Accurate pollen forecasting models can help the self-management of allergic respiratory diseases. Our study introduces and validates, for the first time, a pollen modelling system covering the Veneto Region (Italy) at the 3 km spatial resolution for 2019. The model simulated the pollen dispersion, diffusion and deposition processes, using vegetation cover (VC) maps, phenological pollen emission algorithms, and meteorological forecasting. We have specifically analysed the influence of the spatial resolution of VC maps on predicted airborne pollen concentrations for alder, birch, olive, grass, and ragweed. Two VC datasets were used: CAMS VC: the European CAMS dataset at ca. 10 km horizontal resolution; detailed VC: high-resolution datasets (from 250 m to 1 km spatial resolution). Predicted daily averaged concentrations obtained with CAMS and detailed VC were compared to the observations collected at 15 monitoring stations using model performance indicators and pollen seasonal-derived parameters. A stratified analysis assessed performance variations in lowland versus mountain environments. The results showed a reduction of the root mean square error (RMSE) for alder and birch pollen using the detailed VC (detailed VC vs. CAMS VC: 15.7 vs. 133.6; 17.8 vs. 52.5 p/m, respectively), while higher RMSE resulted for grass (24.5 vs. 20.7 p/m). Similar RMSEs were obtained for olive and ragweed pollen (3.8 vs. 4.0; 3.9 vs. 3.9 p/m, respectively). Results from the differences in Seasonal Pollen Integrals (SPIn) were consistent with the RMSE patterns. The onset of pollen seasons was more accurately predicted than their end. The general improvement of pollen predictions obtained with the detailed VC was particularly evident in the mountains. Incorporating data from detailed vegetation maps into atmospheric dispersion models has significantly improved predictions for arboreal pollen (alder, birch, olive), especially in complex surfaces where high-resolution input data is crucial.

中文翻译：

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植被覆盖空间分辨率对意大利北部空气花粉浓度预测的影响


准确的花粉预测模型可以帮助过敏性呼吸道疾病的自我管理。我们的研究首次引入并验证了 2019 年以 3 公里空间分辨率覆盖威尼托大区（意大利）的花粉建模系统。该模型使用植被覆盖 (VC) 地图模拟了花粉的分散、扩散和沉积过程。 、物候花粉排放算法和气象预报。我们专门分析了 VC 地图的空间分辨率对桤木、桦树、橄榄、草和豚草的预测空气花粉浓度的影响。使用了两个 VC 数据集： CAMS VC：欧洲 CAMS 数据集，位于 ca。 10公里水平分辨率；详细的 VC：高分辨率数据集（从 250 m 到 1 km 空间分辨率）。将使用 CAMS 和详细 VC 获得的预测日平均浓度与使用模型性能指标和花粉季节衍生参数在 15 个监测站收集的观测值进行比较。分层分析评估了低地与山区环境中的性能差异。结果显示，使用详细 VC 可以降低桤木和桦树花粉的均方根误差 (RMSE)（详细 VC 与 CAMS VC：分别为 15.7 与 133.6；17.8 与 52.5 p/m），同时 RMSE 更高草地结果（24.5 vs. 20.7 p/m）。橄榄和豚草花粉也获得了类似的 RMSE（分别为 3.8 与 4.0；3.9 与 3.9 p/m）。季节性花粉积分 (SPIn) 差异的结果与 RMSE 模式一致。花粉季节的开始比花粉季节结束的预测更为准确。通过详细的 VC 获得的花粉预测的总体改进在山区尤其明显。 将详细植被地图的数据纳入大气扩散模型可以显着改善对树栖花粉（桤木、桦树、橄榄树）的预测，特别是在高分辨率输入数据至关重要的复杂表面中。