High-resolution mapping of allergenic pollen risk across China using ensemble machine learning

作者：Jie Yin, Yuan Zhang, Yong Du, Yuhui Ouyang, Chengshuo Wang, Zhiqi Ma, Hongtian Wang, Si Sun, Luo Zhang, Rui Chen · 发表于：Ecotoxicology and Environmental Safety · 年份：2026 · DOI：10.1016/j.ecoenv.2025.119659 · 被引用次数：3 · 研究领域：Allergic Rhinitis and Sensitization、Indoor Air Quality and Microbial Exposure、Air Quality Monitoring and Forecasting

Airborne pollen is a key environmental allergen affecting millions across China. As pollen levels and allergy prevalence continue to rise under rapid urbanization and climate change, developing spatially explicit, long-term pollen datasets becomes increasingly important for public health and ecological risk assessment. In this study, we developed a novel ensemble machine learning framework integrating random forest and gradient boosting models to estimate daily tree and herbaceous pollen concentrations across mainland China from 2011 to 2023. Models were trained using daily pollen data from 27 monitoring sites during 2019–2024 and a rich set of predictors, including meteorological, vegetation, land use, and spatiotemporal variables. By applying the trained models to historical environmental datasets, we reconstructed nationwide daily pollen concentrations for 2011–2023 to extend the temporal coverage beyond the observational record. The models achieved high accuracy, with R 2 values of 0.90 (tree) and 0.89 (herbaceous), and root mean square errors of 0.58 and 0.49, respectively. Tree pollen peaked in early spring in eastern, northeastern, central, and southwestern regions, while herbaceous pollen peaked in late summer in northern and northwestern areas. Seasonal timing, temperature, and vegetation indices were key drivers, with short-term lagged temperature (0–7 days) strongly influencing predictions. This study provides the first nationwide, long-term, daily pollen dataset f...