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МЕЖДУНАРОДНЫЙ ОПЫТ ПРИМЕНЕНИЯ СИСТЕМ ПЫЛЬЦЕВОГО МОНИТОРИНГА

• С. 99-103
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Для цитирования

Лебедев Г.С., Юхновская Ю.Д., Гостеева Е.А., Родионов А.С., Боева О.С., Ханчук И.В., Яворская А.В., Флорен А.М., Сафарова Д.Д. // Информационно-экономические аспекты стандартизации и технического регулирования. 2025. № 5 (86). С. 99–103.

Аннотация

Целью исследования является повышение осведомленности о проблеме аллергии на пыльцу и важности использования современных технологий на основе искусственного интеллекта для мониторинга и контроля аллергенов в окружающей среде. Анализ и поиск литературы был выполнен с использованием основных зарубежных и отечественных источников в общедоступных научных базах данных: PubMed, Cochrane, Elibrary. В качестве ключевых слов использовались: «Пыльцевой мониторинг», «Обнаружение пыльцы», «Распознавание пыльцы», “AI pollen identification”. Результаты: автоматизированный процесс мониторинга пыльцы включает 4 основных этапа: сбор данных, обнаружение, классификация и распространение результатов (публикация отчетов, в сети Интернет). Описанный метод может быть применен для распознавания образцов пыльцы, не поддающихся микроскопической идентификации. Проведенный литературный анализ подтверждает необходимость разработки автоматизированных систем мониторинга, работающих в реальном времени с минимальным включением человека в процесс диагностики

Ключевые слова

пыльцевой мониторинг мониторинг качества воздуха анализ изображений на основе искусственного интеллекта прогнозирование концентрации пыльцы

Об авторах

Лебедев Г. С.

Лебедев Г. С. — Первый МГМУ им. И.М. Сеченова, ( Москва, Россия )

Юхновская Ю. Д.

Юхновская Ю. Д. — Первый МГМУ им. И.М. Сеченова, ( Москва, Россия )

Гостеева Е. А.

Гостеева Е. А. — Первый МГМУ им. И.М. Сеченова, ( Москва, Россия )

Родионов А. С.

Родионов А. С. — БГУ им Доржи Банзарова, ( Улан-Удэ, Россия )

Боева О. С.

Боева О. С. — ФГБНУ «Научно-исследовательский институт фундаментальной и клинической иммунологии», ( Новосибирск, Россия )

Ханчук И. В.

Ханчук И. В. — Первый МГМУ им. И.М. Сеченова,, ( Москва, Россия )

Яворская А. В.

Яворская А. В. — ФГБОУ ВО АМУРСКАЯ ГМА, ( Благовещенск, Россия )

Флорен А. М.

Флорен А. М. — СПбГУ, ( Санкт-Петербург, Россия )

Сафарова Д. Д.

Сафарова Д. Д. — СПбГУ, ( Санкт-Петербург, Россия )

Список литературы

  1. 1. Plaza M.P., Kolek F., Leier-Wirtz V., et al. Detecting airborne pollen using an automatic, real-time monitoring system: evidence from two sites. International journal of environmental research and public health. 2022; 19(4):2471.
  2. 2. Goyal A., Khaiwal R., Mor S. Role of winds and air masses in the transport of non-local pinus sp. pollen at an urban location in North India: A statistical perspective. Atmospheric Pollution Research. 2024;15(3):102014.
  3. 3. Charalampopoulos A, Damialis A., Lazarina M., et al. Spatiotemporal assessment of airborne pol-len in the urban environment: The pollenscape of Thessaloniki as a case study. Atmospheric Envi-ronment. 2021; 247:118185.
  4. 4. Pointner L., Bethanis A., Thaler M., et al. Initiating pollen sensitization – complex source, com-plex mechanisms. Clinical and Translational Allergy. 2020;10(1):36.
  5. 5. Wang Z., Wang Z., Wang L. Automatic 3D pollen recognition based on convolutional neural network. Scientific Programming. 2021; 1:5577307.
  6. 6. Viertel P., König M. Pattern recognition methodologies for pollen grain image classification: A survey. Machine Vision and Applications. 2022;33(1):18.
  7. 7. Hirst J.M. An automatic volumetric spore trap. Annals of Applied Biology. 1952; 39(2): 257–265.
  8. 8. Schiele J., Rabe F., Schmitt M., et al. Automated classification of airborne pollen using neural networks. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2019, pp. 4474-4478
  9. 9. Buters J.T., Antunes C., Galveias A., et al. Pollen and spore monitoring in the world. Clinical and Translational Allergy. 2018; 8(1): 9.
  10. 10. Leru P.M., Eftimie A.M., Anton V.F., Thibaudon M. Five-year data on pollen monitoring, distribution and health impact of allergenic plants in Bucharest and the southeastern region of Romania. Medicina. 2019;55(5): 140.
  11. 11. Oteros J., Pusch G., Weichenmeier I., et al. Automatic and online pollen monitoring. International archives allergy immunol. 2015;167(3): 158–166.
  12. 12. Polling M., Li C., Cao L., et al. Neural networks for increased accuracy of allergenic pollen monitoring. Scientific Reports. 2021. T. 11, № 1. Art 11357.
  13. 13. Wang Z., Bao W., Lin D., Wang Z. A local feature descriptor based on sift for 3D pollen image recognition. IEEE Access. 2019;7: 152658–152666.
  14. 14. Xie Y., Wang Z., Zhao X., Zhu Y. Orientational local binary pattern extraction method for 3D pollen image. Journal of Computer-Aided Design & Computer Graphics. 2018; 30(3): 408.
  15. 15. Galán C., Smith M., Thibaudon M., et al. Pollen monitoring: minimum requirements and reproducibility of analysis. Aerobiologia. 2014; 30(4): 385–395.
  16. 16. Ziska L.H., Makra L., Harry S.K., et al. Temperature-related changes in airborne allergenic pollen abundance and seasonality across the northern hemisphere: a retrospective data analysis. The Lancet Planet Health. 2019;3(3): 124–131.
  17. 17. Anderegg W.R., Abatzoglou J.T., Anderegg L.D., et al. Anthropogenic climate change is worsening North American pollen seasons. Proceedings of the National Academy of Sciences. 2021;118(7): e2013284118.
  18. 18. Warner JO, Kaliner MA, Crisci CD, et al. Allergy practice worldwide: a report by the World Allergy Organization Specialty and Training Council. International archives of allergy and immunology. 2006;139(2): 166–74.
🇬🇧 In English

INTERNATIONAL EXPERIENCE OF USING POLLEN MONITORING SYSTEMS

For citation

Lebedev G.S., Yukhnovskaya Y.D., Gosteeva E.A., Rodionov A.S., et al. International experience of using pollen monitoring systems. Information and Economic Aspects of Standardization and Technical Regulation. 2025; 5 (86): 99–103. (In Russ.).

Abstract

The aim of the study is to raise awareness about the problem of pollen allergy and the importance of using modern arti cial intelligence-based technologies to monitor and control allergens in the environment.The analysis and literature search were performed using the main foreign and domestic sources in publicly available scienti c databases: PubMed, Cochrane, Elibrary. The keywords used were: “Pollen monitoring”, “Pollen detection“, ”Pollen recognition“, “AI pollen identification”. Results: The automated pollen monitoring process includes 4 main stages: data collection, detection, classi cation and dissemination of results (publication of reports on the Internet). The described method can be used to recognize pollen samples that are not amenable to microscopic identi cation.The literature analysis con rms the need to develop automated monitoring systems operating in real time with minimal human involvement in the diagnostic process.

Keywords

pollen monitoring air quality monitoring artificial intelligence-based image analysis and pollen concentration prediction.

About the authors

Lebedev G. S.

Lebedev G. S. — I.M. Sechenov First Moscow State Medical University, ( Moscow, Russia )

Yukhnovskaya Y. D.

Yukhnovskaya Y. D. — I.M. Sechenov First Moscow State Medical University, ( Moscow, Russia )

Gosteeva E. A.

Gosteeva E. A. — I.M. Sechenov First Moscow State Medical University, ( Moscow, Russia )

Rodionov A. S.

Rodionov A. S. — Buryat State University named after D. Banzarov, ( Ulan-Ude, Russia )

Boeva O. S.

Boeva O. S. — Federal State Budgetary Scientific Institution «Research Institute of Fundamental and Clinical Immunology», ( Novosibirsk, Russia )

Khanchuk I. V.

Khanchuk I. V. — I.M. Sechenov First Moscow State Medical University, ( Moscow, Russia )

Yavorskaya A. V.

Yavorskaya A. V. — Federal State Budgetary Educational Institution of Higher Education «Amur State Medical Academy» of the Ministry of Health of the Russian Federation, ( Blagoveshchensk, Russia )

Floren A. M.

Floren A. M. — Saint Petersburg State University, ( St. Petersburg, Russia )

Safarova D. D.

Safarova D. D. — Saint Petersburg State University, ( St. Petersburg, Russia )

References

  1. 1. Plaza M.P., Kolek F., Leier-Wirtz V., et al. Detecting airborne pollen using an automatic, real-time monitoring system: evidence from two sites. International journal of environmental research and public health. 2022; 19(4):2471.
  2. 2. Goyal A., Khaiwal R., Mor S. Role of winds and air masses in the transport of non-local pinus sp. pollen at an urban location in North India: A statistical perspective. Atmospheric Pollution Research. 2024;15(3):102014.
  3. 3. Charalampopoulos A, Damialis A., Lazarina M., et al. Spatiotemporal assessment of airborne pol-len in the urban environment: The pollenscape of Thessaloniki as a case study. Atmospheric Envi-ronment. 2021; 247:118185.
  4. 4. Pointner L., Bethanis A., Thaler M., et al. Initiating pollen sensitization – complex source, com-plex mechanisms. Clinical and Translational Allergy. 2020;10(1):36.
  5. 5. Wang Z., Wang Z., Wang L. Automatic 3D pollen recognition based on convolutional neural network. Scientific Programming. 2021; 1:5577307.
  6. 6. Viertel P., König M. Pattern recognition methodologies for pollen grain image classification: A survey. Machine Vision and Applications. 2022;33(1):18.
  7. 7. Hirst J.M. An automatic volumetric spore trap. Annals of Applied Biology. 1952; 39(2): 257–265.
  8. 8. Schiele J., Rabe F., Schmitt M., et al. Automated classification of airborne pollen using neural networks. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2019, pp. 4474-4478
  9. 9. Buters J.T., Antunes C., Galveias A., et al. Pollen and spore monitoring in the world. Clinical and Translational Allergy. 2018; 8(1): 9.
  10. 10. Leru P.M., Eftimie A.M., Anton V.F., Thibaudon M. Five-year data on pollen monitoring, distribution and health impact of allergenic plants in Bucharest and the southeastern region of Romania. Medicina. 2019;55(5): 140.
  11. 11. Oteros J., Pusch G., Weichenmeier I., et al. Automatic and online pollen monitoring. International archives allergy immunol. 2015;167(3): 158–166.
  12. 12. Polling M., Li C., Cao L., et al. Neural networks for increased accuracy of allergenic pollen monitoring. Scientific Reports. 2021. T. 11, № 1. Art 11357.
  13. 13. Wang Z., Bao W., Lin D., Wang Z. A local feature descriptor based on sift for 3D pollen image recognition. IEEE Access. 2019;7: 152658–152666.
  14. 14. Xie Y., Wang Z., Zhao X., Zhu Y. Orientational local binary pattern extraction method for 3D pollen image. Journal of Computer-Aided Design & Computer Graphics. 2018; 30(3): 408.
  15. 15. Galán C., Smith M., Thibaudon M., et al. Pollen monitoring: minimum requirements and reproducibility of analysis. Aerobiologia. 2014; 30(4): 385–395.
  16. 16. Ziska L.H., Makra L., Harry S.K., et al. Temperature-related changes in airborne allergenic pollen abundance and seasonality across the northern hemisphere: a retrospective data analysis. The Lancet Planet Health. 2019;3(3): 124–131.
  17. 17. Anderegg W.R., Abatzoglou J.T., Anderegg L.D., et al. Anthropogenic climate change is worsening North American pollen seasons. Proceedings of the National Academy of Sciences. 2021;118(7): e2013284118.
  18. 18. Warner JO, Kaliner MA, Crisci CD, et al. Allergy practice worldwide: a report by the World Allergy Organization Specialty and Training Council. International archives of allergy and immunology. 2006;139(2): 166–74.