卷积神经网络促进大城市臭氧的过程理解 时间变化
Zelin Mai1,2, Huizhong Shen1,2, Aoxing Zhang1,2
1Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Environmental science & technology
|March 15, 2024
概括
卷积神经网络 (CNN) 显示,气象因素显著影响日常臭氧污染. 这种先进的方法为城市地区的空气质量管理提供了关键的见解.
科学领域:
- 大气化学 大气化学
- 环境科学 环境科学
- 环境监测中的人工智能
背景情况:
- 臭氧污染在很大程度上受到气象条件的影响,如温度,压力,风和湿度.
- 现有的实证模型往往忽略了大海拔和区域气象对臭氧变异性的影响.
- 超大城市空气质量管理需要了解复杂的时空气象对臭氧动态的影响.
研究的目的:
- 调查三维气象场对中国深臭氧动态的影响,使用卷积神经网络 (CNN).
- 量化各种气象因素对日常,季节性和跨年臭氧变化的贡献.
- 开发一个可解释的AI框架来将臭氧波动归因于非线性气象效应.
主要方法:
- 卷积神经网络 (CNN) 的应用,通常用于图像识别,以分析气象数据和臭氧水平.
- 开发一个优化的CNN模型,覆盖13°×13°的空间域,以捕捉区域影响.
- 使用模型解释技术来识别臭氧变化的关键气象驱动因素.
主要成果:
- 美国有线电视新闻网模型解释了超过70%的每日臭氧变化,超过了传统的经验模型.
- 温度 (16%) 和湿度 (15%) 被确定为每日臭氧波动的主要驱动因素.
- 区域风力发电场在特定时期对臭氧层变化贡献高达40%.
- 美国国家气象局将年间臭氧变化的-5-6μg·m-3归因于天气异常.
结论:
- 可解释的CNN为了解跨时空尺度对臭氧的非线性气象影响提供了强大的工具.
- 这些发现为在不断变化的气候条件下,大城市的有效空气质量管理提供了必要的基于过程的见解.
- 这种人工智能驱动的方法通过考虑更广泛的气象模式,提高了预测和减轻臭氧污染的能力.
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