scholarly journals Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: sparse methods for statistical selection of relevant absorption bands

2016 ◽  
Author(s):  
Satoshi Takahama ◽  
Giulia Ruggeri ◽  
Ann M. Dillner
Keyword(s):  
Infrared Spectroscopy ◽  
Infrared Spectra ◽  
Atmospheric Aerosols ◽  
Model Building ◽  
Quantitative Prediction ◽  
Calibration Model ◽  
Aerosol Composition ◽  
Absorption Bands ◽  
Calibration Models ◽  
Selection Of

Abstract. We present an evaluation of four algorithms for achieving sparsity in Fourier Transform Infrared 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.

scholarly journals Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: sparse methods for statistical selection of relevant absorption bands

2016 ◽  
Vol 9 (7) ◽  
pp. 3429-3454 ◽  
Author(s):  
Satoshi Takahama ◽  
Giulia Ruggeri ◽  
Ann M. Dillner
Keyword(s):  
Infrared Spectra ◽  
Atmospheric Aerosols ◽  
Quantitative Prediction ◽  
Calibration Model ◽  
Vibrational Modes ◽  
Aerosol Composition ◽  
Absorption Bands ◽  
Calibration Models ◽  
Ft Ir ◽  
Selection Of

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.

Fourier-Transform Infrared Spectroscopy of Environmentally Weathered Textile Fabrics for Enhanced Microplastic Identification

2021 ◽  
Author(s):  
Shreyas Patankar ◽  
Ekaterina Vassilenko ◽  
Mathew Watkins ◽  
Anna Posacka ◽  
Peter Ross
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Fourier Transform Infrared Spectroscopy ◽  
Infrared Spectra ◽  
Environmental Fate ◽  
Uv Light ◽  
Natural Fibers ◽  
Fourier Transform Infrared ◽  
Ambient Conditions ◽  
Selection Of

<p>Microplastic pollution in oceans is among the global environmental concerns of our time. Emerging research on ocean environments indicates that microfibers, such as those originating from textiles, are some of the most commonly occurring type of microplastic contaminants. While Fourier-transform infrared spectroscopy (FTIR) is commonly used to identify and characterize pollutant samples obtained from the environment, this identification is challenging because infrared spectra of materials can be modified by exposure to the ocean, air, UV light, and other ambient conditions, in a process referred to as “weathering”. We report preliminary efforts in improving FTIR characterization of microplastics by building a library of infrared spectra of common textile fibers weathered under a selection of ambient conditions. Consumer textile materials including polyester, nylon, cotton, and other, were exposed to a selection of ambient conditions: ocean, air, and wastewater treatment stages, in a controlled weathering experiment. Infrared spectra were monitored for up to 52 weeks, with the resulting data illuminating on the environmental fate and longevity of synthetic and natural fibers. Spectral changes caused by weathering were found to depend strongly on both the composition of the material and the specific ambient conditions. This library of weathered material spectra is useful not only in easier identification of environmental microfibers, but also in helping us estimate the duration and manner of weathering that a given environmental microfiber may have experienced.</p>

scholarly journals Determination of glomalin in agriculture and forest soils by near-infrared spectroscopy

2017 ◽  
Vol 63 (No. 5) ◽  
pp. 226-230 ◽  
Author(s):  
Zbíral Jiří ◽  
Čižmár David ◽  
Malý Stanislav ◽  
Obdržálková Elena
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Forest Soils ◽  
Near Infrared ◽  
Arable Land ◽  
Calibration Model ◽  
Sustainable Land Management ◽  
Chemical Methods ◽  
Calibration Models

Determining and characterizing soil organic matter (SOM) cheaply and reliably can help to support decisions concerning sustainable land management and climate policy. Glomalin was recommended as one of possible indicators of SOM quality. Extracting glomalin from and determining it in soils using classical chemical methods is too complicated and therefore near-infrared spectroscopy (NIRS) was studied as a method of choice for the determination of glomalin. Representative sets of 84 different soil samples from arable land and grasslands and 75 forest soils were used to develop NIRS calibration models. The parameters of the NIRS calibration model (R = 0.90 for soils from arable land and grasslands and R = 0.94 for forest soils) proved that glomalin can be determined in air-dried soils by NIRS with adequate trueness and precision simultaneously with determination of nitrogen and oxidizable carbon.

Organic functional group composition of particulate matter from fresh and aged wood burning and coal combustion

2020 ◽  
Author(s):  
Amir Yazdani ◽  
Nikunj Dudani ◽  
Satoshi Takahama ◽  
Amelie Bertrand ◽  
André S. H. Prévôt ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Particulate Matter ◽  
Infrared Spectra ◽  
Atmospheric Aerosols ◽  
Coal Combustion ◽  
Organic Aerosol ◽  
High Abundance ◽  
Mid Infrared ◽  
Aerosol Mass ◽  
Wood Burning

<p>Particulate matter (PM) affects visibility and climate through light scattering, direct and indirect radiative forcing, and affecting cloud formation [1]. In addition, exposure to ambient fine PM is estimated to have caused 8.9 million deaths worldwide in 2015 [2]. Organic matter (OM), can make up more than half of total fine atmospheric PM, and yet its composition, formation mechanisms, and adverse health effects are not fully characterized due to its sheer compositional complexity. Biomass burning (e.g., residential wood burning, wildfires, and prescribed burning) and coal combustion (for heat and power generation) are two major OM sources, for which the impact of atmospheric aging - including secondary organic aerosol (SOA) formation - is not yet fully clear [3].</p><p>In this study, we investigated the effect of aging on composition and mass concentration of organic aerosols of wood burning (WB) and coal combustion (CC) emissions using two complementary methods, i.e., mid-infrared spectroscopy and aerosol mass spectrometry (AMS). For this purpose, primary aerosols were injected into the Paul Scherrer Institute (PSI) environmental chamber and aged using hydroxyl and nitrate radicals to simulate day-time and night-time oxidation processes in the atmosphere. In these experiments, aerosols reached an oxidative age comparable to that of atmospheric aerosols. A time-of-flight AMS instrument was used to measure the high-time-resolution composition of non-refractory fine PM, while we collected PM<sub>1 </sub>aerosols on PTFE filters before and after four hours of aging for off-line Fourier transform-infrared spectroscopy (FT-IR) measurements.</p><p>AMS and FT-IR estimates of organic aerosol mass concentration were highly correlated (r<sup>2</sup>=0.92); both indicating an approximately three-fold increase in organic aerosol concentration after aging. The OM/OC ratio, indicating the extent of oxidation also agreed closely between the two instruments and increased, on average, from 1.6 (before aging) to 2 (after aging). Mid-infrared spectroscopy, which is able to differentiate among oxygenated species, shows a distinct functional group composition for aged WB aerosols (high abundance of carboxylic acids) and CC aerosols (high abundance of non-acid carbonyls) and detects considerable amounts polycyclic aromatic hydrocarbons (PAHs) for both sources. Mid-infrared spectra of fresh WB and CC aerosols are reminiscent of their parent compounds with differences in specific functional groups suggesting the dominant oxidation pathways for each emission source. Finally, the comparison of mid-infrared spectra of aged WB aerosols in the environmental chamber with that of ambient samples affected by residential wood burning and wildfires reveals interesting similarities regarding the high abundance of alcohols and visible signatures of lignin. This finding is useful for interpreting sources of atmospheric aerosols and better interpretation of their complex mid-infrared spectra.</p><p>--------------------------</p><p>REFERENCES</p><p>[1] M. Hallquist et al., “The formation, properties and impact of secondary organic aerosol: current and emerging issues,” Atmos Chem Phys, 2009.</p><p>[2] R. Burnett et al., “Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter,” Proc. Natl. Acad. Sci., 2018.</p><p>[3] A. Bertrand et al., “Primary emissions and secondary aerosol production potential from woodstoves for residential heating: Influence of the stove technology and combustion efficiency,” Atmos. Environ., 2017.</p>

scholarly journals Characterization of soil organic matter by near infrared spectroscopy – determination of glomalin in different soils

2016 ◽  
Author(s):  
Jiří Zbíral ◽  
David Čižmár ◽  
Stanislav Malý ◽  
Elena Obdržálková
Keyword(s):  
Organic Matter ◽  
Infrared Spectroscopy ◽  
Soil Organic Matter ◽  
Near Infrared Spectroscopy ◽  
Forest Soils ◽  
Near Infrared ◽  
Arable Land ◽  
Soil Samples ◽  
Calibration Model ◽  
Calibration Models

Abstract. Determining and characterizing soil organic matter (SOM) cheaply and reliably can help to support decisions concerning sustainable land management and climate policy. Glomalin, a glycoprotein produced by arbuscular mycorrhizal fungi, was recommended as a promising indicator of SOM quality. But extracting glomalin from and determining glomalin in soils using classical chemical methods is too complicated and time consuming and therefore limits the use of this parameter in large scale surveys. Near infrared spectroscopy (NIRS) is a very rapid, non-destructive analytical technique that can be used to determine many constituents of soil organic matter. Representative sets of 84 different soil samples from arable land and grasslands and 75 forest soils were used to develop reliable NIRS calibration models for glomalin. One calibration model was developed for samples with a low content of glomalin (arable land and grasslands), the second for soils with a high content of glomalin (forest soils), and the third calibration model for all combined soil samples. Calibrations were validated and optimized by leave-one-sample-out-cross-validation (LOSOCV) and by the external validation using eight soil samples (arable land and grassland), and six soil samples (forest soils) not included in the calibration models. Two different calibration models were recommended. One model for arable and grassland soils and the second for forest soils. No statistically significant differences were found between the reference and the NIRS method for both calibration models. The parameters of the NIRS calibration model (RMSECV = 0,70 and R = 0,90 for soils from arable land and grasslands and RMSECV = 3,8 and R = 0,94 for forest soils) proved that glomalin can be determined directly in air-dried soils by NIRS with adequate trueness and precision.

Study of Alkoxide Silica Gels by Infrared Spectroscopy

1989 ◽  
Vol 43 (2) ◽  
pp. 263-267 ◽  
Author(s):  
D. L. Wood ◽  
E. M. Rabinovich
Keyword(s):  
Heat Treatment ◽  
Infrared Spectroscopy ◽  
Absorption Band ◽  
Infrared Spectra ◽  
Fumed Silica ◽  
Silica Gels ◽  
Structural Defects ◽  
Dangling Bonds ◽  
Absorption Bands

The infrared spectra of silica gels made from alkoxides are different from those made from fumed silica, and from the infrared spectra of other amorphous silicas in which the SiO2 network is relatively complete. One important feature of these spectra is the occurrence of an absorption band at 960 cm−1 due to the vibration of dangling -Si-OH bonds in the alkoxide gels, which is not present in many other silicas. There also are less well-defined absorption bands due to structural defects for the alkoxide gels, but their interpretation is speculative. Dried gels made with more concentrated reactants contain more defects, and heat treatment reduces the concentration of dangling bonds, especially above 900°C. For gels containing fluorine, two absorption bands appear at 932 and 980 cm−1, and these are interpreted as arising from terminal -SiF and -SiF2 groups, respectively.

Infrared spectra of muscovites as affected by chemical composition, heating and particle size

Clay Minerals ◽  
1978 ◽  
Vol 13 (3) ◽  
pp. 241-254 ◽  
Author(s):  
Mahmut Sayin ◽  
H. Graf von Reichenbach
Keyword(s):  
Particle Size ◽  
Infrared Spectroscopy ◽  
Chemical Composition ◽  
Infrared Spectra ◽  
Chemical Elements ◽  
Absorption Bands ◽  
Intensity Decrease ◽  
Size Limit ◽  
Frequency Changes ◽  
Major Chemical

AbstractNine muscovite samples from different localities were analysed for all major chemical elements and examined by infrared spectroscopy in the range of 4000–250 cm−1. Iron in the octahedral positions was found to cause an intensity decrease and shifting of some absorption bands. Other structural cations were found not to be effective in producing intensity and frequency changes due to the narrow variations in their amounts. Heating studies indicated that at about 800°C muscovite transforms into a dehydroxylated phase which is stable at least up to 1000°C. Particle size studies showed that 5 µm is the upper size limit for a representative spectrum.

scholarly journals Near-Infrared Spectroscopy for Measuring Urea in Hemodialysis Fluids

2001 ◽  
Vol 47 (7) ◽  
pp. 1279-1286 ◽  
Author(s):  
Christopher V Eddy ◽  
Mark A Arnold
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Spectral Range ◽  
Near Infrared ◽  
Factor Model ◽  
Partial Least Square ◽  
Least Square ◽  
Calibration Model ◽  
Calibration Models ◽  
Sensitivity And Selectivity

Abstract Background: Near-infrared spectroscopy is proposed as a method for providing real-time urea concentrations during hemodialysis treatments. The feasibility of such noninvasive urea measurements is evaluated in undiluted dialysate fluid. Methods: Near-infrared spectra were collected from calibration solutions of urea prepared in dialysate fluid. Spectra were collected over three distinct spectral regions, and partial least-squares calibration models were optimized and compared for each. Selectivity for urea was demonstrated with two-component samples composed of urea and glucose in the dialysate matrix. The clinical significance of this approach was assessed by measuring urea in real hemodialysate samples. Results: Urea absorptions within the combination and short-wavelength, near-infrared spectral regions provided sufficient spectral information for sound calibration models in the dialysate matrix. The combination spectral region had SEs of calibration (SEC) and prediction (SEP) of 0.38 mmol/L and 0.26 mmol/L, respectively, over the 4720–4600 cm−1 spectral range with 5 partial least-square factors. A second calibration model was established over the combination region from a series of solutions prepared with independently variable concentrations of urea and glucose. The best calibration model for urea in the presence of variable glucose concentrations had a SEC of 0.6 mmol/L and a SEP of 0.4 mmol/L for a 5-factor model over the 4600–4350 cm−1 spectral range. There was no significant decrease in SEP when the 4720–4600 cm−1 calibration model was used to measure urea in real samples collected during actual hemodialysis. Conclusions: Urea can be determined with sufficient sensitivity and selectivity for clinical measurements within the matrix of the hemodialysis fluid.

Sign in / Sign up

Export Citation Format

Share Document