Forecasting daily total pollen concentrations on a global scale,Allergy

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Forecasting daily total pollen concentrations on a global scale
Allergy ( IF 12.6 ) Pub Date : 2024-07-12 , DOI: 10.1111/all.16227
László Makra ₁ , Luca Coviello _{2,

3} , Andrea Gobbi ₄ , Giuseppe Jurman ₄ , Cesare Furlanello _{5,

6} , Mauro Brunato ₇ , Lewis H Ziska ₈ , Jeremy J Hess ₉ , Athanasios Damialis ₁₀ , Maria Pilar Plaza Garcia ₁₁ , Gábor Tusnády ₁₂ , Lilit Czibolya ₁ , István Ihász ₁₃ , Áron József Deák ₁ , Edit Mikó ₁₄ , Zita Dorner ₁₅ , Susan K Harry ₁₆ , Nicolas Bruffaerts ₁₇ , Ann Packeu ₁₇ , Annika Saarto ₁₈ , Linnea Toiviainen ₁₈ , Maria Louna-Korteniemi ₁₈ , Sanna Pätsi ₁₈ , Michel Thibaudon ₁₉ , Gilles Oliver ₁₉ , Athanasios Charalampopoulos ₁₀ , Despoina Vokou ₁₀ , Ewa Maria Przedpelska-Wasowicz ₂₀ , Ellý Renée Guðjohnsen ₂₀ , Maira Bonini ₂₁ , Sevcan Celenk ₂₂ , Cumali Ozaslan ₂₃ , Jae-Won Oh ₂₄ , Krista Sullivan ₂₅ , Linda Ford ₂₆ , Michelle Kelly ₂₆ , Estelle Levetin ₂₇ , Dorota Myszkowska ₂₈ , Elena Severova ₂₉ , Regula Gehrig ₃₀ , María Del Carmen Calderón-Ezquerro ₃₁ , César Guerrero Guerra ₃₁ , Manuel Andres Leiva-Guzmán ₃₂ , Germán Darío Ramón ₃₃ , Laura Beatriz Barrionuevo ₃₄ , Jonny Peter ₃₅ , Dilys Berman ₃₆ , Connie H Katelaris ₃₇ , Janet M Davies _{38,

39} , Pamela Burton ₄₀ , Paul J Beggs ₄₁ , Sandra María Vergamini ₄₂ , Rosa María Valencia-Barrera ₄₃ , Claudia Traidl-Hoffmann _{44,

45,

46}

Affiliation

Institute of Economics and Rural Development, Faculty of Agriculture, University of Szeged, Hódmezővásárhely, Hungary.
University of Trento, Trento, Italy.
Enogis s.r.l., Trento, Italy.
Bruno Kessler Foundation, Trento, Italy.
HK3 Lab, Rovereto, Italy.
LIGHT Center, Brescia, Italy.
Department of Information Engineering and Computer Science, University of Trento, Trento, Italy.
Mailman School of Public Health, Columbia University, New York, New York, USA.
Department of Global Health, University of Washington, Seattle, State of Washington, USA.
Department of Ecology, School of Biology, Faculty of Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Environmental Medicine, Faculty of Medicine, University Clinic of Augsburg & University of Augsburg, Augsburg, Germany.
Alfréd Rényi Institute of Mathematics, Budapest, Hungary.
Hungarian Meteorological Service, Budapest, Hungary.
Institute of Animal Science and Wildlife Management, Faculty of Agriculture, University of Szeged, Hódmezővásárhely, Hungary.
Department of Integrated Plant Protection, Hungarian University of Agriculture and Life Science (MATE) (former SZIE), Plant Protection Institute, Gödöllő, Hungary.
Department of Veterinary Medicine, University of Alaska Fairbanks, Fairbanks, Alaska, USA.
Mycology & Aerobiology Service, Brussels, Belgium.
Biodiversity Unit, University of Turku, Turku, Finland.
Réseau National de Surveillance Aérobiologique, Brussieu, France.
Icelandic Institute of Natural History, Garðabær, Iceland.
Department of Hygiene and Health Prevention, ATS (Agency for Health Protection of Metropolitan Area of Milan), Hygiene and Public Health Service, Milan, Italy.
Science and Art Faculty, Biology Department, Aerobiology Laboratory, Uludag University, Bursa, Turkey.
Department of Plant Protection (Weed Science), Dicle University, Diyarbakir, Turkey.
Department of Pediatrics & Adolescent, College of Medicine, Hanyang University, Medical Center, Guri Hospital, Seoul, South Korea.
Clinical Research Institute, Minneapolis, Minnesota, USA.
Asthma and Allergy Center, Bellevue, Nebraska, USA.
University of Tulsa, College of Engineering & Natural Sciences, Department of Biological Science, Tulsa, Oklahoma, USA.
Jagiellonian University, Medical College, Department of Clinical and Environmental Allergology, Kraków, Poland.
Biological Faculty, Lomonosov Moscow State University, Moscow, Russia.
Federal Department of Home Affairs FDHA, Federal Office of Meteorology and Climatology MeteoSwiss, Zurich-Airport, Switzerland.
Centro de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México (UNAM), Circuito Exterior, Ciudad Universitaria, México, Mexico.
Departamento de Química, Facultad de Ciencias, Universidad de Chile, Santiago, Chile.
Hospital Italiano Regional del Sur, Bahía Blanca, Argentina.
Instituto de Alergia e Inmunologia del Sur, AAAeIC Pollen Station, Bahía Blanca, Argentina.
Department of Medicine, Division of Allergy and Clinical Immunology, Groote Schuur Hospital, University of Cape Town, Groote Schuur, South Africa.
Allergy Immunology Department, University of Cape Town Lung Institute, Cape Town, South Africa.
Western Sydney University and Campbelltown Hospital, Campbelltown, New South Wales, Australia.
School of Biomedical Science, Queensland University of Technology, Herston, Queensland, Australia.
Office of Research, Metro North Hospital and Health Service, Herston, Queensland, Australia.
Department of Medicine, Immunology and Allergy, Campbelltown Hospital, Campbelltown, New South Wales, Australia.
School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales, Australia.
Centro de Ciȇncias Biológicas e da Saúde, Museu de Ciȇncias Naturais, University of Caxias do Sul, Caxias do Sul, Brazil.
Departamento de Biología Vegetal, Universidad de León, León, Spain.
Chair of Environmental Medicine, Technical University of Munich, Augsburg, Germany.
Institute of Environmental Medicine, Helmholtz Centre, Munich, Augsburg, Germany.
Department of Environmental Medicine, Faculty of Medicine, University of Augsburg, Augsburg, Germany.

BackgroundThere is evidence that global anthropogenic climate change may be impacting floral phenology and the temporal and spatial characteristics of aero‐allergenic pollen. Given the extent of current and future climate uncertainty, there is a need to strengthen predictive pollen forecasts.MethodsThe study aims to use CatBoost (CB) and deep learning (DL) models for predicting the daily total pollen concentration up to 14 days in advance for 23 cities, covering all five continents. The model includes the projected environmental parameters, recent concentrations (1, 2 and 4 weeks), and the past environmental explanatory variables, and their future values.ResultsThe best pollen forecasts include Mexico City (R2(DL_7) ≈ .7), and Santiago (R2(DL_7) ≈ .8) for the 7th forecast day, respectively; while the weakest pollen forecasts are made for Brisbane (R2(DL_7) ≈ .4) and Seoul (R2(DL_7) ≈ .1) for the 7th forecast day. The global order of the five most important environmental variables in determining the daily total pollen concentrations is, in decreasing order: the past daily total pollen concentration, future 2 m temperature, past 2 m temperature, past soil temperature in 28–100 cm depth, and past soil temperature in 0–7 cm depth. City‐related clusters of the most similar distribution of feature importance values of the environmental variables only slightly change on consecutive forecast days for Caxias do Sul, Cape Town, Brisbane, and Mexico City, while they often change for Sydney, Santiago, and Busan.ConclusionsThis new knowledge of the ecological relationships of the most remarkable variables importance for pollen forecast models according to clusters, cities and forecast days is important for developing and improving the accuracy of airborne pollen forecasts.

中文翻译：

预测全球范围内每日花粉总浓度

背景有证据表明，全球人为气候变化可能会影响花卉物候以及气致敏花粉的时空特征。鉴于当前和未来气候不确定性的程度，有必要加强花粉预测。方法该研究旨在使用 CatBoost (CB) 和深度学习 (DL) 模型提前 14 天预测每日总花粉浓度23个城市，覆盖五大洲。该模型包括预测的环境参数、最近的浓度（1、2 和 4 周）以及过去的环境解释变量及其未来值。结果最佳花粉预测包括墨西哥城（右2 (DL_7) ≈ .7) 和圣地亚哥 (右2第 7 个预报日分别为 (DL_7) ≈ .8)；而布里斯班的花粉预测最弱（右2 (DL_7) ≈ .4) 和首尔 (右2 (DL_7) ≈ .1) 第 7 个预测日。确定每日总花粉浓度的五个最重要环境变量的全局顺序按降序排列：过去每日总花粉浓度、未来 2 m 温度、过去 2 m 温度、过去 28-100 cm 深度土壤温度、和过去 0-7 厘米深度的土壤温度。南卡希亚斯、开普敦、布里斯班和墨西哥城的环境变量特征重要性值分布最相似的城市相关集群在连续预报日仅略有变化，而悉尼、圣地亚哥和釜山则经常变化。结论根据集群、城市和预报天数对花粉预报模型最显着变量重要性的生态关系的新认识对于开发和提高空气花粉预报的准确性非常重要。

更新日期：2024-07-12

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