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dc.contributor.authorMahrt, Fabian
dc.contributor.authorWieder, Jorg
dc.contributor.authorDietlicher, Remo
dc.contributor.authorSmith, Helen R.
dc.contributor.authorStopford, Chris
dc.contributor.authorKanji, Zamin
dc.date.accessioned2019-12-06T01:09:01Z
dc.date.available2019-12-06T01:09:01Z
dc.date.issued2019-06-14
dc.identifier.citationMahrt , F , Wieder , J , Dietlicher , R , Smith , H R , Stopford , C & Kanji , Z 2019 , ' A High Speed Particle Phase Discriminator (PPD-HS) for the classification of airborne particles, as tested in a continuous flow diffusion chamber ' , Atmospheric Measurement Techniques , vol. 12 , no. 6 , pp. 3183–3208 . https://doi.org/10.5194/amt-12-3183-2019
dc.identifier.issn1867-1381
dc.identifier.otherORCID: /0000-0001-8697-4030/work/65667440
dc.identifier.urihttp://hdl.handle.net/2299/21961
dc.description© Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License.
dc.description.abstractA new instrument, the High-speed Particle Phase Discriminator (PPD-HS), developed at the University of Hertfordshire, for sizing individual cloud hydrometeors and determining their phase is described herein. PPD-HS performs an in situ analysis of the spatial intensity distribution of near-forward scattered light for individual hydrometeors yielding shape properties. Discrimination of spherical and aspherical particles is based on an analysis of the symmetry of the recorded scattering patterns. Scattering patterns are collected onto two linear detector arrays, reducing the complete 2-D scattering pattern to scattered light intensities captured onto two linear, one-dimensional strips of light sensitive pixels. Using this reduced scattering information, we calculate symmetry indicators that are used for particle shape and ultimately phase analysis. This reduction of information allows for detection rates of a few hundred particles per second. Here, we present a comprehensive analysis of instrument performance using both spherical and aspherical particles generated in a well-controlled laboratory setting using a vibrating orifice aerosol generator (VOAG) and covering a size range of approximately 3-32 μm. We use supervised machine learning to train a random forest model on the VOAG data sets that can be used to classify any particles detected by PPD-HS. Classification results show that the PPD-HS can successfully discriminate between spherical and aspherical particles, with misclassification below 5% for diameters >3μm. This phase discrimination method is subsequently applied to classify simulated cloud particles produced in a continuous flow diffusion chamber setup. We report observations of small, near-spherical ice crystals at early stages of the ice nucleation experiments, where shape analysis fails to correctly determine the particle phase. Nevertheless, in the case of simultaneous presence of cloud droplets and ice crystals, the introduced particle shape indicators allow for a clear distinction between these two classes, independent of optical particle size. From our laboratory experiments we conclude that PPD-HS constitutes a powerful new instrument to size and discriminate the phase of cloud hydrometeors. The working principle of PPD-HS forms a basis for future instruments to study microphysical properties of atmospheric mixed-phase clouds that represent a major source of uncertainty in aerosol-indirect effect for future climate projections..en
dc.format.extent26
dc.format.extent9756296
dc.language.isoeng
dc.relation.ispartofAtmospheric Measurement Techniques
dc.subjectAtmospheric Science
dc.subjectInstrumentation
dc.titleA High Speed Particle Phase Discriminator (PPD-HS) for the classification of airborne particles, as tested in a continuous flow diffusion chamberen
dc.contributor.institutionSchool of Physics, Astronomy and Mathematics
dc.contributor.institutionCentre for Atmospheric and Climate Physics Research
dc.contributor.institutionCentre for Research in Biodetection Technologies
dc.contributor.institutionParticle Instruments and diagnostics
dc.contributor.institutionCentre for Hazard Detection and Protection Research
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85067259848&partnerID=8YFLogxK
rioxxterms.versionofrecord10.5194/amt-12-3183-2019
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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