• Construction and application o...

*Result*: Construction and application of a high-resolution pollen numerical model system based on phenology and XGBoost.

Title:
Construction and application of a high-resolution pollen numerical model system based on phenology and XGBoost.
Authors:
Li J; Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai, 200438, China; State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China., An X; State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China. Electronic address: anxq@cma.gov.cn., Liu Z; Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai, 200438, China; State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China., Zhao F; Public Meteorological Service Centre, China Meteorological Administration, Beijing, 100081, China., Huang H; Gansu Provincial Traffic Environment Monitoring Center Co., Ltd, Gansu Provincial Transportation Research institute Group Co., Ltd, Gansu, 730000, China., Hou Q; State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China., Wang Y; State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China.
Source:
Environmental pollution (Barking, Essex : 1987) [Environ Pollut] 2026 Feb 15; Vol. 391, pp. 127607. Date of Electronic Publication: 2025 Dec 26.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier Applied Science Publishers Country of Publication: England NLM ID: 8804476 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-6424 (Electronic) Linking ISSN: 02697491 NLM ISO Abbreviation: Environ Pollut Subsets: MEDLINE
Imprint Name(s):
Original Publication: Barking, Essex, England : Elsevier Applied Science Publishers, c1987-
MeSH Terms:
Environmental Monitoring*/methods , Air Pollutants*/analysis , Allergens*/analysis , Pollen* , Models, Theoretical*, Air Pollution/statistics & numerical data ; Seasons ; Beijing ; Boosting Machine Learning Algorithms
Contributed Indexing:
Keywords: Meteorology; Phenology; Pollen; WRF-Chem-pollen; XGBoost
Substance Nomenclature:
0 (Air Pollutants)
0 (Allergens)
Entry Date(s):
Date Created: 20251228 Date Completed: 20260110 Latest Revision: 20260110
Update Code:
20260130
DOI:
10.1016/j.envpol.2025.127607
PMID:
41456852
Database:
MEDLINE

*Further Information*

*The allergenic characteristics of anemophilous pollen differ among taxa, and its atmospheric concentration plays a critical role in influencing allergy risks and health assessments. Based on phenology and artificial intelligence methods, the study constructed a high-precision pollen numerical model system using 15-year pollen monitoring data of Beijing in spring. The model system considered the phenological phases and multiple plant taxa, and based on thermal accumulation, optimized the start and end dates of pollen release for Cupressaceae and Salicaceae (Stage I) and Pinaceae (Stage II). A normalized pollen emission potential model was constructed by integrating dual-threshold temperature accumulation with phenological probability. XGBoost was used to simulate seasonal pollen integral (SPIn), enabling spatiotemporal modeling of spring pollen emission. To represent pollen transport and dispersion in the atmosphere, a new pollen module was incorporated into the WRF-Chem framework, coupling pollen emission with meteorological adjustment factors such as wind speed, precipitation, and humidity, thus forming the WRF-Chem-Pollen system for high-resolution, hourly pollen simulations. The model demonstrated strong performance, with R exceeding 0.60 in 60 % of the years, confirming its spatiotemporal reliability. Further analysis revealed a compound influence of meteorological factors on SPIn, characterized by a "early-season promotion and late-season suppression" effect within distinct time windows. Stage I pollen dominated spring loads, while Stage II contributions were more stable. Overall, this study establishes a comprehensive framework for modeling spring pollen and elucidates the meteorological drivers of pollen release. The findings provide scientific support for pollen exposure assessment, health forecasting, and urban ecological management.
(Copyright © 2025 Elsevier Ltd. All rights reserved.)*

*Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.*