Articles | Volume 15, issue 9
17 May 2022
Research article | 17 May 2022
Contrasting mineral dust abundances from X-ray diffraction and reflectance spectroscopy
Mohammad R. Sadrian et al.
Related subject area
Subject: Aerosols | Technique: Laboratory Measurement | Topic: Data Processing and Information RetrievalFragment ion–functional group relationships in organic aerosols using aerosol mass spectrometry and mid-infrared spectroscopyEvolution under dark conditions of particles from old and modern diesel vehicles in a new environmental chamber characterized with fresh exhaust emissionsSubstantial organic impurities at the surface of synthetic ammonium sulfate particlesQuantification of isomer-resolved iodide chemical ionization mass spectrometry sensitivity and uncertainty using a voltage-scanning approachAssessing the sources of particles at an urban background site using both regulatory instruments and low-cost sensors – a comparative studyHigh-resolution optical constants of crystalline ammonium nitrate for infrared remote sensing of the Asian Tropopause Aerosol LayerAssessing the accuracy of low-cost optical particle sensors using a physics-based approachComparison of dimension reduction techniques in the analysis of mass spectrometry dataDevelopment of a new correction algorithm applicable to any filter-based absorption photometerChemical discrimination of the particulate and gas phases of miniCAST exhausts using a two-filter collection methodExternal and internal cloud condensation nuclei (CCN) mixtures: controlled laboratory studies of varying mixing statesClassification of iron oxide aerosols by a single particle soot photometer using supervised machine learningMethod to measure the size-resolved real part of aerosol refractive index using differential mobility analyzer in tandem with single-particle soot photometerQuantitative capabilities of STXM to measure spatially resolved organic volume fractions of mixed organic ∕ inorganic particlesRevisiting the differential freezing nucleus spectra derived from drop-freezing experiments: methods of calculation, applications, and confidence limitsParticle wall-loss correction methods in smog chamber experimentsImproved real-time bio-aerosol classification using artificial neural networksMachine learning for improved data analysis of biological aerosol using the WIBSA machine learning approach to aerosol classification for single-particle mass spectrometryEvaluation of a hierarchical agglomerative clustering method applied to WIBS laboratory data for improved discrimination of biological particles by comparing data preparation techniquesUsing depolarization to quantify ice nucleating particle concentrations: a new methodReal-time analysis of insoluble particles in glacial ice using single-particle mass spectrometryEvaluation of machine learning algorithms for classification of primary biological aerosol using a new UV-LIF spectrometerSize distribution of particle-associated polybrominated diphenyl ethers (PBDEs) and their implications for healthPredicting ambient aerosol thermal–optical reflectance (TOR) measurements from infrared spectra: extending the predictions to different years and different sitesElectrodynamic balance measurements of thermodynamic, kinetic, and optical aerosol properties inaccessible to bulk methodsMass-specific optical absorption coefficients and imaginary part of the complex refractive indices of mineral dust components measured by a multi-wavelength photoacoustic spectrometerAn experiment to measure raindrop collection efficiencies: influence of rear captureQuantitative single-particle analysis with the Aerodyne aerosol mass spectrometer: development of a new classification algorithm and its application to field dataA modeling approach to evaluate the uncertainty in estimating the evaporation behaviour and volatility of organic aerosolsA model of aerosol evaporation kinetics in a thermodenuder
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,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.
Boris Vansevenant, Cédric Louis, Corinne Ferronato, Ludovic Fine, Patrick Tassel, Pascal Perret, Evangelia Kostenidou, Brice Temime-Roussel, Barbara D'Anna, Karine Sartelet, Véronique Cerezo, and Yao Liu
Atmos. Meas. Tech., 14, 7627–7655,Short summary
A new method was developed to correct wall losses of particles on Teflon walls using a new environmental chamber. It was applied to experiments with six diesel vehicles (Euro 3 to 6), tested on a chassis dynamometer. Emissions of particles and precursors were obtained under urban and motorway conditions. The chamber experiments help understand the role of physical processes in diesel particle evolutions in the dark. These results can be applied to situations such as tunnels or winter rush hours.
Junteng Wu, Nicolas Brun, Juan Miguel González-Sánchez, Badr R’Mili, Brice Temime Roussel, Sylvain Ravier, Jean-Louis Clément, and Anne Monod
Atmos. Meas. Tech. Discuss.,
Revised manuscript accepted for AMTShort summary
This work quantified and tentatively identified the organic impurities on ammonium sulfate aerosols generated in the laboratory. They are likely low volatile and high mass molecules containing oxygen, nitrogen and/or sulfur. Our results show that these organic impurities likely originate from the commercial AS crystals. It is recommended to use AS seeds with caution, especially when small particles are used, in terms of AS purity and water purity when aqueous solutions are used for atomization.
Chenyang Bi, Jordan E. Krechmer, Graham O. Frazier, Wen Xu, Andrew T. Lambe, Megan S. Claflin, Brian M. Lerner, John T. Jayne, Douglas R. Worsnop, Manjula R. Canagaratna, and Gabriel Isaacman-VanWertz
Atmos. Meas. Tech., 14, 6835–6850,Short summary
Iodide-adduct chemical ionization mass spectrometry (I-CIMS) has been widely used to analyze airborne organics. In this study, I-CIMS sensitivities of isomers within a formula are found to generally vary by 1 and up to 2 orders of magnitude. Comparisons between measured and predicted moles, obtained using a voltage-scanning calibration approach, show that predictions for individual compounds or formulas might carry high uncertainty, yet the summed moles of analytes agree reasonably well.
Dimitrios Bousiotis, Ajit Singh, Molly Haugen, David C. S. Beddows, Sebastián Diez, Killian L. Murphy, Pete M. Edwards, Adam Boies, Roy M. Harrison, and Francis D. Pope
Atmos. Meas. Tech., 14, 4139–4155,Short summary
Measurement and source apportionment of atmospheric pollutants are crucial for the assessment of air quality and the implementation of policies for their improvement. This study highlights the current capability of low-cost sensors in source identification and differentiation using clustering approaches. Future directions towards particulate matter source apportionment using low-cost OPCs are highlighted.
Robert Wagner, Baptiste Testa, Michael Höpfner, Alexei Kiselev, Ottmar Möhler, Harald Saathoff, Jörn Ungermann, and Thomas Leisner
Atmos. Meas. Tech., 14, 1977–1991,Short summary
During the Asian summer monsoon period, air pollutants are transported from layers near the ground to high altitudes of 13 to 18 km in the atmosphere. Infrared measurements have shown that particles composed of solid ammonium nitrate are a major part of these pollutants. To enable the quantitative analysis of the infrared spectra, we have determined for the first time accurate optical constants of ammonium nitrate for the low-temperature conditions of the upper atmosphere.
David H. Hagan and Jesse H. Kroll
Atmos. Meas. Tech., 13, 6343–6355,Short summary
Assessing the error of low-cost particulate matter (PM) sensors has been difficult as each empirical study presents unique limitations. Here, we present a new, open-sourced, physics-based model (opcsim) and use it to understand how the properties of different particle sensors alter their accuracy. We offer a summary of likely sources of error for different sensor types, environmental conditions, and particle classes and offer recommendations for the choice of optimal calibrant.
Sini Isokääntä, Eetu Kari, Angela Buchholz, Liqing Hao, Siegfried Schobesberger, Annele Virtanen, and Santtu Mikkonen
Atmos. Meas. Tech., 13, 2995–3022,Short summary
Online mass spectrometry produces large amounts of data. These data can be interpreted with statistical methods, enabling scientists to more easily understand the underlying processes. We compared these techniques on car exhaust measurements. We show differences and similarities between the methods and give recommendations on applicability of the methods on certain types of data. We show that applying multiple methods leads to more robust results, thus increasing reliability of the findings.
Hanyang Li, Gavin R. McMeeking, and Andrew A. May
Atmos. Meas. Tech., 13, 2865–2886,Short summary
We present a new correction algorithm that addresses biases in measurements of aerosol light absorption by filter-based photometers, incorporating the transmission of light through the filter and some aerosol optical properties. It was developed using biomass burning aerosols and tested using rural ambient aerosols. This new algorithm is applicable to any filter-based photometer, resulting in good agreement between different colocated instruments in both the laboratory and the field.
Linh Dan Ngo, Dumitru Duca, Yvain Carpentier, Jennifer A. Noble, Raouf Ikhenazene, Marin Vojkovic, Cornelia Irimiea, Ismael K. Ortega, Guillaume Lefevre, Jérôme Yon, Alessandro Faccinetto, Eric Therssen, Michael Ziskind, Bertrand Chazallon, Claire Pirim, and Cristian Focsa
Atmos. Meas. Tech., 13, 951–967,Short summary
The partitioning of noxious chemical compounds between the particulate and gas phases in combustion emissions is key to delineate their exact impacts on atmospheric chemistry and human health. We developed a two-filter sampling system, a multi-technique analytical approach, and advanced statistical methods to fully characterize the composition of both phases in combustion emissions. We could successfully discriminate samples from a standard soot generator by their volatile–non-volatile species.
Diep Vu, Shaokai Gao, Tyler Berte, Mary Kacarab, Qi Yao, Kambiz Vafai, and Akua Asa-Awuku
Atmos. Meas. Tech., 12, 4277–4289,Short summary
Aerosol–cloud interactions contribute the greatest uncertainty to cloud formation. Aerosol composition is complex and can change quickly in the atmosphere. In this work, we recreate a transition in aerosol mixing state in the laboratory, which (to date) has only been observed in the ambient state. We then report the subsequent changes on cloud condensation nuclei (CCN) activation.
Kara D. Lamb
Atmos. Meas. Tech., 12, 3885–3906,Short summary
Recent atmospheric observations have indicated emissions of iron-oxide-containing aerosols from anthropogenic sources could be 8x higher than previous estimates, leading models to underestimate their climate impact. Previous studies have shown the single particle soot photometer (SP2) can quantify the atmospheric abundance of these aerosols. Here, I explore a machine learning approach to improve SP2 detection, significantly reducing misclassifications of other aerosols as iron oxide aerosols.
Gang Zhao, Weilun Zhao, and Chunsheng Zhao
Atmos. Meas. Tech., 12, 3541–3550,Short summary
A new method is proposed to retrieve the size-resolved real part of the refractive index (RRI). The main principle of deriving the RRI is measuring the scattering intensity by a single-particle soot photometer of a size-selected aerosol. This method is validated by a series of calibration experiments using the components of the known RI. The retrieved size-resolved RRI covers a wide range, from 200 nm to 450 nm, with uncertainty of less than 0.02.
Matthew Fraund, Tim Park, Lin Yao, Daniel Bonanno, Don Q. Pham, and Ryan C. Moffet
Atmos. Meas. Tech., 12, 1619–1633,Short summary
Scanning transmission X-ray microscopy (STXM) is a powerful tool which is able to determine the elemental and functional composition of aerosols on a subparticle level. The current work validates the use of STXM for quantitatively calculating the organic volume fraction of individual aerosols by applying the calculation to lab-prepared samples. The caveats and limitations for this calculation are shown as well.
Atmos. Meas. Tech., 12, 1219–1231,Short summary
The abundance of freezing nuclei in water samples is routinely determined by experiments involving the cooling of sample drops and observing the temperatures at which the drops freeze. This is used for characterizing the nucleating abilities of materials in laboratory preparations or to determine the numbers of nucleating particles in rain, snow, river water or other natural waters. The evaluation of drop-freezing experiments in terms of differential nucleus spectra is advocated in the paper.
Ningxin Wang, Spiro D. Jorga, Jeffery R. Pierce, Neil M. Donahue, and Spyros N. Pandis
Atmos. Meas. Tech., 11, 6577–6588,Short summary
The interaction of particles with the chamber walls has been a significant source of uncertainty when analyzing results of secondary organic aerosol formation experiments performed in Teflon chambers. We evaluated the performance of several particle wall-loss correction methods for aging experiments of α-pinene ozonolysis products. Experimental procedures are proposed for the characterization of particle losses during different stages of these experiments.
Maciej Leśkiewicz, Miron Kaliszewski, Maksymilian Włodarski, Jarosław Młyńczak, Zygmunt Mierczyk, and Krzysztof Kopczyński
Atmos. Meas. Tech., 11, 6259–6270,Short summary
In this study we demonstrate the application of artificial neural networks to the real-time analysis of single-particle fluorescence fingerprints acquired using BARDet (a BioAeRosol Detector). 48 different aerosols including pollens, bacteria, fungi, spores and nonbiological substances were characterized. An entirely new approach to data analysis using a decision tree comprising 22 independent neural networks was discussed. A very high accuracy of aerosol classification in real time resulted.
Simon Ruske, David O. Topping, Virginia E. Foot, Andrew P. Morse, and Martin W. Gallagher
Atmos. Meas. Tech., 11, 6203–6230,Short summary
Pollen, bacteria and fungal spores are common in the environment, can have very important implications for public health and may influence the weather. Biological sensors potentially could be used to monitor quantities of these types of particles. However, it is important to transform the measurements from these instruments into counts of these biological particles. The paper tests a variety of approaches for achieving this aim on data collected in a laboratory.
Costa D. Christopoulos, Sarvesh Garimella, Maria A. Zawadowicz, Ottmar Möhler, and Daniel J. Cziczo
Atmos. Meas. Tech., 11, 5687–5699,Short summary
Compositional analysis of atmospheric and laboratory aerosols is often conducted with mass spectrometry. In this study, machine learning is used to automatically differentiate particles on the basis of chemistry and size. The ability of the machine learning algorithm was then tested on a data set for which the particles were not initially known to judge its ability.
Nicole J. Savage and J. Alex Huffman
Atmos. Meas. Tech., 11, 4929–4942,Short summary
We show the systematic application of hierarchical agglomerative clustering (HAC) to comprehensive bioaerosol and non-bioaerosol laboratory data collected with the wideband integrated bioaerosol sensor (WIBS-4A). This study investigated various input conditions and used individual matchups and computational mixtures of particles; it will help improve clustering results applied to data from the ultraviolet laser and light-induced fluorescence instruments commonly used for bioaerosol research.
Jake Zenker, Kristen N. Collier, Guanglang Xu, Ping Yang, Ezra J. T. Levin, Kaitlyn J. Suski, Paul J. DeMott, and Sarah D. Brooks
Atmos. Meas. Tech., 10, 4639–4657,Short summary
We have developed a new method which employs single particle depolarization to determine ice nucleating particle (INP) concentrations and to differentiate between ice crystals, water droplets, and aerosols. The method is used to interpret measurements collected using the Texas A&M Continuous Flow Diffusion Chamber (TAMU CFDC) coupled to a Cloud and Aerosol Spectrometer with Polarization (CASPOL). This new method extends the range of operating conditions for the CFDC to higher supersaturations.
Matthew Osman, Maria A. Zawadowicz, Sarah B. Das, and Daniel J. Cziczo
Atmos. Meas. Tech., 10, 4459–4477,Short summary
This study presents the first-time attempt at using time-of-flight single particle mass spectrometry (SPMS) as an emerging online technique for measuring insoluble particles in glacial snow and ice. Using samples from two Greenlandic ice cores, we show that SPMS can constrain the aerodynamic size, composition, and relative abundance of most particulate types on a per-particle basis, reducing the preparation time and resources required of conventional, filter-based particle retrieval methods.
Simon Ruske, David O. Topping, Virginia E. Foot, Paul H. Kaye, Warren R. Stanley, Ian Crawford, Andrew P. Morse, and Martin W. Gallagher
Atmos. Meas. Tech., 10, 695–708,Short summary
Particles such as bacteria, pollen and fungal spores have important implications within the environment and public health sectors. Here we evaluate the performance of various different methods for distinguishing between these different types of particles using a new instrument. We demonstrate that there may be better alternatives to the currently used methods which can be further investigated in future research.
Yan Lyu, Tingting Xu, Xiang Li, Tiantao Cheng, Xin Yang, Xiaomin Sun, and Jianmin Chen
Atmos. Meas. Tech., 9, 1025–1037,Short summary
This study presents the particle size distribution of PBDEs in the atmosphere of a megacity and evaluates the contribution of size-fractionated PBDEs' deposition in the human respiratory tract.
Matteo Reggente, Ann M. Dillner, and Satoshi Takahama
Atmos. Meas. Tech., 9, 441–454,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).
S. S. Steimer, U. K. Krieger, Y.-F. Te, D. M. Lienhard, A. J. Huisman, B. P. Luo, M. Ammann, and T. Peter
Atmos. Meas. Tech., 8, 2397–2408,Short summary
Atmospheric aerosol is often subject to supersaturated or supercooled conditions where bulk measurements are not possible. Here we demonstrate how measurements using single particle electrodynamic levitation combined with light scattering spectroscopy allow the retrieval of thermodynamic data, optical properties and water diffusivity of such metastable particles even when auxiliary bulk data are not available due to lack of sufficient amounts of sample.
N. Utry, T. Ajtai, M. Pintér, E. Tombácz, E. Illés, Z. Bozóki, and G. Szabó
Atmos. Meas. Tech., 8, 401–410,
A. Quérel, P. Lemaitre, M. Monier, E. Porcheron, A. I. Flossmann, and M. Hervo
Atmos. Meas. Tech., 7, 1321–1330,
F. Freutel, F. Drewnick, J. Schneider, T. Klimach, and S. Borrmann
Atmos. Meas. Tech., 6, 3131–3145,
E. Fuentes and G. McFiggans
Atmos. Meas. Tech., 5, 735–757,
C. D. Cappa
Atmos. Meas. Tech., 3, 579–592,
Bell, J. F., Farrand, W. H., Johnson, J. R., and Morris, R. V.: Low abundance materials at the Mars Pathfinder landing site: An investigation using spectral mixture analysis and related techniques, Icarus, 158, 56–71, https://doi.org/10.1006/icar.2002.6865, 2002.
Bish, D. L. and Chipera, S. J.: Detection of trace amounts of erionite using x-ray-powder diffraction – erionite In Tuffs of Yucca Mountain, Nevada, And Central Turkey, Clay Miner., 39, 437–445, https://doi.org/10.1346/ccmn.1991.0390413, 1991.
Caquineau, S., Magonthier, M. C., Gaudichet, A., and Gomes, L.: An improved procedure for the X-ray diffraction analysis of low-mass atmospheric dust samples, Eur. J. Mineral., 9, 157–166, 1997.
Cheek, L. C. and Pieters, C. M.: Reflectance spectroscopy of plagioclase-dominated mineral mixtures: Implications for characterizing lunar anorthosites remotely, Am. Mineral., 99, 1871–1892, https://doi.org/10.2138/am-2014-4785, 2014.
Clark, R. N.: Spectral properties of mixtures of montmorillonite and dark carbon grains: Implications for remote sensing minerals containing chemically and physically adsorbed water, J. Geophys. Res., 88, 635–644, https://doi.org/10.1029/JB088iB12p10635, 1983.
Clark, R. N.: Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy, in: Manual of Remote Sensing, Wiley, 3–58, ISBN: 978-0-471-29405-4, 1999
Clark, R. N., King, T. V. V., Klejwa, M., Swayze, G. A., and Vergo, N.: High spectral resolution reflectance spectroscopy of minerals, J. Geophys. Res.-Solid, 95, 12653–12680, https://doi.org/10.1029/JB095iB08p12653, 1990.
Combe, J. P., Le Mouelic, S., Sotin, C., Gendrin, A., Mustard, J. F., Le Deit, L., Launeau, P., Bibring, J. P., Gondet, B., Langevin, Y., Pinet, P., and Team, O. S.: Analysis of OMEGA/Mars express data hyperspectral data using a Multiple-Endmember Linear Spectral Unmixing Model (MELSUM): Methodology and first results, Planet Space Sci., 56, 951–975, https://doi.org/10.1016/j.pss.2007.12.007, 2008.
Cooper, C. D. and Mustard, J. F.: Effects of very fine particle size on reflectance spectra of smectite and palagonitic soil, Icarus, 142, 557–570, https://doi.org/10.1006/icar.1999.6221, 1999.
Delany, A. C., Parkin, D. W., Griffin, J. J., Goldberg, E. D., and Reimann, B. E. F.: Airborne dust collected at Barbados, Geochim. Cosmochim. Ac., 31, 885–900, https://doi.org/10.1016/s0016-7037(67)80037-1, 1967.
Dennison, P. E. and Roberts, D. A.: Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE, Remote Sens. Environ., 87, 123–135, https://doi.org/10.1016/S0034-4257(03)00135-4, 2003.
Downs, R. T. and Hall-Wallace, M.: The American mineralogist crystal structure database, Am. Mineral., 88, 247–250, 2003.
Drits, V. A., Zviagina, B. B., McCarty, D. K., and Salyn, A. L.: Factors responsible for crystal-chemical variations in the solid solutions from illite to aluminoceladonite and from glauconite to celadonite, Am. Mineral., 95, 348–361, https://doi.org/10.2138/am.2010.3300, 2010.
Ducasse, E., Adeline, K., Briottet, X., Hohmann, A., Bourguignon, A., and Grandjean, G.: Montmorillonite estimation in clay-quartz-calcite samples from laboratory SWIR imaging spectroscopy: a comparative study of spectral preprocessings and unmixing methods, Remote Sens., 12, 1723, https://doi.org/10.3390/rs12111723, 2020.
Engelbrecht, J., McDonald, E., Gillies, J., Jayanty, R., Casuccio, G., and Gertler, A.: Characterizing mineral dusts and other aerosols from the middle east—part 2: Grab samples and re-suspensions, Inhal. Toxicol., 21, 327–336, https://doi.org/10.1080/08958370802464299, 2009.
Engelbrecht, J. P., Moosmüller, H., Pincock, S., Jayanty, R. K. M., Lersch, T., and Casuccio, G.: Technical note: Mineralogical, chemical, morphological, and optical interrelationships of mineral dust re-suspensions, Atmos. Chem. Phys., 16, 10809–10830, https://doi.org/10.5194/acp-16-10809-2016, 2016.
Engelbrecht, J. P., Stenchikov, G., Prakash, P. J., Lersch, T., Anisimov, A., and Shevchenko, I.: Physical and chemical properties of deposited airborne particulates over the Arabian Red Sea coastal plain, Atmos. Chem. Phys., 17, 11467–11490, https://doi.org/10.5194/acp-17-11467-2017, 2017.
Fratini, G., Ciccioli, P., Febo, A., Forgione, A., and Valentini, R.: Size-segregated fluxes of mineral dust from a desert area of northern China by eddy covariance, Atmos. Chem. Phys., 7, 2839–2854, https://doi.org/10.5194/acp-7-2839-2007, 2007.
Gaffey, S. J.: Spectral reflectance of carbonate minerals in the visible and near infrared (0.35–2.55 microns); calcite, aragonite, and dolomite, Am. Mineral., 71, 151–162, 1986.
Gaffey, S. J., McFadden, L. A., Nash, D., and Pieters, C. M.: Ultraviolet, visible, and near-infrared reflectance spectroscopy: Laboratory spectra of geologic materials, in: Remote geochemical analysis: Elemental and mineralogical composition, edited by: Pieters, C. M. and Englert P. A. J., Cambridge University Press, Cambridge, 43–78, ISBN: 978-0521402811, 1993.
Ganor, E.: Atmospheric dust in Israel. Sedimentological and meteorological analysis of dust deposition, Hebrew University of Jerusalem, 1975.
Ginoux, P.: Atmospheric chemistry: Warming or cooling dust?, Nat. Geosci., 10, 246–247, https://doi.org/10.1038/ngeo2923, 2017.
Goossens, D.: Quantification of the dry aeolian deposition of dust on horizontal surfaces: an experimental comparison of theory and measurements, Sedimentology, 52, 859–873, https://doi.org/10.1111/j.1365-3091.2005.00719.x, 2005.
Goossens, D. and Offer, Z. Y.: An evaluation of the efficiency of some eolian dust collectors, Soil Technol., 7, 25–35, https://doi.org/10.1016/0933-3630(94)90004-3, 1994.
Goossens, D. and Rajot, J. L.: Techniques to measure the dry aeolian deposition of dust in arid and semi-arid landscapes: a comparative study in West Niger, Earth Surf. Proc. Land., 33, 178–195, https://doi.org/10.1002/esp.1533, 2008.
Goss, N. R., Mladenov, N., Seibold, C. M., Chowanski, K., Seitz, L., Wellemeyer, T. B., and Williams, M. W.: Quantifying particulate matter deposition in Niwot Ridge, Colorado: Collection of dry deposition using marble inserts and particle imaging using the FlowCAM, Atmos. Environ., 80, 549–558, https://doi.org/10.1016/j.atmosenv.2013.08.037, 2013.
Goudie, A. and Middleton, N.: Desert Dust in the Global System, Springer, 1–287, https://doi.org/10.1007/3-540-32355-4, 2006.
Green, R. O., Thompson, D. R., and the EMIT Team: An earth science imaging spectroscopy mission: The earth surface mineral dust source investigation (EMIT), Int. Geosci. Remote Se., 6262–6265, https://doi.org/10.1109/igarss39084.2020.9323741, 2020.
Gualtieri, A. F.: Accuracy of XRPD QPA using the combined Rietveld-RIR method, J. Appl. Crystallogr., 33, 267–278, https://doi.org/10.1107/s002188989901643x, 2000.
Hamilton, V. E. and Christensen, P. R.: Determining the modal mineralogy of mafic and ultramafic igneous rocks using thermal emission spectroscopy, J. Geophys. Res.-Planet., 105, 9717–9733, https://doi.org/10.1029/1999je001113, 2000.
Hamilton, V. E., Christensen, P. R., and McSween, H. Y.: Determination of Martian meteorite lithologies and mineralogies using vibrational spectroscopy, J. Geophys. Res.-Planet., 102, 25593–25603, https://doi.org/10.1029/97je01874, 1997.
Hapke, B.: Bidirectional reflectance spectroscopy: 1. Theory, J. Geophys. Res.-Sol. Ea., 86, 3039–3054, https://doi.org/10.1029/JB086iB04p03039, 1981.
Hartshorn, E. J., McDonald, E. V., Weir, W. B., Sweeney, M., Houseman, S. M., and Lacey, T.: An integrated model combining UAS imagery and PI-SWERL for evaluating intra-landform dust emission variability, Report Prepared for U.S. Army Corps of Engineers, Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, https://www.dri.edu/publication/12515/ (last access: 10 February 2022), 2021.
Hiroi, T. and Pieters, C. M.: Estimation of grain sizes and mixing ratios of fine powder mixtures of common geologic minerals, J. Geophys. Res.-Planet., 99, 10867–10879, https://doi.org/10.1029/94je00841, 1994.
Hunt, G. R.: Spectral signatures of particulate minerals in the visible and near infrared, Geophysics, 42, 501–513, https://doi.org/10.1190/1.1440721, 1977.
Johnson, P. E., Smith, M. O., Taylorgeorge, S., and Adams, J. B.: A semiempirical method for analysis of the reflectance spectra of binary mineral mixtures, J. Geophys. Res., 88, 3557–3561, https://doi.org/10.1029/JB088iB04p03557, 1983.
Kandler, K., Schutz, L., Deutscher, C., Ebert, M., Hofmann, H., Jackel, S., Jaenicke, R., Knippertz, P., Lieke, K., Massling, A., Petzold, A., Schladitz, A., Weinzierl, B., Wiedensohler, A., Zorn, S., and Weinbruch, S.: Size distribution, mass concentration, chemical and mineralogical composition and derived optical parameters of the boundary layer aerosol at Tinfou, Morocco, during SAMUM 2006, Tellus B, 61, 32–50, https://doi.org/10.1111/j.1600-0889.2008.00385.x, 2009.
Keshava, N. and Mustard, J. F.: Spectral unmixing, IEEE Signal Proc. Mag., 19, 44–57, https://doi.org/10.1109/79.974727, 2002.
Klein, C., Hurlbut, C. S., and Dana, J. D.: Analytical Methods in Mineral Science, in: The 22nd edition of the manual of mineral science, J. Wiley, New York, 290–332, ISBN: 0-471-25177-1, 2002.
Kokaly, R. F., Clark R. N., Swayze, G. A., Livo, K. E., Hoefen, T. M., Pearson, N. C., Wise, R. A., Benzel, W. M., Lowers, H. A., Driscoll, R. L., Klein A. J.: USGS Spectral Library Version 7, Reston, VA, Report Rep. 1035, 68 pp., https://doi.org/10.3133/ds1035, 2017.
Lapotre, M. G. A., Ehlmann, B. L., and Minson, S. E.: A probabilistic approach to remote compositional analysis of planetary surfaces, J. Geophys. Res.-Planet., 122, 983–1009, https://doi.org/10.1002/2016je005248, 2017.
Leask, E. K., Ehlmann, B. L.: Identifying and quantifying mineral abundance through VSWIR microimaging spectroscopy: A comparison to XRD and SEM, 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (Whispers), Los Angeles, California, 21–24 August 2016, IEEE, 1–5, https://doi.org/10.1109/WHISPERS.2016.8071774, 2016.
Lucey, P. G.: Model near-infrared optical constants of olivine and pyroxene as a function of iron content, J. Geophys. Res.-Planet., 103, 1703–1713, https://doi.org/10.1029/97je03145, 1998.
Maring, H., Savoie, D. L., Izaguirre, M. A., McCormick, C., Arimoto, R., Prospero, J. M., and Pilinis, C.: Aerosol physical and optical properties and their relationship to aerosol composition in the free troposphere at Izana, Tenerife, Canary Islands, during July 1995, J. Geophys. Res.-Atmos., 105, 14677–14700, https://doi.org/10.1029/2000jd900106, 2000.
Metternicht, G. I. and Fermont, A.: Estimating erosion surface features by linear mixture modeling, Remote Sens. Environ., 64, 254–265. https://doi.org/10.1016/S0034-4257(97)00172-7, 1998.
Miller, R. L. and Tegen, I.: Climate response to soil dust aerosols, J. Climate, 11, 3247–3267, https://doi.org/10.1175/1520-0442(1998)011<3247:crtsda>2.0.co;2, 1998.
Moore, D. M. and Reynolds Jr., R. C.: X-Ray diffraction and the identification and analysis of clay minerals, 2nd edn., Oxford University Press, Oxford, New York, ISBN: 978-0195087130, 1997.
Mustard, J. F. and Pieters, C. M.: Quantitative abundance estimates from bidirectional reflectance measurements, J. Geophys. Res.-Solid, 92, E617–E626, https://doi.org/10.1029/JB092iB04p0E617, 1987.
Mustard, J. F. and Pieters, C. M.: Photometric phase functions of common geologic minerals and applications to quantitative-analysis of mineral mixture reflectance spectra, J. Geophys. Res.-Solid, 94, 13619–13634, https://doi.org/10.1029/JB094iB10p13619, 1989.
Nash, D. B. and Conel, J. E.: Spectral reflectance systematics for mixtures of powdered hypersthene, labradorite, and illmenite, J. Geophys. Res., 79, 1615–1621, https://doi.org/10.1029/JB079i011p01615, 1974.
Offer, Z. Y., Goossens, D., and Shachak, M.: Aeolian deposition of nitrogen to sandy and loessial ecosystems in the negev desert, Journal Arid Environ., 23, 355–363, https://doi.org/10.1016/s0140-1963(18)30609-8, 1992.
Pan, C., Rogers, A. D., and Thorpe, M. T.: Quantitative compositional analysis of sedimentary materials using thermal emission spectroscopy: 2. Application to compacted fine-grained mineral mixtures and assessment of applicability of partial least squares methods, J. Geophys. Res.-Planet., 120, 1984–2001, https://doi.org/10.1002/2015je004881, 2015.
Ramsey, M. S. and Christensen, P. R.: Mineral abundance determination: Quantitative deconvolution of thermal emission spectra, J. Geophys. Res.-Sol. Ea., 103, 577–596, https://doi.org/10.1029/97jb02784, 1998.
Reid, E. A., Reid, J. S., Meier, M. M., Dunlap, M. R., Cliff, S. S., Broumas, A., Perry, K., and Maring, H.: Characterization of African dust transported to Puerto Rico by individual particle and size segregated bulk analysis, J. Geophys. Res.-Atmos., 108, 8591, https://doi.org/10.1029/2002jd002935, 2003.
Reynolds, R. L., Goldstein, H. L., Moskowitz, B. M., Kokaly, R. F., Munson, S. M., Solheid, P., Breit, G. N., Lawrence, C. R., and Derry, J.: Dust deposited on snow cover in the San Juan Mountains, Colorado, 2011–2016: Compositional Variability Bearing on Snow-Melt Effects, J. Geophys. Res.-Atmos., 125, 24, https://doi.org/10.1029/2019jd032210, 2020.
Roberts, D. A., Gardner, M., Church, R., Ustin, S., Scheer, G., and Green, R. O.: Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models, Remote Sens. Environ., 65, 267–279, https://doi.org/10.1016/s0034-4257(98)00037-6, 1998.
Robertson, K. M., Milliken, R. E., and Li, S.: Estimating mineral abundances of clay and gypsum mixtures using radiative transfer models applied to visible-near infrared reflectance spectra, Icarus, 277, 171–186, https://doi.org/10.1016/j.icarus.2016.04.034, 2016.
Rogers, A. D. and Aharonson, O.: Mineralogical composition of sands in Meridiani Planum determined from Mars Exploration Rover data and comparison to orbital measurements, J. Geophys. Res.-Planet., 113, E06S14, https://doi.org/10.1029/2007je002995, 2008.
Sadrian, M. R., Mohammadkhan, S., Mashhadi, N., Alavipanah, S. K., and Dashtakian, K.: Analyzing and investigation of dustfall by MDCO (case study: the city of Ilam), International desert research center, University of Tehran, 2012.
Salisbury, J. W. and Walter, L. S.: Thermal infrared (2.5–13.5 µm) spectroscopic remote sensing of igneous rock types on particulate planetary surfaces, J. Geophys. Res.-Solid, 94, 9192–9202, https://doi.org/10.1029/JB094iB07p09192, 1989.
Shahsavani, A., Naddafi, K., Haghighifard, N. J., Mesdaghinia, A., Yunesian, M., Nabizadeh, R., Arahami, M., Sowlat, M. H., Yarahmadi, M., Saki, H., Alimohamadi, M., Nazmara, S., Motevalian, S. A., and Goudarzi, G.: The evaluation of PM10, PM2.5, and PM1 concentrations during the Middle Eastern Dust (MED) events in Ahvaz, Iran, from April through September 2010, J. Arid Environ., 77, 72–83, https://doi.org/10.1016/j.jaridenv.2011.09.007, 2012.
Singer, R. B.: Near-infrared spectral reflectance of mineral mixtures – systematic combinations of pyroxenes, olivine, and iron-oxides, J. Geophys. Res., 86, 7967–7982, https://doi.org/10.1029/JB086iB09p07967, 1981.
Sokolik, I. N. and Toon, O. B.: Incorporation of mineralogical composition into models of the radiative properties of mineral aerosol from UV to IR wavelengths, J. Geophys. Res.-Atmos., 104, 9423–9444, https://doi.org/10.1029/1998jd200048, 1999.
Sokolik, I. N., Winker, D. M., Bergametti, G., Gillette, D. A., Carmichael, G., Kaufman, Y. J., Gomes, L., Schuetz, L., and Penner, J. E.: Introduction to special section: Outstanding problems in quantifying the radiative impacts of mineral dust, J. Geophys. Res.-Atmos., 106, 18015–18027, https://doi.org/10.1029/2000jd900498, 2001.
Sow, M., Goossens, D., and Rajot, J. L.: Calibration of the MDCO dust collector and of four versions of the inverted frisbee dust deposition sampler, Geomorphology, 82, 360–375, https://doi.org/10.1016/j.geomorph.2006.05.013, 2006.
Tegen, I. and Lacis, A. A.: Modeling of particle size distribution and its influence on the radiative properties of mineral dust aerosol, J. Geophys. Res.-Atmos., 101, 19237–19244, https://doi.org/10.1029/95jd03610, 1996.
Tegen, I., Lacis, A. A., and Fung, I.: The influence on climate forcing of mineral aerosols from disturbed soils, Nature, 380, 419–422, https://doi.org/10.1038/380419a0, 1996.
Thomson, J. L. and Salisbury, J. W.: The midinfrared reflectance of mineral mixtures (7–14 µm), Remote Sens. Environ., 45, 1–13, https://doi.org/10.1016/0034-4257(93)90077-b, 1993.
Thorpe, M. T., Rogers, A. D., Bristow, T. F., and Pan, C.: Quantitative compositional analysis of sedimentary materials using thermal emission spectroscopy: 1. Application to sedimentary rocks, J. Geophys. Res.-Planet., 120, 1956–1983, https://doi.org/10.1002/2015je004863, 2015.
von Holdt, J. R. C., Eckardt, F. D., Baddock, M. C., Hipondoka, M. H. T., and Wiggs, G. F. S.: Influence of sampling approaches on physical and geochemical analysis of aeolian dust in source regions, Aeolian Res., 50, 100684, https://doi.org/10.1016/j.aeolia.2021.100684, 2021.
Mineral dust particles originate from surface soils, primarily in arid regions. They can stay suspended in the atmosphere, impacting Earth's radiation budget. Dust particles will have different perturbation effects depending on their composition. We obtained compositional information on dust collected in an urban setting using two different techniques. We recommended using the combination of measurements to determine the variability in dust mineral abundances.
Mineral dust particles originate from surface soils, primarily in arid regions. They can stay...