Estimating mean molecular weight, carbon number, and OM/OC with mid-infrared spectroscopy in organic particulate matter samples from a monitoring network

Abstract

Abstract. Organic matter (OM) is a major constituent of fine particulate matter which contributes significantly to degradation of visibility, radiative forcing, and causes adverse health effects. However, due to its sheer compositional complexity, OM is difficult to characterize in its entirety. Mid-infrared spectroscopy has previously proven useful in the study of OM by providing extensive information about functional group composition with high mass recovery. Herein, we introduce a new method for obtaining additional characteristics such as mean carbon number and molecular weight of these complex organic mixtures using the aliphatic C–H absorbance profile in mid-infrared spectrum. We apply this technique to spectra acquired non-destructively from Teflon filters used for fine particulate matter quantification at selected sites of Inter-agency Monitoring of PROtected Visual Environments (IMPROVE) network. Since carbon number and molecular weight are important characteristics used by recent models to describe evolution in OM composition, this technique can provide semi-quantitative, observational constraints of these variables at the scale of the network. For this task, multivariate statistical models are trained on calibration spectra prepared from atmospherically relevant laboratory standards and are applied to ambient samples. Then, the physical basis linking the absorbance profile of this relatively narrow region in the mid-infrared spectrum to the molecular structure is investigated using a classification approach. The multivariate statistical models predict mean carbon number and molecular weight that are consistent with previous values of organic-mass-to-organic-carbon (OM/OC) ratios estimated for the network using different approaches. The results are also consistent with temporal and spatial variations in these quantities associated with aging processes, and different source classes (anthropogenic, biogenic, and burning sources). For instance, the models estimate higher mean carbon number for urban samples and smaller, more fragmented molecules for samples in which substantial aging is anticipated.

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

“…We also found that the primary WB and CC aerosols have similar mass spectra to the BBOA and HOA factors, respectively, and OH-aged OAs of both sources are similar to the SV-OOA factor. Furthermore, the spectral variations are consistent with our functional group analysis via FTIR, suggesting that the AMS mass spectra maintain some functional group and source class information even after aging and in spite of the extensive fragmentation (discussed further by Yazdani et al, 2021a). However, even at the moderate levels of aging of this work, a part of this information only exists in higher PCs, which explain the lower variance in the data (e.g., PC2 explains 12 % of the variance across source classes and oxidative aging studied in this work and distinguishes OH-aged CC and NO 3 -aged WB).…”

“…After baseline correction and blank subtraction, the multiple peak-fitting algorithm described by Takahama et al (2013) and implemented by Reggente et al (2019b), functioning based on nonlinear least squares analysis, was applied to the spectra to obtain major FG abundances of aCOH, COOH, aCH, and naCO (Yazdani et al, 2021a). The RONO 2 group abundances were not quantified due to the extensive overlap of its absorbances with other compounds and in order to keep the MIR estimates consistent with those of AMS, for which only total (organic plus inorganic) nitrate was estimated.…”

Measurement and Modeling the Phase Partitioning of Organophosphate Esters Using Their Temperature-Dependent Octanol–Air Partition Coefficients and Vapor Pressures

“…However, ξ OM vary among the sampling events as indicated by the large standard deviations of average values. MW OM may not also be constant (240 g/mol) . This may further contribute to the discrepancy between measured and modeled f g values.…”

“…Two basic assumptions of K OA based models are ξ OCT /ξ OM and MW OCT /MW OM = 1 (eqs 2 and 3). A recent study by Yazdani et al 59 indicated that average MW of atmospheric OM range between 200 (rural) and 240 (urban) g/mol. Octanol has a MW of 130.23 g/mol, therefore MW OCT /MW OM could be ∼0.54 in an urban atmosphere.…”

“…MW OM may not also be constant (240 g/mol). 59 This may further contribute to the discrepancy between measured and modeled f g values. However, MW OM could not be measured easily for modeling purposes.…”