沿海臭氧的动态和中国东部的形成制度:整合趋势分解和机器学习技术
Lei Tong1, Zhuoliang Gu2, Xuchu Zhu2
1Center for Excellence in Regional Atmospheric Environment & Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention & Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Zhejiang Key Laboratory of Urban Environmental Process and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo 315830, China.
Journal of environmental sciences (China)
|April 17, 2025
概括
机器学习揭示了中国城市的挥发性有机化合物 (VOC) 和温度驱动表面臭氧波动. 对于减轻臭氧污染,特别是沿海地区,VOC监管至关重要.
科学领域:
- 大气化学 大气化学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 表面臭氧是中国城市的重要空气污染物,影响公众健康和生态系统.
- 了解臭氧的动态和形成对于制定有效的缓解策略至关重要.
研究的目的:
- 通过创新的机器学习方法,研究中国沿海城市地区的臭氧动态和形成制度.
- 确定影响不同时间尺度的表面臭氧水平的关键驱动因素和周期性.
主要方法:
- 结合趋势分解与随机森林算法进行臭氧分析.
- 分析了2017-2022年间的臭氧数据,检查了年度间的波动,周期性和前体影响.
主要成果:
- 确定了与挥发性有机化合物 (VOC) 和空气温度相关的重大年度臭氧波动.
- 揭示了多方面的周期性 (每日,每周,假日,每年),其中每日和每年组件占主导地位.
- 标志着对VOC敏感的臭氧形成模式 (VOCs/NOx比=0.88) 在周末,节假日和封锁期间臭氧升高.
- 确定太阳辐射和温度作为每日和每年臭氧变化的关键驱动因素.
- 突出了特定的VOC (乙,乙,六,) 对长期臭氧趋势的重大影响.
- 发现的氧化物主要是导致臭氧的随机变化,这是高交通地区的典型特征.
结论:
- 这项研究为中国沿海城市的臭氧动态提供了新的见解,强调了前体物质和气象因素的复杂相互作用.
- 整合统计和机器学习方法对于推进大气污染研究至关重要.
- 紧急的VOC监管是必要的,与数据驱动的洞察力相辅相成的有针对性的缓解策略.
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