Articles | Volume 12, issue 1
https://doi.org/10.5194/amt-12-525-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-12-525-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
28 Jan 2019
Review article |
| 28 Jan 2019
| 28 Jan 2019
Atmospheric particulate matter characterization by Fourier transform infrared spectroscopy: a review of statistical calibration strategies for carbonaceous aerosol quantification in US measurement networks
Satoshi Takahama
CORRESPONDING AUTHOR
ENAC/IIE Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
Ann M. Dillner
Air Quality Research Center, University of California Davis, Davis, CA 95616, USA
Andrew T. Weakley
Air Quality Research Center, University of California Davis, Davis, CA 95616, USA
Matteo Reggente
ENAC/IIE Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
Charlotte Bürki
ENAC/IIE Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
Mária Lbadaoui-Darvas
ENAC/IIE Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
Bruno Debus
Air Quality Research Center, University of California Davis, Davis, CA 95616, USA
Adele Kuzmiakova
ENAC/IIE Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
Anthony S. Wexler
Air Quality Research Center, University of California Davis, Davis, CA 95616, USA
Center for Health and the Environment, University of California, Davis, CA 95616, USA
Mechanical and Aeronautical Engineering, University of California, Davis, CA 95616, USA
Civil and Environmental Engineering, University of California, Davis, CA 95616, USA
Land, Air and Water Resources, University of California, Davis, CA 95616, USA
Related authors
Marife B. Anunciado, Miranda De Boskey, Laura Haines, Katarina Lindskog, Tracy Dombek, Satoshi Takahama, and Ann M. Dillner
Atmos. Meas. Tech., 16, 3515–3529, https://doi.org/10.5194/amt-16-3515-2023, https://doi.org/10.5194/amt-16-3515-2023, 2023
Short summary
Short summary
Organic sulfur compounds are used to identify sources and atmospheric processing of aerosol. Our paper evaluates the potential of using a non-destructive measurement technique to measure organic sulfur compounds in filter samples by assessing their chemical stability over time. Some were stable, but some evaporated or changed chemically. Future work includes evaluating the stability and potential interference of multiple organic sulfur compounds in laboratory mixtures and ambient aerosol.
Amir Yazdani, Satoshi Takahama, John K. Kodros, Marco Paglione, Mauro Masiol, Stefania Squizzato, Kalliopi Florou, Christos Kaltsonoudis, Spiro D. Jorga, Spyros N. Pandis, and Athanasios Nenes
Atmos. Chem. Phys., 23, 7461–7477, https://doi.org/10.5194/acp-23-7461-2023, https://doi.org/10.5194/acp-23-7461-2023, 2023
Short summary
Short summary
Organic aerosols directly emitted from wood and pellet stove combustion are found to chemically transform (approximately 15 %–35 % by mass) under daytime aging conditions simulated in an environmental chamber. A new marker for lignin-like compounds is found to degrade at a different rate than previously identified biomass burning markers and can potentially provide indication of aging time in ambient samples.
Emily Y. Li, Amir Yazdani, Ann M. Dillner, Guofeng Shen, Wyatt M. Champion, James J. Jetter, William T. Preston, Lynn M. Russell, Michael D. Hays, and Satoshi Takahama
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-90, https://doi.org/10.5194/amt-2023-90, 2023
Revised manuscript accepted for AMT
Short summary
Short summary
Infrared spectroscopy is a cost-effective measurement technique to characterize chemical composition of organic aerosol emissions. This technique differentiates the organic matter emission factor from different fuel sources by their characteristic functional groups. Comparison with collocated measurements suggest polycyclic aromatic hydrocarbon concentrations in emissions estimated by conventional chromatography may be substantially under-estimated.
Nikunj Dudani and Satoshi Takahama
Atmos. Meas. Tech., 15, 4693–4707, https://doi.org/10.5194/amt-15-4693-2022, https://doi.org/10.5194/amt-15-4693-2022, 2022
Short summary
Short summary
We designed and fabricated an aerosol collector with high collection efficiency that enables quantitative infrared spectroscopy analysis. By collecting particles on optical windows, typical substrate interferences are eliminated. New methods for fabricating aerosol devices using 3D printing with post-treatment to reduce the time and cost of prototyping are described.
Amir Yazdani, Nikunj Dudani, Satoshi Takahama, Amelie Bertrand, André S. H. Prévôt, Imad El Haddad, and Ann M. Dillner
Atmos. Meas. Tech., 15, 2857–2874, https://doi.org/10.5194/amt-15-2857-2022, https://doi.org/10.5194/amt-15-2857-2022, 2022
Short summary
Short summary
While the aerosol mass spectrometer provides high-time-resolution characterization of the overall extent of oxidation, the extensive fragmentation of molecules and specificity of the technique have posed challenges toward deeper understanding of molecular structures in aerosols. This work demonstrates how functional group information can be extracted from a suite of commonly measured mass fragments using collocated infrared spectroscopy measurements.
Bruno Debus, Andrew T. Weakley, Satoshi Takahama, Kathryn M. George, Anahita Amiri-Farahani, Bret Schichtel, Scott Copeland, Anthony S. Wexler, and Ann M. Dillner
Atmos. Meas. Tech., 15, 2685–2702, https://doi.org/10.5194/amt-15-2685-2022, https://doi.org/10.5194/amt-15-2685-2022, 2022
Short summary
Short summary
In the US, routine particulate matter composition is measured on samples collected on three types of filter media and analyzed using several techniques. We propose an alternate approach that uses one analytical technique, Fourier transform-infrared spectroscopy (FT-IR), and one filter type to measure the chemical composition of particulate matter in a major US monitoring network. This method could be used to add low-cost sites to the network, fill-in missing data, or for quality control.
Mária Lbadaoui-Darvas, Satoshi Takahama, and Athanasios Nenes
Atmos. Chem. Phys., 21, 17687–17714, https://doi.org/10.5194/acp-21-17687-2021, https://doi.org/10.5194/acp-21-17687-2021, 2021
Short summary
Short summary
Aerosol–cloud interactions constitute the most uncertain contribution to climate change. The uptake kinetics of water by aerosol is a central process of cloud droplet formation, yet its molecular-scale mechanism is unknown. We use molecular simulations to study this process for phase-separated organic particles. Our results explain the increased cloud condensation activity of such particles and can be generalized over various compositions, thus possibly serving as a basis for future models.
Amir Yazdani, Ann M. Dillner, and Satoshi Takahama
Atmos. Meas. Tech., 14, 4805–4827, https://doi.org/10.5194/amt-14-4805-2021, https://doi.org/10.5194/amt-14-4805-2021, 2021
Short summary
Short summary
We propose a spectroscopic method for estimating several mixture-averaged molecular properties (carbon number and molecular weight) in particulate matter relevant for understanding its chemical origins. This estimation is enabled by calibration models built and tested using laboratory standards containing molecules with known structure, and can be applied to filter samples of PM2.5 currently collected in existing air pollution monitoring networks and field campaigns.
Amir Yazdani, Nikunj Dudani, Satoshi Takahama, Amelie Bertrand, André S. H. Prévôt, Imad El Haddad, and Ann M. Dillner
Atmos. Chem. Phys., 21, 10273–10293, https://doi.org/10.5194/acp-21-10273-2021, https://doi.org/10.5194/acp-21-10273-2021, 2021
Short summary
Short summary
Functional group compositions of primary and aged aerosols from wood burning and coal combustion sources from chamber experiments are interpreted through compounds present in the fuels and known gas-phase oxidation products. Infrared spectra of aged wood burning in the chamber and ambient biomass burning samples reveal striking similarities, and a new method for identifying burning-impacted samples in monitoring network measurements is presented.
Alexandra J. Boris, Satoshi Takahama, Andrew T. Weakley, Bruno M. Debus, Stephanie L. Shaw, Eric S. Edgerton, Taekyu Joo, Nga L. Ng, and Ann M. Dillner
Atmos. Meas. Tech., 14, 4355–4374, https://doi.org/10.5194/amt-14-4355-2021, https://doi.org/10.5194/amt-14-4355-2021, 2021
Short summary
Short summary
Infrared spectrometry can be applied in routine monitoring of atmospheric particles to give comprehensive characterization of the organic material by bond rather than species. Using this technique, the concentrations of particle organic material were found to decrease 2011–2016 in the southeastern US, driven by a decline in highly aged material, concurrent with declining anthropogenic emissions. However, an increase was observed in the fraction of more moderately aged organic matter.
Eirini Boleti, Christoph Hueglin, Stuart K. Grange, André S. H. Prévôt, and Satoshi Takahama
Atmos. Chem. Phys., 20, 9051–9066, https://doi.org/10.5194/acp-20-9051-2020, https://doi.org/10.5194/acp-20-9051-2020, 2020
Short summary
Short summary
Long-term temporal evolution of ozone concentrations between 2000 and 2015 in Europe was estimated using a signal decomposition technique. The seasonal cycles are correlated with local climate conditions and vary according to geographic region, while ozone levels are indicative of distance to emission sources. The site's environment plays a key role in ozone trends, with the most polluted environments showing the least reduction in ozone, while in less polluted areas ozone has decreased.
Charlotte Bürki, Matteo Reggente, Ann M. Dillner, Jenny L. Hand, Stephanie L. Shaw, and Satoshi Takahama
Atmos. Meas. Tech., 13, 1517–1538, https://doi.org/10.5194/amt-13-1517-2020, https://doi.org/10.5194/amt-13-1517-2020, 2020
Short summary
Short summary
Infrared spectroscopy is a chemically informative method for particulate matter characterization. However, recent work has demonstrated that predictions depend heavily on the choice of calibration model parameters. We propose a means for managing parameter uncertainties by combining available data from laboratory standards, molecular databases, and collocated ambient measurements to provide useful characterization of atmospheric organic matter on a large scale.
Alexandra J. Boris, Satoshi Takahama, Andrew T. Weakley, Bruno M. Debus, Carley D. Fredrickson, Martin Esparza-Sanchez, Charlotte Burki, Matteo Reggente, Stephanie L. Shaw, Eric S. Edgerton, and Ann M. Dillner
Atmos. Meas. Tech., 12, 5391–5415, https://doi.org/10.5194/amt-12-5391-2019, https://doi.org/10.5194/amt-12-5391-2019, 2019
Short summary
Short summary
Organic species are abundant in atmospheric particle-phase (aerosol) pollution and originate from a variety of biogenic and anthropogenic sources. Infrared spectrometry of filter-based atmospheric particle samples can afford a direct measurement of the particulate organic matter concentration and a characterization of its composition. This work discusses recent method improvements and compositions measured in samples from the SouthEastern Aerosol Research and Characterization (SEARCH) network.
Matteo Reggente, Ann M. Dillner, and Satoshi Takahama
Atmos. Meas. Tech., 12, 2287–2312, https://doi.org/10.5194/amt-12-2287-2019, https://doi.org/10.5194/amt-12-2287-2019, 2019
Short summary
Short summary
We compare state-of-the-art models for predicting functional group composition in atmospheric particulate matter across urban and rural samples collected in a US monitoring network. While trends across models are consistent, absolute abundances can be sensitive to selection of calibration standards, spectral processing procedures, and calibration algorithms. Recommendations for further method development for reducing uncertainties are outlined.
Matteo Reggente, Rudolf Höhn, and Satoshi Takahama
Atmos. Meas. Tech., 12, 2313–2329, https://doi.org/10.5194/amt-12-2313-2019, https://doi.org/10.5194/amt-12-2313-2019, 2019
Short summary
Short summary
The infrared spectra of atmospheric particles are rich in chemical information but require sophisticated statistical methods to extract information on account of their complex absorption profiles. We present an open software suite which makes current algorithms used for analysis of such spectra available to the community, with a browser-based interface for general users and modular architecture that facilitates addition of new methods by developers.
Satoshi Takahama and Giulia Ruggeri
Atmos. Chem. Phys., 17, 4433–4450, https://doi.org/10.5194/acp-17-4433-2017, https://doi.org/10.5194/acp-17-4433-2017, 2017
Short summary
Short summary
We formalize a method for classifying carbon atoms in organic aerosols according to their functionalization. This conceptual approach allows estimation of carbon mass from functional group measurements, which previously required a series of assumptions that were not well constrained. We describe how the proposed strategy can lead to better comparisons among functional group measurements, chemically explicit model simulations, and other measurements.
Rob L. Modini and Satoshi Takahama
Atmos. Meas. Tech., 9, 3337–3354, https://doi.org/10.5194/amt-9-3337-2016, https://doi.org/10.5194/amt-9-3337-2016, 2016
Short summary
Short summary
Aerosol measurement techniques with high detection limits often result in poorly time-resolved measurements. We investigated sampling strategies and post-processing methods for constructing hourly resolved aerosol concentration time series from samples collected for 4 to 8 h. We show that this is an effective way to increase measurement time resolution, and that under realistic experimental conditions, simple methods can perform as well as more sophisticated methods.
Satoshi Takahama, Giulia Ruggeri, and Ann M. Dillner
Atmos. Meas. Tech., 9, 3429–3454, https://doi.org/10.5194/amt-9-3429-2016, https://doi.org/10.5194/amt-9-3429-2016, 2016
Short summary
Short summary
We introduce the application of statistical algorithms that allow us to associate various dimensions of aerosol composition to vibrational modes measured by infrared absorption spectroscopy. We demonstrate their use on four organic functional groups for which absorption bands are known and extend the application to interpret bands associated with ambient organic carbon and elemental carbon quantified by an independent measurement technique that is widely used in aerosol monitoring networks.
Giulia Ruggeri, Fabian A. Bernhard, Barron H. Henderson, and Satoshi Takahama
Atmos. Chem. Phys., 16, 8729–8747, https://doi.org/10.5194/acp-16-8729-2016, https://doi.org/10.5194/acp-16-8729-2016, 2016
Short summary
Short summary
Functional groups provide an intermediate level of chemical resolution between full molecular speciation and elemental composition for describing complex mixtures and can be a useful metric in model–measurement comparison of reaction kinetics and secondary organic aerosol formation. We introduce tools to facilitate such comparisons and demonstrate its application in study of the photooxidation of two precursor volatile organic compounds and the gas–particle partitioning of their products.
Adele Kuzmiakova, Ann M. Dillner, and Satoshi Takahama
Atmos. Meas. Tech., 9, 2615–2631, https://doi.org/10.5194/amt-9-2615-2016, https://doi.org/10.5194/amt-9-2615-2016, 2016
Short summary
Short summary
We describe a new method for removing Teflon substrate interference from ambient aerosol infrared spectra such that functional group quantification and spectral clustering (for source classification) can be applied. We demonstrate that this technique produces similar results to a more labor-intensive method used in many field campaigns over the past several years, but is simpler and better constrained by physical criteria that we impose, leading to the possibility of widespread adoption.
Giulia Ruggeri and Satoshi Takahama
Atmos. Chem. Phys., 16, 4401–4422, https://doi.org/10.5194/acp-16-4401-2016, https://doi.org/10.5194/acp-16-4401-2016, 2016
Short summary
Short summary
We present a set of tools for mapping molecular information to functional group composition. This allows us to reduce the complexity of representing the organic aerosol composition, as it consists of hundreds of thousands of different compounds. We describe the tools and methods for validation, and demonstrate several applications in which this tool can facilitate measurement intercomparisons and chemical modeling of aerosol chemistry.
Matteo Reggente, Ann M. Dillner, and Satoshi Takahama
Atmos. Meas. Tech., 9, 441–454, https://doi.org/10.5194/amt-9-441-2016, https://doi.org/10.5194/amt-9-441-2016, 2016
Short summary
Short summary
Organic carbon and elemental carbon are major components of atmospheric PM. Typically they are measured using destructive and relatively expensive methods (e.g., TOR). We aim to reduce the operating costs of large air quality monitoring networks using FT-IR spectra of ambient PTFE filters and PLS regression. We achieve accurate predictions for models (calibrated in 2011) that use samples collected at the same or different sites of the calibration data set and in a different year (2013).
B. R. Ayres, H. M. Allen, D. C. Draper, S. S. Brown, R. J. Wild, J. L. Jimenez, D. A. Day, P. Campuzano-Jost, W. Hu, J. de Gouw, A. Koss, R. C. Cohen, K. C. Duffey, P. Romer, K. Baumann, E. Edgerton, S. Takahama, J. A. Thornton, B. H. Lee, F. D. Lopez-Hilfiker, C. Mohr, P. O. Wennberg, T. B. Nguyen, A. Teng, A. H. Goldstein, K. Olson, and J. L. Fry
Atmos. Chem. Phys., 15, 13377–13392, https://doi.org/10.5194/acp-15-13377-2015, https://doi.org/10.5194/acp-15-13377-2015, 2015
Short summary
Short summary
This paper reports atmospheric gas- and aerosol-phase field measurements from the southeastern United States in summer 2013 to demonstrate that the oxidation of biogenic volatile organic compounds by nitrate radical produces a substantial amount of secondary organic aerosol in this region. This process, driven largely by monoterpenes, results in a comparable aerosol nitrate production rate to inorganic nitrate formation by heterogeneous uptake of HNO3 onto dust particles.
A. M. Dillner and S. Takahama
Atmos. Meas. Tech., 8, 4013–4023, https://doi.org/10.5194/amt-8-4013-2015, https://doi.org/10.5194/amt-8-4013-2015, 2015
Short summary
Short summary
Elemental carbon (EC), a constituent of atmospheric particulate matter (PM), adversely affects climate, visibility and human health. EC is measured in PM monitoring networks world-wide but the method is expensive and destructive to the samples. Here, methods are presented to accurately predict EC using Fourier transform infrared (FT-IR) analysis which is inexpensive and non-destructive. This method complements measurements of organic carbon and organic functional groups made using FT-IR.
H. M. Allen, D. C. Draper, B. R. Ayres, A. Ault, A. Bondy, S. Takahama, R. L. Modini, K. Baumann, E. Edgerton, C. Knote, A. Laskin, B. Wang, and J. L. Fry
Atmos. Chem. Phys., 15, 10669–10685, https://doi.org/10.5194/acp-15-10669-2015, https://doi.org/10.5194/acp-15-10669-2015, 2015
Short summary
Short summary
We report ion chromatographic measurements of gas- and aerosol-phase inorganic species at the SOAS 2013 field study. Our particular focus is on inorganic nitrate aerosol formation via HNO3 uptake onto coarse-mode dust and sea salt particles, which we find to be the dominant source of episodic inorganic nitrate at this site, due to the high acidity of the particles preventing formation of NH4NO3. We calculate a production rate of inorganic nitrate aerosol.
A. M. Dillner and S. Takahama
Atmos. Meas. Tech., 8, 1097–1109, https://doi.org/10.5194/amt-8-1097-2015, https://doi.org/10.5194/amt-8-1097-2015, 2015
Short summary
Short summary
We demonstrate the feasibility of using FT-IR spectra of aerosols and a multivariate calibration to estimate organic carbon (OC) from thermal-optical reflectance analysis. Using 800 IMPROVE samples, we establish that prediction error can be explained by differences in distributions of OC and aerosol composition between calibration and test set. This work is an initial step in proposing a non-destructive analysis method that can reduce the operating costs of large air quality monitoring networks.
Y. You, V. P. Kanawade, J. A. de Gouw, A. B. Guenther, S. Madronich, M. R. Sierra-Hernández, M. Lawler, J. N. Smith, S. Takahama, G. Ruggeri, A. Koss, K. Olson, K. Baumann, R. J. Weber, A. Nenes, H. Guo, E. S. Edgerton, L. Porcelli, W. H. Brune, A. H. Goldstein, and S.-H. Lee
Atmos. Chem. Phys., 14, 12181–12194, https://doi.org/10.5194/acp-14-12181-2014, https://doi.org/10.5194/acp-14-12181-2014, 2014
Short summary
Short summary
Amiens play important roles in atmospheric secondary aerosol formation and human health, but the fast response measurements of amines are lacking. Here we show measurements in a southeastern US forest and a moderately polluted midwestern site. Our results show that gas to particle conversion is an important process that controls ambient amine concentrations and that biomass burning is an important source of amines.
A. L. Corrigan, L. M. Russell, S. Takahama, M. Äijälä, M. Ehn, H. Junninen, J. Rinne, T. Petäjä, M. Kulmala, A. L. Vogel, T. Hoffmann, C. J. Ebben, F. M. Geiger, P. Chhabra, J. H. Seinfeld, D. R. Worsnop, W. Song, J. Auld, and J. Williams
Atmos. Chem. Phys., 13, 12233–12256, https://doi.org/10.5194/acp-13-12233-2013, https://doi.org/10.5194/acp-13-12233-2013, 2013
Mária Lbadaoui-Darvas, Ari Laaksonen, and Athanasios Nenes
Atmos. Chem. Phys., 23, 10057–10074, https://doi.org/10.5194/acp-23-10057-2023, https://doi.org/10.5194/acp-23-10057-2023, 2023
Short summary
Short summary
Heterogeneous ice nucleation is the main ice formation mechanism in clouds. The mechanism of different freezing modes is to date unknown, which results in large model biases. Experiments do not allow for direct observation of ice nucleation at its native resolution. This work uses first principles molecular simulations to determine the mechanism of the least-understood ice nucleation mode and link it to adsorption through a novel modeling framework that unites ice and droplet formation.
Marife B. Anunciado, Miranda De Boskey, Laura Haines, Katarina Lindskog, Tracy Dombek, Satoshi Takahama, and Ann M. Dillner
Atmos. Meas. Tech., 16, 3515–3529, https://doi.org/10.5194/amt-16-3515-2023, https://doi.org/10.5194/amt-16-3515-2023, 2023
Short summary
Short summary
Organic sulfur compounds are used to identify sources and atmospheric processing of aerosol. Our paper evaluates the potential of using a non-destructive measurement technique to measure organic sulfur compounds in filter samples by assessing their chemical stability over time. Some were stable, but some evaporated or changed chemically. Future work includes evaluating the stability and potential interference of multiple organic sulfur compounds in laboratory mixtures and ambient aerosol.
Amir Yazdani, Satoshi Takahama, John K. Kodros, Marco Paglione, Mauro Masiol, Stefania Squizzato, Kalliopi Florou, Christos Kaltsonoudis, Spiro D. Jorga, Spyros N. Pandis, and Athanasios Nenes
Atmos. Chem. Phys., 23, 7461–7477, https://doi.org/10.5194/acp-23-7461-2023, https://doi.org/10.5194/acp-23-7461-2023, 2023
Short summary
Short summary
Organic aerosols directly emitted from wood and pellet stove combustion are found to chemically transform (approximately 15 %–35 % by mass) under daytime aging conditions simulated in an environmental chamber. A new marker for lignin-like compounds is found to degrade at a different rate than previously identified biomass burning markers and can potentially provide indication of aging time in ambient samples.
Emily Y. Li, Amir Yazdani, Ann M. Dillner, Guofeng Shen, Wyatt M. Champion, James J. Jetter, William T. Preston, Lynn M. Russell, Michael D. Hays, and Satoshi Takahama
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-90, https://doi.org/10.5194/amt-2023-90, 2023
Revised manuscript accepted for AMT
Short summary
Short summary
Infrared spectroscopy is a cost-effective measurement technique to characterize chemical composition of organic aerosol emissions. This technique differentiates the organic matter emission factor from different fuel sources by their characteristic functional groups. Comparison with collocated measurements suggest polycyclic aromatic hydrocarbon concentrations in emissions estimated by conventional chromatography may be substantially under-estimated.
Nikunj Dudani and Satoshi Takahama
Atmos. Meas. Tech., 15, 4693–4707, https://doi.org/10.5194/amt-15-4693-2022, https://doi.org/10.5194/amt-15-4693-2022, 2022
Short summary
Short summary
We designed and fabricated an aerosol collector with high collection efficiency that enables quantitative infrared spectroscopy analysis. By collecting particles on optical windows, typical substrate interferences are eliminated. New methods for fabricating aerosol devices using 3D printing with post-treatment to reduce the time and cost of prototyping are described.
Amir Yazdani, Nikunj Dudani, Satoshi Takahama, Amelie Bertrand, André S. H. Prévôt, Imad El Haddad, and Ann M. Dillner
Atmos. Meas. Tech., 15, 2857–2874, https://doi.org/10.5194/amt-15-2857-2022, https://doi.org/10.5194/amt-15-2857-2022, 2022
Short summary
Short summary
While the aerosol mass spectrometer provides high-time-resolution characterization of the overall extent of oxidation, the extensive fragmentation of molecules and specificity of the technique have posed challenges toward deeper understanding of molecular structures in aerosols. This work demonstrates how functional group information can be extracted from a suite of commonly measured mass fragments using collocated infrared spectroscopy measurements.
Bruno Debus, Andrew T. Weakley, Satoshi Takahama, Kathryn M. George, Anahita Amiri-Farahani, Bret Schichtel, Scott Copeland, Anthony S. Wexler, and Ann M. Dillner
Atmos. Meas. Tech., 15, 2685–2702, https://doi.org/10.5194/amt-15-2685-2022, https://doi.org/10.5194/amt-15-2685-2022, 2022
Short summary
Short summary
In the US, routine particulate matter composition is measured on samples collected on three types of filter media and analyzed using several techniques. We propose an alternate approach that uses one analytical technique, Fourier transform-infrared spectroscopy (FT-IR), and one filter type to measure the chemical composition of particulate matter in a major US monitoring network. This method could be used to add low-cost sites to the network, fill-in missing data, or for quality control.
Patrick Obin Sturm and Anthony S. Wexler
Geosci. Model Dev., 15, 3417–3431, https://doi.org/10.5194/gmd-15-3417-2022, https://doi.org/10.5194/gmd-15-3417-2022, 2022
Short summary
Short summary
Large air quality and climate models require vast amounts of computational power. Machine learning tools like neural networks can be used to make these models more efficient, with the downside that their results might not make physical sense or be easy to interpret. This work develops a physically interpretable neural network that obeys scientific laws like conservation of mass and models atmospheric composition more accurately than a traditional neural network.
Christopher D. Wallis, Mason D. Leandro, Patrick Y. Chuang, and Anthony S. Wexler
Atmos. Meas. Tech., 15, 2547–2556, https://doi.org/10.5194/amt-15-2547-2022, https://doi.org/10.5194/amt-15-2547-2022, 2022
Short summary
Short summary
Measuring emissions from stacks requires techniques to address a broad range of conditions and measurement challenges. Here we describe an instrument package held by a crane above a stack to characterize both wet droplet and dried aerosol emissions from cooling tower spray drift in situ. The instrument package characterizes the velocity, size distribution, and concentration of the wet droplet emissions and the mass concentration and elemental composition of the dried PM2.5 and PM10 emissions.
Mária Lbadaoui-Darvas, Satoshi Takahama, and Athanasios Nenes
Atmos. Chem. Phys., 21, 17687–17714, https://doi.org/10.5194/acp-21-17687-2021, https://doi.org/10.5194/acp-21-17687-2021, 2021
Short summary
Short summary
Aerosol–cloud interactions constitute the most uncertain contribution to climate change. The uptake kinetics of water by aerosol is a central process of cloud droplet formation, yet its molecular-scale mechanism is unknown. We use molecular simulations to study this process for phase-separated organic particles. Our results explain the increased cloud condensation activity of such particles and can be generalized over various compositions, thus possibly serving as a basis for future models.
Amir Yazdani, Ann M. Dillner, and Satoshi Takahama
Atmos. Meas. Tech., 14, 4805–4827, https://doi.org/10.5194/amt-14-4805-2021, https://doi.org/10.5194/amt-14-4805-2021, 2021
Short summary
Short summary
We propose a spectroscopic method for estimating several mixture-averaged molecular properties (carbon number and molecular weight) in particulate matter relevant for understanding its chemical origins. This estimation is enabled by calibration models built and tested using laboratory standards containing molecules with known structure, and can be applied to filter samples of PM2.5 currently collected in existing air pollution monitoring networks and field campaigns.
Amir Yazdani, Nikunj Dudani, Satoshi Takahama, Amelie Bertrand, André S. H. Prévôt, Imad El Haddad, and Ann M. Dillner
Atmos. Chem. Phys., 21, 10273–10293, https://doi.org/10.5194/acp-21-10273-2021, https://doi.org/10.5194/acp-21-10273-2021, 2021
Short summary
Short summary
Functional group compositions of primary and aged aerosols from wood burning and coal combustion sources from chamber experiments are interpreted through compounds present in the fuels and known gas-phase oxidation products. Infrared spectra of aged wood burning in the chamber and ambient biomass burning samples reveal striking similarities, and a new method for identifying burning-impacted samples in monitoring network measurements is presented.
Alexandra J. Boris, Satoshi Takahama, Andrew T. Weakley, Bruno M. Debus, Stephanie L. Shaw, Eric S. Edgerton, Taekyu Joo, Nga L. Ng, and Ann M. Dillner
Atmos. Meas. Tech., 14, 4355–4374, https://doi.org/10.5194/amt-14-4355-2021, https://doi.org/10.5194/amt-14-4355-2021, 2021
Short summary
Short summary
Infrared spectrometry can be applied in routine monitoring of atmospheric particles to give comprehensive characterization of the organic material by bond rather than species. Using this technique, the concentrations of particle organic material were found to decrease 2011–2016 in the southeastern US, driven by a decline in highly aged material, concurrent with declining anthropogenic emissions. However, an increase was observed in the fraction of more moderately aged organic matter.
Seyyed Ali Davari and Anthony S. Wexler
Atmos. Meas. Tech., 13, 5369–5377, https://doi.org/10.5194/amt-13-5369-2020, https://doi.org/10.5194/amt-13-5369-2020, 2020
Short summary
Short summary
Traditional instruments for detection and quantification of toxic metals in the atmosphere are expensive. In this study, we have designed, fabricated, and tested a low-cost instrument, which employs cheap components to detect and quantify toxic metals. Advanced machine learning (ML) techniques have been used to improve the instrument's performance. This study demonstrates how the combination of low-cost sensors with ML can address problems that traditionally have been too expensive to be solved.
Patrick Obin Sturm and Anthony S. Wexler
Geosci. Model Dev., 13, 4435–4442, https://doi.org/10.5194/gmd-13-4435-2020, https://doi.org/10.5194/gmd-13-4435-2020, 2020
Short summary
Short summary
Large air quality and climate models calculate different physical and chemical phenomena in separate operators within the overall model, some of which are computationally intensive. Machine learning tools can memorize the behavior of these operators and replace them, but the replacements must still obey physical laws, like conservation principles. This work derives a mathematical framework for machine learning replacements that conserves properties, such as mass or energy, to machine precision.
Eirini Boleti, Christoph Hueglin, Stuart K. Grange, André S. H. Prévôt, and Satoshi Takahama
Atmos. Chem. Phys., 20, 9051–9066, https://doi.org/10.5194/acp-20-9051-2020, https://doi.org/10.5194/acp-20-9051-2020, 2020
Short summary
Short summary
Long-term temporal evolution of ozone concentrations between 2000 and 2015 in Europe was estimated using a signal decomposition technique. The seasonal cycles are correlated with local climate conditions and vary according to geographic region, while ozone levels are indicative of distance to emission sources. The site's environment plays a key role in ozone trends, with the most polluted environments showing the least reduction in ozone, while in less polluted areas ozone has decreased.
Charlotte Bürki, Matteo Reggente, Ann M. Dillner, Jenny L. Hand, Stephanie L. Shaw, and Satoshi Takahama
Atmos. Meas. Tech., 13, 1517–1538, https://doi.org/10.5194/amt-13-1517-2020, https://doi.org/10.5194/amt-13-1517-2020, 2020
Short summary
Short summary
Infrared spectroscopy is a chemically informative method for particulate matter characterization. However, recent work has demonstrated that predictions depend heavily on the choice of calibration model parameters. We propose a means for managing parameter uncertainties by combining available data from laboratory standards, molecular databases, and collocated ambient measurements to provide useful characterization of atmospheric organic matter on a large scale.
Alexandra J. Boris, Satoshi Takahama, Andrew T. Weakley, Bruno M. Debus, Carley D. Fredrickson, Martin Esparza-Sanchez, Charlotte Burki, Matteo Reggente, Stephanie L. Shaw, Eric S. Edgerton, and Ann M. Dillner
Atmos. Meas. Tech., 12, 5391–5415, https://doi.org/10.5194/amt-12-5391-2019, https://doi.org/10.5194/amt-12-5391-2019, 2019
Short summary
Short summary
Organic species are abundant in atmospheric particle-phase (aerosol) pollution and originate from a variety of biogenic and anthropogenic sources. Infrared spectrometry of filter-based atmospheric particle samples can afford a direct measurement of the particulate organic matter concentration and a characterization of its composition. This work discusses recent method improvements and compositions measured in samples from the SouthEastern Aerosol Research and Characterization (SEARCH) network.
Matteo Reggente, Ann M. Dillner, and Satoshi Takahama
Atmos. Meas. Tech., 12, 2287–2312, https://doi.org/10.5194/amt-12-2287-2019, https://doi.org/10.5194/amt-12-2287-2019, 2019
Short summary
Short summary
We compare state-of-the-art models for predicting functional group composition in atmospheric particulate matter across urban and rural samples collected in a US monitoring network. While trends across models are consistent, absolute abundances can be sensitive to selection of calibration standards, spectral processing procedures, and calibration algorithms. Recommendations for further method development for reducing uncertainties are outlined.
Matteo Reggente, Rudolf Höhn, and Satoshi Takahama
Atmos. Meas. Tech., 12, 2313–2329, https://doi.org/10.5194/amt-12-2313-2019, https://doi.org/10.5194/amt-12-2313-2019, 2019
Short summary
Short summary
The infrared spectra of atmospheric particles are rich in chemical information but require sophisticated statistical methods to extract information on account of their complex absorption profiles. We present an open software suite which makes current algorithms used for analysis of such spectra available to the community, with a browser-based interface for general users and modular architecture that facilitates addition of new methods by developers.
Satoshi Takahama and Giulia Ruggeri
Atmos. Chem. Phys., 17, 4433–4450, https://doi.org/10.5194/acp-17-4433-2017, https://doi.org/10.5194/acp-17-4433-2017, 2017
Short summary
Short summary
We formalize a method for classifying carbon atoms in organic aerosols according to their functionalization. This conceptual approach allows estimation of carbon mass from functional group measurements, which previously required a series of assumptions that were not well constrained. We describe how the proposed strategy can lead to better comparisons among functional group measurements, chemically explicit model simulations, and other measurements.
Rob L. Modini and Satoshi Takahama
Atmos. Meas. Tech., 9, 3337–3354, https://doi.org/10.5194/amt-9-3337-2016, https://doi.org/10.5194/amt-9-3337-2016, 2016
Short summary
Short summary
Aerosol measurement techniques with high detection limits often result in poorly time-resolved measurements. We investigated sampling strategies and post-processing methods for constructing hourly resolved aerosol concentration time series from samples collected for 4 to 8 h. We show that this is an effective way to increase measurement time resolution, and that under realistic experimental conditions, simple methods can perform as well as more sophisticated methods.
Satoshi Takahama, Giulia Ruggeri, and Ann M. Dillner
Atmos. Meas. Tech., 9, 3429–3454, https://doi.org/10.5194/amt-9-3429-2016, https://doi.org/10.5194/amt-9-3429-2016, 2016
Short summary
Short summary
We introduce the application of statistical algorithms that allow us to associate various dimensions of aerosol composition to vibrational modes measured by infrared absorption spectroscopy. We demonstrate their use on four organic functional groups for which absorption bands are known and extend the application to interpret bands associated with ambient organic carbon and elemental carbon quantified by an independent measurement technique that is widely used in aerosol monitoring networks.
Giulia Ruggeri, Fabian A. Bernhard, Barron H. Henderson, and Satoshi Takahama
Atmos. Chem. Phys., 16, 8729–8747, https://doi.org/10.5194/acp-16-8729-2016, https://doi.org/10.5194/acp-16-8729-2016, 2016
Short summary
Short summary
Functional groups provide an intermediate level of chemical resolution between full molecular speciation and elemental composition for describing complex mixtures and can be a useful metric in model–measurement comparison of reaction kinetics and secondary organic aerosol formation. We introduce tools to facilitate such comparisons and demonstrate its application in study of the photooxidation of two precursor volatile organic compounds and the gas–particle partitioning of their products.
Adele Kuzmiakova, Ann M. Dillner, and Satoshi Takahama
Atmos. Meas. Tech., 9, 2615–2631, https://doi.org/10.5194/amt-9-2615-2016, https://doi.org/10.5194/amt-9-2615-2016, 2016
Short summary
Short summary
We describe a new method for removing Teflon substrate interference from ambient aerosol infrared spectra such that functional group quantification and spectral clustering (for source classification) can be applied. We demonstrate that this technique produces similar results to a more labor-intensive method used in many field campaigns over the past several years, but is simpler and better constrained by physical criteria that we impose, leading to the possibility of widespread adoption.
Giulia Ruggeri and Satoshi Takahama
Atmos. Chem. Phys., 16, 4401–4422, https://doi.org/10.5194/acp-16-4401-2016, https://doi.org/10.5194/acp-16-4401-2016, 2016
Short summary
Short summary
We present a set of tools for mapping molecular information to functional group composition. This allows us to reduce the complexity of representing the organic aerosol composition, as it consists of hundreds of thousands of different compounds. We describe the tools and methods for validation, and demonstrate several applications in which this tool can facilitate measurement intercomparisons and chemical modeling of aerosol chemistry.
Christopher D. Cappa, Shantanu H. Jathar, Michael J. Kleeman, Kenneth S. Docherty, Jose L. Jimenez, John H. Seinfeld, and Anthony S. Wexler
Atmos. Chem. Phys., 16, 3041–3059, https://doi.org/10.5194/acp-16-3041-2016, https://doi.org/10.5194/acp-16-3041-2016, 2016
Short summary
Short summary
Losses of vapors to walls of chambers can negatively bias SOA formation measurements, consequently leading to low predicted SOA concentrations in air quality models. Here, we show that accounting for such vapor losses leads to substantial increases in the predicted amount of SOA formed from VOCs and to notable increases in the O : C atomic ratio in two US regions. Comparison with a variety of observational data suggests generally improved model performance when vapor wall losses are accounted for.
S. H. Jathar, C. D. Cappa, A. S. Wexler, J. H. Seinfeld, and M. J. Kleeman
Atmos. Chem. Phys., 16, 2309–2322, https://doi.org/10.5194/acp-16-2309-2016, https://doi.org/10.5194/acp-16-2309-2016, 2016
Short summary
Short summary
Multi-generational chemistry schemes applied in regional models do not increase secondary organic aerosol (SOA) mass production relative to traditional "two-product" schemes when both models are fitted to the same chamber data. The multi-generational chemistry schemes do change the predicted composition of SOA and the source attribution of SOA.
C. R. Hoyle, C. Fuchs, E. Järvinen, H. Saathoff, A. Dias, I. El Haddad, M. Gysel, S. C. Coburn, J. Tröstl, A.-K. Bernhammer, F. Bianchi, M. Breitenlechner, J. C. Corbin, J. Craven, N. M. Donahue, J. Duplissy, S. Ehrhart, C. Frege, H. Gordon, N. Höppel, M. Heinritzi, T. B. Kristensen, U. Molteni, L. Nichman, T. Pinterich, A. S. H. Prévôt, M. Simon, J. G. Slowik, G. Steiner, A. Tomé, A. L. Vogel, R. Volkamer, A. C. Wagner, R. Wagner, A. S. Wexler, C. Williamson, P. M. Winkler, C. Yan, A. Amorim, J. Dommen, J. Curtius, M. W. Gallagher, R. C. Flagan, A. Hansel, J. Kirkby, M. Kulmala, O. Möhler, F. Stratmann, D. R. Worsnop, and U. Baltensperger
Atmos. Chem. Phys., 16, 1693–1712, https://doi.org/10.5194/acp-16-1693-2016, https://doi.org/10.5194/acp-16-1693-2016, 2016
Short summary
Short summary
A significant portion of sulphate, an important constituent of atmospheric aerosols, is formed via the aqueous phase oxidation of sulphur dioxide by ozone. The rate of this reaction has previously only been measured over a relatively small temperature range. Here, we use the state of the art CLOUD chamber at CERN to perform the first measurements of this reaction rate in super-cooled droplets, confirming that the existing extrapolation of the reaction rate to sub-zero temperatures is accurate.
Matteo Reggente, Ann M. Dillner, and Satoshi Takahama
Atmos. Meas. Tech., 9, 441–454, https://doi.org/10.5194/amt-9-441-2016, https://doi.org/10.5194/amt-9-441-2016, 2016
Short summary
Short summary
Organic carbon and elemental carbon are major components of atmospheric PM. Typically they are measured using destructive and relatively expensive methods (e.g., TOR). We aim to reduce the operating costs of large air quality monitoring networks using FT-IR spectra of ambient PTFE filters and PLS regression. We achieve accurate predictions for models (calibrated in 2011) that use samples collected at the same or different sites of the calibration data set and in a different year (2013).
B. R. Ayres, H. M. Allen, D. C. Draper, S. S. Brown, R. J. Wild, J. L. Jimenez, D. A. Day, P. Campuzano-Jost, W. Hu, J. de Gouw, A. Koss, R. C. Cohen, K. C. Duffey, P. Romer, K. Baumann, E. Edgerton, S. Takahama, J. A. Thornton, B. H. Lee, F. D. Lopez-Hilfiker, C. Mohr, P. O. Wennberg, T. B. Nguyen, A. Teng, A. H. Goldstein, K. Olson, and J. L. Fry
Atmos. Chem. Phys., 15, 13377–13392, https://doi.org/10.5194/acp-15-13377-2015, https://doi.org/10.5194/acp-15-13377-2015, 2015
Short summary
Short summary
This paper reports atmospheric gas- and aerosol-phase field measurements from the southeastern United States in summer 2013 to demonstrate that the oxidation of biogenic volatile organic compounds by nitrate radical produces a substantial amount of secondary organic aerosol in this region. This process, driven largely by monoterpenes, results in a comparable aerosol nitrate production rate to inorganic nitrate formation by heterogeneous uptake of HNO3 onto dust particles.
A. M. Dillner and S. Takahama
Atmos. Meas. Tech., 8, 4013–4023, https://doi.org/10.5194/amt-8-4013-2015, https://doi.org/10.5194/amt-8-4013-2015, 2015
Short summary
Short summary
Elemental carbon (EC), a constituent of atmospheric particulate matter (PM), adversely affects climate, visibility and human health. EC is measured in PM monitoring networks world-wide but the method is expensive and destructive to the samples. Here, methods are presented to accurately predict EC using Fourier transform infrared (FT-IR) analysis which is inexpensive and non-destructive. This method complements measurements of organic carbon and organic functional groups made using FT-IR.
H. M. Allen, D. C. Draper, B. R. Ayres, A. Ault, A. Bondy, S. Takahama, R. L. Modini, K. Baumann, E. Edgerton, C. Knote, A. Laskin, B. Wang, and J. L. Fry
Atmos. Chem. Phys., 15, 10669–10685, https://doi.org/10.5194/acp-15-10669-2015, https://doi.org/10.5194/acp-15-10669-2015, 2015
Short summary
Short summary
We report ion chromatographic measurements of gas- and aerosol-phase inorganic species at the SOAS 2013 field study. Our particular focus is on inorganic nitrate aerosol formation via HNO3 uptake onto coarse-mode dust and sea salt particles, which we find to be the dominant source of episodic inorganic nitrate at this site, due to the high acidity of the particles preventing formation of NH4NO3. We calculate a production rate of inorganic nitrate aerosol.
S. H. Jathar, C. D. Cappa, A. S. Wexler, J. H. Seinfeld, and M. J. Kleeman
Geosci. Model Dev., 8, 2553–2567, https://doi.org/10.5194/gmd-8-2553-2015, https://doi.org/10.5194/gmd-8-2553-2015, 2015
Short summary
Short summary
Multi-generational oxidation of organic vapors can significantly alter the mass, chemical composition and properties of secondary organic aerosol (SOA). Here, we implement a semi-explicit, constrained multi-generational oxidation model of Cappa and Wilson (2012) in a 3-D air quality model. When compared with results from a current-generation SOA model, we predict similar mass concentrations of SOA but a different chemical composition. O:C ratios of SOA are in line with those measured globally.
A. M. Dillner and S. Takahama
Atmos. Meas. Tech., 8, 1097–1109, https://doi.org/10.5194/amt-8-1097-2015, https://doi.org/10.5194/amt-8-1097-2015, 2015
Short summary
Short summary
We demonstrate the feasibility of using FT-IR spectra of aerosols and a multivariate calibration to estimate organic carbon (OC) from thermal-optical reflectance analysis. Using 800 IMPROVE samples, we establish that prediction error can be explained by differences in distributions of OC and aerosol composition between calibration and test set. This work is an initial step in proposing a non-destructive analysis method that can reduce the operating costs of large air quality monitoring networks.
J. G. Charrier, N. K. Richards-Henderson, K. J. Bein, A. S. McFall, A. S. Wexler, and C. Anastasio
Atmos. Chem. Phys., 15, 2327–2340, https://doi.org/10.5194/acp-15-2327-2015, https://doi.org/10.5194/acp-15-2327-2015, 2015
Short summary
Short summary
We measured the oxidative potential of airborne particles – a property that has been linked to health problems caused by particles – from different emission source mixtures in Fresno, CA. Copper was responsible for the majority of the oxidative potential (as measured by the DTT assay), followed by unknown species (likely organics) and manganese. Sources of copper-rich particles, including vehicles, had higher oxidative potentials.
Y. You, V. P. Kanawade, J. A. de Gouw, A. B. Guenther, S. Madronich, M. R. Sierra-Hernández, M. Lawler, J. N. Smith, S. Takahama, G. Ruggeri, A. Koss, K. Olson, K. Baumann, R. J. Weber, A. Nenes, H. Guo, E. S. Edgerton, L. Porcelli, W. H. Brune, A. H. Goldstein, and S.-H. Lee
Atmos. Chem. Phys., 14, 12181–12194, https://doi.org/10.5194/acp-14-12181-2014, https://doi.org/10.5194/acp-14-12181-2014, 2014
Short summary
Short summary
Amiens play important roles in atmospheric secondary aerosol formation and human health, but the fast response measurements of amines are lacking. Here we show measurements in a southeastern US forest and a moderately polluted midwestern site. Our results show that gas to particle conversion is an important process that controls ambient amine concentrations and that biomass burning is an important source of amines.
A. L. Corrigan, L. M. Russell, S. Takahama, M. Äijälä, M. Ehn, H. Junninen, J. Rinne, T. Petäjä, M. Kulmala, A. L. Vogel, T. Hoffmann, C. J. Ebben, F. M. Geiger, P. Chhabra, J. H. Seinfeld, D. R. Worsnop, W. Song, J. Auld, and J. Williams
Atmos. Chem. Phys., 13, 12233–12256, https://doi.org/10.5194/acp-13-12233-2013, https://doi.org/10.5194/acp-13-12233-2013, 2013
Related subject area
Subject: Aerosols | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
Enhancing characterization of organic nitrogen components in aerosols and droplets using high-resolution aerosol mass spectrometry
Machine learning approaches for automatic classification of single-particle mass spectrometry data
A searchable database and mass spectral comparison tool for the Aerosol Mass Spectrometer (AMS) and the Aerosol Chemical Speciation Monitor (ACSM)
Numerical investigation on retrieval errors of mixing states of fractal black carbon aerosols using single-particle soot photometer based on Mie scattering and the effects on radiative forcing estimation
Performance evaluation of MOMA (MOment MAtching) – a remote network calibration technique for PM2.5 and PM10 sensors
Mapping the performance of a versatile water-based condensation particle counter (vWCPC) with numerical simulation and experimental study
Towards a Universal Hygroscopic Growth Calibration for Low-Cost PM2.5 Sensors
Development and evaluation of an improved offline aerosol mass spectrometry technique
SMEARcore – modular data infrastructure for atmospheric measurement stations
A multiple-charging correction algorithm for a broad-supersaturation scanning cloud condensation nuclei (BS2-CCN) system
A novel probabilistic source apportionment approach: Bayesian Auto-correlated Matrix Factorization
An evaluation of the U.S. EPA's correction equation for PurpleAir sensor data in smoke, dust, and wintertime urban pollution events
Typhoon-associated air quality over the Guangdong–Hong Kong–Macao Greater Bay Area, China: machine-learning-based prediction and assessment
Quantification of primary and secondary organic aerosol sources by combined factor analysis of extractive electrospray ionisation and aerosol mass spectrometer measurements (EESI-TOF and AMS)
A new method for calculating average visibility from the relationship between extinction coefficient and visibility
In situ particle sampling relationships to surface and turbulent fluxes using large eddy simulations with Lagrangian particles
The effect of the averaging period for PMF analysis of aerosol mass spectrometer measurements during offline applications
Calibrating networks of low-cost air quality sensors
Source apportionment resolved by time of day for improved deconvolution of primary source contributions to air pollution
Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data
Rolling vs. seasonal PMF: real-world multi-site and synthetic dataset comparison
Comprehensive detection of analytes in large chromatographic datasets by coupling factor analysis with a decision tree
Combined organic and inorganic source apportionment on yearlong ToF-ACSM dataset at a suburban station in Athens
Retrieval of the sea spray aerosol mode from submicron particle size distributions and supermicron scattering during LASIC
Automated identification of local contamination in remote atmospheric composition time series
Ch3MS-RF: a random forest model for chemical characterization and improved quantification of unidentified atmospheric organics detected by chromatography–mass spectrometry techniques
Regularized inversion of aerosol hygroscopic growth factor probability density function: application to humidity-controlled fast integrated mobility spectrometer measurements
A systematic re-evaluation of methods for quantification of bulk particle-phase organic nitrates using real-time aerosol mass spectrometry
Revisiting matrix-based inversion of scanning mobility particle sizer (SMPS) and humidified tandem differential mobility analyzer (HTDMA) data
Data imputation in in situ-measured particle size distributions by means of neural networks
Analysis of mobile monitoring data from the microAeth® MA200 for measuring changes in black carbon on the roadside in Augsburg
New correction method for the scattering coefficient measurements of a three-wavelength nephelometer
Estimating mean molecular weight, carbon number, and OM∕OC with mid-infrared spectroscopy in organic particulate matter samples from a monitoring network
Modeled source apportionment of black carbon particles coated with a light-scattering shell
Estimation of particulate organic nitrates from thermodenuder–aerosol mass spectrometer measurements in the North China Plain
Aerosol pH indicator and organosulfate detectability from aerosol mass spectrometry measurements
Determination of equivalent black carbon mass concentration from aerosol light absorption using variable mass absorption cross section
Effects of multi-charge on aerosol hygroscopicity measurement by a HTDMA
A new method for long-term source apportionment with time-dependent factor profiles and uncertainty assessment using SoFi Pro: application to 1 year of organic aerosol data
Estimation of pollen counts from light scattering intensity when sampling multiple pollen taxa – establishment of an automated multi-taxa pollen counting estimation system (AME system)
A novel lidar gradient cluster analysis method of nocturnal boundary layer detection during air pollution episodes
Assessment of particle size magnifier inversion methods to obtain the particle size distribution from atmospheric measurements
A global analysis of climate-relevant aerosol properties retrieved from the network of Global Atmosphere Watch (GAW) near-surface observatories
Development of an automatic linear calibration method for high-resolution single-particle mass spectrometry: improved chemical species identification for atmospheric aerosols
A hybrid method for reconstructing the historical evolution of aerosol optical depth from sunshine duration measurements
The influence of the baseline drift on the resulting extinction values of a cavity attenuated phase shift-based extinction monitor (CAPS PMex)
Evaluation of equivalent black carbon source apportionment using observations from Switzerland between 2008 and 2018
Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: method development for probabilistic modeling of organic carbon and organic matter concentrations
Filling the gaps of in situ hourly PM2.5 concentration data with the aid of empirical orthogonal function analysis constrained by diurnal cycles
Gaussian process regression model for dynamically calibrating and surveilling a wireless low-cost particulate matter sensor network in Delhi
Xinlei Ge, Yele Sun, Justin Trousdell, Mindong Chen, and Qi Zhang
Atmos. Meas. Tech., 17, 423–439, https://doi.org/10.5194/amt-17-423-2024, https://doi.org/10.5194/amt-17-423-2024, 2024
Short summary
Short summary
This study aims to enhance the application of the Aerodyne high-resolution aerosol mass spectrometer (HR-AMS) in characterizing organic nitrogen (ON) species within aerosol particles and droplets. A thorough analysis was conducted on 75 ON standards that represent a diverse spectrum of ambient ON types. The results underscore the capacity of the HR-AMS in examining the concentration and chemistry of atmospheric ON compounds, thereby offering insights into their sources and environmental impacts.
Guanzhong Wang, Heinrich Ruser, Julian Schade, Johannes Passig, Thomas Adam, Günther Dollinger, and Ralf Zimmermann
Atmos. Meas. Tech., 17, 299–313, https://doi.org/10.5194/amt-17-299-2024, https://doi.org/10.5194/amt-17-299-2024, 2024
Short summary
Short summary
This research aims to develop a novel warning system for the real-time monitoring of pollutants in the atmosphere. The system is capable of sampling and investigating airborne aerosol particles on-site, utilizing artificial intelligence to learn their chemical signatures and to classify them in real time. We applied single-particle mass spectrometry for analyzing the chemical composition of aerosol particles and suggest several supervised algorithms for highly reliable automatic classification.
Sohyeon Jeon, Michael J. Walker, Donna T. Sueper, Douglas A. Day, Anne V. Handschy, Jose L. Jimenez, and Brent J. Williams
Atmos. Meas. Tech., 16, 6075–6095, https://doi.org/10.5194/amt-16-6075-2023, https://doi.org/10.5194/amt-16-6075-2023, 2023
Short summary
Short summary
A searchable database tool for the Aerosol Mass Spectrometer (AMS) and Aerosol Chemical Speciation Monitor (ACSM) mass spectral datasets was built to improve the efficiency of data analysis using Igor Pro. The tool incorporates the published mass spectra (MS) and sample information uploaded on the website. The tool allows users to compare their own mass spectrum with the reference MS in the database.
Jia Liu, Guangya Wang, Cancan Zhu, Donghui Zhou, and Lin Wang
Atmos. Meas. Tech., 16, 4961–4974, https://doi.org/10.5194/amt-16-4961-2023, https://doi.org/10.5194/amt-16-4961-2023, 2023
Short summary
Short summary
Single-particle soot photometer (SP2) employs the core-shell model to represent coated BC particles, which introduces retrieval errors in the mixing state (Dp/Dc) of BC. We construct fractal models to represent thinly and thickly coated BC particles, and the retrieval errors of the mixing state are investigated from the numerical aspect. We find that errors in Dp/Dc are noteworthy, and the errors in Dp/Dc can further affect the evaluation accuracy of the radiative forcing of BC.
Lena Francesca Weissert, Geoff Steven Henshaw, David Edward Williams, Brandon Feenstra, Randy Lam, Ashley Collier-Oxandale, Vasileios Papapostolou, and Andrea Polidori
Atmos. Meas. Tech., 16, 4709–4722, https://doi.org/10.5194/amt-16-4709-2023, https://doi.org/10.5194/amt-16-4709-2023, 2023
Short summary
Short summary
We apply a previously developed remote calibration framework to a network of particulate matter (PM) sensors deployed in Southern California. Our results show that a remote calibration can improve the accuracy of PM data, which was particularly visible for PM10. We highlight that sensor drift was mostly due to differences in particle composition than monitor operational factors. Thus, PM sensors may require frequent calibration if PM sources vary with different wind conditions or seasons.
Weixing Hao, Fan Mei, Susanne Hering, Steven Spielman, Beat Schmid, Jason Tomlinson, and Yang Wang
Atmos. Meas. Tech., 16, 3973–3986, https://doi.org/10.5194/amt-16-3973-2023, https://doi.org/10.5194/amt-16-3973-2023, 2023
Short summary
Short summary
Airborne aerosol instrumentation plays a crucial role in understanding the spatial distribution of ambient aerosol particles. This study investigates a versatile water-based condensation particle counter through simulations and experiments. It provides valuable insights to improve versatile water-based condensation particle counter (vWCPC) aerosol measurement and operation for the community.
Milan Y. Patel, Pietro F. Vannucci, Jinsol Kim, William M. Berelson, and Ronald C. Cohen
EGUsphere, https://doi.org/10.5194/egusphere-2023-1701, https://doi.org/10.5194/egusphere-2023-1701, 2023
Short summary
Short summary
Low-cost particulate matter (PM) sensors are becoming increasingly common in community monitoring and atmospheric research, but these sensors require proper calibration to provide accurate reporting. Here, we propose a hygroscopic growth calibration scheme that evolves in time to account for seasonal changes in hygroscopic growth. In San Francisco and Los Angeles, CA, applying a seasonal hygroscopic growth calibration can account for sensor biases driven by the seasonal cycles in PM composition.
Christina N. Vasilakopoulou, Kalliopi Florou, Christos Kaltsonoudis, Iasonas Stavroulas, Nikolaos Mihalopoulos, and Spyros N. Pandis
Atmos. Meas. Tech., 16, 2837–2850, https://doi.org/10.5194/amt-16-2837-2023, https://doi.org/10.5194/amt-16-2837-2023, 2023
Short summary
Short summary
The offline aerosol mass spectrometry technique is a useful tool for the source apportionment of organic aerosol in areas and periods during which an aerosol mass spectrometer is not available. In this work, an improved offline technique was developed and evaluated in an effort to capture most of the partially soluble and insoluble organic aerosol material, reducing the uncertainty of the corresponding source apportionment significantly.
Anton Rusanen, Kristo Hõrrak, Lauri R. Ahonen, Tuomo Nieminen, Pasi P. Aalto, Pasi Kolari, Markku Kulmala, Tuukka Petäjä, and Heikki Junninen
Atmos. Meas. Tech., 16, 2781–2793, https://doi.org/10.5194/amt-16-2781-2023, https://doi.org/10.5194/amt-16-2781-2023, 2023
Short summary
Short summary
We present a framework for setting up SMEAR (Station for Measuring Ecosystem–Atmosphere Relations) type measurement station data flows. This framework, called SMEARcore, consists of modular open-source software components that can be chosen to suit various station configurations. The benefits of using this framework are automation of routine operations and real-time monitoring of measurement results.
Najin Kim, Hang Su, Nan Ma, Ulrich Pöschl, and Yafang Cheng
Atmos. Meas. Tech., 16, 2771–2780, https://doi.org/10.5194/amt-16-2771-2023, https://doi.org/10.5194/amt-16-2771-2023, 2023
Short summary
Short summary
We propose a multiple-charging correction algorithm for a broad-supersaturation scanning cloud condensation nuclei (BS2-CCN) system which can obtain high time-resolution aerosol hygroscopicity and CCN activity. The correction algorithm aims at deriving the activation fraction's true value for each particle size. The meaningful differences between corrected and original κ values (single hygroscopicity parameter) emphasize the correction algorithm's importance for ambient aerosol measurement.
Anton Rusanen, Anton Björklund, Manousos Manousakas, Jianhui Jiang, Markku T. Kulmala, Kai Puolamäki, and Kaspar R. Daellenbach
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-70, https://doi.org/10.5194/amt-2023-70, 2023
Revised manuscript accepted for AMT
Short summary
Short summary
We present a Bayesian non-negative matrix factorization model that performs better on our test datasets than currently widely used models. Its advantages are better use of time information and providing a direct error estimation. We believe this could lead to better estimates of emission sources from measurements.
Daniel A. Jaffe, Colleen Miller, Katie Thompson, Brandon Finley, Manna Nelson, James Ouimette, and Elisabeth Andrews
Atmos. Meas. Tech., 16, 1311–1322, https://doi.org/10.5194/amt-16-1311-2023, https://doi.org/10.5194/amt-16-1311-2023, 2023
Short summary
Short summary
PurpleAir sensors (PASs) are low-cost tools to measure fine particulate matter (PM) concentrations. However, the raw PAS data have significant biases, so the sensors must be corrected. We analyzed data from numerous sites and found that the standard correction to the PAS Purple Air data is accurate in urban pollution events and smoke events but leads to a 6-fold underestimate in the PM2.5 concentrations in dust events. We propose a new correction algorithm to address this problem.
Yilin Chen, Yuanjian Yang, and Meng Gao
Atmos. Meas. Tech., 16, 1279–1294, https://doi.org/10.5194/amt-16-1279-2023, https://doi.org/10.5194/amt-16-1279-2023, 2023
Short summary
Short summary
The Guangdong–Hong Kong–Macao Greater Bay Area suffers from summertime air pollution events related to typhoons. The present study leverages machine learning to predict typhoon-associated air quality over the area. The model evaluation shows that the model performs excellently. Moreover, the change in meteorological drivers of air quality on typhoon days and non-typhoon days suggests that air pollution control strategies should have different focuses on typhoon days and non-typhoon days.
Yandong Tong, Lu Qi, Giulia Stefenelli, Dongyu Simon Wang, Francesco Canonaco, Urs Baltensperger, André Stephan Henry Prévôt, and Jay Gates Slowik
Atmos. Meas. Tech., 15, 7265–7291, https://doi.org/10.5194/amt-15-7265-2022, https://doi.org/10.5194/amt-15-7265-2022, 2022
Short summary
Short summary
We present a method for positive matrix factorisation (PMF) analysis on a single dataset that includes measurements from both EESI-TOF and AMS in Zurich, Switzerland. For the first time, we resolved and quantified secondary organic aerosol (SOA) sources. Meanwhile, we also determined the retrieved EESI-TOF factor-dependent sensitivities. This method provides a framework for exploiting semi-quantitative, high-resolution instrumentation for quantitative source apportionment.
Zefeng Zhang, Hengnan Guo, Hanqing Kang, Jing Wang, Junlin An, Xingna Yu, Jingjing Lv, and Bin Zhu
Atmos. Meas. Tech., 15, 7259–7264, https://doi.org/10.5194/amt-15-7259-2022, https://doi.org/10.5194/amt-15-7259-2022, 2022
Short summary
Short summary
In this study, we first analyze the relationship between the visibility, the extinction coefficient, and atmospheric compositions. Then we propose to use the harmonic average of visibility data as the average visibility, which can better reflect changes in atmospheric extinction coefficients and aerosol concentrations. It is recommended to use the harmonic average visibility in the studies of climate change, atmospheric radiation, air pollution, environmental health, etc.
Hyungwon John Park, Jeffrey S. Reid, Livia S. Freire, Christopher Jackson, and David H. Richter
Atmos. Meas. Tech., 15, 7171–7194, https://doi.org/10.5194/amt-15-7171-2022, https://doi.org/10.5194/amt-15-7171-2022, 2022
Short summary
Short summary
We use numerical models to study field measurements of sea spray aerosol particles and conclude that both the atmospheric state and the methods of instrument sampling are causes for the variation in the production rate of aerosol particles: a critical metric to learn the aerosol's effect on processes like cloud physics and radiation. This work helps field observers improve their experimental design and interpretation of measurements because of turbulence in the atmosphere.
Christina Vasilakopoulou, Iasonas Stavroulas, Nikolaos Mihalopoulos, and Spyros N. Pandis
Atmos. Meas. Tech., 15, 6419–6431, https://doi.org/10.5194/amt-15-6419-2022, https://doi.org/10.5194/amt-15-6419-2022, 2022
Short summary
Short summary
Offline aerosol mass spectrometer (AMS) measurements can provide valuable information about ambient organic aerosols when online AMS measurements are not available. In this study, we examine whether and how the low time resolution (usually 24 h) of the offline technique affects source apportionment results. We concluded that use of the daily averages resulted in estimated average contributions that were within 8 % of the total OA compared with the high-resolution analysis.
Priyanka deSouza, Ralph Kahn, Tehya Stockman, William Obermann, Ben Crawford, An Wang, James Crooks, Jing Li, and Patrick Kinney
Atmos. Meas. Tech., 15, 6309–6328, https://doi.org/10.5194/amt-15-6309-2022, https://doi.org/10.5194/amt-15-6309-2022, 2022
Short summary
Short summary
How sensitive are the spatial and temporal trends of PM2.5 derived from a network of low-cost sensors to the calibration adjustment used? How transferable are calibration equations developed at a few co-location sites to an entire network of low-cost sensors? This paper attempts to answer this question and offers a series of suggestions on how to develop the most robust calibration function for different end uses. It uses measurements from the Love My Air network in Denver as a test case.
Sahil Bhandari, Zainab Arub, Gazala Habib, Joshua S. Apte, and Lea Hildebrandt Ruiz
Atmos. Meas. Tech., 15, 6051–6074, https://doi.org/10.5194/amt-15-6051-2022, https://doi.org/10.5194/amt-15-6051-2022, 2022
Short summary
Short summary
We present a new method to conduct source apportionment resolved by time of day using the underlying approach of positive matrix factorization. We report results for four example time periods in two seasons (winter and monsoon 2017) in Delhi, India. Compared to the traditional approach, we extract a larger number of factors that represent the expected sources of primary organic aerosol. This method can capture diurnal time series patterns of sources at low computational cost.
Alireza Moallemi, Rob L. Modini, Tatyana Lapyonok, Anton Lopatin, David Fuertes, Oleg Dubovik, Philippe Giaccari, and Martin Gysel-Beer
Atmos. Meas. Tech., 15, 5619–5642, https://doi.org/10.5194/amt-15-5619-2022, https://doi.org/10.5194/amt-15-5619-2022, 2022
Short summary
Short summary
Aerosol properties (size distributions, refractive indices) can be retrieved from in situ, angularly resolved light scattering measurements performed with polar nephelometers. We apply an established framework to assess the aerosol property retrieval potential for different instrument configurations, target applications, and assumed prior knowledge. We also demonstrate how a reductive greedy algorithm can be used to determine the optimal placements of the angular sensors in a polar nephelometer.
Marta Via, Gang Chen, Francesco Canonaco, Kaspar R. Daellenbach, Benjamin Chazeau, Hasna Chebaicheb, Jianhui Jiang, Hannes Keernik, Chunshui Lin, Nicolas Marchand, Cristina Marin, Colin O'Dowd, Jurgita Ovadnevaite, Jean-Eudes Petit, Michael Pikridas, Véronique Riffault, Jean Sciare, Jay G. Slowik, Leïla Simon, Jeni Vasilescu, Yunjiang Zhang, Olivier Favez, André S. H. Prévôt, Andrés Alastuey, and María Cruz Minguillón
Atmos. Meas. Tech., 15, 5479–5495, https://doi.org/10.5194/amt-15-5479-2022, https://doi.org/10.5194/amt-15-5479-2022, 2022
Short summary
Short summary
This work presents the differences resulting from two techniques (rolling and seasonal) of the positive matrix factorisation model that can be run for organic aerosol source apportionment. The current state of the art suggests that the rolling technique is more accurate, but no proof of its effectiveness has been provided yet. This paper tackles this issue in the context of a synthetic dataset and a multi-site real-world comparison.
Sungwoo Kim, Brian M. Lerner, Donna T. Sueper, and Gabriel Isaacman-VanWertz
Atmos. Meas. Tech., 15, 5061–5075, https://doi.org/10.5194/amt-15-5061-2022, https://doi.org/10.5194/amt-15-5061-2022, 2022
Short summary
Short summary
Atmospheric samples can be complex, and current analysis methods often require substantial human interaction and discard potentially important information. To improve analysis accuracy and computational cost of these large datasets, we developed an automated analysis algorithm that utilizes a factor analysis approach coupled with a decision tree. We demonstrate that this algorithm cataloged approximately 10 times more analytes compared to a manual analysis and in a quarter of the analysis time.
Olga Zografou, Maria Gini, Manousos I. Manousakas, Gang Chen, Athina C. Kalogridis, Evangelia Diapouli, Athina Pappa, and Konstantinos Eleftheriadis
Atmos. Meas. Tech., 15, 4675–4692, https://doi.org/10.5194/amt-15-4675-2022, https://doi.org/10.5194/amt-15-4675-2022, 2022
Short summary
Short summary
A yearlong ToF-ACSM dataset was used to characterize ambient aerosols over a suburban Athenian site, and innovative software for source apportionment was implemented in order to distinguish the sources of the total non-refractory species of PM1. A comparison between the methodology of combined organic and inorganic PMF analysis and the conventional organic PMF took place.
Jeramy L. Dedrick, Georges Saliba, Abigail S. Williams, Lynn M. Russell, and Dan Lubin
Atmos. Meas. Tech., 15, 4171–4194, https://doi.org/10.5194/amt-15-4171-2022, https://doi.org/10.5194/amt-15-4171-2022, 2022
Short summary
Short summary
A new method is presented to retrieve the sea spray aerosol size distribution by combining submicron size and nephelometer scattering based on Mie theory. Using available sea spray tracers, we find that this approach serves as a comparable substitute to supermicron size distribution measurements, which are limited in availability at marine sites. Application of this technique can expand sea spray observations and improve the characterization of marine aerosol impacts on clouds and climate.
Ivo Beck, Hélène Angot, Andrea Baccarini, Lubna Dada, Lauriane Quéléver, Tuija Jokinen, Tiia Laurila, Markus Lampimäki, Nicolas Bukowiecki, Matthew Boyer, Xianda Gong, Martin Gysel-Beer, Tuukka Petäjä, Jian Wang, and Julia Schmale
Atmos. Meas. Tech., 15, 4195–4224, https://doi.org/10.5194/amt-15-4195-2022, https://doi.org/10.5194/amt-15-4195-2022, 2022
Short summary
Short summary
We present the pollution detection algorithm (PDA), a new method to identify local primary pollution in remote atmospheric aerosol and trace gas time series. The PDA identifies periods of contaminated data and relies only on the target dataset itself; i.e., it is independent of ancillary data such as meteorological variables. The parameters of all pollution identification steps are adjustable so that the PDA can be tuned to different locations and situations. It is available as open-access code.
Emily B. Franklin, Lindsay D. Yee, Bernard Aumont, Robert J. Weber, Paul Grigas, and Allen H. Goldstein
Atmos. Meas. Tech., 15, 3779–3803, https://doi.org/10.5194/amt-15-3779-2022, https://doi.org/10.5194/amt-15-3779-2022, 2022
Short summary
Short summary
The composition of atmospheric aerosols are extremely complex, containing hundreds of thousands of estimated individual compounds. The majority of these compounds have never been catalogued in widely used databases, making them extremely difficult for atmospheric chemists to identify and analyze. In this work, we present Ch3MS-RF, a machine-learning-based model to enable characterization of complex mixtures and prediction of structure-specific properties of unidentifiable organic compounds.
Jiaoshi Zhang, Yang Wang, Steven Spielman, Susanne Hering, and Jian Wang
Atmos. Meas. Tech., 15, 2579–2590, https://doi.org/10.5194/amt-15-2579-2022, https://doi.org/10.5194/amt-15-2579-2022, 2022
Short summary
Short summary
New nonparametric, regularized methods are developed to invert the growth factor probability density function (GF-PDF) from humidity-controlled fast integrated mobility spectrometer measurements. These algorithms are computationally efficient, require no prior assumptions of the GF-PDF distribution, and reduce the error in inverted GF-PDF. They can be applied to humidified tandem differential mobility analyzer data. Among all algorithms, Twomey’s method retrieves GF-PDF with the smallest error.
Douglas A. Day, Pedro Campuzano-Jost, Benjamin A. Nault, Brett B. Palm, Weiwei Hu, Hongyu Guo, Paul J. Wooldridge, Ronald C. Cohen, Kenneth S. Docherty, J. Alex Huffman, Suzane S. de Sá, Scot T. Martin, and Jose L. Jimenez
Atmos. Meas. Tech., 15, 459–483, https://doi.org/10.5194/amt-15-459-2022, https://doi.org/10.5194/amt-15-459-2022, 2022
Short summary
Short summary
Particle-phase nitrates are an important component of atmospheric aerosols and chemistry. In this paper, we systematically explore the application of aerosol mass spectrometry (AMS) to quantify the organic and inorganic nitrate fractions of aerosols in the atmosphere. While AMS has been used for a decade to quantify nitrates, methods are not standardized. We make recommendations for a more universal approach based on this analysis of a large range of field and laboratory observations.
Markus D. Petters
Atmos. Meas. Tech., 14, 7909–7928, https://doi.org/10.5194/amt-14-7909-2021, https://doi.org/10.5194/amt-14-7909-2021, 2021
Short summary
Short summary
Inverse methods infer physical properties from a measured instrument response. Measurement noise often interferes with the inversion. This work presents a general, domain-independent, accessible, and computationally efficient software implementation of a common class of statistical inversion methods. In addition, a new method to invert data from humidified tandem differential mobility analyzers is introduced. Results show that the approach is suitable for inversion of large-scale datasets.
Pak Lun Fung, Martha Arbayani Zaidan, Ola Surakhi, Sasu Tarkoma, Tuukka Petäjä, and Tareq Hussein
Atmos. Meas. Tech., 14, 5535–5554, https://doi.org/10.5194/amt-14-5535-2021, https://doi.org/10.5194/amt-14-5535-2021, 2021
Short summary
Short summary
Aerosol size distribution measurements rely on a variety of techniques to classify the aerosol size and measure the size distribution. However, due to the instrumental insufficiency and inversion limitations, the raw dataset contains missing gaps or negative values, which hinder further analysis. With a merged particle size distribution in Jordan, this paper suggests a neural network method to estimate number concentrations at a particular size bin by the number concentration at other size bins.
Xiansheng Liu, Hadiatullah Hadiatullah, Xun Zhang, L. Drew Hill, Andrew H. A. White, Jürgen Schnelle-Kreis, Jan Bendl, Gert Jakobi, Brigitte Schloter-Hai, and Ralf Zimmermann
Atmos. Meas. Tech., 14, 5139–5151, https://doi.org/10.5194/amt-14-5139-2021, https://doi.org/10.5194/amt-14-5139-2021, 2021
Short summary
Short summary
A monitoring campaign was conducted in Augsburg to determine a suitable noise reduction algorithm for the MA200 Aethalometer. Results showed that centred moving average (CMA) post-processing effectively removed spurious negative concentrations without major bias and reliably highlighted effects from local sources, effectively increasing spatio-temporal resolution in mobile measurements. Evaluation of each method on peak sample reduction and background correction further supports the reliability.
Jie Qiu, Wangshu Tan, Gang Zhao, Yingli Yu, and Chunsheng Zhao
Atmos. Meas. Tech., 14, 4879–4891, https://doi.org/10.5194/amt-14-4879-2021, https://doi.org/10.5194/amt-14-4879-2021, 2021
Short summary
Short summary
Considering nephelometers' major problems of a nonideal Lambertian light source and angle truncation, a new correction method based on a machine learning model is proposed. Our method has the advantage of obtaining data with high accuracy while achieving self-correction, which means that researchers can get more accurate scattering coefficients without the need for additional observation data. This method provides a more precise estimation of the aerosol’s direct radiative forcing.
Amir Yazdani, Ann M. Dillner, and Satoshi Takahama
Atmos. Meas. Tech., 14, 4805–4827, https://doi.org/10.5194/amt-14-4805-2021, https://doi.org/10.5194/amt-14-4805-2021, 2021
Short summary
Short summary
We propose a spectroscopic method for estimating several mixture-averaged molecular properties (carbon number and molecular weight) in particulate matter relevant for understanding its chemical origins. This estimation is enabled by calibration models built and tested using laboratory standards containing molecules with known structure, and can be applied to filter samples of PM2.5 currently collected in existing air pollution monitoring networks and field campaigns.
Aki Virkkula
Atmos. Meas. Tech., 14, 3707–3719, https://doi.org/10.5194/amt-14-3707-2021, https://doi.org/10.5194/amt-14-3707-2021, 2021
Short summary
Short summary
The Aethalometer model is used widely for estimating the contributions of fossil fuel emissions and biomass burning to black carbon. The calculation is based on measured absorption Ångström exponents, which is ambiguous since it not only depends on the dominant absorber but also on the size and internal structure of the particles, core size, and shell thickness. The uncertainties of the fractions of absorption by eBC from fossil fuel and biomass burning are evaluated with a core–shell Mie model.
Weiqi Xu, Masayuki Takeuchi, Chun Chen, Yanmei Qiu, Conghui Xie, Wanyun Xu, Nan Ma, Douglas R. Worsnop, Nga Lee Ng, and Yele Sun
Atmos. Meas. Tech., 14, 3693–3705, https://doi.org/10.5194/amt-14-3693-2021, https://doi.org/10.5194/amt-14-3693-2021, 2021
Short summary
Short summary
Here we developed a method for estimation of particulate organic nitrates (pON) from the measurements of a high-resolution aerosol mass spectrometer coupled with a thermodenuder based on the volatility differences between inorganic nitrate and pON. The results generally had improvements in reducing negative values due to the influences of a high concentration of inorganic nitrate and a constant ratio of NO+ to NO2+ of organic nitrates (RON).
Melinda K. Schueneman, Benjamin A. Nault, Pedro Campuzano-Jost, Duseong S. Jo, Douglas A. Day, Jason C. Schroder, Brett B. Palm, Alma Hodzic, Jack E. Dibb, and Jose L. Jimenez
Atmos. Meas. Tech., 14, 2237–2260, https://doi.org/10.5194/amt-14-2237-2021, https://doi.org/10.5194/amt-14-2237-2021, 2021
Short summary
Short summary
This work focuses on two important properties of the aerosol, acidity, and sulfate composition, which is important for our understanding of aerosol health and environmental impacts. We explore different methods to understand the composition of the aerosol with measurements from a specific instrument and apply those methods to a large dataset. These measurements are confounded by other factors, making it challenging to predict aerosol sulfate composition; pH estimations, however, show promise.
Weilun Zhao, Wangshu Tan, Gang Zhao, Chuanyang Shen, Yingli Yu, and Chunsheng Zhao
Atmos. Meas. Tech., 14, 1319–1331, https://doi.org/10.5194/amt-14-1319-2021, https://doi.org/10.5194/amt-14-1319-2021, 2021
Chuanyang Shen, Gang Zhao, and Chunsheng Zhao
Atmos. Meas. Tech., 14, 1293–1301, https://doi.org/10.5194/amt-14-1293-2021, https://doi.org/10.5194/amt-14-1293-2021, 2021
Short summary
Short summary
Aerosol hygroscopicity measured by the humidified tandem differential mobility analyzer (HTDMA) is affected by multiply charged particles from two aspects: (1) number contribution and (2) the weakening effect. An algorithm is proposed to do the multi-charge correction and applied to a field measurement. Results show that the difference between corrected and measured size-resolved κ can reach 0.05, highlighting that special attention needs to be paid to the multi-charge effect when using HTDMA.
Francesco Canonaco, Anna Tobler, Gang Chen, Yulia Sosedova, Jay Gates Slowik, Carlo Bozzetti, Kaspar Rudolf Daellenbach, Imad El Haddad, Monica Crippa, Ru-Jin Huang, Markus Furger, Urs Baltensperger, and André Stephan Henry Prévôt
Atmos. Meas. Tech., 14, 923–943, https://doi.org/10.5194/amt-14-923-2021, https://doi.org/10.5194/amt-14-923-2021, 2021
Short summary
Short summary
Long-term ambient aerosol mass spectrometric data were analyzed with a statistical model (PMF) to obtain source contributions and fingerprints. The new aspects of this paper involve time-dependent source fingerprints by a rolling technique and the replacement of the full visual inspection of each run by a user-defined set of criteria to monitor the quality of each of these runs more efficiently. More reliable sources will finally provide better instruments for political mitigation strategies.
Kenji Miki and Shigeto Kawashima
Atmos. Meas. Tech., 14, 685–693, https://doi.org/10.5194/amt-14-685-2021, https://doi.org/10.5194/amt-14-685-2021, 2021
Short summary
Short summary
Laser optics have long been used in pollen counting systems. To clarify the limitations and potential new applications of laser optics for automatic pollen counting and discrimination, we determined the light scattering patterns of various pollen types, tracked temporal changes in these distributions, and introduced a new theory for automatic pollen discrimination.
Yinchao Zhang, Su Chen, Siying Chen, He Chen, and Pan Guo
Atmos. Meas. Tech., 13, 6675–6689, https://doi.org/10.5194/amt-13-6675-2020, https://doi.org/10.5194/amt-13-6675-2020, 2020
Short summary
Short summary
Air pollution has an important impact on human health, climatic patterns, and the ecological environment. The complexity of the nocturnal boundary layer (NBL), combined with its strong physio-chemical effect, induces worse polluted episodes. Therefore, we present a new approach named cluster analysis of gradient method (CA-GM) to overcome the multilayer structure and remove the fluctuation of NBL height using raw data resolution.
Tommy Chan, Runlong Cai, Lauri R. Ahonen, Yiliang Liu, Ying Zhou, Joonas Vanhanen, Lubna Dada, Yan Chao, Yongchun Liu, Lin Wang, Markku Kulmala, and Juha Kangasluoma
Atmos. Meas. Tech., 13, 4885–4898, https://doi.org/10.5194/amt-13-4885-2020, https://doi.org/10.5194/amt-13-4885-2020, 2020
Short summary
Short summary
Using a particle size magnifier (PSM; Airmodus, Finland), we determined the particle size distribution using four inversion methods and compared each method to the others to establish their strengths and weaknesses. Furthermore, we provided a step-by-step procedure on how to invert measured data using the PSM. Finally, we provided recommendations, code and data related to the data inversion. This is an important paper, as no operating procedure exists regarding how to process measured PSM data.
Paolo Laj, Alessandro Bigi, Clémence Rose, Elisabeth Andrews, Cathrine Lund Myhre, Martine Collaud Coen, Yong Lin, Alfred Wiedensohler, Michael Schulz, John A. Ogren, Markus Fiebig, Jonas Gliß, Augustin Mortier, Marco Pandolfi, Tuukka Petäja, Sang-Woo Kim, Wenche Aas, Jean-Philippe Putaud, Olga Mayol-Bracero, Melita Keywood, Lorenzo Labrador, Pasi Aalto, Erik Ahlberg, Lucas Alados Arboledas, Andrés Alastuey, Marcos Andrade, Begoña Artíñano, Stina Ausmeel, Todor Arsov, Eija Asmi, John Backman, Urs Baltensperger, Susanne Bastian, Olaf Bath, Johan Paul Beukes, Benjamin T. Brem, Nicolas Bukowiecki, Sébastien Conil, Cedric Couret, Derek Day, Wan Dayantolis, Anna Degorska, Konstantinos Eleftheriadis, Prodromos Fetfatzis, Olivier Favez, Harald Flentje, Maria I. Gini, Asta Gregorič, Martin Gysel-Beer, A. Gannet Hallar, Jenny Hand, Andras Hoffer, Christoph Hueglin, Rakesh K. Hooda, Antti Hyvärinen, Ivo Kalapov, Nikos Kalivitis, Anne Kasper-Giebl, Jeong Eun Kim, Giorgos Kouvarakis, Irena Kranjc, Radovan Krejci, Markku Kulmala, Casper Labuschagne, Hae-Jung Lee, Heikki Lihavainen, Neng-Huei Lin, Gunter Löschau, Krista Luoma, Angela Marinoni, Sebastiao Martins Dos Santos, Frank Meinhardt, Maik Merkel, Jean-Marc Metzger, Nikolaos Mihalopoulos, Nhat Anh Nguyen, Jakub Ondracek, Noemi Pérez, Maria Rita Perrone, Jean-Eudes Petit, David Picard, Jean-Marc Pichon, Veronique Pont, Natalia Prats, Anthony Prenni, Fabienne Reisen, Salvatore Romano, Karine Sellegri, Sangeeta Sharma, Gerhard Schauer, Patrick Sheridan, James Patrick Sherman, Maik Schütze, Andreas Schwerin, Ralf Sohmer, Mar Sorribas, Martin Steinbacher, Junying Sun, Gloria Titos, Barbara Toczko, Thomas Tuch, Pierre Tulet, Peter Tunved, Ville Vakkari, Fernando Velarde, Patricio Velasquez, Paolo Villani, Sterios Vratolis, Sheng-Hsiang Wang, Kay Weinhold, Rolf Weller, Margarita Yela, Jesus Yus-Diez, Vladimir Zdimal, Paul Zieger, and Nadezda Zikova
Atmos. Meas. Tech., 13, 4353–4392, https://doi.org/10.5194/amt-13-4353-2020, https://doi.org/10.5194/amt-13-4353-2020, 2020
Short summary
Short summary
The paper establishes the fiducial reference of the GAW aerosol network providing the fully characterized value chain to the provision of four climate-relevant aerosol properties from ground-based sites. Data from almost 90 stations worldwide are reported for a reference year, 2017, providing a unique and very robust view of the variability of these variables worldwide. Current gaps in the GAW network are analysed and requirements for the Global Climate Monitoring System are proposed.
Shengqiang Zhu, Lei Li, Shurong Wang, Mei Li, Yaxi Liu, Xiaohui Lu, Hong Chen, Lin Wang, Jianmin Chen, Zhen Zhou, Xin Yang, and Xiaofei Wang
Atmos. Meas. Tech., 13, 4111–4121, https://doi.org/10.5194/amt-13-4111-2020, https://doi.org/10.5194/amt-13-4111-2020, 2020
Short summary
Short summary
Single-particle aerosol mass spectrometry (SPAMS) is widely used to detect chemical compositions and sizes of individual aerosol particles. However, it has a major issue: the mass accuracy of high-resolution SPAMS is relatively low. Here we developed an automatic linear calibration method to greatly improve the mass accuracy of SPAMS spectra so that the elemental compositions of organic peaks, such as Cx, CxHy, CxHyOz and CxHyNO peaks, can be directly identified just based on their m / z values.
William Wandji Nyamsi, Antti Lipponen, Arturo Sanchez-Lorenzo, Martin Wild, and Antti Arola
Atmos. Meas. Tech., 13, 3061–3079, https://doi.org/10.5194/amt-13-3061-2020, https://doi.org/10.5194/amt-13-3061-2020, 2020
Short summary
Short summary
This paper proposes a novel and accurate method for estimating and reconstructing aerosol optical depth from sunshine duration measurements under cloud-free conditions at any place and time since the late 19th century. The method performs very well when compared to AErosol RObotic NETwork measurements and operates an efficient detection of signals from massive volcanic eruptions. Reconstructed long-term aerosol optical depths are in agreement with the dimming/brightening phenomenon.
Sascha Pfeifer, Thomas Müller, Andrew Freedman, and Alfred Wiedensohler
Atmos. Meas. Tech., 13, 2161–2167, https://doi.org/10.5194/amt-13-2161-2020, https://doi.org/10.5194/amt-13-2161-2020, 2020
Short summary
Short summary
The effect of the baseline drift on the resulting extinction values of three CAPS PMex monitors with different wavelengths was analysed for an urban background station. A significant baseline drift was observed, which leads to characteristic measurement artefacts for particle extinction. Two alternative methods for recalculating the baseline are shown. With these methods the extinction artefacts are diminished and the effective scattering of the resulting extinction values is reduced.
Stuart K. Grange, Hanspeter Lötscher, Andrea Fischer, Lukas Emmenegger, and Christoph Hueglin
Atmos. Meas. Tech., 13, 1867–1885, https://doi.org/10.5194/amt-13-1867-2020, https://doi.org/10.5194/amt-13-1867-2020, 2020
Short summary
Short summary
Black carbon (BC) is an important atmospheric pollutant and can be monitored by instruments called aethalometers. A pragmatic data processing technique called the
aethalometer modelcan be used to apportion aethalometer observations into traffic and woodburning components. We present an exploratory data analysis evaluating the aethalometer model and use the outputs for BC trend analysis across Switzerland. The aethalometer model's robustness and utility for such analyses is discussed.
Charlotte Bürki, Matteo Reggente, Ann M. Dillner, Jenny L. Hand, Stephanie L. Shaw, and Satoshi Takahama
Atmos. Meas. Tech., 13, 1517–1538, https://doi.org/10.5194/amt-13-1517-2020, https://doi.org/10.5194/amt-13-1517-2020, 2020
Short summary
Short summary
Infrared spectroscopy is a chemically informative method for particulate matter characterization. However, recent work has demonstrated that predictions depend heavily on the choice of calibration model parameters. We propose a means for managing parameter uncertainties by combining available data from laboratory standards, molecular databases, and collocated ambient measurements to provide useful characterization of atmospheric organic matter on a large scale.
Kaixu Bai, Ke Li, Jianping Guo, Yuanjian Yang, and Ni-Bin Chang
Atmos. Meas. Tech., 13, 1213–1226, https://doi.org/10.5194/amt-13-1213-2020, https://doi.org/10.5194/amt-13-1213-2020, 2020
Short summary
Short summary
A novel gap-filling method called the diurnal-cycle-constrained empirical orthogonal function (DCCEOF) is proposed. Cross validation indicates that this method gives high accuracy in predicting missing values in daily PM2.5 time series by accounting for the local diurnal phases, especially by reconstructing daily extrema that cannot be accurately restored by other approaches. The DCCEOF method can be easily applied to other data sets because of its self-consistent capability.
Tongshu Zheng, Michael H. Bergin, Ronak Sutaria, Sachchida N. Tripathi, Robert Caldow, and David E. Carlson
Atmos. Meas. Tech., 12, 5161–5181, https://doi.org/10.5194/amt-12-5161-2019, https://doi.org/10.5194/amt-12-5161-2019, 2019
Short summary
Short summary
Here we present a simultaneous Gaussian process regression (GPR) and linear regression pipeline to calibrate and monitor dense wireless low-cost particulate matter sensor networks (WLPMSNs) on the fly by using all available reference monitors across an area. Our approach can achieve an overall 30 % prediction error at a 24 h scale, can differentiate malfunctioning nodes, and track drift. Our solution can substantially reduce manual labor for managing WLPMSNs and prolong their lifetimes.
Cited articles
Abdi, H.: Partial least squares regression and projection on latent structure
regression (PLS Regression), WIRES
Comput. Stat., 2, 97–106, https://doi.org/10.1002/wics.51, 2010. a
Afseth, N. K. and Kohler, A.: Extended multiplicative signal correction in
vibrational spectroscopy, a tutorial, Chemometr. Intell.
Lab., 117, 92–99, https://doi.org/10.1016/j.chemolab.2012.03.004, 2012. a
Aggarwal, C. C.: Outlier Analysis, Springer Publishing Company,
Incorporated, New York,
2013. a
Aida, M. and Dupuis, M.: IR and Raman intensities in vibrational spectra from
direct ab initio molecular dynamics: D2O as an illustration, J.
Mol. Struc.-Theochem., 633, 247–255,
https://doi.org/10.1016/S0166-1280(03)00280-X, 2003. a
Aitken, A. C.: IV. – On Least Squares and Linear Combination of
Observations, P. Roy. Soc. Edinb., 55, 42–48,
https://doi.org/10.1017/S0370164600014346, 1936. a
Akaike, H.: A new look at the statistical model identification, IEEE
T. Automat. Contr., 19, 716–723,
https://doi.org/10.1109/TAC.1974.1100705, 1974. a
Akhter, M. S., Chughtai, A. R., and Smith, D. M.: The Structure of Hexane
Soot
I: Spectroscopic Studies, Applied Spectrosc., 39, 143–153,
https://doi.org/10.1366/0003702854249114, 1985. a
Akimoto, H., Bandow, H., Sakamaki, F., Inoue, G., Hoshino, M., and Okuda, M.:
Photooxidation of the propylene-NOx-air system studied by long-path Fourier
transform infrared spectrometry, Environ. Sci. Technol.,
14, 172–179, https://doi.org/10.1021/es60162a007, 1980. a
Allen, D. T. and Palen, E.: Recent advances in aerosol analysis by infrared
spectroscopy, J. Aerosol Sci., 20, 441–455,
https://doi.org/10.1016/0021-8502(89)90078-5, 1989. a
Allen, D. T., Palen, E. J., Haimov, M. I., Hering, S. V., and Young, J. R.:
Fourier-transform Infrared-spectroscopy of Aerosol Collected In A
Low-pressure Impactor (LPI/FTIR) – Method Development and Field Calibration,
Aerosol Sci. Tech., 21, 325–342,
https://doi.org/10.1080/02786829408959719, 1994. a
Andries, E. and Kalivas, J. H.: Interrelationships between generalized Tikhonov
regularization, generalized net analyte signal, and generalized least squares
for desensitizing a multivariate calibration to interferences, J. Chemometr., 27, 126–140, https://doi.org/10.1002/cem.2501, 2013. a
Arlot, S. and Celisse, A.: A survey of cross-validation procedures for model
selection, Statistics Surveys, 4, 40–79, https://doi.org/10.1214/09-SS054,
2010. a
Arnold, A., Nallapati, R., and Cohen, W. W.: A Comparative Study of Methods for
Transductive Transfer Learning, in: Seventh IEEE International Conference on
Data Mining Workshops (ICDMW 2007), 77–82, https://doi.org/10.1109/ICDMW.2007.109,
2007. a
ASTM D7844-12: Standard Test Method for Condition Monitoring of Soot in
In-Service Lubricants by Trend Analysis using Fourier Transform Infrared
(FT-IR) Spectrometry, Standard D7844-12, West Conshohocken, PA,
https://doi.org/10.1520/D7844-12, 2017. a
ASTM E1655-17: Standard Practices for Infrared Multivariate Quantitative
Analysis, Standard E1655-17, West Conshohocken, PA, https://doi.org/10.1520/E1655-17,
2017. a, b
Bao, L., Yuan, X., and Ge, Z.: Co-training partial least squares model for
semi-supervised soft sensor development, Chemometr. Intell.
Lab., 147, 75–85, https://doi.org/10.1016/j.chemolab.2015.08.002, 2015. a
Barone, V., Baiardi, A., Biczysko, M., Bloino, J., Cappelli, C., and Lipparini,
F.: Implementation and validation of a multi-purpose virtual spectrometer for
large systems in complex environments, Phys. Chem. Chem.
Phys., 14, 12404–12422, https://doi.org/10.1039/C2CP41006K, 2012. a
Barone, V., Biczysko, M., and Bloino, J.: Fully anharmonic IR and Raman
spectra of medium-size molecular systems: accuracy and interpretation,
Phys. Chem. Chem. Phys., 16, 1759–1787,
https://doi.org/10.1039/C3CP53413H, 2014. a
Barth, A.: SpecInfo: An integrated spectroscopic information system, J. Chem.
Inf. Comp. Sci., 33, 52–58,
https://doi.org/10.1021/ci00011a009, 1993. a
Baumann, K. and Clerc, J. T.: Computer-assisted IR spectra prediction –
linked similarity searches for structures and spectra, Anal.
Chim. Acta, 348, 327–343, https://doi.org/10.1016/S0003-2670(97)00238-9, 1997. a
Behler, J. and Parrinello, M.: Generalized Neural-Network Representation of
High-Dimensional Potential-Energy Surfaces, Phys. Rev. Lett.,
98, 146401, https://doi.org/10.1103/PhysRevLett.98.146401, 2007. a
Bernasconi, M., Silvestrelli, P. L., and Parrinello, M.: Ab Initio Infrared
Absorption Study of the Hydrogen-Bond Symmetrization in Ice, Phys.
Rev. Lett., 81, 1235–1238, https://doi.org/10.1103/PhysRevLett.81.1235, 1998. a
Bickel, S., Brückner, M., and Scheffer, T.: Discriminative Learning for
Differing Training and Test Distributions, in: Proceedings of the
24th International Conference on Machine Learning,
ICML'07, 81–88, ACM, New York, NY, USA,
https://doi.org/10.1145/1273496.1273507, 2007. a
Binfeng, Y. and Haibo, J.: Near-infrared calibration transfer via support
vector machine and transfer learning, Anal. Methods, 7,
2714–2725, https://doi.org/10.1039/C4AY02462A, 2015. a
Bishop, C. M.: Pattern recognition and machine learning, Springer, New York,
NY, 2009. a
Blando, J. D., Porcja, R. J., Li, T. H., Bowman, D., Lioy, P. J., and Turpin,
B. J.: Secondary formation and the Smoky Mountain organic aerosol: An
examination of aerosol polarity and functional group composition during SEAVS
RID F-6148-2011, Environ. Sci. Technol., 32, 604–613,
https://doi.org/10.1021/es970405s, 1998. a
Bogard, J. S., Johnson, S. A., Kumar, R., and Cunningham, P. T.: Quantitative
analysis of nitrate ion in ambient aerosols by Fourier-transform infrared
spectroscopy, Environ. Sci. Technol., 16, 136–140,
https://doi.org/10.1021/es00097a004, 1982. a
Borggaard, C. and Thodberg, H. H.: Optimal minimal neural interpretation of
spectra, Anal. Chem., 64, 545–551,
https://doi.org/10.1021/ac00029a018, 1992. a
Bornemann, L., Welp, G., Brodowski, S., Rodionov, A., and Amelung, W.: Rapid
assessment of black carbon in soil organic matter using mid-infrared
spectroscopy, Org. Geochem., 39, 1537–1544,
https://doi.org/10.1016/j.orggeochem.2008.07.012, 2008. a, b
Brereton, R. G.: One-class classifiers, J. Chemometr., 25,
225–246, https://doi.org/10.1002/cem.1397, 2011. a
Brereton, R. G.: Hotelling's T squared distribution, its relationship to the F
distribution and its use in multivariate space, J.
Chemometr., 30, 18–21, https://doi.org/10.1002/cem.2763, 2016. a
Brereton, R. G. and Lloyd, G. R.: Re-evaluating the role of the Mahalanobis
distance measure, J. Chemometr., 30, 134–143,
https://doi.org/10.1002/cem.2779, 2016. a, b
Bro, R. and Eldén, L.: PLS works, J. Chemometr., 23, 69–71,
https://doi.org/10.1002/cem.1177, 2009. a
Brown, P. J., Fearn, T., and Vannucci, M.: Bayesian Wavelet Regression on
Curves With Application to a Spectroscopic Calibration Problem, J.
Am. Stat. Assoc., 96, 398–408,
https://doi.org/10.1198/016214501753168118, 2001. a
Brown, R. J. C., Beccaceci, S., Butterfield, D. M., Quincey, P. G., Harris,
P. M., Maggos, T., Panteliadis, P., John, A., Jedynska, A., Kuhlbusch, T.
A. J., Putaud, J.-P., and Karanasiou, A.: Standardisation of a European
measurement method for organic carbon and elemental carbon in ambient air:
results of the field trial campaign and the determination of a measurement
uncertainty and working range, Environmental Science: Processes &
Impacts, 19, 1249–1259, https://doi.org/10.1039/C7EM00261K, 2017. a, b
Burbidge, J. B., Magee, L., and Robb, A. L.: Alternative Transformations to
Handle Extreme Values of the Dependent Variable, J.
Am. Stat. Assoc., 83, 123–127, 1988. a
Cain, J. P., Gassman, P. L., Wang, H., and Laskin, A.: Micro-FTIR study of soot
chemical composition-evidence of aliphatic hydrocarbons on nascent soot
surfaces, Phys. Chem. Chem. Phys., 12, 5206–5218,
https://doi.org/10.1039/b924344e, 2010. a, b
Camci, F., Chinnam, R. B., and Ellis, R. D.: Robust kernel distance
multivariate control chart using support vector principles,
Int. J. Prod. Res., 46, 5075–5095,
https://doi.org/10.1080/00207540500543265, 2008. a
Cappelli, C. and Biczysko, M.: Time-Independent Approach to Vibrational
Spectroscopies, in: Computational Strategies for Spectroscopy, edited by:
Barone, V., 309–360, John Wiley & Sons, Inc.,
https://doi.org/10.1002/9781118008720.ch7, 2011. a
Car, R. and Parrinello, M.: Unified Approach for Molecular Dynamics and
Density-Functional Theory, Phys. Rev. Lett., 55, 2471–2474,
https://doi.org/10.1103/PhysRevLett.55.2471, 1985. a
Caruana, R.: Multitask Learning, Mach. Learn., 28, 41–75,
https://doi.org/10.1023/A:1007379606734, 1997. a
Ceriotti, M., Fang, W., Kusalik, P. G., McKenzie, R. H., Michaelides, A.,
Morales, M. A., and Markland, T. E.: Nuclear Quantum Effects in Water and
Aqueous Systems: Experiment, Theory, and Current Challenges,
Chem. Rev., 116, 7529–7550, https://doi.org/10.1021/acs.chemrev.5b00674,
2016. a
Chapelle, O., Schölkopf, B., and Zien, A.: Semi-Supervised Learning, 1st
Edn., The MIT Press, Cambridge, 2010. a
Chen, Q., Ikemori, F., Higo, H., Asakawa, D., and Mochida, M.: Chemical
Structural Characteristics of HULIS and Other Fractionated Organic Matter in
Urban Aerosols: Results from Mass Spectral and FT-IR Analysis,
Environ. Sci. Technol., 50, 1721–1730,
https://doi.org/10.1021/acs.est.5b05277, 2016. a
Chen, T. and Yang, Y.: Interpretation of non-linear empirical data-based
process models using global sensitivity analysis, Chemometr.
Intell. Lab., 107, 116–123,
https://doi.org/10.1016/j.chemolab.2011.02.006, 2011. a
Chen, T., Morris, J., and Martin, E.: Gaussian process regression for
multivariate spectroscopic calibration, Chemometr. Intell.
Lab., 87, 59–71, 2007. a
Chen, W.-R., Bin, J., Lu, H.-M., Zhang, Z.-M., and Liang, Y.-Z.: Calibration
transfer via an extreme learning machine auto-encoder, Analyst, 141,
1973–1980, https://doi.org/10.1039/C5AN02243F, 2016. a, b
Cheng, C.-H., Lehmann, J., and Engelhard, M. H.: Natural oxidation of black
carbon in soils: Changes in molecular form and surface charge along a
climosequence, Geochim. Cosmochim. Ac., 72, 1598–1610,
https://doi.org/10.1016/j.gca.2008.01.010, 2008. a
Chong, I. G. and Jun, C. H.: Performance of some variable selection methods
when multicollinearity is present, Chemometr. Intell.
Lab., 78, 103–112, https://doi.org/10.1016/j.chemolab.2004.12.011,
2005. a, b, c
Chow, J. C.: Measurement Methods to Determine Compliance with Ambient Air
Quality Standards for Suspended Particles, J. Air Waste
Manage., 45, 320–382, https://doi.org/10.1080/10473289.1995.10467369,
1995. a
Chow, J. C., Watson, J. G., Pritchett, L. C., Pierson, W. R., Frazier, C. A.,
and Purcell, R. G.: The dri thermal/optical reflectance carbon analysis
system: description, evaluation and applications in U.S. Air quality studies,
Atmos. Environ. A-Gen., 27, 1185–1201,
https://doi.org/10.1016/0960-1686(93)90245-T, 1993. a
Chow, J. C., Watson, J. G., Chen, L.-W. A., Arnott, W. P., Moosmüller,
H., and Fung, K.: Equivalence of Elemental Carbon by Thermal/Optical
Reflectance and Transmittance with Different Temperature Protocols,
Environ. Sci. Technol., 38, 4414–4422,
https://doi.org/10.1021/es034936u, 2004. a
Chow, J. C., Watson, J. G., Chen, L.-W. A., Chang, M. O., Robinson, N. F.,
Trimble, D., and Kohl, S.: The IMPROVE_A Temperature Protocol for
Thermal/Optical Carbon Analysis: Maintaining Consistency with a Long-Term
Database, J. Air Waste Manage., 57,
1014–1023, https://doi.org/10.3155/1047-3289.57.9.1014, 2007a. a, b, c, d
Chow, J. C., Yu, J. Z., Watson, J. G., Ho, S. S. H., Bohannan, T. L., Hays,
M. D., and Fung, K. K.: The application of thermal methods for determining
chemical composition of carbonaceous aerosols: A review, J.
Environ. Sci. Heal. A, 42, 1521–1541, https://doi.org/10.1080/10934520701513365,
2007b. a
Chow, J. C., Lowenthal, D. H., Chen, L.-W. A., Wang, X., and Watson, J. G.:
Mass reconstruction methods for PM2.5: a review, Air Qual.
Atmos. Hlth., 8, 243–263, https://doi.org/10.1007/s11869-015-0338-3, 2015. a
Christian, T. J., Kleiss, B., Yokelson, R. J., Holzinger, R., Crutzen, P. J.,
Hao, W. M., Shirai, T., and Blake, D. R.: Comprehensive laboratory
measurements of biomass-burning emissions: 2. First intercomparison of
open-path FTIR, PTR-MS, and GC-MS/FID/ECD, J. Geophys.
Res.-Atmos., 109, D02311, https://doi.org/10.1029/2003JD003874, 2004. a
Christie, B. D. and Munk, M. E.: Structure generation by reduction: a new
strategy for computer-assisted structure elucidation, J.
Chem. Inf. Comp. Sci., 28, 87–93,
https://doi.org/10.1021/ci00058a009, 1988. a
Corrigan, A. L., Russell, L. M., Takahama, S., Äijälä, M., Ehn,
M., Junninen, H., Rinne, J., Petäjä, T., Kulmala, M., Vogel, A. L.,
Hoffmann, T., Ebben, C. J., Geiger, F. M., Chhabra, P., Seinfeld, J. H.,
Worsnop, D. R., Song, W., Auld, J., and Williams, J.: Biogenic and biomass
burning organic aerosol in a boreal forest at Hyytiälä, Finland,
during HUMPPA-COPEC 2010, Atmos. Chem. Phys., 13, 12233–12256,
https://doi.org/10.5194/acp-13-12233-2013, 2013. a
Cortes, C., Mohri, M., and Weston, J.: A General Regression Technique for
Learning Transductions, in: Proceedings of the 22Nd
International Conference on Machine Learning, ICML '05,
153–160, ACM, New York, NY, USA, https://doi.org/10.1145/1102351.1102371, 2005. a
Coury, C. and Dillner, A. M.: A method to quantify organic functional groups
and inorganic compounds in ambient aerosols using attenuated total
reflectance FTIR spectroscopy and multivariate chemometric techniques,
Atmos. Environ., 42, 5923–5932,
https://doi.org/10.1016/j.atmosenv.2008.03.026, 2008. a
Cross, E. S., Williams, L. R., Lewis, D. K., Magoon, G. R., Onasch, T. B.,
Kaminsky, M. L., Worsnop, D. R., and Jayne, J. T.: Use of electrochemical
sensors for measurement of air pollution: correcting interference response
and validating measurements, Atmos. Meas. Tech., 10, 3575–3588,
https://doi.org/10.5194/amt-10-3575-2017, 2017. a
Culp, M. and Michailidis, G.: An Iterative Algorithm for Extending Learners
to a Semi-Supervised Setting, J. Comput. Graph.
Stat., 17, 545–571, https://doi.org/10.1198/106186008X344748, 2008. a, b, c, d
Cunningham, P. T. and Johnson, S. A.: Spectroscopic observation of acid sulfate
in atmospheric particulate samples, Science, 191, 77–79,
https://doi.org/10.1126/science.1856, 1976. a
Cunningham, P. T., Johnson, S. A., and Yang, R. T.: Variations in chemistry of
airborne particulate material with particle size and time,
Environ. Sci. Technol., 8, 131–135,
https://doi.org/10.1021/es60087a002, 1974. a
Cziczo, D. J., Nowak, J. B., Hu, J. H., and Abbatt, J. P. D.: Infrared
spectroscopy of model tropospheric aerosols as a function of relative
humidity: Observation of deliquescence and crystallization, J. Geophys. Res.-Atmos., 102, 18843–18850,
https://doi.org/10.1029/97JD01361, 1997. a
Day, D. A., Liu, S., Russell, L. M., and Ziemann, P. J.: Organonitrate group
concentrations in submicron particles with high nitrate and organic fractions
in coastal southern California, Atmos. Environ., 44,
1970–1979, https://doi.org/10.1016/j.atmosenv.2010.02.045, 2010. a
de Juan, A. and Tauler, R.: Multivariate Curve Resolution (MCR) from 2000:
Progress in Concepts and Applications, Crit. Rev. Anal.
Chem., 36, 163–176, https://doi.org/10.1080/10408340600970005, 2006. a, b
De Maesschalck, R., Jouan-Rimbaud, D., and Massart, D.: The Mahalanobis
distance, Chemometr. Intell. Lab., 50, 1–18,
https://doi.org/10.1016/S0169-7439(99)00047-7, 2000. a
Debus, B., Takahama, S., Weakley, A. T., Seibert, K., and Dillner, A. M.:
Long-Term Strategy for Assessing Carbonaceous Particulate Matter
Concentrations from Multiple Fourier Transform Infrared (FT-IR) Instruments:
Influence of Spectral Dissimilarities on Multivariate Calibration
Performance, Appl. Spectrosc., 0, 0003702818804574,
https://doi.org/10.1177/0003702818804574, 2018. a, b, c
Decesari, S., Facchini, M. C., Mircea, M., Cavalli, F., and Fuzzi, S.:
Solubility properties of surfactants in atmospheric aerosol and cloud/fog
water samples, J. Geophys. Res.-Atmos., 108, 4685,
https://doi.org/10.1029/2003JD003566, 2003. a
deJong, S.: Simpls – An Alternative Approach To Partial Least-squares
Regression, Chemometr. Intell. Lab., 18,
251–263, https://doi.org/10.1016/0169-7439(93)85002-X, 1993. a
Denham, M. C.: Prediction intervals in partial least squares, J.
Chemometr., 11, 39–52, 1997. a
DeNoyer, L. and Dodd, J. G.: Smoothing and Derivatives in Spectroscopy, John
Wiley & Sons, Ltd, https://doi.org/10.1002/0470027320.s4501, 2006. a
Despagne, F. and Luc Massart, D.: Neural networks in multivariate calibration,
Analyst, 123, 157–178, https://doi.org/10.1039/A805562I, 1998. a
Difoggio, R.: Examination of Some Misconceptions about Near-Infrared Analysis,
Appl. Spectrosc., 49, 67–75, https://doi.org/10.1366/0003702953963247,
1995. a
Dillner, A. M.: Change to artifact correction method for OC carbon fractions,
available at:
http://vista.cira.colostate.edu/improve/Data/QA_QC/Advisory/da0032/da0032_OC_artifact.pdf,
last access: 18 February 2018. a
Dodd, J. G. and DeNoyer, L.: Curve-Fitting: Modeling Spectra, John Wiley &
Sons, Ltd, https://doi.org/10.1002/0470027320.s4503, 2006. a
Domingos, P.: A Few Useful Things to Know About Machine Learning,
Commun. ACM, 55, 78–87, https://doi.org/10.1145/2347736.2347755,
2012. a
Douak, F., Melgani, F., Alajlan, N., Pasolli, E., Bazi, Y., and Benoudjit, N.:
Active learning for spectroscopic data regression, J.
Chemometr., 26, 374–383, https://doi.org/10.1002/cem.2443, 2012. a
Doughty, D. C. and Hill, S. C.: Automated aerosol Raman spectrometer for
semi-continuous sampling of atmospheric aerosol, J.
Quant. Spectrosc. Ra., 188, 103–117,
https://doi.org/10.1016/j.jqsrt.2016.06.042, 2017. a
Dubois, J. E., Mathieu, G., Peguet, P., Panaye, A., and Doucet, J. P.:
Simulation of infrared spectra: an infrared spectral simulation program
(SIRS) which uses DARC topological substructures, J. Chem.
Inf. Comp. Sci., 30, 290–302, https://doi.org/10.1021/ci00067a013,
1990. a
Duyckaerts, G.: The infra-red analysis of solid substances. A review,
Analyst, 84, 201–214, https://doi.org/10.1039/AN9598400201, 1959. a
Efron, B. and Tibshirani, R.: Improvements on Cross-Validation: The .632+
Bootstrap Method, J. Am. Stat. Assoc., 92,
548–560, 1997. a
Eilers, P. H. C.: Parametric Time Warping, Anal. Chem., 76,
404–411, https://doi.org/10.1021/ac034800e, 2004. a
Elyashberg, M., Blinov, K., Molodtsov, S., Smurnyy, Y., Williams, A. J., and
Churanova, T.: Computer-assisted methods for molecular structure elucidation:
realizing a spectroscopist's dream, J. Cheminformatics, 1, 3,
https://doi.org/10.1186/1758-2946-1-3, 2009. a
Esbensen, K. H. and Geladi, P.: Principles of Proper Validation: use and abuse
of re-sampling for validation, J. Chemometr., 24, 168–187, https://doi.org/10.1002/cem.1310, 2010. a
Faber, K. and Kowalski, B. R.: Propagation of measurement errors for the
validation of predictions obtained by principal component regression and
partial least squares, J. Chemometr., 11, 181–238,
https://doi.org/10.1002/(SICI)1099-128X(199705)11:3<181::AID-CEM459>3.0.CO;2-7,
1997a. a
Faber, K. and Kowalski, B. R.: Improved prediction error estimates for
multivariate calibration by correcting for the measurement error in the
reference values, Appl. Spectrosc., 51, 660–665,
https://doi.org/10.1366/0003702971941061, 1997b. a
Faber, N. K. M. and Bro, R.: Standard error of prediction for multiway PLS: 1.
Background and a simulation study, Chemometr. Intell.
Lab., 61, 133–149, https://doi.org/10.1016/S0169-7439(01)00204-0, 2002. a, b
Faber, N. M., Song, X. H., and Hopke, P. K.: Sample-specific standard error of
prediction for partial least squares regression, Trac-Trend.
Anal. Chem., 22, 330–334, https://doi.org/10.1016/S0165-9936(03)00503-X,
2003. a, b
Faber, P., Drewnick, F., Bierl, R., and Borrmann, S.: Complementary online
aerosol mass spectrometry and offline FT-IR spectroscopy measurements:
Prospects and challenges for the analysis of anthropogenic aerosol particle
emissions, Atmos. Environ., 166, 92–98,
https://doi.org/10.1016/j.atmosenv.2017.07.014, 2017. a, b
Farrés, M., Platikanov, S., Tsakovski, S., and Tauler, R.: Comparison of
the variable importance in projection (VIP) and of the selectivity ratio (SR)
methods for variable selection and interpretation, J.
Chemometr., 29, 528–536, https://doi.org/10.1002/cem.2736, 2015. a
Fawcett, T.: An introduction to ROC analysis, Pattern Recogn.
Lett., 27, 861–874, https://doi.org/10.1016/j.patrec.2005.10.010, 2006. a
Fearn, T.: Discriminant Analysis, in: Handbook of Vibrational Spectroscopy,
John Wiley & Sons, Ltd, https://doi.org/10.1002/0470027320.s4302, 2006. a
Feudale, R. N., Woody, N. A., Tan, H., Myles, A. J., Brown, S. D., and Ferré,
J.: Transfer of multivariate calibration models: a review, Chemometr.
Intell. Lab., 64, 181–192,
https://doi.org/10.1016/S0169-7439(02)00085-0, 2002. a
Filzmoser, P., Gschwandtner, M., and Todorov, V.: Review of sparse methods in
regression and classification with application to chemometrics, J.
Chemometr., 26, 42–51, https://doi.org/10.1002/cem.1418, 2012. a
Fischer, S., Ueltschi, T., El-Khoury, P., Mifflin, A., Hess, W., Wang, H.,
Cramer, C., and Govind, N.: Infrared and Raman Spectroscopy from Ab Initio
Molecular Dynamics and Static Normal Mode Analysis: The C-H Region of DMSO as
a Case Study, J. Phys. Chem. B, 120,
1429–1436, https://doi.org/10.1021/acs.jpcb.5b03323, 2016. a
Flores, E., Viallon, J., Moussay, P., and Wielgosz, R. I.: Accurate Fourier
Transform Infrared (FT-IR) Spectroscopy Measurements of Nitrogen
Dioxide (NO2) and Nitric Acid (HNO3) Calibrated with Synthetic
Spectra, Appl. Spectrosc., 67, 1171–1178,
https://doi.org/10.1366/13-07030, 2013. a
Flores, E., Viallon, J., Moussay, P., Griffith, D. W. T., and Wielgosz, R. I.:
Calibration Strategies for FT-IR and Other Isotope Ratio Infrared
Spectrometer Instruments for Accurate δ13C and
δ18O
Measurements of CO2 in Air, Anal. Chem., 89,
3648–3655, https://doi.org/10.1021/acs.analchem.6b05063, 2017. a
Foster, R. D. and Walker, R. F.: Quantitative determination of crystalline
silica in respirable-size dust samples by infrared spectrophotometry,
Analyst, 109, 1117–1127, https://doi.org/10.1039/AN9840901117, 1984. a
Friedel, R. and Carlson, G.: Difficult carbonaceous materials and their
infra-red and Raman spectra. Reassignments for coal spectra, Fuel, 51,
194–198, https://doi.org/10.1016/0016-2361(72)90079-8, 1972. a
Friedel, R. A. and Carlson, G. L.: Infrared spectra of ground graphite,
J. Phys. Chem., 75, 1149–1151,
https://doi.org/10.1021/j100678a021, 1971. a
Fu, G.-H., Xu, Q.-S., Li, H.-D., Cao, D.-S., and Liang, Y.-Z.: Elastic Net
Grouping Variable Selection Combined with Partial Least Squares Regression
(EN-PLSR) for the Analysis of Strongly Multi-collinear Spectroscopic Data,
Appl. Spectrosc., 65, 402–408, https://doi.org/10.1366/10-06069, 2011. a
Gaigeot, M.-P.: Alanine Polypeptide Structural Fingerprints at Room
Temperature: What Can Be Gained from Non-Harmonic Car–Parrinello Molecular
Dynamics Simulations, J. Phys. Chem. A, 112,
13507–13517, https://doi.org/10.1021/jp807550j, 2008. a
Gaigeot, M.-P. and Sprik, M.: Ab Initio Molecular Dynamics Computation of the
Infrared Spectrum of Aqueous Uracil, J. Phys. Chem.
B, 107, 10344–10358, https://doi.org/10.1021/jp034788u, 2003. a
Gaigeot, M.-P., Martinez, M., and Vuilleumier, R.: Infrared spectroscopy in the
gas and liquid phase from first principle molecular dynamics simulations:
application to small peptides, Mol. Phys., 105, 2857–2878,
https://doi.org/10.1080/00268970701724974, 2007. a
Galle, B., Klemedtsson, L., and Griffith, D. W. T.: Application of a Fourier
transform IR system for measurements of N2O fluxes using
micrometeorological methods, an ultralarge chamber system, and conventional
field chambers, J. Geophys. Res.-Atmos., 99,
16575–16583, https://doi.org/10.1029/94JD00264, 1994. a
Gastegger, M., Behler, J., and Marquetand, P.: Machine learning molecular
dynamics for the simulation of infrared spectra, Chem. Sci., 8,
6924–6935, https://doi.org/10.1039/C7SC02267K, 2017. a, b
Gasteiger, J.: The central role of chemoinformatics, Chemometr.
Intell. Lab., 82, 200–209,
https://doi.org/10.1016/j.chemolab.2005.06.022, 2006. a
Ge, Z. and Song, Z.: Nonlinear Probabilistic Monitoring Based on the Gaussian
Process Latent Variable Model, Ind. Eng. Chem.
Res., 49, 4792–4799, https://doi.org/10.1021/ie9019402, 2010. a
Geisser, S.: The Predictive Sample Reuse Method with Applications, J. Am.
Stat. Assoc., 70, 320–328,
https://doi.org/10.1080/01621459.1975.10479865, 1975. a
Geladi, P. and Kowalski, B. R.: Partial least-squares regression: a tutorial,
Anal. Chim. Acta, 185, 1–17,
https://doi.org/10.1016/0003-2670(86)80028-9, 1986. a, b
Gibson, E. R., Hudson, P. K., and Grassian, V. H.: Physicochemical properties
of nitrate aerosols: Implications for the atmosphere, J.
Phys. Chem. A, 110, 11785–11799, https://doi.org/10.1021/jp063821k, 2006. a
Gilardoni, S., Russell, L. M., Sorooshian, A., Flagan, R. C., Seinfeld, J. H.,
Bates, T. S., Quinn, P. K., Allan, J. D., Williams, B., Goldstein, A. H.,
Onasch, T. B., and Worsnop, D. R.: Regional variation of organic functional
groups in aerosol particles on four US east coast platforms during the
International Consortium for Atmospheric Research on Transport and
Transformation 2004 campaign, J. Geophys.
Res.-Atmos., 112, D10S27, https://doi.org/10.1029/2006JD007737, 2007. a, b
Gowen, A. A., Downey, G., Esquerre, C., and O'Donnell, C. P.: Preventing
over-fitting in PLS calibration models of near-infrared (NIR)
spectroscopy data using regression coefficients, J.
Chemometr., 25, 375–381, https://doi.org/10.1002/cem.1349, 2011. a, b
Gribov, L. A. and Elyashberg, M. E.: Symbolic logic methods for spectrochemical
investigations, J. Mol. Struct., 5, 179–198,
https://doi.org/10.1016/0022-2860(70)80002-3, 1970. a
Griffith, D. W. T.: Synthetic Calibration and Quantitative Analysis of
Gas-Phase FT-IR Spectra, Appl. Spectrosc., 50, 59–70,
https://doi.org/10.1366/0003702963906627, 1996. a
Griffith, D. W. T. and Galle, B.: Flux measurements of NH3,
N2O and
CO2 using dual beam FTIR spectroscopy and the flux–gradient technique,
Atmos. Environ., 34, 1087–1098,
https://doi.org/10.1016/S1352-2310(99)00368-4, 2000. a
Griffith, D. W. T. and Jamie, I. M.: Fourier Transform Infrared Spectrometry
in Atmospheric and Trace Gas Analysis, in: Encyclopedia of Analytical
Chemistry, John Wiley & Sons, Ltd,
https://doi.org/10.1002/9780470027318.a0710, 2006. a
Griffith, D. W. T., Leuning, R., Denmead, O. T., and Jamie, I. M.: Air–land
exchanges of CO2, CH4 and N2O measured by FTIR spectrometry and
micrometeorological techniques, Atmos. Environ., 36,
1833–1842, https://doi.org/10.1016/S1352-2310(02)00139-5, 2002. a
Griffiths, P. R.: Introduction to Vibrational Spectroscopy, John Wiley & Sons,
Ltd, https://doi.org/10.1002/0470027320.s0102, 2006. a
Gujral, P., Amrhein, M., Ergon, R., Wise, B. M., and Bonvin, D.: On
multivariate calibration with unlabeled data, J. Chemometr.,
25, 456–465, https://doi.org/10.1002/cem.1389, 2011. a, b
Gussoni, M., Castiglioni, C., and Zerbi, G.: Vibrational Intensities:
Interpretation and Use for Diagnostic Purposes, in: Handbook of Vibrational
Spectroscopy, John Wiley & Sons, Ltd, https://doi.org/10.1002/0470027320.s4205, 2006. a
Halevy, A., Norvig, P., and Pereira, F.: The Unreasonable Effectiveness of
Data, IEEE Intell. Syst., 24, 8–12, https://doi.org/10.1109/MIS.2009.36,
2009. a
Hallquist, M., Wenger, J. C., Baltensperger, U., Rudich, Y., Simpson, D.,
Claeys, M., Dommen, J., Donahue, N. M., George, C., Goldstein, A. H.,
Hamilton, J. F., Herrmann, H., Hoffmann, T., Iinuma, Y., Jang, M., Jenkin, M.
E., Jimenez, J. L., Kiendler-Scharr, A., Maenhaut, W., McFiggans, G., Mentel,
Th. F., Monod, A., Prévôt, A. S. H., Seinfeld, J. H., Surratt, J. D.,
Szmigielski, R., and Wildt, J.: The formation, properties and impact of
secondary organic aerosol: current and emerging issues, Atmos. Chem. Phys.,
9, 5155–5236, https://doi.org/10.5194/acp-9-5155-2009, 2009. a
Hammer, S., Griffith, D. W. T., Konrad, G., Vardag, S., Caldow, C., and
Levin, I.: Assessment of a multi-species in situ FTIR for precise atmospheric
greenhouse gas observations, Atmos. Meas. Tech., 6, 1153–1170,
https://doi.org/10.5194/amt-6-1153-2013, 2013. a
Hanst, P. L., Wong, N. W., and Bragin, J.: A long-path infra-red study of Los
Angeles smog, Atmos. Environ., 16, 969–981,
https://doi.org/10.1016/0004-6981(82)90183-4, 1982. a
Harrington, P. d. B., Urbas, A., and Wan, C.: Evaluation of Neural Network
Models with Generalized Sensitivity Analysis, Anal. Chem.,
72, 5004–5013, https://doi.org/10.1021/ac0004963, 2000. a
Hase, F., Frey, M., Blumenstock, T., Groß, J., Kiel, M., Kohlhepp, R.,
Mengistu Tsidu, G., Schäfer, K., Sha, M. K., and Orphal, J.: Application
of portable FTIR spectrometers for detecting greenhouse gas emissions of the
major city Berlin, Atmos. Meas. Tech., 8, 3059–3068,
https://doi.org/10.5194/amt-8-3059-2015, 2015. a
Hasegawa, T.: Principal Component Regression and Partial Least Squares
Modeling, in: Handbook of Vibrational Spectroscopy, John Wiley & Sons, Ltd,
https://doi.org/10.1002/0470027320.s4604, 2006. a, b
Hawkins, L. N. and Russell, L. M.: Oxidation of ketone groups in transported
biomass burning aerosol from the 2008 Northern California Lightning Series
fires, Atmos. Environ., 44, 4142–4154,
https://doi.org/10.1016/j.atmosenv.2010.07.036, 2010. a
Hayes, M. H.: Statistical Digital Signal Processing and Modeling, 1st Edn.,
John Wiley
& Sons, Inc., New York, NY, USA, 1996. a
Hazama, K. and Kano, M.: Covariance-based locally weighted partial least
squares for high-performance adaptive modeling, Chemometr. Intell. Lab., 146, 55–62,
https://doi.org/10.1016/j.chemolab.2015.05.007, 2015. a
Helland, K., Berntsen, H. E., Borgen, O. S., and Martens, H.: Recursive
algorithm for partial least squares regression, Chemometr. Intell. Lab., 14, 129–137,
https://doi.org/10.1016/0169-7439(92)80098-O, 1992. a
Hemmer, M. C.: Expert Systems in Chemistry Research, Taylor & Francis, Inc.,
Bristol, PA, USA, 2007. a
Henry, R. C., Lewis, C. W., Hopke, P. K., and Williamson, H. J.: Review of
receptor model fundamentals, Atmos. Environ., 18,
1507–1515, https://doi.org/10.1016/0004-6981(84)90375-5, 1984. a
Hoerl, A. E. and Kennard, R. W.: Ridge Regression – Applications To
Nonorthogonal Problems, Technometrics, 12, 69–82,
https://doi.org/10.2307/1267352, 1970. a
Holes, A., Eusebi, A., Grosjean, D., and Allen, D. T.: FTIR analysis of aerosol
formed in the photooxidation of 1,3,5-trimethylbenzene, Aerosol Sci.
Tech., 26, 516–526, https://doi.org/10.1080/02786829708965450, 1997. a
Hopke, P. K.: Target transformation factor analysis, Chemometr. Intell. Lab.,
6, 7–19,
https://doi.org/10.1016/0169-7439(89)80061-9, 1989. a
Höskuldsson, A.: Prediction Methods in Science and Technology: Basic Theory,
Vol. 1, Thor Publishing, Copenhagen, 1996. a
Höskuldsson, A.: Variable and subset selection in PLS regression,
Chemometr. Intell. Lab., 55, 23–38,
https://doi.org/10.1016/S0169-7439(00)00113-1, 2001. a
Hotelling, H.: The Generalization of Student's Ratio, Ann.
Math. Stat., 2, 360–378, https://doi.org/10.1214/aoms/1177732979, 1931. a
Huang, J., Smola, A. J., Gretton, A., Borgwardt, K. M., and Scholkopf, B.:
Correcting Sample Selection Bias by Unlabeled Data, in: Proceedings of the
19th International Conference on Neural Information
Processing Systems, NIPS'06, 601–608, MIT Press, Cambridge, MA, USA,
2006. a
Huber, P. J. and Ronchetti, E. M.: Robust Statistics, Wiley Series in
Probability and Statistics, 2nd
Edn., John Wiley & Sons, Ltd., https://doi.org/10.1002/9780470434697, 2009. a
Hung, H.-M., Chen, Y.-Q., and Martin, S. T.: Reactive Aging of Films of
Secondary Organic Material Studied by Infrared Spectroscopy,
J. Phys. Chem. A, 117, 108–116, https://doi.org/10.1021/jp309470z,
2013. a
Hurst, D. F., Griffith, D. W. T., and Cook, G. D.: Trace gas emissions from
biomass burning in tropical Australian savannas, J.
Geophys. Res.-Atmos., 99, 16441–16456,
https://doi.org/10.1029/94JD00670, 1994. a
Isaksson, T. and Aastveit, A. H.: Classification Methods, in: Handbook of
Vibrational Spectroscopy, John Wiley & Sons, Ltd,
https://doi.org/10.1002/0470027320.s4304, 2006. a
Ishiyama, T. and Morita, A.: Molecular Dynamics Simulation of Sum Frequency
Generation Spectra of Aqueous Sulfuric Acid Solution, J.
Phys. Chem. C, 115, 13704–13716, https://doi.org/10.1021/jp200269k, 2011. a
Ivanov, S. D., Witt, A., and Marx, D.: Theoretical spectroscopy using molecular
dynamics: theory and application to CH5+ and its isotopologues,
Phys. Chem. Chem. Phys., 15, 10270–10299,
https://doi.org/10.1039/C3CP44523B, 2013. a
Jackson, J. E.: A User's Guide to Principal Components, Wiley Series in
Probability and Statistics, John Wiley & Sons, https://doi.org/10.1002/0471725331,
2004. a
Janson, L., Fithian, W., and Hastie, T. J.: Effective degrees of freedom: a
flawed metaphor, Biometrika, 102, 479–485,
https://doi.org/10.1093/biomet/asv019, 2015. a
Johnson, N. L.: Systems of Frequency Curves Generated By Methods of
Translation, Biometrika, 36, 149–176, 1949. a
Jouan-Rimbaud, D., Bouveresse, E., Massart, D. L., and de Noord, O. E.:
Detection of prediction outliers and inliers in multivariate calibration,
Anal. Chim. Acta, 388, 283–301,
https://doi.org/10.1016/S0003-2670(98)00626-6, 1999. a, b
Kalivas, J. H.: Overview of two-norm (L2) and one-norm (L1) Tikhonov
regularization variants for full wavelength or sparse spectral multivariate
calibration models or maintenance, J. Chemometr., 26,
218–230, https://doi.org/10.1002/cem.2429, 2012. a, b
Kariya, T. and Kurata, H.: Generalized Least Squares, Wiley Ser. Prob.
Stat., Chichester,
2004. a
Kelley, A. M.: Condensed-Phase Molecular Spectroscopy and Photophysics,
John Wiley & Sons, Hoboken, 2013. a
Kennard, R. W. and Stone, L. A.: Computer Aided Design of Experiments,
Technometrics, 11, 137–148, https://doi.org/10.1080/00401706.1969.10490666,
1969. a
Kidd, C., Perraud, V., and Finlayson-Pitts, B. J.: New insights into secondary
organic aerosol from the ozonolysis of α-pinene from combined infrared
spectroscopy and mass spectrometry measurements, Phys. Chem.
Chem. Phys., 16, 22706–22716, https://doi.org/10.1039/C4CP03405H, 2014. a
Kim, J., Shusterman, A. A., Lieschke, K. J., Newman, C., and Cohen, R. C.:
The BErkeley Atmospheric CO2 Observation Network: field calibration
and evaluation of low-cost air quality sensors, Atmos. Meas. Tech., 11,
1937–1946, https://doi.org/10.5194/amt-11-1937-2018, 2018. a
Kim, S., Kano, M., Nakagawa, H., and Hasebe, S.: Estimation of active
pharmaceutical ingredients content using locally weighted partial least
squares and statistical wavelength selection, Int. J.
Pharmaceut., 421, 269–274, https://doi.org/10.1016/j.ijpharm.2011.10.007, 2011. a
Kirchgessner, D. A., Piccot, S. D., and Chadha, A.: Estimation of methane
emissions from a surface coal mine using open-path FTIR spectroscopy and
modeling techniques, Chemosphere, 26, 23–44,
https://doi.org/10.1016/0045-6535(93)90410-7, 1993. a
Kirchner, U., Scheer, V., and Vogt, R.: FTIR Spectroscopic Investigation of the
Mechanism and Kinetics of the Heterogeneous Reactions of NO2 and HNO3 with
Soot, J. Phys. Chem. A, 104, 8908–8915,
https://doi.org/10.1021/jp0005322, 2000. a
Koop, T., Bookhold, J., Shiraiwa, M., and Poeschl, U.: Glass transition and
phase state of organic compounds: dependency on molecular properties and
implications for secondary organic aerosols in the atmosphere, Phys.
Chem. Chem. Phys., 13, 19238–19255, https://doi.org/10.1039/c1cp22617g,
2011. a
Kortüm, G.: Reflectance Spectroscopy: Principles, Methods, Applications,
Springer, Berlin-Heidelberg, 1969. a
Kourti, T. and MacGregor, J. F.: Process analysis, monitoring and diagnosis,
using multivariate projection methods, Chemometr. Intell. Lab., 28, 3–21, https://doi.org/10.1016/0169-7439(95)80036-9, 1995. a, b
Krämer, N. and Sugiyama, M.: The Degrees of Freedom of Partial Least Squares
Regression, J. Am. Stat. Assoc., 106,
697–705, https://doi.org/10.1198/jasa.2011.tm10107, 2011. a, b
Krost, K. J. and McClenny, W. A.: Fourier Transform Infrared Spectrometric
Analysis for Particle-Associated Ammonium Sulfate, Appl.
Spectrosc., 46, 1737–1740, https://doi.org/10.1366/0003702924926763, 1992. a
Krost, K. J. and McClenny, W. A.: FT-IR Transmission Spectroscopy for
Quantitation of Ammonium Bisulfate in Fine-Particulate Matter Collected on
Teflon Filters, Appl. Spectrosc., 48, 702–705,
https://doi.org/10.1366/000370294774368983, 1994. a
Kubicki, J. D. and Mueller, K. T.: Computational Spectroscopy in Environmental
Chemistry, in: Computational Spectroscopy, 323–351, Wiley-VCH Verlag
GmbH & Co. KGaA, https://doi.org/10.1002/9783527633272.ch11, 2010. a
Kuhn, M. and Johnson, K.: Applied Predictive Modeling, SpringerLink :
Bücher, Springer New York, https://doi.org/10.1007/978-1-4614-6849-3, 2013. a, b, c
Kulkarni, A. D., Rai, D., Bartolotti, L. J., and Pathak, R. K.: Microsolvation
of methyl hydrogen peroxide: Ab initio quantum chemical approach,
J. Chem. Phys., 131, 054310, https://doi.org/10.1063/1.3179753, 2009. a
Kulkarni, P., Baron, P. A., and Willeke, K.: Aerosol Measurement:
Principles, Techniques, and Applications, John Wiley & Sons, Hoboken,
2011. a
Kuzmiakova, A., Dillner, A. M., and Takahama, S.: An automated baseline
correction protocol for infrared spectra of atmospheric aerosols collected on
polytetrafluoroethylene (Teflon) filters, Atmos. Meas. Tech., 9, 2615–2631,
https://doi.org/10.5194/amt-9-2615-2016, 2016. a
Kvalheim, O. M.: Interpretation of partial least squares regression models by
means of target projection and selectivity ratio plots, J.
Chemometr., 24, 496–504, https://doi.org/10.1002/cem.1289, 2010. a
Lack, D. A., Moosmueller, H., McMeeking, G. R., Chakrabarty, R. K., and
Baumgardner, D.: Characterizing elemental, equivalent black, and refractory
black carbon aerosol particles: a review of techniques, their limitations and
uncertainties, Anal. Bioanal. Chem., 406, 99–122,
https://doi.org/10.1007/s00216-013-7402-3, 2014. a
Laskin, J., Laskin, A., and Nizkorodov, S. A.: Mass Spectrometry Analysis in
Atmospheric Chemistry, Anal. Chem., 90, 166–189,
https://doi.org/10.1021/acs.analchem.7b04249, 2018. a
Latecki, L. J., Lazarevic, A., and Pokrajac, D.: Outlier Detection with
Kernel Density Functions, in: Machine Learning and Data Mining in
Pattern Recognition, 61–75, Springer, Berlin, Heidelberg,
https://doi.org/10.1007/978-3-540-73499-4_6, 2007. a
Leardi, R. and Nørgaard, L.: Sequential application of backward interval
partial least squares and genetic algorithms for the selection of relevant
spectral regions, J. Chemometr., 18, 486–497,
https://doi.org/10.1002/cem.893, 2004. a, b
Lee, E., Chan, C. K., and Paatero, P.: Application of positive matrix
factorization in source apportionment of particulate pollutants in Hong Kong,
Atmos. Environ., 33, 3201–3212,
https://doi.org/10.1016/S1352-2310(99)00113-2, 1999. a
Li, B., Morris, J., and Martin, E. B.: Model selection for partial least
squares regression, Chemometr. Intell. Lab.,
64, 79–89, 2002. a
Li, Y.-J., Liu, P.-F., Bergoend, C., Bateman, A. P., and Martin, S. T.:
Rebounding hygroscopic inorganic aerosol particles: Liquids, gels, and
hydrates, Aerosol Sci. Tech., 51, 388–396,
https://doi.org/10.1080/02786826.2016.1263384, 2017. a
Lin, Z., Pei, Y., Chen, Z., Shi, X., Qiao, Y., Shi, X., and Qiao, Y.: Improving
the creditability and reproducibility of variables selected from near
infrared spectra, in: 2013 Ninth International Conference on Natural
Computation (ICNC), 1370–1376, https://doi.org/10.1109/ICNC.2013.6818193,
2013. a
Lindgren, F., Geladi, P., and Wold, S.: The Kernel Algorithm For PLS,
J. Chemometr., 7, 45–59, https://doi.org/10.1002/cem.1180070104,
1993. a
Liu, F. T., Ting, K. M., and Zhou, Z. H.: Isolation Forest, in: 2008 Eighth
IEEE International Conference on Data Mining, 413–422,
https://doi.org/10.1109/ICDM.2008.17, 2008. a
Liu, J.: Developing a soft sensor based on sparse partial least squares with
variable selection, J. Process Contr., 24, 1046–1056,
https://doi.org/10.1016/j.jprocont.2014.05.014, 2014. a
Long, J. R., Gregoriou, V. G., and Gemperline, P. J.: Spectroscopic calibration
and quantitation using artificial neural networks, Anal.
Chem., 62, 1791–1797, https://doi.org/10.1021/ac00216a013, 1990. a
Luinge, H. J., van der Maas, J. H., and Visser, T.: Partial least squares
regression as a multivariate tool for the interpretation of infrared spectra,
Chemometr. Intell. Lab., 28, 129–138,
https://doi.org/10.1016/0169-7439(95)80045-B, 1995. a
Ma, Y., Gong, W., and Mao, F.: Transfer learning used to analyze the dynamic
evolution of the dust aerosol, J. Quant. Spectrosc.
Ra., 153, 119–130, https://doi.org/10.1016/j.jqsrt.2014.09.025, 2015. a
MacDonald, S. A. and Bureau, B.: Fourier Transform Infrared Attenuated Total
Reflection and Transmission Spectra Studied by Dispersion Analysis,
Appl. Spectrosc., 57, 282–287, 2003. a
MacGregor, J. F. and Kourti, T.: Statistical process control of multivariate
processes, Control Eng. Pract., 3, 403–414,
https://doi.org/10.1016/0967-0661(95)00014-L, 1995. a
Mader, P. P., MacPhee, R. D., Lofberg, R. T., and Larson, G. P.: Composition of
Organic Portion of Atmospheric Aerosols in the Los Angeles Area,
Ind. Eng. Chem., 44, 1352–1355,
https://doi.org/10.1021/ie50510a047, 1952. a
Mahalanobis, P.: On the Generalised Distance in Statistics,
Proceedings National Institute of Science, India, 2, 49–55, 1936. a
Malli, B., Birlutiu, A., and Natschläger, T.: Standard-free calibration
transfer – An evaluation of different techniques, Chemometr. Intell. Lab., 161, 49–60,
https://doi.org/10.1016/j.chemolab.2016.12.008, 2017. a
Malm, W. C. and Hand, J. L.: An examination of the physical and optical
properties of aerosols collected in the IMPROVE program, Atmos.
Environ., 41, 3407–3427, https://doi.org/10.1016/j.atmosenv.2006.12.012, 2007. a, b
Malm, W. C., Schichtel, B. A., and Pitchford, M. L.: Uncertainties in
PM2.5
Gravimetric and Speciation Measurements and What We Can Learn from Them,
J. Air Waste Manage., 61, 1131–1149,
https://doi.org/10.1080/10473289.2011.603998, 2011. a
Marcou, G., Delouis, G., Mokshyna, O., Horvath, D., Lachiche, N., and Varnek,
A.: Transductive Ridge Regression in Structure-Activity Modeling,
Mol. Inform., 36, 1700112, https://doi.org/10.1002/minf.201700112,
2017. a
Maria, S. F., Russell, L. M., Turpin, B. J., and Porcja, R. J.: FTIR
measurements of functional groups and organic mass in aerosol samples over
the Caribbean, Atmos. Environ., 36, 5185–5196,
https://doi.org/10.1016/S1352-2310(02)00654-4, 2002. a
Maria, S. F., Russell, L. M., Turpin, B. J., Porcja, R. J., Campos, T. L.,
Weber, R. J., and Huebert, B. J.: Source signatures of carbon monoxide and
organic functional groups in Asian Pacific Regional Aerosol
Characterization Experiment (ACE-Asia) submicron aerosol types,
J. Geophys. Res.-Atmos., 108, 8637,
https://doi.org/10.1029/2003JD003703, 2003. a, b, c, d
Marsalek, O. and Markland, T. E.: Quantum Dynamics and Spectroscopy of Ab
Initio Liquid Water: The Interplay of Nuclear and Electronic Quantum
Effects, J. Phys. Chem. Lett., 8, 1545–1551,
https://doi.org/10.1021/acs.jpclett.7b00391, 2017. a
Marx, D.: Ab Initio Molecular Dynamics: Basic Theory and Advanced Methods,
Cambridge University Press, 1st Edn., Cambridge, UK, New York, 2009. a
McClenny, W. A., Childers, J. W., Rōhl, R., and Palmer, R. A.: FTIR
transmission spectrometry for the nondestructive determination of ammonium
and sulfate in ambient aerosols collected on teflon filters,
Atmos. Environ., 19, 1891–1898,
https://doi.org/10.1016/0004-6981(85)90014-9, 1985. a, b, c
Medders, G. R. and Paesani, F.: Infrared and Raman Spectroscopy of Liquid Water
through “First-Principles” Many-Body Molecular Dynamics, J.
Chem. Theory Comput., 11, 1145–1154, https://doi.org/10.1021/ct501131j,
2015. a
Mehmood, T., Liland, K. H., Snipen, L., and Saebo, S.: A review of variable
selection methods in Partial Least Squares Regression, Chemometr. Intell. Lab., 118, 62–69,
https://doi.org/10.1016/j.chemolab.2012.07.010, 2012. a, b
Meier, A. and Notholt, J.: Determination of the isotopic abundances of heavy
O3 as observed in Arctic ground-based FTIR-spectra, Geophys.
Res. Lett., 23, 551–554, https://doi.org/10.1029/96GL00374, 1996. a
Mevik, B. and Wehrens, R.: The pls package: Principal component and partial
least squares regression in R, J. Stat. Softw., 18,
1–24, https://doi.org/10.18637/jss.v018.i02, 2007. a
Molinaro, A. M., Simon, R., and Pfeiffer, R. M.: Prediction error estimation: a
comparison of resampling methods, Bioinformatics, 21, 3301–3307,
https://doi.org/10.1093/bioinformatics/bti499, 2005. a
Montgomery, D.: Statistical Quality Control,
7th Ed., John Wiley & Sons, Hoboken,
2013. a
Mosteller, F. and Tukey, J.: Data Analysis, including Statistics, in: Revised
Handbook of Social Psychology, edited by: Lindzey, G. and Aronson, E., Vol. 2,
80–203, Addison Wesley, 1968. a
Munk, M. E.: Computer-Based Structure Determination: Then
and Now, J. Chem. Inf. Comp. Sci., 38,
997–1009, https://doi.org/10.1021/ci980083r, 1998. a
Mylonas, D. T., Allen, D. T., Ehrman, S. H., and Pratsinis, S. E.: The Sources
and Size Distributions of Organonitrates In Los Angeles Aerosol,
Atmos. Environ. A-Gen., 25, 2855–2861,
https://doi.org/10.1016/0960-1686(91)90211-O, 1991. a
Nelder, J. A. and Wedderburn, R. W. M.: Generalized Linear Models,
J. R. Stat. Soc. A Stat., 135,
370–384, 1972. a
Nomikos, P. and MacGregor, J. F.: Multivariate SPC Charts for Monitoring
Batch Processes, Technometrics, 37, 41–59,
https://doi.org/10.1080/00401706.1995.10485888, 1995. a
Nordlund, T. M.: Quantitative Understanding of Biosystems: An Introduction to
Biophysics, CRC Press, New York, 2011. a
Novakov, T.: The role of soot and primary oxidants in atmospheric chemistry,
Sci. Total Environ., 36, 1–10,
https://doi.org/10.1016/0048-9697(84)90241-9, 1984. a
Novic, M. and Zupan, J.: Investigation of Infrared Spectra-Structure
Correlation Using Kohonen and Counterpropagation Neural Network,
J. Chem. Inf. Comp. Sci., 35, 454–466,
https://doi.org/10.1021/ci00025a013, 1995. a
Nozière, B., Kalberer, M., Claeys, M., Allan, J., D'Anna, B., Decesari, S.,
Finessi, E., Glasius, M., Grgić, I., Hamilton, J. F., Hoffmann, T., Iinuma,
Y., Jaoui, M., Kahnt, A., Kampf, C. J., Kourtchev, I., Maenhaut, W., Marsden,
N., Saarikoski, S., Schnelle-Kreis, J., Surratt, J. D., Szidat, S.,
Szmigielski, R., and Wisthaler, A.: The Molecular Identification of Organic
Compounds in the Atmosphere: State of the Art and Challenges, Chem.
Rev., 115, 3919–3983, https://doi.org/10.1021/cr5003485, 2015. a
Ofner, J.: Formation of secondary organic aerosol and its processing by
atmospheric halogen species – a spectroscopic study, PhD thesis,
University of Bayreuth, 2011. a
Olivieri, A. C.: Practical guidelines for reporting results in single- and
multi-component analytical calibration: A tutorial, Anal. Chim.
Acta, 868, 10–22, https://doi.org/10.1016/j.aca.2015.01.017, 2015. a
Olivieri, A. C., Faber, N. M., Ferré, J., Boqué, R., Kalivas, J. H., and
Mark, H.: Uncertainty estimation and figures of merit for multivariate
calibration (IUPAC Technical Report), Pure Appl. Chem.,
78, 633–661, https://doi.org/10.1351/pac200678030633, 2006. a
Oppenheimer, C. and Kyle, P. R.: Probing the magma plumbing of Erebus volcano,
Antarctica, by open-path FTIR spectroscopy of gas emissions, J. Volcanol. Geoth. Res., 177, 743–754,
https://doi.org/10.1016/j.jvolgeores.2007.08.022, 2008. a
Ottaway, J., Farrell, J. A., and Kalivas, J. H.: Spectral Multivariate
Calibration without Laboratory Prepared or Determined Reference Analyte
Values, Anal. Chem., 85, 1509–1516,
https://doi.org/10.1021/ac302705m, 2012. a, b
Paatero, P.: Least squares formulation of robust non-negative factor analysis,
Chemometr. Intell. Lab., 37, 23–35,
https://doi.org/10.1016/S0169-7439(96)00044-5, 1997. a
Pagliai, M., Cavazzoni, C., Cardini, G., Erbacci, G., Parrinello, M., and
Schettino, V.: Anharmonic infrared and Raman spectra in Car-Parrinello
molecular dynamics simulations, J. Chem. Phys., 128,
224514, https://doi.org/10.1063/1.2936988, 2008. a
Painter, P. C., Snyder, R. W., Starsinic, M., Coleman, M. M., Kuehn, D. W., and
Davis, A.: Fourier Transform IR Spectroscopy, in: Coal and Coal
Products: Analytical Characterization Techniques, Vol. 205 of
ACS Symposium Series, 47–76, American Chemical Society,
https://doi.org/10.1021/bk-1982-0205.ch003, https://doi.org/10.1021/bk-1982-0205.ch003, 1982. a
Paiva, J. G. S., Schwartz, W. R., Pedrini, H., and Minghim, R.:
Semi-Supervised Dimensionality Reduction based on Partial Least Squares
for Visual Analysis of High Dimensional Data, Comput. Graph.
Forum, 31, 1345–1354, https://doi.org/10.1111/j.1467-8659.2012.03126.x, 2012. a
Palen, E. J., Allen, D. T., Pandis, S. N., Paulson, S. E., Seinfeld, J. H., and
Flagan, R. C.: Fourier-transform Infrared-analysis of Aerosol Formed In the
Photooxidation of Isoprene and Beta-pinene, Atmos. Environ. A-Gen., 26, 1239–1251, https://doi.org/10.1016/0960-1686(92)90385-X,
1992. a
Palen, E. J., Allen, D. T., Pandis, S. N., Paulson, S., Seinfeld, J. H., and
Flagan, R. C.: Fourier-transform Infrared-analysis of Aerosol Formed In the
Photooxidation of 1-octene, Atmos. Environ. A-Gen., 27, 1471–1477, https://doi.org/10.1016/0960-1686(93)90133-J, 1993. a
Pan, S. J. and Yang, Q.: A Survey on Transfer Learning, IEEE
T. Knowl. Data En., 22, 1345–1359,
https://doi.org/10.1109/TKDE.2009.191, 2010. a
Pan, S. J., Tsang, I. W., Kwok, J. T., and Yang, Q.: Domain Adaptation via
Transfer Component Analysis, IEEE T. Neural Networ.,
22, 199–210, https://doi.org/10.1109/TNN.2010.2091281, 2011. a
Paulson, S. E., Pandis, S. N., Baltensperger, U., Seinfeld, J. H., Flagan,
R. C., Palen, E. J., Allen, D. T., Schaffner, C., Giger, W., and Portmann,
A.: Characterization of Photochemical Aerosols From Biogenic Hydrocarbons,
J. Aerosol Sci., 21, S245–S248,
https://doi.org/10.1016/0021-8502(90)90230-U, 1990. a
Pedone, A., Biczysko, M., and Barone, V.: Environmental Effects in
Computational Spectroscopy: Accuracy and Interpretation, Chem. Phys. Chem.,
11, 1812–1832, https://doi.org/10.1002/cphc.200900976, 2010. a
Petzold, A., Ogren, J. A., Fiebig, M., Laj, P., Li, S.-M., Baltensperger, U.,
Holzer-Popp, T., Kinne, S., Pappalardo, G., Sugimoto, N., Wehrli, C.,
Wiedensohler, A., and Zhang, X.-Y.: Recommendations for reporting “black
carbon” measurements, Atmos. Chem. Phys., 13, 8365–8379,
https://doi.org/10.5194/acp-13-8365-2013, 2013. a, b
Phatak, A., Reilly, P. M., and Penlidis, A.: An approach to interval estimation
in partial least squares regression, Anal. Chim. Acta, 277,
495–501, https://doi.org/10.1016/0003-2670(93)80461-S, 1993. a
Pickle, T., Allen, D. T., and Pratsinis, S. E.: The sources and size
distributions of aliphatic and carbonyl carbon in Los Angeles aerosol,
Atmos. Environ. A-Gen., 24, 2221–2228,
https://doi.org/10.1016/0960-1686(90)90253-J, 1990. a
Pimentel, M. A., Clifton, D. A., Clifton, L., and Tarassenko, L.: A review of
novelty detection, Signal Processing, 99, 215–249,
https://doi.org/10.1016/j.sigpro.2013.12.026, 2014. a
Pitts, J. N., Finlayson-Pitts, B. J., and Winer, A. M.: Optical systems unravel
smog chemistry, Environ. Sci. Technol., 11, 568–573,
https://doi.org/10.1021/es60129a014, 1977. a
Pitts, J. N., Sanhueza, E., Atkinson, R., Carter, W. P. L., Winer, A. M.,
Harris, G. W., and Plum, C. N.: An investigation of the dark formation of
nitrous acid in environmental chambers, Int. J. Chem. Kinet., 16, 919–939, https://doi.org/10.1002/kin.550160712, 1984. a
Pollard, M., Jaklevic, J., and Howes, J.: Fourier Transform Infrared and
Ion-Chromatographic Sulfate Analysis of Ambient Air Samples, Aerosol
Sci. Tech., 12, 105–113, https://doi.org/10.1080/02786829008959330, 1990. a, b
Popovicheva, O. B., Kireeva, E. D., Shonija, N. K., Vojtisek-Lom, M., and
Schwarz, J.: FTIR analysis of surface functionalities on particulate matter
produced by off-road diesel engines operating on diesel and biofuel,
Environ. Sci. Pollut. R., 22, 4534–4544,
https://doi.org/10.1007/s11356-014-3688-8, 2014. a
Pratt, K. A. and Prather, K. A.: Mass spectrometry of atmospheric
aerosolsuRecent developments and applications. Part I: Off-line mass
spectrometry techniques, Mass Spectrom. Rev., 31, 1–16,
https://doi.org/10.1002/mas.20322, 2012. a
Presto, A. A., Hartz, K. E. H., and Donahue, N. M.: Secondary organic aerosol
production from terpene ozonolysis. 2. Effect of NOx concentration,
Environ. Sci. Technol., 39, 7046–7054,
https://doi.org/10.1021/es050400s, 2005. a
Putrino, A. and Parrinello, M.: Anharmonic Raman Spectra in High-Pressure Ice
from Ab Initio Simulations, Phys. Rev. Lett., 88, 176401,
https://doi.org/10.1103/PhysRevLett.88.176401, 2002. a
Qin, S. J.: Recursive PLS algorithms for adaptive data modeling,
Comput. Chem. Eng., 22, 503–514,
https://doi.org/10.1016/S0098-1354(97)00262-7, 1998. a
Quarti, C., Milani, A., and Castiglioni, C.: Ab Initio Calculation of the IR
Spectrum of PTFE: Helical Symmetry and Defects, J. Phys.
Chem. B, 117, 706–718, https://doi.org/10.1021/jp3102145, 2013. a
Ranney, A. P. and Ziemann, P. J.: Microscale spectrophotometric methods for
quantification of functional groups in oxidized organic aerosol,
Aerosol Sci. Tech., 50, 881–892,
https://doi.org/10.1080/02786826.2016.1201197, 2016. a
Reff, A., Turpin, B. J., Offenberg, J. H., Weisel, C. P., Zhang, J., Morandi,
M., Stock, T., Colome, S., and Winer, A.: A functional group characterization
of organic PM2.5 exposure: Results from the RIOPA study RID C-3787-2009,
Atmos. Environ., 41, 4585–4598,
https://doi.org/10.1016/j.atmosenv.2007.03.054, 2007. a, b
Reggente, M., Dillner, A. M., and Takahama, S.: Predicting ambient aerosol
thermal–optical reflectance (TOR) measurements from infrared spectra:
extending the predictions to different years and different sites, Atmos.
Meas. Tech., 9, 441–454, https://doi.org/10.5194/amt-9-441-2016, 2016. a, b, c, d, e, f, g, h, i, j, k, l, m
Rinnan, Å.: Pre-processing in vibrational spectroscopy – when, why and
how, Anal. Meth., 6, 7124–7129, https://doi.org/10.1039/C3AY42270D,
2014. a
Robb, E. W. and Munk, M. E.: A neural network approach to infrared spectrum
interpretation, Microchim. Acta, 100, 131–155,
https://doi.org/10.1007/BF01244838, 1990. a
Rosipal, R. and Krämer, N.: Overview and Recent Advances in Partial Least
Squares, in: Subspace, Latent Structure and Feature Selection, edited by:
Saunders, C., Grobelnik, M., Gunn, S., and Shawe-Taylor, J., Vol. 3940 of
Lecture Notes in Computer Science, 34–51, Springer Berlin
Heidelberg, https://doi.org/10.1007/11752790_2, 2006. a, b
Rossi, M., Ceriotti, M., and Manolopoulos, D. E.: How to remove the spurious
resonances from ring polymer molecular dynamics, J.
Chem. Phys., 140, 234116, https://doi.org/10.1063/1.4883861,
2014a. a
Rossi, M., Liu, H., Paesani, F., Bowman, J., and Ceriotti, M.: Communication:
On the consistency of approximate quantum dynamics simulation methods for
vibrational spectra in the condensed phase, J. Chem.
Phys., 141, 181101, https://doi.org/10.1063/1.4901214, 2014b. a, b
Russell, L. M., Bahadur, R., Hawkins, L. N., Allan, J., Baumgardner, D., Quinn,
P. K., and Bates, T. S.: Organic aerosol characterization by complementary
measurements of chemical bonds and molecular fragments, Atmos.
Environ., 43, 6100–6105, https://doi.org/10.1016/j.atmosenv.2009.09.036, 2009. a, b, c
Russell, L. M., Bahadur, R., and Ziemann, P. J.: Identifying organic aerosol
sources by comparing functional group composition in chamber and atmospheric
particles, P. Natl. Acad. Sci. USA, 108, 3516–3521, https://doi.org/10.1073/pnas.1006461108,
2011. a, b
Russolillo, G.: Non-Metric Partial Least Squares, Electron. J. Stat., 6,
1641–1669, https://doi.org/10.1214/12-EJS724, 2012. a
Russwurm, G. M.: Compendium Method TO-16: Long-path Open-path Fourier
Transform Infrared Monitoring of Atmospheric Gases, 16.1–16.41, US
Environmental Protection Agency, 1999. a
Russwurm, G. M. and Childers, J. W.: Open-Path Fourier Transform Infrared
Spectroscopy, in: Handbook of Vibrational Spectroscopy, John Wiley & Sons,
Ltd, https://doi.org/10.1002/0470027320.s2112, 2006. a
Ruthenburg, T. C., Perlin, P. C., Liu, V., McDade, C. E., and Dillner, A. M.:
Determination of organic matter and organic matter to organic carbon ratios
by infrared spectroscopy with application to selected sites in the IMPROVE
network, Atmos. Environ., 86, 47–57,
https://doi.org/10.1016/j.atmosenv.2013.12.034, 2014. a, b
Sadezky, A., Muckenhuber, H., Grothe, H., Niessner, R., and Pöschl, U.:
Raman microspectroscopy of soot and related carbonaceous materials: Spectral
analysis and structural information, Carbon, 43, 1731–1742,
https://doi.org/10.1016/j.carbon.2005.02.018, 2005. a
Saeys, W., De Ketelaere, B., and Darius, P.: Potential applications of
functional data analysis in chemometrics, J. Chemometr., 22,
335–344, https://doi.org/10.1002/cem.1129, 2008. a
Saeys, Y., Inza, I., and Larrañaga, P.: A review of feature selection
techniques in bioinformatics, Bioinformatics, 23, 2507–2517,
https://doi.org/10.1093/bioinformatics/btm344, 2007. a
Sasaki, S., Abe, H., Ouki, T., Sakamoto, M., and Ochiai, S.: Automated
structure elucidation of several kinds of aliphatic and alicyclic compounds,
Anal. Chem., 40, 2220–2223, https://doi.org/10.1021/ac50158a061, 1968. a
Savitzky, A. and Golay, M. J. E.: Smoothing and Differentiation of Data by
Simplified Least Squares Procedures, Anal. Chem., 36,
1627–1639, https://doi.org/10.1021/ac60214a047, 1964. a
Sax, M., Zenobi, R., Baltensperger, U., and Kalberer, M.: Time resolved
infrared spectroscopic analysis of aerosol formed by photo-oxidation of
1,3,5-trimethylbenzene and alpha-pinene, Aerosol Sci.
Tech., 39, 822–830, https://doi.org/10.1080/02786820500257859, 2005. a
Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., and Platt, J.:
Support Vector Method for Novelty Detection, in: Proceedings of the
12th International Conference on Neural Information
Processing Systems, NIPS'99, 582–588, MIT Press, Cambridge, MA, USA,
1999. a
Schütze, C., Lau, S., Reiche, N., Sauer, U., Borsdorf, H., and Dietrich, P.:
Ground-based Remote Sensing with Open-path Fourier- transform
Infrared (OP-FTIR) Spectroscopy for Large-scale Monitoring of
Greenhouse Gases, Enrgy Proced., 37, 4276–4282,
https://doi.org/10.1016/j.egypro.2013.06.330, 2013. a
Schuur, J. and Gasteiger, J.: Infrared Spectra Simulation of Substituted
Benzene Derivatives on the Basis of a 3D Structure Representation,
Anal. Chem., 69, 2398–2405, https://doi.org/10.1021/ac9611071, 1997. a
Schwarz, G.: Estimating the Dimension of a Model, Ann. Stat., 6, 461–464,
1978. a
Selzer, P., Gasteiger, J., Thomas, H., and Salzer, R.: Rapid Access to
Infrared Reference Spectra of Arbitrary Organic Compounds: Scope and
Limitations of an Approach to the Simulation of Infrared Spectra by
Neural Networks, Chem.-Eur. J., 6, 920–927,
https://doi.org/10.1002/(SICI)1521-3765(20000303)6:5<920::AID-CHEM920>3.0.CO;2-W, 2000. a
Serneels, S., Croux, C., and Van Espen, P. J.: Influence properties of partial
least squares regression, Chemometr. Intell. Lab., 71, 13–20, https://doi.org/10.1016/j.chemolab.2003.10.009, 2004. a
Serradilla, J., Shi, J., and Morris, A.: Fault detection based on Gaussian
process latent variable models, Chemometr. Intell. Lab., 109, 9–21, https://doi.org/10.1016/j.chemolab.2011.07.003, 2011. a
Shao, L. and Griffiths, P. R.: Information Extraction from a Complex
Multicomponent System by Target Factor Analysis, Anal. Chem.,
82, 106–114, https://doi.org/10.1021/ac901246x, 2010. a
Shurvell, H.: Spectra–Structure Correlations in the Mid- and Far-Infrared, in:
Handbook of Vibrational Spectroscopy, John Wiley & Sons, Ltd,
https://doi.org/10.1002/0470027320.s4101, 2006. a
Silvestrelli, P. L., Bernasconi, M., and Parrinello, M.: Ab initio infrared
spectrum of liquid water, Chem. Phys. Lett., 277, 478–482,
https://doi.org/10.1016/S0009-2614(97)00930-5, 1997. a, b
Solomon, P. A., Crumpler, D., Flanagan, J. B., Jayanty, R., Rickman, E. E., and
McDade, C. E.: U.S. National PM2.5 Chemical Speciation Monitoring
Networks–CSN and IMPROVE: Description of networks, J. Air
Waste Manage., 64, 1410–1438,
https://doi.org/10.1080/10962247.2014.956904, 2014. a, b, c, d
Spellicy, R. L. and Webb, J. D.: Atmospheric Monitoring Using Extractive
Techniques, in: Handbook of Vibrational Spectroscopy, John Wiley & Sons,
Ltd, https://doi.org/10.1002/0470027320.s2111, 2006. a
Steele, D.: Infrared Spectroscopy: Theory, in: Handbook of Vibrational
Spectroscopy, John Wiley & Sons, Ltd, https://doi.org/10.1002/0470027320.s0103, 2006. a
Stone, M.: Cross-Validatory Choice and Assessment of Statistical Predictions,
J. R. Stat. Soc. B,
36, 111–147, 1974. a
Sugiyama, M., Nakajima, S., Kashima, H., Buenau, P. V., and Kawanabe, M.:
Direct Importance Estimation with Model Selection and Its Application
to Covariate Shift Adaptation, in: Advances in Neural Information
Processing Systems 20, edited by: Platt, J. C., Koller, D., Singer, Y.,
and Roweis, S. T., 1433–1440, Curran Associates, Inc., Red Hook, 2008. a
Takahama, S. and Dillner, A. M.: Model selection for partial least squares
calibration and implications for analysis of atmospheric organic aerosol
samples with mid-infrared spectroscopy, J. Chemometr., 29,
659–668, https://doi.org/10.1002/cem.2761, 2015. a
Takahama, S. and Ruggeri, G.: Technical note: Relating functional group
measurements to carbon types for improved model-measurement comparisons of
organic aerosol composition, Atmos. Chem. Phys., 17, 4433–4450,
https://doi.org/10.5194/acp-17-4433-2017, 2017. a
Takahama, S., Schwartz, R. E., Russell, L. M., Macdonald, A. M., Sharma, S.,
and Leaitch, W. R.: Organic functional groups in aerosol particles from
burning and non-burning forest emissions at a high-elevation mountain site,
Atmos. Chem. Phys., 11, 6367–6386, https://doi.org/10.5194/acp-11-6367-2011,
2011. a, b
Takahama, S., Johnson, A., and Russell, L. M.: Quantification of Carboxylic and
Carbonyl Functional Groups in Organic Aerosol Infrared Absorbance Spectra,
Aerosol Sci. Tech., 47, 310–325,
https://doi.org/10.1080/02786826.2012.752065, 2013. a, b, c
Takahama, S., Ruggeri, G., and Dillner, A. M.: Analysis of functional groups
in atmospheric aerosols by infrared spectroscopy: sparse methods for
statistical selection of relevant absorption bands, Atmos. Meas. Tech., 9,
3429–3454, https://doi.org/10.5194/amt-9-3429-2016, 2016. a, b, c, d, e, f, g, h, i
Thissen, U., Pepers, M., Üstün, B., Melssen, W. J., and Buydens, L.
M. C.: Comparing support vector machines to PLS for spectral regression
applications, Chemometr. Intell. Lab., 73,
169–179, 2004. a
Tibshirani, R.: Regression shrinkage and selection via the Lasso, J. R. Stat.
Soc. B, 58, 267–288,
1996. a
Tibshirani, R. J.: Degrees of Freedom and Model Search, ArXiv
e-prints, 2014. a
Tikhonov, A. N. and Arsenin, V. I.: Solutions of ill-posed problems, Halsted
Press, New York, 1977. a
Torrey, L. and Shavlik, J.: Transfer learning, Handbook of Research on
Machine Learning Applications and Trends: Algorithms, Methods, and
Techniques, 1, 242, IGI Global, Hershey, 2009. a
Trygg, J.: O2-PLS for qualitative and quantitative analysis in multivariate
calibration, J. Chemometr., 16, 283–293,
https://doi.org/10.1002/cem.724, 2002. a
Tsai, A. C., Liou, M., Simak, M., and Cheng, P. E.: On hyperbolic
transformations to normality, Comput. Stat. Data
An., 115, 250–266, https://doi.org/10.1016/j.csda.2017.06.001, 2017. a
Tsai, Y. I. and Kuo, S.-C.: Development of diffuse reflectance infrared
Fourier transform spectroscopy for the rapid characterization of aerosols,
Atmos. Environ., 40, 1781–1793,
https://doi.org/10.1016/j.atmosenv.2005.11.023, 2006. a
Tuazon, E. C., Winer, A. M., and Pitts, J. N.: Trace pollutant concentrations
in a multiday smog episode in the California South Coast Air Basin by long
path length Fourier transform infrared spectroscopy, Environ.
Sci. Technol., 15, 1232–1237, https://doi.org/10.1021/es00092a014, 1981. a
Ţucureanu, V., Matei, A., and Avram, A. M.: FTIR Spectroscopy for
Carbon
Family Study, Crit. Rev. Anal. Chem., 46, 502–520,
https://doi.org/10.1080/10408347.2016.1157013, 2016. a
Tuinstra, F. and Koenig, J. L.: Raman Spectrum of Graphite, J. Chem. Phys.,
53, 1126–1130, https://doi.org/10.1063/1.1674108, 1970. a
Turrell, G.: Theory of Infrared Spectroscopy, in: Encyclopedia of
Analytical Chemistry, John Wiley & Sons, Ltd,
https://doi.org/10.1002/9780470027318.a5607,
2006. a
U.S. EPA: Method 320 Measurement of vapor phase organic and inorganic
emissions by extractive Fourier transform infrared (FTIR) spectroscopy, 14219–14228,
1998. a
van der Voet, H.: Comparing the predictive accuracy of models using a simple
randomization test, Chemometr. Intell. Lab.,
25, 313–323, https://doi.org/10.1016/0169-7439(94)85050-X, 1994. a
Venables, W. N. and Ripley, B. D.: Modern Applied Statistics with S,
Springer, New York, 2003. a
Virtanen, A., Joutsensaari, J., Koop, T., Kannosto, J., Yli-Pirila, P.,
Leskinen, J., Makela, J. M., Holopainen, J. K., Poeschl, U., Kulmala, M.,
Worsnop, D. R., and Laaksonen, A.: An amorphous solid state of biogenic
secondary organic aerosol particles, Nature, 467, 824–827,
https://doi.org/10.1038/nature09455, 2010. a
Walczak, B. and Massart, D.: Local modelling with radial basis function
networks, Chemometr. Intell. Lab., 50,
179–198, https://doi.org/10.1016/S0169-7439(99)00056-8, 2000. a
Walczak, B. and Wegscheider, W.: Non-linear modelling of chemical data by
combinations of linear and neural net methods, Anal. Chim. Acta,
283, 508–517, https://doi.org/10.1016/0003-2670(93)85264-K, 1993. a
Wang, L.-L., Lin, Y.-W., Wang, X.-F., Xiao, N., Xu, Y.-D., Li, H.-D., and Xu,
Q.-S.: A selective review and comparison for interval variable selection in
spectroscopic modeling, Chemometr. Intell. Lab., 172, 229–240, https://doi.org/10.1016/j.chemolab.2017.11.008, 2017. a
Weakley, A., Miller, A., Griffiths, P., and Bayman, S.: Quantifying silica in
filter-deposited mine dusts using infrared spectra and partial least squares
regression, Anal. Bioanal. Chem., 406, 4715–4724,
https://doi.org/10.1007/s00216-014-7856-y, 2014. a, b
Weakley, A. T., Takahama, S., and Dillner, A. M.: Ambient aerosol composition
by infrared spectroscopy and partial least-squares in the chemical speciation
network: Organic carbon with functional group identification, Aerosol
Sci. Tech., 50, 1096–1114, https://doi.org/10.1080/02786826.2016.1217389,
2016. a, b, c, d, e, f, g, h, i, j, k
Weakley, A. T., Takahama, S., and Dillner, A. M.: Thermal/optical reflectance
equivalent organic and elemental carbon determined from federal reference and
equivalent method fine particulate matter samples using Fourier transform
infrared spectrometry, Aerosol Sci. Tech., 52, 1048–1058,
https://doi.org/10.1080/02786826.2018.1504161, 2018a. a
Weakley, A. T., Takahama, S., Wexler, A. S., and Dillner, A. M.: Ambient
aerosol composition by infrared spectroscopy and partial least squares in the
chemical speciation network: Multilevel modeling for elemental carbon,
Aerosol Sci. Tech., 52, 642–654,
https://doi.org/10.1080/02786826.2018.1439571, 2018b. a, b, c, d, e, f, g, h, i, j, k
Wei, S., Kulkarni, P., Ashley, K., and Zheng, L.: Measurement of Crystalline
Silica Aerosol Using Quantum Cascade Laser-Based Infrared Spectroscopy,
Sci. Rep., 7, 13860, https://doi.org/10.1038/s41598-017-14363-3,
2017. a
Weigel, U. M. and Herges, R.: Simulation of infrared spectra using artificial
neural networks based on semiempirical and empirical data, Anal.
Chim. Acta, 331, 63–74, https://doi.org/10.1016/0003-2670(96)00203-6, 1996. a
Weymuth, T., Haag, M. P., Kiewisch, K., Luber, S., Schenk, S., Jacob, C. R.,
Herrmann, C., Neugebauer, J., and Reiher, M.: MOVIPAC: Vibrational
spectroscopy with a robust meta-program for massively parallel standard and
inverse calculations, J. Comput. Chem., 33,
2186–2198, https://doi.org/10.1002/jcc.23036, 2012. a
Wiklund, S., Nilsson, D., Eriksson, L., Sjostrom, M., Wold, S., and Faber, K.:
A randomization test for PLS component selection, J.
Chemometr., 21, 427–439, https://doi.org/10.1002/cem.1086, 2007. a
Wise, B. M. and Gallagher, N. B.: The process chemometrics approach to process
monitoring and fault detection, J. Process Contr., 6,
329–348, https://doi.org/10.1016/0959-1524(96)00009-1, 1996. a
Wise, B. M. and Roginski, R. T.: A Calibration Model Maintenance Roadmap,
IFAC-PapersOnLine, 48, 260–265, https://doi.org/10.1016/j.ifacol.2015.08.191,
2015. a, b, c, d
Witt, A., Ivanov, S. D., Shiga, M., Forbert, H., and Marx, D.: On the
applicability of centroid and ring polymer path integral molecular dynamics
for vibrational spectroscopy, J. Chem. Phys., 130,
194510, https://doi.org/10.1063/1.3125009, 2009. a
Wold, H.: Estimation of Principal Components and Related Models by Iterative
Least squares, in: Multivariate Analysis, 391–420, Academic Press, 1966. a
Wold, S.: Cross-Validatory Estimation of the Number of Components in Factor and
Principal Components Models, Technometrics, 20, 397–405,
https://doi.org/10.1080/00401706.1978.10489693, 1978. a
Wold, S.: Discussion: PLS in Chemical Practice, Technometrics, 35,
136–139, https://doi.org/10.2307/1269657, 1993. a
Wold, S., Ruhe, A., Wold, H., and Dunn, III, W. J.: The Collinearity Problem
in Linear Regression. The Partial Least Squares (PLS) Approach to
Generalized Inverses, SIAM J. Sci. Stat.
Comp., 5, 735–743, https://doi.org/10.1137/0905052, 1984. a
Wold, S., Antti, H., Lindgren, F., and Öhman, J.: Orthogonal signal
correction of near-infrared spectra, Chemometr. Intell. Lab., 44, 175–185, https://doi.org/10.1016/S0169-7439(98)00109-9, 1998. a
Wold, S., Trygg, J., Berglund, A., and Antti, H.: Some recent developments in
PLS modeling, Chemometr. Intell. Lab., 58,
131–150, https://doi.org/10.1016/S0169-7439(01)00156-3, 2001. a
Yao, J., Fan, B., Doucet, J.-P., Panaye, A., Yuan, S., and Li, J.:
SIRS-SS: A System for Simulating IR/Raman Spectra. 1.
Substructure/Subspectrum Correlation, J. Chem. Inf.
Comp. Sci., 41, 1046–1052, https://doi.org/10.1021/ci010010z, 2001. a
Yokelson, R. J., Susott, R., Ward, D. E., Reardon, J., and Griffith, D. W. T.:
Emissions from smoldering combustion of biomass measured by open-path
Fourier transform infrared spectroscopy, J. Geophys.
Res.–Atmos., 102, 18865–18877, https://doi.org/10.1029/97JD00852, 1997. a
Zadrozny, B.: Learning and Evaluating Classifiers Under Sample Selection
Bias, in: Proceedings of the Twenty-first International Conference on
Machine Learning, ICML '04, 114 pp., ACM, New York, NY, USA,
https://doi.org/10.1145/1015330.1015425, 2004.
a
Zeng, G., Holladay, S., Langlois, D., Zhang, Y., and Liu, Y.: Kinetics of
Heterogeneous Reaction of Ozone with Linoleic Acid and its Dependence on
Temperature, Physical State, RH, and Ozone Concentration, J. Phys. Chem. A, 117, 1963–1974, https://doi.org/10.1021/jp308304n, 2013. a
Zezula, P., Amato, G., Dohnal, V., and Batko, M.: Similarity Search: The Metric
Space Approach, Advances in Database Systems, Springer US, 2006. a
Zhang, L. and Garcia-Munoz, S.: A comparison of different methods to estimate
prediction uncertainty using Partial Least Squares (PLS): A practitioner's
perspective, Chemometr. Intell. Lab., 97,
152–158, https://doi.org/10.1016/j.chemolab.2009.03.007, 2009. a
Zhang, X., Kano, M., and Li, Y.: Locally weighted kernel partial least squares
regression based on sparse nonlinear features for virtual sensing of
nonlinear time-varying processes, Comput. Chem. Eng.,
104, 164–171, https://doi.org/10.1016/j.compchemeng.2017.04.014, 2017. a
Zhao, N., Wu, Z.-s., Zhang, Q., Shi, X.-y., Ma, Q., and Qiao, Y.-j.:
Optimization of Parameter Selection for Partial Least Squares Model
Development, Sci. Rep., 5, 11647, https://doi.org/10.1038/srep11647,
2015. a
Zhao, R., Lee, A. K. Y., and Abbatt, J. P. D.: Investigation of Aqueous-Phase
Photooxidation of Glyoxal and Methylglyoxal by Aerosol Chemical Ionization
Mass Spectrometry: Observation of Hydroxyhydroperoxide Formation,
J. Phys. Chem. A, 116, 6253–6263,
https://doi.org/10.1021/jp211528d, 2012. a
Zhou, L. M., Hopke, P. K., Stanier, C. O., Pandis, S. N., Ondov, J. M., and
Pancras, J. P.: Investigation of the relationship between chemical
composition and size distribution of airborne particles by partial least
squares and positive matrix factorization, J. Geophys.
Res.-Atmos., 110, D07S18, https://doi.org/10.1029/2004JD005050, 2005. a
Zimmerman, N., Presto, A. A., Kumar, S. P. N., Gu, J., Hauryliuk, A.,
Robinson, E. S., Robinson, A. L., and R. Subramanian: A machine learning
calibration model using random forests to improve sensor performance for
lower-cost air quality monitoring, Atmos. Meas. Tech., 11, 291–313,
https://doi.org/10.5194/amt-11-291-2018, 2018. a
Short summary
Mid-infrared spectra of particulate matter (PM) samples are complex but chemically informative and present an opportunity for cost-effective measurement of PM provided that quantitative calibration models can be built. We review an emerging strategy for building statistical calibration models using collocated measurements, interpreting the physical bases for such models and evaluating the suitability of existing calibration models to new samples.
Mid-infrared spectra of particulate matter (PM) samples are complex but chemically informative...
