对中国东部空气质量变化的外部强迫因素的气候调节:对PM2.5季节性预测的影响
Jinghui Ma1, Yuanhao Qu2, Zhongqi Yu2
1Shanghai Typhoon Institute, Shanghai Meteorological Service, Shanghai 200030, China; Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China; Shanghai Key Laboratory of Meteorology and Health, Shanghai Meteorological Service, Shanghai 200030, China.
The Science of the total environment
|September 26, 2023
概括
预测中国东部季节性PM2.5空气污染对空气质量控制至关重要. 北极海冰成为冬季PM2.5度的主要驱动因素,使实时预测建模成为可能.
科学领域:
- 环境科学 环境科学
- 大气科学 大气科学
- 数据科学数据科学数据科学
背景情况:
- 气象条件对空气污染事件产生重大影响,需要准确预测季节性PM2.5度变化,以有效管理空气质量.
- 了解PM2.5的时空动态至关重要,特别是在中国东部等脆弱地区.
研究的目的:
- 分析冬季中国东部PM2.5度异常的长期时空变化.
- 确定影响冬季PM2.5度的关键外部强迫因素.
- 开发和验证一种深度学习模型,用于预测季节性冬季PM2.5度.
主要方法:
- 实证直角函数 (EOF) 分析被用来研究从1980年到2018年的PM2.5异常.
- 对秋季前的外部强迫因素 (海冰,海温,雪地) 进行了回归分析.
- 使用长短期记忆 (LSTM) 深度学习算法构建了预测模型,结合了9个选定的关键因素.
主要成果:
- 该研究确定北极海冰 (ASI) 是中国东部冬季PM2.5度的最大贡献者.
- 开发的LSTM模型证明了预测和观察到的PM2.5分布之间的一致性,偏差在15μg·m−3 (2016-2018) 之内.
- 在过去十年 (2009-2018) 中,在关键城市的预测值和观察值之间观察到高相关系数 (0.42-0.93).
结论:
- 该LSTM模型为中国东部季节性冬季PM2.5污染提供了可靠的实时预测工具.
- 识别像北极海冰这样的关键预测因素可以提高我们预测和减轻空气污染事件的能力.
- 这些发现为脆弱地区的空气质量控制战略提供了更好的前景.
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