For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
1
views
42
references
 Top references
cited by
2
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      117
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Development and application of a method to classify airborne pollen taxa concentration using light scattering data

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Although automated pollen monitoring networks using laser optics are well-established in Japan, it is thought that these methods cannot distinguish between pollen counts when evaluating various pollen taxa. However, a method for distinguishing the pollen counts of two pollen taxa was recently developed. In this study, we applied such a method to field evaluate the data of the two main allergens in Japan, Chamaecyparis obtusa and Cryptomeria japonica. We showed that the method can distinguish between the pollen counts of these two species even when they are simultaneously present in the atmosphere. This result indicates that a method for automated and simple two pollen taxa monitoring with high spatial density can be developed using the existing pollen network.

          Related collections

          Most cited references42

          • Record: found
          • Abstract: not found
          • Article: not found

          Automatic and Online Pollen Monitoring

          Ingrid Weichenmeier, Rouven Möller, Stefani Röseler … (2015)
          Article 0 comments Cited 33 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found
            Is Open Access

            Real-time pollen monitoring using digital holography

            Eric Sauvageat, Yanick Zeder, Kevin Auderset … (2020)
            Abstract. We present the first validation of the Swisens Poleno, currently the only operational automatic pollen monitoring system based on digital holography. The device provides in-flight images of all coarse aerosols, and here we develop a two-step classification algorithm that uses these images to identify a range of pollen taxa. Deterministic criteria based on the shape of the particle are applied to initially distinguish between intact pollen grains and other coarse particulate matter. This first level of discrimination identifies pollen with an accuracy of 96 %. Thereafter, individual pollen taxa are recognized using supervised learning techniques. The algorithm is trained using data obtained by inserting known pollen types into the device, and out of eight pollen taxa six can be identified with an accuracy of above 90 %. In addition to the ability to correctly identify aerosols, an automatic pollen monitoring system needs to be able to correctly determine particle concentrations. To further verify the device, controlled chamber experiments using polystyrene latex beads were performed. This provided reference aerosols with traceable particle size and number concentrations in order to ensure particle size and sampling volume were correctly characterized.
            Article 0 comments Cited 31 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found
              Is Open Access

              Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps

              Ingrida Šaulienė, Laura Šukienė, Gintautas Daunys … (2019)
              Abstract. Pollen-induced allergies are among the most prevalent non-contagious diseases, with about a quarter of the European population being sensitive to various atmospheric bioaerosols. In most European countries, pollen information is based on a weekly-cycle Hirst-type pollen trap method. This method is labour-intensive and requires narrow specialized abilities and substantial time, so that the pollen data are always delayed and subject to sampling- and counting-related uncertainties. Emerging new approaches to automatic pollen monitoring can, in principle, allow for real-time availability of the data with no human involvement. The goal of the current paper is to evaluate the capabilities of the new Plair Rapid-E pollen monitor and to construct a first-level pollen recognition algorithm. The evaluation was performed for three devices located in Lithuania, Serbia and Switzerland, with independent calibration data and classification algorithms. The Rapid-E output data include multi-angle scattering images and the fluorescence spectra recorded at several times for each particle reaching the device. Both modalities of the Rapid-E output were treated with artificial neural networks (ANNs) and the results were combined to obtain the pollen type. For the first classification experiment, the monitor was challenged with a large variety of pollen types and the quality of many-to-many classification was evaluated. It was shown that in this case, both scattering- and fluorescence-based recognition algorithms fall short of acceptable quality. The combinations of these algorithms performed better, exceeding 80 % accuracy for 5 out of 11 species. Fluorescence spectra showed similarities among different species, ending up with three well-resolved groups: (Alnus, Corylus, Betula and Quercus), (Salix and Populus) and (Festuca, Artemisia and Juniperus). Within these groups, pollen is practically indistinguishable for the first-level recognition procedure. Construction of multistep algorithms with sequential discrimination of pollen inside each group seems to be one of the possible ways forward. In order to connect the classification experiment to existing technology, a short comparison with the Hirst measurements is presented and the issue of false positive pollen detections by Rapid-E is discussed.
              Article 0 comments Cited 26 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                Kenji Miki: mikik@keio.jp
                Journal
                Journal ID (nlm-ta): Sci Rep
                Journal ID (iso-abbrev): Sci Rep
                Title: Scientific Reports
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2045-2322
                Publication date (Electronic): 16 November 2021
                Publication date PMC-release: 16 November 2021
                Publication date Collection: 2021
                Volume: 11
                Electronic Location Identifier: 22371
                Affiliations
                [1 ]GRID grid.26091.3c, ISNI 0000 0004 1936 9959, Faculty of Science and Technology, , Keio University, ; 3-14-1, Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522 Japan
                [2 ]GRID grid.32197.3e, ISNI 0000 0001 2179 2105, Tokyo Institute of Technology Earth-Life Science Institute, ; 2-12-1-IE-1, Ookayama, Meguro-ku, Tokyo, 152-8550 Japan
                [3 ]Yamatronics Corporation, 2-1, Heiseicho, Yokosuka, Kanagawa 238-0013 Japan
                [4 ]NPO Pollen Information Association, 2-7-4, Motookubo, Narashino, Chiba 275-0012 Japan
                Article
                Publisher ID: 1919
                DOI: 10.1038/s41598-021-01919-7
                PMC ID: 8595647
                PubMed ID: 34785742
                SO-VID: c8d1102f-54f6-4827-b99f-f2b98c741b5b
                Copyright © © The Author(s) 2021
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 16 August 2021
                Date accepted : 8 November 2021
                Categories
                Subject: Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2021

                ScienceOpen disciplines: Uncategorized
                Keywords: environmental sciences,optics and photonics
                Data availability:
                ScienceOpen disciplines: Uncategorized
                Keywords: environmental sciences, optics and photonics

                Comments

                Comment on this article

                scite_

                Similar content117

                See all similar

                Cited by2

                See all cited by

                Most referenced authors427

                See all reference authors