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

Organic matter (OM) is a major constituent of fine particulate matter, which contributes significantly to degradation of visibility and 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 the 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 the Inter-agency Monitoring of PROtected Visual Environments (IMPROVE) network. Since carbon number and molecular weight are important characteristics used by recent conceptual 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 statistical models estimate higher mean carbon number for urban samples and smaller, more fragmented molecules for samples in which substantial aging is anticipated.

Chemical characterization of organic aerosols with a focus on biomass burning and mid-infrared spectroscopy

Amir Yazdani

Aerosols play a significant role in the atmosphere through affecting the radiative budget, cloud condensation nuclei activity, and visibility. They also cause adverse health effects leading to premature deaths. A major fraction of aerosols is organic matter (OM), which has a complex composition, is produced by various sources, and undergoes chemical transformation in the atmosphere, making its full characterization difficult. Biomass burning has become a major source of OM with an increasing effect due to more frequent large wildfires around the world resulting partly from climate change.In this thesis, Fourier transform infrared spectroscopy (FTIR) has been employed in six different projects as the main tool to characterize OM from various sources especially biomass burning in order to understand its formation and evolution in the atmosphere. This technique has been used to analyze the functional group (FG) composition of OM, specific spectral profiles from different sources, and biomass burning marker signatures in a nondestructive and cost-efficient manner. FTIR has also been combined with other analytical techniques. First, we used the spectral profiles in the aliphatic CH region of IR spectra to extract information about the molecular structure of atmospheric organic aerosols in terms of their mean carbon number and molecular weight. We found that urban, rural, and biomass burning aerosols have distinct mean molecular weights and carbon numbers. FTIR and aerosol mass spectrometry (AMS) were used in the second project to characterize burning aerosols in an environmental simulation chamber. We showed the agreement of these two instruments in terms of the OM mass concentration and elemental ratios (H:C, and O:C). We found that AMS spectra contained functional group information that agreed with that of FTIR even for moderately aged aerosols. In the third project, we used univariate and multivariate statistics to combine FTIR and AMS measurements to better understand and interpret the complex AMS mass spectra in terms of the FG composition and to estimate the high-time-resolution FG composition of combustion aerosols during the course of aging. In another environmental chamber study (the fourth project), we developed a procedure to estimate the often-neglected evolution of primary biomass burning aerosols during daytime and nighttime chemical agings. We found that at least 15 % of the primary aerosol mass undergoes chemical transformation at relatively short time scales in the order of a day in the atmosphere. Organic biomass burning markers were among the fastest decaying species, making the identification of aged biomass burning aerosols in the atmosphere challenging. In the fifth project, we used the biomass burning marker signatures in the FTIR spectra for the first time to identify and quantify atmospheric samples affected by wood smoke. The FTIR-based identification method agreed well with those using ion chromatography and satellite observations. This method, which is one of the few scalable to large air pollution monitoring networks, was applied to around 20,000 filters collected across the US in 2015 to estimate the impact of biomass burning. In the sixth project, we used FTIR to characterize OM emitted from different cookstoves and fuels. We found similarities between the spectra of unburned fuels and OM emissions and measured high abundances of aromatics and polycyclic aromatic hydrocarbons in the particulate emissions.

EPFL2022

Quantification of amine functional groups and their influence on OM/OC in the IMPROVE network

Satoshi Takahama

Recently, we developed a method using FT-IR spectroscopy coupled with partial least squares (PLS) regression to measure the four most abundant organic functional groups, aliphatic C-H, alcohol OH, carboxylic acid OH and carbonyl C = O, in atmospheric particulate matter. These functional groups are summed to estimate organic matter (OM) while the carbon from the functional groups is summed to estimate organic carbon (OC). With this method, OM and OM/OC can be estimated for each sample rather than relying on one assumed value to convert OC measurements to OM. This study continues the development of the FT-IR and PLS method for estimating OM and OM/OC by including the amine functional group. Amines are ubiquitous in the atmosphere and come from motor vehicle exhaust, animal husbandry, biomass burning, and vegetation among other sources. In this study, calibration standards for amines are produced by aerosolizing individual amine compounds and collecting them on PTFE filters using an IMPROVE sampler, thereby mimicking the filter media and collection geometry of ambient standards. The moles of amine functional group on each standard and a narrow range of amine-specific wavenumbers in the FT-IR spectra (wavenumber range 1 550-1 500 cm(-1)) are used to develop a PLS calibration model. The PLS model is validated using three methods: prediction of a set of laboratory standards not included in the model, a peak height analysis and a PLS model with a broader wavenumber range. The model is then applied to the ambient samples collected throughout 2013 from 16 IMPROVE sites in the USA. Urban sites have higher amine concentrations than most rural sites, but amine functional groups account for a lower fraction of OM at urban sites. Amine concentrations, contributions to OM and seasonality vary by site and sample. Amine has a small impact on the annual average OM/OC for urban sites, but for some rural sites including amine in the OM/OC calculations increased OM/OC by 0.1 or more.

Pergamon-Elsevier Science Ltd2018

Chemical characterization of organic aerosols with a focus on biomass burning and mid-infrared spectroscopy

Amir Yazdani

EPFL2022

Quantification of amine functional groups and their influence on OM/OC in the IMPROVE network

Satoshi Takahama

Pergamon-Elsevier Science Ltd2018