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Globally, billions of people burn fuels indoors for cooking and heating, which contributes to millions of chronic illnesses and premature deaths annually. Additionally, residential burning contributes significantly to black carbon emissions, which have the highest global warming impacts after carbon dioxide and methane. In this study, we use Fourier transform infrared spectroscopy (FTIR) to analyze fine-particulate emissions collected on Teflon membrane filters from 15 cookstove types and 5 fuel types. Emissions from three fuel types (charcoal, kerosene, and red oak wood) were found to have enough FTIR spectral response for functional group (FG) analysis. We present distinct spectral profiles for particulate emissions of these three fuel types. We highlight the influential FGs constituting organic carbon (OC) using a multivariate statistical method and show that OC estimates by collocated FTIR and thermal–optical transmittance (TOT) are highly correlated, with a coefficient determination of 82.5 %. As FTIR analysis is fast and non-destructive and provides complementary FG information, the analysis method demonstrated herein can substantially reduce the need for thermal–optical measurements for source emissions. [ABSTRACT FROM AUTHOR]

Globally, billions of people burn fuels indoors for cooking and heating, which contributes to millions of chronic illnesses and premature deaths annually. Additionally, residential burning contributes significantly to black carbon emissions, which have the highest global warming impacts after carbon dioxide and methane. In this study, we use Fourier transform infrared spectroscopy (FTIR) to analyze fine particulate emissions collected on Teflon membrane filters from fifteen cookstove types and five fuel types. Emissions from three fuel types (charcoal, kerosene, and red oak wood) were found to have enough FTIR spectral response for functional group (FG) analysis. We present distinct spectral profiles for particulate emissions of these three fuel types. We highlight the influential FGs constituting organic carbon (OC) using a multivariate statistical method and show that OC estimates by collocated FTIR and thermal optical transmittance (TOT) are highly correlated, with a coefficient of determination of 82.5%. As FTIR analysis is fast, non-destructive, and provides complementary FG information, the analysis method demonstrated herein can substantially reduce the need for thermal-optical measurements for source emissions. [ABSTRACT FROM AUTHOR]

Aerosol mass spectrometry (AMS) and mid-infrared spectroscopy (MIR) are two analytical methods for characterizing the chemical composition of organic matter (OM). While AMS provides high-temporal-resolution bulk measurements, the extensive fragmentation during the electron ionization makes the characterization of OM components limited. The analysis of aerosols collected on polytetrafluoroethylene (PTFE) filters using MIR, on the other hand, provides functional group information with reduced sample alteration but results in a relatively low temporal resolution. In this work, we compared and combined MIR and AMS measurements for several environmental chamber experiments of combustion-related aerosols to achieve a better understanding of the AMS spectra and the OM chemical evolution with aging. Fresh emissions of wood and coal burning were injected into an environmental simulation chamber and aged with hydroxyl and nitrate radicals. A high-resolution time-of-flight AMS measured the bulk chemical composition of fine OM. Fine aerosols were also sampled on PTFE filters before and after aging for the offline MIR analysis. After comparing AMS and MIR bulk measurements, we used multivariate statistics to identify the functional groups associated the most with the AMS OM for different aerosol sources and oxidants. We also identified the key fragment ions resulting from molecules containing each functional group for the complex OM generated from biomass and fossil fuel combustion. Finally, we developed a statistical model that enables the estimation of the high-time-resolution functional group composition of OM using collocated AMS and MIR measurements. AMS spectra can be used to interpolate the functional group measurements by MIR using this approach. The latter allows us to better understand the evolution of OM during the aging process. [ABSTRACT FROM AUTHOR]

Aerosol mass spectrometry (AMS) and mid-infrared spectroscopy (MIR) are two analytical methods for characterizing the chemical composition of OM. While AMS provides high-temporal-resolution bulk measurements, the extensive fragmentation during the electron impact (EI) ionization makes the characterization of OM components limited. The analysis of aerosols collected on PTFE filters using MIR, on the other hand, provides functional group (FG) information with reduced sample alteration but results in a relatively low temporal resolution. In this work, we compared and combined MIR and AMS measurements for several environmental chamber experiments to achieve a better understanding of the AMS spectra and the OM chemical evolution by aging. Fresh emissions of wood and coal burning were injected into an environmental simulation chamber and aged with hydroxyl and nitrate radicals. A high-resolution time-of-flight (HR-TOF) AMS measured the bulk chemical composition of fine PM. Fine aerosols were also sampled on PTFE filters before and after aging for the offline MIR analysis. After comparing AMS and MIR bulk measurements, we used multivariate statistics to identify the influential functional groups contributing to AMS OM mass for different aerosol sources and aging processes. We also identified the key mass fragments resulting from each functional group for the complex OM generated from biomass and fossil fuel combustion. Finally, we developed a statistical model that enables estimation of the high-time-resolution functional group composition of OM using collocated AMS and MIR measurements. Using this approach, AMS spectra can be used to interpolate the functional group measurements by MIR, allowing us to better understand the evolution of OM during the aging process. [ABSTRACT FROM AUTHOR]

Organic species within atmospheric particles vary widely in molecular structure. The variety of molecules that comprise the aerosol make it rich in information about its sources and chemical life cycle but also make particulate organic matter (OM) difficult to characterize and quantify. In Part 1 of this pair of papers, we described a direct method for measuring the composition and concentrations of OM in aerosol samples that is compatible with routine monitoring of air quality. This method uses Fourier transform infrared (FT-IR) spectrometry of filter-based aerosol samples to quantify bonds, or functional groups, that represent the majority of organic composition. Summation of these functional groups gives OM. In this paper, functional group and OM concentrations are directly measured in 8 years of aerosol samples collected at two rural and two urban sites in the Southeastern Aerosol Research and Characterization (SEARCH) network. FT-IR spectrometry with a multivariate calibration is used to quantify the concentrations of aliphatic C-H (aCH), carboxylic acid (COOH), oxalate (oxOCO ; representing carboxylates), non-acid and non-oxalate carbonyl (naCO), and alcohol O-H (aCOH) in approximately 3500 filter samples collected every third day from 2009 through 2016. In addition, measurements are made on samples from all days in 2016. To the best of our knowledge, this is the longest time period over which this type of analysis has been applied, and this work also demonstrates the application of a more chemically complete and less destructive method than in prior work using alternate techniques. A decline in the total OM is observed from 2011 to 2016 due to a decrease in the more oxygenated functional groups (carboxylic acid and oxalate) and is attributed to anthropogenic SO2 and/or volatile organic compound (VOC) emissions reductions. The trend in OM composition is consistent with those observed using more time- and labor-intensive analytical techniques. Concurrently, the fractional contributions of aCOH and naCO to OM increased, which might be linked to monoterpene-derived secondary OM, with plausible influences from decreasing NOx and/or increasing O3 concentrations. In addition, this work demonstrates that OM to organic carbon (OM/OC) ratios in the southeastern US (SE US) did not appreciably change over the study time period as a result of these competing functional group contributions to OM. Monthly observations support the sources suggested by these overall trends, including evidence of strong biogenic and photo-oxidation influences. Daily samples from 2016 further elucidate the consistent impact of meteorology and biomass burning events on shorter-term OM variability, including prescribed burning in the winter or spring and wildfires in the autumn, although these sources did not appear to be strong contributors to long-term OM or composition trends in the SE US. These shorter-term and spatial observations reinforce the results of the broader dataset and serve to evaluate the applicability of FT-IR spectrometry measurement to trends analysis on various timescales relevant to routine monitoring of aerosol composition. [ABSTRACT FROM AUTHOR]

The Fourier transform infrared (FTIR) spectra of fine particulate matter (PM 2.5) 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. [ABSTRACT FROM AUTHOR]

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 - poses several challenges. For instance, a subset of model parameters lead to calibrations that test almost indistinguishably well against laboratory standards generate substantially different predictions in ambient samples. Furthermore, additional parameters related to molecular structure are required to estimate carbon content from functional group (FG) abundance. However, since many carbon atoms can be branched (not fully functionalized) or polyfunctional, these parameters are not well constrained for ambient sample mixtures. In this work, we present a probabilistic framework to characterize combinations of these parameters that are consistent with field measurements of organic carbon (OC), for which estimates from thermal optical reflectance (TOR) measurements are used. Uncertainties in this probabilistic framework characterize the plausibility of many different parameter values that yield acceptable predictions (to the extent that they can be evaluated) neglected in conventional estimates of statistical uncertainties. Based on calibrations of aliphatic CH, alcohol COH, carboxylic acid COO, carboxylate COO, and amine NH2, we find model parameters for approximately homogeneous groups of samples determined from cluster analysis of FTIR spectra 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). These groups are interpreted as being predominantly influenced by dust, residential wood burning, wildfire, urban sources, and biogenic aerosols. The resulting calibrations reproduce TOR OC concentrations (R² = 0.96) and provide OM/OC values 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 balance 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. [ABSTRACT FROM AUTHOR]

Comprehensive techniques to describe the organic composition of atmospheric aerosol are needed to elucidate pollution sources, gain insights into atmospheric chemistry, and evaluate changes in air quality. Fourier transform infrared absorption (FT-IR) spectrometry can be used to characterize atmospheric organic matter (OM) and its composition via functional groups of aerosol filter samples in air monitoring networks and research campaigns. We have built FT-IR spectrometry functional group calibration models that improve upon previous work, as demonstrated by the comparison of current model results with those of previous models and other OM analysis methods. Laboratory standards that simulated the breadth of the absorbing functional groups in atmospheric OM were made: particles of relevant chemicals were first generated, collected, and analyzed. Challenges of collecting atmospherically relevant particles and spectra were addressed by including interferences of particle water and other inorganic aerosol constituents and exploring the spectral effects of intermolecular interactions. Calibration models of functional groups were then constructed using partial least-squares (PLS) regression and the collected laboratory standard data. These models were used to quantify concentrations of five organic functional groups and OM in 8 years of ambient aerosol samples from the southeastern aerosol research and characterization (SEARCH) network. The results agreed with values estimated using other methods, including thermal optical reflectance (TOR) organic carbon (OC; R2=0.74) and OM calculated as a difference between total aerosol mass and inorganic species concentrations (R2=0.82). Comparisons with previous calibration models of the same type demonstrate that this new, more complete suite of chemicals has improved our ability to estimate oxygenated functional group and overall OM concentrations. Calculated characteristic and elemental ratios including OM/OC , O/C , and H/C agree with those from previous work in the southeastern US, substantiating the aerosol composition described by FT-IR calibration. The median OM/OC ratio over all sites and years was 2.1±0.2. Further results discussing temporal and spatial trends of functional group composition within the SEARCH network will be published in a forthcoming article. [ABSTRACT FROM AUTHOR]

Comprehensive techniques to describe the organic composition of atmospheric aerosol are needed to elucidate pollution sources, gain insights into atmospheric chemistry and evaluate changes in air quality. Fourier Transform Infrared absorption (FT-IR) spectrometry can be used to characterize atmospheric organic matter (OM) and its composition via functional groups on aerosol filter samples in air monitoring networks and research campaigns. We have built FT-IR spectrometry functional group calibration models that improve upon previous work. Laboratory standards that simulated the breadth of the absorbing functional groups in atmospheric OM were made: particles of relevant chemicals were first generated, collected, and analyzed. Challenges of collecting atmospherically relevant particles and spectra were addressed by including interferences of particle water and other inorganic aerosol constituents and exploring the spectral effects of inter-molecular interactions. Calibration models of functional groups were then constructed using partial least squares (PLS) regression and the collected laboratory standard data. These models were used to quantify concentrations of five organic functional groups and OM in eight years of ambient aerosol samples from the southeastern aerosol research and characterization (SEARCH) network. The results agreed with values estimated using other methods, including thermal optical reflectance (TOR) organic carbon (OC; R² = 0.74) and OM calculated as a difference between total aerosol mass and inorganic species concentrations (R² = 0.82). Comparisons with previous calibration models of the same type demonstrate that this new, more complete suite of chemicals has improved our ability to estimate oxygenated functional group and overall OM concentrations. Calculated characteristic and elemental ratios including OM/OC, O/C and H/C agree with those from previous work in the southeastern US, substantiating the aerosol composition described by FT-IR calibration. The median OM/OC ratio over all sites and years was 2.1 ± 0.2. Further results discussing temporal and spatial trends of functional group composition within the SEARCH network will be published in a forthcoming article. [ABSTRACT FROM AUTHOR]

Peak fitting (PF) and partial least squares (PLS) regression have been independently developed for estimation of functional groups (FGs) from Fourier transform infrared (FTIR) spectra of ambient aerosol collected on Teflon filters. PF is a model that quantifies the functional group composition of the ambient samples by fitting individual Gaussian line shapes to the aerosol spectra. PLS is a data-driven, statistical model calibrated to laboratory standards of relevant compounds and then extrapolated to ambient spectra. In this work, we compare the FG quantification using the most widely used implementations of PF and PLS, including their model parameters, and also perform a comparison when the underlying laboratory standards and spectral processing are harmonized. We evaluate the quantification of organic FGs (alcohol COH , carboxylic COOH , alkane CH, carbonyl CO) and ammonium, using external measurements (organic carbon (OC) measured by thermal optical reflectance (TOR) and ammonium by balance of sulfate and nitrate measured by ion chromatography). We evaluate our predictions using 794 samples collected in the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network (USA) in 2011 and 238 laboratory standards from Ruthenburg et al. (2014) (available at 10.1016/j.atmosenv.2013.12.034). Each model shows different biases. Overall, estimates of OC by FTIR show high correlation with TOR OC. However, PLS applied to unprocessed (raw spectra) appears to underpredict oxygenated functional groups in rural samples, while other models appear to underestimate aliphatic CH bonds and OC in urban samples. It is possible to adjust model parameters (absorption coefficients for PF and number of latent variables for PLS) within limits consistent with calibration data to reduce these biases, but this analysis reveals that further progress in parameter selection is required. In addition, we find that the influence of scattering and anomalous transmittance of infrared in coarse particle samples can lead to predictions of OC by FTIR which are inconsistent with TOR OC. We also find through several means that most of the quantified carbonyl is likely associated with carboxylic groups rather than ketones or esters. In evaluating state-of-the-art methods for FG abundance by FTIR, we suggest directions for future research. [ABSTRACT FROM AUTHOR]

Peak-fitting (PF) and partial least squares (PLS) regression have been independently developed for estimation of functional groups (FGs) from Fourier Transform Infrared (FTIR) spectra of ambient aerosol collected on Teflon filters. PF is a model that quantifies the functional group composition of the ambient samples by fitting individual Gaussian line shapes to the aerosol spectra. PLS is a data-driven, statistical model calibrated to laboratory standards of relevant compounds and then extrapolated to ambient spectra. In this work, we compare the FG quantification using the most widely used implementations of PF and PLS including their model parameters, and also perform a comparison when the underlying laboratory standards and spectral processing are harmonized. We evaluate the quantification of organic FGs (alcohol COH, carboxylic COOH, alkane CH, carbonyl CO) and ammonium, using external measurements (OC by thermal optical reflectance and ammonium by balance of sulfate and nitrate measured by ion chromatography). Moreover, we explore the sensitivity of FG, OC, OM/OC predictions to a series of possible input parameters available for each algorithm. We evaluate our predictions using 794 samples collected in the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network (USA) in 2011 and 238 laboratory standards from Ruthenburg et al. (https://doi.org/10.1016/j.atmosenv.2013.12.034, 2014). Each model shows different biases. Overall, estimates of OC by FT-IR show high correlation with TOR OC. However, PLS applied to unprocessed (raw spectra) appears to underpredict oxygenated functional groups in rural samples, while other models appear to underestimate aliphatic CH bonds and OC in urban samples. It is possible to adjust model parameters (absorption coefficients for PF and number of latent variables for PLS) within limits consistent with calibration data to reduce these biases, but this analysis reveals that further progress in parameter selection is required. In addition, we find that the influence of scattering and anomalous transmittance of infrared in coarse particle samples can lead to predictions of OC by FTIR which are inconsistent with TOR OC. We also find through several means that most of the quantified carbonyl is likely associated with carboxylic groups rather than ketones or esters. In evaluating state-of-the-art methods for FG abundance by FTIR, we suggest directions for future research. [ABSTRACT FROM AUTHOR]

The Fourier-transform infrared (FT-IR) spectra of ambient fine aerosols were used with partial least-squares (PLS) regression to accurately, inexpensively, and nondestructively predict organic carbon (OC) on polytetrafluoroethylene (PTFE) filters in the U.S. Environmental Protection Agencies' Chemical Speciation Network (CSN). Recently, a similar FT-IR method was used for OC determination in the rural United States Interagency Monitoring of PROtected Visual Environments network, with the present work extending the method to urban aerosols with low mass loadings. In the present study, FT-IR spectra were calibrated to collocated thermal/optical reflectance (TOR) OC measurements following numerical processing with a second derivative filter, backward Monte Carlo unimportant variable elimination, and a quadratic discriminant analysis-PLS vapor correction routine. After processing and vapor correcting spectra, the number of model components (latent variables) were reduced from thirty-five to three with only the first PLS component patently predicting OC. The two lesser components modeled PTFE and inorganic interference remaining in the spectra. A wavenumber ranking procedure— using both the variable importance in projection and bootstrapped confidence intervals— underscored the primacy of aliphatic C-H stretches and carbonyl vibrations for OC prediction. Aliphatic deformations, amines, organonitrate, carboxylate, and aromatic vibrations were also valuable for OC quantification. This study demonstrates that PLS models quantifying TOR OC are explicable in terms of organic functional group absorption after judiciously processing FT-IR spectra. Copyright © 2016 American Association for Aerosol Research [ABSTRACT FROM PUBLISHER]

An Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopic method was used to measure organic functional groups and inorganic ions at Tonto National Monument (TNM), an Interagency Monitoring of Protected Visual Environments (IMPROVE) sampling site in a rural area near Phoenix, Arizona. Functional groups and ions from common aerosol compound classes such as aliphatic and aromatic CH, methylene, methyl, aldehydes/ketones, carboxylic acids, ammonium sulfate and nitrate as well as functional groups from difficult to measure compound classes such as esters/lactones, acid anhydrides, carbohydrate hydroxyl and ethers, amino acids, and amines were quantified. On average, ∼33% of the PM1.0 mass was composed of organic aerosol. The average (standard deviation) composition of the organic aerosol at TNM was 34% (6%) biogenic functional groups, 21% (5%) oxygenated functional groups, 28% (7%) aliphatic hydrocarbon functional groups (aliphatic CH, methylene and methyl) and 17% (1%) aromatic hydrocarbon functional groups. Compositional analysis, functional group correlations, and back trajectories were used to identify three types of events with source signatures: primary biogenic-influenced, urban-influenced, and regional background. The biogenic-influenced event had high concentrations of amino acids and carbohydrate hydroxyl and ether, as well as aliphatic CH and aromatic CH functional groups and qualitatively high levels of silicate. The urban-influenced events had back trajectories traveling directly from the Phoenix area and high concentrations of hydrocarbons, oxygenated functional groups, and inorganic ions. This aerosol characterization suggests that both primary emissions in Phoenix and secondary formation of aerosols from Phoenix emissions had a major impact on the aerosol composition and concentration at TNM. The regional background source had low concentrations of all functional groups, but had higher concentrations of biogenic functional groups than the urban source. [Copyright &y& Elsevier]

Abstract: Mass of ambient particulate organic matter (OM) is often estimated by multiplying the organic carbon (OC) mass by a fixed factor that typically ranges from 1.4 to 1.8. In this paper, we develop a non-destructive, mid-infrared spectroscopic (MIR) technique to measure OM in PM2.5 collected on PTFE filters (commonly called “teflon” filters). MIR techniques measure absorption by functional groups within organic molecules; that is, carbon atoms bonded to oxygen (O) or hydrogen (H), O bonded to H and other elements bonded together in organic molecules that comprise OM. We developed laboratory standards of atmospherically relevant organic compounds as the basis for calibrating the MIR absorption to the moles of functional groups. A multivariate regression technique was used to develop calibrations for quantifying alkane CH, alcohol OH, carboxylic acid OH, and carbonyl, which likely comprise the bulk of OM in most ambient samples. OM is estimated as the sum of masses attributed to these functional groups. The precision of the OM measurement is 6.9 μg (relative precision is 7%) and the minimum detection limit is 4.8 μg, corresponding to an ambient concentrations of 0.21 μg m−3 and 0.15 μg m−3 respectively, for the sampling protocol used here. The method was employed to estimate OM, OC (estimated as the sum of the mass of carbon in the alkane CH and carbonyl functional groups) and OM/OC from one year of routinely collected filters at seven Interagency Monitoring of Protected Visual Environments (IMPROVE) sites. In the IMPROVE network, OC is measured using a thermal optical method and OM is estimated for regional haze tracking simply as 1.8 × OC. Using the MIR technique on one year of samples from 7 IMPROVE sites, the median OM/OC ratio was 1.69 with the 10th and 90th percentiles of 1.46 and 2.01. Phoenix, AZ had the lowest annual-median value of 1.56 and Mesa Verde, CO had the highest of 1.83. For more than half of the sites, the lowest monthly-median OM/OC ratio occurred in winter. The sample, site median and seasonal median OM/OC ratios obtained from MIR analysis indicate that a single estimate of OM/OC does not represent the observed variability in OM/OC. [Copyright &y& Elsevier]