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在美国密西西比州的COVID-19封锁期间,区域移动对空气质量的影响,使用机器学习

Francis Tuluri¹, Reddy Remata², Wilbur L Walters³

¹Department of Industrial Systems & Technology, Jackson State University, Jackson, MS 39217, USA.

International journal of environmental research and public health

|June 10, 2023

概括

由于COVID-19的封锁,大大减少了交通,导致密西西比州的空气质量得到改善. 二氧化和一氧化碳水平下降,而臭氧水平略有增加,与减少过境和喘率相关.

科学领域:

环境科学环境科学
公共卫生公共卫生
数据科学数据科学数据科学

背景情况:

在全球范围内,COVID-19封锁大大减少了流动性和运输.
人类活动减少所带来的预期的次要效应是改善空气质量.
本研究侧重于密西西比 (MS),一个非大都市和非工业地区,以评估这些影响.

研究的目的:

为了检查COVID-19锁定诱导的流动性变化对密西西比州空气质量的影响.
分析空气污染物度及其与锁定期间的交通数据的相关性.
验证在危机期间估计空气质量变化的分析工具的使用.

主要方法:

从EPA (2011-2020年) 收集的空气污染物数据 (PM2.5,PM10,O3,NO2,SO2,CO) 和来自NOAA的天气数据.
利用2020年的谷歌交通数据来表示运输变化.
在R Studio中使用统计和机器学习工具,包括天气规范化建模,以比较观察和预测的空气质量.

主要成果:

天气正常化的机器学习预测了NO2,O3和CO水平的显著差异 (p < 0.05).
由于封锁,NO2的平均度下降了-4.1ppb,CO的平均度下降了-0.088ppm.
臭氧 (O3) 度增加了0.002 ppm,而过境减少了50.5%.

结论:

这项研究证实了COVID-19封锁期间流动性减少与密西西比州空气质量变化之间的联系.
观察到的空气质量变化与交通减少和喘患病率减少一致.
简单的分析工具可以有效地帮助决策者在流行病或自然灾害期间管理空气质量.

关键词:

在 COVID-19 疫情中,空气质量空气质量是什么机器学习建模机器学习 python编程 python编程是什么统计描述性分析是一种统计描述性分析.运输和流动性的使用.

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