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

[摘要] Organic carbon (OC) can constitute 50% or more of the mass ofatmospheric particulate matter. Typically, organic carbon is measured from aquartz fiber filter that has been exposed to a volume of ambient air andanalyzed using thermal methods such as thermal-optical reflectance (TOR).Here, methods are presented that show the feasibility of using Fouriertransform infrared (FT-IR) absorbance spectra from polytetrafluoroethylene(PTFE or Teflon) filters to accurately predict TOR OC. This work marks aninitial step in proposing a method that can reduce the operating costs oflarge air quality monitoring networks with an inexpensive, non-destructiveanalysis technique using routinely collected PTFE filter samples which, inaddition to OC concentrations, can concurrently provide information regardingthe composition of organic aerosol. This feasibility study suggests that theminimum detection limit and errors (or uncertainty) of FT-IR predictions are on par with TOR OC such that evaluation of long-term trends andepidemiological studies would not be significantly impacted. To develop andtest the method, FT-IR absorbance spectra are obtained from 794 samples fromseven Interagency Monitoring of PROtected Visual Environment (IMPROVE) sitescollected during 2011. Partial least-squares regression is used to calibratesample FT-IR absorbance spectra to TOR OC. The FTIR spectra are divided intocalibration and test sets by sampling site and date. The calibration producesprecise and accurate TOR OC predictions of the test set samples by FT-IR asindicated 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%). Theseperformance metrics can be achieved with various degrees of spectralpretreatment (e.g., including or excluding substrate contributions to theabsorbances) and are comparable in precision to collocated TOR measurements.FT-IR spectra are also divided into calibration and test sets by OC mass andby OM / OC ratio, which reflects the organic composition of the particulatematter and is obtained from organic functional group composition; thesedivisions also leads to precise and accurate OC predictions. Low OCconcentrations have higher bias and normalized error due to TOR analyticalerrors and artifact-correction errors, not due to the range of OC mass of thesamples in the calibration set. However, samples with low OC mass can be usedto predict samples with high OC mass, indicating that the calibration islinear. Using samples in the calibration set that have differentOM / OC or ammonium / OC distributions than the test set leads toonly a modest increase in bias and normalized error in the predicted samples.We conclude that FT-IR analysis with partial least-squares regression is arobust method for accurately predicting TOR OC in IMPROVE network samples –providing complementary information to the organic functional groupcomposition and organic aerosol mass estimated previously from the same setof sample spectra (Ruthenburg et al., 2014).

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