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Predicting Ambient Aerosol Thermal-optical Reflectance (Tor) Measurements from Infrared Spectra: Organic Carbon : Volume 8, Issue 3 (05/03/2015)

By Dillner, A. M.

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Book Id: WPLBN0003999615
Format Type: PDF Article :
File Size: Pages 13
Reproduction Date: 2015

Title: Predicting Ambient Aerosol Thermal-optical Reflectance (Tor) Measurements from Infrared Spectra: Organic Carbon : Volume 8, Issue 3 (05/03/2015)  
Author: Dillner, A. M.
Volume: Vol. 8, Issue 3
Language: English
Subject: Science, Atmospheric, Measurement
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Takahama, S., & Dillner, A. M. (2015). Predicting Ambient Aerosol Thermal-optical Reflectance (Tor) Measurements from Infrared Spectra: Organic Carbon : Volume 8, Issue 3 (05/03/2015). Retrieved from

Description: University of California, Davis, Davis, California, USA. 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).

Predicting ambient aerosol thermal-optical reflectance (TOR) measurements from infrared spectra: organic carbon

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