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将区域气象过程映射到北中国平原和长江三角洲的臭氧变化,中国

Feng Hu^1,2, Pinhua Xie^1,2,3,4, Jin Xu²

¹School of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei 230026, China.

Environmental science & technology

|May 8, 2025

概括

高度臭氧 (O3) 污染更好地使用一个新的AI框架来预测,该框架集成了多尺度气象数据. 这种方法提高了对臭氧水平和高污染事件的预测准确度.

科学领域:

环境科学环境科学
大气中的化学成分
人工智能的人工智能是人工智能.

背景情况:

高度的臭氧对人类健康和生态系统构成风险.
目前用于臭氧预测的机器学习模型往往忽视了区域气象场 (STRMF) 复杂的时空演变.

研究的目的:

开发和评估一个新的臭氧预测框架,其中包括STRMFs.
增强气象驱动因素对臭氧变化的解释能力.

主要方法:

设计了一个顺序卷积长期短期记忆网络 (CNN-LSTM) 框架.
使用STRMF跨多个时空尺度的场景使用全球预测系统 (GFS) 数据创建.
模型的性能被评估为臭氧度预测精度 (AOCP) 和预测高臭氧污染事件 (PHOE) 的精度.

主要成果:

扩大气象数据的时空尺度改善了AOCP和PHOE,证实了臭氧变异性与多尺度气象过程的联系.
通过整合STRMF,CNN-LSTM框架解释了超过85%的每日臭氧变化.
该模型成功阐明了臭氧度对台风转移的反应.

结论:

整合全面的气象数据显著提高了臭氧预测的准确性和污染事件的预测.
开发的CNN-LSTM框架为了解和预测臭氧污染动态提供了强大的工具.
这种方法支持及时发出污染警报,并强调区域气象学的关键作用.

关键词:

高度臭氧污染的污染.机器学习是机器学习.臭氧预测臭氧预测连续卷积式LSTM网络是连续卷积式LSTM网络.气象场的时间空间演变.

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