使用可解释的机器学习来解释政策对空气污染的影响:伦敦的COVID-19封锁
Liang Ma1, Daniel J Graham1, Marc E J Stettler1
1Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, United Kingdom.
由于COVID-19的封锁,交通量大大减少,但对伦敦的空气污染产生了不同的影响. 二氧化减少,而臭氧和PM10显示的变化很小,突出显示了复杂的空气质量反应.
科学领域:
- 环境科学 环境科学
- 公共卫生 公共卫生
- 城市规划 城市规划
背景情况:
- COVID-19封锁为研究空气污染变化提供了独特的机会.
- 了解这些变化将为未来的排放控制和空气质量政策提供信息.
研究的目的:
- 分析英国第一个国家封锁对伦敦空气污染的影响.
- 调查减少交通量与空气质量反应之间的关系.
主要方法:
- 回归不连续性设计用于锁定效应的因果分析.
- 可解释机器学习解释预测模型输出以确定关键因素.
主要成果:
- 道路交通减少了多达65%,但对空气污染的反应是不相称的和异质的.
- 二氧化 (NO2) 的变化范围从-50%到0%,臭氧 (O3) 从0%到+4%,PM10从-5%到0%.
- 没有观察到PM2.5的显著变化;与空间特征相关的NO2减少和加剧了现有的不平等.
结论:
- 锁定引发的交通减少并没有统一地减少伦敦的空气污染.
- 空气质量改善在空间上是可变的,受到公路货运和城市中心的接近等因素的影响.
- 现有的空气污染不平等性被放大,富裕地区的NO2减少更大.
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