., Hidy, G.M.,
Kleinman, M.T., Watson, J.G. and Wyzga, R.E. (2014).
Public Health and Components of Particulate Matter:
The Changing Assessment of Black Carbon-Critical
Review Discussion. J. Air Waste Manage. Assoc. 64:
1221–1231.
Evans, D.E., Harrison, R.M. and Ayres, J.G. (2003). The
Generation and Characterisation of Elemental Carbon
Aerosols for Human Challenge Studies. J. Aerosol Sci.
34: 1023–1041.
Gargava, P., Chow, J.C., Watson, J.G. and Lowenthal, D.H.
(2014). Speciated PM10 Emission Inventory for Delhi,
India. Aerosol Air Qual. Res. 14: 1515–1526.
Georgiev, G.T. and Butler, J.J. (2007). Long-Term
Calibration Monitoring of Spectralon Diffusers BRDF
in the Air-Ultraviolet. Appl. Opt. 46: 7892–7899.
Grabowsky, J., Streibel, T., Sklorz, M., Chow, J.C.,
Mamakos, A. and Zimmermann, R. (2011). Hyphenation
of a Carbon Analyzer to Photo-Ionization Mass
Spectrometry to Unravel the Organic Composition of
Particulate Matter on a Molecular Level. Anal. Bioanal.
Chem. 401: 3153–3164.
Grahame, T.J., Klemm, R.J. and Schlesinger, R.B. (2014).
Public Health and Components of Particulate Matter:
The Changing Assessment of Black Carbon: Critical
Review. J. Air Waste Manage. Assoc. 64: 620–660.
Hansen, A.D.A., Rosen, H. and Novakov, T. (1984). The
Aethalometer - An Instrument for the Real-Time
Measurement of Optical Absorption by Aerosol
Particles. Sci. Total Environ. 36: 191–196.
Hansen, D.A., Edgerton, E., Hartsell, B., Jansen, J., Burge,
H., Koutrakis, P., Rogers, C., Suh, H., Chow, J.C.,
13
Zielinska, B., McMurry, P.H., Mulholland, J., Russell,
A. and Rasmussen, R. (2006). Air Quality Measurements
for the Aerosol Research and Inhalation Epidemiology
Study. J. Air Waste Manage. Assoc. 56: 1445–1458.
Heal, M.R. and Quincey, P.G. (2012). The Relationship
Between Black Carbon Concentration and Black Smoke:
A More General Approach. Atmos. Environ. 54: 538–
544.
Hoff, R.M. and Christopher, S.A. (2009). Remote Sensing
of Particulate Pollution From Space: Have We Reached
the Promised Land? A Critical Review. J. Air Waste
Manage. Assoc. 59: 645–675.
Huang, W., Cao, J.J., Tao, Y.B., Dai, L.Z., Lu, S.E., Hou,
B., Wang, Z. and Zhu, T. (2012). Seasonal Variation of
Chemical Species Associated with Short-Term Mortality
Effects of PM2.5 in Xi'an, a Central City in China. Am. J.
Epidemiol. 175: 556–566.
Huntzicker, J.J., Johnson, R.L., Shah, J.J. and Cary, R.A.
(1982). Analysis of Organic and Elemental Carbon in
Ambient Aerosols by a Thermal-Optical Method. In
Particulate Carbon: Atmospheric Life Cycle, Wolff,
G.T. and Klimisch, R.L. (Eds.), Plenum Press, New
York, NY, p. 79–88.
IMPROVE (2015). Interagency Monitoring of Protected
Visual Environments. Prepared by National Park
Service, Ft. Collins, CO.
ISO (1993). ISO 9835:1993 Ambient air -- Determination
of a Black Smoke Index. Prepared by International
Organization for Standardization, Geneva, Switzerland.
Jäger, C., Henning, T., Schlögl, R. and Spillecke, O. (1999).
Spectral Properties of Carbon Black. J. Non-Cryst. Solids
258: 161–179.
Kim, E. and Hopke, P.K. (2004a). Source Apportionment of
Fine Particles at Washington, DC, Utilizing TemperatureResolved Carbon Fractions. J. Air Waste Manage. Assoc.
54: 773–785.
Kim, E. and Hopke, P.K. (2004b). Improving Source
Identification of Fine Particles in a Rural Northeastern US
Area Utilizing Temperature-Resolved Carbon Fractions. J.
Geophys. Res. 109: 1–13.
Kim, E., Hopke, P.K. and Edgerton, E.S. (2004). Improving
Source Identification of Atlanta Aerosol Using
Temperature Resolved Carbon Fractions in Positive
Matrix Factorization. Atmos. Environ. 38: 3349–3362.
Kim, E. and Hopke, P.K. (2005). Improving Source
Apportionment of Fine Particles in the Eastern United
States Utilizing Temperature-Resolved Carbon Fractions.
J. Air Waste Manage. Assoc. 55: 1456–1463.
Labsphere (2000). Spectralon® Diffuse Reflectance
Standards: High Lambertian Reflectance over their
Effective Spectral Range. Prepared by Labsphere, Inc.,
North Sutton, NH.
Lindberg, J.D., Douglass, R.E. and Garvey, D.M. (1999).
Atmospheric Particulate Absorption and Black Carbon
Measurement. Appl. Opt. 38: 2369–2376.
Maykut, N.N., Lewtas, J., Kim, E. and Larson, T.V. (2003).
Source Apportionment of PM2.5 at an Urban IMPROVE
Site in Seattle, Washington. Environ. Sci. Technol. 37:
5135–5142.
ARTICLE IN PRESS
14
Chow et al., Aerosol and Air Quality Research, x: 1–15, xxxx
Medalia, A.I. and Richards, L.W. (1972). The Tinting
Strength of Black Carbon. J. Colloid Interface Sci. 40:
233–252.
Moore, R.H., Ziemba, L.D., Dutcher, D., Beyersdorf, A.J.,
Chan, K., Crumeyrolle, S., Raymond, T.M., Thornhill,
K.L., Winstead, E.L. and Anderson, B.E. (2014). Mapping
the Operation of the Miniature Combustion Aerosol
Standard (Mini-CAST) Soot Generator. Aerosol Sci.
Technol. 48: 467–479.
Moosmüller, H., Chakrabarty, R.K., Ehlers, K.M. and
Arnott, W.P. (2011). Absorption Angstrom Coefficient,
Brown Carbon, and Aerosols: Basic Concepts, Bulk
Matter, and Spherical Particles. Atmos. Chem. Phys. 11:
1217–1225.
Moosmüller, H., Engelbrecht, J.P., Skiba, M., Frey, G.,
Chakrabarty, R.K. and Arnott, W.P. (2012). Single
Scattering Albedo of Fine Mineral Dust Aerosols
Controlled by Iron Concentration. J. Geophys. Res. 117:
D11210.
Murphy, D.M., Chow, J.C., Leibensperger, E.M., Malm,
W.C., Pitchford, M.L., Schichtel, B.A., Watson, J.G.
and White, W.H. (2011). Decreases in Elemental Carbon
and Fine Particle Mass in the United States. Atmos.
Chem. Phys. 11: 4679–4686.
Park, K., Chow, J.C., Watson, J.G., Trimble, D.L.,
Doraiswamy, P., Arnott, W.P., Stroud, K.R., Bowers,
K., Bode, R., Petzold, A. and Hansen, A.D.A. (2006).
Comparison of Continuous and Filter-Based Carbon
Measurements at the Fresno Supersite. J. Air Waste
Manage. Assoc. 56: 474–491.
Pervez, S., Chakrabarty, R.K., Dewangan, S., Watson,
J.G., Chow, J.C., Matawle, J.L. and Pervez, Y. (2015).
Cultural and Ritual Burning Emission Factors and
Activity Levels in India. Aerosol Air Qual. Res. 15: 72–
80.
Petzold, A., Schloesser, H., Sheridan, P.J., Arnott, W.P.,
Ogren, J.A. and Virkkula, A. (2005). Evaluation of
Multiangle Absorption Photometry for Measuring Aerosol
Light Absorption. Aerosol Sci. Technol. 39: 40–51.
Quincey, P.G. (2007). A Relationship Between Black
Smoke Index and Black Carbon Concentration. Atmos.
Environ. 41: 7964–7968.
Quincey, P.G., Butterfield, D.M., Green, D. and Fuller,
G.W. (2011). Black Smoke and Black Carbon: Further
Investigation of the Relationship Between These Ambient
Air Metrics. Atmos. Environ. 45: 3528–3534.
Richards, L.W. (1984). Suggested Units for Quantities
Related to Visibility in the Atmosphere. J. Air Pollut.
Control Assoc. 34: 378–379.
Sandradewi, J., Prevot, A.S.H., Weingartner, E.,
Schmidhauser, R., Gysel, M. and Baltensperger, U.
(2008a). A Study of Wood Burning and Traffic Aerosols in
an Alpine Valley Using a Multi-Wavelength Aethalometer.
Atmos. Environ. 42: 101–112.
Sandradewi, J., Prevot, A.S.H., Szidat, S., Perron, N., Alfarra,
M.R., Lanz, V.A., Weingartner, E. and Baltensperger, U.
(2008b). Using Aerosol Light Absorption Measurements
for the Quantitative Determination of Wood Burning
and Traffic Emission Contributions to Particulate Matter.
Environ. Sci. Technol. 42: 3316–3323.
Sheridan, P.J., Arnott, W.P., Ogren, J.A., Andrews, E.,
Atkinson, D.B., Covert, D.S., Moosmüller, H., Petzold,
A., Schmid, B., Strawa, A.W., Varma, R. and Virkkula,
A. (2005). The Reno Aerosol Optics Study: An Evaluation
of Aerosol Absorption Measurement Methods. Aerosol
Sci. Technol. 39: 1–16.
Travis, J.C., Smith, M.V., Rasberry, S.D. and Kramer,
G.W. (2000). Technical specifications for certification
of spectrophotometric NTRMs. Report Number 260-140
Prepared by Naitonal Institute of Standards and
Technology, Gaithersberg, MD.
U.S.EPA (1999). 40 CFR Part 51 - Regional Haze
Regulations: Final Rule. Fed. Regist. 64: 35714–35774.
U.S.EPA (2005). Regional Haze Regulations and Guidelines
for Best Available Retrofit Technology (BART)
Determinations. Fed. Regist. 70: 39104–39172.
U.S.EPA (2012). Report to Congress on Black Carbon.
Report Number EPA-450/D-12-001 Prepared by U.S.
Environmental Protection Agency, Washington DC.
U.S.EPA (2015). Chemical Speciation. Prepared by U.S.
Environmental Protection Agency, Research Triangle
Park, NC.
Upstone,
S.
(2002).
Validating
UV/Visible
Spectrophotometers. Prepared by Perkin-Elmer,
Waltham, Ma.
Wang, L., Li, Z.Q., Tian, Q.J., Ma, Y., Zhang, F.X.,
Zhang, Y., Li, D.H., Li, K.T. and Li, L. (2013). Estimate
of Aerosol Absorbing Components of Black Carbon,
Brown Carbon, and Dust From Ground-Based Remote
Sensing Data of Sun-Sky Radiometers. J. Geophys. Res.
118: 6534–6543.
Wang, X.L., Chancellor, G., Evenstad, J., Farnsworth, J.E.,
Hase, A., Olson, G.M., Sreenath, A. and Agarwal, J.K.
(2009). A Novel Optical Instrument for Estimating Size
Segregated Aerosol Mass Concentration in Real Time.
Aerosol Sci. Technol. 43: 939–950.
Watson, J.G., Chow, J.C., Lowenthal, D.H., Pritchett, L.C.,
Frazier, C.A., Neuroth, G.R. and Robbins, R. (1994).
Differences in the Carbon Composition of Source Profiles
for Diesel- and Gasoline-Powered Vehicles. Atmos.
Environ. 28: 2493–2505.
Watson, J.G., Chow, J.C., Bowen, J.L., Lowenthal, D.H.,
Hering, S.V., Ouchida, P. and Oslund, W. (2000). Air
Quality Measurements from the Fresno Supersite. J. Air
Waste Manage. Assoc. 50: 1321–1334.
Watson, J.G. (2002). Visibility: Science and Regulation 2002 Critical Review. J. Air Waste Manage. Assoc. 52:
628–713.
Watson, J.G., Chow, J.C. and Chen, L.-W.A. (2005).
Summary of Organic and Elemental Carbon/Black Carbon
Analysis Methods and Intercomparisons. Aerosol Air
Qual. Res. 5: 65–102.
Watson, J.G., Chen, L.-W.A., Chow, J.C., Lowenthal, D.H.
and Doraiswamy, P. (2008). Source Apportionment:
Findings From the U.S. Supersite Program. J. Air Waste
Manage. Assoc. 58: 265–288.
Watson, J.G., Chow, J.C., Chen, L.-W.A. and Frank, N.H.
(2009). Methods to Assess Carbonaceous Aerosol
ARTICLE IN PRESS
Chow et al., Aerosol and Air Quality Research, x: 1–15, xxxx
Sampling Artifacts for IMPROVE and Other Long-Term
Networks. J. Air Waste Manage. Assoc. 59: 898–911.
Zhang, X.Y., Wang, Y.Q., Niu, T., Zhang, X.C., Gong, S.L.,
Zhang, Y.M. and Sun, J.Y. (2012). Atmospheric Aerosol
Compositions in China: Spatial/Temporal Variability,
Chemical Signature, Regional Haze Distribution and
Comparisons with Global Aerosols. Atmos. Chem. Phys.
15
12: 779–799.
Received for review, February 20, 2015
Revised, April 6, 2015
Accepted, April 18, 2015