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| Titel |
Predicting ambient aerosol thermal–optical reflectance measurements from infrared spectra: elemental 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. 10 ; Nr. 8, no. 10 (2015-10-02), S.4013-4023 |
| Datensatznummer |
250116621
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| Publikation (Nr.) |
 copernicus.org/amt-8-4013-2015.pdf |
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| Zusammenfassung |
| Elemental carbon (EC) is an important constituent of atmospheric particulate
matter because it absorbs solar radiation influencing climate and visibility
and it adversely affects human health. The EC measured by thermal methods
such as thermal–optical reflectance (TOR) is operationally defined as the
carbon that volatilizes from quartz filter samples at elevated temperatures
in the presence of oxygen. Here, methods are presented to accurately predict
TOR EC using Fourier transform infrared (FT-IR) absorbance spectra from
atmospheric particulate matter collected on polytetrafluoroethylene (PTFE or
Teflon) filters. This method is similar to the procedure
developed for OC in prior work (Dillner and Takahama, 2015). Transmittance
FT-IR analysis is rapid, inexpensive and nondestructive to the PTFE filter
samples which are routinely collected for mass and elemental analysis in
monitoring networks. FT-IR absorbance spectra are obtained from 794 filter
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 collocated TOR EC
measurements. The FT-IR spectra are divided into calibration and test sets.
Two calibrations are developed: one developed from uniform
distribution of samples across the EC mass range (Uniform EC) and one
developed from a uniform distribution of Low EC mass samples (EC < 2.4 μg,
Low Uniform EC). A hybrid approach which applies the Low EC
calibration to Low EC samples and the Uniform EC calibration to all other
samples is used to produce predictions for Low EC samples that have mean
error on par with parallel TOR EC samples in the same mass range and an
estimate of the minimum detection limit (MDL) that is on par with TOR EC
MDL. For all samples, this hybrid approach leads to precise and accurate TOR
EC predictions by FT-IR as indicated by high coefficient of determination
(R2; 0.96), no bias (0.00 μg m−3, a concentration value based on
the nominal IMPROVE sample volume of 32.8 m3), low error
(0.03 μg m−3) and reasonable normalized error (21 %). 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 and accuracy to collocated TOR measurements. Only
the normalized error is higher for the FT-IR EC measurements than for
collocated TOR. FT-IR spectra are also divided into calibration and test
sets by the ratios OC/EC and ammonium/EC to determine the impact of OC and
ammonium on EC prediction. We conclude that FT-IR analysis with partial
least squares regression is a robust method for accurately predicting TOR EC
in IMPROVE network samples, providing complementary information to TOR OC
predictions (Dillner and Takahama, 2015) and the organic functional group
composition and organic matter estimated previously from the same set
of sample spectra (Ruthenburg et al., 2014). |
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