| 北京城区气传花粉含量分季节预测模型 |
| 摘要点击 2135 全文点击 446 投稿时间：2023-12-01 修订日期：2024-02-23 |
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| 中文关键词 气传花粉含量 格兰杰检验 预测模型 气象因子 机器学习 |
| 英文关键词 airborne pollen content Granger causality test prediction model meteorological factors machine learning |
| DOI 10.13227/j.hjkx.20241110 |
| 作者 | 单位 | E-mail | | 郑祚芳 | 北京城市气象研究院, 北京 100089
中国气象局城市气象重点开放实验室, 北京 100089
北京城市气象工程技术研究中心, 北京 100089 | zfzheng@ium.cn | | 王耀庭 | 北京城市气象研究院, 北京 100089
中国气象局城市气象重点开放实验室, 北京 100089
北京城市气象工程技术研究中心, 北京 100089 | | | 祁文 | 铜陵市气象局, 铜陵 244061 | | | 高华 | 北京城市气象研究院, 北京 100089
中国气象局城市气象重点开放实验室, 北京 100089
北京城市气象工程技术研究中心, 北京 100089 | hgao@ium.cn |
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| 中文摘要 |
| 气传花粉是主要大气污染物的生物成分之一,可引起人群过敏反应,导致一系列过敏性疾病的发生,准确的花粉含量预测可给易感人群更有效的帮助. 基于2021~2022年花粉季北京城区多站点实测数据,分析了花粉含量时空分布特征及与气象因子间的联系. 发现花粉含量与气象因子间存在较一致的空间相关性,但这种相关性具有明显的季节性差异. 在此基础上,应用格兰杰检验方法遴选出影响北京城区花粉含量的主要气象因子,结合支持向量机和多元线性回归理论分别建立了北京城区空气花粉含量分季节预测模型. 应用2023年实测数据检验表明,两种分季节预测模型在2023年春季预测准确率分别为61.2%和60.1%,秋季预测准确率为68.1%和66.7%,性能均优于现有业务预测模型,尤其对重污染事件预测中的跨等级误差改善明显,为进一步完善北京地区花粉含量预测技术提供了新途径. |
| 英文摘要 |
| Airborne pollen is considered to be one of the air pollutants that can cause allergic reactions in humans, leading to the occurrence or aggravation of a series of allergic diseases. The latest study showed that the positive rate of pollen allergens in allergic rhinitis patients in urban areas of Beijing exceeded 80%. Accurate prediction of pollen content could provide more effective assistance to susceptible populations. Based on the measured data from multiple stations in the urban area of Beijing during the pollen season from 2021 to 2022, the spatiotemporal distribution characteristics of pollen content were analyzed. The results showed that the main meteorological factors affecting spring pollen content in the urban area of Beijing were daily average wind speed, 3-day average temperature, water vapor pressure, daily average, temperature, and accumulated temperature. The main meteorological factors affecting autumn pollen content were 3-day average temperature, water vapor pressure, minimum surface temperature, and daily average temperature. In addition, it was found that there was a consistent spatial correlation between the current air pollen content and meteorological elements in the urban area of Beijing, but this correlation had significant seasonal differences. Furthermore, the Granger causality test method was applied to select the main meteorological factors that affected airborne pollen content in the urban area of Beijing, and two prediction models for air pollen content in the Beijing urban area for different seasons were established based on the support vector machine method （SVM） and multiple linear regression theory. The test of the prediction results for 2023 showed that both the SVM model considering seasonal differences and the multiple linear regression model could predict the daily distribution trend of pollen content well. The overall correlation coefficients between the predicted pollen content and the measured values were 0.693 and 0.636 （P <0.01）, respectively. Additionally, both models had good predictive ability for several severe content pollen pollution events within the year. In the spring of 2023, the prediction accuracy of the SVM model and linear model were 61.2% and 60.1%, respectively. During autumn, the prediction accuracy was 68.1% and 66.7%, respectively. The performance was better than that of existing business models, especially in the cross-level error improvement of heavy pollution event prediction. The research results provide reference value for further improving the prediction technology of airborne pollen content in the Beijing area. |
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