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| Titel |
Predicting ambient aerosol thermal-optical reflectance (TOR) measurements from infrared spectra: organic carbon |
| VerfasserIn |
A. M. Dillner, S. Takahama |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 8, no. 3 ; Nr. 8, no. 3 (2015-03-05), S.1097-1109 |
| Datensatznummer |
250116203
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| Publikation (Nr.) |
 copernicus.org/amt-8-1097-2015.pdf |
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| Zusammenfassung |
| Organic carbon (OC) can constitute 50% or more of the mass of
atmospheric particulate matter. Typically, organic carbon is measured from a
quartz fiber filter that has been exposed to a volume of ambient air and
analyzed using thermal methods such as thermal-optical reflectance (TOR).
Here, methods are presented that show the feasibility of using Fourier
transform infrared (FT-IR) absorbance spectra from polytetrafluoroethylene
(PTFE or Teflon) filters to accurately predict TOR OC. This work marks an
initial step in proposing a method that can reduce the operating costs of
large air quality monitoring networks with an inexpensive, non-destructive
analysis technique using routinely collected PTFE filter samples which, in
addition to OC concentrations, can concurrently provide information regarding
the composition of organic aerosol. This feasibility study suggests that the
minimum detection limit and errors (or uncertainty) of FT-IR predictions are on par with TOR OC such that evaluation of long-term trends and
epidemiological studies would not be significantly impacted. To develop and
test the method, FT-IR absorbance spectra are obtained from 794 samples from
seven Interagency Monitoring of PROtected Visual Environment (IMPROVE) sites
collected during 2011. Partial least-squares regression is used to calibrate
sample FT-IR absorbance spectra to TOR OC. The FTIR spectra are divided into
calibration and test sets by sampling site and date. The calibration produces
precise and accurate TOR OC predictions of the test set samples by FT-IR as
indicated by high coefficient of variation (R2; 0.96), low bias
(0.02 μg m−3,
the nominal IMPROVE sample volume is 32.8 m3), low error
(0.08 μg m−3) and low normalized error (11%). These
performance metrics can be achieved with various degrees of spectral
pretreatment (e.g., including or excluding substrate contributions to the
absorbances) and are comparable in precision to collocated TOR measurements.
FT-IR spectra are also divided into calibration and test sets by OC mass and
by OM / OC ratio, which reflects the organic composition of the particulate
matter and is obtained from organic functional group composition; these
divisions also leads to precise and accurate OC predictions. Low OC
concentrations have higher bias and normalized error due to TOR analytical
errors and artifact-correction errors, not due to the range of OC mass of the
samples in the calibration set. However, samples with low OC mass can be used
to predict samples with high OC mass, indicating that the calibration is
linear. Using samples in the calibration set that have different
OM / OC or ammonium / OC distributions than the test set leads to
only a modest increase in bias and normalized error in the predicted samples.
We conclude that FT-IR analysis with partial least-squares regression is a
robust method for accurately predicting TOR OC in IMPROVE network samples –
providing complementary information to the organic functional group
composition and organic aerosol mass estimated previously from the same set
of sample spectra (Ruthenburg et al., 2014). |
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