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Automated pollen monitoring system using laser optics for observing seasonal changes in the concentration of total airborne pollen

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Abstract

The development of a simple and automatic pollen measurement methodology is required to manage allergic problems caused by airborne pollen. We developed a device and algorithm to automatically monitor airborne pollen by using basic laser optics technology. The device measures the sideward and forward scattering intensities of laser light from each particle. Because this device provides detailed temporal variation in the pollen concentration, the dispersal dynamics of airborne pollen can be effectively analyzed. We compared the pollen counts obtained with the automated method and standard Hirst-type method. Scatter-plot graphs were constructed of the forward and sideward scattering intensities of the observed particles. An extract window methodology was used to estimate the concentrations of the major allergenic pollens. The extract window parameters were obtained for major types of allergenic pollen. The results suggest the possibility of developing a device for monitoring several types of airborne pollen simultaneously. We determined the standard extract window to be used for estimating the concentration of all types of airborne pollen together. A field experiment was performed to evaluate the automated monitoring system with the standard extract window. The estimated temporal variation pattern of the total airborne pollen concentration agreed well with the observed temporal variation pattern for the whole pollen season. The pollen monitor was able to estimate the overall features of seasonal changes in the total airborne pollen concentration.

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References

  • Aronne, G., Cavuoto, D., & Eduardo, P. (2001). Classification and counting of fluorescent pollen using an image analysis system. Biotechnic and Histochemistry, 76, 35–40.

    Article  CAS  Google Scholar 

  • Bechar, A., Gan-Mor, S., Vaknin, Y., Shemulevich, I., Ronen, B., & Eisikowitch, D. (1997). An image-analysis technique for accurate counting of pollen on stigmas. New Phytologist, 137, 639–643.

    Article  Google Scholar 

  • Bennett, K. D. (1990). Pollen counting on a pocket computer. New Phytologist, 114, 275–280.

    Article  Google Scholar 

  • Chen, C., Hendriks, E. A., Duin, R. P. W., Reiber, J. H. C., Hiemstra, P. S., de Weger, L. A., et al. (2006). Feasibility study on automated recognition of allergenic pollen: Grass, birch and mugwort. Aerobiologia, 22, 275–284.

    Article  Google Scholar 

  • Crouzy, B., Stella, M., Konzelmann, T., Calpini, B., & Clot, B. (2016). All-optical automatic pollen identification: Towards an operational system. Atmospheric Environment, 140, 202–212.

    Article  CAS  Google Scholar 

  • Galan, C., Smith, M., Thibaudon, M., Frenguelli, G., Oteros, J., Gehrig, R., Berger, U., Clot, B., Brandao, R., EAS QC Working Group. (2014). Pollen monitoring: Minimum requirements and reproducibility of analysis. Aerobiologia, 30, 385–395.

    Article  Google Scholar 

  • Gottardini, E., Rossi, S., Cristofolini, F., & Benedetti, L. (2007). Use of Fourier transform infrared (FT-IR) spectroscopy as a tool for pollen identification. Aerobiologia, 23, 211–219.

    Article  Google Scholar 

  • Harder, L. D. (1990). Pollen removal by bumblebees and its implications for pollen dispersal. Ecology, 71, 1110–1125.

    Article  Google Scholar 

  • Healy, D. A., O’Connor, D. J., Burke, A. M., & Sodeau, J. R. (2012). A laboratory assessment of the waveband integrated bioaerosol sensor (WIBS-4) using individual samples of pollen and fungal spore material. Atmospheric Environment, 60, 534–543.

    Article  CAS  Google Scholar 

  • Hinz, K. P., Greweling, M., Drews, F., & Spengler, B. (1999). Data processing in on-line laser mass spectrometry of inorganic, organic, or biological airborne particles. Journal of the American Society for Mass Spectrometry, 10, 648–660.

    Article  CAS  Google Scholar 

  • Kawashima, S., & Takahashi, Y. (1995). Modelling and simulation of mesoscale dispersion processes for airborne cedar pollen. Grana, 34, 142–150.

    Article  Google Scholar 

  • Kawashima, S., & Takahashi, Y. (1999). An improved simulation of mesoscale dispersion of airborne cedar pollen using a flowering-time map. Grana, 38, 316–324.

    Article  Google Scholar 

  • Kawashima, S., Clot, B., Fujita, T., Takahashi, Y., & Nakamura, K. (2007). An algorithm and a device for counting airborne pollen automatically using laser optics. Atmospheric Environment, 41, 7987–7993.

    Article  CAS  Google Scholar 

  • Kiselev, D., Bonacina, L., & Wolf, J.-P. (2011). Individual bioaerosol particle discrimination by multi-photon excited fluorescence. Optics Express, 19, 24516–24521.

    Article  CAS  Google Scholar 

  • Kiselev, D., Bonacina, L., & Wolf, J.-P. (2013). A flash-lamp based device for fluorescence detection and identification of individual pollen grains. Review of Scientific Instruments, 84, 033302.

    Article  Google Scholar 

  • Landsmeer, S. H., Hendriks, E. A., De Weger, L. A., Reiber, J. H. C., & Stoel, B. C. (2009). Detection of pollen grains in multifocal optical microscopy images of air samples. Microscopy Research and Technique, 72, 424–430.

    Article  Google Scholar 

  • Longhi, S., Cristofori, A., Gatto, P., Cristofolini, F., Grando, M. S., & Gottardini, E. (2009). Biomolecular identification of allergenic pollen: A new perspective for aerobiological monitoring? Annals of Allergy, Asthma & Immunology, 103, 508–514.

    Article  CAS  Google Scholar 

  • Marcos, J. V., Nava, R., Cristóbal, G., Redondo, R., Escalante-Ramírez, B., Bueno, G., et al. (2015). Automated pollen identification using microscopic imaging and texture analysis. Micron, 68, 36–46.

    Article  Google Scholar 

  • Mishchenko, M. I., Hovenier, J. W., & Travis, L. D. (2000). Light scattering by nonspherical particles: Theory, measurements, and applications. San Diego: Academic Press.

    Google Scholar 

  • Mitsumoto, K., Yabusaki, K., Kobayashi, K., & Aoyagi, H. (2010). Development of a novel real-time pollen-sorting counter using species-specific pollen autofluorescence. Aerobiologia, 26, 99–111.

    Article  Google Scholar 

  • O’Connor, D. J., Healy, D. A., & Sodeau, J. R. (2013). The on-line detection of biological particle emissions from selected agricultural materials using the WIBS-4 (waveband integrated bioaerosol sensor) technique. Atmospheric Environment, 80, 415–425.

    Article  Google Scholar 

  • O’Connor, D. J., Healy, D. A., Hellebust, S., Buters, J. T. M., & Sodeau, J. R. (2014). Using the WIBS-4 (waveband integrated bioaerosol sensor) technique for the on-line detection of pollen grains. Aerosol Science and Technology, 48, 341–349.

    Article  Google Scholar 

  • Oteros, J., Pusch, G., Weichenmeier, I., Heimann, U., Möller, R., Röseler, S., et al. (2015). Automatic and online pollen monitoring. International Archives of Allergy and Immunology, 167, 158–166.

    Article  Google Scholar 

  • Ranzato, M., Taylor, P. E., House, J. M., Flagan, R. C., LeCun, Y., & Perona, P. (2007). Automatic recognition of biological particles in microscopic images. Pattern Recognition Letters, 28, 31–39.

    Article  Google Scholar 

  • Rittenour, W. R., Hamilton, R. G., Beezhold, D. H., & Green, B. J. (2012). Immunologic, spectrophotometric and nucleic acid based methods for the detection and quantification of airborne pollen. Journal of Immunological Methods, 383, 47–53.

    Article  CAS  Google Scholar 

  • Stanley, W. R., Kaye, P. H., Foot, V. E., Barrington, S. J., Gallagher, M., & Gabey, A. (2011). Continuous bioaerosol monitoring in a tropical environment using a UV fluorescence particle spectrometer. Atmospheric Science Letters, 12, 195–199.

    Article  Google Scholar 

  • Takahashi, Y., Aoyama, M., Abe, E., Aita, T., Kawashima, S., Ohta, N., et al. (2008). Development of electron spin resonance radical immunoassay for measurement of airborne orchard grass (Dactylis glomerata) pollen antigens. Aerobiologia, 24, 53–59.

    Article  Google Scholar 

  • Wagner, J., & Macher, J. (2012). Automated spore measurements using microscopy, image analysis, and peak recognition of near-monodisperse aerosols. Aerosol Science and Technology, 46, 862–873.

    Article  CAS  Google Scholar 

  • Xu, R. (2000). Particle characterization: Light-scattering methods. Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Young, H. J., & Stanton, M. L. (1990). Influences of floral variation on pollen removal and seed production in wild radish. Ecology, 71, 536–547.

    Article  Google Scholar 

Download references

Acknowledgements

We thank Dr. Seiichiro Yonemura of the National Institute for Agro-Environmental Sciences for the greatly inspiring discussions. In addition, we thank Ms. Yuriko Arakawa of the secretary section at the Graduate School of Agriculture, Kyoto University, for performing numerous tasks on our behalf. This research did not receive any specific grant from funding agencies in the public, commercial, or nonprofit sectors.

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Authors and Affiliations

  1. Graduate School of Agriculture, Kyoto University, Kyoto, 606-8502, Japan

    Shigeto Kawashima & Soken Matsuda

  2. Le Réseau National de Surveillance Aérobiologique, 69690, Brussieu, France

    Michel Thibaudon & Gilles Oliver

  3. Yamato Engineering Company Ltd., Heiseicho, Yokosuka, 238-0013, Japan

    Toshio Fujita

  4. Federal Office of Meteorology and Climatology MeteoSwiss, 1530, Payerne, Switzerland

    Natalie Lemonis & Bernard Clot

Authors
  1. Shigeto Kawashima
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  2. Michel Thibaudon
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  3. Soken Matsuda
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  4. Toshio Fujita
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  5. Natalie Lemonis
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  6. Bernard Clot
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  7. Gilles Oliver
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Correspondence to Shigeto Kawashima.

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Kawashima, S., Thibaudon, M., Matsuda, S. et al. Automated pollen monitoring system using laser optics for observing seasonal changes in the concentration of total airborne pollen. Aerobiologia 33, 351–362 (2017). https://doi.org/10.1007/s10453-017-9474-6

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  • DOI: https://doi.org/10.1007/s10453-017-9474-6

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