Abstract
Abstract. Various vibrational modes present in molecular mixtures of laboratory and atmospheric aerosols give rise to complex Fourier transform infrared (FT-IR) absorption spectra. Such spectra can be chemically informative, but they often require sophisticated algorithms for quantitative characterization of aerosol composition. Naïve statistical calibration models developed for quantification employ the full suite of wavenumbers available from a set of spectra, leading to loss of mechanistic interpretation between chemical composition and the resulting changes in absorption patterns that underpin their predictive capability. Using sparse representations of the same set of spectra, alternative calibration models can be built in which only a select group of absorption bands are used to make quantitative prediction of various aerosol properties. Such models are desirable as they allow us to relate predicted properties to their underlying molecular structure. In this work, we present an evaluation of four algorithms for achieving sparsity in FT-IR spectroscopy calibration models. Sparse calibration models exclude unnecessary wavenumbers from infrared spectra during the model building process, permitting identification and evaluation of the most relevant vibrational modes of molecules in complex aerosol mixtures required to make quantitative predictions of various measures of aerosol composition. We study two types of models: one which predicts alcohol COH, carboxylic COH, alkane CH, and carbonyl CO functional group (FG) abundances in ambient samples based on laboratory calibration standards and another which predicts thermal optical reflectance (TOR) organic carbon (OC) and elemental carbon (EC) mass in new ambient samples by direct calibration of infrared spectra to a set of ambient samples reserved for calibration. We describe the development and selection of each calibration model and evaluate the effect of sparsity on prediction performance. Finally, we ascribe interpretation to absorption bands used in quantitative prediction of FGs and TOR OC and EC concentrations.
Highlights
Atmospheric aerosols or particulate matter (PM) can range in size from a few nanometers to tens of micrometers and exist as complex mixtures of organic compounds, black carbon, sea salt and other inorganic salts, mineral dust, trace elements, and water (Seinfeld and Pandis, 2006)
The sensitivity of RMSECV to models formulated with different non-zero variables (NZVs) are shown in Figs. 1 and 2 for raw and baseline corrected spectra, respectively (FGs are shown in fixed order from highest to lowest wavenumber of absorption bands – alcohol hydroxyl (aCOH), cCOH, aCH, and CO – in all figures)
Additional consideration is required as RMSECV indicated for laboratory standard spectra may not necessarily reflect the prediction error when extrapolated to ambient sample spectra (Takahama and Dillner, 2015), and predictions of functional group (FG) cannot be evaluated individually as no reference measurements exist for these samples
Summary
Atmospheric aerosols or particulate matter (PM) can range in size from a few nanometers to tens of micrometers and exist as complex mixtures of organic compounds, black carbon, sea salt and other inorganic salts, mineral dust, trace elements, and water (Seinfeld and Pandis, 2006). While absorption bands of isolated molecules culminate in a series of narrow peaks, condensed phase spectra pose challenges for interpretation as these peaks significantly overlap due to heterogeneous broadening of bands from similar bonds vibrating in slightly altered chemical environments (Kelley, 2012). This phenomenon is salient for atmospheric PM, as it comprises a mixture of many different components, with the organic fraction alone consisting of thousands of different types of molecules (e.g., Hamilton et al, 2004). In the face of such complexity, statistical approaches are useful in building quantitative models for calibration
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