Atmospheric particulate matter characterization by Fourier transform infrared spectroscopy: a review of statistical calibration strategies for carbonaceous aerosol quantification in US measurement networks

Abstract

Abstract. Atmospheric particulate matter (PM) is a complex mixture of many different substances and requires a suite of instruments for chemical characterization. Fourier transform infrared (FT-IR) spectroscopy is a technique that can provide quantification of multiple species provided that accurate calibration models can be constructed to interpret the acquired spectra. In this capacity, FT-IR spectroscopy has enjoyed a long history in monitoring gas-phase constituents in the atmosphere and in stack emissions. However, application to PM poses a different set of challenges as the condensed-phase spectrum has broad, overlapping absorption peaks and contributions of scattering to the mid-infrared spectrum. Past approaches have used laboratory standards to build calibration models for prediction of inorganic substances or organic functional groups and predict their concentration in atmospheric PM mixtures by extrapolation. In this work, we review recent studies pursuing an alternate strategy, which is to build statistical calibration models for mid-IR spectra of PM using collocated ambient measurements. Focusing on calibrations with organic carbon (OC) and elemental carbon (EC) reported from thermal–optical reflectance (TOR), this synthesis serves to consolidate our knowledge for extending FT-IR spectroscopy to provide TOR-equivalent OC and EC measurements to new PM samples when TOR measurements are not available. We summarize methods for model specification, calibration sample selection, and model evaluation for these substances at several sites in two US national monitoring networks: seven sites in the Interagency Monitoring of Protected Visual Environments (IMPROVE) network for the year 2011 and 10 sites in the Chemical Speciation Network (CSN) for the year 2013. We then describe application of the model in an operational context for the IMPROVE network for samples collected in 2013 at six of the same sites as in 2011 and 11 additional sites. In addition to extending the evaluation to samples from a different year and different sites, we describe strategies for error anticipation due to precision and biases from the calibration model to assess model applicability for new spectra a priori. We conclude with a discussion regarding past work and future strategies for recalibration. In addition to targeting numerical accuracy, we encourage model interpretation to facilitate understanding of the underlying structural composition related to operationally defined quantities of TOR OC and EC from the vibrational modes in mid-IR deemed most informative for calibration. The paper is structured such that the life cycle of a statistical calibration model for FT-IR spectroscopy can be envisioned for any substance with IR-active vibrational modes, and more generally for instruments requiring ambient calibrations.

A review of experimental techniques for aerosol hygroscopicity studies

“…Fourier transform infrared spectroscopy (FTIR), a vibrational absorption spectroscopy, has been widely employed in laboratory (Goodman et al, 2000;Eliason et al, 2003;Asad et al, 2004;Hung et al, 2005;Najera et al, 2009;Li et al, 2010;Tan et al, 2016;Tang et al, 2016b) and field work (Maria et al, 2002;Russell et al, 2011;Takahama et al, 2013Takahama et al, , 2019Kuzmiakova et al, 2016) to characterize the chemical composition of aerosol particles. It can also be used in aerosol hygroscopicity studies.…”

Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: method development for probabilistic modeling of organic carbon and organic matter concentrations

“…Bayesian inference has been used previously in atmospheric modeling (e.g., Pinder et al, 2006;San Martini et al, 2006;Thompson et al, 2011;Henderson et al, 2012;Wang et al, 2013;Tukiainen et al, 2016) for estimating under-constrained parameters using field observations in several different contexts. We adopt this approach to provide probabilistic estimates to unknown parameters; starting from prior distributions derived from laboratory measurements and available molecular structures, and updating them based on their plausibility for modeling OC as reported by TOR.…”

Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: method development for probabilistic modeling of organic carbon and organic matter concentrations

“…We apply this method to the Interagency Monitoring of Protected Visual Environments (IMPROVE) 2011 and 2013 data set used by Reggente et al (2016) and Takahama et al (2019), except that the Baengnyeong Island, South Korea, site is excluded to focus on the US sites ( Figure 1 the FG-OC over a range of parameters considered. Because of the inclusion of COOH (for which λ C,COOH = 1) and additional fixed contributions from several FGs, the mass recovery parameter α in eq.…”