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

Abstract

Abstract. The Fourier transform infrared (FTIR) spectra of fine particulate matter (PM2.5) contain many important absorption bands relevant for characterizing organic matter (OM) and obtaining organic matter to organic carbon (OM∕OC) ratios. However, extracting this information quantitatively – accounting for overlapping absorption bands and relating absorption to molar abundance – and furthermore relating abundances of functional groups to that of carbon atoms poses several challenges. In this work, we define a set of parameters that model these relationships and apply a probabilistic framework to identify values consistent with collocated field measurements of thermal–optical reflectance organic carbon (TOR OC). Parameter values are characterized for various sample types identified by cluster analysis of sample FTIR spectra, which are available for 17 sites in the Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring network (7 sites in 2011 and 10 additional sites in 2013). The cluster analysis appears to separate samples according to predominant influence by dust, residential wood burning, wildfire, urban sources, and biogenic aerosols. Functional groups calibrations of aliphatic CH, alcohol COH, carboxylic acid COOH, carboxylate COO, and amine NH2 combined together reproduce TOR OC concentrations with reasonable agreement (r=0.96 for 2474 samples) and provide OM∕OC values generally consistent with our current best estimate of ambient OC. The mean OM∕OC ratios corresponding to sample types determined from cluster analysis range between 1.4 and 2.0, though ratios for individual samples exhibit a larger range. Trends in OM∕OC for sites aggregated by region or year are compared with another regression approach for estimating OM∕OC ratios from a mass closure equation of the major chemical species contributing to PM fine mass. Differences in OM∕OC estimates are observed according to estimation method and are explained through the sample types determined from spectral profiles of the PM.

Characterization of primary and aged wood burning and coal combustion organic aerosols in environmental chamber and its implications for atmospheric aerosols

“…The average OM:OC estimates for samples influenced by wildfires and residential wood burning were reported to be 1.65 and 1.45, respectively (Bürki et al, 2020) compared to 1.65 and 2 (AMS and MIR spectroscopy obtained close values) for primary and aged WB aerosols in this work. The average OM:OC estimates for these atmospheric burning-influenced samples 450 are clearly closer to that of primary WB aerosols in chamber.…”

“…While each FG is not a marker for any specific source, their proportions could possibly be informative when used with multivariate methods. Cluster analysis by Bürki et al (2020), for example, identified 45 burning-influenced samples (in 3050 samples) based on their spectral similarity (i.e. FG proportions and organic-to-inorganic ratio), which were supported, to some extent, by matching their collection time and location with the known burning events (e.g.…”

“…The PTFE filters were analyzed using a Bruker-Tensor 27 FT-IR instrument equipped with a liquid nitrogen-cooled, wide-band mercury-cadmium-telluride (MCT) detector, at a resolution of 4 cm −1 . In this work, atmospheric samples were divided into four mutually exclusive sub-groups: urban, rural, residential wood burning, and wildfire (residential wood burning, and wildfire samples were identified by Bürki et al, 2020). Bürki et al (2020) separated and identified the burning-influenced samples in the same dataset using cluster analysis and subsequent analysis of the clusters.…”

“…They divided the burning-influenced samples into residential wood burning and wildfire sub-categories by extending further down the hierarchical tree. The residential wood burning sub-category was labeled according to its occurrence during winter months at Phoenix, AZ (Bürki et al, 2020), where residential wood burning commonly takes place (Pope et al, 2017). The wildfire sub-category was labeled due to its occurrence during a known fire event (Rim Fire, CA, 2013).…”

“…The OM:OC ratio is an important parameter often reported for aerosols collected at monitoring networks (Boris et al, 2019;Ruthenburg et al, 2014;Bürki et al, 2020;Hand et al, 2019;Reggente et al, 2019a). We compared 445 OM:OC ratios from these chamber samples to burning samples identified by Bürki et al (2020). They used a probabilistic framework on MIR spectra, which resulted OC estimates that were consistent with those from collocated TOR measurements.…”