一种拓方法分析空气污染物动态在COVID-19前后封锁时代的变化
Vine Nwabuisi Madukpe1,2, Chukwuma Bright Ugoala3,2, Nkechi Nnadi4
1School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia.
Environmental monitoring and assessment
|June 2, 2025
概括
由于COVID-19的封锁,美国各地的空气污染物行为发生了变化. 虽然大多数污染物恢复到封锁前的模式,但臭氧 (O3) 显示了持续的新模式,表明了长期的环境影响.
科学领域:
- 环境科学 环境科学
- 大气化学 大气化学
- 数据科学数据科学数据科学
背景情况:
- 从2020年开始,COVID-19大流行导致了前所未有的全球性干扰.
- 这些干扰导致了人类行为和城市空气质量的显著,但模两可的变化.
- 了解这些变化对空气污染物动态的长期影响对于环境政策至关重要.
研究的目的:
- 调查COVID-19封锁引起的美国空气污染物行为变化的持续性.
- 为了确定污染物模式是否恢复到封锁前的动态或建立新的长期趋势.
- 评估封锁措施对关键空气污染物的影响:PM10,PM2.5,NO2,CO和O3.
主要方法:
- 来自美国500个监测站 (2017-2023) 的每日污染物度分析.
- 应用Mapper算法,一个拓数据分析 (TDA) 技术.
- 构建和分析年度拓网络,度分布图和网络密度指标.
主要成果:
- 在所有分析的污染物中,在2020年封锁期间观察到的污染物模式的广泛破坏.
- 大多数污染物在随后的几年中显示部分回归到封锁前的行为.
- 臭氧 (O3) 在封锁后表现出持久,明显的新结构模式,而一氧化碳 (CO) 显示出最小的变化.
结论:
- 拓数据分析 (TDA) 有效地揭示了空气质量数据中的复杂模式,补充了传统方法.
- COVID-19封锁对美国空气污染物行为产生了持久的,多样化的影响,特别是对O3.
- 调查结果强调需要细微的环境监测和政策制定,以应对长期的污染转移.
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